Chul Lee

Check out the latest episode of our Tech Teams Today podcast with Yongsheng Wu, VP of Engineering at Granica - now available on all major podcast platforms. #AI #EngineeringLeadership #DataScience #MachineLearning #TechTeamsToday #Revelo

4

It was great to hear Robert Wong Julie Willem Angie McAllister, Ph.D. and Markus Kunesch on the recent episode of the @Ed-Technical podcast -- the first of a three-part series about how we're applying Google AI to our work in education and learning: https://lnkd.in/e7hQi6a2. This episode delves deep into LearnLM, how we're making Gemini tuned for pedagogy and how we're integrating this technology into our mainstream products to improve learning outcomes, and evolve how people learn on Google products. It explains inspiring new product ideas, and the steps we take across the company to understand and enhance the journey of learning in a responsible and innovative way. #LearnLM #AI #Education #Google

67

So much fun talking about the power of LLMs with Amazon Bedrock and Anthropic Claude. In summary, the ever-evolving landscape of GenAI, it's becoming clear that there won't be a single LLM (Large Language Model) to rule them all. The rise of application-specific LLMs heralds a new era, where various models will carve out niches to excel in. This diversity opens up a wide playing field for innovation and tailored solutions. When it comes to deploying AI models, striking the right balance in evaluation is paramount. We must weigh factors like cost, speed, and precision to ensure optimal performance and resource utilization. Finding this equilibrium ensures that our GenAI solutions are not just efficient but also effective in real-world scenarios. Anthropic Claude emerges as a frontrunner in the realm of LLMs, distinguished by its exceptional capabilities and groundbreaking advancements. Boasting unparalleled scale, efficiency, and adaptability, Anthropic Claude represents a pinnacle of GenAI innovation. Its ability to seamlessly integrate with Amazon Bedrock amplifies its prowess, providing users with an unparalleled ecosystem for knowledge integration and generation. With state-of-the-art architecture and a commitment to pushing the boundaries of what's possible, Anthropic Claude stands as a testament to the transformative potential of GenAI in shaping the future of technology and beyond. Prompt engineering holds the key to maximizing the potential of applications. Crafting prompts that consider persona, context, tags, and positioning can significantly enhance the model's ability to comprehend and generate relevant responses. This strategic approach empowers AI systems to better serve their intended purposes. In the realm of GenAI architectures, a robust knowledge base is essential. Amazon Bedrock stands out as a key component for architectures like RAG (Retrieval-Augmented Generation), providing a solid foundation to fuel intelligent decision-making and content generation. As we march towards AI integration, bridging the gap with legacy applications is crucial. Agents equipped with open APIs offer a seamless way to facilitate communication and interoperability, ensuring a smooth transition into the AI-driven future. Let's continue to explore, innovate, and collaborate as we navigate the exciting frontier of GenAI together! #aws #LanguageModels #Innovation #TechTrends #AIIntegration #webdevcon #claude #bedrock

68

I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

70

3 Comments

🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

164

37 Comments

My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

15

Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

23

The afternoon Keynote @ Stripe Sessions was Fireside Chat between Patrick Collison (CEO of Stripe) and Jensen Huang (CEO of NVIDIA). Patrick was an excellent moderator — asked Jensen really probing, nuanced questions that Jensen answered with his usual sharp wit and unique (and impressively well rationalized) perspective. A couple of quotes among many that stuck with me: - “Current CPU installed market is $1 trillion of hardware, all of which will be upgraded to GPUs over the next 5 years. And I would estimate about another $1 trillion will be spent on top of that during the same time period as companies try to do more with AI. Add those together — and I think at least $2 trillion will be spent on GPUs in the next 5 years.” - “I like to go after Zero-Billion Dollar Markets — nonexistent markets that I envision & can rationalize well, but will be very hard, expensive & time-consuming to achieve.”

1

So excited to see our team's work featured here! These are fundamentals directions for recommender systems, essential for advancing the field. Excited to see how others will build on this and drive deeper insights. https://lnkd.in/gkqpq7HS

134

4 Comments

The term “AI Hallucination” describes instances where LLMs generate incorrect, misleading, or nonsensical results. However, recent discussions in psychology suggest that “confabulation” is a more accurate term (https://lnkd.in/gvYwBGCk). While hallucination implies a false sensory experience, confabulation refers to the creation of false memories—more akin to what AI does. Understanding these nuances helps us harness AI’s potential while mitigating its shortcomings. At GumGum, we thoughtfully evaluate state-of-the-art technology with rigorous quantitative testing and meticulous manual spot-checking before incorporating it into our existing workflows. This ensures our datasets are curated to remove anomalies and yield top-performing models.

5

1 Comment

This Thumbtack blog post deserves a read for anyone involved in AI transformation within their teams. Because it distills more than a year of strategy, ideas and wins for a mid size consumer tech company in a 5 minute read. The Trust and Safety efforts around detecting policy violations via LLMs is also called out as the Technology Demonstrator for AI use 🔥

11

4 Comments

Can probabilistic models, like LLMs and Nate Silver's election forecast model, ever truly be perfect, with no errors? According to Nate Silver: "We don’t talk about it much, but there’s a small amount of error introduced into the model because it’s probabilistic rather than deterministic. The margin of error on 40,000 simulations is about ± 0.5 points of win probability for either candidate; after 80,000, it declines, but only to ± 0.35." Sounds like a familiar refrain around Infactory as we discuss the limitations of LLMs... P.S. Vote!

11

1 Comment

Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

16

3 Comments

AI & SaaS: A Tale of Two Futures What if Excel and other SaaS apps don’t vanish but evolve, blending seamlessly with AI? In a recent Interview (https://reurl.cc/eGz06j), Satya Nadella, Microsoft’s CEO, suggests the “agent era” will replace traditional applications. That’s Nadella’s vision: AI as a co-star, enhancing our favorite tools—think Excel with Python instead of Excel replaced by something else. But here’s the twist: 🔸 I see AI’s disruptive power surpassing any need for standalone software. 🔸 Why open an app when a smart agent can directly pull, manipulate, and display data on demand? Nadella believes AI + SaaS = Harmony. He sees Copilot orchestrating tasks, quietly boosting productivity. Developers still matter, refining user experiences to work with AI rather than against it. I foresee a more dramatic shift. If AI can generate everything from sales forecasts to text summaries instantly, maybe the spreadsheets and CRMs we rely on will fade away. Where do you stand? 📌 Collaboration or Disruption? 📌 AI enhancing SaaS or AI replacing it? No matter which path emerges, one thing’s certain: AI is rewriting the rules—and the next chapter of SaaS will be far from business as usual. Your thoughts? Let’s discuss. #AI #SaaS #FutureOfWork #Microsoft 

4

While large organizations can be slow to adapt, nimble startups have a unique opportunity to innovate and lead the charge. Why? Because the best place for these AI copilots and agents to operate is within existing workflows and systems of record (SORs) where data already lives. This is where Creao AI comes in—helping businesses seamlessly integrate AI into their existing processes, transforming the way they work. 🌐 At Creao AI, we're enabling businesses to do just that with our customizable AI workflow platform. From domain-specific adaptability to scalable deployment and autonomous workflow generation, we’re transforming how teams operate across industries. 💡Our platform doesn't just adapt; it evolves alongside you. We’re making AI accessible, intuitive, and personal—allowing anyone to build powerful workflows, from solo entrepreneurs to global enterprises. #AI #WorkflowAutomation #DigitalTransformation #FutureOfWork #TechInnovation #AIForBusiness #CreaoAI

3

“It’s all about core product creation at the companies that use our AI,” SandboxAQ CEO Jack Hidary told VentureBeat. “And so if you want to create a drug, a diagnostic, a new material or you want to do risk management at a big bank, that’s where quantitative models shine.” OOh and you want an enterprise use case? How's this LQMs for a better version of a Monte Carlo simulation. A Monte Carlo simulation is a classic form of computational algorithm that uses random sampling to get results. With the SandboxAQ LQM approach, a financial services firm can scale in a way that a Monte Carlo simulation can’t enable. “If I have a portfolio and I want to know what the tail risk is given changes in this portfolio,” said Hidary. “What I’d like to do is I’d like to create 300 to 500 million versions of that portfolio with slight changes to it, and then I want to look at the tail risk.”

6