Latest news with #AttentionIsAllYouNeed
Time of India
30-07-2025
- Science
- Time of India
8 Indian-origin AI and ML experts in the US: Where did they study
Eight Indian-origin researchers shaping AI and ML in the US. (AI Image) In the dynamic field of artificial intelligence, researchers of Indian origin have played a central role in shaping some of the most impactful innovations of the past two decades. Their contributions range from foundational advances in machine learning and computational theory to breakthroughs in natural language processing and computer vision. These scholars' academic journeys reflect a deep integration across premier institutions in India, the UK, and the US, underscoring the global nature of AI research. Eight Indian-origin researchers who have worked or are currently working in the US are highlighted below, focusing on where they studied and their key academic contributions. Ashish Vaswani Ashish Vaswani was born in India and completed his undergraduate studies at BIT Mesra. He received his PhD from the University of Southern California in 2014. Vaswani is best known as the lead author of the landmark paper "Attention Is All You Need," which introduced the Transformer architecture—now a fundamental component of modern AI systems, including large language models like GPT. His work has had a profound impact on the field of natural language processing. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like The Most Beautiful Women In The World Undo Jitendra Malik Jitendra Malik (born 1960) was born in India and completed his undergraduate degree in electrical engineering from the Indian Institute of Technology (IIT) Kanpur. He went on to earn his PhD at Stanford University in 1985. Malik is a professor at the University of California, Berkeley, and a renowned figure in computer vision. His work on image segmentation, object recognition, and deep visual understanding has helped define the field over several decades. Anima Anandkumar Anima Anandkumar was born in Mysore, India, and completed her undergraduate studies at IIT Madras. She later earned her PhD in electrical engineering and computer science in the US. Anandkumar is the Bren Professor of Computing at Caltech and former Director of AI Research at NVIDIA. Her contributions include work on tensor decomposition, deep learning architectures, and climate-focused AI models like FourCastNet. She is also an advocate for diversity and ethical AI. Vasant Honavar Vasant G. Honavar was born in India and earned his undergraduate degree from BMS College of Engineering, Bangalore University. He pursued his MS in electrical and computer engineering at Drexel University and completed his PhD in computer science at the University of Wisconsin–Madison. Honavar is currently a professor at Penn State University. His research spans machine learning, bioinformatics, and causal inference, with a focus on the integration of AI and data science across disciplines. Trapit Bansal Trapit Bansal completed his undergraduate education in mathematics and scientific computing at IIT Kanpur. He earned his PhD in computer science from the University of Massachusetts Amherst. Bansal has contributed to advances in reinforcement learning and chain-of-thought reasoning in large language models. He recently joined Meta's Superintelligence Lab, where he works on state-of-the-art AI systems and their alignment. Sanjeev Arora Sanjeev Arora (born 1968) was born in India and received his PhD in computer science from the University of California, Berkeley, in 1994 under the supervision of Umesh Vazirani. He is a professor at Princeton University and leads the Princeton Language and Intelligence Initiative. Arora's research spans approximation algorithms, learning theory, and computational complexity. He is especially known for his work on the PCP theorem and has been awarded the Gödel Prize twice for foundational contributions to theoretical computer science. Eshan Chattopadhyay Eshan Chattopadhyay is an Indian-origin computer scientist who earned his PhD from the University of Texas at Austin under the guidance of David Zuckerman. He is currently an associate professor at Cornell University. In 2025, he was awarded the prestigious Gödel Prize for constructing a two-source randomness extractor that works even with weak randomness—a breakthrough in theoretical computer science that has implications for secure AI computation and learning algorithms. Deepak Pathak Deepak Pathak completed his undergraduate degree at IIT Kanpur before moving to Carnegie Mellon University for graduate studies. He is currently an assistant professor at CMU and co-founder of Skild AI, a robotics company. His research focuses on embodied intelligence, self-supervised learning, and enabling AI agents to interact meaningfully with the physical world. His work bridges the gap between machine learning and real-world robotics. TOI Education is on WhatsApp now. Follow us here . Ready to navigate global policies? Secure your overseas future. Get expert guidance now!
Time of India
18-07-2025
- Science
- Time of India
The coming of agentic AI: The next era of human-machine synergy
Artificial Intelligence (AI) has travelled from the confines of research labs to every aspect of our daily lives. Over the past several decades, we have witnessed an extraordinary transformation; from rule-based systems to neural networks, from statistical AI to large language models (LLMs), and now, to the threshold of Agentic AI. The trending buzzword now, which is a paradigm where machines can reason, plan, adapt, and act with increasing autonomy and human-like capability. This evolution has not only showcased the power of technological progress but has also continuously enriched human life in meaningful ways. The evolution of AI The story of AI began in the 1950s with the advent of symbolic AI; systems designed to reason using logic and handcrafted rules. While foundational, these early systems were rigid, unable to adapt to real-world complexity. The 1980s brought expert systems, which encoded domain knowledge explicitly. Though revolutionary for tasks like medical diagnosis and financial modelling, their maintenance proved unsustainable at scale. The real shift came in the late 1990s and 2000s with the re-entry of machine learning. Instead of handcrafting intelligence, we began teaching machines to learn patterns from data. Algorithms like decision trees, support vector machines, and eventually deep learning architectures unlocked the ability to process images, speech, and text at scale. In 2012, a convolutional neural network achieved ground breaking accuracy in image classification marking the arrival of deep learning as a dominant force. We saw a seismic shift in AI capabilities with the advent of transformer architectures, introduced in the seminal 2017 paper 'Attention Is All You Need.' This innovation enabled models to understand large language context, paving the way for Large Language Models (LLMs) capable of generating fluent, context-aware responses and performing tasks from summarization to reasoning. Landmark models like BERT revolutionized understanding through bidirectional context, while generative models like the GPT series demonstrated unprecedented abilities in content creation, dialogue, and code generation. This progress was driven by advanced algorithms, massive datasets, and exponentially growing computational power, catalysing the shift from narrow, task-specific AI to general-purpose systems with emergent intelligence, paving the way for Agentic AI. The rise of agentic AI Today, we stand at the edge of another monumental shift: the emergence of Agentic AI. Agentic AI systems exhibit autonomy, goal-oriented behaviour, memory, reasoning, and the ability to interact and modify plans as per real-world environment. This is built on the foundation of powerful Large Language Models (LLMs), enhanced with capabilities for self-reflection, memory, and planning. These systems not only understand and generate language but can also evaluate their own actions and adapt their behaviour, enabling continuous improvement and proactive task execution. Agentic AI's core capabilities: 1. Perception and Awareness : Understands real-world inputs across text, vision, and audio. 2. Reasoning and Planning: Makes strategic decisions, breaks down goals, and adapts through learning. 3. Autonomous Execution: Carries out tasks across systems, learns from feedback, and improves over time. Together, this LLM-driven intelligence and reflective agent architectures blur the line between tool and teammate. These systems can proactively initiate tasks, collaborate, and continuously evolve mirroring the human-like cognitive flexibility and purpose-driven actions. Impact on human life Each wave of AI advancement has expanded our collective capability. Symbolic AI gave us expert systems in finance and medicine. Machine learning unlocked personalization powering recommendation engines, fraud detection, and predictive analytics. Deep learning brought breakthroughs in vision and speech - enabling virtual assistants, real-time translation, autonomous vehicles, and medical imaging. Agentic AI takes this further by transforming how we interact with machines. Imagine an AI assistant that doesn't just draft your emails, but understands your calendar, reads context from past meetings, and autonomously books travel, schedules follow-ups, and flags opportunities, continuously learning from your preferences. In enterprise, Agentic AI will streamline complex workflows. In healthcare, it can serve as a tireless collaborator, synthesizing patient data, flagging anomalies and coordinating care across departments. In education, it will act as an always-available tutor, adjusting teaching strategies in real-time to individual student needs. In scientific research, Agentic systems can formulate hypotheses, run simulations, and interpret results at a speed and scale previously unimaginable. The road ahead: Promise and responsibility As we venture deeper into the era of Agentic AI, the possibilities are infinite. We foresee: Cognitive Companions: Agents capable of dialogue, empathy modelling, and proactive Digital Workers: Agents execute complex business processes with minimal Interfaces: AI that adapts to user preferences and behaviours, providing intuitive and context-aware Human Intelligence: Seamless collaboration between human creativity and machine precision to solve grand Collaboration: Closely working with other AI agents to solve complex problems through specialized expertise. It is important to remember that agentic systems must be designed with rigorous safeguards; embedding transparency, fairness, interpretability, and alignment with human values. Robust testing, continuous red-teaming, and human-in-the-loop oversight will be vital to ensure trust and accountability. Conclusion The evolution of Agentic AI mirrors our growing understanding of both computation and cognition. More than building smarter machines, we are shaping a new interface between human intention and digital action. Agentic AI holds the promise of being our most powerful collaborator yet, the one that understands, learns, and acts on our behalf. As we look ahead, let us embrace this transformative moment with optimism and responsibility. The future of Agentic AI is not just technological, it is deeply human.
Yahoo
06-06-2025
- Business
- Yahoo
Cohere seeks $500m funding to advance AI development
Cohere, a Canadian artificial intelligence start-up, is reportedly seeking to raise more than $500m (C$685m) in a new funding round. This move aims to strengthen its position in the competitive AI landscape, alongside industry leaders such as OpenAI, Google, and Anthropic. Cohere is targeting a valuation of more than $5.5bn, reported Financial Times, citing sources familiar with the discussions. According to the media report, the valuation could potentially reach between $6bn and $6.5bn, although discussions remain in the early stages. This anticipated funding would position Cohere among the most valuable start-ups in the AI sector, despite trailing behind US competitors that have seen significant valuation increases. In April 2025, OpenAI achieved a $300bn valuation, up from $157bn in 2024, while Anthropic's funding round in March increased its valuation to $61.5bn. Cohere was founded by former Google researchers, including CEO Aidan Gomez, a co-author of the influential "Attention Is All You Need" paper, which introduced the transformer AI architecture. Unlike its competitors, Cohere has not launched a consumer-facing app, instead it is focusing on enterprise and privacy-centric solutions. The company has developed "open" models like the Aya multilingual models, accessible for developers to build upon, entering a market with competitors such as Meta and start-ups Mistral and DeepSeek. Cohere's founders, including Gomez, Nick Frosst, and Ivan Zhang, are also pursuing contracts with tech giants such as Google, Microsoft, and Amazon, which offer their own AI models to enterprises. Cohere has doubled its annual recurring revenue in the past four months, surpassing $100m last month. "A lot of the consumer adoption happened right away," said a source close to Cohere. "Enterprise tends to be slower in adoption but stickier in terms of users. Companies aren't known to adopt tech early." The development of advanced AI models demands significant financial investment for training and computing power. Nearly three years after OpenAI's ChatGPT sparked the AI boom, investors are eager to see returns on their investments in AI model creators. Cohere has also launched North, a platform enabling businesses to build AI agents for office tasks, although it is currently available to a limited number of customers. In December 2024, it was reported that Cohere plans to build an 'multibillion-dollar' AI data centre in Canada with financial support from the Canadian government. "Cohere seeks $500m funding to advance AI development" was originally created and published by Verdict, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Errore nel recupero dei dati Effettua l'accesso per consultare il tuo portafoglio Errore nel recupero dei dati Errore nel recupero dei dati Errore nel recupero dei dati Errore nel recupero dei dati
India Today
30-04-2025
- Business
- India Today
Gemini 2.5 Pro is so good that it is Empire Strikes Back moment in AI fight between Google and OpenAI
At a time when OpenAI and its ChatGPT models have hogged all the limelight it is easy to forget that Google is the OG in AI. The company has been at it for over a decade, with its AI efforts largely spearheaded by researchers at DeepMind. In fact, the current AI revolution and breakthroughs are a direct result of a paper — and which has now attained a mythical reputation — called Attention Is All You Need. The paper was written by Google researchers. And yet, when we think of AI we think of OpenAI and ChatGPT. With Google Gemini 2.5 Pro, the latest model from the tech giant, this is changing. advertisementThe reason why OpenAI has hogged all the limelight is because it has been better at turning AI research into actual products. While Google has been the lab leader, OpenAI has been better at bringing its AI tools to the public. Google, in a way, was caught off-guard by ChatGPT 3 in 2022. The company scrambled to respond, and in a furry even ended up making a few mistakes. Its initial challenges to the GPT 3 were not good at all. In 2025, to use a much-cliched phrase, the Empire Is Striking Back. And it is doing so with the Gemini 2.5 Pro (experimental). Since it arrived on the scene a few weeks ago, the Gemini 2.5 Pro (experimental) has wowed its users. This is the first AI system from Google that feels as good as — and in many instances better — than the best tools available from OpenAI. And whether in actual use, or in benchmarks, the Gemini 2.5 Pro has excelled. Take a look at the benchmarks: advertisement But more than the benchmarks the new Gemini has impressed users. Social media — mostly X aka Twitter because that is where action is — nowadays is full of thumbs ups from people saying how Gemini 2.5 Pro excels at coding, or writing, or analysing documents. Unlike the recent ChatGPT 4o, which turned into a parrot eulogising and flattering its users, the Gemini has a measured tone and a more authoritative personality. It does not by any means sound unhelpful. Instead, it is measured in a way a professional helper would be. When I try an AI tool like Gemini, DeepSeek or ChatGPT, I usually ask it to write a paragraph or two in the style of Jon Fosse. Now, Fosse is a writer — and here I am talking of his work translated in English — who uses a minimalistic language and a sparse voice, but one which gets its force from the way the Norwegian writer creates rhythm with repetitions. For AI, coding is easy nowadays. It excels at logic. But writing a few lines of fiction is still a difficult job for it. And on that, it is very difficult for an AI tool to write a paragraph that reflects the style used by Fosse. They can write in Hemingway style — well, sort of — but not Fosse. In my experience, so far the only AI tool that has managed some sort of coherency in this has been the Gemini 2.5 Pro. advertisementThere is another reason why the Gemini 2.5 Pro is such a key AI tool. Unlike the best tools from the likes of OpenAI and Anthropic, the Gemini 2.5 Pro is free to use. In this aspect it is somewhat like DeepSeek R1. But unlike DeepSeek R1, which often found itself getting a hammering due to load on its servers, Gemini 2.5 Pro can probably handle millions of users simultaneously. The backend that powers Google services is one of the best, if not the absolute best, that any tech company has. In many ways, this makes the Gemini 2.5 Pro the first top-class AI model that most people in the world can as good as the Gemini 2.5 Pro is, there is another aspect to it, and that is how Google vs OpenAI is going to look like in future. So far it seemed that OpenAI was landing all the blows. But I have a suspicion that the Gemini 2.5 Pro is just the beginning of a counter strike. Google has some of the best AI researchers. It also has the kind of data that other companies can only dream of. And finally it has a massive scale — in everything — which OpenAI currently doesn't still needs to get better at showing its AI tools to people. For example, its NotebookLLM, which can turn a paragraph into a long podcast, is superb. Similarly, there are a number of other Gemini and Google AI tools that are top class in what they do. But they remain scattered across Google services and products, and in many cases accessing them requires — like literally — an engineering degree because Google has placed them in virtual silos that are accessible only to developers. But I feel these are the niggles that a company like Google will sort out in the coming months. The new Gemini interface in itself is a big improvement in terms of accessibility and user experience compared to what we had a year ago. A more pressing matter for Google was to prove that it could match ChatGPT and Claude. With the Gemini 2.5 Pro it has done that.
Yahoo
22-04-2025
- Automotive
- Yahoo
Rivian elects Cohere's CEO to its board in latest signal the EV maker is bullish on AI
Aidan Gomez, the co-founder and CEO of generative AI startup Cohere, has joined the board of EV maker Rivian, according to a regulatory filing. The appointment is the latest sign that Rivian sees promises in applying AI to its own venture while positioning itself as a software leader — and even provider — within the automotive industry. Rivian increased the size of the board and elected Gomez, whose term will expire in 2026, according to the filing. Gomez has had a long career as a data scientist and AI expert. He launched Cohere in 2019 with co-founders Nick Frosst and Ivan Zhang with a focus on training AI foundation models for enterprises. The generative AI startup sells its services to companies such as Oracle and Notion. Prior to starting Cohere, Gomez was a researcher at Google Brain, the deep learning division at Google led by Nobel Prize winner Geoffrey Hinton. Gomez is also known for "Attention Is All You Need," a 2017 technical paper he co-authored that laid the foundation for many of the most capable generative AI models today. Gomez's skillset could be particularly useful for Rivian as the EV maker navigates a new $5.8 billion joint venture with Volkswagen Group to develop software. Under the joint venture, Rivian will share its electrical architecture expertise with a Volkswagen Group — including its many brands — and is expected to license existing intellectual property rights to the joint venture. It's possible the joint venture will sell its tech to other companies in the future. Rivian has also been working on an AI assistant for its EVs since 2023, Rivian's chief software officer, Wassym Bensaid, told TechCrunch during an interview in March. The AI work, which is specifically on the orchestration layer or framework for an AI assistant, sits outside the joint venture with VW, Bensaid said at the time. Gomez's expertise in AI and as a data scientist is clearly attractive to Rivian founder and CEO RJ Scaringe, who noted in a statement that his "thinking and expertise will support Rivian as we integrate new, cutting-edge technologies into our products, services and manufacturing." This article originally appeared on TechCrunch at Sign in to access your portfolio
