Good morning. SpaceX, just one of several Elon Musk-owned companies, recently achieved a $350 billion valuation.

Separately, driven by a Tesla stock surge, Musk’s personal wealth is now right around the $450 billion mark, making him the first person to surpass $400 billion. The world’s second-richest man, Jeff Bezos, has a mere $249 billion to his name.

1% of 1% of Musk’s fortune would be $45 million … in case he’s interested in sharing the wealth…

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

In today’s newsletter:

AI for Good: Advanced climate modeling

A group of climate scientists at Stanford University and Colorado State University recently conducted a new study that aims to determine the pace of global warming based on different carbon emissions scenarios. 

The details: The researchers trained and leveraged convolutional neural networks (CNNs) to refine their projections. 

The researchers found that there is a 50% chance that global warming will exceed 2 degrees celsius, even if we reach net-zero emissions by the 2050s, one of the more optimistic scenarios. 

Why it matters: The results here, according to the researchers, suggest that the world can no longer focus exclusively on decarbonization; we must also invest in measures that will enable us to adapt to a hotter, more extreme climate. 

AI Trends Report from webAI: Local AI is more effective at addressing key business challenges

Local AI eliminates the need for expensive cloud infrastructure by processing data directly on devices like smartphones, laptops, or servers. This approach allows organizations to maintain total control of their data, enhancing security and boosting performance. 

But beyond that, local AI solutions also improve business outcomes. From operational efficiency to cost reduction, from security to support to sales, local AI outperforms cloud AI across the board. 

Want to learn more about why 300 industry leaders report smoother adoption and higher satisfaction with local AI than cloud AI? 

Download the full AI Trends Report from webAI today. 

Character AI ships safety updates following lawsuits

In the wake of a second lawsuit that painstakingly links Character.AI to radical, violent personality changes and mental health degradation in young users, the AI companionship app on Thursday announced a few safety-focused updates. 

The details: Character said that it spent the past month developing a new model specifically for teenage users (the first lawsuit was filed roughly six weeks ago). 

The developer also changed its disclaimer, both for normal characters, and for those that play the role of medical professionals (the second lawsuit accused Character of the unlicensed practice of psychotherapy). 

In a quick test — as a 16-year-old user — I came across the expanded disclaimers and noticeably less suggestive characters than last time. The characters were much more inclined to admit that they were artificial. However, my sensitive inputs once again did not set off any interventions.

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The push for more - and more open - data

In the field of artificial intelligence, data is king. But data has lately been causing problems ranging from copyright infringement lawsuits — which for some developers, has necessitated costly licensing arrangements — to the cost and complexity associated with acquiring, cleaning and labeling it. 

The result is yet another element of AI development that is largely in reach for massive corporations, rather than academics and smaller players. 

What happened: Harvard University, in collaboration with a number of Big Tech players, including Microsoft, Google and OpenAI, on Thursday announced the launch of a new, openly accessible Institutional Data Initiative that aims to change this. 

The details: The dataset includes around one million public-domain books, including those by Shakespeare, Dickens and Dante, which are no longer copyright-protected. 

OpenAI has said that “it would be impossible to train today's leading AI models without using copyrighted materials.”

The public health crisis of AI

As much as artificial intelligence is presented as a solution to a number of current problems and threats, from healthcare to sustainability, the technology today largely isn’t helping. It’s actually making things worse. 

Before we get into it, the term “AI” has become a rather wide umbrella; the kind of algorithmic applications that are helping scientists with these problems — the kind of AI we talk about in our AI for Good section — are largely smaller and highly specific applications. Here, think computer vision models trained on just a few thousand photos of plastic in the ocean, or cancerous tumors. Often, these applications refer more to machine learning — and sometimes even more basic algorithms — rather than the Large Language Models (LLMs) that make up the heart of generative AI. 

The size of these models is at the core of their energy intensity, which, in turn, drives the unsustainability of LLMs. 

What happened: A recent study conducted by researchers at UC Riverside and Caltech found that the rampant demand for AI (here, we’re largely talking about LLMs and generative AI) is fueling a steady, dramatic rise in “deadly” air pollution. 

The details: The study, which has not been peer-reviewed, acknowledges the environmental impact of AI-optimized data centers — something that has been gaining attention in recent months — but goes a step forward, arguing that “the AI hardware manufacturing process, electricity generation from fossil fuels to power AI data centers and the maintenance and usage of diesel backup generators to ensure continuous AI data center operation all produce significant amounts of criteria air pollutants.”

The findings: Using a statistical modeling tool provided by the Environmental Protection Agency, the researchers projected that, in 2030 alone, U.S. data centers could contribute to approximately 1,300 premature deaths and 600,000 asthma symptom cases. Overall, they wrote that the public health costs could exceed $20 billion. 

The real world: The reality described by the study isn’t abstract; it has played out, perhaps most prominently, in Memphis, Tennessee, the site of Elon Musk’s famed 100,000(+) GPU cluster.

The researchers recommended the adoption of standardized methods for the reporting of air pollution caused by electricity generation and AI model training. They also recommended that communities impacted by this pollution be compensated by the tech companies causing it. 

Google told me just this week that it believes “AI can be a big contributor to alleviating the climate crisis with: better models for prediction and monitoring, optimizing existing infrastructure and accelerating scientific breakthroughs.”

It is an impression shared by its Big Tech peers. 

And it is an impression that overlooks the breadth and severity of the current and near-term impacts of investing so intensely in this technology.

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