Meme-makers and misinformation peddlers are embracing artificial intelligence tools to create convincing fake videos on the cheap. It wouldn't be completely out of character for Joe Rogan, the comedian turned podcaster, to endorse a "libido-boosting" coffee brand for men. But when a video circulating on TikTok recently showed Mr. Rogan and his guest, Andrew Huberman, hawking the coffee, some eagle-eyed viewers were shocked -- including Dr. Huberman. "Yep that's fake," Dr. Huberman wrote on Twitter after seeing the ad, in which he appears to praise the coffee's testosterone-boosting potential, even though he never did. The ad was one of a growing number of fake videos on social media made with technology powered by artificial intelligence. Experts said Mr. Rogan's voice appeared to have been synthesized using A.I. tools that mimic celebrity voices.

Joe Rogan is known to read a few sponsored ads at the beginning of his podcast, but a video of him promoting a libido booster for men is a deepfake - and people fear this is the start of new scams and a wave of misinformation. The'eerily real' clip shows Rogan discussing the brand Alpha Grind with guest Professor Andrew D. Huberman on The Joe Rogan Experience podcast, stating the product is all over TikTok and is available for purchase on Amazon. The 28-second video does look realistic, but some segments reveal it was created by artificial intelligence - some commentary jumps instead of naturally flowing. Huberman responded to the video on Twitter, saying: 'They created a false conversation. We were talking about something very different.'

In the latest installment of CMO Review with Erik Huberman, presented by Entrepreneur Network partner Business Rockstars, Huberman covers the real-world connection between artificial intelligence and marketing. Huberman says the future of marketing is influenced by artificial intelligence. He notes that though the term "AI" is widely used in marketing, there is nothing truly artificially intelligent just yet. These tools are technically at the intelligence of a mouse. The downfall of AI is that it is not properly suited to pick up nuances, like humans can.

In the latest installment of CMO Review with Erik Huberman, presented by Entrepreneur Network partner Business Rockstars, Huberman covers the real-world connection between artificial intelligence and marketing. Huberman says the future of marketing is influenced by artificial intelligence. He notes that though the term "AI" is widely used in marketing, there is nothing truly artificially intelligent just yet. These tools are technically at the intelligence of a mouse. The downfall of AI is that it is not properly suited to pick up nuances, like humans can.

Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates. 1 INTRODUCTION Learning procedures for connectionist networks are essentially statistical devices for performing inductive inference. There is a tradeoff between two goals: on the one hand, we want such devices to be as general as possible so that they are able to learn a broad range of problems.

Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates. 1 INTRODUCTION Learning procedures for connectionist networks are essentially statistical devices for performing inductive inference. There is a tradeoff between two goals: on the one hand, we want such devices to be as general as possible so that they are able to learn a broad range of problems.

Bernardo A. Huberman Dynamics of Computation XeroxPARC Palo Alto, CA 94304 Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates. 1 INTRODUCTION Learning procedures for connectionist networks are essentially statistical devices for performing inductiveinference. There is a tradeoff between two goals: on the one hand, we want such devices to be as general as possible so that they are able to learn a broad range of problems.