Good morning. This is a big week for Big Tech earnings. Microsoft reports second-quarter earnings on Tuesday, Meta reports on Wednesday and Apple and Amazon report on Thursday.

Bank of America estimated that, led by this array of Magnificent Seven giants, more than a third of aggregate S&P 500 earnings will report this week. It will be a big week to watch as investors begin to look for returns against a field of high and growing investments in artificial intelligence.

— Ian Krietzberg, Editor-in-Chief, The Deep View

In today’s newsletter:

AI for Good: Machine learning for water quality

In a world with steadily deteriorating global water quality, machine learning offers the first step of a solution; it’s really good at predicting things. For a long time, though, the challenge with applying it to predict water quality was one of insufficient data. 

But last year, scientists developed a machine learning model designed to predict river water quality. 

The details: To overcome issues of data insufficiency, the model was fed on data gathered by remote sensors. 

Why it matters: The researchers said that, given the link between climate extremes and water quality, “understanding of climate change implications for water quality is critical for the future of water quality management.”

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Google DeepMind’s major math breakthrough

Last week, Google DeepMind unveiled AlphaProof and AlphaGeometry 2, new AI models designed to overcome the problem-solving shortcomings of large language models to act as a more usable tool in the domain of mathematics. 

Together, the two models solved four of the six problems from this year’s International Math Olympiad, the first time an AI model has achieved the same level as a silver medalist in the IMO. 

The details: Human competitors are given two 4.5-hour sessions to submit answers. Google’s models solved some problems very quickly (within minutes, according to a statement) and others took much longer (three days, for instance). 

“However, what it does is way beyond what a pure brute-force search would be capable of, so there is clearly something interesting going on when it operates,” he said. “We'll all have to watch this space.”

It has at the same time been criticized by researchers for providing “no technical details whatsoever but just bragging about non-verifiable results.”

Study: Synthetic data isn’t the breakthrough it’s cracked up to be

Data is a central ingredient in the construction of AI models. Because of this, developers have spent the past few years scraping the entire corpus of the internet in order to train progressively larger models.

Even amid academic warnings that we will soon run out of data to train these models, developers have begun turning to synthetic data for training purposes; that is, data generated by an LLM, used to train an LLM. But a new study published in Nature found that this is not a tenable approach. 

The details: The study found that “indiscriminate use of model-generated content in training causes irreversible defects in the resulting models.”

AI researcher and cognitive scientist Gary Marcus said in response that the “only way we will move significantly forward is to develop new 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚𝙨 — likely neurosymbolic — that are less tied to the idiosyncrasies of specific training set.”

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Last week this morning: Apple’s agreement with the White House and everything else we missed

It has been a busy week. 

So busy, in fact, that we weren’t able to cover everything important that happened over the past week. So, this morning, we’re going to take a brief look at, well, everything we missed. 

Apple’s voluntary agreement with the White House

What happened: The White House on Friday published a statement detailing the progress federal agencies have made in the nine months since President Joe Biden issued his executive order on artificial intelligence. 

The FTC weighs in on open-weights models

Just a few weeks before Meta announced the release of its latest generative AI model, the U.S. Federal Trade Commission (FTC) tackled the issue of open-versus-closed AI in a blog post. 

The details: The FTC said that, like open-source software, open-weights models have the “potential to be a positive force for innovation and competition.”

The UK has an AI action plan

Jumping across the sea to our friends in London, the U.K. on Friday announced the establishment of a new AI action plan — which will be led by tech entrepreneur Matt Clifford. 

The details: Its purpose is to “identify how AI can drive economic growth and deliver better outcomes for people across the country.”

US & EU joint statement on genAI competition

In our last bit of government-related news, the FTC, U.S. Department of Justice, U.K. Competition and Markets Authority and European Commission recently published a joint statement on the state of competition in the world of generative AI. 

The details: The statement recognized a number of risks to competition in the sector, mainly that the key “ingredients” necessary for AI — chips, compute, data and talent — “potentially put a small number of companies in a position to exploit existing or emerging bottlenecks across the AI stack and to have outsized influence over the future development of these tools.”

Other stuff to know: Twitter very quietly default-enrolled its users into a new option that allows the company to train its AI model, Grok, on user posts. 

And last up, JPMorgan, according to an internal memo seen by the FT, has been rolling out an internal large language model chatbot that it is billing as a “research assistant” for employees. Around 50,000 employees now have access to the model. 

This comes in the face of rather glaring LLM weaknesses, in that their propensity for inaccurate output and hallucination makes them a questionable choice for a “research assistant.” 

Ok, that was a lot. 

Here’s hoping this week is a little calmer. 

But somehow, I doubt it. 

Happy Monday.

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