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On Tuesday, Microsoft announced a new, freely available lightweight AI language model named Phi-3-mini, which is simpler and less expensive to operate than traditional large language models (LLMs) like OpenAI's GPT-4 Turbo . Its small size is ideal for running locally, which could bring an AI model of similar capability to the free version of ChatGPT to a smartphone without needing an Internet connection to run it.

The AI field typically measures AI language model size by parameter count. Parameters are numerical values in a neural network that determine how the language model processes and generates text. They are learned during training on large datasets and essentially encode the model's knowledge into quantified form. More parameters generally allow the model to capture more nuanced and complex language-generation capabilities but also require more computational resources to train and run.

Some of the largest language models today, like Google's PaLM 2 , have hundreds of billions of parameters. OpenAI's GPT-4 is rumored to have over a trillion parameters but spread over eight 220-billion parameter models in a mixture-of-experts configuration. Both models require heavy-duty data center GPUs (and supporting systems) to run properly.

Enlarge / A time-lapse showing how an insect's wing adopts very specific positions during flight. (credit: Florian Muijres, Dickinson Lab)

About 350 million years ago, our planet witnessed the evolution of the first flying creatures. They are still around, and some of them continue to annoy us with their buzzing. While scientists have classified these creatures as pterygotes, the rest of the world simply calls them winged insects.

There are many aspects of insect biology, especially their flight , that remain a mystery for scientists. One is simply how they move their wings. The insect wing hinge is a specialized joint that connects an insect’s wings with its body. It’s composed of five interconnected plate-like structures called sclerites. When these plates are shifted by the underlying muscles, it makes the insect wings flap.

Until now, it has been tricky for scientists to understand the biomechanics that govern the motion of the sclerites even using advanced imaging technologies. “The sclerites within the wing hinge are so small and move so rapidly that their mechanical operation during flight has not been accurately captured despite efforts using stroboscopic photography, high-speed videography, and X-ray tomography,” Michael Dickinson, Zarem professor of biology and bioengineering at the California Institute of Technology (Caltech), told Ars Technica.

Enlarge / A cropped image showing Raw TV's poster for the Netflix documentary What Jennifer Did , which features a long front tooth that leads critics to believe it was AI-generated. (credit: Raw TV )

An executive producer of the Netflix hit What Jennifer Did has responded to accusations that the true crime documentary used AI images when depicting Jennifer Pan, a woman currently imprisoned in Canada for orchestrating a murder-for-hire scheme targeting her parents .

What Jennifer Did shot to the top spot in Netflix's global top 10 when it debuted in early April, attracting swarms of true crime fans who wanted to know more about why Pan paid hitmen $10,000 to murder her parents. But quickly the documentary became a source of controversy, as fans started noticing glaring flaws in images used in the movie, from weirdly mismatched earrings to her nose appearing to lack nostrils, the Daily Mail reported , in a post showing a plethora of examples of images from the film.

Futurism was among the first to point out that these flawed images (around the 28-minute mark of the documentary) "have all the hallmarks of an AI-generated photo, down to mangled hands and fingers, misshapen facial features, morphed objects in the background, and a far-too-long front tooth." The image with the long front tooth was even used in Netflix's poster for the movie.

Enlarge / A sample image from Microsoft for "VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time." (credit: Microsoft )

On Tuesday, Microsoft Research Asia unveiled VASA-1 , an AI model that can create a synchronized animated video of a person talking or singing from a single photo and an existing audio track. In the future, it could power virtual avatars that render locally and don't require video feeds—or allow anyone with similar tools to take a photo of a person found online and make them appear to say whatever they want.

"It paves the way for real-time engagements with lifelike avatars that emulate human conversational behaviors," reads the abstract of the accompanying research paper titled, "VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time." It's the work of Sicheng Xu, Guojun Chen, Yu-Xiao Guo, Jiaolong Yang, Chong Li, Zhenyu Zang, Yizhong Zhang, Xin Tong, and Baining Guo.

The VASA framework (short for "Visual Affective Skills Animator") uses machine learning to analyze a static image along with a speech audio clip. It is then able to generate a realistic video with precise facial expressions, head movements, and lip-syncing to the audio. It does not clone or simulate voices (like other Microsoft research ) but relies on an existing audio input that could be specially recorded or spoken for a particular purpose.

Enlarge (credit: Getty Images | Benj Edwards )

On Thursday, Meta unveiled early versions of its Llama 3 open-weights AI model that can be used to power text composition, code generation, or chatbots. It also announced that its Meta AI Assistant is now available on a website and is going to be integrated into its major social media apps, intensifying the company's efforts to position its products against other AI assistants like OpenAI's ChatGPT, Microsoft's Copilot, and Google's Gemini.

Like its predecessor, Llama 2 , Llama 3 is notable for being a freely available, open-weights large language model (LLM) provided by a major AI company. Llama 3 technically does not quality as "open source" because that term has a specific meaning in software (as we have mentioned in other coverage ), and the industry has not yet settled on terminology for AI model releases that ship either code or weights with restrictions (you can read Llama 3's license here ) or that ship without providing training data. We typically call these releases "open weights" instead.

At the moment, Llama 3 is available in two parameter sizes: 8 billion (8B) and 70 billion (70B), both of which are available as free downloads through Meta's website with a sign-up. Llama 3 comes in two versions: pre-trained (basically the raw, next-token-prediction model) and instruction-tuned (fine-tuned to follow user instructions). Each has a 8,192 token context limit.

(credit: Getty Images)

Last October, I received an email with a hell of an opening line: “I fired a nuke at the US Copyright Office this morning.”

The message was from Elisa Shupe, a 60-year-old retired US Army veteran who had just filed a copyright registration for a novel she’d recently self-published. She’d used OpenAI's ChatGPT extensively while writing the book. Her application was an attempt to compel the US Copyright Office to overturn its policy on work made with AI, which generally requires would-be copyright holders to exclude machine-generated elements.