Creative processes are typically divided into three types: combinatorial, exploratory, and transformational. Here, we provide a graphical theory of transformational scientific creativity, synthesizing Boden's insight that trans-formational creativity arises from changes in the "enabling constraints" of a conceptual space (Boden 1992) and Kuhn's structure of scientific revolutions as resulting from paradigm shifts (Kuhn 1962). We prove that modifications made to axioms of our graphical model have the most transformative potential and then illustrate how several historical instances of transforma-tional creativity can be captured by our framework.

Artificial intelligence (AI) captured the world's attention in 2023 with the emergence of pre-trained models such as GPT, on which the conversational AI system ChatGPT is based. For the first time, we can converse with an entity, however imperfectly, about anything, as we do with other humans. This new capability provided by pre-trained models has created a paradigm shift in AI, transforming it from an application to a general-purpose technology that is configurable to specific uses. Whereas historically an AI model was trained to do one thing well, it is now usable for a variety of tasks such as general conversations; assistance; decision making; and the generation of documents, code, and video--for which it was not explicitly trained. The scientific history of AI provides a backdrop for evaluating and discussing the capabilities and limitations of this new technology, and the challenges that lie ahead.

Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model which is able to achieve good results with comparatively low amounts of data. We show this by predicting 19F and 13C NMR chemical shifts of small molecules in specific solvents.

In The Structure of Scientific Revolutions, the philosopher of science Thomas Kuhn observed that scientists spend long periods taking small steps. They pose and solve puzzles while collectively interpreting all data within a fixed worldview or theoretical framework, which Kuhn called a paradigm. The scientists wring their hands, reexamine their assumptions and eventually make a revolutionary shift to a new paradigm, a radically different and truer understanding of nature. For several years, the particle physicists who study nature's fundamental building blocks have been in a textbook Kuhnian crisis. The crisis became undeniable in 2016, when, despite a major upgrade, the Large Hadron Collider in Geneva still hadn't conjured up any of the new elementary particles that theorists had been expecting for decades.

Researchers have developed a new early-warning system for self-driving vehicles -- leveraging artificial intelligence (AI) capable of learning from thousands of real traffic scenarios, according to a new study executed with the BMW Group and published in the journal IEEE Transactions on Intelligent Transportation Systems. In other words, you may soon ride in a self-driving car with an AI's figurative finger on the buzzer -- to keep you from dying in transit by giving seven seconds' warning of crucial situations the cars can't handle on their own. And so far, the AI can do it with more than 85% accuracy. The drive to increase safety for self-driving cars feels almost self-explanatory, but efforts typically rely on complicated models designed to enhance vehicles' ability to analyze the traffic behavior of users. But driving on public roads always comes with risk and uncertainty.

In a brightly coloured shipping container in east London, Rubens Filho is asking me to pick a card. "Any card," he says, fanning the pack out face down. "And don't worry, you can show me. I pull out the seven of spades, and show it to him; he gets me to sign my name on it with a marker pen. Then he slides it back into the middle of the pack, puts the cards back into their box and puts the box on the table in front of us. "Now," he says with a grin, "the magic begins." Filho is 51, tall, handsome and infectiously enthusiastic about the power of magic tricks and illusions. Born in Brazil, he's been a keen magician since adolescence. He came to Britain in 2012 to work in advertising, before, in 2015, setting up Abracademy, a startup dedicated to bringing magic – and in particular the skills needed to perform it – to the rest of us. "I think magic has a such a positive twist," he says. "It brings this soft approach that's hard to explain, this role of creating something beautiful." But he is also fascinated by the relationship between magic and neuroscience and psychology, and set up Abracademy Labs, an offshoot of Abracademy, to explore this connection. "Magic has lived in the'glitches' of the brain for a long time," he says. "How you see things, how you form beliefs, how you experience wonder.

Errol Morris feels that Thomas Kuhn saved him from a career he was not suited for--by having him thrown out of Princeton. In 1972, Kuhn was a professor of philosophy and the history of science at Princeton, and author of The Structure of Scientific Revolutions, which gave the world the term "paradigm shift." As Morris tells the story in his recent book, The Ashtray, Kuhn was antagonized by Morris' suggestions that Kuhn was a megalomaniac and The Structure of Scientific Revolutions was an assault on truth and progress. To say the least, Morris, then 24, was already the iconoclast who would go on to make some of the most original documentary films of our time. After launching the career he was suited for with The Gates of Heaven in 1978, a droll affair about pet cemeteries, Morris earned international acclaim with The Thin Blue Line, which led to the reversal of a murder conviction of a prisoner who had been on death row. In 2004, Morris won an Academy Award for The Fog of War, a dissection of former Secretary of Defense Robert McNamara, a major architect of the Vietnam War. His 2017 film, Wormwood, a miniseries on Netflix, centers on the mystery surrounding a scientist who in 1975 worked on a biological warfare program for the Army, and suspiciously fell to his death from a hotel room. The Ashtray--Morris explains the title in our interview below--is as arresting and idiosyncratic as Morris' films.

The 18th-century Irish philosopher Bishop George Berkeley concluded that, since all we know of the universe is what our senses convey to us, things in the world exist only to the extent that we perceive them. They have no material reality, but are phenomena in and of our minds, or the mind of God. Samuel Johnson famously countered this philosophy by kicking a large stone and saying, "I refute it thus!" Two hundred years later, while American campuses roiled with protests against the Vietnam War, the philosopher, historian, and physicist Thomas Kuhn met with a grad student at Princeton's legendary Institute for Advanced Study to discuss the student's paper. The professor and student disagreed on some fundamental ideas, and the conversation grew heated.

Recent work in natural language processing has looked at learning text to code translation models using parallel pairs of text and code samples from example source code libraries (for a review, see Neubig (2016)). In particular, Richardson and Kuhn (2017a,b); Richardson et al. (2018) look at learning to translate short text descriptions to function signature representations as a first step towards modeling the semantics of function documentation. Examples pairs of docstring and function signature representations are shown in Figure 1; using such pairs, the goal is to learn a general model that can robustly translate a given description of a function to a formal representation of that function. Initially, these datasets were proposed as a synthetic resource for studying semantic parser induction (Mooney, 2007), or for building models that learn to translate text to formal meaning representations from parallel data (see Richardson et al. (2017) for a proposal on using these datasets for the inverse problem of data-to-text generation). To date, we have built around 45 API datasets across 11 popular programming languages (e.g., Python, Java, C, Scheme, Haskell, PHP) and 7 natural languages (see Richardson (2017)), each using an ad hoc rendering of the target function signature representations. In this brief note, we define a unified syntax for expressing these representations, as well as a systematic mapping into first-order logic and a small subject domain model. In doing this, we aim to answer the following question: what do these function signatures that are being learned actually mean, and how can they be used for solving more complex natural language understanding problems (for a similar idea, see Bos (2016))? By recasting the learned representations in terms of classical logic, the hope is that our datasets will in particular be made more accessible to studies on natural language based program synthesis (Raza et al., 2015) and natural language programming more generally. In what follows, we first define a general syntax for these representations, then discuss the mapping into logic and the various applications that motivate our particular approach and subject domain model.

Artificial intelligence, or AI, is everywhere these days, from self-driving cars and voice-activated software like Siri and Alexa. It's being used in fields from criminal justice to finance. So this year in All Tech Considered, we're going to spend some time exploring AI. Its leadership wants to dominate the tech world. It's one way China can beat possible competitors and adversaries.