Human sensory systems are very good at recognizing objects that we see or words that we hear, even if the object is upside down or the word is spoken by a voice we’ve never heard.

Computational models known as deep neural networks can be trained to do the same thing, correctly identifying an image of a dog regardless of what color its fur is, or a word regardless of the pitch of the speaker’s voice. However, a new study from MIT neuroscientists has found that these models often also respond the same way to images or words that have no resemblance to the target.

When these neural networks were used to generate an image or a word that they responded to in the same way as a specific natural input, such as a picture of a bear, most of them generated images or sounds that were unrecognizable to human observers. This suggests that these models build up their own idiosyncratic “invariances”—meaning that they respond the same way to stimuli with very different features.

Microsoft has unveiled a new dataset to help build interactive AI assistants for everyday tasks.

Extensive dataset of egocentric videos

According to Microsoft researchers Xin Wang and Neel Joshi, the dataset, called “HoloAssist,” is the first of its kind to include egocentric videos of humans performing physical tasks, as well as associated instructions from a human tutor.

At the same time, Mudrik has been trying to figure out what this diversity of theories means for AI. She’s working with an interdisciplinary team of philosophers, computer scientists, and neuroscientists who recently put out a white paper that makes some practical recommendations on detecting AI consciousness. In the paper, the team draws on a variety of theories to build a sort of consciousness “report card”—a list of markers that would indicate an AI is conscious, under the assumption that one of those theories is true. These markers include having certain feedback connections, using a global workspace, flexibly pursuing goals, and interacting with an external environment (whether real or virtual).

In effect, this strategy recognizes that the major theories of consciousness have some chance of turning out to be true—and so if more theories agree that an AI is conscious, it is more likely to actually be conscious. By the same token, a system that lacks all those markers can only be conscious if our current theories are very wrong. That’s where LLMs like LaMDA currently are: they don’t possess the right type of feedback connections, use global workspaces, or appear to have any other markers of consciousness.

The trouble with consciousness-by-committee, though, is that this state of affairs won’t last. According to the authors of the white paper, there are no major technological hurdles in the way of building AI systems that score highly on their consciousness report card. Soon enough, we’ll be dealing with a question straight out of science fiction: What should one do with a potentially conscious machine?

Adobe will premiere the first-ever TV commercial powered by its Firefly generative AI during high-profile sports broadcasts on Monday night. The commercial for Adobe Photoshop highlights creative capabilities enabled by the company’s AI technology.

Set to air during MLB playoffs and Monday Night Football, two of the most-watched live events on television, the new Adobe spot will showcase Photoshop’s Firefly-powered Generative Fill feature. Generative Fill uses AI to transform images based on text prompts.

With Adobe’s new commercial, generative AI will enter the mainstream spotlight, reaching audiences beyond just tech circles. While early adopters have embraced AI tools, a recent study found 44% of U.S. workers have yet to use generative AI, indicating its capabilities remain unknown to many.

As you might imagine for a scroll that has been buried under mounds of volcanic ash from Mount Vesuvius for close to 2,000 years, the rolled-up papyrus excavated from the ancient Roman city of Herculaneum is rather difficult to open, let alone read – but AI has found a way.

Scholars from the University of Kentucky launched the Vesuvius Challenge in March, releasing thousands of X-ray images of charred, carbonized Herculaneum scrolls together with untrained artificial intelligence software that could be used to interpret the scans.

Now two students have claimed the first prizes to be awarded: Luke Farritor, a computer science student at the University of Nebraska-Lincoln, and Youssef Nader, a biorobotics grad student at the Free University of Berlin in Germany.