Song Xiong

Interesting article from Bloomberg about life at Google's X in a time of layoffs and cost reduction at https://lnkd.in/gmw3GG7C. X is a component of Alphabet (Google's parent company) that incubates new ideas. They have been responsible for some great successes - getting Waymo and a lot of Google's AI work started - but they've also had projects that didn't survive - like Google's attempt to provide Internet access via weather balloons. When you work at a company that has hundreds of billions of dollars in revenues, it's hard to get a lot of interest in an entirely new business idea that brings in hundreds of millions. If you have a project that improves ads revenue by that amount, or decreases capital expenditures, you can do it, but if you want to, say, build wearable tech, it becomes hard to attract the attention of marketing or product. So, new ideas often die on the vine. For the most part, this is good and proper. Companies may have the financial wherewithal to branch out, but more often than not, they consider it a distraction. And they are probably right - it's hard to argue that Meta was right to divert such a huge chunk of their resources to chase after the metaverse. They've (rightfully) pulled back, and are more focused on their core business again. On the flip side, if you are in the tech sector, and you don't innovate, you are likely to get left behind at some point. So, you need to make and manage these investments somehow. When I joined Google, the CEO had a 70-20-10 rule - 70% of effort went to core businesses, 20% to broader efforts to improve Google, and 10% to stuff that's completely blue-sky. This is probably not completely right, but it is also probably not completely wrong. X is the division of Alphabet that is *specifically* designed to bring blue-sky products to life. Unsurprisingly, they've had to pivot and rethink their strategy many, many times. The article interviews a number of people who have worked at X over the years; it's well worth a perusal, for folks who are interested in the story.

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Today I'm sharing this 🔥 video from our recent Engineering onsite at Demandbase HQ in SF! https://lnkd.in/eFAFR6QG We were so fortunate to host Zhengxuan Wu and Aryaman Arora, two upcoming AI researchers from Stanford University Department of Computer Science. Their approach to fine tuning, called ReFT, targets the representations emitted by neural net layers rather than the weights of the layers themselves. This makes it very flexible when using the same base model for swapping multiple fine tunes. The memory overhead is also very low for their intervention weights. Another highlight is how powerful it can be. One POC they showed forced the LLM to speak in only emojis exhibiting ReFT's power to control output. Control is crucial when, for example, you want to prevent your bot from offering customers unauthorized refunds!! If you're already familiar with LoRA, it should be pretty simple to use with only a few extra hyperparameters -- details in the video. Here's a link to the repo. Give them a star! https://lnkd.in/eJScNbzK Thanks again for letting us host your first external talk Aryaman and Zen. It's very exciting tech!!

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Check out the "Titan Text Premier Demo" built by my team on this page

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Having lived in SF bay area for so long, I came to know the big coffee culture scene here. I was totally consumed by it a while ago and spent years trying to attain the “perfect" latte. I published a fun coffee story and my entire home setup, which took weeks of research and multiple iterations, in my blog link in bio. Meanwhile, to all fellow coffee loving tech peeps, here are some top tips that no one told me. Put it in practice at home or in microkitchens. - A big factor in preparing milk suitable for latte art is pressure of the wand. If this is important to you, you need to get a machine that has a strong enough pressure. - When pulling shots, put in 18g of beans and extract 30g of espresso. - The darker the roast, the coarser the setting should be on the grinder, the lighter the roast, the finer the setting should be. - When adjusting the grind setting, do so while the grinder is working if there are beans in the hopper. Otherwise, a bean can get stuck between the burrrs. - Pulling a shot should take 20-30 sec in time, or based on observation. It should look like caramel going out, not a waterfall. - Eventually work your way to using a bottomless portafilter. It punishes you for all the small imperfections when preparing your coffee puck, like any tilt, and uneven distribution. It does so by spraying the espresso all over the place. The more you can get it to flow uniformly, the better your preparation of espresso, objectively. - Seal your beans in a vacuum, so they don’t go stale. The more stale the beans, the more minor adjustment you need to make to grind finer. - When steaming milk in your pitcher, you can tell if it’s done by holding the pitcher and as soon as the heat becomes unbearable i.e. stings, it’s done. - Taking a latte art course at a local coffee shop was the best boost for me to start nailing latte art.

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A data-driven approach to driving wound healing outcomes Marcella Gomez, University of California, Santa Cruz https://lnkd.in/gAkWZYJT Meet: https://lnkd.in/g4AAWkcK YouTube Stream: https://lnkd.in/gwtqaTmf Talk details: https://lnkd.in/gWn3B3cU Subscribe for talk announcements: https://lnkd.in/g5ciuNuX Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori. Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. Here, we discuss work in wound healing, for accelerating wound closure. We argue that timing of treatments is as critical to consider as the choice of drug or therapy. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices enhanced with deep learning can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and preliminary work in transcriptomic based classification of wound states. Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics and Associate Dean for Diversity, Equity, and Inclusion for Baskin Engineering. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in the broad field of bio-control leveraging methods in machine learning and control theory. Applications range from controlling single-cell response to driving complex systems such as wound healing. #modeling #simulation #medicine #healing #trauma #predictivemodeling #datascience #data #woundhealing #biology #bioengineering #systemanalysis #sensing #omics

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Our Kumo.AI blog post on Hybrid GNNs is out. https://lnkd.in/gpzr8TiF We have seen success with our Hybrid GNNs across several marketplace problems. Hybrid GNNs are a novel innovation at Kumo.AI because they model both repeated and explorative user interactions within a single GNN framework. The GNN automatically infers whether a user tends to do repeat purchases or tends to be more exploratory (ie buying new unseen items) and learns a repetition scalar per user. What we love about this approach is that it takes away from the standard recommendation systems architecture where you have multiple candidate generators - some for new unseen items, some for repeat behavior and then have to combine them in a final multi-objective optimization framework. Such systems which are a combination of multiple systems are hard to maintain, re-train and operationalize. On the Kaggle H&M challenge where most winning teams usually have approaches that require large amounts of feature engineering and the final system is a complex ensemble of approaches needing months to build, the Kumo.AI approach which is a 3 line predictive query that runs a Hybrid GNN under the hood is top 1%. Thanks Matthias Fey and Weihua Hu for the algorithms and innovation.

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Guo Liu and his team are on the cutting edge of personalizing AI models. For software engineers keen on exploring #LLM and #Alexa, Guo's work is definitely worth checking out! #AI #ArtificialIntelligence #SoftwareEngineering

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🍜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

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A successful systems design and delivery strategy begins by assuming that change is likely the only constant. Be adaptable, or fail.

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Amazon Bedrock Studio 😎. "With Bedrock Studio, you - and your teams - can sow a million different generative AI ideas, care and feed them, and watch the beautiful results bloom in-front of your eyes. " #aws #artificialintelligence #genai

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I produced a demonstration video showcasing a personal assistant implemented by me on an AI terminal device, such as an AI-enabled PC. In this video, I illustrate how leveraging Nvidia technology and the LangChain framework enables efficient utilization of large language models within our application. By utilizing Nvidia-accelerated data centers, we call open-source models, including Mistral, to process user queries through retrieval-augmented generation. This approach effectively organizes and synthesizes information from the web, delivering users timely and accurate responses. demo video: https://lnkd.in/gUtxKBHf Step-by-step tutorial: https://lnkd.in/gcv8NBNM source code: https://lnkd.in/gPw8XjMj #NVIDIADevContest and #LangChain NVIDIA AI @NVIDIAAI NVIDIA Taiwan Happy AI hacking!

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*Open Source AI is the Path Forward* Open source AI represents the world’s best shot at harnessing this technology to create the greatest economic opportunity and security for everyone. Meta's release of Llama 3.1 marks a significant milestone in advancing open source AI. The benchmark results are impressive! The gap between closed-source and open-source models is quickly vanishing. Check them out here: https://llama.meta.com/ If you feel like doing a bit more reading, this blog by Mark Zuckerberg is just a gem! https://lnkd.in/d48iBtiR

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Istio Service Mesh: great to see Ambient moving up to Beta, delta xDS becoming the default, and api-gateway becoming a first class citizen in Istio. https://lnkd.in/gYPmnTuj

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Amazon-to-Google-to-Amazon #a2g2a thought of the day: interesting to see how RTO (Return-to-Office) outcomes are entirely different for the two companies. In my post today, I don't really want to go down the path of debating whether RTO is "good" or "bad". There's very emotional and valid opinions on both sides, and it affects people's personal lives in real ways. Please be respectful. Whether you agree with the policy or not, for it to actually work it does need *critical mass*. Both companies have essentially the same RTO policy (3 days/week expectation), but there's significant differences is in how the message was delivered and enforced - and the outcome. Google’s 3-days-per-week was mostly a “pretty-please, would-you, if-you-don't-mind” with no actual enforcement (but some vague threats). So most people ignored it. They still do. Leadership got frustrated and the message got less polite over time, but nothing really changed. Even as of 3 weeks ago, when I was still a Google employee, the buildings were fairly empty. That made being physically in the office significantly less useful, other than enjoying the perks of free food and massages by myself (and occasionally with 1 or 2 coworkers). Amazon’s 3-days-per-week came with actual enforcement. Amazon used badge reader data to track when people were actually coming to the office, with consequences. It does feel a bit draconian, but you can’t argue with the effectiveness of saying “we have a policy that we believe is business-critical and we’ll enforce it or else.” I appreciate that when I come to the office, I can find my coworkers there and we can have f2f meetings. There’s critical mass for RTO to actually be effective. Amazon has picked a lane. Personally, I have not figured out how to truly be productive and happy working remotely in the last 4 years. I respect and appreciate the fact that some people have, but I haven’t. I do like the option to stay home a day or two, skipping my long commute, working from a lounge chair in my backyard on a sunny day. But there’s nothing like the energy in the room of a good group meeting, or the chemistry of a face-to-face 1:1, or the enthusiasm of two engineers standing in front of a physical whiteboard drawing boxes and arrows with markers brainstorming about the future. Or the bonding of sharing a meal or a walk between buildings with a coworker. I feel energized when I’m physically in the office. It literally charges my batteries. I’ve been coming to the office 4x/week and loving it. 

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Sustainability is no longer just a buzzword; it's pervasive. Amazon's leadership principle of "Success and Scale Bring Broad Responsibility" translates this from corporate strategy to daily habits, hence reshaping industries and driving innovation. #Sustainability #SustainableInnovation #Amazon #AmazonPrinciples #NetZero #CarbonNeutral #RenewableEnergy #WasteReduction

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