Vijay Karunamurthy

I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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Chatbots spewing hate speech is a problem for engineers and security teams alike. Building safe and reliable AI systems is a problem that runs deep! Check out my latest blog to learn about how we got here and what you can do to deploy AI that you can trust. I’ve even drawn up some illustrations to keep you entertained on the way: 

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We’re experiencing a massive platform shift to LLMs. Lightspeed's Michael Mignano and I discussed what this shift means for Grammarly, what the industry needs to do to ensure #genAI adoption and value creation, and where I predict true efficiency gains through AI will come from. Hint: it’s not about creating more content. Thanks for having me on, Michael! https://lnkd.in/gpruc99i

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A good read on building with LLMs https://applied-llms.org/

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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.”

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Zoom has been using the power of LLMs to enable novel platform features like AI Companion. Excited to see that #DBRX will enable enterprises like Zoom to cost-effectively build, train, and serve their own custom LLMs! Learn more about DBRX here👇

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We're thrilled to announce that Meta's Llama 3 models are now available in Amazon Bedrock. These models are designed to empower you in building, experimenting, and responsibly scaling your generative artificial intelligence (AI) applications, offering enhanced capabilities across various use cases. Pre-trained and instruction-tuned, you can now simply and easily add new capabilities into your Gen AI projects. Because these models have been meticulously trained on a massive dataset of over 15 trillion tokens, you will have much more capacity for nuance, analysis, dialogue and more. Check out these use cases and be inspired: ·      Llama 3 8B: Ideal for scenarios with limited computational power and resources, as well as edge devices. This model excels in tasks such as text summarization, classification, sentiment analysis, and language translation. ·      Llama 3 70B: Tailored for content creation, conversational AI, language understanding, research development, and enterprise applications. This powerhouse model shines in various tasks including text summarization, accuracy, classification, sentiment analysis, language modeling, dialogue systems, code generation, and instruction-following. Ready to elevate your AI game with Amazon Bedrock? There is more detail in our blog post by my AWS colleague Channy Yun linkedin.com/in/channy #Meta #Llama3 #AmazonBedrock #AIInnovation #EmpoweringTech

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Big CONGRATS to Sarah Russell on pulling the covers off her incredible work launching Project Bellwether at Google X -- helping humans respond more efficiently to natural disasters, mitigate the impact of catastrophic events, and better understand our planet’s behavior. From the TechCrunch article: "The Bellwether team has thus far produced two tools. The first is designed to forecast the risk of wildfire “up to five years into the future.” The second is a response tool that helps first responders “identify critical infrastructure” in the wake of natural disaster or extreme weather." https://lnkd.in/gQJEGtNf

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Yes most large #VC firms are overrated and generally suck because they are run my arrogant white men https://lnkd.in/gviBibWs I liked your example of Vinod Khosla of Khosla Ventures who made a prediction in 2012 that AI could replace 80% of what doctors do. He suggested that the healthcare industry could be significantly disrupted by the application of algorithms and artificial intelligence. (Sources: Impact Lab, The Healthcare Blog, Venture Beat.) In a 2016 paper titled “20 Percent Doctor Included”, Khosla doubled down on this idea, arguing that a significant portion of healthcare could be automated. He continued to advocate for the transformative potential of AI in healthcare, predicting in 2023 that within 5 to 6 years, the FDA would approve a primary care app qualified to practice medicine like a primary care physician. None of Khosla’s predictions have come true so far. In fact, the United States is experiencing the largest shortage of human physicians in its history.

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This is a quick way (Bill came in below his 4 minute limit!) to learn more about what we're building with Prak.

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For all of you space enthusiasts, excited to share the results of some work over the last 5 months between my friends at B612 and Google Cloud. Using GKE, BigQuery and Cloud Storage, Edward Lu and his team, researchers from the University of Washington along with Massimo Mascaro and some folks from Google, were able to comb through existing archived astronomical surveys and discover 27,500 new high confidence asteroid discovery candidates, including more than 100 that are classified as "Near Earth" 😳 And with the data pipelines established, we can now move to using AI to automate classification and risk based analysis at a rate that will allow us to match the pace of cosmological data growth with insights and actions. As Ed put it, being able to match the photonic capabilities of telescopes with the electrons in data centers at massive scale promises to unlock more discoveries like this, including supernovae explosions, minor planet discoveries and maybe even help us avoid the wrong kind of impacts ;) https://lnkd.in/gJa72Zn2 https://lnkd.in/gJRnH5pB

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I will be attending this session May 9th. #AIagents #AI

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Daniel Wilson and Reza Rassool Thanks for the opportunity to join the conversation, and more importantly to contribute to the critical work of Kwaai Personal AI -- turning AI into a meaningful benefit versus a threat in people's lives. (My comments start about 30 min in, after Reza's introduction to Kwaai))

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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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*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

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Can’t agree more on this as we are seeing more early stage startups building more useful AI-native agents instead of copilots on existing workflow.

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It’s exciting to witness how the hyperscalers are enabling a hyper-collaborative marketplace ecosystem that allows the multiplication of revenue opportunities. I see this happening in two ways 1️⃣ ISVs are being encouraged by hyperscalers to build with interoperability in mind 🔧 All three #hyperscalers have, over the course of the last few years, incrementally deepened their partnerships with on-page "competitors" like Salesforce and Snowflake to break data silos and leverage #GenAI on more comprehensive data sets. AWS, GCP and Azure are not just strengthening their own associations, but they are also actively encouraging collaboration between these ISVs to drive interoperability, aimed at the creation of joint solutions that significantly expand the universe of customers who can benefit. Snowflake’s most recent Polaris Catalog launch also reaffirms its commitment to interoperability with a wider library of industry players (beyond the three clouds) including hyperscaler partners like Salesforce and Confluent. That brings me to point 2…. 2️⃣ Hyperscalers are actively inviting the participation of channel partners to piece complementary solutions together 🤝 As ISVs focus on creating compatible offerings with each other, and with hyperscaler services, buyers have the opportunity to find end-to-end solutions for specific use cases right from the marketplace catalog. To do so, they’re relying on partners to identify, connect and manage these increasingly complex technologies. This presents a tremendous opportunity for channel partners to add value to these marketplace-led deals by helping piece solutions together into a comprehensive whole. Hyperscalers are also cognizant that the “channel is the glue between them and the lower enterprise, midmarket and SMB,” as I’ve hear my friend and founder of Ultimate Partner Vince Menzione put it, and are actively developing strategies to incorporate channels into marketplace deal workflows. There’s a clear signal to shift the perception of marketplaces from being potential threats to traditional partnership associations, to what they really are – growth opportunities. Channel partners who recognize this sooner rather than later, have biggest opportunity to capture net new business.

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