Idris Kothari

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

7

1 Comment

Flow Computing, 100X CPU performance company, a fairly new addition to our portfolio, just published their 4M€ pre-seed round. I ended up making pretty hefty predictions in the press release in relation to their impact on advancing the baseline performance of computing. Anyhow, I think it's true - at least what comes to next 10 years or so, possibly much longer. Lets hope so. :-D It's not just I don't want to turn out to be proven dumb optimist (which I perhaps am regardless), but this level of performance gains will have quite substantial effect on many industries... #flowcomputing #supercpu #parallelprocessing #computing #seriousperformance #cputech #deeptech I'm sure the entire Butterfly Ventures team is as thrilled as I am. In deed, super cool tech from VTT with solid scientific backing, peer reviewed articles, IP portfolio and excellent founder team. Also super happy to collaborate with Sarsia, Superhero Capital, Stephen Industries and FOV Ventures.

79

4 Comments

https://lnkd.in/gr6DCzCQ Demand for NVIDIA’s Blackwell Platform Expected to Boost TSMC’s CoWoS Total Capacity by Over 150% in 2024. #nvidia #artificialintelligence #llms #generativeai #gpu #servers #datacenter #cloud #ai #Semiconductorindustry #electronics #semiconductor #supplychain #engineering #manufacturing #technology #computerchips #business #innovation #semiconductors #chips #chipmaker #foundry credit: trendforce

9

NVIDIA's market cap surpassed $2.8 trillion. Yet, Jensen Huang never completed its business plan. As he says, business plans are easy to write. Anyone can write them. I have been stressing this lately - Business plans and pitch decks are just tools for guiding strategy and communicating vision. In fact, startups deviate a lot from their early business plans. How do you secure investments then? At early stages, the team gets funded, not the startup. Investors look for specific qualities in the founding team. Some qualities that matter: > Background and Expertise: Proven track records and relevant experience are crucial. Demonstrate your skills and accomplishments. > Vision: Investors need to understand and believe in your idea. > Passion and Perseverance: Investors want to see that you will push through challenges. > Ability to Adapt: The startup landscape is dynamic. Flexibility and quick learning are important traits. That’s how the CEO of NVIDIA convinced the investors - through his reputation. Startup building starts way before its launch. Focus on what makes the most impact first. . . #entrepreneurship #venturecapital #startup #mergers #acquisitions Serial Entrepreneur & Investor Helping Startups Become Unstoppable – David Hauser

209

10 Comments

Why did we decide to build Chirp Wireless on #SUI instead of #Solana ⁉️ I shared a detailed explanation of our thought process with CoinDesk in this piece 👇🏼

5

1 Comment

Nice demo talk from the creator of #Devin during the recent AI Engineer #worldfair. Nice demonstration and articulation of what the "autonomous software engineer" agent can do. Given scrutiny about previous Devin demos, I want to assume these were all bullet proof authentic. There were Q&A, and I wish someone had asked for his opinion on allegations for fake demos, and comparison to open Devin and other open source projects. Anyhow, IMHO, the key point he made - yyX more (human) software developers that are zzX more efficient - basically by lowering the bar and delegation. What he didn't say: 1) These engineers will also be much cheaper 2) There will also be no code / low code versions for non engineers. These raise interesting questions about the future of the software business. As engineering becomes cheaper and more accessible, will we see a shift in build vs buy, especially in the application layer - assuming hyper customized experiences could be tailor made at low cost and low risk. https://lnkd.in/e4KH4CfC #ai #innovation #productivity #startups #futureofwork

2

Some observations about Nvidia following yesterday's earnings report: • Nvidia’s market cap is just 12% away from overtaking Apple. • The other Magnificent 7 companies account for 40% of Nvidia’s revenue • The massive upgrade that AI demands is lengthening the boom phase of this chip cycle. However, we can see that Nvidia's quarterly sequential revenue growth is moderating: Jul / Apr 2023: 88% Oct / Jul 2023: 34% Jan/ Oct 2023: 22% Apr / Jan 2024: 18%

11

1 Comment

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

5

Key Points from Jensen Huang's Quote on AI: 1. Current State: $1 trillion in data centers use outdated general-purpose computing. 2. Future Shift: Transition to accelerated computing with GPUs, replacing up to $2 trillion in infrastructure. 3. AI Industrial Revolution: AI produces valuable floating-point numbers (tokens) at high volumes, similar to a new industrial revolution. 4. AI Factories: New data centers will produce these AI tokens, transforming electricity into AI capabilities. 5. Economic Impact: AI tokens will drive $100 trillion in new products, services, and industry productivity. 6. Need for Financial Platforms: Platforms like Stripe will be crucial to monetize and transact in this new AI-driven economy.

13

I've been thinking a lot about how Tesla's vehicle infrastructure can leverage inference compute, distributing computational load and reducing data center energy usage. Dr. John Gibb does a nice job of summarizing these concepts in his video (Starting around 8:10). Inference Arbitrage: A Game Changer for AI Compute As AI continues to evolve, the demand for computational power is skyrocketing. Giants like OpenAI, Meta, and Google are pouring billions into training and inference compute, facing looming shortages of transformers and electricity. Here's where inference arbitrage, a novel concept leveraging Tesla's infrastructure, comes into play. The Challenge: Soaring AI Compute Costs Leading AI companies are investing billions to power their AI programs. However, the energy and computational needs are outpacing infrastructure capabilities. This bottleneck isn't just financial; it's physical. The growing demand for AI services could soon exceed available power and hardware. The Solution: Inference on the Edge xAI, in collaboration with Tesla, offers a groundbreaking solution: using Tesla vehicles for AI inference tasks. Tesla's fleet, equipped with powerful hardware and large batteries, can perform AI inference during idle times (about 90% of the time), reducing strain on central data centers How Inference Arbitrage Works Distributed Computing: Instead of relying solely on massive, centralized compute farms, inference requests (e.g., generating an email or image) are processed by idle Tesla vehicles. This distributed approach taps into the existing infrastructure efficiently. Energy Optimization: Tesla vehicles can perform inference tasks using their batteries during peak electricity demand times, avoiding high energy costs. At off-peak times, they recharge at lower costs, ensuring energy-efficient operations. Commercial Benefits: This model allows Tesla owners to earn money by utilizing their vehicle's idle time for AI computations. Tesla benefits from additional revenue streams with minimal overhead, and xAI gains a scalable, cost-effective solution for AI service delivery. Impact on the AI Industry By integrating AI inference with Tesla's distributed compute network, xAI can deliver services faster and more economically than competitors. This innovative use of existing resources not only addresses the infrastructure bottleneck but also offers a sustainable path forward for AI growth. Conclusion Inference arbitrage provides a strategic edge for xAI and Tesla, combining AI innovation with smart energy use. As the demand for AI compute continues to rise, this approach could redefine the landscape, positioning xAI and Tesla as leaders in efficient, scalable AI service delivery. Stay tuned as this exciting development unfolds and reshapes the future of AI. #ElectricVehicles #InferenceCompute #DataCenters #Electrification #AI #FrontierTechnology #FutureFrontier

4

1 Comment

Etched Raises $120M in Funding to Expand AI Chip Development - Significant Funding Secured: Etched, based in Cupertino, CA, raised $120 million in a funding round led by Primary Venture Partners and Positive Sum Ventures. - Innovative AI Chip: The company is known for its transformer-specialized AI chip, Sohu, which supports transformer inference for models from major AI companies. - Expansion Plans: The funds will be used to expand operations and further develop their AI chip technology. Subscribe to our daily newsletter here for more AI news https://lnkd.in/d-BJ_6eb #Technology #AI #Innovation #Entrepreneurship #VentureCapital #Startups #BigData #Engineering #Analytics #Productivity

3

https://lnkd.in/grt_6pVK Jim Keller on fire. 🔥 U.S. chip designer aims to bring down AI prices pushed up by Nvidia. #nvidia #artificialintelligence #llms #chatgpt #generativeai #gpu #servers #datacenter #cloud #ai #technology #innovation #business #Semiconductorindustry #electronics #semiconductor #supplychain credit: Nikkei Asia

144

I experienced the mobile computing shift in the trenches as a PM of Samsung’s mobile processors and later in the C-Suite as a consultant. Reflecting on those days, I see distinct similarities between the nascent stages of mobile technology and the current, early days of artificial intelligence. Some observations: OPERATING SYSTEM WARS VS. FOUNDATIONAL MODEL WARS In 2007, the mobile OS war was in full swing and it was not at all clear who would win. Apple had the hype factor, while Windows Mobile seemed to be a sure thing due to its familiarity and domination of the Office productivity suite. Nokia, with its Symbian OS, had the largest market share, and Blackberry had the early cult following. In a few short years, however, the mobile OS space quickly became a duopoly of iOS (premium & closed) and Android (free! and open source). In the early days of mobile, the momentum of app developers created insurmountable moats. Today the moats in foundation models are created by CapEx and access to data. I don’t necessarily think the AI foundational model wars will rationalize into a duopoly, but I do think that 1) a smaller number of players will emerge as the winners, 2) open source will have an important role and will force closed players to refine their strategies, and 3) developers will have to pay close attention to which models emerge victorious. Looking back to the mobile OS wars, startups that tied their fate to Windows Mobile or Symbian are probably not around today. THE ENTREPRENEURIAL LEARNING CURVE Apple was not early to the smartphone game. Palm, Blackberry, and others had mobile devices in consumers’ hands. The proliferation of smartphones, which Apple masterfully accelerated, was enabled by inflections of existing technologies such as fast, cheap, and low power processors, fast mobile networks, software assisted GPS, capacitive multi-touch, and more. Following the launch of the iPhone in 2007, it took entrepreneurs a few years, and lots of experimentation in leveraging these technologies before companies like Uber (2009), Whatsapp (2009), and Instagram (2010) emerged. Before that, there were countless startups that saw early experimental adoption and then quickly fizzled out (e.g., Flashlight apps, iBeer, Bump). Fast forward 17 years. It has now been 18 months since the public release of ChatGPT sparked the frenzy of today’s AI boom. The rate of model development and the corresponding boom in AI entrepreneurial activity have been dizzying. But much like the 2+ years that it took entrepreneurs to create truly iconic businesses in the mobile world, I believe that entrepreneurs are emerging from the experimentation phase and today are building AI native companies that will fundamentally change the way we do things for many years to come.

89

2 Comments

NVIDIA's acquisition of Deci AI is a perfect acknowledgement of the increasingly important role of model optimization to #AI deployment. #AI development and deployment is full of trade-off and neural networks architecture search (#nas) helps developers that are constantly to identify the most optimal AI model in a resource-constrained real world environment.

16

And with lowest power - uses only 16 RDU chips to achieve this performance!

56

1 Comment

A new world of trust evolves … as organisations must migrate their public key methods away from RSA, ECDSA, ECDH and EdDSA …

10

2 Comments

Worth reading: Generative AI is not going to build your engineering team for you. So, so good - peppered with gem quotes and observations- chuckle. https://lnkd.in/gZBKtU4X Thanks to Eric Brooke for sharing.

16