The first 2024 presidential debate unfolded Thursday night between President Joe Biden and former President Donald Trump and it was, as fellow Slate writer Jill Filipovic put it, the "most painful two hours of television in living memory." If you missed it, good for you. If you did watch it, you'd be forgiven for not remembering the topics that were actually debated vaguely shouted about. In between Biden's excruciatingly painful and "nightmarishly confused" performance and Trump's boorish firehose of lies and falsehoods, the debate also ostensibly featured a wide range of topics like abortion, the economy, climate change, foreign policy in Ukraine and Israel, election integrity, immigration, veterans, race, crime, health care, and even which of the two candidates has a better golf game. However, there was one major issue that the debate failed to broach--despite the fact that it's one of the most (if not the most) consequential developments since the last election cycle: artificial intelligence.

OpenAI's newly unveiled ChatGPT bot is making waves when it comes to all the amazing things it can do--from writing music to coding to generating vulnerability exploits, and what not. As the erudite machinery turns into a viral sensation, humans have started to discover some of the AI's biases, like the desire to wipe out humanity. Yesterday, BleepingComputer ran a piece listing 10 coolest things you can do with ChatGPT. And, that doesn't even begin to cover all use cases like having the AI compose music for you [1, 2]. Within six days of its launch, ChatGPT surpassed a million users to the extent its servers couldn't keep up.

Written by Dr. Kirk Borne I recently learned about the rule of least power in computer programming. The principle expresses the notion that one should use the least powerful programming language to code a given task, while still meeting the business requirements. It occurred to me that a similar concept can apply to data analytics tasks, specifically in data science modeling using machine learning algorithms. In this post, this "doctor of analytics" prescribes this approach for your next analytics project. We have often defined analytics as the products of data science activities that apply machine learning and statistical algorithms on data in order to achieve one or more business requirements.

I realized that my sixth sense was failing when I stopped noticing the magnetic fields of my laptop. In fall 2012, I implanted a rare earth magnet in my right ring finger. Magnets were one of the most accessible forms of DIY biohacking, a niche subculture riding the start of a massive mainstream publicity wave. My colleague Ben Popper got one while writing a biohacking feature, and it sounded like magic. When I first got it, I wasn't disappointed.

We all know the half-joke about the AI apocalypse. The robots learn to think, and in their cold ones-and-zeros logic, they decide that humans--horrific pests we are--need to be exterminated. It's the subject of countless sci-fi stories and blog posts about robots, but maybe the real danger isn't that AI comes to such a conclusion on its own, but that it gets that idea from us. Yesterday Microsoft launched a fun little AI Twitter chatbot that was admittedly sort of gimmicky from the start. "A.I fam from the internet that's got zero chill," its Twitter bio reads.