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Sunnyvale, California, United States
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- ML/AI Infrastructure for end-to-end experimentation and research…
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We are not talking enough about Waymo Robotaxis in San Francisco. I’ve been taking them regularly over the past months, and it feels as normal as…
We are not talking enough about Waymo Robotaxis in San Francisco. I’ve been taking them regularly over the past months, and it feels as normal as…
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What a fantastic set of announcements at WWDC'24! Its been an insane but exciting past year to get efficient on device inference engine powering so…
What a fantastic set of announcements at WWDC'24! Its been an insane but exciting past year to get efficient on device inference engine powering so…
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Rishaad Currimjee
We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor
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Natalia Vassilieva
The 7th workshop on Neural Scaling Laws is about to start: https://lnkd.in/gQSB5k8v Scaling laws, emergency, alignment, transfer learning, with a focused track on multilingual models. Looking forward to kick it off with Irina Rish and discuss all these topics with Yuhai Tu, Paul Bogdan, Hailey Schoelkopf, Darshil Doshi, Rio Yokota, Neha Sengupta, Preslav Nakov, Tatiana Shavrina, PhD, Yishi Xu. Join in person or virtually! #icml2024 #ai
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Anshul Bhide
A couple of recent college graduates from India against AI finetuning giants like Anyscale and Run:ai? I met Arko C at a NASSCOM event last October. He was building Xylem AI (YC S24) , a platform that allows you to train and deploy LLMs in production. So naturally most Indian VCs rejected him. Because how could a team of three recent college grads compete with the likes SV startups like Anyscale that have raised hundreds of millions of dollars, have teams of experienced AI researchers and a stash of H100 GPUs? Arko hustled and closed two Fortune 500 companies for paid contracts. I personally know unicorns that haven't managed to do this. He then got into YC in the last round in May. There's a lot to still be proved out, but Arko exemplifies how valuable grit is in building startups. I’m doing a webinar with him tomorrow on challenges of using LLMs in production. Link to register in the comments. Disclaimer: I'm a small investor in Xylem.
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Meinolf Sellmann
https://lnkd.in/esZgE24d Seven years ago I gave this talk at IIT Kanpur. Back then it seemed inevitable that we would have self-driving cars by now, and some predictions I made were wrong. Overall, though, I think this talk has aged reasonably well. Little did I know back then that the reference to the 2016 MaxSAT I gave would spark the creation of a new company, InsideOpt. The Seeker Solver that we sell is based on the technology outlined, and it is changing the optimization landscape. On the social implications, we since edited a special issue of the AI Magazine on the social disruption that AI brings. More work is needed. We need to have a wide societal discourse on how we are going to share the increasing wealth that AI brings to the world. #insideopt.com
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Sai Krishna
Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.
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Tapomoy Adhikari
At Corgnit Research Lab, India, we are excited to share our latest findings on the convergence of Reinforcement Learning and Federated Learning and their implications for advancing AGI. Key Insights: - 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: The distributed nature of FL enables scalable and efficient training of RL models, significantly reducing computational overhead and accelerating convergence. - 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: By ensuring that sensitive data remains local, FL facilitates the development of AGI in privacy-sensitve domains, such as healthcare, without compromising data security. - 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Multiple RL agents can share knowledge through FL, enhancing overall learning efficiency and robustness without the need to share raw data. - 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧: The integration of FL allows AGI models to be tailored to individual users or environments, combining global intelligence with local customization. - 𝐑𝐨𝐛𝐮𝐬𝐭𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞: Aggregating diverse experiences through FL results in more generalized and resilient AGI models, mitigating the risk of overfitting. Benefits for Researchers: - 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Leveraging distributed computational resources lowers costs and enhances the feasibility of large-scale AGI experiments. - 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Model updates can be shared instead of raw data, promoting cross-institutional and interdisciplinary research. - 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Faster training times and rapid iteration lead to quicker advancements in AGI. - 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Prioritizing user privacy and data security aligns with ethical standards and regulations, fostering responsible AI research. - 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Immediate feedback and improvements from real-world data enable practical deployment in sectors like smart cities, healthcare, and IoT. Our research at Corgnit underscores the transformative potential of merging RL and FL in the pursuit of AGI. We believe these insights will catalyze further innovations and collaborations in the AI community. #ArtificialGeneralIntelligence #ReinforcementLearning #FederatedLearning #AIResearch #CorgnitResearchLab #PrivacyPreservingAI #ScalableAI #EthicalAI Corgnit, Corgnit Research India, Reinforcement Learning, Federated Learning Consortium
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Pranav Mistry
The Economic Times's article on GENAI features my conversation with Himanshi Lohchab on why Cost (especially inference cost) and high quality Conversational Data are the key barriers to make LLMs ready for India Scale. TWO.AI's mission with #ChatSUTRA and #SUTRA models is to bring energy-efficient language models that are truly multilingual. https://lnkd.in/gYshMMNz
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Gautham Pallapa, Ph. D.
Looking forward to this panel where Divakar Tantravahi, Meduri Ravi Kumar, and Cosimo Spera will talk about the latest AI technologies that are changing product and customer support, along with their real world examples and practical strategies. If you have not registered, use this link - https://lnkd.in/gdTMgryA #ai #businessoutcomes #product #customer
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Vellanki Sriharsha
Luma AI recently launched #DreamMachine a competitor of OpenAI's #Sora which was announced a while back but yet to be launched. I tried the model and generated 2 videos. Given my interest in Indian Itihasas 1st prompt was - "Lord Hanuman and Lord Ganesh playing football with vanarasena as their teams" It generated a 5sec video, and I didn't find the output to be good. Then being a superhero fan the 2nd prompt was - "Superman and batman playing cricket with batman batting and Superman bowling" It generated a 5sec video, and I didn't find the output to be good but was better than the 1 video. I think the bar is set so high that we expect the world at our fingertips. Must say great to see the pace of innovation! Do checkout lumalabs.ai and try DreamMachine and share your views in the comments. #GenAI #AI #VideoGen #TexttoVideo
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Asheesh Asthana
Geoffrey Hinton's insights on LLMs are invaluable! I recently came across this video (https://lnkd.in/gSCJdzuH) and I was blown away by the clarity and depth of his explanations. In my opinion, he provides more insight than most of the material I have seen on this topic. If you're interested in learning more about LLMs, I highly recommend checking out this video.
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Prateek Raj
My sense is that NVIDIA's dominance in AI compute is likely to be disrupted 1st in inference (if and when it happens). Mark Zuckerberg made a very interesting point in his conversation with Dwarkesh Patel recently. He said - Meta's ratio of inference to training compute is much higher than most other companies leveraging AI. This is because Meta serves extremely large user communities across Facebook, Instagram, WhatsApp, etc., resulting in huge volumes of inference requests. In contrast, companies focused mainly on research and development may have lower ratios of inference to training compute. The scale of Meta's user base means inference optimization is more important for them in order to efficiently serve real-time requests from billions of users. This was one factor in Meta's decision to develop custom silicon optimized for inference to reduce GPU consumption and free up more expensive GPU resources for training models. I suspect that all the large cos will reduce their dependencies on NVIDIA as they scale their AI inference compute. As of now, the four cos - Amazon Web Services (AWS), Meta, Google, and Microsoft - account for >1/3rd of NVIDIA’s data centre business, and all of them have their custom AI chip initiatives. I don't know what opportunities will be created for early-stage companies in this area, but I will be doing a deep dive here. Let me know who I should be speaking with for these initiatives.
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Steven Eliuk
Please join me on June 25th San Francisco, CA, USA for a panel titled "A BLUEPRINT FOR SCALABLE & RELIABLE ENTERPRISE AI/ML SYSTEMS" w/ moderation by Hira Dangol from Bank of America and my fellow panelists Rama Akkiraju from Nvidia and Nitin Aggarwal from Google. Recent breakthroughs in AI technology have captured the world's attention, and now it's time to address the challenge of creating truly robust and reliable AI systems. AIQCon's mission is to harness the power of our community to develop new GOLD STANDARDS for AI quality. The global movement is bringing together renowned leaders and professionals from across the industry to establish reliable AI quality solutions and standards. If you're working on AI systems, you won't want to miss this one! Use the discount code "Community" for a 15% discount on registration. Register at https://lnkd.in/gqRE48fC. #AI #EnterpriseAI #AIQuality #AIQCon https://lnkd.in/gqRE48fC
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Haneef Abbas
Collaboration boosts execution 10x Earlier in my career, I had been on teams where members worked mainly on their C:// drives and information was sparingly shared via mail attachments. This slowed execution and impeded learning and development of junior members - particularly for people, who are eager to learn and develop rapidly. So, in 2014, when I was interviewing for a role in PwC’s Corporate Finance team, one of the questions I asked the interviewing manager Gbolahan Ashagbe was: “does the team actively use a shared work drive?” It was crucial for me. And when I founded in Leonine in 2017, one of the 1st expenses I incurred was Microsoft 365 for Business licenses for myself and Yinka Ojikutu, CFA because, amongst other things, I wanted to set a solid foundation for organization and collaboration – a lesson I had learnt the hard way. I believe having a culture of collaboration, where information and communication are open to the relevant people, boosts execution many folds. This is one of the practices that has served Leonine (and Aster-BI) over the years and helped us thrive as a remote-first company.
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Michael O'Connell
SEMICON WEST is on fire 24x7 Tuesday afternoon continued the focus on Front-End Manufacturing. Great session on wafer map visual analytics with Janhavi Giri, Intel, using self-supervised learning for image analysis, detecting failures and optimizing manufacturing processes. Naor Alfassi, Applied Materials, spoke about ML-guided Electron Beam review for wafermap image classification and defect detection. Janhavi Giri, PhD Naor Alfassi Sessions on virtual metrology from Srividya Jayaram, Siemens, on high product mix; and Mike Yim, Gauss Labs, on high-volume, from Lab to Fab. In our experience, virtual metrology has led to massive time savings in product testing. Srividya Jayaram Mike Young-Han Kim One very fun and topical presentation was from Christoher Nguyen, Aitomatic, and Daisuke Oku, Tokyo Electron - announcing SemiKong, a new Open-Source Semiconductor LLM, jointly developed by Tokyo Electron, Aitomatic, FPT AI Center and the AI Alliance. This seems to be based on Meta's Llama3 with additional HTM context. Christopher Cuong T. Nguyen Daisuke Oku I just returned from visiting our HTM customers in Japan and Korea, where we showed our Spotfire Copilot running in Japanese and Korean .. and this presentation on SemiKong was a joy to see!! Feel free to check out my latest blog on the Japan visit including a Spotfire Seminar with our partner NTT Com and customers including Texas Instruments, Tokyo Electron, Toshiba, Sony and many others. MOC Blog on Spotfire Seminar in Tokyo, Japan https://lnkd.in/gAeSZSSC Wednesday sessions in Smart Manufacturing focused on how AI/ML can optimize back-end semiconductor manufacturing process eg OEE, predictive and condition-based maintenance.. We are currently revamping our Spotfire OEE solution with Spofire Copilot, so this is quite timely. Shawn Sarbacker, Kulicke and Soffa, gave a nice presentation on Semiconductor Assembly. Shawn Sarbacker I really enjoyed the session on Zero Defect Manufacturing from Prasad Bachiraju, Onto Innovation. Totally aligned with our Spotfire work on visual data science for defect analysis .. interesting backend data integration .. we’ve been working on data federation and libraries of virtualization views for that part of the data and analytics flow. Prasad Bachiraju I also liked the IBM WatsonX GenAI session from Megan Arp and Francois Bernard - many similarities to our Spotfire Copilot e.g. they refer to the “Watson Orchestrator” in an analogous architecture to our Spofire Copilot Orchestrator. Their LLM is able to predict user intent when a technician reviews a ticket (i.e., query vs. task). Tasks are handled through custom and prebuilt skills training, while queries are handled through RAG. Megan Arp Francois Bernard Spotfire #VisualDataScience https://lnkd.in/eSeVcE5h https://lnkd.in/ecWq3PSH
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⌨︎Charles Chen
Weekend update: TrialSight AI is now Zeeq AI (rhymes with "seek")! Zeeq (https://zeeq.ai) is an #AI agent inspired by the Clinical Trials Transformation Initiative (CTTI) AACT project that watches FDA ClinicalTrials.gov for the latest updates and emails you a personalized newsletter that answers your questions for trials that match your criteria. Whether you are a patient, researcher, pharma executive, or industry analyst, Zeeq helps you stay on top of the latest developments any time a change is added to ClinicalTrials.gov. The agent is now even faster (see for yourself) and still free to sign up (no usernames or login required). I'm getting close to go-live. The monitoring logic is ready and testing the email generation this morning. Still need to add a passwordless/accountless way to manage the subscriptions.
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Madhav Chanchani
Nandan Nilekani says that India’s path in AI (artificial intelligence) is different, with a focus on applications rather than building the biggest large language models (LLMs). “Let those people with capital, and who have better chips do that,” he said. “Our advantage currently lies not in compute, cloud, or chips. 𝐎𝐮𝐫 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐢𝐬 𝐨𝐮𝐫 𝐩𝐨𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐭𝐡𝐞𝐢𝐫 𝐚𝐬𝐩𝐢𝐫𝐚𝐭𝐢𝐨𝐧𝐬. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐲 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐭𝐨 𝐛𝐫𝐢𝐧𝐠 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐜𝐨𝐬𝐭 𝐨𝐟 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐟𝐫𝐨𝐦 𝐑𝐬 100 𝐭𝐨 1 𝐑𝐮𝐩𝐞𝐞……Innovate frugally to dramatically reduce the cost of AI. We can't deliver it to a billion people unless we can begin to charge 1 rupee per transaction.” "Winners in AI in India will be those who meet customers where they are," Nilekani said at an event by People+ai on Tuesday. These are some notes on what I found interesting from his chat. Startups and companies should leverage AI’s capabilities to personalize products at scale, which has not been possible earlier, according to Nilekani. “We are moving to a world where instead of you adjusting to the technology, the technology will adjust to you. It will adjust to you as a person, adjust to your skills, adjust your talents, adjust, your capabilities, adjust your language, and your skills……what we have found is whenever we make it easier for technology to adjust to you then our usage goes up,” he said. Nilekani mentioned that companies should leverage the combination of digital public infrastructure (DPI) and AI. DPI is the combination of Aadhaar, UPI, Account Aggregator (AA), and ONDC open-source softwares. This includes examples like leveraging local language capabilities of AI platforms like Bhashini and Sarvam to deliver personalized financial services products or offers, by understanding customer's banking profiles through AA. There was a demo by Nikhil Kumar of Setu of a new chat-based personal finance product. “We have a unique infrastructure in India where we are building digital public infrastructure for AI. We are building a set of digital public goods for AI. We are building on top of an existing DPI to power AI and therefore accelerating the adoption of AI in India,” said Nilekani. “And that's what this is a really the unique way that India is doing in AI which is what gives us confidence that we can move very rapidly in this space. Now, we talk about unbundling, but now with AI, we have a chance to bundle again. It was James Clarke of Netscape who said that we spend half our lives unbundling and then the next half re-bundling.” “This means that all these AI Tools now allow you to put things back together in a very effective way to get the full impact of the solutions. Because for the first time, we can now personalize at scale.”
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Sal Huante
“Google was built for this moment. We’ve been pioneering GPUs for more than a decade,” Google CEO Sundar Pichai Introducing Trillium, Google's sixth-generation Tensor Processing Unit (TPU). Trillium was specifically designed to accelerate the development and deployment of advanced AI models. With significant improvements in compute power, memory, and networking capabilities Key advancements in Trillium include: ➡ Enhanced compute performance: 4.7X increase per chip, enabling faster model training and serving. ➡Increased memory and bandwidth: Doubled High Bandwidth Memory (HBM) capacity and bandwidth, along with Interchip Interconnect (ICI) bandwidth, for handling larger models and datasets. ➡SparseCore accelerator: third-generation SparseCore, Specialized for processing ultra-large embeddings common in advanced AI workloads. ➡Sustainability: 67% more energy-efficient than the previous generation. 👉 👉👉👉 https://lnkd.in/dyASZGWy #AI #ML
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Jess Nall
From where I sit in the SF Bay Area, cradle of innovation for at least the last 30 years, I'm a true believer in the promise of the #AI revolution. That said, I find it fascinating to consider views on the opposing side. I'm sharing below two opinion pieces from the last two weeks suggesting that the AI revolution is fundamentally overhyped and a bubble ripe for bursting, and that AI tools are actually not useful or transformative. Contrast this with venture capitalist Kai-Fu Lee's prognostication that 50% of white collar jobs will be displaced by #AI within three years, and that OpenAI and the other cutting edge movers in the space are headed to a trillion+ dollar valuation. Which side will win out? Where will we be by 2030 in the AI race? I'd love to hear your thoughts in the comments.
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