⚙️ Report: The hidden market of body-centric data

November 25, 2024

Good morning. There’s been a lot of talk about scaling laws, lately.

The industry is increasingly — if a little quietly — accepting the fact that scaling laws aren’t quite working anymore. The effort now is shifting beyond simply scaling compute and data and moving instead toward scaling time; time spent training, time spent post-training and time developers give a model to respond to a query.

As Dr. Gary Marcus wrote, “scaling can only take us so far; the time has come for fresh invention.”

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

In today’s newsletter:

🧠 AI for Good: Brain mapping
💰 Amazon to invest additional $4 billion in Anthropic
🏛️ Judge partially dismisses another copyright lawsuit against OpenAI
🚨 Report: The hidden market of body-centric data

Source: Google Research & Lichtman Lab (Harvard University). Renderings by D. Berger (Harvard University)

We’ve talked before about just how little researchers know or understand about the human brain, human cognition and human consciousness. Even as some labs are attempting to design artificially intelligent systems that better emulate the many complexities and intricacies of human cognition, other researchers are leveraging the data-parsing capabilities of current models in an attempt to better understand the human brain.

I’m talking about brain mapping.

The details: Earlier this year, a group of neuroscientists at Harvard University teamed up with researchers at Google to construct an AI-generated map of part of the human brain.

Harvard researchers first collected thousands of cross-sectional images of a tiny brain tissue sample (that was 3 millimeters long, or one-millionth of the total human brain).
Google researchers then developed an AI tool that, by processing those images, was able to create a 3D model of that minute piece of tissue. Describing this one small sample required more than 1 million gigabytes of data.

This one small sample contained 50,000 cells and about 150 million synapses, a demonstration of just how complex the human mind is.

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Source: Anthropic

Amazon on Friday said that it would invest an additional $4 billion in heavyweight AI startup Anthropic, bringing the tech giant’s total investment in the developer up to $8 billion.

Anthropic said that Amazon will retain a minority stake in the company, though the exact percentage is unknown.

The details: As part of the partnership, Amazon Web Services — Amazon’s cloud arm — has become Anthropic’s “primary training partner,” in addition to serving as the startup’s primary cloud provider. This means that Anthropic will use Amazon’s in-house chips to train and deploy its future models, instead of Nvidia.

Anthropic’s current valuation is not clear; according to The Information, the company has been talking to investors about a new round of funding that would value the company between $30 billion and $40 billion (OpenAI just raised $6.6 billion at a $156 billion valuation).
The Information reported several months ago that Anthropic expects to burn through $2.7 billion in cash on $800 million in annualized revenue in 2024, making a cash raise somewhat urgent.

These Big Tech partnerships have attracted regulatory scrutiny around the world, with the FTC opening investigations into Anthropic’s relationship with Amazon and Google, as well as Microsoft’s partnership with OpenAI (Google has invested $2 billion in Anthropic, for a 10% stake in the company).

The U.K. Competition and Markets Authority recently cleared Google’s partnership with Anthropic, several months after clearing the startup’s partnership with Amazon.

Anthropic’s market share doubled to 24% this year, according to a recent report, while OpenAI’s fell significantly to 34% — the gap between the two is steadily shrinking.

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The Pentagon’s battle inside the U.S. for control of a new Cyber Force (CNBC).
OpenAI considers taking on Google with web browser (The Information).
You can now try Microsoft’s Recall AI feature on a Copilot Plus PC (The Verge).
The COP29 deal is even smaller than it looks (Semafor).
Courts in Buenos Aires are using ChatGPT to draft rulings (Rest of World).

If you want to get in front of an audience of 200,000+ developers, business leaders and tech enthusiasts, get in touch with us here.

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Software Development Manager: Google, New York, NY

Tesla has the highest fatal accident rate of all car brands, according to a new analysis of NHTSA data, of 5.6 per billion vehicle miles, above the overall average of 2.8.
A judge denied OpenAI’s motion to compel the New York Times to share information regarding the ways in which the company’s own employees use generative AI: “This case is about whether Defendant trained their LLMs using Plaintiff’s copyrighted material, and whether that use constitutes copyright infringement. It is not a referendum on the benefits of Gen AI, on Plaintiff’s business practices, or about whether any of Plaintiff’s employees use Gen AI at work.”

Source: Unsplash

Last week, a judge completely — and with prejudice — granted Microsoft’s motion to dismiss the copyright infringement lawsuit brought against the firm by media outlet The Intercept. The judge also partially granted OpenAI’s motion for a dismissal.

The details: The initial lawsuit accused both OpenAI and Microsoft of violating two sections — b(1) and b(3) — of U.S. copyright code 1202.

The judge granted OpenAI’s motion for dismissal under section b(3), also with prejudice, but is allowing The Intercept’s claim of OpenAI’s violation of section b(1) to proceed past the motion-to-dismiss stage.
The dismissals “with prejudice” mean that The Intercept will not be able to re-file its case.

Let’s take a look at the law: Section b(3) of the copyright code holds that no one, without the express permission of the copyright holder, shall “distribute … copies of works … knowing that copyright management information has been removed or altered without authority of the copyright owner or the law.”

Section b(1) — the last remaining vestige of The Intercept’s argument — holds that no person shall, without the express permission of the copyright holder, “intentionally remove or alter any copyright management information.”

Similar to the recently dismissed case brought by RawStory and AlterNet, the central pillar of this case had to do with the removal of copyright-identifying information, such as headlines and bylines; it did not aim to address whether the act of training commercialized generative AI models on copyrighted content constitutes copyright infringement itself.

To that end, while it will be interesting to see how this case turns out, this case will not decide the copyright question of generative AI. That burden falls instead on the New York Times and the Authors Guild, each of which has brought sweeping copyright infringement cases against OpenAI.

Source: Unsplash

We hear often that, when it comes to artificial intelligence, data is king.

This is perhaps best exemplified by the gradual adjustments in social media terms’ of service over the past few years; now, all of our data, beyond being used to fuel algorithms and hyper-targeted advertisements, is additionally being used to train generative AI models.

While this has become relatively common knowledge, the scope of it isn’t. And according to new research out of the Mozilla Foundation, one category of this environment of data collection has been rapidly growing since the onset of the pandemic: body-centric data.

What it means: Here, body-centric data refers to “information about people’s biological and psychological characteristics — from fingerprints used to unlock phones and face scans for security to intimate data from fitness and fertility trackers, mental health apps and digital medical records.”

The implications of the broad consumption of this type of data are numerous and potentially severe, from cybersecurity breaches to data misuse, consent violations, discrimination against vulnerable populations, biometric persecution and widespread surveillance.
The report found that the market for body-centric data is expected to top $500 billion by 2030, a boom that comes as “health-related cybersecurity breaches and ransom attacks have skyrocketed more than 4,000% — from 18 incidents in 2009 to 745 in 2023 in the United States alone.”

The findings: The report found that the rapid advancement and subsequent introduction of generative AI tools facilitates “continuous data processing without clear user awareness, thereby increasing the risk of data misuse.”

This, combined with gaps in current regulation surrounding the use of highly sensitive biometric data, increases the risk of potential data misuse, including algorithmic discrimination that can impact health outcomes.
For example, as the report notes, AI systems used to diagnose skin cancer — often lacking comprehensive training data for darker skin tones — can result “in lower diagnostic accuracy for individuals with darker skin.”

The report recommended the reconstruction of data privacy legislation that specifically classifies and defends against the collection and use of sensitive data, including bodily data.

It also recommends an expansion of HIPAA in the U.S. to cover all health-related information — for instance, the data collected by fitness watches and health apps, which is not currently subject to the same restrictions and protections as information shared with your doctor — and an increase in transparency around data collection, data usage and opt-outs.

“We need a new approach to our digital interactions that recognizes the fundamental rights of individuals to safeguard their bodily data, an issue that speaks directly to human autonomy and dignity,” Mozilla fellow Julia Keserű said. “As technology continues to advance, it is critical that our laws and practices evolve to meet the unique challenges of this era.”

The concerns laid out in this report echo and mirror concerns that researchers have been sharing for years. Several recent reports have highlighted risks of algorithmic discrimination and hallucination resulting in negative outcomes for patients, even as some hospitals push for integration.

This is the dual nature of generative AI. The same data and the same systems that could be used to help people, can also be weaponized against people, which makes specific regulation extremely necessary.

We need to try to prevent misuse before these things become more widespread. And we need to start caring about how our data is being used before this regime of data consumption and application — without our consent — becomes even more established as the fully acceptable norm.