Tommy Flowers: nothing like the machine he proposed had ever been contemplated. Tommy Flowers: nothing like the machine he proposed had ever been contemplated. Move over, Alan Turing: meet the working-class hero of Bletchley Park you didn't see in the movies The Oxbridge-educated boffin is feted as the codebreaking genius who helped Britain win the war. But should a little-known Post Office engineer named Tommy Flowers be seen as the real father of computing? T his is a story you know, right? It's early in the war and western Europe has fallen. Only the Channel stands between Britain and the fascist yoke; only Atlantic shipping lanes offer hope of the population continuing to be fed, clothed and armed. But hunting "wolf packs" of Nazi U-boats pick off merchant shipping at will, coordinated by radio instructions the Brits can intercept but can't read, thanks to the fiendish Enigma encryption machine.

Breakthroughs, discoveries, and DIY tips sent every weekday. A trove of forgotten papers penned by famed World War II codebreaker Alan Turing has sold for the record-setting price of 627,000. But the June 17 auction almost never happened. At one point, the long-lost archival materials from the father of modern computer science were nearly pulverized by a paper shredder. Alan Turing was many things during his brief and ultimately tragic life: renowned mathematician, computer theorist, marathon runner, philosopher, and an invaluable codebreaker.

Artificial intelligence (AI) chatbots like ChatGPT have been designed to replicate human speech as closely as possible to improve the user experience. But as AI gets more and more sophisticated, it's becoming difficult to discern these computerised models from real people. Now, scientists at University of California San Diego (UCSD) reveal that two of the leading chatbots have reached a major milestone. Both GPT, which powers OpenAI's ChatGPT, and LLaMa, which is behind Meta AI on WhatsApp and Facebook, have passed the famous Turing test. Devised by British WWII codebreaker Alan Turing Alan Turing in 1950, the Turing test or'imitation game' is a standard measure to test intelligence in a machine.

We introduce Chunk-Distilled Language Modeling (CD-LM), an approach to text generation that addresses two challenges in current large language models (LLMs): the inefficiency of token-level generation, and the difficulty of adapting to new data and knowledge. Our method combines deep network-based LLMs with a straightforward retrieval module, which allows the generation of multi-token text chunks at a single decoding step. Our retrieval framework enables flexible construction of model- or domain-specific datastores, either leveraging the internal knowledge of existing models, or incorporating expert insights from human-annotated corpora. This adaptability allows for enhanced control over the language model's distribution without necessitating additional training. We present the CD-LM formulation along with performance metrics demonstrating its ability to improve language model performance and efficiency across a diverse set of downstream tasks. Code and data will be made publicly available.

A humanoid robot is slated to become first of its kind to have its artwork sold by a major auction house. On October 16, Sotheby's announced it will soon begin accepting bids starting at 120,000 for "AI God." The abstract portrait of Alan Turing was painted by Ai-Da, an ongoing, experimental AI-powered robotics project that cites a pivotal 1980's transhumanist feminist manifesto as its inspiration. The auction is scheduled to run from October 31st through November 7th. Completed in 2019 by gallerist Aidan Meller in collaboration with Oxford University researchers and the robotics company, Engineered Arts, Ai-Da uses cameras to capture visual inputs that onboard graphics algorithms then use to formulate generative images with some human guidance and adjustments.

The world has seen the emergence of machines based on pretrained models, transformers, also known as generative artificial intelligences for their ability to produce various types of content, including text, images, audio, and synthetic data. Without resorting to preprogramming or special tricks, their intelligence grows as they learn from experience, and to ordinary people, they can appear human-like in conversation. This means that they can pass the Turing test, and that we are now living in one of many possible Turing futures where machines can pass for what they are not. However, the learning machines that Turing imagined would pass his imitation tests were machines inspired by the natural development of the low-energy human cortex. They would be raised like human children and naturally learn the ability to deceive an observer. These ``child machines,'' Turing hoped, would be powerful enough to have an impact on society and nature.

Since it was first proposed in 1950, passing the'Turing test' has been seen as one of the highest goals in AI. But now, researchers claim that ChatGPT has become the first AI to pass this famous test for human intelligence. Proposed by computer pioneer Alan Turing, it claims that an AI should be considered truly intelligent if people can't tell if they are speaking to a human or machine. In a pre-print paper, cognitive scientists from UC San Diego argue that the ChatGPT-4 can fool human test subjects more than half of the time. However, the researchers say this might say more about the Turing test than it does about the intelligence of modern AI.

In the wake of large language models, there has been a resurgence of claims and questions about the Turing test and its value for AI, which are reminiscent of decades of practical "Turing" tests. If AI were quantum physics, by now several "Schr\"odinger's" cats could have been killed. Better late than never, it is time for a historical reconstruction of Turing's beautiful thought experiment. In this paper I present a wealth of evidence, including new archival sources, give original answers to several open questions about Turing's 1950 paper, and address the core question of the value of Turing's test.

Alan Turing was a British mathematician born on June 23, 1912 In Maida Vale, London, to father Julius, a civil servant, and mother Ethel, the daughter of a railway engineer. His talents were recognised early on at school but he struggled with his teachers when he began boarding at Sherborne School aged 13 because he was too fixated on science. Turing continued to excel at maths but his time at Sherborne was also rocked by the death of his close friend Christopher Morcom from tuberculosis. Morcom was described as Turing's'first love' and he remained close with his mother following his death, writing to her on Morcom's birthday each year. He then moved on to Cambridge where he studied at King's College, graduating with a first class degree in mathematics.

A growing area of research investigates augmenting language models with tools (e.g., search engines, calculators) to overcome their shortcomings (e.g., missing or incorrect knowledge, incorrect logical inferences). Various few-shot tool-usage strategies have been proposed. However, there is no systematic and fair comparison across different strategies, or between these strategies and strong baselines that do not leverage tools. We conduct an extensive empirical analysis, finding that (1) across various datasets, example difficulty levels, and models, strong no-tool baselines are competitive to tool-assisted strategies, implying that effectively using tools with in-context demonstrations is a difficult unsolved problem; (2) for knowledge-retrieval tasks, strategies that *refine* incorrect outputs with tools outperform strategies that retrieve relevant information *ahead of* or *during generation*; (3) tool-assisted strategies are expensive in the number of tokens they require to work -- incurring additional costs by orders of magnitude -- which does not translate into significant improvement in performance. Overall, our findings suggest that few-shot tool integration is still an open challenge, emphasizing the need for comprehensive evaluations of future strategies to accurately assess their *benefits* and *costs*.