We model human and animal learning by computing with high-dimensional vectors (H = 10,000 for example). The architecture resembles traditional (von Neumann) computing with numbers, but the instructions refer to vectors and operate on them in superposition. The architecture includes a high-capacity memory for vectors, analogue of the random-access memory (RAM) for numbers. The model's ability to learn from data reminds us of deep learning, but with an architecture closer to biology. The architecture agrees with an idea from psychology that human memory and learning involve a short-term working memory and a long-term data store. Neuroscience provides us with a model of the long-term memory, namely, the cortex of the cerebellum. With roots in psychology, biology, and traditional computing, a theory of computing with vectors can help us understand how brains compute. Application to learning by robots seems inevitable, but there is likely to be more, including language. Ultimately we want to compute with no more material and energy than used by brains. To that end, we need a mathematical theory that agrees with psychology and biology, and is suitable for nanotechnology. We also need to exercise the theory in large-scale experiments. Computing with vectors is described here in terms familiar to us from traditional computing with numbers.

Bold technology predictions pave the road to humility. Even titans like Albert Einstein own a billboard or two along that humbling freeway. In a classic example, John von Neumann, who pioneered modern computer architecture, wrote in 1949, "It would appear that we have reached the limits of what is possible to achieve with computer technology." Among the myriad manifestations of computational limit-busting that have defied von Neumann's prediction is the social psychologist Frank Rosenblatt's 1958 model of a human brain's neural network. He called his device, based on the IBM 704 mainframe computer, the "Perceptron" and trained it to recognize simple patterns.

We provide partial implementations of von Neumann's universal constructor and universal copier, starting out with three types of simple building blocks using minimal assumptions. Using the same principles, we also construct Turing machines. Combining both, we arrive at a proposal for a self-replicating Turing machine. Our construction allows for mutations if desired, and we give a simple description language.

Thomas Haigh (thomas.haigh@gmail.com) is a professor of history at the University of Wisconsin--Milwaukee, WI, USA, and a Comenius visiting professor at Siegen University, Germany.

As artificial intelligence travels through the solar system and gets to explore the heliosphere (enclosing the planets), it will adapt by making decisions that enable it to do its job. Many people in the field of astrobiology are in favour of the so-called post-biological cosmos vision. Is it because of the desire to conquer space that we humans are sowing the seeds of our own destruction in favour of artificial intelligence? Or are we unconsciously following some sort of master plan in which flesh and blood beings are destined to become extinct and be hybridised by silicon and synthetic materials? As for the mind, memory, consciousness, could there also be a place for humans in a robot's brain?

Let me start by saying a few things that seem obvious," Geoffrey Hinton, "Godfather" of deep learning, and one of the most celebrated scientists of our time, told a leading AI conference in Toronto in 2016. "If you work as a radiologist you're like the coyote that's already over the edge of the cliff but hasn't looked down." Deep learning is so well-suited to reading images from MRIs and CT scans, he reasoned, that people should "stop training radiologists now" and that it's "just completely obvious within five years deep learning is going to do better." Fast forward to 2022, and not a single radiologist has been replaced. Rather, the consensus view nowadays is that machine learning for radiology is harder than it looks1; at least for now, humans and machines complement each other's strengths.2 Deep learning is at its best when all we need are rough-ready results. Few fields have been more filled with hype and bravado than artificial intelligence. It has flitted from fad to fad decade ...

Chess is not a game. Chess is a well-defined form of computation. You may not be able to work out the answers, but in theory, there must be a solution, a right procedure in any position---John von Neumann The advent of computer chess engines based, such as AlphaZero, LCZero and Stockfish 14 NNUE, provides us with the ability to study optimal play. AI chess algorithms are based on pattern matching, efficient search and data-centric methods rather than rules based. Together with an objective functions based on maximising the probability of winning, we can now see what optimal play and strategies look like. One caveat is the black-box nature of these algorithms and lack of insight into the features that are empirically learned from self play.

Stand aside humanity, you're holding up the progress. We've passed the point of usefulness for Homo sapiens, now is the dawning of the Homo Faber era. The idea that "I think therefore I am" has become quaint in this new age of builders and creators. But has our continued obsession with technology and progress actually managed to instead set back our capacity for humanity? In his new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, author and pioneering researcher in the field of natural language processing, Erik J Larson, investigates the efforts to build computers that process information like we do and why we're much farther away from having human-equivalent AIs than most futurists would care to admit.

The term Artificial Intelligence (AI) was created and used for the first time in the mid-1940s. However, it was not until 1956 that it was formally used for the first time in a small gathering attended by some psychologists, physiologists and computer scientists. Since its inception, AI has had some successes in enabling computers to perform (on a limited basis) some tasks that are normally done by the human mind. It is today that the technological aspects of AI have become more visible. Public interest in AI and its coverage by the media have increased tremendously in recent years. More and more people see AI as an emerging technology with great potential and future social significance.

Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player--or much of a poker fan, in fact--but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely--a view shared years later by Sandholm in his research with artificial intelligence. "Poker is the main benchmark and challenge program for games of imperfect information," Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.