If you have been tracking anything about anything in AI, you would know that this year belongs to generative AI. Generative AI models have accomplished what was unthinkable five years ago. Creative labour, once considered to be under the realm of humans, has been overtaken by machines. Machines can now create things entirely new – write code, poetry, stories, design 3D products, create images and videos with little to no human help. Since AI research in these areas moves at a breakneck pace, investors are flocking startups working with generative AI models.

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Sarah Guo, founder of venture capital firm Conviction, organized a buzzy salon at a posh bar in San Francisco last week that drew an animated crowd of engineers, entrepreneurs, and financiers. Guo's event was just one of several held last week in San Francisco by investors and technologists excited by the commercial potential of what has been dubbed "generative AI." Her guests included AI engineers from large tech companies, fellow investors, and entrepreneurs building businesses powered by recent advances in algorithms that generate text or images. One of the guests of honor was Clement Delangue, CEO of Hugging Face, a company that hosts a number of open source generative AI projects, including one that recently sparked a frenzy of AI memes. He answered questions from engineers thinking about jumping onto the bandwagon with generative AI startups of their own. "It's just the hottest area from a fundraising perspective right now," Guo says.

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Seek AI launched today a cloud-based AI platform that automates some of the repetitive work that data professionals perform. Thy are often asked by business users to write new code to query a database to answer ad-hoc questions. Using generative AI like DALL-E, Stable Diffusion or GPT-3, Seek AI automates this process, improving the productivity of data professionals. Business users can access Seek AI's natural language interface by means of email, Slack, text, and a range of customer relationship management (CRM) systems. In an interview with Authority Magazine, Seek AI co-founder and CEO Sarah Nagy highlighted the challenge of managing the tradeoff between data accuracy and accessibility: "On one hand, accessibility allows less technical folks to start interacting with the knowledge wellspring that is a company's data. On the other hand, what good is a wellspring of polluted water (i.e. The Talend second annual Data Health Barometer, based on a recent worldwide survey of 900 data ...

Text-to-image generative models have achieved unprecedented success in generating high-quality images based on natural language descriptions. However, it is shown that these models tend to favor specific social groups when prompted with neutral text descriptions (e.g., 'a photo of a lawyer'). Following Zhao et al. (2021), we study the effect on the diversity of the generated images when adding ethical intervention that supports equitable judgment (e.g., 'if all individuals can be a lawyer irrespective of their gender') in the input prompts. To this end, we introduce an Ethical NaTural Language Interventions in Text-to-Image GENeration (ENTIGEN) benchmark dataset to evaluate the change in image generations conditional on ethical interventions across three social axes -- gender, skin color, and culture. Through ENTIGEN framework, we find that the generations from minDALL.E, DALL.E-mini and Stable Diffusion cover diverse social groups while preserving the image quality. Preliminary studies indicate that a large change in the model predictions is triggered by certain phrases such as 'irrespective of gender' in the context of gender bias in the ethical interventions. We release code and annotated data at https://github.com/Hritikbansal/entigen_emnlp.

Stock image provider Shutterstock is embracing AI-generated art. The company plans on offering customers access to OpenAI's DALL-E 2, a program that can produce professional-grade images from a mere text description. Customers will be able to log in, type in a description for the desired picture they'd like to create, and watch DALL-E 2 churn out the corresponding image in seconds. The technology promises to open up art creation to anyone. But the same AI programs are sparking controversy.

NASA previously announced that it would create a panel to study "unidentified aerial phenomena" (UAP), aka UFOs -- while saying it doesn't believe they're "extraterrestrial in origin." Now, the space agency has unveiled the 16-member panel that will focus on these unclassified sightings, chaired by David Spergel, former head of astrophysics at Princeton University. Other members include Anamaria Berea, a research affiliate at the SETI (Search for Extraterrestrial Life) Institute in California; retired NASA astronaut and test pilot Scott Kelly; and astrophysicists, science journalists and more. The US government is effectively running two tracks of UFO probes. There's also a Pentagon group looking into UAPs reported by military pilots and investigated by US defense and intelligence officials.

At first glance, generative AI might seem like more of a curiosity than an enterprise-technology tool, said Peter van der Putten, director of the AI Lab at software firm Pegasystems Inc. "Creating cute pictures of a corgi in a house made of sushi isn't exactly a profitable business case, at least not for large enterprises," Mr. van der Putten said. And yet, he said, "generative AI startups are popping up left and right, in areas such as marketing, support, service and other content creation." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Jasper, an Austin, Texas-based startup launched last year, has developed a generative AI platform designed to auto-generate promotional blog posts and other marketing materials. Amid a sharp decline in venture-capital investing deals, Jasper last week announced a $125 million Series A fundraising round, which set its private-market valuation above $1 billion, the company said.

GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code. Prior work exploring OpenAI Codex, the underlying model that powers Copilot, has shown it performs well on typical CS1 problems thus raising concerns about the impact it will have on how introductory programming courses are taught. However, little is known about the types of problems for which Copilot does not perform well, or about the natural language interactions that a student might have with Copilot when resolving errors. We explore these questions by evaluating the performance of Copilot on a publicly available dataset of 166 programming problems. We find that it successfully solves around half of these problems on its very first attempt, and that it solves 60\% of the remaining problems using only natural language changes to the problem description. We argue that this type of prompt engineering, which we believe will become a standard interaction between human and Copilot when it initially fails, is a potentially useful learning activity that promotes computational thinking skills, and is likely to change the nature of code writing skill development.