The Case That A.I. Is Thinking ChatGPT does not have an inner life. Yet it seems to know what it's talking about. How convincing does the illusion of understanding have to be before you stop calling it an illusion? Dario Amodei, the C.E.O. of the artificial-intelligence company Anthropic, has been predicting that an A.I. "smarter than a Nobel Prize winner" in such fields as biology, math, engineering, and writing might come online by 2027. He envisions millions of copies of a model whirring away, each conducting its own research: a "country of geniuses in a datacenter." In June, Sam Altman, of OpenAI, wrote that the industry was on the cusp of building "digital superintelligence." "The 2030s are likely going to be wildly different from any time that has come before," he asserted. Meanwhile, the A.I. tools that most people currently interact with on a day-to-day basis are reminiscent of Clippy, the onetime Microsoft Office "assistant" that was actually more of a gadfly. A Zoom A.I. tool suggests that you ask it "What are some meeting icebreakers?" or instruct it to "Write a short message to share gratitude." Siri is good at setting reminders but not much else. A friend of mine saw a button in Gmail that said "Thank and tell anecdote." When he clicked it, Google's A.I. invented a funny story about a trip to Turkey that he never took. The rushed and uneven rollout of A.I. has created a fog in which it is tempting to conclude that there is nothing to see here--that it's all hype. There is, to be sure, plenty of hype: Amodei's timeline is science-fictional.

Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data. Previous research has looked into this problem from the perspective of i.i.d. data. Here, we develop a framework, GraCE-VAE, for non-i.i.d. settings, in which structured context in the form of network data is available. GraCE-VAE integrates discrepancy-based variational autoencoders with graph neural networks to jointly recover the true latent causal graph and intervention effects. We show that the theoretical results of identifiability from i.i.d. data hold in our setup. We also empirically evaluate GraCE-VAE against state-of-the-art baselines on three genetic perturbation datasets to demonstrate the impact of leveraging structured context for causal disentanglement.

Mogadishu, Somalia – Ending the United States' "forever wars" was a major slogan of Donald Trump's 2024 election campaign, during which he and many of his supporters spoke out against American resources and lives being put to waste in conflicts across the globe. But on February 1, a mere 10 days after being inaugurated for a second time, President Trump announced that the US had carried out air strikes targeting senior leadership of ISIL (ISIS) in Somalia. "These killers, who we found hiding in caves, threatened the United States," his post on X read. This marked Trump's first military action overseas, but it wouldn't be his last. In the time since, the US has provided weapons and support to Israel in its wars in Gaza and across the Middle East; it has launched strikes on Yemen; and even attacked Iran's nuclear facilities.

Despite the potential of Large Language Models (LLMs) as writing assistants, they are plagued by issues like coherence and fluency of the model output, trustworthiness, ownership of the generated content, and predictability of model performance, thereby limiting their usability. In this position paper, we propose to adopt Norman's seven stages of action as a framework to approach the interaction design of intelligent writing assistants. We illustrate the framework's applicability to writing tasks by providing an example of software tutorial authoring. The paper also discusses the framework as a tool to synthesize research on the interaction design of LLM-based tools and presents examples of tools that support the stages of action. Finally, we briefly outline the potential of a framework for human-LLM interaction research.

Your New Year's treat is a short story that seems appropriate, given the growing popularity (and fear) about the new image and language generating AIs, such as ChatGPT and DALL-e. Even I can't get that earworm out of my head. It's drives me crazy, but you can't help but love it. Despite his words, Rory didn't look scared, in fact he gloated, a sneer across his lips, as he leaned back in his chair, took a drag on his cigar, and released the smoke in a slow stream to join the white mist hanging in a cloud above the desk separating him and his partner, David. "This is gonna change the music world, maybe even go beyond that." "I never would have said it would work, but damn if it didn't. Musicman put together a song that went right to the top of the charts. No one can stop humming it. Once it was on the internet, and our response bots started with the likes and the shares, it took off like a California wildfire."

The U.S. Army will lean on artificial intelligence to assist two fundamental abilities aboard the forthcoming Bradley infantry fighting vehicle replacement, officials said. Gen. Geoffrey Norman, the director of the Next Generation Combat Vehicles Cross-Functional Team, said both AI-enabled targeting and navigation are "absolutely essential" for the Optionally Manned Fighting Vehicle, or OMFV. "What we're seeking initially is well within the realm of the feasible. We've demonstrated those capabilities in the field in really rigorous environments," Norman said Oct. 12 at the Association of the U.S. Army annual conference. "But as we look toward the future, and what we want to see downstream, we want to be able to accept maturing technologies."

Maverick's next wingman could be a drone. In the movies, fighter pilots are depicted as highly trained military aviators with the skills and experience to defeat adversaries in thrilling aerial dogfights. New technologies, though, are set to redefine what it means to be a "Top Gun," as algorithms, data and machines take on a bigger role in the cockpit -- changes hinted at in "Top Gun: Maverick." "A lot of people talk about, you know, the way of the future, possibly taking the pilot out of the aircraft," said 1st Lt. Walker Gall, an F-35 pilot with the U.S. 48th Fighter Wing based at RAF Lakenheath in England. "That's definitely not something that any of us look forward to." "I'd like to keep my job as long as possible, but I mean, it's hard to argue with newer and newer technology," he said.

Maverick's next wingman could be a drone. In the movies, fighter pilots are depicted as highly trained military aviators with the skills and experience to defeat adversaries in thrilling aerial dogfights. New technologies, though, are set to redefine what it means to be a "Top Gun," as algorithms, data and machines take on a bigger role in the cockpit -- changes hinted at in "Top Gun: Maverick." "A lot of people talk about, you know, the way of the future, possibly taking the pilot out of the aircraft," said 1st Lt. Walker Gall, an F-35 pilot with the U.S. 48th Fighter Wing based at RAF Lakenheath in England. "That's definitely not something that any of us look forward to." "I'd like to keep my job as long as possible, but I mean, it's hard to argue with newer and newer technology," he said.

One night in October, 2009, a young man lay in an fMRI scanner in Liège, Belgium. Five years earlier, he'd suffered a head trauma in a motorcycle accident, and since then he hadn't spoken. He was said to be in a "vegetative state." A neuroscientist named Martin Monti sat in the next room, along with a few other researchers. For years, Monti and his postdoctoral adviser, Adrian Owen, had been studying vegetative patients, and they had developed two controversial hypotheses.

The world's first psychopath AI serves as a reminder that although newer, improved, and increasingly complex algorithms continue to be developed, the need for good, unbiased data will always be paramount. The Rorschach test, named after its creator Hermann Rorschach, is a psychological test that uses a subject's interpretation of a series of ambiguous inkblots to assess her personality, emotional tendencies, and mental biases. Depending on a person's perception of the different, seemingly abstract patterns, psychologists are said to gain insights into her thinking process. Although different people perceive the ten images (that make up the test) in different ways, people with mental disorders, such as schizophrenia and psychopathy, come up with extraordinarily skewed and, sometimes, morbid interpretations of these blotted patterns. For instance, for an image that normally would evoke responses such as'a moth' or'a bat', a psychopathic response would be something like "pregnant woman falls at construction story".