AI, like humans, learns from examples. Given enough data and time, an AI model can make sense of the statistical relationships well enough to generate predictions. That's how OpenAI's GPT-3 writes text from poetry to computer code, and how apps like Google Lens recognize objects such as lampshades in photos of bedrooms. Historically, the data to train as well as test AI has come mostly from public sources on the web. But these sources are flawed.

Johannes Gutenberg's printing press, introduced in the fifteenth century, transformed society ... [ ] through language. The creation of machines that can understand language may have an even greater impact. Language is the cornerstone of human intelligence. The emergence of language was the most important intellectual development in our species' history. It is through language that we formulate thoughts and communicate them to one another. Language enables us to reason abstractly, to develop complex ideas about what the world is and could be, and to build on these ideas across generations and geographies. Almost nothing about modern civilization would be possible without language. Building machines that can understand language has thus been a central goal of the field of artificial intelligence dating back to its earliest days. It has proven maddeningly elusive.

We login to the API using our personal token and retrieve the 200 most popular songs. We exclude things like acoustic versions, because we don't want too many duplicate lyrics polluting the data. We also exclude the headers, lest your crush get suspicious when reading words like [Intro] or [Chorus] in your beautifully personalized Valentine's Day poem. In our case, we will use GPT-2 as the pre-trained model. The reason for this model is that more recent models like GPT-J are too large to retrain on most accessible compute resources.

Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning. It is a big challenge to scale ABSA to a large number of new domains. This paper aims to train a unified model that can perform zero-shot ABSA without using any annotated data for a new domain. We propose a method called contrastive post-training on review Natural Language Inference (CORN). Later ABSA tasks can be cast into NLI for zero-shot transfer. We evaluate CORN on ABSA tasks, ranging from aspect extraction (AE), aspect sentiment classification (ASC), to end-to-end aspect-based sentiment analysis (E2E ABSA), which show ABSA can be conducted without any human annotated ABSA data.

Large datasets underlying much of current machine learning raise serious issues concerning inappropriate content such as offensive, insulting, threatening, or might otherwise cause anxiety. This calls for increased dataset documentation, e.g., using datasheets. They, among other topics, encourage to reflect on the composition of the datasets. So far, this documentation, however, is done manually and therefore can be tedious and error-prone, especially for large image datasets. Here we ask the arguably "circular" question of whether a machine can help us reflect on inappropriate content, answering Question 16 in Datasheets. To this end, we propose to use the information stored in pre-trained transformer models to assist us in the documentation process. Specifically, prompt-tuning based on a dataset of socio-moral values steers CLIP to identify potentially inappropriate content, therefore reducing human labor. We then document the inappropriate images found using word clouds, based on captions generated using a vision-language model. The documentations of two popular, large-scale computer vision datasets -- ImageNet and OpenImages -- produced this way suggest that machines can indeed help dataset creators to answer Question 16 on inappropriate image content.

These jaw-breaking developments gave rise to expectations from AI and made many curious about upcoming trends and advances in the field. Thus, this article will highlight some of the key forthcoming developments in AI, poised to make it more potent and impactful. Language modeling is machine understanding and generation of natural languages, which is used in applications such as speech recognition, machine translation, handwriting recognition, question answering and information retrieval. Since OpenAI released GPT-3, the most powerful language model ever built, it has been in the limelight due to its breathtaking language capabilities. For example, it has been demonstrated that--with proper human priming--GPT-3 can generate creative fiction, working computer code and compose introspective business memos.

Google parent Alphabet's subsidiary DeepMind AI has been developing various kinds of machine learning and AI systems that can perform complicated tasks. Now, the company has unveiled a new AI code-generation system called "AlphaCode," which reached a competitive level of performance in programming competitions for the first time. Seemingly, it is at par with an average human coder and could potentially take away your job in the future! It is revealed that AlphaCode can write computer programs at a competitive level, which is a first for an AI-based code-generation model. The company tested the AI's abilities using competitions hosted on Codeforces. Ten contests (newer for AlphaCode's skills) were selected and as a result, the AI was able to surpass a median competitor.

I studied engineering at university and, like most of my contemporaries, found that I sometimes needed to write computer programs to do certain kinds of calculations. These pieces of utilitarian software were written in languages now regarded as the programming equivalent of Latin – Fortran, Algol and Pascal – and what I learned from the experience was that I was not a born hacker. The software I wrote was clumsy and inefficient and more talented programmers would look at it and roll their eyes, much as Rory McIlroy might do if required to play a round with an 18-handicap golfer. But it did the job and in that sense was, in the laconic phrase sometimes used by the great computer scientist Roger Needham, "good enough for government work". And what I took away from the experience was a lifelong respect for programmers who can write elegant, efficient code. Anyone who thinks programming is easy has never done it.

A "new milestone in competitive programming" was trumpeted by Google's British AI subsidiary DeepMind earlier this month, when it unveiled AlphaCode, a system it claims can write fully fledged computer programmes that compare favourably to the work of humans. Software development has long been pinpointed as an area where AI can have a significant impact, and with the advances AlphaCode and other systems offer, is the prospect of machines replacing human coders a realistic one? DeepMind says AlphaCode is capable of understanding a problem then writing a programme which solves that problem. It says it has tested the system against people who took part in coding contests and found that its results rank within the top 54% of human participants. "Solving competitive programming problems is a really hard thing to do, requiring both good coding skills and problem-solving creativity in humans," said Google software engineer Petr Mitrichev, who takes part in coding competitions.

GPT-3 or Generative Pre-trained Transformer 3 is a language model that was created by OpenAI, an artificial intelligence research laboratory in San Francisco. The 175-billion parameter deep learning model is capable of producing human-like text and was trained on large text datasets with hundreds of billions of words. When OpenAI released GPT-3, in June 2020, the neural network's apparent grasp of the language was uncanny. It could generate convincing sentences, converse with humans, and even autocomplete code. GPT-3 was also monstrous in scale--larger than any other neural network ever built.