On a recent commute to work, I texted my distant family about our fantasy baseball league, which was nice because I felt connected to them for a second. Then I switched apps and became enraged by a stupid opinion I saw on X, which I shouldn't be using anymore due to its advanced toxicity and mind-numbing inanity. Many minutes passed before I was able to stop reading the stupid replies to the stupid original post and relax the muscles of my face. This is the duality of the phone: It connects me to my loved ones, and sometimes I think it's ruining my life. I need it and I want it, but sometimes I hate it and I fear it.

Last spring, the clothing brand Levi Strauss & Co. announced plans to introduce "customized AI-generated models" into its online shopping platforms. These "body-inclusive avatars" would come in a range of sizes, ages, and skin tones and would help Levi's create a more "diverse" lineup in a way the company considered "sustainable." A lot of (real) people were appalled. Why not give those jobs to actual humans of the sizes, ages, and skin tones Levi's sought? Was "sustainable" just PR-speak for "cheaper"?

Since the tech industry began its love affair with machine learning about a decade ago, US lawmakers have chattered about the potential need for regulation to rein in the technology. No proposal to regulate corporate AI projects has got close to becoming law--but OpenAI's release of ChatGPT in November has convinced some senators there is now an urgent need to do something to protect people's rights against the potential harms of AI technology. At a hearing held by a Senate Judiciary subcommittee yesterday attendees heard a terrifying laundry list of ways artificial intelligence can harm people and democracy. Senators from both parties spoke in support of the idea of creating a new arm of the US government dedicated to regulating AI. The idea even got the backing of Sam Altman, CEO of OpenAI.

Scott Petry, co-founder and CEO of Authentic8, maker of Silo, a platform for secure and controlled access to the web. Security misconfiguration and broken authentication. It plays out time and again: A bad person invents a way to attack a computer or a network. A good person discovers the attack and figures out how to detect future attacks. More good people build on that work and learn how to block them.

If you believed everything you read, artificial intelligence (AI) is the savior of cybersecurity. According to Capgemini, 80% of companies are counting on AI to help identify threats and thwart attacks. That's a big ask to live up to because, in reality, few nonexperts really understand the value of AI to security or whether the technology can effectively address information security's many potential use cases. A cynic would call out the proliferation of claims about using AI for what it is -- marketing hype. Even the use of the term "AI" is misleading.

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here? The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video above).

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here?: The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video, above.)