Microsoft, Google, Salesforce and other big tech companies are rushing to incorporate generative AI into more of their products as regular people, Wall Street investors and the companies' customers have grown incredibly interested in the technology. Earlier this year, Microsoft announced a multibillion-dollar deal with OpenAI, a smaller company that kicked off the latest AI wave by publicly launching its DALL-E image generator and ChatGPT chatbot. Since then, it has integrated ChatGPT-based tech into its Bing search engine, productivity tools such as Word and Excel and cloud software that it sells to other businesses.

For years now, "artificial intelligence" has been a hot buzzword in the cybersecurity industry, promising tools that spot suspicious behavior on a network, quickly figure out what's going on, and guide incident response if there's an intrusion. The most credible and useful of services, though, have actually been machine learning algorithms trained to spot characteristics of malware and other dubious network activity. Now, as generative AI tools proliferate, Microsoft says it has finally built a service for defenders that's worthy of all the hype. Two weeks ago, the company launched Microsoft 365 Copilot, which builds on a partnership with OpenAI along with Microsoft's own work on large language models. The company is rolling out Security Copilot, a sort of security field notebook that integrates system data and network monitoring from security tools like Microsoft Sentinel and Defender and even third-party services.

With the surging demand for generative AI, this week saw preparatory developments for the next wave of AI. Companies are fast-tracking the development of AI products, and generative AI tools are closer to becoming consumer products than ever before. They are already becoming powerful assistants for writers and programmers and rapidly taking on more challenges. The open-source community is also making significant progress in running local LLMs. For instance, Facebook's LLama model has continued to be a focal point for building in the academic and open source community following the leaked weights on 4Chan.

This is part two of a two part series. Please see the first part if you're unfamiliar with PyBullet. We'll want our environment to be neatly structured so that others can install it with pip and run it quickly as any other OpenAI Gym environment. A complete tutorial on packaging projects can be found in the Python documentation. The following segment is mostly boilerplate and can be skimmed; it is roughly what is covered here, tailored to out environment -- viewing that link is encouraged.

Step 2: Use Whisper to transcribe the audio to text: https://github.com/openai/whisper Prompt: "Provide a summary of the following pasted text. Format in a style that can be emailed to a team, it should be in the style of condensed Meeting minutes and easily digestible, but includes all the salient points.

Researchers don't know why text- and image-generating AI models self-correct for some biases after simply being asked to do so. What does that person look like? If you ask Stable Diffusion or DALL-E 2, two of the most popular AI image generators, it's a white man with glasses. Last week, I published a story about new tools developed by researchers at AI startup Hugging Face and the University of Leipzig that let people see for themselves what kinds of inherent biases AI models have about different genders and ethnicities. Although I've written a lot about how our biases are reflected in AI models, it still felt jarring to see exactly how pale, male, and stale the humans of AI are.

ChatGPT, the powerful new artificial intelligence tool from OpenAI that can answer questions, chat with humans, and generate text, has dominated headlines in the past few months. The tool is advanced enough to pass law school exams (though with fairly low scores), but it has also veered into strange conversations and has shared misinformation. It also highlights an important area that companies using or thinking about using AI need to confront: how to embrace AI in a way that doesn't harm humans. "Leadership involves absolutely centering the human and being rigorous before releasing into the wild things that affect these humans," saidRenée Richardson Gosline, a senior lecturer and principal research scientist at MIT Sloan. "Having the courage and ethics to say we want to cultivate a system and a relationship with our customers whereby we don't simply always extract, but we also share value -- that's what leads to loyalty in the long term."

Accountants are among the professionals whose careers are most exposed to the capabilities of generative artificial intelligence, according to a new study. The researchers found that at least half of accounting tasks could be completed much faster with the technology. The same was true for mathematicians, interpreters, writers and nearly 20% of the U.S. workforce, according to the study by researchers at the University of Pennsylvania and OpenAI, the company that makes the popular AI tool ChatGPT.

ChatGPT has dazzled with its poetry, prose, and academic test scores. Now prepare for the precocious chatbot to find your next flight, recommend a restaurant with good seating, and fetch you a sandwich, too. Last week, OpenAI, the company behind ChatGPT, announced that a slew of companies including Expedia, OpenTable, and Instacart have created plugins to let the chatbot access their services. Once a user activates a plugin, they will be able to ask ChatGPT to perform tasks that would normally require using the web or opening an app, and hopefully see the dutiful bot scurry off to do it. The move potentially heralds a big shift in how people use computers, apps, and the web, with clever AI programs completing chores on their behalf.

Learning-based solutions are efficient, but are they trustworthy enough to be embedded in a robot cooperating with or assisting humans? In this blogpost, Daniele Meli explores this question, and reviews logic programming as a route to trustworthy autonomous (and cooperative) robotic systems. As part of the 37th AAAI Conference on Artificial Intelligence (AAAI2023), 32 different workshops were held, covering a wide range of topics. We heard from the organisers of four of these workshops, who told us their key takeaways from their respective events. These were split into two articles: 1) #AAAI2023 workshops round-up 1: AI for credible elections, and responsible human-centric AI, and 2) #AAAI2023 workshops round-up 2: health intelligence and privacy-preserving AI. Hosted by the Alan Turing Institute, AI UK is a two-day conference that showcases artificial intelligence and data science research, development, and policy in the UK. This year, the event took place on 21 and 22 March, and we covered the panel discussion session on the role and impact of science journalism. AAAI have updated their publication policy to deal with AI systems: "It is AAAI's policy that any AI system, including Generative Models such as Chat-GPT, BARD, and DALL-E, does not satisfy the criteria for authorship of papers published by AAAI and, as such, also cannot be used as a citable source in papers published by AAAI".