Wednesday, March 19, 2025
Google Wiz
Tuesday, March 18, 2025
Transformational Education
During the senior leadership team of college board (prior to students session), got two personal questions beyond other topics.
1. What's the materialistic motivation in industry to earn Doctorate?
2. Is MBA required for industry leader role?
In my lens, both are surprising, provoking and powerful queries from industry to institute.
My answers were
1. Zero benefits on hike, promotion, etc; but personally complete transformation on deep learning of the computers between 1992 masters and 2022 PhD
2. Original (silly) drive was to become the alumini of prestigious legacy red bricks CEG, Madras, which got disqualified after my high school. During MBA classes, learnt few formal methods/techniques to shape myself as true leader in the industry.
In tum, both Q&A emphasize my favorite motivational quote - "Education is only way to transform poor in the society".
Thursday, February 27, 2025
Microsoft Quantum
A couple reflections on the quantum computing breakthrough we just announced...
Most of us grew up learning there are three main types of matter that matter: solid, liquid, and gas. Today, that changed.After a nearly 20-year pursuit, we’ve created an entirely new state of matter, unlocked by a new class of materials, topoconductors, that enable a fundamental leap in computing.
It powers Majorana 1, the first quantum processing unit built on a topological core.
We believe this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years.
The qubits created with topoconductors are faster, more reliable, and smaller.
They are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor.
Imagine a chip that can fit in the palm of your hand yet is capable of solving problems that even all the computers on Earth today combined could not!
Sometimes researchers have to work on things for decades to make progress possible.
It takes patience and persistence to have big impact in the world.
And I am glad we get the opportunity to do just that at Microsoft.
This is our focus: When productivity rises, economies grow faster, benefiting every sector and every corner of the globe.
It’s not about hyping tech; it’s about building technology that truly serves the world.
Wednesday, February 26, 2025
Token embedding vector
Token embedding vectors are a way to represent words or sub words as numerical vectors in a high-dimensional space.
These vectors capture the semantic meaning of the tokens, allowing AI models to understand the relationships between words and their context within a sentence or document.
- Tokenization: The input text is first broken down into individual units called tokens. These can be words, sub words, or even characters, depending on the specific model and task.
- Embedding: Each token is then mapped to a corresponding vector in a high-dimensional space. This mapping is learned by the model during training, where it analyzes vast amounts of text data to understand the relationships between words.
- Vector Representation: The resulting vectors are dense and continuous, meaning that each element in the vector is a real number. The position of a token in this vector space reflects its semantic meaning, with similar words having vectors that are closer together.
Monday, February 17, 2025
Text Analytics
Text Analytics is Microsoft AI library with the below tasks
- Tokenization: Breaking text into individual words or phrases (tokens).
- Stop Word Removal: Removing common words (e.g., "the," "a," "is") that don't carry much meaning.
- Stemming/Lemmatization: Reducing words to their root form (e.g., "running" to "run"). Stemming is a simpler, rule-based approach, while lemmatization uses dictionaries and is more accurate.
- Part-of-Speech Tagging: Identifying the grammatical role of each word (e.g., noun, verb, adjective).
- Named Entity Recognition (NER): Identifying named entities like people, organizations, and locations.
- Sentiment Analysis: Determining the emotional tone of text (positive, negative, neutral).
- Topic Modeling: Discovering underlying topics in a collection of documents.
- Text Classification: Assigning categories or labels to text.
Thursday, February 6, 2025
Insane Journey of DeepSeek
The Insane Story of DeepSeek’s Founder, Liang Wenfeng
DeepSeek didn’t just build an AI—Liang Wenfeng turned constraints into competitive advantages.
Here’s how setbacks fueled game-changing innovations.
Implications:
- DeepSeek’s efficiency-first AI models are shifting the competitive landscape.
- Compute scarcity forces companies to rethink AI scaling strategies.
- China’s AI labs are proving that cost-effective models can challenge U.S. AI dominance.
- Nvidia’s stock volatility underscores market sensitivity to compute-efficient AI models.
Looking Ahead
- AI innovation is no longer about who spends the most—it’s about who does more with less.
- Investors and businesses must adapt to a world where efficiency is king.
- The AI race is evolving fast—staying informed isn’t optional.
Monday, January 27, 2025
DeepSeek R1
Hot AI topic of this week "DeepSeek" a Chinese AI startup challenges the dominance of established players like OpenAI, Google and Meta
DeepSeek's emergence as a disruptive force in the AI landscape is undeniable. Its innovative techniques, cost-efficient solutions and optimization strategies have challenged the status quo and forced established players to re-evaluate their approaches.
While DeepSeek faces challenges, its commitment to open-source collaboration and efficient AI development has the potential to reshape the future of the industry. As the AI race intensifies, DeepSeek's journey will be one to watch closely.
DeepSeek-R1 Release fact sheet at https://api-docs.deepseek.com/news/news250120
Today, market is getting huge impact due to DeepSeek release.