Global private investment and the number of AI startups decreased in 2022, while the industry's adoption of the technology has plateaued compared to previous years, according to new data. This revelation hits at a time when AI hype is at an all-time high. Commercial tools capable of generating images, text, code, video, audio, and even music are rapidly improving and becoming increasingly convincing. Companies across different industries are looking to deploy generative AI features to revamp existing products and services or create new ones. Analysts are predicting the boom will increase global productivity and change the labor force, while experts are debating whether the technology poses an existential threat to humanity.

It's not trivial to set up, but super exciting to run your own model. Let me tell you how to start out and what outcome you can expect. BLOOM -- BigScience Large Open-science Open-access Multilingual Language Model is a transformer-based language model created by 1000 researchers (more on the BigScience project). It was trained on about 1,6 TB pre-processed multilingual text. It is free -- everybody who wants to, can try it out.

If you’ve been following the — very recent — news on Generative Artificial Intelligence, you might have noticed a pattern: most people publicly and loudly warning us about the potential perils of AI…

Recent improvements in machine learning and deep learning algorithms, as well as the accessibility of enormous amounts of data and processing power, have fuelled the rapid evolution of AI technology. Large-scale language models like GPT-3, as well as research in other fields like robotics and computer vision, are just a few of the substantial contributions that OpenAI has made to the field of artificial intelligence. Without any prior experience of AI, we will learn how to use the OpenAI API and build an AI application in this tutorial. OpenAI is a research organisation that aims to advance artificial intelligence in a way that is safe and beneficial for humanity. Founded in 2015, the organisation has quickly established itself as a leader in the field of AI research and development.

Generative AI, or AI used to create new images, text and sound based on prompts and training data, has had a contentious history in the game development community recently. While generative AI has become a commonly used tool for those who create user-generated content (UGC), its use as a tool for game developers has come with criticism. In particular, some users question why AI should be used when a human developer could do the job. Despite this pushback, game developers and publishers have started openly using AI tools. Major games companies such as Unity, Epic Games, Roblox and Ubisoft have all announced generative AI integrations in their development kits.

Over the past few months, AI chatbots like ChatGPT have captured the world's attention due to their ability to converse in a human-like way on just about any subject. But they come with a serious drawback: They can present convincing false information easily, making them unreliable sources of factual information and potential sources of defamation. Why do AI chatbots make things up, and will we ever be able to fully trust their output? We asked several experts and dug into how these AI models work to find the answers. AI chatbots such as OpenAI's ChatGPT rely on a type of AI called a "large language model" (LLM) to generate their responses. An LLM is a computer program trained on millions of text sources that can read and generate "natural language" text--language as humans would naturally write or talk.

If you've seen photos of a teapot shaped like an avocado or read a well-written article that veers off on slightly weird tangents, you may have been exposed to a new trend in artificial intelligence (AI). Machine learning systems called DALL-E, GPT and PaLM are making a splash with their incredible ability to generate creative work. These systems are known as "foundation models" and are not all hype and party tricks. So how does this new approach to AI work? And will it be the end of human creativity and the start of a deep-fake nightmare?

Generating synthetic data through generative models is gaining interest in the ML community and beyond. In the past, synthetic data was often regarded as a means to private data release, but a surge of recent papers explore how its potential reaches much further than this -- from creating more fair data to data augmentation, and from simulation to text generated by ChatGPT. In this perspective we explore whether, and how, synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. Just as importantly, we discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data -- the most important of which is quantifying how much we can trust any finding or prediction drawn from synthetic data.

Large language models (LLMs), such as OpenAI's Codex, have demonstrated their potential to generate code from natural language descriptions across a wide range of programming tasks. Several benchmarks have recently emerged to evaluate the ability of LLMs to generate functionally correct code from natural language intent with respect to a set of hidden test cases. This has enabled the research community to identify significant and reproducible advancements in LLM capabilities. However, there is currently a lack of benchmark datasets for assessing the ability of LLMs to generate functionally correct code edits based on natural language descriptions of intended changes. This paper aims to address this gap by motivating the problem NL2Fix of translating natural language descriptions of code changes (namely bug fixes described in Issue reports in repositories) into correct code fixes. To this end, we introduce Defects4J-NL2Fix, a dataset of 283 Java programs from the popular Defects4J dataset augmented with high-level descriptions of bug fixes, and empirically evaluate the performance of several state-of-the-art LLMs for the this task. Results show that these LLMS together are capable of generating plausible fixes for 64.6% of the bugs, and the best LLM-based technique can achieve up to 21.20% top-1 and 35.68% top-5 accuracy on this benchmark.

Artificial intelligence is one of the dominant topics about the future in travel, and we're all over it at Skift. In November, OpenAI publicly released breakthrough generative AI technology, and a number of big-name travel companies have already responded. Expedia, Kayak, and more -- including multiple startups -- have started releasing experimental technologies that could lead to transformations in the way users plan and book travel. But the potential does not stop there. Advancements in AI could change the way hotels manage revenue and customer service, the way travel tech companies operate internally, and even the way airplanes and airports get designed.