Good morning, and happy Friday. This week, Mark Zuckerberg and Meta unveiled Orion, a prototype model of the company’s AR glasses designed to merge the virtual and physical world in one seamless interface. 

But that all got drowned out by the leadership chaos (again) at OpenAI. Now, Sam Altman is all that’s left … this has certainly not made me trust OpenAI more. 

Didn’t think I could trust them any less, but here we are. 

In today’s newsletter: 

— Ian Krietzberg, Editor in Chief, The Deep View

MBZUAI Research: AI-generated text detection

Shortly after ChatGPT — and the ever-lengthening list of similar GenAI chatbots — demonstrated to people (and students) around the world their apparent ability to spit out essays in seconds, came the technological response: GenAI text detectors. 

The problem with these generators — as we’ve reported — is that they are just as unreliable as the systems generating the text in the first place. Instances of false accusations of GenAI-powered cheating have spiked in high schools and colleges across the globe. 

Recent research from the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) adds more credence to the idea that AI-powered text detection isn’t a reliable method. 

The details: The researchers presented a large-scale multi-generator, multi-domain and multilingual dataset — dubbed “M4” — for machine-generated text detection. 

Why it matters: This adds weight to two separate ideas in the realm of AI. One, that LLMs are nothing more than predictive text generators incapable of reasoning; here, the fact that the systems struggle when the domain isn’t included in the training data is telling. And two, that such detectors should not be used as an absolute indicator of machine-generated text in any scenario, especially in schools.

To learn more about MBZUAI’s research visit their website.  

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.

AI for Good: Carbon sequestration

Agricultural practices have a significant impact on the environment. In terms of carbon emissions alone, agriculture — in a number of ways — is responsible for around 10% to 12% of global carbon emissions.

Part of the problem here is that widespread tilling breaks up shallow root systems, causing naturally sequestered carbon to re-enter the atmosphere. 

What happened: New research published earlier this year points to a potential solution, one that combines a different approach to root systems with machine learning tech. 

Why it matters: Improving levels of carbon in the soil is good for crop health, reducing global emissions and increasing sustainability and climate change resilience. 

The FTC’s AI crackdown

The United States Federal Trade Commission (FTC) is taking legal action against five companies for deceptive AI-related practices. 

The details: The cases come as part of the FTC’s Operation AI Comply sweep. The FTC is taking action against DoNotPay, Ascend ECom, Ecommerce Empire Builders, Rytyr and FBA Machine. 

“Using AI tools to trick, mislead, or defraud people is illegal,” FTC Chair Lina Khan said. “The FTC’s enforcement actions make clear that there is no AI exemption from the laws on the books. By cracking down on unfair or deceptive practices in these markets, FTC is ensuring that honest businesses and innovators can get a fair shot and consumers are being protected.”

Bring on the lawsuits. 

We talk a lot about regulation, and what the FTC is doing here is a great form of it — lawmaking will move slow, but federal regulators can move much more quickly. And these movements can help set the stage for a future environment that helps skim some of the scum from the sector here. 

As long as we’ve had an internet, we’ve had internet scams. They’re not going away; AI just opened up a new opportunity for a slightly different breed of scam. 

Don’t fall for them.

🤔 Your thought process:

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💭 A poll before you go

Your view on Colorado’s new AI-powered call center: 

More than a third of you said it sounds okay, so long as there are proper guardrails. 25% said it sounds great. The rest are concerned about bugs that are common to LLMs and generative AI, including hallucination and bias. 

Something else: 

“The rapid language translation seems to be a fine addition to emergency response as long as it’s accurate. The wholesale elimination of bystander reporting seems to be a loss of data that could be used.” 

Sounds okay, but needs guardrails: 

“The concept is great. I hope there are no bugs that may cost life or injury.” 

Thanks for reading today’s edition of The Deep View!

We’ll see you in the next one.