Fox News Correspondent, William La Jeunesse, joins'Fox News Sunday' to discuss the evolution of A.I. and the push lawmakers are making to regulate it. ADAPT: The Trump administration's recent announcement of a sweeping deregulatory agenda for generative artificial intelligence (Gen AI) has created ripples across industries. This policy shift has implications for professionals and businesses alike, signaling a future where Gen AI development will accelerate quickly. If you want your work and business to survive this new acceleration, you need to adapt quickly to our increasingly disrupted environment. Zachary Levi attends the UK premiere of Shazam!

The Agency

Humans have been trying to talk to animals ever since we figured out how to form words. In modern times, we turn to technology for the solution--giving our dogs talking buttons to paw at, or trying to use artificial intelligence to help us understand whales. The latest and perhaps most direct approach at human-animal communication is a voice-activated collar that gives your pet the power to talk back to you. John McHale, a self-described "tech guy" based out of Austin, Texas, has a company called Personifi AI. The startup's goal, as the name implies, is to create tech that will "personify everything," as McHale puts it.

A mysterious sound emitted from the deepest part of the ocean has finally been identified as a Bryde's whale. Now, artificial intelligence is helping researchers track the elusive whale species responsible for the call. The puzzle began in 2014 when researchers recorded a sound resembling a moan followed by metallic sweeping pings over the Pacific Ocean's Mariana Trench. "Your average person would not think that it was made by an animal – they would think it was some ship or the Navy," says Ann Allen at the National Oceanic and Atmospheric Administration (NOAA). Years later, additional recordings of the sound, which researchers call a biotwang, were eventually linked to sightings of Bryde's whales (Balaenoptera brydei) near the Mariana Islands.

We propose a new method, instruction back-and-forth translation, to construct high-quality synthetic data grounded in world knowledge for aligning large language models (LLMs). Given documents from a web corpus, we generate and curate synthetic instructions using the backtranslation approach proposed by Li et al.(2023a), and rewrite the responses to improve their quality further based on the initial documents. Fine-tuning with the resulting (backtranslated instruction, rewritten response) pairs yields higher win rates on AlpacaEval than using other common instruction datasets such as Humpback, ShareGPT, Open Orca, Alpaca-GPT4 and Self-instruct. We also demonstrate that rewriting the responses with an LLM outperforms direct distillation, and the two generated text distributions exhibit significant distinction in embedding space. Further analysis shows that our backtranslated instructions are of higher quality than other sources of synthetic instructions, while our responses are more diverse and complex than those obtained from distillation. Overall we find that instruction back-and-forth translation combines the best of both worlds -- making use of the information diversity and quantity found on the web, while ensuring the quality of the responses which is necessary for effective alignment.

German auto manufacturer Porsche announced its second-ever model of an all-electric vehicle. The new EV Macan comes in two forms--the Macan 4 and the slightly beefier Macan Turbo. Both of these all-electric SUVs have high-performance 100-kilowatt-hour batteries. These are also the first Porsches to use an 800-volt architecture, which allows the cars to charge from 10 to 80 percent in 21 minutes at a 270-kilowatt DC fast charger. The cars will be just as quick on the road: The Macan 4 puts out the equivalent of 402 horsepower, and the Turbo model puts out 630 hp. Zero to 60 times are 4.9 seconds for the Macan 4 and 3.1 seconds for the Turbo.

In order to find patterns, it first needs to learn what the pattern is. Cornell's library of birdsong recordings provides the training that the AI needs to learn which sounds are bird songs and which ones are you watering the garden. Cornell has been tweaking its neural net for some time. If you'd like to experience this without investing in a Haikubox, you can grab Cornell's Merlin Bird ID app, which relies on a small subset of the data and an AI processor similar to what the Haikubox uses. Haikubox creator David Mann told WIRED that the Haikubox uses a modified version of BirdNet, which is called BirdNet for Haikubox.

Picture this, again; you walk into a grocery store, you get the groceries you want, and then walk out. No lines, no payment, just walk out. With it's sensor fusion, computer vision and deep learning technology, Amazon is able to identify you and charge your account, without you having to do anything. You scan your phone via the Amazon app, walk in, shop, and then leave. In order for this to work, I'll let you figure out what data points you have to give up.

There's nothing more annoying than indulging in a delicious cheese board at a restaurant, only to forget what the varieties are in front of you are. But the days of scrambling to find the menu could be a thing of the past, thanks to a new app called Cheezam. Cheezam was inspired by the music-finding app, Shazam, and uses artificial intelligence (AI) to identify different cheeses from a single photo. Served in a simmering pot with a selection of dunkable ingredients, cheese fondue is without a doubt one of the most popular dishes among cheese lovers around the world. Now, the famous Swiss dish can be prepared, stirred up and served by a robot chef.

Voice AI company SoundHound is set to go public on the Nasdaq via a SPAC transaction at a nearly $2.1 billion valuation in early 2022, blank check company Archimedes and its target announced. The last time you heard SoundHound's name might have been several years ago when it was considered a lesser-known Shazam competitor. Now it's worth 5.25x what Apple paid for the leading music recognition service – some $400 million, in a transaction that closed in the fall of 2018. That was five years after TechCrunch reported on SoundHound's "struggles to exit Shazam's shadow," despite boasting more than 175 million users. So what happened that would make SoundHound now significantly more valuable than its British counterpart?