Erick W.

NVIDIA stock increased 338,850% since IPO and is now the world's largest company by mkt cap ($3.35T). It wasn’t easy. Jensen Huang shares the most important thing when building a company: "...The company building process is mechanical. It's interesting. Lots of trial and error. It's organic. People matter. Personalities matter. ...And then I would say probably the most important thing is to realize that when you're building a company and a product, skill matters. Intellect matters. Training matters. But it's not enough. The part of it that is important to realize about building companies is that it's a challenging and painful and oftentimes extraordinarily scary thing to do. And so unless you have passion, unless you really love the process of building the company and what you're trying to do, it's going to be incredibly, incredibly challenging."

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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Learn a development pattern to systematically improve the accuracy and reliability of LLM applications in our new short course, Improving Accuracy of LLM Applications, built in partnership with Lamini and Meta, and taught by Lamini’s CEO, Sharon Zhou, PhD, and Meta’s Senior Director of Partner Engineering, Amit Sangani. (Disclosure: I am an investor in Lamini.) The path to tuning an LLM application can be complex. In this course, you'll learn a systematic sequence of steps for improving accuracy by reducing hallucinations:  - Create an evaluation dataset to measure model accuracy - Add prompt engineering and self-reflection - Fine-tune your model including "memory-tuning" which is a new method of embedding facts in an LLM Using the Llama 3-8B parameter model, you will: - Build a text-to-SQL agent with a custom schema and simulate situations where it hallucinates - Understand the difference between instruction fine-tuning, which gives pre-trained LLMs instructions to follow, and memory fine-tuning - See how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further I appreciate Meta releasing the Llama's family of open models -- this course gives an example of the unique type of work that developers can do with such models. Please sign up here: https://lnkd.in/gyxHwPkp

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Fascinating. I’m a huge believer in 1:1s to empower my team. They’re touch points, as I have faith in their ability to do the job (that’s why we hired them!) - is everything on track, do you need more resources/expertise, and, most importantly, are you okay? The flip side of removing so much bureaucracy from an organisation is compelling - having so many people closer helps disseminate knowledge and remove the game of chain-of command “telephone”. I’m not sure it’s very well suited to multiple personality types, neurodivergent folks, etc. re: creating an environment where they can thrive. And I’ve seen countless people from CEOs to 1LMs burn out from having 9+ direct reports. 1) what do you think? 2) does it change for remote organisations?

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Please check our latest press release. RAIVEN CAPITAL has sharpened our thesis regarding #AI as we move forward with our second global fund. Supreet Singh Manchanda

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The best things come in small packages 👝 🚨 Pay attention to the rise of Small Language Models (SLMs)! They will become important going forward: 1️⃣ Efficiency: SLMs achieve comparable or superior performance to LLMs with fewer parameters. Microsoft's new Phi-3 series outperforms larger models on benchmarks like MMLU & MT-bench (Google: https://lnkd.in/gn5zYZHg Microsoft: https://lnkd.in/ghK8fm7V Meta: https://lnkd.in/gkbM7pGs) 2️⃣ Specialization: Trained on curated, high-quality datasets. With 3.8B-14B parameters & advanced datasets, these transformer decoder models match GPT-3.5's capabilities in a compact form. Accurate benchmarking/eval will be important. 3️⃣ Interpretability: Neurons in SLMs are easier to analyze, improving model transparency. 4️⃣ Cost-effective: Less resource-intensive (vs training large models) I have a chance to meet a few exciting startups here. Reach out if you are working on this space 🙋🏻‍♂️ #startups #llm #slm #smalllanguagemodels #phi3 #largelanguagemodels #gpt4 #google #gemma #llama3 #ai #ml #edgeai

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There are two interesting and relatively new/emerging revenue models in hardware space. (1) Software Enabled Hardware Hardware is sold at lower but slowly improving gross margins (eg 20-40%), and software recurring revenues layered on top. Software sells at higher margins, enabling blended margins to appear in 50%+ category. These are applied in situations where hardware is not expected to be replaced or greatly improved upon in deployment for a significant number of years to come. Consider your car, or military equipment, or even infrastructure equipment. As always, there are often multiple business/revenue models utilized by different players in the same industry. (2) Hardware As A Service Hardware and software are bundled and sold with annually recurring revenues. Often multi-year contracts are signed to be able to amortize hardware over time, and enable blended gross margins over the length of the contract to be high (approaching 60-75%). This is more common in situations where vendor takes responsibility to upgrade hardware as needed along the way to provide maximum and highest functionality to the customer. Hardware inventory costs are, over time, passed on to suppliers or third party financing parties in lieu of few gross margin points. Consider robots as a service in factories (ala Formic), or AI-enabled equipment (eg Evolv, Lumafield etc). Hardware innovation will require deeper investigation, experimentation, and understanding of business/revenue models to fit customer needs as well as to maximize value for the innovating company. Strong and experienced management teams work hard to minimize sales friction, inventory costs etc, but also maximize gross margins, customer stickiness, and ability to upsell during the life of a contract.

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Recently, Anthropic shared its perspective on California's proposed AI regulation bill, SB 1047, with the California Assembly. While recognizing the importance of safe AI development, Anthropic has raised substantial concerns about the bill's current form, which could potentially hinder innovation and harm the United States' competitive edge in AI. Anthropic's key points include shifting the focus from "pre-harm enforcement" to a more adaptable "outcome-based deterrence" model. They also advocate for regulations that focus more narrowly on frontier AI safety to avoid redundancy with federal requirements. Additionally, they suggest giving regulatory authority to the Government Operations Agency instead of creating a new state agency. Anthropic believes that these amendments will encourage innovation in risk reduction practices and incentivize companies to adopt measures that prevent catastrophic risks. In contrast, the AI Salon Policy & Civic Engagement Guild shares Anthropic's commitment to ethical AI and safe development but has broader concerns about the bills. We believe that overly burdensome regulations may discourage AI development and lead to job losses. Compliance requirements favor large companies, potentially driving smaller startups out of the market, and overregulation might cause California to fall behind other regions in AI development. Furthermore, compliance under penalty of perjury imposes significant personal risk on developers, and strict regulations could disadvantage California-based companies in the global AI market. On behalf of the over 1700+ members of the AI Salon, we urge our larger community to engage in this crucial dialogue about AI regulation. By advocating for well-balanced policies that protect public interests without stifling innovation, we can ensure California remains a leader in ethical and responsible AI development. Please sign the letters of support and join us in shaping a future where AI is developed and deployed with ethical integrity, social responsibility, and inclusive governance. Together, we can bridge the gap between AI technology and policymaking, fostering an environment where innovation thrives alongside robust safety measures. https://lnkd.in/e6MRBmcX #AIRegulation #EthicalAI #Innovation #PolicyEngagement #Anthropic #AISalon #PublicPolicy #TechPolicy Kyle Shannon Leah Fasten Brandon Tidd Stacie L. Matthew Karabinos, MAT Vikki Baptiste Jon Potter Jon Leland Cyndi Coon Daniele Hall

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AI that can use computers like humans? Anthropic just made it a reality. Anthropic's latest announcement is reshaping the AI landscape: 1. Upgraded Claude 3.5 Sonnet with industry-leading software engineering skills 2. New Claude 3.5 Haiku model matching top-tier performance at lower cost and speed 3. Groundbreaking "computer use" capability allowing AI to navigate interfaces like humans 4. Significant improvements in coding, with Claude 3.5 Sonnet outperforming all publicly available models on SWE-bench Verified 5. Early adoption by tech giants for automating complex, multi-step processes This leap forward in AI capabilities opens up exciting possibilities for automation, software development, and open-ended research tasks. However, it also raises important questions about responsible deployment and potential risks. What are your thoughts on the implications of AI systems that can use computers like humans? How might this change the future of work and technology? #ArtificialIntelligence #TechInnovation #FutureOfWork #AIEthics #SoftwareEngineering Share your insights in the comments!

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New Tesla Patent Reveals Clues to an Exciting Future of Humanoid Robots and AI Integration In a interesting discussion, Herbert Ong and Scott Walter dive deep into Tesla's recently published patent application WO2024072984A1 (link in comments). Entitled "Actuator and actuator design methodology," the patent primarily focuses on controlling robot movement through strategic actuator placement. However, the document also unveils important clues about the potential for end-to-end neural net training of humanoid robots, reminiscent of Tesla's approach to training its vehicles with FSD. Ong and Walter explore the concept of personalized humanoid skills. Imagine purchasing a bot with built-in capabilities, then having the option to augment and customize those skills through a "Skills Store" akin to Apple's App Store. This opens up a world of possibilities for tailoring robots to specific tasks and preferences. The discussion also touches on the potential for various robot form factors, each designed to excel at particular skills. Picture a tennis-playing robot that comes pre-trained with basic volleying instructions, but requires a "Skills Upgrade" to perform at a master level. This raises questions about the need to manufacture and train a diverse range of robot body types and sizes, mirroring how humans possess ideal physiological characteristics for different activities. Later, Ong and Walter discuss a scenario in which Tesla builds the hardware – including automobiles, humanoids, and other bot form factors – while xAI develops the brains. This arrangement opens up opportunities for licensing agreements between the two companies, that may help the companies navigate future antitrust concerns as these technologies are deployed at scale. The convergence of advanced robotics and artificial intelligence holds the potential to catalyze a profound societal transformation. As Tesla and xAI continue to push the boundaries of what's possible, I can envision a future where intelligent, adaptable robots become integral to various aspects of our lives – from manufacturing and transportation to healthcare, recreation, and beyond. The seamless integration of cutting-edge hardware and software will revolutionize the way we live and work, ushering in an era of unprecedented efficiency, productivity, and innovation. While there are still challenges to overcome and ethical considerations to address, the prospect of these combined technologies driving positive change on a global scale is awe-inspiring. #FrontierFrontier #Humanoids #AI #Robots

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Some responses to this article have taken the point of view that muffling the angry customer calls is itself harmful. What do you think? Here is my take: Drawing on my background in business operations, where navigating crises is part of the job, I've learned that anger and extreme emotions rarely lead to effective solutions. The truth is, understanding the severity of a problem doesn't require being yelled at. Discovering and resolving issues can be achieved without resorting to verbal abuse or harassment. In fact, such negative behaviors only hinder the problem-solving process. What responsible employer would ever condone a customer mistreating their employee? I've found myself in the role of a frustrated customer on several occasions. Yet, I understand that the service agent is seldom the cause of the issue. Therefore, I refrain from directing any anger towards them. This doesn't make me a saint—it's about basic respect and civility toward another person.

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Absolutely simple yet powerful way to look at leadership. Its an "ability to reason thru" to contribute positively when people come to you or show you piece of work/situation etc. One can easily use this to find out true leaders at workplace. Designations dont make leaders but ability does (even without any formal designation ) #Leadership

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Come join the Future of Frontier Technologies AI & blockchain event panel in Santa Monica! ✨ We are hosting an event on Friday, May 3rd during @Farcaster's FarCon weekend. Which will bring many blockchain experts & builders to LA! Emerging LA is collaborating with Lightspeed's Faction VC as a co-host, with Partner Timothy K. moderating a panel. The panel will discuss the questions that you are probably wondering about these technologies: • How can AI and blockchain work together? • How will these frontier technologies impact industries today? • What are the real-world use cases that these technologies solve? • What are the current dynamics between innovation and regulation? ​One on hand, AI offers the promise of abundance, democratizing content creation but will open the door to (deep)fakes and mistrust. On the other hand, ​Blockchain offers digital scarcity, authentication, and trust in a world inundated with digital replicas. --- The audience will be filled with founders, technical builders, investors, and industry executives in sectors that are most relevant to these trends: Gaming, media, content, finance, social. The early bird ticket sold out quickly last week and the general admission tickets expire on Wednesday, so don't miss out and grab yours soon! Partners for this event include Sunset Ventures and AI LA as well as others in town: • LA schools & clubs: Wharton Club of Southern California, Wharton Digital Assets Club, Harvard Kennedy School, Harvard Blockchain , and Blockchain at USC • CPG startups & Brands: Studio TBD, doorsa, Red Bull --- PROMO CODE DISCOUNTS! If you are a student or alum from Penn/Wharton, Harvard, USC, reach out and ask for code!! 😉 See you on Friday! (Link to event in comments) #ai #blockchain #farcon #web3 #LA

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https://lnkd.in/g6p235_Q Google Cloud is giving Y Combinator startups access to a dedicated, subsidized cluster of Nvidia graphics processing units and Google tensor processing units to build AI models. It’s part of Google Cloud’s effort to cozy up with promising early-stage AI startups, in hopes some of them will evolve into massive, compute-hungry business. “We want to surround them with a lot of love and warmth early in their lifecycle so they get familiarized with building and working on the Google Cloud Platform,” said general manager for Google Cloud’s startups and AI business, James Lee, in an interview with TechCrunch. “As they stick around, we grow as they grow, and we become a partner of theirs throughout their lifecycle.” #AIstartup Google OpenAI NVIDIA   Blog – SAAS – Deploying FPA Solution on the Cloud - https://lnkd.in/gC9Mk8eF

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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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CS Leaders based out of the SF Bay Area: Join us in Menlo Park next Friday (6/13) for our inaugural "Application of AI in CS" monthly luncheon. Our featured guest, Ashvin Vaidyanathan (VP of Customer Success and Insights at LinkedIn), will kick us off with how the LinkedIn CS team is embracing AI to increase customer value and efficiency. We will follow that with a roundtable discussion on how AI can help with your current priorities, how are you leveraging AI today, are there any adoption hurdles that you are running in to? https://lu.ma/g9u8mu8q

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Multi-agent systems have been steadily gaining adoption, but it’s important to understand that they aren’t a blanket solution.  In fact, they introduce a range of complexities around coordination, consistency, and coherence that single-agent setups avoid. In my conversation with Chi Wang, the creator of AutoGen, he underscored the importance of matching your product architecture to the problem you’re trying to solve. If your use case involves narrowly defined tasks, a lone agent may be the simpler, more efficient choice. Other factors to consider before making the single agent versus multi agent decision? Response speed, decision-making frequency, inter-agent communication needs, latency, and bandwidth. Check out my full conversation with Chi here:

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I recently took a deep-dive into an article written by Laurin Brechter on AI Quantum Intelligence's Blog about A/B Testing. Folks, this isn't just ordinary A/B testing. 💡 Brechter is discussing Bayesian A/B Testing. With traditional A/B testing, businesses can experiment to see how different versions of a webpage or marketing asset perform in terms of click-through rates and user engagement. Conversion rates- the percentage of visitors who turn into customers takes the helm in this strategy. So how is Bayesian A/B Testing different? Applying Bayes' Theorem in these tests provides a flexible and complete picture of the full range of possible outcomes and their corresponding probabilities. ✔ Gone are the days of just aiming for an increase in each conversion. ✔ Instead, it presents a wealth of statistical data that gives robust insights into user engagement and click-through rates. My prediction on this? 🔮 The use of Bayesian A/B Testing marks a new dawn in business and marketing optimization strategies. Harnessing the power of detailed Bayesian A/B testing would give businesses the agility to adapt their marketing strategy to meet more precise, evidence-based objectives. What's particularly insightful here is: - The ability to capture a full range of possible outcomes, not just the best/worst case scenarios. - It allows businesses to better pivot with their marketing strategies based on these robust insights. The inspiring thing about Bayesian A/B Testing is how it mirrors the horizon of possibilities: expansive, unknown, but reachable and fathomable through technology, analytics, and a dash of boldness! Remember: Business optimization is not just about playing, but also about winning the right moves. Now that’s an invigorating thought! Let’s redefine boundaries and challenge norms by embracing dynamic tools like Bayesian A/B testing geared towards sharp, prescient decision-making. #ABTESTING #BAYESIANTHEORY #BUSINESSOPTIMIZATION #MARKETINGSTRATEGIES #AIQUANTUMINTELLIGENCE.

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Generative AI is outpacing the internet and PCs in adoption speed. Nearly 40% of U.S. adults have embraced this technology in just two years, compared to 20% for the internet and PCs in their early days. This rapid uptake signals a seismic shift in how we work and interact with technology. Are you ready for the AI revolution? #GenerativeAI #TechAdoption #FutureOfWork #AIRevolution #DigitalTransformation

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