As we are into 2026, this moment offers more than a calendar transition—it offers clarity. The past year tested assumptions, accelerated adoption, and forced recalibration across technology, business, and society.

For me personally, 2025 was a milestone year. After reflecting on two decades in the industry, I published my second book, Jagjeevan: Living Larger Than Life, in both Hindi and English, while continuing to engage readers through Exploring the Metaverse. I contributed over 30 articles and papers across IoT solutions, XR AI → GenAi → Agentic AI, and motivation, participated in 10+ global forums and standards discussions, and was fortunate to see two patents granted along the way.

But beyond personal milestones,

2025 will be remembered as the year AI stopped being an experiment and became an assumption. It was the year the industry tested AI-first thinking at scale—and learned where it breaks.

Below are my five key observations from 2025, and how I see them evolving in 2026.

1. The Age of Abundant AI Content—and the Return of Human Value

2025 flooded the world with AI-generated content: blogs, ads, music, videos, images, 3D assets, even books. Content became cheap, fast, and everywhere—and paradoxically, less valuable.

Social media was already saturated with influence-driven narratives; AI influencers only amplified the trust deficit. We are reaching to a point where volume erasing the meaning.

I spoke about this in my recent session, “The Heart and Mind of Authorship: What Makes Writing Human in an AI Era”, where I shared how I used AI while writing Jagjeevan. AI helped with grammar, flow, structure, and language, but never with intent. I repeatedly reminded my “intelligent but obedient assistant”:

Don’t tell me what to write. Help me say what I already mean. 

That distinction matters.

2026 Outlook

The soul of content will matter more than its scale.

2. From GenAI to Agentic AI: Automation Learns to Act

2025 was the breakout year for Agentic AI.

We moved beyond content generation and search into reasoning, decision-making, and action. AI agents started performing real tasks—handling calls, qualifying leads, scheduling meetings, following up, even troubleshooting. Ref : AI Receptionist.

Receptionists, sales assistants, HR coordinators, trip planners, healthcare, education and more have an agent equivalent.

This isn’t just rebranding. This is a paradigm shift.

2026 Outlook

Agents will stay—but only those designed with humanity in mind will succeed.

3. AI-First Modernization (Not AI-Only)

2025 taught us a hard lesson: AI-first does not mean AI-only.

AI-First Software Delivery (AIFSD) rose rapidly—tools that could design, code, test, deploy, and monitor systems end-to-end. We built demos before products existed. Speed was astonishing.

But maintenance told a different story.

Fixing AI-generated systems—especially without human context—was often harder than building from scratch. Specs mattered more than ever. When intent was unclear, AI amplified the confusion.

2026 Outlook

AI is a multiplier—but only when humans set direction.

4. Standardization Returns to Emerging Tech

2025 reignited momentum in XR, spatial computing, digital twins, robotics and immersive intelligence. Platforms like Android XR and expanding Meta ecosystems signal maturity—not fragmentation.

What’s next is convergence.

2026 Outlook

Innovation scales only when foundations stabilize.

5. The Rediscovery of Human Work

Remote vs. office debates faded in 2025. What remained was more fundamental: the value of work itself.

AI raised productivity—but also surfaced a deeper truth: intelligence without humanity has no meaning. The question is no longer “Will AI replace us?” but “Where must humans remain essential?

2026 Outlook

This aligns deeply with the vision I outlined in Exploring the Metaverse—a future where immersive AI enhances human potential rather than erasing it.

Conclusion: Humanity Evolves, Not Disappears

2025 shattered illusions. It showed us what AI can do—and where it must stop. It reminded us that technology without values scales risk, not progress.

As we step into 2026, the opportunity is clear: Build AI that serves humans, not replaces them. Design systems that amplify meaning, not noise.

The future isn’t artificial. It’s augmented—with intention.

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Mr. Kuldeep Singh is a seasoned technology professional with a rich background spanning nearly two decades, where he has consistently demonstrated expertise in various roles. Mr. Kuldeep Singh journey has encompassed diverse positions, from a software developer and enterprise architect to an engineering director, practice head, and technical product manager. Currently associated with Thoughtworks, he has also significantly contributed to Nagarro and Quark Media House during his illustrious career.

In the tech industry, Mr. Kuldeep Singh stands out as a trailblazer, playing a pivotal role in pioneering Centers of Excellence for emerging technologies like IoT, AR/VR, and Web3. Mr. Kuldeep Singh’s leadership extends to overseeing complex data projects in estimations, forecasting, and optimization, designing highly scalable, cloud-native, and microservices-based architectures. Mr. Kuldeep Singh emphasizes bringing technology to the core of the business and advocates for development practices such as CI/CD, Test-Driven Development (TDD), and eXtreme Programming (XP). Mr. Kuldeep Singh extensive experience spans diverse domains, including manufacturing, aviation, education, retail, telecom, healthcare, commodity trading, and more.

Beyond his technical roles, Mr. Kuldeep Singh is a prolific writer and speaker, actively sharing insights on technology, leadership, and motivation. Mr. Kuldeep Singh extensive portfolio includes over 100 articles, some of which have gained recognition in esteemed publications such as Economics Times, Tech.de, industry4o.com, Analytics India and Thoughtworks Insights

Academically, Mr. Kuldeep Singh holds a B. Tech. in Computer Engineering from NIT, Kurukshetra. Mr. Kuldeep Singh’s commitment to knowledge includes active involvement in events, mentoring and judging initiatives, and volunteering for ideations, hackathons, and nonprofit causes, collaborating with organizations like NASSCOM, Meta, Ministry of Electronics and Information Technology (MeitY) — Government of India, Smart India Hackathon, Digital India, FICCI, AICTE, IEEE, The VRARA, Metaverse India Policy and Standards (MIPS), and academic institutions like IIT, NITs, and other universities across India. Mr. Kuldeep Singh is also engaged with World Metaverse Council, Responsible Metaverse Alliance, and others to collectively build and share the community by open source.

With a passion for empowering others and a steadfast commitment to innovation, Mr. Kuldeep Singh stands as a thought leader in the ever-evolving landscape of technology.

Mr. Kuldeep Singh has built his career empowering businesses with the Tech@Core approach. Mr. Kuldeep Singh has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep Singh has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep Singh also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep Singh has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep Singh is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. Mr. Kuldeep Singh has worked with Nagarro as a Director of Technology.

Mr. Kuldeep Singh holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep Singh also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep Singh is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep Singh is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep Singh is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep Singh can be Contacted at:

Linkedin | Twitter | Facebook | Instagram | Blog | E-mail (1) | E-mail (2)

Website (P) | Website (O) | Github

Also read Mr. Kuldeep Singh ’s earlier articles :

The post Humanizing AI appeared first on Industry4o.com.

Unlock the power of Generative AI by learning to interact with large language models programmatically—and take it further with fine-tuning for customized responses.

If you’re a software maker feeling the fear of missing out (FOMO) on the Gen AI revolution, you’re not alone. The way we build and interact with software is evolving rapidly. “Google it” or “check Stack Overflow” is no longer the go-to approach — AI-first, conversational tools are becoming the new standard across development, design, and decision-making. Simply using tools like ChatGPT or Gemini as chatbots isn’t enough anymore. When even the biggest tech paradigms are being disrupted, what does that mean for traditional development roles?

Here’s the good news: it’s not too late to get started — and this guide will take you from FOMO to flow, introducing you to Gen AI concepts and showing you how to start building with confidence.

Let’s start by exploring the gen AI architecture that brings it all Gen AI concept together, and then we will get into developing Gen AI applications and practice these core concepts.

Overview of Gen AI Architecture

More than a chatbot — building blocks of intelligent, context-aware applications

You’ve probably heard about LLMs — Large Language Models — floating around in conversations, headlines, or team meetings. They’re trained on gigantic amounts of text (imagine reading everything on Facebook, X, LinkedIn, and more), which helps them understand and generate human-like responses. Ask the right question (a.k.a. a prompt), and boom — you get a surprisingly good answer.

On their own, LLMs are a bit like goldfish.

They don’t remember what happened a moment ago, they don’t keep track of ongoing conversations or your specific context between interactions.

To build smart, conversational, and personalized Gen AI applications, you need more than just an LLM. You need a supporting architecture — a set of layers that provide memory, knowledge, and prompt optimization to help the AI feel more intelligent and useful. Here’s a quick look:

1. User Interface (Front End)

This is where the magic starts — from the user’s point of view. It might be a chat window, voice assistant, or a smart feature inside your app. The frontend captures input (text, images, voice) and displays responses in a way that suits your use case.

In tools like ChatGPT, users are expected to write clear prompts. That’s because LLMs — though incredibly knowledgeable — can’t read your mind. They respond best when you ask precisely. This is often called Prompt Engineering, but honestly, it’s more of a Prompting Art — knowing how to talk to the model so it truly understands what you want.

In business apps, the frontend may also help shape or enhance user input behind the scenes — so even vague questions become high-quality prompts before reaching the application layer.

2. Application Logic (Backend)

This is the brain behind the scenes. The backend handles user input, talks to the LLM, manages memory, adds relevant context, and delivers meaningful responses. Think of it as your app’s AI concierge, organizing everything to make the magic happen smoothly.

Here are the key component of application logic:

• Memory System – LLMs don’t remember anything between interactions. To make conversations feel natural, your app needs to manage memory. A memory system stores previous interactions and sends important parts to the LLM as context with every prompt, so it can respond more intelligently — like remembering what the user asked earlier.
• Knowledge Base – LLMs are trained on general data — they might not know about you or or example about your company’s policies, product details, or niche topics. That’s where a Knowledge Base comes in.

This system retrieve relevant info from your own data (like FAQs, documents, or databases) and augment it to the user prompt before sending it to the LLM. This process is called Retrieval-Augmented Generation (RAG).

• The data is stored in a vector database, where each document/content is represented by a mathematical fingerprint (called an embedding).
• When a user asks something, we compare their query’s embedding to the stored embeddings (using similarity search) and fetch the most relevant content.
• This content is then injected into the prompt, giving the LLM exactly what it needs to give better, grounded answers.

Together, memory and knowledge retrieval make your Gen AI app feel smarter, more helpful, and deeply customized to your domain.

3. Large Language Models (LLMs)

At the heart of Gen AI applications are large language models like GPT, trained on massive datasets to understand and generate human-like language. These models are available through platforms such as Hugging Face, OpenAI, and others.

Fine-Tuning: A method to adapt a general-purpose, pre-trained LLM for specific business needs. Fine-tuning adjusts the model using domain-specific data, improving relevance and performance for targeted applications.

Some time we need to implement all of these concepts to achieve better result as per our business need. Next will develop all these concepts step by step.

Developing Gen AI Application

You might be surprised how easy it is to build your first Gen AI app. The best part? You don’t need to train your own LLM. Big players like OpenAI, Google, Anthropic, and Hugging Face already provide powerful models that you can use through simple APIs.

In this example, we’ll use Google’s Gemini API, which is still free within usage limits — perfect for trying things out.

Setup LLM APIs Access

Accessing Gen AI Model Programmatically

For simplicity, I’m using a Google Colab Notebook — no setup needed beyond your browser! But feel free to try this on your local machine too.

The fun part? You can even use Gen AI to help you write Gen AI code. Learning by doing the same in collab or in your favorite tool — what’s cooler than that?

Developing First Gen AI Application — Colab Exercise

from google import genai
from google.colab import userdata

def query_gemini(prompt):
    client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))

    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=[prompt]
    )
    return response.text

prompt = "Tell me about Kuldeep"

print(query_gemini(prompt))

So, what happens here?

I ask the Gemini model: “Tell me about Kuldeep.” And it tries its best. But wait… the response might be generic. Something like:

“Kuldeep is a common name in India…” need more information…

That’s not what you were hoping for, right?

That’s because LLMs don’t know which Kuldeep you mean unless you give them more context.

This is where conversation and memory layers come into play. Let’s explore how to have a more meaningful interaction with the model in the next example by providing additional background during the conversation.

Conversation for LLM

What you get from a Gen AI model heavily depends on how you ask. The better your prompt, the better the answer. This is where techniques like giving the model a persona, adding context, or showing examples come in — these are called one-shot or few-shot prompting.

Sometimes, a single prompt isn’t enough. Just like in real conversations, you might need to go back and forth, ask follow-ups, or break things down into steps. This is where ideas like chain of thought or tree of thought prompting help — guiding the AI step by step to a better outcome.

To have a proper conversation with an LLM we need implement memory component to store the conversations, and pass on with every conversation. And the good news? Gemini makes this easy with its built-in chat API that remembers what you’ve said.

Check out the code notebook here:Developing a Chatbot with Gemini

Here’s a basic chatbot python program with memory:

from google import genai
from google.colab import userdata

client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))
chat = client.chats.create(model="gemini-2.0-flash")

def run_chatbot():
    print("Hello! I am thinkuldeep, how can I help you! Type 'quit' to exit.")
    chat_history = []

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            print("thinkuldeep: Goodbye!")
            break

        response = chat.send_message(user_input)
        print(f"thinkuldeep: {response.text}")

# Start the chatbot
run_chatbot()

Sample Conversation:

Hello! I am thinkuldeep, how can I help you! Type 'quit' to exit.
You: tell me about kuldeep  
thinkuldeep: "Kuldeep" is a common name… can you give me more context?

You: he works at thoughtworks and wrote "Exploring the Metaverse"  
thinkuldeep: Ah, you're likely referring to Kuldeep Singh… [gives useful details]

You: book title is "Exploring the Metaverse: Redefining the Reality in Digital Age"  
thinkuldeep: Thanks! That helps narrow it down…

As you can see, adding a bit more information helped the model give a more relevant answer. But it still can’t tell us, say, his contact info or specifics that aren’t popularly available in its training data.

In the next example, we’ll bring in custom knowledge and pass that knowledge to LLMs to get better response.

Implementing Basic RAG

Large Language Models (LLMs) like Gemini are powerful, but they aren’t all-knowing. They might not have the latest information about specific people, use cases, or private knowledge.

That’s where RAG comes in — Retrieval-Augmented Generation is a technique that fetches relevant information from an external source (like a website or document) and augments the prompt with this context before sending it to the LLM.

Let’s walk through a simple RAG implementation that pulls data from Kuldeep’s About page and uses it to generate accurate responses.

Notebook: Implementing Basic RAG

1: Retrieve

Pull relevant information from the source page:

def retrive(url):
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.content, "html.parser")
        text_content = ""
        for paragraph in soup.find_all("p"):
            text_content += paragraph.get_text(strip=True) + "\n"
        return text_content
    except Exception as e:
        return f"An error occurred: {e}"

2: Augment

Inject the retrieved context into the prompt:

def augment(prompt, context):
    return f"{prompt} where context is {context}"

3: Generate

Send the augmented prompt to Gemini:

def generation(prompt):
    client = genai.Client(api_key=userdata.get('GOOGLE_API_KEY'))
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=[prompt]
    )
    return response.text

4: RAG in Action

prompt = "Tell me about Kuldeep"
context = retrive("https://thinkuldeep.com/about/")
improved_prompt = augment(prompt, context)
print(generation(improved_prompt))

That’s it! You’ll get a detailed response based on the real data from Kuldeep’s website. The second part of the same notebook integrates it in our chatbot example to initialize the context before chatting, and we get all accurate response in natural language.

Here’s a glimpse of how that interaction goes:

Hello! Ask me about Kuldeep Singh! Type 'quit' to exit.

You: how to contact him  
thinkuldeep: Based on the provided info, you can reach Kuldeep via:
-  thinkuldeep@gmail.com
-  kuldeeps@thoughtworks.com
-  LinkedIn: kuldeep-reck
-  X (Twitter): thinkuldeep
-  Instagram: thinkuldeep
-  Facebook: kuldeep.reck

You: where does he work  
thinkuldeep: He currently works at Thoughtworks as a Global Emerging Technology Leader and Principal Consultant.

You: does he write books  
thinkuldeep: Yes! Kuldeep is the author of *"Exploring the Metaverse"* and *"MyThoughtworkings"*, and has contributed to others as well.

You: what is his next book  
thinkuldeep: His upcoming book is titled *"Jagjivan – Living Larger than Life"*.

You: quit  
thinkuldeep: Goodbye!

So we have seen RAG approach can give accurate, contextual answers by pulling in external information. This approach require all the contextual information to be passed along with the prompt, and that’s not practical and costly affair when using hosted LLMs. We need to augment the prompt with selected and most relevant context. Let’s understand it more in next section.

RAG using Embeddings and Similarity Search

As explained earlier, embeddings are numerical representations of text that capture semantic meaning. When we generate embeddings for all our contextual data and store them in a vector database, we create a searchable knowledge base.

These embeddings can then be compared to the user’s query using similarity metrics — like cosine similarity — to find the most relevant content. Once identified, that content can be appended to the user’s prompt to provide a more accurate and context-aware response from the LLM.

Let’s walk through how this works using Gemini APIs and a simple example. — RAG Using Embeddings and Similarity Search — GitHub

1: Create Embeddings and Build the Knowledge Base

Let’s start by building a small dataset of page titles and URLs from thinkuldeep.com, and then generate embeddings using Gemini APIs.

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))

model = 'models/embedding-001'

def pageEmbeddings(title, text):
  return genai.embed_content(model=model,
                             content=text,
                             task_type="retrieval_document",
                             title=title)["embedding"]
def knowledgeBase():
  pages = [ 
   {"title": "Open-sources and Community Applications", "content": "https://thinkuldeep.com/about/open-sources/"},
   {"title": "Live Streaming, Webinars","content": "https://thinkuldeep.com/about/streaming/"},
   {"title": "Patents Granted","content": "https://thinkuldeep.com/about/patents/"},
   {"title": "Awards received and public coverage", "content": "https://thinkuldeep.com/about/recognitions/"},
   {"title": "Me, my family and some moments, travel and trips", "content": "https://thinkuldeep.com/about/moments/"},
   { "title": "Book foreword, reviewed and authored", "content": "https://thinkuldeep.com/about/books/"}
  ]

  df = pd.DataFrame(pages)
  df.columns = ['Title', 'Url']
  df['Embeddings'] = df.apply(lambda row: pageEmbeddings(row['Title'], row['Url']), axis=1)
  return df

This creates a dataframe where each row contains a title, URL, and its corresponding embedding vector.

2: Find the Best Match with Similarity Search

Now, we search for the most relevant page based on the user’s input by comparing embedding similarity.

def findBestPage(query, dataframe):
  query_embedding = genai.embed_content(model=model,
                                        content=query,
                                        task_type="retrieval_query")
  dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding["embedding"])
  idx = np.argmax(dot_products)
  return dataframe.iloc[idx]['Url']

prompt = "Tell me in brief Kuldeep's travels"

bestPage = findBestPage(prompt, knowledgeBase())

print(bestPage)
# Output: https://thinkuldeep.com/about/moments/

3: Implementing RAG with Context Augmentation

Now let’s use this best-matched page as additional context when generating a response.

def run_chatbot():
    print("Hello! ask me about Kuldeep Singh! Type 'quit' to exit.")
    generation(augment("", retrive("https://thinkuldeep.com/about/")))
    df = knowledgeBase();

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            print("thinkuldeep: Goodbye!")
            break
        bestPageMatching = findBestPage(user_input, df)
        response = generation(augment(user_input, retrive(bestPageMatching)))
        print(f"thinkuldeep: {response.text} \n\n")

run_chatbot()

This chatbot dynamically retrieves and appends the most relevant context to the user’s query using similarity search and embeddings, resulting in richer, more accurate responses.

Fine-Tuning

So far, we’ve worked with general-purpose LLMs using prompt engineering to steer their behavior. However, no matter how well we craft our prompts, public APIs and general models may still fall short of producing responses that align exactly with our desired tone, format, or accuracy.

That’s where fine-tuning comes into play.

Fine-tuning allows us to train a model on our own dataset, enabling it to respond in the specific way we want.

Below is a basic example of fine-tuning a Gemini model using Google’s Generative AI tools.

Link to the full notebook on GitHub

import google.generativeai as genai
from google.colab import userdata 
import time

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))

basemodel = "models/gemini-1.5-flash-001-tuning"

trainingdata = [
    {"text_input": "Who is Kuldeep", "output": "Hi, Thanks for Asking,  Kuldeep Singh is a computer engineer with 20 years of IT experience. All the best - thinkuldeep.com"},
    {"text_input": "Where does Kuldeep Work", "output": "Hi, Thanks for Asking, He works at Thoughtworks. All the best - thinkuldeep.com"},
    # ... more training examples ...
]
operation = genai.create_tuned_model(
    display_name="thinkuldeep",
    source_model=basemodel,
    epoch_count=5,
    batch_size=4,
    learning_rate=0.001,
    training_data=trainingdata,
)

for status in operation.wait_bar():
    time.sleep(10)

print(operation.result())

This would create a fine-tuned model something like this : tunedModels/thinkuldeep-1bja29uyplkib4xx1pcfer0yjhf5

Once created, we can use it like any other generative model:

genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))
model = genai.GenerativeModel(model_name="tunedModels/thinkuldeep-1bja29uyplkib4xx1pcfer0yjhf5")
prompt = "Tell me about Kuldeep"
print(model.generate_content(prompt).text)

Hi, Thanks for Asking. He is a passionate AI engineer. All the best - thinkuldeep.com

Even with a very limited dataset, the model adapts to the style we trained it for — consistently using a polite opening and closing format. That’s the power of fine-tuning. Of course, real-world scenarios would require much larger and more diverse datasets, along with proper evaluation and validation processes.

Conclusion

We’ve now walked through several core concepts of Generative AI — from prompt engineering to embeddings, vector search, function calling, and finally, fine-tuning. These foundational techniques open up incredible opportunities for building intelligent, interactive, and personalized AI systems.

That said, this is just the beginning. The field of GenAI is vast and evolving rapidly. As we move forward, we must also recognize the ethical responsibilities that come with it. Building responsible, safe, and trustworthy AI requires:

• Transparent usage of data,
• Adherence to privacy standards,
• Thoughtful model alignment,
• And awareness of hallucination and misuse risks.

Always be cautious about the data you feed into prompts – it may inadvertently become part of training sets and surface in future responses. The industry still faces challenges in setting strong standards and guardrails for responsible use.

Let’s keep exploring, learning, and sharing as we shape the future of intelligent systems.

All the best — thinkuldeep.com

A variation of this article is originally published at AI Practices Publication

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Mr. Kuldeep Singh is a seasoned technology professional with a rich background spanning nearly two decades, where he has consistently demonstrated expertise in various roles. His journey has encompassed diverse positions, from a software developer and enterprise architect to an engineering director, practice head, and technical product manager. Currently associated with Thoughtworks, he has also significantly contributed to Nagarro and Quark Media House during his illustrious career.

In the tech industry, Kuldeep stands out as a trailblazer, playing a pivotal role in pioneering Centers of Excellence for emerging technologies like IoT, AR/VR, and Web3. His leadership extends to overseeing complex data projects in estimations, forecasting, and optimization, designing highly scalable, cloud-native, and microservices-based architectures. He emphasizes bringing technology to the core of the business and advocates for development practices such as CI/CD, Test-Driven Development (TDD), and eXtreme Programming (XP). His extensive experience spans diverse domains, including manufacturing, aviation, education, retail, telecom, healthcare, commodity trading, and more.

Beyond his technical roles, Kuldeep is a prolific writer and speaker, actively sharing insights on technology, leadership, and motivation. His extensive portfolio includes over 100 articles, some of which have gained recognition in esteemed publications such as Economics Times, Tech.de, industry4o.com, Analytics India and Thoughtworks Insights

Academically, Kuldeep holds a B. Tech. in Computer Engineering from NIT, Kurukshetra. His commitment to knowledge includes active involvement in events, mentoring and judging initiatives, and volunteering for ideations, hackathons, and nonprofit causes, collaborating with organizations like NASSCOM, Meta, Ministry of Electronics and Information Technology (MeitY) — Government of India, Smart India Hackathon, Digital India, FICCI, AICTE, IEEE, The VRARA, Metaverse India Policy and Standards (MIPS), and academic institutions like IIT, NITs, and other universities across India. He is also engaged with World Metaverse Council, Responsible Metaverse Alliance, and others to collectively build and share the community by open source.

With a passion for empowering others and a steadfast commitment to innovation, Kuldeep stands as a thought leader in the ever-evolving landscape of technology.

Mr. Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Mr. Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

The post Building Gen AI Solutions appeared first on Industry4o.com.

The world of technology is evolving at lightning speed. Every week, new AI models, frameworks, and services make headlines. With the hype around LLMs, Agentic AI, MCPs, and A2A systems, it’s easy to feel like we’re always playing catch-up.

But here’s the truth: without strong fundamentals, chasing the “next big thing” is like building a skyscraper on sand. A strong foundation ensures stability. That’s why, before diving deep into Generative AI, it’s critical to revisit the path that got us here.

This article is structured as a learning guide — a quick explainer – so you can use it to build the right base for your GenAI journey (and even prepare for AI/GenAI certifications).

We’ll cover the evolutionary path:

Machine Learning → Deep Learning → Generative AI → NLP → Speech → Computer Vision → Agentic AI

1. Machine Learning (ML): The Starting Point

At its core, machine learning (ML) is about teaching machines to learn from experience, just as humans do. Models are essentially mathematical equations trained on large datasets to predict outcomes (labels) based on given inputs (features).

The process of building ML models involves training on datasets, adjusting parameters, validating results on test data, and repeating this cycle until the results are satisfactory.

Two primary techniques define ML:

• Supervised learning: where we have labeled data with known outcomes.
• Unsupervised learning: where no labels exist, and the machine must find hidden patterns or groupings.

1.1 Supervised Machine Learning

It is generally built on historical observations where we have clear relation between outcome (label — y) and parameters ( x1, x2, x3…). and a model can be derived to represent that relationship in a mathematical form the model.

y = f(x1, x2, x3, …)

With this, we can predict outcomes for new data.

• Regression is used when predicting numeric values (e.g., predicting rainfall based on temperature and wind speed).
• Classification is used when predicting categories (e.g., diagnosing diseases from patient records).
  • Binary classification: two categories (true/false, positive/negative).
  • Multiclass classification: more than two categories.

Key steps in supervised learning include:

• Splitting data into training and test sets.
• Applying algorithms like Linear Regression (for regression) or Logistic Regression (for classification).
• Evaluating results using techniques such as confusion matrices, F1 score, MAE, MSE, or R².
• Iterating until results reach an acceptable accuracy.

1.2 Unsupervised Machine Learning

In contrast, unsupervised learning deals with data that has no labels. The goal is to uncover structure and relationships hidden within the data.

A common technique is K-Means clustering, where data points are grouped into clusters based on similarity. The algorithm keeps adjusting centroids until the clusters stabilize.

Evaluation here involves measuring separation between clusters — using metrics like silhouette scores or distances from cluster centers.

In many real-world scenarios, hybrid approaches combine supervised and unsupervised methods, allowing clusters to be labeled and then used for prediction tasks.

2. Deep Learning: Inspired by the Brain

Deep learning takes ML a step further by mimicking how the human brain processes information through neural networks.

Each artificial neuron applies a function to inputs, weighted by importance, and passes the output through an activation function to decide whether the signal continues.

Through repeated training (epochs), weights are adjusted using methods like gradient descent until errors are minimized. Networks with many layers are called deep neural networks (DNNs), which can handle complex tasks in regression, classification, natural language, and computer vision.

3. Generative AI: The Creative Leap

Generative AI (GenAI) represents a leap forward. Unlike traditional ML, it doesn’t just predict — it creates. With natural language prompts, GenAI can produce text, images, code, audio, and more. We have covered Generative AI Fundamental in recent article on Building Gen AI solutions.

The evolution here is tied to natural language processing (NLP):

• Early methods relied on tokenization and embeddings to represent words numerically.
• RNNs allowed sequential predictions but struggled with long contexts.
• Transformers revolutionized NLP by enabling parallel processing and introducing attention mechanisms.

This led to powerful transformer architectures like:

• BERT (encoder-based, by Google) for understanding context.
• GPT (decoder-based, by OpenAI) for generating coherent content.

Today’s LLMs are built on these foundations. Gen AI advancement has good impact on traditional AI ways of natural language, audio processing, and computer visions.

4. Natural Language Processing (NLP)

Core NLP tasks include:

• Language detection
• Sentiment analysis
• Named entity recognition
• Text classification
• Translation
• Summarization
• Conversational AI

Many cloud providers now bundle most of these into ready-to-use services over LLMs, but understanding the underlying mechanics helps us better use and evaluate them.

5. Speech Processing

Modern speech systems convert speech to text and text to speech in real time. Thanks to deep neural networks, today’s synthetic voices sound natural — closing the gap between human and machine communication.

These capabilities power applications in accessibility, customer support, and live transcription.

6. Computer Vision

Computer vision enables machines to interpret visual information. Traditionally, convolutional neural networks (CNNs) dominated this field, excelling at tasks like image classification, object detection, and segmentation.

But now, transformers and multimodal models extend these abilities further. By combining image encoders with text embeddings, models can understand and generate across modalities — describing images in natural language or generating images from text.

7. Agentic AI: The Next Stage

Agentic AI systems can not only process information but take autonomous actions, chaining multiple models and tools together.

• Example: AI agents booking a flight, writing an itinerary, and syncing your calendar — without manual intervention.
• Built on strong ML, DL, and GenAI foundations.

Conclusion

Generative AI is redefining how we interact with technology, but the path here matters. Without grounding in ML, DL, NLP, and CV, it’s easy to misuse or overestimate GenAI.

Think of this journey as layers of a course:

• ML (prediction)
• DL (complex data handling)
• GenAI (creation)
• NLP, Speech, CV (specializations)
• Agentic AI (autonomy)

Certification Tip: Most GenAI certifications (from Google, Microsoft, AWS, or Stanford/DeepLearning.AI) expect you to understand this progression, not just how to call an API.

By stepping back, reinforcing fundamentals, and then advancing, we ensure that our GenAI journey isn’t just hype-driven — but future-ready.

All the best — thinkuldeep.com

A variation of this article is originally published at AI Practices Publication

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Mr. Kuldeep Singh is a seasoned technology professional with a rich background spanning nearly two decades, where he has consistently demonstrated expertise in various roles. His journey has encompassed diverse positions, from a software developer and enterprise architect to an engineering director, practice head, and technical product manager. Currently associated with Thoughtworks, he has also significantly contributed to Nagarro and Quark Media House during his illustrious career.

In the tech industry, Kuldeep stands out as a trailblazer, playing a pivotal role in pioneering Centers of Excellence for emerging technologies like IoT, AR/VR, and Web3. His leadership extends to overseeing complex data projects in estimations, forecasting, and optimization, designing highly scalable, cloud-native, and microservices-based architectures. He emphasizes bringing technology to the core of the business and advocates for development practices such as CI/CD, Test-Driven Development (TDD), and eXtreme Programming (XP). His extensive experience spans diverse domains, including manufacturing, aviation, education, retail, telecom, healthcare, commodity trading, and more.

Beyond his technical roles, Kuldeep is a prolific writer and speaker, actively sharing insights on technology, leadership, and motivation. His extensive portfolio includes over 100 articles, some of which have gained recognition in esteemed publications such as Economics Times, Tech.de, industry4o.com, Analytics India and Thoughtworks Insights

Academically, Kuldeep holds a B. Tech. in Computer Engineering from NIT, Kurukshetra. His commitment to knowledge includes active involvement in events, mentoring and judging initiatives, and volunteering for ideations, hackathons, and nonprofit causes, collaborating with organizations like NASSCOM, Meta, Ministry of Electronics and Information Technology (MeitY) — Government of India, Smart India Hackathon, Digital India, FICCI, AICTE, IEEE, The VRARA, Metaverse India Policy and Standards (MIPS), and academic institutions like IIT, NITs, and other universities across India. He is also engaged with World Metaverse Council, Responsible Metaverse Alliance, and others to collectively build and share the community by open source.

With a passion for empowering others and a steadfast commitment to innovation, Kuldeep stands as a thought leader in the ever-evolving landscape of technology.

Mr. Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Mr. Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

The post A Path to AI → GenAI → Agentic AI appeared first on Industry4o.com.

AI is transforming the software development process, but there are many misconceptions about its capabilities. This article explores the common myths surrounding AI in software development and sheds light on its reality.

In recent times, Artificial Intelligence (AI) has gained momentum across various fields, especially with the introduction of Generative AI (GenAI). AI is now widely accessible, often sparking discussions about its potential, its misuse, and, unfortunately, several myths. As the hype around AI intensifies, so does the Fear of Missing Out (FOMO), leading many to adopt it without fully understanding its true value.

Software development, like many industries, has evolved over time and now faces the integration of AI into its processes, practices, and standards. At Thoughtworks, we are scaling AI-assisted software development, equipping teams with practical insights. Here are my personal takeaways and lessons from practicing AI-assisted software development:

Myth 1: AI will replace software development teams

A prevalent misconception is that AI will eliminate the need for developers, business analysts, quality analysts, experience designers, and other related roles. Many fear losing their jobs to AI.

Reality: AI is far from replacing human intelligence. The idea of achieving Artificial General Intelligence (AGI) or superintelligence is still largely speculative. While AI will not replace humans, those who embrace and effectively use AI will outpace those who don’t. AI should be viewed as a powerful assistant. It can help with tasks like writing business specifications from meeting notes, fast-tracking user stories, and even generating code. However, human oversight is crucial to ensure the quality and appropriateness of the output.

Yes, there are tools that allow users to create web dashboards or mobile apps from textual prompts, but this does not negate the need for developers. Developers are still essential for guiding AI, refining its output, and, importantly, creating the platforms that AI relies on.

Myth 2: AI can handle all software development tasks

Another myth is that since AI can code, it can handle the bulk of software development.

Reality: Coding constitutes less than half of the entire software development process. While AI may speed up coding (by around 30–50%), it may solve about 50–60% of development challenges. AI assists not just in coding but also in writing user stories, acceptance criteria, and more. However, the process still requires human input for tasks such as requirement agreements, evolutionary architectural design, refactoring, testing driven development and quality assurance.

In my experience, tools like GitHub Co-pilot Chat are incredibly useful. For instance, Co-pilot can generate a method to calculate the total price of an order or suggest test cases in less than a second.

/* Calculate order total price. */
public Double calculateTotalPrice(final Order order) {
    return order.getLineItems().stream()
        .mapToDouble(lineItem -> lineItem.getProduct().getPrice() * lineItem.getQuantity())
        .sum();
}

There are some great tools Tabnine, Cursor, CodeSense that can provide better experience, but developers must ensure that the generated code meets security, performance, and coding standards. AI can assist, but it doesn’t replace critical development practices like Test-Driven Development (TDD) or refactoring, but these practices need to evolve.

Myth 3: AI-generated software is future-proof

Some believe that AI-generated software will be more advanced and better equipped for the future than one built with traditional methods.

Reality: AI is trained on historical data, meaning it replicates both the good and bad patterns of the past. AI-generated artifacts still require validation and must pass through quality gates. While AI can assist in creating solutions, human oversight remains essential to ensure innovation and to avoid issues like AI “inbreeding,” where AI begins learning from its own outputs, potentially leading to hallucinations and suboptimal results. AI is built on historical knowledge and must be guided to innovate effectively.

Myth 4: AI-assisted software development is just about learning tools

There’s a myth that mastering AI-assisted software development simply means learning AI tools.

Reality: AI-assisted development represents a paradigm shift. It’s not just about using tools but also about adapting how we think and communicate with AI. Since AI tools rely heavily on natural language inputs, developers must refine their ability to write concise, clear, and expressive natural language prompts to get the desired outcome from AI. They need to be the great storyteller before a programmer.

Another key area is defining success metrics. Traditional productivity measures may no longer apply in the AI-assisted world. Teams need to clarify which tasks AI can handle and where human effort is still needed.

Myth 5: “Prompt Engineering” will become a job role

With the rise of GenAI, some predicted that “prompt engineering” would become a dedicated job role.

Reality: While prompt engineering is a valuable skill, it’s unlikely to become a standalone career. User interfaces and tools will continue to evolve, simplifying the process of interacting with AI, just as no one has a career solely based on searching Google. The more familiar you are with a tool, the better you become at using it effectively. Similarly, prompting is about practice. As AI tools improve, prompting will become second nature.

In conclusion, AI-assisted software development is not about replacing developers/makers but empowering them to work more efficiently. It’s important to understand that AI is a tool to assist, not a magic solution that can do everything. As we continue to integrate AI into software development, the focus will be on finding the right balance between human ingenuity and AI efficiency.

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Kuldeep Singh is a seasoned technology professional with a rich background spanning nearly two decades, where he has consistently demonstrated expertise in various roles. His journey has encompassed diverse positions, from a software developer and enterprise architect to an engineering director, practice head, and technical product manager. Currently associated with Thoughtworks, he has also significantly contributed to Nagarro and Quark Media House during his illustrious career.

In the tech industry, Kuldeep stands out as a trailblazer, playing a pivotal role in pioneering Centers of Excellence for emerging technologies like IoT, AR/VR, and Web3. His leadership extends to overseeing complex data projects in estimations, forecasting, and optimization, designing highly scalable, cloud-native, and microservices-based architectures. He emphasizes bringing technology to the core of the business and advocates for development practices such as CI/CD, Test-Driven Development (TDD), and eXtreme Programming (XP). His extensive experience spans diverse domains, including manufacturing, aviation, education, retail, telecom, healthcare, commodity trading, and more.

Beyond his technical roles, Kuldeep is a prolific writer and speaker, actively sharing insights on technology, leadership, and motivation. His extensive portfolio includes over 100 articles, some of which have gained recognition in esteemed publications such as Economics Times, Tech.de, industry4o.com, Analytics India and Thoughtworks Insights

Academically, Kuldeep holds a B. Tech. in Computer Engineering from NIT, Kurukshetra. His commitment to knowledge includes active involvement in events, mentoring and judging initiatives, and volunteering for ideations, hackathons, and nonprofit causes, collaborating with organizations like NASSCOM, Meta, Ministry of Electronics and Information Technology (MeitY) — Government of India, Smart India Hackathon, Digital India, FICCI, AICTE, IEEE, The VRARA, Metaverse India Policy and Standards (MIPS), and academic institutions like IIT, NITs, and other universities across India. He is also engaged with World Metaverse Council, Responsible Metaverse Alliance, and others to collectively build and share the community by open source.

With a passion for empowering others and a steadfast commitment to innovation, Kuldeep stands as a thought leader in the ever-evolving landscape of technology.

Mr. Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Mr. Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

The post Myths & Realities of AI appeared first on Industry4o.com.

The post Exploring the Metaverse – A Synopsis appeared first on Industry4o.com.

As we say goodbye to 2023 and welcome 2024, it’s a time to think about the good and tough parts of the year. Not everything goes the way we want, but we can learn from both the good and bad experiences. I see 2023 as a year of making things better, like fixing the base of a building. I’ve picked my top five observations on foundational changes from this year, and some might continue into 2024. Now, it’s exciting to see them happen and see how they make a difference.

Navigating restructuring and contractions

In 2023, industry giants like Google, Amazon, and Microsoft underwent significant contractions and restructuring, and the overall industry faced a substantial loss of 2.5 lac jobs in a year, 50% higher than previous year. These measures, projected as responses to the prevailing micro-economic situation, also involved cost-cutting in operational expenses, perks, and salary increments. This trend of optimization is expected to persist into 2024.

However, true optimization extends beyond cost reduction—it involves strategic investment in the right areas. In 2024, organizations are likely to shift towards recognizing this crucial aspect of optimization; aligning investments for long-term success. Those quick to realize this need and adapt will likely thrive, while those resistant to change may face challenges in navigating the landscape beyond 2024.

Shaping the generation AI

AI isn’t new, but in 2023, a whole new era started—Generation AI. Everything, from products to giants like Google, felt its impact. Microsoft went all-in on the OpenAI episode, “.ai” domains gained popularity over the traditional “.com,” and nearly every digital thing now has AI or assisted with AI, including this article.

What started as generative AI hype is now a reality, profoundly altering the landscape of digital work. Take a look at posts on LinkedIn and other platforms—crafted with emojis, bullets, refined English, grammar, and captivating graphics. Even the emails we receive are nicely composed, and we know that it is not because every content editor suddenly acquired these skills; GenAI took the place. Skills that were crucial before might not carry the same weight now, as AI steps in either to replace or enhance them:

This change is substantial, and in 2024, it will manifest in the day-to-day, challenging individuals to adapt. Those dismissing it as a mere hype or a passing trend risk being left behind permanently. Embrace the change, accept, learn, utilize, and challenge established norms.

Reimagining how we work: No going back

As 2023 concludes, the notion of reverting to traditional work norms is challenged. Organizations, amidst contractions, attempted a return to the office and may continue more in 2024, while others sought innovative alternatives. Schools adapting to online modes during disruptions exemplify a shift away from pre-COVID practices. Likewise, businesses are integrating remote work as a staple, acknowledging a generation reveling in workation. Flexi work is considered essential for better DEI strategy.

The question arises: Is a return to the pre-COVID norm even feasible, or should we embrace a more pragmatic, profound, and productive approach?

In 2023, conversations stirred around these sentiments, challenging conventional agile methods. Sumeet Moghe’s Async-First Playbook and asyncagile.org stand as proof. The Async-First Manifesto prioritizes effective, intentional work with room for experimentation, discarding outdated practices linked to fixed locations and times.

This signifies a fundamental paradigm shift that would help focus on right no matter whether you work from the office or somewhere. The tussle between office-first, hybrid or remote-first would continue in 2024, but better focus on deep work, steering clear of superficial tasks.

Clear your calendar, embrace the power of GenAI, and start expressing yourself better. “Don’t be busy in 2024, but go deep!”

Measuring tech beyond funding

In 2023, the fall of Byju’s, once hailed as an edtech success, debunked the myth that a startup’s funding dictates the triumph or failure of technology. This setback didn’t signal the failure of online education; instead, it ushered in a new era marked by diverse and accessible learning methods. Traditional educational institutions also adapted, offering an array of courses that don’t necessitate physical presence, providing individuals with more choices to acquire skills.

Similarly, people raise their eyebrows when they hear XR and metaverse, especially with Meta’s shift from metaverse emphasis to AI. Despite skepticism, the interconnection between XR, metaverse, and AI remains evident.

As we approach 2024, expect the unexpected—ideas previously deemed impractical will become commonplace. Widespread acceptance of technologies will manifest in people unknowingly incorporating tech like XR or others their daily lives, similar to what they are already experiencing – virtual trials, playful camera features, scanning and artificially generating the environment or simply changing backgrounds on images or while on video calls.

More voices will join the conversation on technology, discussing use cases, concerns, and navigating beyond the allure of hype or funding. As part of this sprint, I’m working on my book, Exploring the Metaverse: Redefining Reality in the Digital Age. .

Embracing open and responsible tech ecosystem

A notable trend that unfolded in 2023 was the emphasis on fostering an open and experimentation-friendly environment, exemplified by emerging initiatives like OpenXR, OpenBCI, OpenAI and hundreds of AI models on hugging faces. Organizations and individuals globally shifted their focus towards open-sourcing, 2023 saw a sharp rise of first time contributors. A surge of commitment to responsible tech and sustainable development emerged. Notably, products began including green nudges such as carbon emission while showing flights, software getting green, carbon aware software architecture, solutions and infrastructure.

The momentum towards ethical and responsible development is anticipated to gain further momentum in 2024. Industries will shift from merely discussing Diversity, Equity, and Inclusion (DEI) to integrating these principles into their core business strategies—moving beyond rhetoric to tangible action. This evolution is poised to create collaborative spaces, where like-minded producers and consumers converge, co-building and aligning their efforts towards ethical practices and sustainability.

Expect to witness a surge in such partnerships in the coming years.

Conclusion

Absolutely, 2023 has been a year marked by challenges, but within those challenges lie valuable lessons and opportunities for growth. It’s a year that shattered myths about foundations and traditions, urging us to think beyond the established norms. In this dynamic landscape, nothing remains fixed, not our beliefs, practices, or ways of working. The key is to evolve continuously to meet the demands of the time.

As we transition into 2024, it presents a unique opportunity to witness the impact of the changes set in motion in 2023. It’s a chance to course correct, align with evolving needs, and embrace the transformative potential that lies ahead. Let’s navigate the future with adaptability, openness, and a commitment to continuous improvement.

Embrace Change, Seek Growth

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Mr. Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Mr. Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Mr. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

Mr. Kuldeep has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

Mr. Kuldeep is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Mr. Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. Mr. Kuldeep also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. Mr. Kuldeep is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

The post Fundamental Shifts & 2024 Anticipation appeared first on Industry4o.com.

eXtended Reality (XR/AR/VR) technology is changing the reality of almost every industry today by enhancing user experiences. XR adoption in the museum industry is also quite natural, let’s understand how it is shaping up the Museums of Future.

Museum is a place that displays artifacts from history, culture, science, art and more. As per International Council of Museums (ICOM), there are around 55000 museums worldwide designed with different themes serving different types of artifacts. The museums are all about providing user experience where visitors immerse in the environment provided. Over the years, physical design and layout of the museum including colors, interior / exterior, arrangement of artifacts, audio/visuals guides, tips are playing key roles in improving the user experience.

Museum visitors typically want to cover more in a short time, and keeping all types of visitors engaged is tedious, especially the younger ones who are well equipped today’s gadgets, and for them traditional brochures and signage methods may not work well. So museums are finding ways of evolving user experience with technological advancements, earlier paper based brochures, navigation maps, tips are now converting into smart touch based displays, websites, or even to the sensor based touch-less interactions. Mobile apps or smart devices are replacing the traditional audio taps. Now, navigating through the museum is getting driven by technology, and this pandemic has induced the need to go virtual. Museum industry is exploring ARVR based technology to provide new ways of interactions and enhance the existing experiences to be ready for future, where the task from the metaversal museum will be quite different, it would demand not only digital (virtual) replicas of the museum itself but also the whole experience need to evolve.

Let’s understand different use cases for the museum industry that can be extended with XR technology as follows.

Extended Navigation – The Museum Explorer

Museums are generally spread in multiple floors, buildings and have large space and an easily accessible navigation path for indoor and outdoor has been a key experience driver. A Museum’s experience has to be a journey, starting from a visitor entering the premise, park vehicle, booking, and entering the museum, visiting different artifacts, till a visitor leaves from the premise.

Traditionally museums provide paper based info brochures, maps, navigational signages, point of attractions, and audio guides in different languages with markers. These ways are not easy to follow for specially abled, or people with non-supported language, these are hard to maintain too

There has been some effort already in building indoor maps from Google similar to standard google map, and now there are number AR applications are being built for Indoor navigation, example is shown below in Fig#1 from OfficeExplorer App from ThoughtWorks. In such case, visitor can use an AR application in a smartphone/tablet or smart glass like ThinkReality A3, and follow the path shown in the phone overlaid on the environments, or they can follow some digital character that appears in the app.

The digital character can well talk to the visitor and guide them on the path to the area of interest, or take them to the point of attraction. A visitor may also choose the language of their choice. So instead of maintaining fixed audio taps, devices and paper manuals, museums can now maintain navigational paths digitally, not just that the path can be designed and controlled as per the need, crowd dynamically. They can monitor who is following what path, how much time people spend differently, what area is less explored, and can think of improving the point attraction, so it gets better.

Visitors may be shown different navigation paths with time/waiting time, point of attractions etc, and they can choose what suits them best. There can be a dedicated navigation path configured for specially abled, or people who need special attention. Visitors can pre-download the app, and may pre-book the navigations, so they get personalized experience, and enjoy the experience

Extended Artifacts – Tell Me More

The purpose of a museum is to display a set of artifacts from history, science, art, culture or so. Information about these artifacts are typically available in form of brochures, printed boards near the artifacts. Some museums also adapted audio devices/visual displays near the artifacts that can tell information about the artifacts.

In recent times, artifacts getting digitally tagged with QRCode/BarCode, RFID etc. Visitors are using these tag reader phone apps/devices to scan the tags and get the relevant information about the artifacts, this is a less flexible way to manage artifacts information than the traditional paper based crude ways. Fig#4 shows digital information overlaid near the artifact it scanned in Fig#3

With advancements in computer vision and cloud technology, artifacts can now be tagged and persistent as Anchors (e.g Google Cloud Anchor) with respect to the environment. The XR applications can have image and object detection capability that allow showing augmented information directly on the physical objects. For example, Fig 5 shows information about the book (such as booking rating, views, number of pages in the book, reviews etc.) just by looking at this book. This is a snapshot from TellMeMore app built by Thoughtworks

The AR app shown in Fig#5 is pre-trained for this book image, similarly all the physical artifacts in a museum can be scanned and an AR app can be pre-trained for that. We can augment any information such as Audio, Video, Image, Text or Document, Website etc at any position relative to the artifact.

This type of application improves visitor experience by bringing contextual information at ease as per their preference. In addition to that, it would allow faster editing/configuring the museum and onboarding new artifacts. We can well measure the telemetry data around usage of content, and see which artifacts are accessed well, what kind of information visitors are interested in, and update the content based on that. It would also save on resources consumed for managing these artifacts, we can divert the effort where it needs attention most.

Extended Visitor Engagement – Look Around

Keeping the visitor engaged is one of the important tasks while designing the museum experience. Traditional ways are getting out dated, and engaging the modern crowd would need modern solutions. Current generation is well versed with smart gadgets, and actively connected on social media.

XR can well help in improving visitor engagement by gamifying the experience. For example introducing games like treasure hunts and pokemon go where visitors can signup and compete in groups. At Thoughtworks, we have built an app named LookARound, that allows users to scan some QR Codes configured at last space, and visitors attempt to collect goodies appearing randomly around the area of attraction.

This type of app helps in designing large spaces like museums for better customer engagement, in the search of goodies people will explore the places which we want them to visit. Since visitors also come in a group this could be a great app for experience enhancements.

Other ways of improving engagement is to let users provide reviews, feedback, rating on the artifacts directly by looking at artifacts from the XR apps, and other visitors can also see the reviews and ratings, they can also publish these on their social media channels.

There could be experience booths for visitor engagements for having advanced experience like time travel described in the next section. There could be other value added services for taking to food joints, offers, advertisements etc.

Extended Immersive Experience — Time Travel

We have talked about user experience enhancement in previous sections, and here we will talk about taking that experience to the next level with XR. Visitors look at the artifact wearing a smart glass, it would change the whole environment related to that artifact, it would be like the visitor has traveled in that time, and would see people wearing dresses of that era, see buildings, design of that time. An example of this shown below.

This can be further enhanced by multi user collaboration, where a group can join in and be part of the same session. Visitor may choose and try clothings from history and take selfie in those clothes, ornaments, and share these with others

Another example could be, looking at dinosaurs at a museum would take us to the dinosaur era, and the whole environment would change to a jungle and we see dinosaurs roaming around with sounds and animations.

Similarly, if we are in a car museum, and looking at a car, then it can allow visitors to experience the car as follows.

Zero Museum – Open Museum

Zero museum is a concept coming out with a museum without any physical artifacts, we still have a physical museum but with empty space. Artifacts are shown all virtual, either projected in 3D or shown via smart headsets or AR/VR enabled phone/tablets.

This is a very interesting concept, and the same space can be used for different types of museum, and even at a time multiple visitors can see the same place differently.

Another concept of museum is coming to think of the museum as a city scale experience, like turning all parks or open space of the city into museums or art galleries, where people can search such places and go there to experience it . In fact people may also contribute in submitting the content.

Virtual Museum

Last but not the least, Virtual Museum, where the whole museum experience can be replicated virtually. There are multiple ways of doing that. Visitors can experience it directly on a web browser, and access the museum as a 360 degree tour, example here.

or users can try experiencing a museum in virtual reality headsets. They can walk inside a museum using a controller in the device or walk naturally as today’s device may well track the user’s movement.

This experience is not just experiencing the museum artifacts but also it can also replicate people who are visiting the virtual museums at that time, their avatars can come, and interact with you. It is called metaversal museum experience, and nowadays even NFT museum are also getting hyped, which aim to bring artists and the creator community together, and drive the creator economy.

Museums can build VR training and education materials that they will run with schools, and audiences in a studio setup in the premise as additional services.

Conclusion

I have documented the ways to extend the museum experience using XR technology, where users get the enhanced experience using XR enabled phone/tablet or XR smart glasses/headsets. All different types of AR/VR/MR use cases are possible for museums. XR can cater both the audiences who want to physically visit the museum and those who want to experience it remotely. It can provide a touch less/contact less, and safe experience for physical visitors, and for immersive virtual museum experience for the ones who don’t want to come for physical visit or they can’t come due certain medical conditions. Metaverse will also bring new ways of experiencing the museum.

XR brings flexibility to change the museum as per the visitors’ needs, and can provide a very personalized experience. Visitors would see a different theme/style of the museum on every visit, or in future, we will be able to configure museums as per my need.

About the Author :

Mr. Kuldeep Singh
Engineering Director
ThoughtWorks

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Engineering Director and Head of XR Practice in India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

Mr. Kuldeep can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Github : https://github.com/thinkuldeep

Also read Mr. Kuldeep’s earlier articles :

This article is originally published on XR Practices Publication

Banner image courtesy : https://www.culturele-vacatures.nl

The post Use cases of XR in Museum Industry appeared first on Industry4o.com.

Self driving cars are a good example of what artificial intelligence can accomplish today. Such tech-centered advances are built on the back of highly trained Machine Learning (ML) algorithms.

These algorithms require exhaustive data that help the AI effectively model all possible scenarios. Accessing and pooling all that data needs complex and very expensive real-world hardware setups, aside from the extensive time it takes to gather the necessary training-data in the cloud (to train ML). Additionally during the learning process, hardware is prone to accidents, wear and tear which raises the cost and lead time in training the AI.

Technology leaders like GE have side stepped this hurdle by simulating real world scenarios using controlled-environment setups. This controlled environment simulations coined the term ‘Digital Twins’. A digital twin can model a physical object, process, or person with abstractions that might be necessary to bring the real world behaviour in digital simulation. This is useful to data scientists who model and generate the needed data to train complex AI in a cost effective way.

An example is GE’s Asset Digital Twin – the software representation of a physical asset (like turbines). Infact, GE was able to demonstrate a savings of $1.6 billion by using digital twins for remote monitoring.

Research predicts the digital twin market will hit $26 billion by 2025, and major consumers of this technology being the automotive and manufacturing sector.

Tools at hand for digital twins

Through experimentation, Extended Reality (XR) and tools used to create XR have been found best suited to leverage digital twins with ease. Also, one of the most popular tools used to create digital twins is [Unity3D], also mentioned in ThoughtWorks Technology Radar.

Unity3D ensures an easier production of AI for prototyping in robotics, autonomous vehicles and other industrial applications.

Use case: autonomous vehicle parking

Self driving cars have a wide domain of variables to consider — lane detection, lane following, signal detection, obstacle detection etc. In our experiment at ThoughtWorks, we attempted to solve one of the problems; train a typical sedan-like-car to autonomously park itself in a parking lot through XR simulation.

The outcome of this experiment only produced signals such as the extent of throttle, brake and steering needed to successfully park the car in an empty lot. Other parameters like the clutch and gear shift were not calculated as they were not needed in the simulation. The outcome was applied in the simulation environment by a car controller script.

This simulation was made up of two parts, one was the car or the agent and the second was the environment within which we were training the car. In our project, the car was prefabricated (CAD model of car) and attached with a Rigidbody physics element (that simulates mass and gravity during execution).

The simulated car was given the following values:

A total body mass was about 1500kg
A car controller with front wheel drive
Brake on all 4 wheels
Motor Torque of 400
Brake Torque of 200
Max. steering angle of 30 degrees
Ray cast sensors (rays projecting out from the car body for obstacle detection) in all four directions of the car. In the real world, ray cast sensors would be the LIDAR-like vision sensors.

The picture below, displays the ray cast sensors attached to the car:

Raycast sensors in all sides of the car to detect obstacles

Ray cast sensors in action during simulation

The car’s digital twin was set up in the learning environment. A neural network configuration of four hidden layers and 512 neurons per hidden layer was configured. The reinforcement training was configured to run 10 million cycles.

The Reinforcement Learning (RL) was set up to run in episodes that would end or re-start based on the following parameters:

• The agent has successfully parked in the parking lot
• The agent has met with an accident (hitting a wall, tree or another car)
• The episode length has exceeded the given number of steps

For our use case, an episode length of 5000 was chosen.

The rewarding scheme for RL was selected as follows:

• The agent receives a penalty if it meets with any accident and the episode re-starts
• The agent receives a reward if it parks itself in an empty slot
• The ‘amount of reward’ for successful parking was based on:
  • How aligned the car is to the demarcations in the parking lot
  • If parked facing the wall, the agent would receive a marginal reward
  • If parked facing the road (so that leaving the lot is easier), the agent would receive a large reward

A training farm was set up with 16 agents to speed up the learning process. A simulation manager randomized the training environment with parking lots having varying lot occupancy and the agent was placed at random spots in the parking area between episodes – to ensure the learning covered a wide array of scenarios.

Reinforcement learning with multiple agents parallelly

The tensor graph after 5 million cycles looked like this:

Not only had the agent increased its reward, it had also optimized the number of steps to reach the optimal solution (right hand side of graph). On an average it took 40 signals (like accelerate, steer, brake) to take the agent from the entrance to the empty parking lot.

Here are some interesting captures taken during training: ​

Agent has learnt to park both side of the parking lot

Agent learnt to take elegant maneuvers by reversing

Agent learnt to park from random locations of the lot

Way forward

We’ve just looked at how digital twins can effectively fast track production of artificial intelligence for use in autonomous vehicles at a prototyping stage. This applies to the industrial robotics space as well.

In robotics, specifically, the digital twins approach can help train better path-planning and optimization for moving robots. If we model the rewarding points around the Markov decision process, we can save energy, reduce load and improve cycle time for repetitive jobs – because the deep learning mechanisms maximize rewards in a lesser number of steps where possible.

In conclusion, XR simulation using digital twins to train ML models can be used to fast track prototyping AI for industrial applications.

About the Author :

Mr. Kuldeep Singh
Head of XR Practice
ThoughtWorks

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Principal Consultant, Engineering and Head of XR Practice, India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

He can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

About the Author:

Mr. Raju Kandaswamy
Lead Consultant
ThoughtWorks

Also read their earlier articles :

The post Training autonomous vehicles with XR simulation appeared first on Industry4o.com.

Before we get there,

Do you know Extended Reality (XR) is one of the art forms practiced in Ancient India?

It’s called Indrajaal (இந்திர ஜாலம்: Tamil) — The art of Magic, illusion and deception. Listed in the 64 art forms that were practiced in ancient India. Wondering how Magic, illusion and deception is related to Extended Reality? Read on…

In fact, the earliest form of Augmented Reality was put to use by stage magicians. The first ever stage illusion of a ghost was produced in 1862 where a glass panel placed at an angle in front of the stage reflecting the ghost artist acting below the stage is made to appear on stage due to Refraction of light by the glass. This effect was called “Pepper’s Ghost” and produced the desired augmentation for the audience. Augmented reality is a century old technique

Most of the modern day Smart glass technology (Hololens, Lenovo ThinkReality, nReal) uses this Pepper’s Ghost effect at a very small scale in the wearing glass to produce the Mixed reality effect. In a nutshell, most smart glasses hold the glass that does the refraction of light mounted in the front and the glass is black tinted to make sure that the refraction is visible for various lighting conditions. A small projector beams the content on the glass surface, the refraction of light gives an illusion that the content appears at a distance from the viewer wearing the glass. That’s the art of illusion elevated by current scientific improvements in electronics.

The technology has gone through several transformations and it has reached our hands almost in the form factor of a wearing glass today. There are however advancements in this domain which lead me to project where the future is headed.

Wearable form factor is reduced to the form factor of contact lens. The early prototypes released by Mojo vision promises just that.

This prototype wearable contact lens produces monochromatic augmented reality for the wearer.

This is just the beginning of the huge potential that XR has to offer for humans. The interaction with this form of XR system is currently limited to the retina movements. This may change seeing the advancements that are made to the BCI or BMI (Brain computer interface, Brain Machine Interface) and AI.

While this may sound more science fiction at this point, projecting further on this space and trends I cannot exclude the possibility of a “Hallucinating XR”.

Neuralink — Most of us know that this Elon Musk backed startup is working in the space of brain implants to stimulate cortex and sensory parts of the brain to attain desirable effects.

While using Neuralink as an enabler to record and induce neural stimulations, there are advancements in decoding these signals into visual imagery

Decoding models trained on stimulus-induced brain activity in visual cortical areas showed accurate classification, detection, and identification of contents.

Source: https://www.science.org/doi/10.1126/science.1234330

Combining Neuralink with AI models trained for visual decoding may open up possibilities for recording visual experiences from the visual cortex of the brain. If we reverse the process we get the desired stimulations to induce visual imagery in a brain signal encoded form. An experience in this form the end user will start to hallucinate an extended reality experience. The possibility of this happening is still far enough with low probability, But we cannot hide the fact that the science is inching towards it.

Ethical & Responsible Tech:

While this possibility may look scary as with any other technological innovation. While we are working on these technologies, we must also work in parallel on regulatory and ethical use of this technology so that we retain our human values and respect our biological evolution process.

About the Author:

Mr. Raju Kandaswamy
Lead Consultant
ThoughtWorks

The post Hallucinating XR appeared first on Industry4o.com.

Metaverse is the next Internet, that’s what we summarized it in the previous article. We discussed it’s characteristics and building blocks. In this article we will discuss some prominent use cases of the meta verse. We understand the metaverse as a trillion dollar opportunity in the next few years. It is growing at real fast speed, but without effective rules and regulations, without effective standards and practices, and we will also touch upon this concerning part of meta verse.

Use-cases of Metaverse

The Internet today has already created some metaverse, when we connect to the internet, we are knowingly and unknowingly becoming live to the world. The apps on our phone are getting active on the internet, and ready to talk to the world. Tomorrow’s internet will be more immersive, we will actually go into the internet.

Here are few use cases of metaverse:

Location Agnostic Work Environment

Work location is becoming irrelevant for many of the businesses, need of bring tools and technology that can make work environment free from location, is arising. Covid19 has pushed this heavily, and businesses that could not achieve this in some or other form have lost to pandemic. Today, many enterprises are talking about different mode of work, to work from home, hybrid or work from anywhere. Metaverse will smoothen this transition for enterprises and consumers. Working from metaverse would be a common thing. Just like Microsoft Mesh, or Meta’s Horizon World does. Here are few key objectives

• Remote Expert Collaboration – Experts don’t need to travel at the exact location
• Be in your comfort – In the metaverse, my avatar can replicate me while working, and I don’t always need to be on camera. Always on camera is so stressful for long hours.
• Everything Digital – Metaverse will have the potential to have digital copies of almost everything, that means I don’t need to carry a lot of hardware, devices or instruments all the time to use them. I would be able to work on digital twins with almost the same efficiency.
• Infinite and Interoperable Space – Metaverse will provide opportunity to infinitely scale the work environment, and take it anywhere, interoperable across devices mobile/PC/smart glasses and more.. More importantly customize it as per need with minimal cost.
• 3D for 3D – Traditionally all the tools used for designing 3D products are on 2D. We have been taught in Engineering Design how to represent a 3D on 2D paper on screen. This is all going to be changed in the metaverse. 3D objects will be designed in 3D, that’s more natural, and without any material wastage. It will make things faster to market.
• Eco-friendly – Yes, the metaversal work environment will be much more eco-friendly to the environment as well as to the pocket. It would run on minimal operation cost, and with minimal waste.

Skilling and Reskilling

Industry has to tackle skilled workforce shortage and onboarding new people on the skills. As per Global Talent Crunch report, 10000 baby-boomers reaching retirement age every day for next 19 years, covid19 making them retire even faster. By 2023, 75% of global workforce will be millenials, and traditional methods of onboarding and training are outdated for these millennials. On top of that, enterprise complexity is increasing, need to co-work with machines is arising. Workforce safety compliance requirements are making things complex.

Onboarding and training, with remote collaboration in metaverse can solve some of above challenges, and improve the efficiency. The future of work and workforce will be more metaverse ready.

Who need what training, and for how much hours, auto-suggestion, contextual on the job guidance, co-working with machine, remote controlling the machine, all will be possible in AI empowered metaverse. Humans will be empowered more with technology to take better and faster decisions. They we will able to record the work, and rewind and make correction in the work they have done.

Assets and Marketplaces

One of characteristic Metaverse is – self sustainable, that provide it’s own way to fund creator and co-creators. A new economy of creators and co-creators is growing. Co-creation is not new, whole social media market is built on it, YouTube, Snap AR Lens, Stories, or Insta Reels are all example of co-creation.

Metaverse claim to fund the content creators transparently, with NFT (Non-Fungible Token). Eg. Original GIF of below Nyan Cat was sold at $600K.

NFT technology allow metaverse users to own digital assets, and trade them on marketplaces, original creator would get the loyalty on every transaction. There are number of marketplaces are booming in this area for example Cryptopunks, Hashmasks, SuperRare,Rarible, Sandbox, NFTically, WazirX.

The use-case for NFT would be all the confidentials/copyright assets can be made open on the marketplace, as a user I don’t need to fake it, can directly use original assets with the originator’s permission. For example if I want to bring a Ducati bike 3D model in my game, I can get it directly from Dukati on the marketplace, instead of searching for it. So metaverse will not only benefit the creators but also make the digital content more accessible to the users.

Real Estate and Event Management

As metaverse will be a self-sustainable marketplace for digital content. Everything of physical world can have it’s digital version. Companies like Niantic, are building a whole 3D map to augment on physical world. Just like physical things, the viral things will also be traded. So people will buy/sell virtual land on metaverse, and build house/building and sell it or rent it, or earn from advertisements on them. See it happening here.

Just like physical properties, people will own properties in metaverse, similarly events and concerts will be organised in metaverse. There are number of VR events happening around the world from convocation, award shows, virtual conferences, concerts and more.

Advertisement in metaverse will be very big opportunities, and can make everything free of cost and open for people to join in, or even people who join/refer can be paid for joining the events.

Meet and Greet

Metaverse is all about human connections and Meet and greet is a basic use case of metaverse, within capabilities of real world and beyond it. Metaverse can bring the real power of being human.

Here is how we will Meet and Greet in Metaverse –

Meeting the ones who are no more with us.

Metaversal ability for the specially abled

Multiple social identities — people will have multiple social identities in metaverse.

It DOSEs when meeting — Dopamine, Oxytocin, Serotonin, and Endorphins are the chemicals in brain, and our feeling when we meet, and remember is based on ratio these chemicals. It is observed, that when people meet in VR, similar chemical reaction happens in brain. This is very big thing.

Metaversal therapy — enhanced from VRT, and more will happen, may be metaverse as a drug.

We discussed key use cases of metaverse, let’s now get into concerning part of metaverse.

Concerning part of Metaverse

Just like any technological evolution there is always some concerning part, Metaverse do have some concerns, as it will be exposing us and our information more to the world. Metaverse is being treated a dystopia by many.

Protecting Identity

In Metaverse, people may want to keeps multiple social identity but safeguarding metaversal identify would be the difficult part, without strong laws and regulations. At this stage it is not very difficult to impersonate others. Our future identity is at Risk. Think of following,

• What if someone represent you in your office, society, without your knowledge?
• What if someone accessing your property/assets without your knowledge?
• Social media trials (metaversal trial in future) can cause the irreversible impact on social reputation, health and wealth, by the time the truth prevails.
• It would be difficult to differentiate the real or fake

We may need Universal Virtual Identity, and must need attentions from governments, institutions, legal authorities, enterprises and people to come together and safeguard our future identity.

Protecting Privacy

Maintaining privacy would be another big concern in metaverse, we will be exposing more information, and we will be more vulnerable to exploits. We will always be under surveillance, when everyone roaming around with smart glasses or smart contact lens with camera on. Most of the vehicles will have cameras.

New type of restrictions will be imposed, for example, you may not be allowed to enter at certain area with glasses, or there will be detector that detects the camera devices. Physical places may be going aways, lot of businesses will be impacted, people will have fear of coming out of their homes.

What will be legal options if privacy is compromised in metaverse? Difficult part would how to define the compromised privacy, will it be acceptable in physical justice system?

Mind Blowing Impacts

Metaverse is an immersive experience and we will be going into the internet, into people’s imagination, and mind. We talked about DOSE (Dopamine, Oxytocin, Serotonin, and Endorphins), how their chemical equation can impact our feelings. How about changing that chemical equation by metaverse? think of drug addictions, that is also a result of some chemical balance in the brain. Similar addiction has been observed with games, and can Metaverse be a drug, Look at this Forbes Article Legal Heroin.

So metaverse has capability to influence the decisions, if it goes in wrong hand, impact will be huge. If we get compromised, what will be the legal option? That is another big concern explained in the next section.

Law and Order

Metaverse is growing without meaningful rules and regulations. What is right and what is wrong in Metaverse is not known. Crime in the real world may not be a crime metaverse. For example, killing someone, cheating, posing weapons is not a crime in the metaverse. Can we deal with crime in a metaverse with a physical legal system?

• Will we have a metaverse constitution?
• Will we have a metaverse court?
• How to identify crime, do we such tools and tech?
• Who will maintain law and order? will there be virtual police?
• What would be punishments? Block people/ put them in virtual jail?

I don’t have answer to all of these at this stage, but what we can do is get ready for it.

Getting ready for metaverse

We have defined metaverse, discussed use-cases and concerns. Some of it might look just the hype or scary at this stage, only time will tell what will be real and what not. I believe when there is a problem, there is a solution, innovation happens when there is a need to innovate.

In the long run, not using metaverse or staying away from it would not be an option. No matter how scary it looks, it is coming closer than we think.

What we can do

• Create awareness — start using it
• Get trained — build/co-create on it
• Adopt ethically — Find right use-cases that can solve real world problems
• Contribute — Build practices, standards and contribute share with communities.
• Join hands with enterprises and governments, institutions, systems and people.

Most importantly, we can Play our part, promote ethical use of metaverse, and educate on unethical use of it.

Keep sharing, keep contributing.

About the Author :

Mr. Kuldeep Singh
Head of XR Practice
ThoughtWorks

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Principal Consultant, Engineering and Head of XR Practice, India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

He can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Also read his earlier articles :

The post Getting into the Metaverse – Part 2 appeared first on Industry4o.com.

Meta verse is the most enquired word after Facebook renamed itself “Meta” to align to their metaversal vision. However, the term “metaverse” was coined 3 decade ago by Neal Stephenson, in his 1992 science-fiction novel “Snow Crash,” which envisions a virtual reality-based successor to the internet. In the novel, people use digital avatars of themselves to explore the online world, often as a way of escaping a dystopian reality.

Today, Metaverse is being considered as a $800 billion path with double digit growth in next 5 years, where industry is trying to get most out of it by co-creating with communities, and focusing on it’s cost-effective universal acceptance. Metaverse is supposed to be driven by technology and business related to content, AI, ARVR, IoT, and Blockchain. However, the definition of metaverse is not very clear, it is very loosely defined by individuals and enterprises as per their interests, purpose, use-cases and understanding.

In this article, we will try to understand the metaverse in more detail and attempt to define it, and its characteristics. We cover metaverse use cases and concerns in our next article.

Defining Metaverse

Root of the metaverse has always been from science-fiction gaming and entertainment, so definition would also converge from there. Let’s look at Oasis of Ready Player One, it could be one of its kind.

“People come to Oasis for all the things they can do, because of all the things they can be”

Dictionary meaning of the word “Meta” is “beyond”, a universe beyond this real life could be the metaverse. Just like what Second Life defines

If you are traveling beyond this life, where weight of tradition cannot follow…

There is an endless list of such games eg. Fortnite, Decentraland, The Sandbox, Moonieverse, where community based co-creation and play-to-earn kind on platforms are being built, it’s some sort of mini-metaverses.

Metaverse can’t just be gaming and entertainment, it’s going beyond it. The way Meta and Microsoft defines it as new ways of collaboration, and human connection, and working in Metaverse.

Niantic, best known for bringing Augmented Reality at scale by games like Pokémon Go, they define metaverse as a better reality of the same universe and call it real world metaverse. Niantic aims to build 3D maps and augment the whole world with their Lightship platform for developers and creators communities. Google CEO Sundar Pichai looks at metaverse as immersive computing in AR. Google, who pioneer in ARVR (XR), most of XR devices today run’s on Google’s Android OS today, and now they are building Operating System for AR, may be a key enabler to Metaverse devices.

After looking at multiple metaverse definitions from different enterprises, we can say there are few common aspects such as co-creation, openness, currency, self-sustainability, communities, sharing, collaboration and network. Before getting into a more formal definition of Metaverse, let’s first understand two words : “Metadata” and “Internet”. Metadata is the “other data”, “extra data”, that can refer something, for example email-Id, phone number, address, interests, what we like/dislike, things we own, places we go.. all are metadata for us. The way metadata is connected with us, let’s call it the Intranet of Metadata. Similarly everyone has metadata, and the way we all are connected is the Internet.

Metaverse and it’s characteristics

Now we understand the metadata and internet, and the Metaverse can be defined as follows

“Metaverse is an universe built up on the network of metadata around us and about us. In a universe where the real and fake blur together, photo-realistic avatars are making use of virtual identities, virtual intellectual properties / assets, virtual societies and their own virtual currencies”

Here are the characteristics of Metaverse

• Metaverse is Open and Independent – Metaverse has to be open to use, open to build on top of it, and It must not be controlled by any one.
• Metaverse is One and Interoperable – Metaverse has to be one and only one, and interoperable and integratable across software and hardwares.
• Metaverse is for everyone and self-sustainable – Metaverse should be accessible for everyone, and has to be self-sustainable where people can buy and sell things and earn to survive there.

In nutshell, Metaverse is the next Internet, it is touted to be the next natural step in the evolution of the internet with use cases to boot.

AI, XR, IOT, Blockchain, Cloud, 5G/6G will be the building blocks of Metaverse.

Getting closer to Metaverse

We are getting closer to the metaverse, it is the evolution of the internet. Today’s “connecting to the internet” is becoming “joining the metaverse”. Evolution is a natural process, and we will be getting into the internet, no matter how good or bad we feel.

Metaverse is not only about a few of the big enterprises we discussed in the earlier sections of this article, it is much bigger than all of them combined. Look at the below list, 100s of XR devices already there and many are in making to catch the metaversal wave.

Number of development, partnerships happening to boost the adoption faster, for example Qualcomm Snapdragon XR platform will be boosting normal looking smart glasses development, joining hands with enterprise like Lenovo, Motorola, Niantic, Holoone and more.

Jobs Markets and hiring situations are changing, there is a huge surge in demand for AR/VR engineers in recent years, however there is still a gap in the skills required for metaverse and skills already exists. Metaversal development needs skills and creativity of an Artist, Scientist, Mathematician, Physics, Sound and Lights, and they have to a movie director.

New jobs titles are coming such as Chief Metaverse Officer (to define metaverse strategy for the products, replacing existing social media strategists job), Metaverse Security/Safety Officer and Metaverse Planner. Storyteller roles will be more common in experience designing for the metaverse. Content Creators/free launcher economy will play a big role and will make people self-sustainable in metaverse market. Play2Earn is getting popularized these days and, in future people might be earning just by being in the metaverse.

We are going faster into the future than we think, in next the 5 years, we will see what we have not seen in last the 10 years. At this speed, we are getting into metaverse with new use cases to solve and new concerns to deal with. In the next article we have covered key use-cases and concerns of metaverse, and what we can do about it.

About the Author :

Mr. Kuldeep Singh

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Principal Consultant, Engineering and Head of XR Practice, India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

He can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Also read his earlier articles :

The post Getting into the Metaverse – Part 1 appeared first on Industry4o.com.

Enterprise XR is gaining traction by making the pandemic-proof workplaces a reality. Here we describe the key considerations of enterprise XR journey.

The IMF called the COVID-19 crisis ‘unlike any other,’ where the global growth contraction for 2020 was estimated at -3.5 percent.

Here is a quick overview of the biggest challenges that CXOs (and their businesses) have faced over the recent past, to better illustrate how the pandemic is affecting businesses:

• Businesses are grappling with slowing global supply chains
• Sales and production growth is further impacted with a subsequent dive in opportunities for partnerships and growth
• The flattened or negative growth market result in less manpower and resources to work with, further affecting productivity

The five biggest challenges that CXOs are facing today

On the other hand, consumer behaviour has irrevocably changed– the way people shop, consume education, travel etc. In trying to meet these challenges, leaders are crafting comprehensive business continuity plans that include what’s known as the ‘pandemic-proof infrastructure.’

Pandemic or crisis proof infrastructure is characterized by contactless, hygienic, location agnostic, remote and/or virtual working environments.

Such a setup will help business leaders:

• Adapt to and upgrade work environments to better suit the new normal
• Onboard new and old employees into a new work environment
• Better understand and work with quickly evolving business and customer priorities

Given the scenario described above, we clearly see the potential that emerging tech lik eXtended Reality (XR) has to help enterprises crisis-proof their workspaces.

Enterprise XR (EXR) goes mainstream

XR tech has progressed from Gartner’s hype cycle and the confines of the consumer (gaming) industry to become a mature technology ready for business consumption.

According to Digi-Capital – “Enterprise VR active users are beginning to reach critical mass in the training vertical…Enterprise VR training company, Strivr partnered with Walmart to roll out 17,000 Oculus Go headsets loaded with its software to 4,700 stores and 1 million employees, as well as Verizon using it to train 22,000 employees across the US.”

And, when we look at reports like IDC’s prediction of global AR/VR spends reaching $136.9 billion by 2024, or news of Microsoft winning a U.S. Army contract for augmented reality headsets, worth up to $21.9 billion – it’s evident that (public and/or private) enterprise-wide adoption of XR is on the rise.

So, can XR really crisis-proof workplaces?

XR tech can simulate physical environments in immersive, virtual ones, thus creating a life-like experience. This helps entire workforces collaborate across remote locations, building on the ‘work from anywhere’ norm. Those principles also allow XR to be an efficient medium for remote upskilling of a large workforce.

A recent HBR Analytic Services study stated, “[XR is] a key part of enterprise digital transformation initiatives. It is emerging as an important tool to improve employee productivity, training, and customer service.”

Five considerations for EXR journeys

ThoughtWorks has put together the top five things to consider when business leaders embark on their Enterprise eXtended Reality journeys:

1. Enterprise integration: EXR solutions have to integrate with various enterprise systems – from Identity and Access Management (IAM) to Enterprise Risk Management (ERM) to Customer Relationship Management (CRM) and other proprietary services.

They must comply with organizational standards, processes and policies by integrating with Mobile Device Management (MDM) solutions. Additionally, EXR solutions should allow for a consistent user experience (and design) across an organization’s systems (e.g. a single sign across websites, mobile or desktop apps).

2. XR content management: Conversations around XR content (like what’s used in gaming) are largely focused on the consumption stage i.e. the visualization and immersive quality of the content. However, the journey begins earlier, at the generation and publication stages, where EXR is yet to make significant progress.

It’s ideal for Enterprise XR to support entire content management pipelines – generating multi-format communique leveraging CAD, CAM, 3D Model, 360° videos etc. This ensures proactive and seamless integration across media platforms/channels. It also encourages efficient accessibility, publishing and managing of the content.

3. Customizations: EXR adoption will grow when solutions are software and hardware agnostic and customizable to the needs of enterprises. For instance, EXR must be compatible with software apps and web services running on cloud, on-premise, on hybrid setups, and with evolving infrastructure.

Enterprise XR adoption benefits from a vibrant partner ecosystem as that guarantees integration with a variety of devices, development tools and frameworks like Unity, Unreal, Vuforia, and others, alongside industry leading components like Google ARCore, Apple ARKit, MRTK etc. EXR also expects interoperability to enable existing mobile and web apps with XR, and this helps leverage existing investments.

4. Monetizing solutions: EXR solutions can be a critical use case with measurable ROI. Organizations can implement turnkey XR solutions for remote assistance, guided workflow and training; remote data visualization, design collaboration and compliance. These solutions can be monetized by offering them to wider industrial usage.

5. Standardization: The Enterprise XR space should implement software development best practices like Agile principles, open source, etc. Standardization matters because there are a multitude of tooling and tech stacks used for EXR solutions.

While XR has the potential to reorient the enterprise environment, enterprises face multiple challenges with the XR development lifecycle. One gap is the automation of testing XR applications. ThoughtWorks developed Arium – an open-source, lightweight, extensible, platform-agnostic framework that helps write automation tests that are built specifically for XR applications.

Would like to conclude this article with a quotation from ThoughtWorker, Margaret Plumley, Product Manager and Researcher –

“Most people when they think of AR, VR, they think of games, they think of consumers, but they’ve actually been used in enterprises with really positive returns. You’re seeing actual cases where time on task is reduced by anywhere from eight to 30% in industry. So, there are some very real enterprise level applications out there with very real results.”

About the Authors :

Mr. Kuldeep Singh

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Principal Consultant, Engineering and Head of XR Practice, India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

He can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

Also read Mr. Kuldeep’s earlier article :

The post Making pandemic-proof workplaces a reality with XR appeared first on Industry4o.com.

Metaverse – a technological evolution

Understanding the metaverse as a technological evolution

Metaverse is the most talked, understood and misunderstood topic these days. In most of my sessions people ask what is the technology stack for Metaverse? what should I learn to stay relevant? etc.. I will try to cover it in this article. In my earlier article I tried to define the Metaverse, and my friend Vinodh Arumugam also attempted the same here. It is defined so loosely that every other industry today is trying to define it in their own way to suits their narrative. It would end up creating many sub-metaverses, with different set of features and, would lead to many confusions, and conflict of interests. Any how this will evolve with time.

Current stage of metaverse is like “a solution waiting for the problems to happen”, there are lot of promises and claims without real acceptable usages. This situation is changing soon with evolution of technology and businesses, and more acceptance to metaverse are in making. Acceptable definition of Metaverse will also evolve with time. We are still far from the true metaverse, and today metaverse hype is just the evolution of technology and it is a set of technology pieces coming together, it may continue like that. Here are the building blocks of Metaverse. Evolution within these building blocks driving the metaverse adoption.

eXtended Reality (XR)

Evolution of user experience and interactions is the key driver for XR adaption. Now access to a camera and internet is a very basic thing the users, norms are changing, and people are accepting to be live on camera, and sharing more with the world. This is brining many metaversal use-cases in life. There has been lot of advancement in ARVR devices in recent years, that is making the new user experience possible.

Head Mounted Displays to Normal looking Smart Glass — Earlier bulky ARVR head mounted devices are turning into light weight and normal looking smart glasses with better capability than ever. Eg. Lenovo A3, Nreal and 250+ devices in line. Not just that it is now getting into as small as contract lenses.

Mobile phones are evolving as wearable computing unit to power the smart glasses/other wearables. Most head mounted devices today are tethered from PC or mobile phones, or some android based computing units. This enabled bringing traditional developed practices to be reused and improvised for XR development.

Mobile XR — Mobile phones itself is capable of running ARVR application without a need of smart glass. Google ARCore and Apple ARKit has made it possible for mobile phone developer to build XR application for Mobile phones. Unity 3D Engine, and Unity AR Foundation has scaled it the next level along with other 3D engines like unreal, vuforia and more. Smart phones are working to include advanced capabilities like depth sensing, and semantic scene understanding out of the box.

WebXR — Web based XR technology is bring XR experience directly on the web browser. There are many player making it possible with traditional WebGL, or using favourite javascript libraries ThreeJS, BabylonJS or likes of 8th Wall.

OpenXR — an open initiative for bringing multiple devices, maker, and communities closure to the characteristics of the Metaverse. OpenXR is being adapted by Qualcomm Snapdragon XR platform, and Qualcomm is fuelling huge in metaverse. More and more XR devices (Lenovo A3, NReal, and more) will be using XR2/XR2 chipset from Qualcomm. SDK’s from Meta and Microsoft Hololens has also support OpenXR.

Volumetric Displays or projectors are also growing and bringing XR experience with wearing any display devices.

Smart Mirrors with camera are coming to track the person moment and show contextual information right in front of user. Heads up displays are also traditionally available as Augmented Reality usecases.

Other Peripheral devices like hectic gloves, trackpads, keyboards, are all growing and evolving.

Artificial Intelligence (AI)

As we talked about in last section, user interaction is evolving faster, the XR devices or development kit are are coming with out of the box artificially intelligent capability to understand the environments semantically, as follows

Identity detection — XR capable devices as now capable identify humans by detecting the face, iris, skin texture, finger print etc. Authentication and Authorisation metaverse will be a key think to safe guard identify where people will have multiple metaversal identifies. These capabilities are important for metaversal development.

Image and Object Recognition — Scanning the scene and understanding is semantically is now important part to share the environment in metaverse or creating the digital twin of it. Devices and SDKs are coming with built in Image and Object recognition, and plane detection capability. Tracking the detected objects naturally is the important part where they get occluded with other real or virtual objects out there.

Gesture detection — XR interactions have been evolved now, users can use their hands naturally with different hand gestures. They don’t need to use physical controllers. Use can also control the XR apps with eye tracking, or facial expression too. Not just that, now XR devices are evolving to detect full body posture detection, and can track it well. The applications can react to full body movement. You might have seen a metaversal conference call where avatars are with only upper half of the body, but it will be changing soon, as the AI now head worn devices can also predict movement bottom half of the body. Ref here.

Voice recognition — Avatars or digital twins of us are replacing us in metaverse, and they need to replicate/animate our voice, lip movement, and more. Voice recognition is at the core of it, and not just that the XR devices now support voice based interactions to control the XR applications.

Spatial tracking — Realtime tracking of device with respect to the environment is a basic need for XR application, and it is called Simultaneous Localisation And Mapping (SLAM). Most devices are now capable of tracking in 6DOF (Degree of Freedom).

Spatial Sound — Sound is very important for spatial application, and it need to reflect naturally based on the environment. Read more details here, and hear it out below;

Image/Video Processing — Live video processing and deep learning, and sensing the environment is becoming useful tech for metaverse usecases. See here RealityScan, and NVIDIA Instant NeRF.

NVidia Instant NeRF

Brain Computer Interface (BCI) — is the next big bet in evolving interactions, controlling metaverse just by thoughts. See some development, Snap acquired NextMind, Galea from OpenBCI, or emotiv

Cloud Technologies

Cloud technologies has also evolved with time, and it has been acting as a catalyst for scaling emerging tech. There has always been a battle of private/public/hybrid cloud and the balancing game between these. Infra management and maintenance is always a key cost consideration in digital modernisation.

While XR devices are getting more powerful, and capable but that is at the cost like Lower battery life, bigger form factor, overheating, more wiring etc. This limit them for longer usage. Cloud based XR, is looked as scale solution for XR, following evolutions are helping the adaption

Cloud Rendering — Rendering XR content put huge load on XR devices and currently not all XR content can be rendered in these devices. Lot of effort is being put in to optimising the XR content for different devices. This restricts the use of photorealistic content in XR, mean sub-optimal experience. XR is all about believing in the Virtual content just like it is real, and this breaks the experience. Device SDK can provide an automatic way to switch between local vs cloud rendering (near or far cloud). Read here Azure Remote Rendering and NVIDIA CloudXR

Cloud Anchors — Another key cloud requirement is mapping the world. Finding the feature points in the environment and storing those feature point on cloud. This is called Cloud Anchors. Read here about google cloud anchors and Spatial Anchors from Microsoft Azure.

Other general cloud usage continue here too for realtime data transfer using WebTRC/Websocket and storing the data on cloud. or integrating with other SAAS and managing the XR devices with MDM solutions.

Internet Of Things (IoT)

XR devices are already acting as hub of IoT eco-system, that has number connected sensors, has internet, and can do some level of local computing and can well talk to connected cloud services. So the XR devices are more evolved IoT devices in one sense. They can well connect to other IoT devices over the different IoT protocols (BLE, Zigbee, IR etc) to understand the environment even better.

In the industrial context, connected eco-system can bring more fruitful results. For example, bring contextual information about a machine when user is in its proximity and machine can be accurately identified by merging multiple identification methods like image/object recognition and IR/proximity data.

Not just that, IoT eco-system can well handshake XR devices and process or read data from it and behave accordingly. In nutshell XR devices can become ear and eyes for the traditional devices, and the Enterprise Metaverse is all aiming for it, to bring humans and machine/robots closer, let them work together.

Blockchain

Metaverse is evolving in multiple directions, sustainability is one of the characteristic of metaverse. Content creation and making it available to public consumption is important part of metaverse survival, and adaptation to general use. Currently there is no standard way to distribute or monetise on digital content. You might say why am I discussing this here in blockchain section, how is blockchain linked to metaverse?

One reason is NFT which is an evolution of blockchain technology, that claims to boost content creation economy and where creators can monetise on their asset. I am not very sure how long this will fly, but currently it is really hyped. Number of NFT based market places booming in this area for example Cryptopunks, Hashmasks, SuperRare, Rarible, Sandbox, NFTically, WazirX.

Another reason is, another characteristic of metaverse that talks metaverse not being controlled/owned centrally by one, rather it should be driven from communities/people. This lead to de-centralised architecture for metaverse solutions and Blackchain distributed app architecture comes closure to it. So it’s more of “de-centralisation” that is related to metaverse than the blockchain. De-centralisation in form of near remote computing is also a factor here. Decentraland is building marketplace on blockchain tech, giants like samsung are also adopting it.

The traditional payment methods are evolving to include NFT/Crypto based payment mode, and whole new era of advertisement business for virtual real-state coming in to play. Current stage of blockchain based architecture may not suffice speed the metaverse need, but more people use it, more it will evolve naturally. There is always a tussle between forces for centralisation (government, enterprises, authorities, auditors) and decentralisation (community, people, open eco-system etc), and will continue like that.

nG Network

We are talking about huge data transfer, the network speed has to be really high to meet the metaverse need of real time environment sharing, and live collaborations. Evolution to 5G/6G will bring more metaverse adoption. It’s all about experience, and if immersion breaks it would not look real. It has to be close to real world. There would be no storage in device, as the devices will go very light-eight wearables, all can be processed remotely and transferred via the super fast network.

Telecom provider also evolving, see MEC computing with 5G by Verizon.

XR devices also need to go wireless, and solution like LiFi are evolving to catch the game. Even charging to these devices evolving with wireless charger. 5G rollout in trial, I hope it will get faster for enterprise use-cases first and then it will to get hand of consumer. So enterprise metaverse will take 5G/6G advantage faster than that of consumer side, enterprise can have controlled high speed network.

Satellites internet is also evolving and claims to provide high speed, low latency network. other development happening on quantum network for safer internet.

Conclusion

Evolution is a natural process, and so is true for technology or businesses. Technology emerges and get tested, conceptualised for real use. There is always some loopholes, constraints and limitations with emerging tech or business, it goes in to multiple evolutions before it become ready for prime times. The same is true for the Metaverse, that is being driven from some set of technological evolutions. More it get explored, more it will evolve, and we hope to reach true stage of the metaverse in future.

Characteristics of Metaverse

• Metaverse is Open and Independent — Metaverse has to be open to use, open to build on top of it, and It must not be controlled by any one.
• Metaverse is One and Interoperable — Metaverse has to be one and only one, and interoperable and integratable across software and hardwares.
• Metaverse is for everyone and self-sustainable — Metaverse should be accessible for everyone, and has to be self-sustainable where people can buy and sell things and earn to survive there.

In nutshell, Metaverse is the next Internet — it is touted to be the next natural step in the evolution of the internet with use cases to boot.

About the Author

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Principal Consultant, Engineering and Head of XR Practice, India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

He can be Contacted at

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

The post Metaverse – a technological evolution appeared first on Industry4o.com.