Welcome back AI enthusiasts!
In today’s AI Report:
💵Amazon Invests Over $500 Million Into Nuclear Power
🗞The New York Times Takes Legal Action Against Perplexity AI
💭Meta Scientists Develop “Thinking” LLMs
🛠Trending Tools
💰Funding Frontlines
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
AMAZON
💵Amazon Invests Over $500 Million Into Nuclear Power
Image Source: Canva’s AI Image Generators/Magic Media
Amazon Web Services (AWS), Amazon’s cloud computing platform that deploys software solutions, is investing over $500 million into nuclear power.
Key Details:
AWS has signed an agreement with Dominion Energy, a utility company that offers clean, safe, reliable, and affordable energy solutions.
The agreement explores building Small Modular Reactors (SMRs) near Dominion Energy’s existing North Anna Power Station.
SMRs are smaller, more efficient nuclear reactors designed to be built in factories and shipped to operational sites for installation.
SMRs boast faster construction times, allowing them to come online sooner.
AWS needs SMRs to power the growing demand for Generative AI (GenAI) platforms. “We’re seeing the need for GigaWatts (GW) of power in the coming years,” said AWS CEO Matt Garman.
GenAI uses AI models trained on text, image, audio, video, and code data to generate new content.
AWS plans to invest $35 billion by 2040 to establish multiple data center campuses across Virginia powered by SMRs.
Why It’s Important:
Big Tech companies are investing billions of dollars into nuclear power due to the growing demand for GenAI platforms.
Earlier this week, Google partnered with Kairos Power to build “several small nuclear-power reactors” to fuel AI ambitions.
Last month, Microsoft signed a historic 20-year deal with Constellation Energy to reopen Three Mile Island near Middletown, PA, to access nuclear power for AI demands.
THE NEW YORK TIMES
📰The New York Times Takes Legal Action Against Perplexity AI
The New York Times just sent a “cease and desist” letter to Perplexity AI for violating copyright laws.
Key Details:
Perplexity AI is a free AI-powered search engine that rivals Google Search. In January, Jeff Bezos participated in a $73.6 million Series B funding round for Perplexity AI.
The New York Times claims Perplexity AI is leveraging the media outlet’s articles to develop AI-generated summaries without their permission, which violates copyright law.
“Perplexity AI and its business partners have been unjustly enriched by using, without authorization, the Times’s expressive, carefully written and researched, and edited journalism without a license,” the media outlet wrote.
Perplexity AI says it’s open to working with publishers and will respond to the “cease and desist” letter by the October 30th, 2024 deadline.
The New York Times also filed a lawsuit against Microsoft and OpenAI over abusing the newspaper’s intellectual property to train Microsoft’s Bing Search and OpenAI’s ChatGPT.
Why It’s Important:
Microsoft, OpenAI, and Perplexity AI could potentially argue that AI models fall under transformative use (i.e., add new expression, meaning, or messaging to the original work) and bypass copyright law. The AI models learn patterns from the media outlet’s content rather than directly copying it.
They could cite Google LLC v. Oracle America, Inc., where the Supreme Court ruled that Google’s use of Oracle’s Java API Code to create the Android platform was transformative use because it served a new purpose for a substantially different product, even though it involved copyrighted content.
🩺 PULSE CHECK
Do you think Perplexity AI’s situation falls under transformative use?
AI RESEARCH
💭Meta Scientists Develop “Thinking” LLMs
Image Source: Meta Fundamental AI Research (FAIR)/“Thinking LLMs: General Instruction Following With Thought Generation”/Screenshot
Meta scientists developed Thought Preference Optimization (TPO), a method for training Large Language Models (LLMs) to “think” before answering questions.
LLMs are AI models pre-trained on vast amounts of data to generate human-like text, such as OpenAI’s ChatGPT.
One of the main challenges with LLMs is their tendency to respond to questions without contemplating the complexity of the question. For simple questions, immediate responses may be sufficient. However, LLMs often fail to answer complex problems that require reasoning and problem-solving.
To solve this issue, Meta scientists hypothesized how to get LLMs to pause, create internal thoughts, and evaluate those internal thoughts before generating responses.
IPO achieves this by instructing LLMs to divide outputs into two parts:
Thought Process: Multiple thoughts are generated for each question and paired together.
Final Response: The Thought Process pairs are evaluated through preference optimization. The best Thought Process pairs are selected for further training iterations.
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💰FUNDING FRONTLINES
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