The United States government is taking on one of the world's most powerful companies: Google. A court battle kicks off on Tuesday in which the U.S. Justice Department will argue that Google abused its power as a monopoly to dominate the search engine business. It's the government's first major monopoly case to make it to trial in decades and the first in the age of the modern internet. The Justice Department's case hinges on claims that Google illegally orchestrated its business dealings so that it's the first search engine people see when they turn on their phones and web browsers. The government says Google's goal was to stomp out competition. "This lawsuit strikes at the heart of Google's grip over the internet for millions of American consumers, advertisers, small businesses, and entrepreneurs beholden to an unlawful monopolist," said former Attorney General William Barr when the case was first filed in October 2020. Now nearly three years later, with millions of pages of documents produced and depositions from more than 150 people, the case is going to trial.  The government's case challenges how tech companies are able to amass power and control the products people now use daily in their lives. The outcome of the case could change how tech giants are able to do business and, in effect, how the internet is run. Google, which is worth $1.7 trillion, controls around 90% of the U.S. search engine market. It's put together a massive legal team and brought on outside law firms to help fight its case. The company says its search product is superior to competitors and that is why it dominates the industry. Google says if people don't want to use its search engine, they can just switch to another. "People don't use Google because they have to — they use it because they want to," Kent Walker, one of Google's top lawyers and its president of global affairs, wrote in an emailed statement. "It's easy to switch your default search engine — we're long past the era of dial-up internet and CD-ROMs."  The last antitrust case of this magnitude took place in 1998, when the Justice Department sued Microsoft. That trial centered around claims that Microsoft illegally grouped its various products together in a way that both stifled competition and compelled people to use its products. The judge ruled in favor of the Justice Department in that case, saying Microsoft violated antitrust laws and held "an oppressive thumb on the scale of competitive fortune." The Justice Department's case against Google is strikingly similar and its lawyers are angling for the same outcome. "That case was about a monopolist tech platform and the government won," says Rebecca Haw Allensworth, a professor at Vanderbilt Law School who specializes in antitrust law. "And so, everybody has viewed that as a kind of blueprint for how we might enforce the laws against the current tech giants." Learn more by visiting OUR FORUM.

It hasn't even been a year since OpenAI released ChatGPT, and already generative AI is everywhere. It's in classrooms; it's in political advertisements; it's in entertainment and journalism and a growing number of AI-powered content farms. Hell, generative AI has even been integrated into search engines, the great mediators and organizers of the open web. People have already lost work to the tech, while new and often confounding AI-related careers seem to be on the rise. Though whether it sticks in the long term remains to be seen, at least for the time being generative AI seems to be cementing its place in our digital and real lives. And as it becomes increasingly ubiquitous, so does the synthetic content it produces. But in an ironic twist, those same synthetic outputs might also stand to be generative AI's biggest threat. That's because underpinning the growing generative AI economy is human-made data. Generative AI models don't just cough up human-like content out of thin air; they've been trained to do so using troves of material that actually was made by humans, usually scraped from the web. But as it turns out, when you feed synthetic content back to a generative AI model, strange things start to happen. Think of it like data inbreeding, leading to increasingly mangled, bland, and all-around bad outputs. (Back in February, Monash University data researcher Jathan Sadowski described it as "Habsburg AI," or "a system that is so heavily trained on the outputs of other generative AI's that it becomes an inbred mutant, likely with exaggerated, grotesque features.") It's a problem that looms large. AI builders are continuously hungry to feed their models more data, which is generally being scraped from an internet increasingly laden with synthetic content. If there's too much destructive inbreeding, could everything just... fall apart? To understand this phenomenon better, we spoke to machine learning researchers Sina Alemohammad and Josue Casco-Rodriguez, Ph.D. students in Rice University's Electrical and Computer Engineering department, and their supervising professor, Richard G. Baraniuk. In collaboration with researchers at Stanford, they recently published a fascinating — though yet to be peer-reviewed — paper on the subject, titled "Self-Consuming Generative Models Go MAD." MAD, which stands for Model Autophagy Disorder, is the term they've coined for AI's apparent self-allergy. In their research, it took only five cycles of training on synthetic data for an AI model's outputs to, in the words of Baraniuk, "blow up." It's a fascinating glimpse at what just might be generative AI's Achilles heel. If so, what does it all mean for regular people, the burgeoning AI industry, and the internet itself? In-depth details can be found on OUR FORUM.