Nvidia could be primed to be the next AWS

Tuesday - March 26 - 2024 

Good morning. Nvidia and Amazon Web Services, the lucrative cloud arm of Amazon, have a surprising amount in common. For starters, their core businesses emerged from a happy accident.

For starters, their core businesses emerged from a happy accident. For AWS, it was realizing that it could sell the internal services — storage, compute and memory — that it had created for itself in-house.

For Nvidia, it was the fact that the GPU, created for gaming purposes, was also well suited to processing AI workloads.

The NSA, sometimes jokingly said to stand for No Such Agency, has long hired top math and computer science talent. Its technical leaders have been early and avid users of advanced computing and AI.

And yet when Herrera spoke with me by phone about the implications of the latest AI boom from NSA headquarters in Fort Meade, Maryland, it seemed that, like many others, the agency has been stunned by the recent success of the large language models behind ChatGPT and other hit AI products.

The conversation has been lightly edited for clarity and length.

Today’s newsletter : 

• Nvidia could be primed to be the next AWS

• Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

• Large language models can help home robots recover from errors without human help

• A Deepfake Nude Generator Reveals a Chilling Look at Its Victims

• Large Language Models’ Emergent Abilities Are a Mirage

• The NSA Warns That US Adversaries Free to Mine Private Data May Have an AI Edge

• The Filmmaker Who Says AI Is Reparations

• Here’s Proof You Can Train an AI Model Without Slurping Copyrighted Content

• 8 Google Employees Invented Modern AI. Here’s the Inside Story

• Google DeepMind’s New AI Model Can Help Soccer Teams Take the Perfect Corner

Nvidia could be primed to be the next AWS
Summary:

Nvidia and Amazon Web Services, the lucrative cloud arm of Amazon, have a surprising amount in common.

For starters, their core businesses emerged from a happy accident.

For AWS, it was realizing that it could sell the internal services — storage, compute and memory — that it had created for itself in-house.

For Nvidia, it was the fact that the GPU, created for gaming purposes, was also well suited to processing AI workloads.

That eventually led to some explosively growing revenue in recent quarters.

Nvidia’s revenue has been growing at triple digits, moving from $7.1 billion in Q1 2024 to $22.1 billion Q4 2024.

That’s a pretty amazing trajectory, although the vast majority of that growth was in the company’s data center business.

While Amazon never experienced that kind of intense growth spurt, it has consistently been a big revenue driver for the e-commerce giant, and both companies have experienced first market advantage.

Over the years, though, Microsoft and Google have joined the market creating the Big Three cloud vendors, and it is expected that other chip makers will eventually begin to gain meaningful market share, too, even as the revenue pie continues to grow over the next several years.

Large language models use a surprisingly simple mechanism to retrieve some stored knowledge
Summary:

Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex.

Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don't fully grasp how they work.

In an effort to better understand what is going on under the hood, researchers at MIT and elsewhere studied the mechanisms at work when these enormous machine-learning models retrieve stored knowledge.

They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts.

Moreover, the model uses the same decoding function for similar types of facts.

Linear functions, equations with only two variables and no exponents, capture the straightforward, straight-line relationship between two variables.

Large language models can help home robots recover from errors without human help
Summary:

There are countless reasons why home robots have found little success post-Roomba. Pricing, practicality, form factor and mapping have all contributed to failure after failure.

Even when some or all of those are addressed, there remains the question of what happens when a system makes an inevitable mistake.

This has been a point of friction on the industrial level, too, but big companies have the resources to address problems as they arise.

We can’t, however, expect consumers to learn to program or hire someone who can help any time an issue arrives. Thankfully, this is a great use case for large language models (LLMs) in the robotics space, as exemplified by new research from MIT.

A study set to be presented at the International Conference on Learning Representations (ICLR) in May purports to bring a bit of “common sense” into the process of correcting mistakes.

“It turns out that robots are excellent mimics,” the school explains.“

But unless engineers also program them to adjust to every possible bump and nudge, robots don’t necessarily know how to handle these situations, short of starting their task from the top.”

A Deepfake Nude Generator Reveals a Chilling Look at Its Victims
Summary:

As AI-powered image generators have become more accessible, so have websites that digitally remove the clothes of people in photos.

One of these sites has an unsettling feature that provides a glimpse of how these apps are used: two feeds of what appear to be photos uploaded by users who want to “nudify” the subjects.

The feeds of images are a shocking display of intended victims. WIRED saw some images of girls who were clearly children.

Other photos showed adults and had captions indicating that they were female friends or female strangers.

The site’s homepage does not display any fake nude images that may have been produced to visitors who aren’t logged in.

Large Language Models’ Emergent Abilities Are a Mirage
Summary:

Two years ago, in a project called the Beyond the Imitation Game benchmark, or BIG-bench, 450 researchers compiled a list of 204 tasks designed to test the capabilities of large language models, which power chatbots like ChatGPT.

On most tasks, performance improved predictably and smoothly as the models scaled up—the larger the model, the better it got.

But with other tasks, the jump in ability wasn’t smooth. The performance remained near zero for a while, then performance jumped.

Other studies found similar leaps in ability.

The authors described this as “breakthrough” behavior; other researchers have likened it to a phase transition in physics, like when liquid water freezes into ice.

In a paper published in August 2022, researchers noted that these behaviors are not only surprising but unpredictable, and that they should inform the evolving conversations around AI safety, potential, and risk.

They called the abilities “emergent,” a word that describes collective behaviors that only appear once a system reaches a high level of complexity.

^News
What else is nex?

The NSA Warns That US Adversaries Free to Mine Private Data May Have an AI Edge

Electrical engineer Gilbert Herrera was appointed research director of the US National Security Agency in late 2021, just as an AI revolution was brewing inside the US tech industry.

The Filmmaker Who Says AI Is Reparations

Willonius Hatcher was looking for a way in.
He’d tried just about everything to break into Hollywood, and because there no longer exists a traditional entry point into its hallowed pantheon of performers—we can thank the internet for doing away with all notions of conventional success—the pursuit of it sometimes felt like a mirage.

Here’s Proof You Can Train an AI Model Without Slurping Copyrighted Content

IN 2023, OPENAI told the UK parliament that it was “impossible” to train leading AI models without using copyrighted materials.

It’s a popular stance in the AI world, where OpenAI and other leading players have used materials slurped up online to train the models powering chatbots and image generators, triggering a wave of lawsuits alleging copyright infringement.

8 Google Employees Invented Modern AI. Here’s the Inside Story

EIGHT NAMES ARE listed as authors on “Attention Is All You Need,” a scientific paper written in the spring of 2017.

They were all Google researchers, though by then one had left the company. When the most tenured contributor, Noam Shazeer, saw an early draft, he was surprised that his name appeared first, suggesting his contribution was paramount.

“I wasn’t thinking about it,” he says.

Google DeepMind’s New AI Model Can Help Soccer Teams Take the Perfect Corner

The most exciting young coach in soccer might not be at Bayer Leverkusen or Stade de Reims, or even Bologna FC. It might be at Google DeepMind.

For the past few years, the search giant’s artificial intelligence division has been working with Liverpool Football Club to bring AI to the world’s most popular sport.

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