Christianna Clark

Pathways to Tech Week #2 at  Headstarter: Being surrounded by people who are building amazing software and companies is a truly inspiring experience. My favorite speakers were: • Tariq Ahmed: First Engineer at AppLovin ($30Billion IPO) • Julian Alvarez 🚀: CEO of Wisdolia • Hamza Ali: CEO of Olostep  Here's what I have learned from them: 𝟭- "𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗮𝗻𝗱 𝗽𝗶𝘃𝗼𝘁" Tariq, one of the first engineers at AppLovin, shared that AppLovin tried several things in their early stages that failed. However, the company had incredible success when it pivoted to mobile advertising; They didn't have expertise in the field. Today if you've experienced in-app advertising, AppLovin was likely behind it. 𝟮- "𝗦𝘁𝘂𝗱𝘆 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀, 𝘆𝗼𝘂'𝗹𝗹 𝗯𝗲 𝘀𝗵𝗼𝗰𝗸𝗲𝗱 𝗮𝘁 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻." Julian runs Wisdolia a startup aimed at helping college students actively study. By simply asking what types of students they were. His team found that medical students were the ones the ones who converted the most into paying customers. It seemed evident because they were the most overwhelmed by the amount of information they had cram. But the evidence in their face was a lightbulb moment for Wisdolia. 𝟯- "𝗦𝗰𝗿𝗮𝗽𝗽𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝘄𝗲'𝗹𝗹 𝗴𝗲𝘁 𝘆𝗼𝘂 𝗳𝗮𝗿." Hamza, CEO of Olostep, describes his company as "a company that enables AI agents to browse the web just like humans". I was fascinated when I heard that, and as the Olostep team acquires more customers they value scrappy and people that initiative on board. Those qualities set engineers apart when building successful products.

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Very useful insights for folks who are starting to dig further and further into the realm of LLM. An easier GUI approach for fine tuning LLMs.

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I was truly inspired by Logan Havern, who featured on Headstarter AI Pathways to Tech as a speaker Logan is founder and CEO at Datalogz, solving biggest challenge with data management in Business Intelligence. From early days at Texas A&M University , where he studied Industrial Engineering and nurtured his interest in entrepreneurship, he played around with his startup ideas and built Zuntik and Blip Biometrics. He quit his job at JetBlue to work on his idea which he saw potential in when 400 people signed up via reddit post, which is now Datalogz From going all in 18 months after quiting job with zero revenue, living in NYC, burning through savings and getting ghosted by investors to raising millions of dollars is testament to resilience. Also, his insights on how to get first 1000 users to use your product was super intriguing. Here is what shared: 1. Focus on core pain points 2. Conduct user discovery before even writing a single line of code 3. Don't just think about consumer problems consider enterprise level problems as well

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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!

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Have you been trying to learn AI and finding that there's just a lot? A lot of new vocabulary, a lot of linear algebra, a lot of conflicting opinions...just a lot. Yeah, me too. One of the things that has frustrated me most is that a lot of the new vocabulary seems to just be new names for things I already know. Or existing concepts or algorithms with small changes. To address this, Jason Davenport and I decided to create videos where we explain AI concepts in words developers already use. Let me know what you think and if there are any terms or topics you want us to do an episode about. https://lnkd.in/gP2p2dU7

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The 2024 edition of the Stanford AI Index is out, and highlights PaLM-E and RT-2 as this past year's research spotlights in robotics: https://lnkd.in/gemZZzXJ

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Open source has always been a major part of my career. I spent 13 years building MongoDB and its open source developer ecosystem, and at Viam all of the software that runs on a user's device, our APIs, and our SDKs are open source. Now, I am excited to share that Viam is now an official member of the Open Source Robotics Alliance. Let’s talk more about why open source is important to us:  Security - If machines are inside your house, your office, etc. security and privacy are paramount, and you'd want to know exactly what’s happening with that data. Open source not only makes companies responsible to write good software, but lets the community easily vet it. Innovation - I have said before, no one wants to contribute to a closed source ecosystem. We want developers to bring their ideas to life using Viam. To do so, open source is necessary, as it’s harder to contribute to closed source, and not nearly as rewarding. It keeps corporations in check - When betting your business on a technology, you are responsible for making sure there are some checks in place. By making everything on-machine open source, it means that if something happens, there are ways for users to move forward using open source without Viam. This is all just the tip of the iceberg. Open source has the power to democratize robotics, remove barriers to interoperability, and make automation much easier than it is today. Read more here: https://lnkd.in/eTc8p_Dk Open Robotics #opensource #openrobotics #viam #mongodb #robotics #opensourceroboticsalliance #OSRA

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If you think compute scaling will plateau, think again. We are in the baby states of inference time compute. VLLMs coupled with computer tools, reward learning and RL can leverage orders of magnitude more inference time compute than what we have seen with foundation models. Here’s a concrete training architecture (replace \pi with a large pre-trained vLLM and add it to the reward learner too, \R is the space of all computer tools)

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Legit quotable and actionable advice: * Power of Threes - 3 mentors, 3 peers, 3 mentees. * If you want to go fast, go alone. If you want to go far, go together

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We’ve run almost a billion inference requests at Beam Here are a few things we’ve learned about real-time inference APIs 1. For small models, you can often run multiple workers in parallel on the same GPU and increase throughput 2. Weights should be cached on disk or in distributed storage. You don't want to be downloading large files from cloud storage during an inference request 3. Cross-region latency matters. Regional caching is hard and requires a lot of planning 4. You can't use the same autoscaling strategy for every model. You have to see how your workload performs in the real world and choose a strategy that makes sense When you run APIs powered by large ML models, performance isn’t guaranteed. What other strategies have you employed in your ML pipelines to optimize performance?

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I've been in my Gemini Era! #Geminibaby But I'm not just talking about being born in the most incredible month ever #June. I'm referring to Google's Gemini AI product. I wanted to share with my network the top 3 ways I use Gemini to help save me time, make life easier and even get discounts or gift cards when I've had a good or bad customer service experience. 1. Did you know Gemini can help draft customer service emails? Have you ever wanted to contact corporate, but felt you didn't have the time nor energy to articulate your frustration? In less than 3 mins. you can craft an email to offer feedback on if your food was cold, a flight was delayed, or even if a waiter spilled your espresso martini. 🍸 (true story) 2. Had an amazing hotel 🏨 experience? Recently I visited Nobu Los Cabos, and everything from the staff, service, food and coffee were amazing. They reached out several times asking for a review. I input everything I loved about my experience and voila! I had 3 paragraphs of goodness to share on hotels .com and a Google review. I was able to help amazing staff get recognized for my positive experience. 3. Need help with a quick day trip itinerary? While traveling, I like to see as much as possible. One of my personality traits is #maximizer. On a recent trip to Europe, I wanted to do a day trip to 🇨🇭 Switzerland. I input top things to see in 24 hours into the #Gemini search bar and it was able to flush me out a full itinerary. Mapped all of the places in Google maps - and started knocking things off my To Do list. I had fun, my plan was efficient, and even got to see the amazing views of the Grossmünster church. So go on and try it out. I know some folks are weary of how AI will affect our futures, but I wanted to share my experience on how it very much helps me in my regular day to day life. I hope these tips help someone! P.S. Be sure to catch the Google I/O in 10 mins. recap. https://lnkd.in/d4GB6UtH

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Anysphere, the startup behind the AI-powered coding assistant Cursor, has secured $60 million in a Series A round, achieving a $400 million valuation. The funding was co-led by Andreessen Horowitz and Thrive Capital, with participation from Stripe's CEO Patrick Collison. Founded by MIT students Michael Truell, Sualeh Asif, Arvid Lunnemark, and Aman Sanger, Anysphere is making waves in the competitive AI coding assistant market, which includes rivals like Cognition and Magic. The company previously raised $11 million in a seed round led by OpenAI Startup Fund. With growing revenue and usage, Anysphere is poised to challenge established players like GitHub Copilot, which has seen significant success under Microsoft's ownership. As the demand for efficient coding tools rises, Anysphere's latest funding round underscores investor confidence in the burgeoning AI coding sector.

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Check out the great conversation I had with Shaw Talebi, founder of the TDE podcast! We talked about AI, Education, being a YouTuber, Quantum Computing, and the implications of AI in society. https://lnkd.in/eTw-UK-f

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Exciting work from Meta and World Resources Institute who have used AI to create the most detailed map ever made of trees across the planet. This allows anyone to query the height of forest canopies or even individual trees (when they stand isolated enough). The data is now freely available. As a fun test, I used the provided browser (https://lnkd.in/ghxQFmxt) to find one of my favorite neighborhood trees - the giant sequoia in Seattle's Leschi Park. The data accurately shows this tree towering above the others in the park (screenshots in comments below). The team used AI at Meta's DINOv2 visual transformer architecture in order to infer the height of trees given just a satellite or aerial color image as input. This is a very similar technique to 3D photos that you may have seen in your Facebook feed (https://lnkd.in/gXCygk5H), where the depth of an image is computed via AI in order give a 3D effect. I'm looking forward to this work continuing and becoming an important way in which the world measures, reports, and verifies the carbon stored in trees. As industries (from big tech to airlines) purchase biosphere carbon credits to offset emissions, it's critical that they have the best data possible. Improved imagery and AI offer a path to getting there. More info in the project video below.

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Today's guest is the amazing Google DeepMind research scientist, Dr. Rosanne Liu (pictured here in San Francisco this past weekend with myself and Jason Yosinski, who introduced me to Rosanne). Rosanne:  • Is a Research Scientist at Google DeepMind in California. • Is Co-Founder and Executive Director of ML Collective, a non-profit that provides global ML research training and mentorship. • Was a founding member of Uber AI Labs, where she served as a Senior Research Scientist. • She has published deep learning research in top academic venues such as NeurIPS, ICLR, ICML and Science, and her work has been covered in publications like WIRED and the MIT Tech Review. • Holds a PhD in Computer Science from Northwestern University. Today’s episode, particularly in the second half when we dig into Rosanne’s fascinating research, is relatively technical so will probably appeal most to hands-on practitioners like data scientists and ML engineers. In today’s episode, Rosanne details:  • The problem she founded the ML Collective to solve. • How her work on the “intrinsic dimension” of deep learning models inspired the now-standard LoRA approach to fine-tuning LLMs. • The thorny problems with LLM evaluation benchmarks and how they might be solved. • The pros and cons of curiosity- vs goal-driven ML research. • The positive impacts of diversity, equity and inclusion in the ML community. The "Super Data Science Podcast with Jon Krohn" is freely available on all major podcasting platforms and we publish a video version on YouTube. This is Episode #797! #superdatascience #machinelearning #ai #deeplearning #llms

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