This article is a description of Charles Babbage's first computer program, which he sketched out almost 200 years ago, in 1837. The Analytical Engine (AE), the computer for which the program was intended, did not actually exist; sadly, it was to remain unfinished. Only some portions of Babbage's calculating machine were built during the lifetime of the English mathematician and inventor. Had it been completed, it would have been the world's first computer.1,3 Of course, many algorithms had already been described before Babbage--for computing the greatest common divisor (GCD), for example--but Babbage's code is the first attempt to specify how to mechanize complex algorithms with a computer.

Prior work has shown that the ordering in which concepts are shown to a commonsense generator plays an important role, affecting the quality of the generated sentence. However, it remains a challenge to determine the optimal ordering of a given set of concepts such that a natural sentence covering all the concepts could be generated from a pretrained generator. To understand the relationship between the ordering of the input concepts and the quality of the generated sentences, we conduct a systematic study considering multiple language models (LMs) and concept ordering strategies. We find that BART-large model consistently outperforms all other LMs considered in this study when fine-tuned using the ordering of concepts as they appear in CommonGen training data as measured using multiple evaluation metrics. Moreover, the larger GPT3-based large language models (LLMs) variants do not necessarily outperform much smaller LMs on this task, even when fine-tuned on task-specific training data. Interestingly, human annotators significantly reorder input concept sets when manually writing sentences covering those concepts, and this ordering provides the best sentence generations independently of the LM used for the generation, outperforming a probabilistic concept ordering baseline

When Y2K seemed like the world's most pressing technological concern, the Mexican-Canadian artist Rafael Lozano-Hemmer was using a dictionary and a set of grammatical rules to teach a computer how to write questions. The program he built can make enquiries in Spanish, English, German and French, in 4.7tn possible combinations. When the artwork showed at the San Francisco Museum of Modern Art last year, it still had 271,000 years of new questions to ask. Which is to say, Lozano-Hemmer has been working with generative technology long enough to have learned a powerful lesson: "There is no such thing as a neutral algorithm." This lesson was reiterated to the Bafta-winning media artist in a spectacular, humiliating fashion at Miami Art Basel a little over a decade ago.

Large language models (LLMs) exploit in-context learning (ICL) to solve tasks with only a few demonstrations, but its mechanisms are not yet well-understood. Some works suggest that LLMs only recall already learned concepts from pre-training, while others hint that ICL performs implicit learning over demonstrations. We characterize two ways through which ICL leverages demonstrations. Task recognition (TR) captures the extent to which LLMs can recognize a task through demonstrations -- even without ground-truth labels -- and apply their pre-trained priors, whereas task learning (TL) is the ability to capture new input-label mappings unseen in pre-training. Using a wide range of classification datasets and three LLM families (GPT-3, LLaMA and OPT), we design controlled experiments to disentangle the roles of TR and TL in ICL. We show that (1) models can achieve non-trivial performance with only TR, and TR does not further improve with larger models or more demonstrations; (2) LLMs acquire TL as the model scales, and TL's performance consistently improves with more demonstrations in context. Our findings unravel two different forces behind ICL and we advocate for discriminating them in future ICL research due to their distinct nature.

Last November, at the Stockholm University of the Arts, a human and an AI made music together. The performance began with musician David Dolan playing a grand piano into a microphone. As he played, a computer system, designed and overseen by composer and Kingston University researcher Oded Ben-Tal, "listened" to the piece, extracting data on pitch, rhythm, and timbre. Then, it added its own accompaniment, improvising just like a person would. Some sounds were transformations of Dolan's piano; some were new sounds synthesized on the fly.

I had so much fun getting GPT-3 to generate simple one-line Valentine's Day cards last year that this year I decided to see if I could generate cards with more complicated messages. I focused on the classic "roses are red, violets are blue" rhyme, figuring that language models like GPT-3 would have seen lots of examples of this structure during their internet training. Rhyming poetry is notoriously difficult for text-generating algorithms, and I wanted to make it easy. To find out what I should draw, I added to its text with "Illustration is of". And I would create the card according to its instructions.

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OpenAI API adds'text-davinci-003' to its list of main GPT-3 models, which can do all tasks other models can do while also ensuring high quality, longer output, and better instruction-following. Davinci is the most competent and can perform all tasks the other models can, often with fewer instructions. It works specifically well with tasks requiring in-depth knowledge of the subject matter, such as summarising texts for a specific audience and creative content development. However, the new capabilities of Davinci also require more computing resources leading to higher costs per API call and lesser speed than other models. For example, it is good at deducing solutions to various logical problems and outlining character motivations.

On a summer Monday evening in 1833, Ada Byron and her mother Anne Isabella "Annabella" Byron went to the home of English mathematician Charles Babbage. Twelve days earlier, when the younger Byron met Babbage at a high society soiree, she had been taken with his description of a machine he was building. The hand-cranked apparatus of bronze and steel used stacks of cogs, hammer-like metal arms, and thousands of numbered wheels to automatically solve mathematical equations. But the Difference Engine, as Babbage called it, was incomplete. He had finished a small prototype that stood about two-and-a-half feet tall.

Last week, OpenAI removed the waitlist for the application programming interface to GPT-3, its flagship language model. Now, any developer who meets the conditions for using the OpenAI API can apply and start integrating GPT-3 into their applications. Since the beta release of GPT-3, developers have built hundreds of applications on top of the language model. But building successful GPT-3 products presents unique challenges. You must find a way to leverage the power of OpenAI's advanced deep learning models to provide the best value to your users while keeping your operations scalable and cost-efficient.