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- 🤖 Meta Spends Billions on Nvidia AI Chips
🤖 Meta Spends Billions on Nvidia AI Chips
PLUS: Google DeepMind’s Geometry AI Model
James Roesser
January 19, 2024
Welcome back AI enthusiasts!
In today’s AI Report:
💵Meta Spends Billions on Nvidia AI Chips
🧠Google DeepMind’s Geometry AI Model
🛠5 Trending Tools
💰Venture Capital Updates
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
META
💵Meta Spends Billions on Nvidia AI Chips
Image Source: Laure Andrillon/REUTERS
Meta is spending billions of dollars on Nvidia’s H100 Tensor Core GPUs to power AI research and future projects.
Key Details:
In an Instagram Reels post on Thursday, Meta CEO Mark Zuckerberg explained the company’s “future roadmap for AI requires building a massive computer infrastructure.”
The computer infrastructure will include 350,000 Nvidia H100 Tensor Core GPUs.
Analysts at Raymond James estimate Nvidia is selling H100 Tensor Core GPUs for $25,000 to $30,000. These estimates would equate to $9 billion in expenditures for Meta.
During Meta’s third-quarter earnings call for 2023, the company stated total expenses will reach $99 billion in 2024 to fuel computer expansion.
Why It’s Important:
Meta is preparing to develop Artificial General Intelligence (AGI) comparable to human-level intelligence.
Meta’s chief scientist, Yann LeCun, stated: “If you think AGI is in, the more GPUs you have to buy.”
🩺 PULSE CHECK
Will Meta’s recent computing investments accelerate their AI development?Vote Below to View Live Results |
GOOGLE DEEPMIND
🧠Google DeepMind’s Geometry AI Model
Image Source: Ryan Browne/CNBC
Google DeepMind researchers showcased AlphaGeometry, an AI system that solves complex geometry problems at the level of Olympiad gold medalist students.
Key Details:
The International Mathematical Olympiad is an annual competition for the world’s smartest high school mathematicians.
Google DeepMind leveraged this event to test and fine-tune AlphaGeometry’s math and reasoning abilities.
Language models identify patterns and relationships in data but cannot reason rigorously or explain their decisions. AlphaGeometry’s symbolic deduction engine formulates likely solutions to math problems.
AlphaGeometry combines neural networks and symbolic logic engines to search for geometric proofs verified by millions of synthetic training examples.
In a benchmark test of 30 Olympiad geometry problems, AlphaGeometry solved 25 problems under competition time limits, achieving gold medalist scores.
Why It’s Important:
The AI system rediscovered known theorems during data generation and established novel generalizations of test problems.
Linear algebra and geometry are critical for developing neural networks, which are the building blocks of deep learning (i.e., teaching computers to process data similar to the human brain).
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💰VENTURE CAPITAL UPDATES
💼WHO’S HIRING?
Tesla (Hawthorne, CA): Digital Design Developer Intern, Summer 2024
SPAN (San Francisco, CA): Frontend Web Intern, Summer 2024
Crowdstrike (Remote): Technical Operations Engineer Intern, Summer 2024
Activision (Irvine, CA): Game Design Intern, Summer 2024
Rocket Lab (Long Beach, CA): Software Engineer/Co-Op Intern, Summer 2024
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