Matt Rappaport’s Post

What if the next generation data center is your Tesla? Tesla's latest earnings call introduced the concept of "distributed inference," proposing to utilize Tesla's vast fleet of vehicles as a global network for AI processing. Idle Tesla cars can be turned into nodes for distributed computing, similar to Amazon Web Services. With plans to expand AI training capacity and leverage the computational power of potentially 100 million Tesla, it can be a serious contender in the AI provider business. One of the bottle necks of the current development in AI is the lack of compute and the relatively high cost for training large language models. Could high tech cars be the solution for such service? The only service that I can think of that a vehicle is offering indirectly now is an ads spot on the bodies of the vehicle and maybe the delicious food truck where you can get hot food from. What else did I miss here? Could this actually be pulled off? Coverage from The Verge: https://buff.ly/3We2sq8

Arbitrary parameterisation validation to build, and test, dependant on the design of the computing machinery model, and the set of principles algorithms, might support better source the IP automation product/service. Technical implementation of computing machinery intellectual property products might help better assess the analytical, and empirical use of AI-based models, requires the setting of parameters that have a significant effect on model performance and results. Training and testing performance should be continually assessed and managed based on the intended purpose. Office supervisory documenting complex AI algorithm models, computing machinery data storage irrespective of the cryptographic mechanisms, uniform encryption data integrity, systems typically support access zone at the file level or block level, unauthorized access, user-level or group-level encryption, authenticating the users. The access rights security boundaries protocol appropriately combining analysis of the source code and of the data with methods based on standards for documenting AI algorithms, predictive models and datasets, and leveraging the methods and benchmarking datasets, both curated by the auditor.

As the AI enthusiasm continues in the marketplace, i am sharing this fair assessment on the status of the full self driving beta software at tesla. Worth a read. Tesla's software is now down to max 1-2 interventions per drive in most cases... with Tesla currently expanding its computing power by an order of magnitude, and driving data coming in exponentially, the now end to end video trained neural net (synthetic human brain) should be able to close this gap to perhaps 1 intervention every 3 drives, then every week, then every month etc... This is not a matter of if anymore, just when. As the writer notes at the end, "Lots of time, after all, is wasted in cars." An exciting one to watch. Tesla Martin Viecha #futureisbright #vision #artificialintelligence https://lnkd.in/exUS77xm

#Tech #Technology #DevOps #Automation #BigData #DataAnalytics #Data #DataEngineering #AI #ML Elon Musk Can’t Solve Tesla’s China Crisis With His Desperate Asia Visit: Tesla’s deal with Baidu isn’t new, the mapping data Tesla will collect likely can’t leave China, and Full Self-Driving can’t compete with the more advanced Chinese alternatives. #ArtificialIntelligence #MachineLearning #DataScience

The recent financial outcomes for Tesla underscore a pivotal phase in the company's journey. While Q3 saw a reduction in profits, a deeper analysis reveals strategic investments in AI and a recalibration of market strategies. This comprehensive review sheds light on why Tesla is a multifaceted tech giant, not just a carmaker. Discover more about Tesla's strategic moves and what they signal for the future in our latest article https://lnkd.in/e2NGmGmr #Tesla #Innovation #MarketAnalysis #FutureTech #StrategicInvestment #AI #Cybertruck #ElonMusk

A server on wheels - Tesla has an idea to use the unused compute power of all their cars to run AI models. They dream of a future where when the cars are parked and not moving, they could team up to handle tasks like inferencing. “If you imagine the future perhaps where there’s a fleet of 100 million Teslas and on average, they have got like maybe a kilowatt of inference compute. That’s 100 gigawatts of inference compute, distributed all around the world.” https://lnkd.in/gdPJegzp

***Tesla's AI Ambitions Challenged: Musk's Priorities and Resource Allocation Under Scrutiny. What does it mean?*** It has been a very interesting news day for Tesla/Musk/AI/xAI. -Elon Musk's vision for Tesla as an AI and robotics powerhouse, fueled by Nvidia's coveted H100 AI chips, is facing turbulence. While Musk publicly projected an aggressive procurement of 85,000 H100 chips and a $10 billion AI investment for Tesla, internal Nvidia emails reveal a starkly different reality. -Internal Conflicts: Prioritizing X Over Tesla Leaked communications indicate that Musk diverted a crucial shipment of 12,000 H100 GPUs, initially earmarked for Tesla, to his social media platform, X. This decision, favoring X's AI infrastructure development, has reportedly delayed Tesla's supercomputer deployment by months, potentially hindering its progress in autonomous vehicle and robotics technology. This revelation has sparked investor concerns, as Nvidia is a key supplier to both Tesla and other tech giants like Apple, Amazon, and AMD, all vying for limited chip supply from Taiwan Semiconductor Manufacturing Company (TSMC). -Conflicting Statements: Raising Investor Doubts The emails also expose discrepancies between Musk's public statements and internal figures, raising questions about his commitment to Tesla amidst his involvement in multiple ventures. These discrepancies, coupled with Tesla's declining sales and tarnished reputation, have amplified shareholder concerns about Musk's divided attention. -Musk's Expansive Vision: Balancing Priorities As Musk juggles his various companies, including SpaceX, Neuralink, The Boring Co., and his AI startup xAI, Tesla shareholders are increasingly wary of potential conflicts of interest. The lines between these entities are blurred, with xAI sharing resources with X and Tesla employees transitioning to Musk's other ventures. -The Stakes: Innovation and Investor Confidence Tesla's ability to innovate in the competitive AI landscape is crucial for its future success. However, Musk's resource allocation decisions and conflicting priorities have shaken investor confidence. This internal conflict underscores the challenges faced by companies led by visionary leaders with sprawling ambitions across multiple industries. -The Road Ahead: A Critical Juncture Tesla now stands at a critical juncture, navigating internal conflicts, supply chain constraints, and investor scrutiny. The company's ability to balance Musk's ambitious vision with the realities of resource allocation and market challenges will be pivotal in determining its trajectory in the AI and automotive sectors. Source: CNBC #automotivenews #ai #automotiveengineering #xai #supplychain #manufacturingengineering #nvidia #tsmc #apple #artificialintelligence

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🚀 What if you own Tesla but you don't have X 🌐 Elon Musk's decided to divert $500 million worth of Nvidia AI chips from Tesla to his social media platform, X. Tesla’s former head of AI infrastructure, Tim Zaman, recently defended this move, highlighting the immense logistical challenges of setting up a supercomputer. Zaman pointed out that managing and deploying thousands of GPUs is a complex, military-like operation. He emphasized that without an immediate use at Tesla, these valuable resources would have been wasted sitting in a warehouse. Musk confirmed this reasoning, underscoring the necessity of maximizing the utilization of these AI chips. The point is before making a huge investment on AI/GenAI hardware it is a must to run a site readiness and deep analysis on how you will operationalize this unprecedented infrastructure. Or you have to give it to X "If you own one :)". https://lnkd.in/dRWWaPyj