Breakthroughs, discoveries, and DIY tips sent every weekday. Fine artist Laura Shape uses quite an unexpected medium in her visual artwork. It lends striking patterns to her abstract canvases, while helping restore rivers, reefs, and wetlands. Shape uses the leather of invasive species--specifically lionfish, carp, and Burmese pythons. "I use those materials to make vibrant, textured, abstract acrylic pieces," she tells Popular Science via video call.

Crews in Oviedo, Florida, were investigating underground pipes for anomalies when their robotic camera ran into a 5-foot alligator. A city crew in Florida spotted a 5-foot alligator lurking in a stormwater pipe while investigating the pipes with a robotic camera last week, officials said Tuesday. The stormwater crew in the city of Oviedo, located about 20 miles northeast of Orlando, was on Lockwood Boulevard to check on a series of potholes that appeared in the roadway on Friday, the city said in a Facebook post. The crew used a four-wheel robotic camera to go into the pipes below the road and investigate any anomalies such as leaking pipes, cracks or other defects underground, officials said. However, crews instead found a different kind of anomaly while searching the underground pipes.

This is Work in Progress, a newsletter by Derek Thompson about work, technology, and how to solve some of America's biggest problems. Sign up here to get it every week. Recently I gave myself an assignment: Come up with a framework for explaining generative AI, such as ChatGPT, in a way that illuminates the full potential of the technology and helps me make predictions about its future. By analogy, imagine that it's the year 1780 and you get a glimpse of an early English steam engine. You might say: "This is a device for pumping water out of coal mines."

Recently, I asked a number of AI researchers this question. The responses i received vary considerably; it turns out there is not much agreement about the risks or implications. Non-experts are even more confused about AI and its attendant challenges. Part of the problem is that "artificial intelligence" is an ambiguous term. By AI one can mean a Roomba vacuum cleaner, a self-driving truck, or one of those death-dealing Terminator robots.

How worried should we be about artificial intelligence? Recently, I asked a number of AI researchers this question. The responses I received vary considerably; it turns out there is not much agreement about the risks or implications. Non-experts are even more confused about AI and its attendant challenges. Part of the problem is that "artificial intelligence" is an ambiguous term.

Hector Levesque thinks his computer is stupid--and that yours is, too. Siri and Google's voice searches may be able to understand canned sentences like "What movies are showing near me at seven o'clock?," but what about questions--"Can an alligator run the hundred-metre hurdles?"--that nobody has heard before? Any ordinary adult can figure that one out. But if you type the question into Google, you get information about Florida Gators track and field. Watson, the computer system that won "Jeopardy!,"

Once, I dreamed I was at a man's funeral. According to the deceased's instructions, each of his toes were to be buried in tiny, individual coffins. When I woke up, I wondered, "What could it mean?" According to some neuroscience research on dreams, like that of the Harvard psychiatrist Allan Hobson, the coffined toes might mean absolutely nothing. In his view, dreams are essentially narratives that our frontal brain areas piece together from chaotic brain signals originating from the brain stem. I might look back on that dream and derive some meaning from it after the fact, but it's not like those coffined toes are my brain's way of telling me something important.

As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high-school student. For the fourth grade level questions, I argue that questions that require the understanding of time, impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high-school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs. I argue that these are more useful benchmarks than existing standardized tests such as the SATs or Regents tests. Since the questions in standardized tests are designed to be hard for people, they often leave many aspects of what is hard for computers but easy for people untested

Terrifying, toothy, and with tears you can never trust, crocodiles are fearsome predators. But added to their ability to lay perfectly still with mouth agape for hours on end is the most powerful bite in the animal kingdom. Now research has shed light on exactly how crocodilians got their bite, and it's thanks to a second jaw joint which helps to spread out the full force when their teeth snap shut. New research has shed light on how crocodiles and alligators get their bite. The reptiles have a second joint in their jaws which helps to spread out the full force of their powerful bites, stabilising the jaw and keeping grip.

A good explanation among many others of logical consequence is that given a possibly empty set of assumptions A that are said to entail x, written A x, one is bound to believe x whenever it is the case that she believes the conjunction of all elements of A. This explanation shows a weak correspondence with the Tarskian semantic definition of logical consequence, according to which A entails x if and only if x is true under all interpretations (models) that make all elements of A true. But the relevant term here is "bound" ("is obliged to"), and the talk is of a normative conception of logical consequence. But where does this command come from? To understand some of the implications of what we call here a normative account of logical consequence, we will consider a notion which is not very well understood until the present day: that of "defeasible subsumption". To grasp this notion, it suffices to consider the following example.