Ike Nassi

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here: https://lnkd.in/ga6tD5rt

6,433

199 Comments

Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this. I’ll explain in a moment why having a new test is important. The public thinks achieving AGI means computers will be as intelligent as people and be able to do most or all knowledge work. I’d like to propose a new test. The test subject — either a computer or a skilled professional human — is given access to a computer that has internet access and software such as a web browser and Zoom. The judge will design a multi-day experience for the test subject, mediated through the computer, to carry out work tasks. For example, an experience might consist of a period of training (say, as a call center operator), followed by being asked to carry out the task (taking calls), with ongoing feedback. This mirrors what a remote worker with a fully working computer (but no webcam) might be expected to do. A computer passes the Turing-AGI Test if it can carry out the work task as well as a skilled human. Most members of the public likely believe a real AGI system will pass this test. Surely, if computers are as intelligent as humans, they should be able to perform work tasks as well as a human one might hire. Thus, the Turing-AGI Test aligns with the popular notion of what AGI  means. Here’s why we need a new test: “AGI” has turned into a term of hype rather than a term with a precise meaning. A reasonable definition of AGI is AI that can do any intellectual task that a human can. When businesses hype up that they might achieve AGI within a few quarters, they usually try to justify these statements by setting a much lower bar. This mismatch in definitions is harmful because it makes people think AI is becoming more powerful than it actually is. I’m seeing this mislead everyone from high-school students (who avoid certain fields of study because they think it’s pointless with AGI’s imminent arrival) to CEOs (who are deciding what projects to invest in, sometimes assuming AI will be more capable in 1-2 years than any likely reality). The original Turing Test, which required a computer to fool a human judge, via text chat, into being unable to distinguish it from a human, has been insufficient to indicate human-level intelligence. The Loebner Prize competition actually ran the Turing Test and found that being able to simulate human typing errors — perhaps even more than actually demonstrating intelligence — was needed to fool judges. A main goal of AI development today is to build systems that can do economically useful work, not fool judges. Thus a modified test that measures ability to do work would be more useful than a test that measures the ability to fool humans. [Truncated for length. Full text: https://lnkd.in/gSn2KB3R ] 

3,718

284 Comments

Barbarians at the Gate (https://lnkd.in/gkEBX79s) is a very interesting new paper from some great folks at Berkeley, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it: https://lnkd.in/gxS3s4v4 The core point of the paper is that AI can be effective at automatically driving the 'number go up' part of systems research. Given an evaluator, AI can automate progress on implementation. I believe this is 100% correct. The paper also talks about how more focus will have to go into developing evaluators. Again, I 100% agree, but think they're understating their case. Building evaluators (basically tests) is, I believe, going to become the focus of systems and software over the next decade. Which leaves researchers with the hard problem of choosing which problems to go after. That's never been easy in academic systems, and is only going to become more of a challenge. But the best systems research is driven by choosing problems well! This vision for AI could lead to lots more of the least interesting systems research. The "number go up" kind. Which could make it harder than ever to identify the truly innovative and visionary work. We're already seeing that elsewhere in science. So, mostly, I think the paper is right, but not yet asking the biggest question: what should systems research be?

276

12 Comments

An important episode of #theArtofthePossible with Tony Rodriguez talking to professor Hany Farid about truth in the age of GenAI. Some of the key points: - AI-generated content creates doubt about the authenticity of real events. - The “liar’s dividend” enables people to dismiss inconvenient truths as fake. - The importance of a multi-dimensional solution to address AI-driven misinformation. - Better regulation of AI technologies is critical to prevent misuse. And, finally, talking about Coalition for Content Provenance and Authenticity (C2PA) and what the watermarking tech and task force brings to the standard. Digimarc https://lnkd.in/eCm3QMsK

14

1 Comment

Excited to see spotlights on Bay Area startups like @Troiomics and @Snorkel AI - From research to real-world results: Triomics and Snorkel AI are proving how AI can transform lives. Whether it’s accelerating clinical trial matching for cancer patients or revolutionizing data labeling, these innovators are building with purpose. https://lnkd.in/gWDYzcJZ #Healthcare #DataAndAI #Azure #MicrosoftForStartups

20

My guest on "Inside AI", Yale professor Nicholas Christakis, is one of the most important scientists of our age whose work bridges medicine, sociology, and #AI. Originally a hospice doctor, Nicholas truly embodies the renaissance man - a polymath who examines human nature through multiple lenses across many disciplines. His now classic papers on human social networks showed how our connections shape everything from health to happiness. His book "Connected" (co-authored with James Fowler) made this research accessible to a wider audience. In "Blueprint," his most recent book, Nicholas presents compelling evidence that humans are fundamentally wired for kindness, cooperation, friendship, and love. In this episode, Nicholas discussed some the insights from his experiments integrating AI into social collaboration settings. You'll hear how these experiences have shaped his views on how we should incorporate AI into our societies - thoughtfully, with both optimism and caution. A few quotes: 1️⃣ "We are inventing 'dumb AI' to supplement human interaction... All the AI has to do is be like a catalyst... allow the humans to solve their own problem because they're sufficiently smart. They just need a little lubrication." - on the surprising idea that simple AI can be effective in human groups 2️⃣ "What my laboratory has been studying is human-human interaction in the presence of machines. What kind of programming of these AI agents can optimize human performance and human experience?" - on the profound question about how AI changes human dynamics 3️⃣ "We have found cases in which AI can subvert altruistic tendencies in groups... [In one experiment] people's 'ethical chip' had degraded, they never recovered the ability to take turns or be altruistic with each other [after relying on automation]." - on the potential downsides 4️⃣ "If we want to have a utopian rather than dystopian future, as we add AI agents into our midst interacting with us socially, those agents ideally should be programmed... to respect our innate sociality. To the extent that they do not, I think they could harm us." - on designing AI for a better future Listen to the conversation on "Inside AI" by the EPFL AI Center: Apple Podcasts: https://lnkd.in/e5qgiSfC Spotify: https://lnkd.in/esbbUEXu

159

11 Comments

Generative AI’s greatest potential lies in the physical world, where the ability to simulate, design, and iterate faster can reshape trillion-dollar sectors. That’s why we’re thrilled to announce that N47 is leading Luminary Cloud’s $72M Series B. The Luminary team is pioneering the Physics AI Model Factory, underpinned by a GPU-native physics simulation platform. This technology is designed from the ground up to unleash the performance of modern GPUs and generate the high-value datasets that power next-generation AI. Now, engineers can run hundreds of design iterations overnight instead of waiting days. AI models trained on physics-grounded data can suggest new aerodynamic designs or optimize energy systems in ways previously impossible, and companies across industries can accelerate their innovation cycles while reducing cost. With co-founders Juan Alonso and Pete Schlampp, Luminary has the technical depth and operational experience to define the new era of Physics AI. We’re proud to partner with them as they create the future of simulation and redefine what’s possible in the built world. Learn more about Luminary Cloud’s vision here: https://lnkd.in/gbjeWrvy 

119

6 Comments

Delighted to share new Arc Institute work from our group on AI-accelerated lab-in-the-loop, in Science Magazine today. One of the most remarkable things about biology is that it's digital. DNA, RNA, proteins: these are all sequences, and their function is directly encoded in their sequence of letters. But a protein of length N has 20^N possible variants and the vast majority are non-functional. Evolution spent billions of years finding the functional needles in this haystack through random exploration and natural selection. For modern biomedicine, we need to solve this in days to weeks. The process of scientific research is fundamentally a search problem, and we basically do guess and check. We've trained predictive models of biology, like our Evo series of DNA language models, to learn the evolutionary constraints on biological sequences. Such models learn a fitness landscape of what evolution has explored. But the fitness landscape is not the same as the function you actually care about: whether an enzyme catalyzes faster, whether an antibody binds tighter, whether a CRISPR tool edits better. The core question is how do you connect the knowledge of these models to the functional search that has to happen in the physical lab? MULTI-evolve is one of our first answers. It's a full-stack, AI-lab-in-the-loop framework that "jumps" directly to hyperactive multi-mutant proteins via ML-guided evolution. We combine an ensemble of protein language models pretrained on all proteins across evolution to discover beneficial mutations, then systematically measure pairwise combinations to learn the epistatic landscape (e.g. which mutations are synergistic vs. antagonistic), and extrapolate to predict powerful higher-order combinations of 5-7+ mutations. We also built MULTI-assembly, a molecular biology method to physically construct these complex multi-mutants cheaply and quickly, regardless of protein length (previously a major bottleneck). We applied this to three very different proteins: APEX (a proximity labeling enzyme), CRISPR-Cas13d (for RNA trans-splicing), and a therapeutic anti-CD122 antibody, achieving up to 256-fold improvement. For CD122, we simultaneously optimized both binding affinity and antibody expression, navigating real trade-offs between competing developability objectives. We built Arc Institute to be a full-stack biology and AI research organization. We've been training frontier AI models for biology, like Evo, Evo 2, State, Stack, etc. But the whole point of having frontier AI capabilities and experimental biologists under a single physical roof is to close the loop between computation and the wet lab. MULTI-evolve is one of Arc's first examples of AI lab-in-the-loop, and there will be much more to come. This work was a wonderful collaboration with Silvana Konermann and Brian Hie, and led by the remarkably driven and persistent Vincent Tran with Matthew Nemeth, Liam Bartie, Sita C., Alison Fanton, and Chad Moon.

1,066

19 Comments

The Brookings Institution has identified five distinct AI clusters in US metros: 🌟 Superstars 🌟 Star Hubs 🌟 Emerging Centers 🌟 Focused Movers 🌟 Nascent Adopters Standing out as an AI Star Hub, the Phoenix-Mesa-Chandler metro area ranks 17th nationally. 🚀 Anticipating positive spillover effects from TSMC's expansion in Arizona, benefiting both the semiconductor ecosystem and AI sector. #AI #Innovation #aidatacenters #semiconductors State of Arizona City of Chandler, Arizona City of Phoenix City of Mesa City of Tempe

10

🚀 Breaking Down Memory Walls for AI and Science! Fascinating read from Pacific Northwest National Laboratory (PNNL) on their groundbreaking "active memory" computing architecture, purpose-built for massive AI and scientific workloads. At the heart of this system is technology from XConn Technologies: a high-performance CXL (Compute Express Link) switch. This isn't just an incremental upgrade—it's a paradigm shift. The XConn CXL switch enables a revolutionary pool of shared memory, allowing CPUs and GPUs to access a massive, unified memory space at incredible speeds. This directly tackles the biggest bottleneck in high-performance computing: data movement. For national labs like PNNL, this means unlocking unprecedented scale for scientific discovery, from energy research to advanced materials science. A powerful example of how core infrastructure innovation (CXL) is enabling the next leap in computational science. https://lnkd.in/gCGCtgx6 #AI #HPC #HighPerformanceComputing #CXL #ComputeExpressLink #DataCenter #Innovation #GPU #ScientificComputing #NationalLabs #XConnTechnologies #PNNL

22