My in-laws own a little two-bedroom beach bungalow. It's part of a condo development that hasn't changed much in fifty years. The units are connected by brick paths that wind through palm trees and tiki shelters to a beach. Nearby, developers have built big hotels and condo towers, and it's always seemed inevitable that the bungalows would be razed and replaced. But it's never happened, probably because, according to the association's bylaws, eighty per cent of the owners have to agree to a sale of the property.

Two years ago, OpenAI released the public beta of DALL-E 2, an image-generation tool that immediately signified that we'd entered a new technological era. Trained off a huge body of data, DALL-E 2 produced unsettlingly good, delightful, and frequently unexpected outputs; my Twitter feed filled up with images derived from prompts such as close-up photo of brushing teeth with toothbrush covered with nacho cheese. Suddenly, it seemed as though machines could create just about anything in response to simple prompts. You likely know the story from there: A few months later, ChatGPT arrived, millions of people started using it, the student essay was pronounced dead, Web3 entrepreneurs nearly broke their ankles scrambling to pivot their companies to AI, and the technology industry was consumed by hype. The generative-AI revolution began in earnest.

Ten years ago, the Oxford philosopher Nick Bostrom published Superintelligence, a book exploring how superintelligent machines could be created and what the implications of such technology might be. One was that such a machine, if it were created, would be difficult to control and might even take over the world in order to achieve its goals (which in Bostrom's celebrated thought experiment was to make paperclips). The book was a big seller, triggering lively debates but also attracting a good deal of disagreement. Critics complained that it was based on a simplistic view of "intelligence", that it overestimated the likelihood of superintelligent machines emerging any time soon and that it failed to suggest credible solutions for the problems that it had raised. But it had the great merit of making people think about a possibility that had hitherto been confined to the remoter fringes of academia and sci-fi. Now, 10 years later, comes another shot at the same target.

Recent weeks have not been kind to OpenAI. The release of the company's latest model, GPT-4o, has been somewhat overshadowed by a series of accusations leveled at both the company and its CEO, Sam Altman. This comes at the same time that several high-profile employees, including co-founder and chief scientist Ilya Sutskever, have chosen to leave the company. This is not the first time the Silicon Valley startup has been embroiled in scandal. In November, Altman was briefly ousted from the company after the board found he had not been "consistently candid" with them.

Thanks to great advancements in computational power, new algorithms, and better labeling of data, machine learning applications have flourished in recent years. From customer service chatbots to content recommendations, it now feels like this technology is everywhere. Unfortunately, much of machine learning's potential is still left on the table. Technical constrictions and complicated barriers lie in the way of this artificial intelligence (AI) subset becoming mainstream. Once these issues are resolved, more organizations and consumers than ever before will be able to practically leverage machine learning for their own benefit.

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.

It's the most cunning robocall scam I've encountered -- and the fact that I've fallen for it more than once tells you how successful it can be. You pick it up and say "hello." There's a brief silence and then a woman's voice says, "Oh, hi there!" She offers an embarrassed laugh. "I'm sorry, I was having a little trouble with my headset!"