This June, in the political battle leading up to the 2024 US presidential primaries, a series of images were released showing Donald Trump embracing one of his former medical advisers, Anthony Fauci. In a few of the shots, Trump is captured awkwardly kissing the face of Fauci, a health official reviled by some US conservatives for promoting masking and vaccines during the COVID-19 pandemic.
“It was obvious” that they were fakes, says Hany Farid, a computer scientist at the University of California, Berkeley, and one of many specialists who examined the pictures. On close inspection of three of the photos, Trump’s hair is strangely blurred, the text in the background is nonsensical, the arms and hands are unnaturally placed and the details of Trump’s visible ear are not right. All are hallmarks — for now — of generative artificial intelligence (AI), also called synthetic AI.
Many sophisticated machine learning (ML) products recently have been introduced as general-purpose content-creation tools. The one that has garnered the most attention was ChatGPT, a chatbot powered by the large language model (LLM) GPT-3.5.
An LLM is a type of ML model that performs various natural language processing tasks—such as recognizing, summarizing, translating, and generating text; answering questions; and carrying on a conversation. An LLM is developed by deep learning techniques, and training its artificial neural networks requires a massive amount of data. Deep learning is a type of ML, and ML is a subfield of AI. Since ChatGPT outputs new content as a response to a user’s inquiry, it is considered a tool in the realm of generative AI.
Editor’s Note: Read more, see link below for original item…
As usual, librarians will have to deal with this, if only because we live in the same reality as the rest of the world and have not been granted a pass on modernity so that we can sit by the library fireplace with our library cat and read all day. In case you haven’t heard, we’ve gone digital.
The search giant is testing generative writing and other AI features for its Workspace apps.
By Nina Raemont, March 14, 2023 8:12 a.m. PT
Google plans to bring new AI-powered tools to its suite of Workspace apps. In a blog post on Tuesday, the search giant said it’s starting by testing generative AI writing features in Gmail and Docs that can help people get started on the writing process.
“Simply type a topic you’d like to write about, and a draft will instantly be generated for you,” reads Google’s post. “With your collaborative AI partner you can continue to refine and edit, getting more suggestions as needed.”
The tool, Google suggests, can be used to help create things like customized job descriptions or invitations for a kid’s birthday party. The company is also exploring ways to incorporate AI tools into Slides, Sheets, Meet and Chat.
With the recent news that the ChatGPT AI can pass a theory of mind test, how far away are we from an artificial intelligence that fully understands the goals and beliefs of others?
By Edd Gent, 14 February 2023 , updated 22 February 2023
SUPERHUMAN artificial intelligence is already among us. Well, sort of.
When it comes to playing games like chess and Go, or solving difficult scientific challenges like predicting protein structures, computers are well ahead of us.
But we have one superpower they aren’t even close to mastering: mind reading.
Humans have an uncanny ability to deduce the goals, desires and beliefs of others, a crucial skill that means we can anticipate other people’s actions and the consequences of our own. Reading minds comes so easily to us, though, that we often don’t think to spell out what we want.
If AIs are to become truly useful in everyday life – to collaborate effectively with us or, in the case of self-driving cars, to understand that a child might run into the road after a bouncing ball – they need to establish similar intuitive abilities.
You can ask Google, Alexa, Cortana, Watson, or Siri—but will you be able to ask your local library? A century or so ago, electricity was a new, quasi-magical thing—a novelty with few applications. Back then, nobody could have predicted that it would give rise to telephones, production lines, and microchips. And yet, electricity transformed every industry, including agriculture, healthcare, transportation, and manufacturing. As a foundational springboard for so many new innovations, that novelty was the most important engineering achievement of the 20th century.
Today, our interactions with AI are mostly novel (“Siri, why did the chicken cross the road?”)—and the results crude—but so were the first lightbulbs and photographs.