Enlarge / The robots exploring a simulated alien environment. (credit: ETH Zurich / Takahiro Miki )

While rovers have made incredible discoveries, their wheels can hold them back, and erratic terrain can mean damage. There is no replacing something like Perseverance , but sometimes rovers could use a leg up, and they could get that from a small swarm of four-legged robots.

They look like giant metal insects, but the trio of ANYmal robots customized by researchers at ETH Zurich was tested in environments as close to the harsh lunar and Martian terrain as possible. Robots capable of walking could assist future rovers and mitigate the risk of damage from sharp edges or loss of traction in loose regolith. Not only do the ANYmals’ legs help them literally step over obstacles, but these bots work most efficiently as a team. They are each specialized for particular functions but still flexible enough to cover for each other—if one glitches, the others can take over its tasks.

“Our technology can enable robots to investigate scientifically transformative targets on the Moon and Mars that are unreachable at present using wheeled rover systems,” the research team said in a study recently published in Science Robotics.

Enlarge / This image has a warm, nostalgic feel for many of us. (credit: SETI Institute )

Distributed computing erupted onto the scene in 1999 with the release of SETI@home, a nifty program and screensaver (back when people still used those) that sifted through radio telescope signals for signs of alien life.

The concept of distributed computing is simple enough: You take a very large project, slice it up into pieces, and send out individual pieces to PCs for processing. There is no inter-PC connection or communication; it’s all done through a central server. Each piece of the project is independent of the others; a distributed computing project wouldn't work if a process needed the results of a prior process to continue. SETI@home was a prime candidate for distributed computing: Each individual work unit was a unique moment in time and space as seen by a radio telescope.

Twenty-one years later, SETI@home shut down, having found nothing. An incalculable amount of PC cycles and electricity wasted for nothing. We have no way of knowing all the reasons people quit (feel free to tell us in the comments section), but having nothing to show for it is a pretty good reason.

Enlarge / The Tunnel Falls chip in its natural habitat (dilution refrigeration hardware not shown). (credit: Intel)

Intel does a lot of things, but it's mostly noted for making and shipping a lot of processors, many of which have been named after bodies of water. So, saying that the company is set to start sending out a processor called Tunnel Falls would seem unsurprising if it weren't for some key details. Among them: The processor's functional units are qubits, and you shouldn't expect to be able to pick one up on New Egg. Ever.

Tunnel Falls appears to be named after a waterfall near Intel's Oregon facility, where the company's quantum research team does much of its work. It's a 12-qubit chip, which places it well behind the qubit count of many of Intel's competitors—all of which are making processors available via cloud services. But Jim Clarke, who heads Intel's quantum efforts, said these differences were due to the company's distinct approach to developing quantum computers.

Intel being Intel

So far, both the large companies and startups that are developing quantum computers have been focused on a single technology (transmons, trapped ions, etc.) that they're betting they can be the first to scale to useful qubit counts and error rates. To the extent that they have customers, those customers are simply developing the expertise needed to use the processors should they ever become viable. That can easily be achieved by accessing the hardware via a cloud service and using a software developer's kit instead of directly controlling the hardware. So, that's what nearly everyone other than Intel has been focused on providing.

Enlarge / IBM's Eagle processor has reached Rev3, which means lower noise qubits. (credit: IBM )

Today's quantum processors are error-prone. While the probabilities are small—less than 1 percent in many cases—each operation we perform on each qubit, including basic things like reading its state, has a significant error rate. If we try an operation that needs a lot of qubits, or a lot of operations on a smaller number of qubits, then errors become inevitable.

Long term, the plan is to solve that using error-corrected qubits . But these will require multiple high-quality qubits for every bit of information, meaning we'll need thousands of qubits that are better than anything we can currently make. Given that we probably won't reach that point until the next decade at the earliest, it raises the question of whether quantum computers can do anything interesting in the meantime.

In a publication in today's Nature, IBM researchers make a strong case for the answer to that being yes. Using a technique termed "error mitigation," they managed to overcome the problems with today's qubits and produce an accurate result despite the noise in the system. And they did so in a way that clearly outperformed similar calculations on classical computers.

Enlarge (credit: Anas Photography )

Anyone who has taken a basic computer science class has undoubtedly spent time devising a sorting algorithm—code that will take an unordered list of items and put them in ascending or descending order. It's an interesting challenge because there are so many ways of doing it and because people have spent a lot of time figuring out how to do this sorting as efficiently as possible.

Sorting is so basic that algorithms are built into most standard libraries for programming languages. And, in the case of the C++ library used with the LLVM compiler, the code hasn't been touched in over a decade.

But Google's DeepMind AI group has now developed a reinforcement learning tool that can develop extremely optimized algorithms without first being trained on human code examples. The trick was to set it up to treat programming as a game.

Enlarge (credit: Parradee Kietsirikul)

Anyone who has had to go back and retype a word on their smartphone because autocorrect chose the wrong one has had some kind of experience writing with AI . Failure to make these corrections can allow AI to say things we didn’t intend. But is it also possible for AI writing assistants to change what we want to say?

This is what Maurice Jakesch, a doctoral student of information science at Cornell, wanted to find out. He created his own AI writing assistant based on GPT-3, one that would automatically come up with suggestions for filling in sentences—but there was a catch. Subjects using the assistant were supposed to answer the question “Is social media good for society?” The assistant, however, was programmed to offer biased suggestions for how to answer that question.

Assisting with bias

AI can be biased despite not being alive. Though these programs can only “think” to the degree that human brains figure out how to program them to, their creators may end up embedding personal biases in the software. Alternatively, if trained on a dataset that has a limited or biased representation, the final product may display biases .

Enlarge (credit: Columbia University ROAM Lab )

From bionic limbs to sentient androids, robotic entities in science fiction blur the boundaries between biology and machine. Real-life robots are far behind in comparison. While we aren’t going to reach the level of Star Trek’s Data anytime soon, there is now a robot hand with a sense of touch that is almost human.

One thing robots have not been able to achieve is a level of sensitivity and dexterity high enough to feel and handle things as humans do. Enter a robot hand developed by a team of researchers at Columbia University. (Five years ago, we covered their work back when this achievement was still a concept.)

This hand doesn’t just pick things up and put them down on command. It is so sensitive that it can actually “feel” what it is touching, and it's dextrous enough to easily change the position of its fingers so it can better hold objects, a maneuver known as "finger gaiting." It is so sensitive it can even do all this in the dark, figuring everything out by touch.

Enlarge (credit: Peter Dazeley )

While learning high-level mathematics is no easy feat, teaching math concepts can often be just as tricky. That may be why many teachers are turning to ChatGPT for help. According to a recent Forbes article , 51 percent of teachers surveyed stated that they had used ChatGPT to help teach, with 10 percent using it daily. ChatGPT can help relay technical information in more basic terms, but it may not always provide the correct solution, especially for upper-level math.

An international team of researchers tested what the software could manage by providing the generative AI program with challenging graduate-level mathematics questions. While ChatGPT failed on a significant number of them, its correct answers suggested that it could be useful for math researchers and teachers as a type of specialized search engine.

Portraying ChatGPT’s math muscles

The media tends to portray ChatGPT’s mathematical intelligence as either brilliant or incompetent. “Only the extremes have been emphasized,” explained Frieder Simon , a University of Oxford PhD candidate and the study’s lead author. For example, ChatGPT aced Psychology Today’s Verbal-Linguistic Intelligence IQ Test, scoring 147 points, but failed miserably on Accounting Today’s CPA exam. “There’s a middle [road] for some use cases; ChatGPT is performing pretty well [for some students and educators], but for others, not so much,” Simon elaborated.

Enlarge / The D-Wave hardware is, quite literally, a black box. (credit: D-Wave)

Before we had developed the first qubit, theoreticians had done the work that showed that a sufficiently powerful gate-based quantum computer would be able to perform calculations that could not realistically be done on traditional computing hardware. All that is needed is to build hardware capable of implementing the theorists' work.

The situation was essentially reversed when it came to quantum annealing . D-Wave started building hardware that could perform quantum annealing without a strong theoretical understanding of how its performance would compare to standard computing hardware. And, for practical calculations, the hardware has sometimes been outperformed by more traditional algorithms.

On Wednesday, however, a team of researchers, some at D-Wave, others at academic institutions, is releasing a paper comparing its quantum annealer with different methods of simulating its behavior. The results show that actual hardware has a clear advantage over simulations, though there are two caveats: errors start to cause the hardware to deviate from ideal performance, and it's not clear how well this performance edge translates to practical calculations.