ChatGPT can readily traverse the vast amounts of information on the internet to answer almost any ad-hoc question users pose. That it does so via natural language, in close to real-time, is indicative of the immense advancements of Generative Artificial Intelligence--and of Natural Language Generation, in particular. ChatGPT's practical utility spans most tasks associated with language, including creating annotated training datasets for data scientists, to creating highly specific reports, emails, or papers for almost any facet of business or academia. Not surprisingly, vendors of all types are rushing to implement this language model to improve solutions for everything from Business Intelligence to content services. "It's still got its own set of limitations," admitted Abhishek Gupta, Principal Data Scientist, and Engineer at Talentica.

In a Thursday blog post, Microsoft pitches the feature as a way to create "very specific" visuals when they're working on social media posts or slideshows and documents. While this has been possible in a variety of ways before -- you could use OpenAI's DALL-E, Microsoft's Bing image creator site, the built-in image generator in Bing Chat, or one of the many other image generators -- putting it right in Edge's sidebar makes it much easier to ask an AI to make you some pictures while you're doing something else on the web.

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.