Home Learn MIT Technology Review My deepfake shows how priceless our data is within the age of AI Now read the remainder of The Algorithm

MIT Technology Review My deepfake shows how priceless our data is within the age of AI Now read the remainder of The Algorithm

MIT Technology Review
My deepfake shows how priceless our data is within the age of AI
Now read the remainder of The Algorithm

Deepfakes are getting good. Like, really good. Earlier this month I went to a studio in East London to get myself digitally cloned by the AI video startup Synthesia. They made a hyperrealistic deepfake that looked and sounded identical to me, with realistic intonation. It’s a good distance away from the glitchiness of earlier generations of AI avatars. The tip result was mind-blowing. It could easily idiot someone who doesn’t know me well.

Synthesia has managed to create AI avatars which can be remarkably humanlike after just one yr of tinkering with the most recent generation of generative AI. It’s equally exciting and daunting fascinated by where this technology goes. It would soon be very difficult to distinguish between what’s real and what just isn’t, and this can be a particularly acute threat given the record variety of elections happening world wide this yr. 

We will not be ready for what’s coming. If people develop into too skeptical in regards to the content they see, they may stop believing in anything in any respect, which could enable bad actors to reap the benefits of this trust vacuum and lie in regards to the authenticity of real content. Researchers have called this the “liar’s dividend.” They warn that politicians, for instance, could claim that genuinely incriminating information was fake or created using AI. 

I just published a story on my deepfake creation experience, and on the massive questions on a world where we increasingly can’t tell what’s real. Read it here. 

But there may be one other big query: What happens to our data once we submit it to AI firms? Synthesia says it doesn’t sell the information it collects from actors and customers, even though it does release a few of it for tutorial research purposes. The corporate uses avatars for 3 years, at which point actors are asked in the event that they wish to renew their contracts. If that’s the case, they arrive into the studio to make a brand new avatar. If not, the corporate deletes their data.

But other firms will not be that transparent about their intentions. As my colleague Eileen Guo reported last yr, firms comparable to Meta license actors’ data—including their faces and  expressions—in a way that permits the businesses to do whatever they need with it. Actors are paid a small up-front fee, but their likeness can then be used to coach AI models in perpetuity without their knowledge. 

Even when contracts for data are transparent, they don’t apply in the event you die, says Carl Öhman, an assistant professor at Uppsala University who has studied the web data left by deceased people and is the creator of a brand new book, . The information we input into social media platforms or AI models might find yourself benefiting firms and living on long after we’re gone. 

“Facebook is projected to host, inside the following couple of a long time, a few billion dead profiles,” Öhman says. “They’re not likely commercially viable. Dead people don’t click on any ads, but they take up server space nevertheless,” he adds. This data may very well be used to coach recent AI models, or to make inferences in regards to the descendants of those deceased users. The entire model of knowledge and consent with AI presumes that each the information subject and the corporate will survive ceaselessly, Öhman says.

Our data is a hot commodity. AI language models are trained by indiscriminately scraping the online, and that also includes our personal data. A few years ago I tested to see if GPT-3, the predecessor of the language model powering ChatGPT, has anything on me. It struggled, but I discovered that I used to be capable of retrieve personal information about MIT Technology Review’s editor in chief, Mat Honan. 

High-quality, human-written data is crucial to training the following generation of powerful AI models, and we’re on the verge of running out of free online training data. That’s why AI firms are racing to strike deals with news organizations and publishers to access their data treasure chests. 

Old social media sites are also a possible gold mine: when firms exit of business or platforms stop being popular, their assets, including users’ data, get sold to the best bidder, says Öhman. 

“MySpace data has been bought and sold multiple times since MySpace crashed. And something similar may occur to Synthesia, or X, or TikTok,” he says. 

Some people may not care much about what happens to their data, says Öhman. But securing exclusive access to high-quality data helps cement the monopoly position of huge corporations, and that harms us all. That is something we want to grapple with as a society, he adds. 

Synthesia said it can delete my avatar after my experiment, but the entire experience did make me consider all of the cringeworthy photos and posts that haunt me on Facebook and other social media platforms. I feel it’s time for a purge.

Now read the remainder of The Algorithm

Deeper Learning

Chatbot answers are all made up. This recent tool helps you determine which of them to trust.

Large language models are famous for his or her ability to make things up—in truth, it’s what they’re best at. But their inability to inform fact from fiction has left many businesses wondering if using them is definitely worth the risk. A brand new tool created by Cleanlab, an AI startup spun out of MIT, is designed to supply a clearer sense of how trustworthy these models really are. 

A BS-o-meter for chatbots: Called the Trustworthy Language Model, it gives any output generated by a big language model a rating between 0 and 1, based on its reliability. This lets people select which responses to trust and which to throw out. Cleanlab hopes that its tool will make large language models more attractive to businesses anxious about how much stuff they devise. Read more from Will Douglas Heaven.

Bits and Bytes

Here’s the defense tech at the middle of US aid to Israel, Ukraine, and Taiwan
President Joe Biden signed a $95 billion aid package into law last week. The bill will send a major quantity of supplies to Ukraine and Israel, while also supporting Taiwan with submarine technology to assist its defenses against China. (MIT Technology Review) 

Rishi Sunak promised to make AI secure. Big Tech’s not playing ball.
The UK’s prime minister thought he secured a political win when he got AI power players to conform to voluntary safety testing with the UK’s recent AI Safety Institute. Six months on, it seems pinkie guarantees don’t go very far. OpenAI and Meta haven’t granted access to the AI Safety Institute to do prerelease safety testing on their models. (Politico) 

Contained in the race to seek out AI’s killer app
The AI hype bubble is beginning to deflate as firms try to seek out a strategy to make profits out of the eye-wateringly expensive technique of developing and running this technology. Tech firms haven’t solved among the fundamental problems slowing its wider adoption, comparable to the indisputable fact that generative models always make things up. (The Washington Post)  

Why the AI industry’s thirst for brand new data centers can’t be satisfied
The present boom in data-hungry AI means there may be now a shortage of parts, property, and power to construct data centers. (The Wall Street Journal) 

The buddies who became rivals in Big Tech’s AI race
This story is a captivating look into probably the most famous and fractious relationships in AI. Demis Hassabis and Mustafa Suleyman are old friends who grew up in London and went on to cofound AI lab DeepMind. Suleyman was ousted following a bullying scandal, went on to start out his own short-lived startup, and now heads rival Microsoft’s AI efforts, while Hassabis still runs DeepMind, which is now Google’s central AI research lab. (The Latest York Times) 

This creamy vegan cheese was made with AI
Startups are using artificial intelligence to design plant-based foods. The businesses train algorithms on data sets of ingredients with desirable traits like flavor, scent, or stretchability. Then they use AI to comb troves of knowledge to develop recent combos of those ingredients that perform similarly. (MIT Technology Review) 


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