ChatGPT is one of the most advanced and popular chatbots in the world, powered by OpenAI's generative pre-trained transformer (GPT) models. ChatGPT can generate natural and engaging responses to a variety of topics and contexts, using its large-scale language understanding and generation capabilities. However, until recently, ChatGPT had a major limitation: it could not access the internet or any external data sources or services. This meant that ChatGPT could only rely on its internal knowledge and memory, which might be outdated, incomplete, or inaccurate. For example, if you asked ChatGPT about the latest news, sports scores, or weather forecasts, it would not be able to give you a reliable answer.

Whether it's based on hallucinatory beliefs or not, a gold rush has started over the last several months to make money from generative AI models like ChatGPT. You can practically hear the shrieks from corner offices around the world: "What is our ChatGPT play? How do we make money off this?" But while companies and executives want to cash in, the likely impact of generative AI on workers and the economy on the whole is far less obvious. Will ChatGPT make the already troubling income and wealth inequality in the US and many other countries even worse, or could it in fact provide a much-needed boost to productivity?

Content warning: Please note that this blog includes examples of toxic and offensive language generated by OpenAI's GPT-3. On its release in June 2020, OpenAI's GPT-3 represented a major breakthrough in natural language generation (NLG). With near human-like performance, GPT-3 is able to pen news articles, write complex code, and solve algebra problems -- without being explicitly trained to do so. It can even write your CV. While race and gender biases are well documented in NLG systems, research into LGBTQ bias is lacking in comparison and is thus the main focus of this blog post.

The internet has been enthralled with the rise of AI starting with the introduction of the Artificial Intelligence image generation program DALL-E 2 to the public in November 2022. Programs like ChatGPT have already begun to change how we use the internet, and huge organizations such as Microsoft and the British government have started investing hundreds of millions of dollars into creating new AI technology. AI is going to be around for a while and, like with every other advancement in technology, will inevitably cause some problems. Programs like ChatGPT have the potential to radically change how we interact with our devices, but that potential needs to be realized while understanding the limits of the technology and protecting the rights of workers. Before we unpack what limits may need to be placed on AI, it is important to understand what makes the tech useful. Recently, the introduction of the massively improved GPT-4 has shown that AI has the potential to be a great asset to society.

MICROSOFT is adding artificial intelligence to its platform at lightning speeds. In the latest preview of Bing, the Windows maker introduced generative AI-assisted Knowledge Card 2.0 and Stories in Bing, the company announced in a blog on March 24. With'Stories', Bing can draft AI-generated stories to help users consume information via small data packets. It will use "text, images, video and audio" to deliver the results. These cater to "visual and auditory learners" and are available in different languages. "English, French, Japanese, German, Spanish, Russian, Dutch, Italian, Portuguese, Polish and Arabic" are the supported languages, Microsoft adds.

Generative AI is the is a technological marvel destined to change the way we work, but what does it do and what are its use cases for CTOs? Where better to start than with a definition straight from the horse's mouth? ChatGPT-4, from Microsoft-backed OpenAI, describes generative AI as "…a type of artificial intelligence that involves training algorithms to generate new content based on a given set of parameters or data. This technology has been used in a variety of fields, from image and video generation to natural language processing and music creation". Apart from being endearingly modest, generative AI also has the potential to transform economies.

She wanted to know if I had any suggestions, and asked what I thought all the new advances meant for lawmakers. I've spent a few days thinking, reading, and chatting with the experts about this, and my answer morphed into this newsletter. Though GPT-4 is the standard bearer, it's just one of many high-profile generative AI releases in the past few months: Google, Nvidia, Adobe, and Baidu have all announced their own projects. In short, generative AI is the thing that everyone is talking about. And though the tech is not new, its policy implications are months if not years from being understood.

New GPT chatbot learns slight tonal difference between "Hey" and "Heyyyy." Scientists develop first A.I. police dog that does not require oiling. Concentration of untruths emanating from ChatGPT bot earns it the nickname "the new George Santos." Futurist Michio Kaku tells "All Things Considered" that the iPhone is now officially steampunk. Stanford Vision and Learning Lab announces new DALL-E variant that is able to generate image and aroma.

Analysis OpenAI this week introduced ChatGPT plugins, a way to extend the scope of its chatbot language model beyond the slurry of internet training data to bespoke business information. So wary is OpenAI of all the ways that ChatGPT and its other models can misfire that the company begins its announcement by reassuring readers that its cautious rollout follows from its desire to address "safety and alignment challenges." It does so with good reason – large language models (LLMs), referred to euphemistically as artificial intelligence or just AI, are seen by some to be venomous constructs that must be contained. LLMs are also limited to whatever information can be accessed or derived from their training data. As OpenAI puts it, "This information can be out-of-date and is one-size fits all across applications. Furthermore, the only thing language models can do out-of-the-box is emit text. This text can contain useful instructions, but to actually follow these instructions you need another process."

Picsart's artificial intelligence research team (PAIR) has built a new generative model that can create entirely new video content from only text descriptions. The technology, often described as text-to-video generative artificial intelligence (AI), has been released as an open-source demonstration on Twitter and has been published on GitHub and Hugging Face. The team behind it has also published a research paper describing the methodology. "Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain," the researchers explain.