Have you heard of AutoGPT? While ChatGPT requires multiple prompts, AutoGPT iterates, asking its own next question, and includes short and long-term memory. Described as an experimental open-source application, it’s worth understanding these iterations. In another useful outcome, a recent paper by two Fed Staffers tested if AI could decipher all the complicated Fedspeak – the Federal Reserves’s communication style. It turns out it can.
Reddit said on Tuesday that it plans to charge AI companies for access to its API. Meanwhile, Microsoft is reportedly working on its own AI chips, per the Information report. Microsoft has been developing the chips in secret since 2019, and some Microsoft and OpenAI employees already have access to them to test how well they perform for the latest large language models like GPT-4.
ConnectWise is now adding AI-assisted PowerShell scripting in its Asio platform to two of its products, Automate and RMM. The company said this is Automate’s first integration into the Asio platform and that the underlying power of Asio made rapid development and deployment of the ChatGPT integration possible. Atlassian, with their new AI-driven “virtual teammate,” offers features like AI-generated summaries Confluence and test plans in Jira or reworking responses to customers in Jira Service Management.
And while we’re talking OpenAI, the Washington Post has released its research into what data sets were used to create the large language mode, looking at Google’s C4 data set, a massive snapshot of the contents of 15 million websites. The top three are patents.google.com, Wikipedia, and scribd.com, a subscription-only digital library. But there are others in there, including at least 27 other sites identified by the U.S. government as markets for piracy and counterfeits in the data set.
And I have three ethics-related threads to pick up.
A German artist has declined a photography prize after revealing that AI generated the winning image. “I applied as a cheeky monkey to find out if the competitions are prepared for AI images to enter. They are not,” he wrote. “We, the photo world, need an open discussion. A discussion about what we want to consider photography and what not. Is the umbrella of photography large enough to invite AI images to enter—or would this be a mistake? With my refusal of the award, I hope to speed up this debate.”
Second, A Bloomberg piece focuses on the use of ChatGPT for therapy. Despite warnings about privacy and medical concerns, the article interviews several users using it to supplement their traditional mental health services.
And third, another Bloomberg report cites 18 current and former Google workers and screenshots of internal messages. In these internal discussions, one employee noted how Bard would frequently give users dangerous advice, whether on topics like how to land a plane or scuba diving. Another said, “Bard is worse than useless: please do not launch.” Bloomberg says the company even “overruled a risk evaluation” submitted by an internal safety team, saying the system was not ready for general use.
Why do we care?
Two I want to focus on. First, unexpected outcomes. Did you predict that AI could parse the Fed? Or that it might create a new chip war? Or that users will find ways to use it despite warnings not to do so? (Security-minded listeners are probably not surprised by that one). And we’ve gone from nothing to continual product release in such a short time, which leads to the second reason.
The importance of learning the models as the value of service providers. Customers will look to technologists to help them solve problems with AI, and the models are all different. Not only should you parse the value of the feature, but insight into the model beneath will tell a lot about the function of the results.