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Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, heres a handy roundup of the last weeks stories in the world of machine learning, along with notable research and experiments we didnt cover on their own.

In one of the more surprising stories of the past week, Italys data protection authority (DPA) blocked OpenAIs viral AI-powered chatbot, ChatGPT, citing concerns that the tool breaches the European Unions General Data Protection Regulation. The DPA is reportedly opening an investigation into whether OpenAI unlawfully processed peoples data, as well as over the lack of any system to prevent minors from accessing the tech.

Its unclear what the outcome might be; OpenAI has 20 days to respond to the order. But the DPAs move could have significant implications for companies deploying machine learning models not just in Italy, but anywhere within the European Union.

As Natasha notes in her piece about the news, many of OpenAIs models were trained on data scraped from the internet, including social networks like Twitter and Reddit. Assuming the same is true of ChatGPT, because the company doesnt appear to have informed people whose data it has repurposed to train the AI, it might well be running afoul of GDPR across the bloc.

GDPR is but one of the many potential legal hurdles that AI, particularly generative AI (e.g. text- and art-generating AI like ChatGPT), faces. Its becoming clearer with each mounting challenge that itll take time for the dust to settle. But thats not scaring away VCs, who continue to pour capital into the tech like theres no tomorrow.

Will those prove to be wise investments, or liabilities? Its tough to say at present. Rest assured, though, that well report on whatever happens.

Here are the other AI headlines of note from the past few days:

At AI enabler Nvidia, BioNeMo is an example of their new strategy, where the advance is not so much that its new, but that its increasingly easy for companies to access. The new version of this biotech platform adds a shiny web UI and improved fine-tuning of a bunch of models.

A growing portion of pipelines are dealing with heaps of data, amounts weve never seen before, hundreds of millions of sequences we have to feed into these models, said Amgens Peter Grandsard, who is leading a research division using AI tech. We are trying to obtain operational efficiency in research as much as we are in manufacturing. With the acceleration that tech like Nvidias provides, what you could have done last year for one project, now you can do five or 10 using the same investment in tech.

This book excerpt by Meredith Broussard over at Wired is worth reading. She was curious about an AI model that had been used in her cancer diagnosis (shes OK) and found it incredibly fiddly and frustrating to try to take ownership of and understand that data and process. Medical AI processes clearly need to consider the patient more.

Actually nefarious AI applications make for new risks, for instance attempting to influence discourse. Weve seen what GPT-4 is capable of, but it was an open question whether such a model could create effective persuasive text in a political context. This Stanford study suggests so: When people were exposed to essays arguing a case in issues like gun control and carbon taxes, AI-generated messages were at least as persuasive as human-generated messages across all topics. These messages were also perceived as more logical and factual. Will AI-generated text change anyones mind? Hard to say, but it seems very likely that people will increasingly put it to use for this kind of agenda.

Machine learning has been put to use by another group at Stanford to better simulate the brain as in, the tissue of the organ itself. The brain is not just complex and heterogeneous, but much like Jell-O, which makes both testing and modeling physical effects on the brain very challenging, explained professor Ellen Kuhl in a news release. Their new model picks and chooses between thousands of brain modeling methods, mixing and matching to identify the best way to interpret or project from the given data. It doesnt reinvent brain damage modeling, but should make any study of it faster and more effective.

Out in the natural world,a new Fraunhofer approach to seismic imaging applies ML to an existing data pipeline that handles terabytes of output from hydrophones and airguns. Ordinarily this data would have to be simplified or abstracted, losing some of its precision in the process, but the new ML-powered process allows analysis of the unabridged dataset.

Interestingly, the researchers note that this would ordinarily be a boon to oil and gas companies looking for deposits, but with the move away from fossil fuels, it can be put to more climate-friendly purposes like identifying potential CO2 sequestration sites or potentially damaging gas buildups.

Monitoring forests is another important task for climate and conservation research, and measuring tree size is part of it. But this task involves manually checking trees one by one. A team at Cambridge built an ML model that uses a smartphone lidar sensor to estimate trunk diameter, having trained it on a bunch of manual measurements. Just point the phone at the trees around you and boom. The system is more than four times faster, yet accurate beyond their expectations, said lead author of the study, Amelia Holcomb: I was surprised the app works as well as it does. Sometimes I like to challenge it with a particularly crowded bit of forest, or a particularly oddly shaped tree, and I think theres no way it will get it right, but it does.

Because its fast and requires no special training, the team hopes it can be released widely as a way to collect data for tree surveys, or to make existing efforts faster and easier. Android only for now.

Lastly, enjoy this interesting investigation and experiment by Eigil zu Tage-Ravn of seeing what a generative art model makes of the famous painting in the Spouter-Inn described in chapter 3 of Moby-Dick.

Original post:
The week in AI: The pause request heard round the world - TechCrunch