💰 Sequoia Capital's AI Investments Soar
PLUS: Stanford's "Foundation Model Transparency Index" reveals a transparency gap among AI giants like OpenAI, Google, and Meta, and more!
Casey Clifton
October 24, 2023
Welcome back, today we're covering:
Funding: Sequoia Capital goes all in on AI with 60% of investments now in AI companies.
Big Tech: Apple spending $1B+ annually on secretive AI projects.
Robotics: New Eureka algorithm uses GPT-4 to speed up robot learning.
PLUS: Stanford's "Foundation Model Transparency Index" reveals a transparency gap among AI giants like OpenAI, Google, and Meta, and more!
Off we go!
News & Insights
💰 Inside Sequoia Capital’s Growing AI Portfolio
Sequoia Capital's AI investments have surged from 16% to nearly 60% in 2023. They're not just banking on AI as a standalone sector, but as a catalyst across various domains. Stephanie Zhan, partner at Sequoia, suggests every company will have an AI angle, with AI seen as an accelerator, not the main event. Remember, this isn't about AI companies, it's about problem-solving entities using AI to get ahead.
The investment firm now boasts about 70 AI-focused portfolio companies, with examples ranging from legal tech to open-source developments. Sequoia's commitment is evident in its evergreen fund model, a long-term strategy that's all about nurturing transformative technologies. crunchbase
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👀 Apple Has Its Sights Set Firmly on AI
Apple's AI endeavors are finally gaining momentum. Bloomberg's Mark Gurman revealed that Apple is stepping up its game in the AI arms race, with its own large language model, Ajax, in the pipeline. A portion of the estimated $1 billion yearly spend is directed towards this project, with AI and Machine Learning head, John Giannandrea, at the helm. With the likes of iMessage, Siri, Apple Music, and Keynote being primed for a revamp, Apple’s AI integration seems to be a comprehensive approach.
Apple's strategy leans towards competency over haste. Craig Federighi, the driving force behind the AI-infused iOS, and Eddy Cue, infusing AI into Apple applications, are indicative of the company's intent to deliver a more personalized and efficient user experience, akin to Spotify's auto-generated playlists and PowerPoint's auto deck generation. zdnet
Why does this matter?
Apple's entry into the AI space, albeit late, presents an interesting paradox. While it could be seen as playing catch-up, Apple's typical 'wait and perfect' model might just work to their advantage in carving out a niche in the AI landscape, offering robust and competent technology.
💡 With GPT-4 Overseeing Training, Robots Can Learn Much Faster
Eureka, a novel algorithm, leverages OpenAI's GPT-4 to enhance robotic dexterity like never before. With the ability to design highly effective training goals, Eureka allows robots to rapidly learn complex tasks. The secret sauce? It's in the blend of high-level reasoning and low-level motor control, accelerated by parallel simulations running multiple trials simultaneously.
The algorithm outshines human-written reward functions by a considerable margin. Instead of the traditional approach of one task at a time in a lab, Eureka uses virtual, three-dimensional physics simulations akin to video game worlds, enabling mass parallel training and therefore, significantly reducing training time. Nvidia calls this approach "rapid reward evaluation via massively parallel reinforcement learning." arstechnica
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What Else Is Happening
🗞️ More News
FCC plans to probe potential threats posed by AI-enhanced robocalls.
Google Cloud VP asserts that generative AI, which has allowed Google to excel, will be the driving force behind the future of companies' AI development.
ToolFormer introduces a method for guiding AI models to use external tools, enhancing their problem-solving capabilities.
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Oct 23, 2023
🔭 Research Review
Stanford Researchers Challenge OpenAI, Others On AI Transparency In New Report
Stanford University's recent report, "The Foundation Model Transparency Index," is a wake-up call for AI giants like OpenAI, Google, Meta, and others, citing a significant transparency deficit. The report unearths the opaque aspects of the data and human labor involved in AI model training, emphasizing the necessity for more comprehensive disclosures.
The crux of the matter is the rising dependence on foundation models, including the likes of ChatGPT. As these AI systems become more ingrained in everyday operations, understanding their biases and limitations is no longer optional. Stanford's call to action underscores the need for businesses, academia, policymakers, and consumers alike to comprehend the confines of these models for safe application, meaningful regulation, and informed consumption. arstechnica
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Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.
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