The Humane AI Pin is expected to start shipping in March. On Friday, the company posted on X (Twitter) that "those who placed priority orders will receive their Ai Pins first when we begin shipping in March." The company had previously given an "early 2024" estimate for the screen-less wearable device designed to replace a smartphone. Humane, founded by former Apple employees Bethany Bongiorno and Imran Chaudhri, views the smartphone (still their ex-employer's bread and butter) as on its last legs. "The last era has plateaued," TechCrunch reported Chaudhri as saying in a November press briefing.

Ken Kocienda walks toward me, with a small white square pinned to his shirt. "Play songs written by Prince, but not performed by Prince," he says. The Sinéad O'Connor version of'Nothing Compares 2 U'--a song originally written by Prince--begins to play. A green volume meter, pause button, and next-song button appear on his hand. He twists his wrist clockwise, and the volume rises. Anticlockwise, and the song gets quieter.

In case you missed the hype, Humane is a startup founded by ex-Apple executives that's working on a device called the "Ai Pin" that uses projectors, cameras and AI tech to act as a sort of wearable AI assistant. Now, the company has unveiled the Ai Pin in full at a Paris fashion show (Humane x Coperni) as a way to show off the device's new form factor. "Supermodel Naomi Campbell is the first person outside of the company to wear the device in public, ahead of its full unveiling on November 9," Humane wrote. The company describes the device as a "screenless, standalone device and software platform built from the ground up for AI." It's powered by an "advanced" Qualcomm Snapdragon platform and equipped with a mini-projector that takes the place of a smartphone screen, along with a camera and speaker. It can perform functions like AI-powered optical recognition, but is also supposedly "privacy-first" thanks to qualities like no wake word and thus no "always on" listening." Humane first flaunted the capabilities of the Ai Pin at a Ted Talk back in May led by co-founder Imran Chaudhri, who previously worked on the iPhone user interface. Though the device itself was mostly hidden, he showed off a variety of sci-fi like features, including the ability to take a call while projecting details on his hand.

A large and complex knowledge base that models some aspect of the real world can rarely be fully specified. Two examples of such underspecification are that (i) some of the cardinality constraints are omitted; (ii) some properties of all individual instances of a class are specialized across a class hierarchy, but specific references to which particular values are specialized are omitted. Such knowledge bases are of great practical interest as they are the basis of an empirically tested knowledge acquisition system that has been used to construct a knowledge base from a significant portion of a biology textbook. In this paper, we formalize an underspecified knowledge base using answer set programming, and give a set of rules called UMAP that support inheritance reasoning in such a knowledge base.

Imran Chaudhri, who created the iPhone's user interfaces and interactions, illustrated this point bluntly onstage at the Fast Company European Innovation Festival in Milan today. "Siri never worked for me," the accented British-American designer acknowledged, "and we worked on it." Chaudhri, who spent more than 20 years at Apple before cofounding the still-in-stealth startup Humane, was speaking on a panel about the pursuit of inclusive AI with Michael Jones, the senior director of product at Salesforce AI Research. As evidence of AI's susceptibility to bias increases, the pair agreed that having diverse data sets is essential to creating automated systems that transcend--rather than replicate--the flaws of the real world. "We have an implicit bias in society today," Chaudhri said.

In this paper, I summarize the results of a decade-plus of research and development driven by the vision that human knowledge can be grounded in a small number of prototypical components that can be extended through composition and analogy. These ideas have been embodied in a system called AURA, which has been used to engineer an expressive knowledge base for an intelligent biology textbook. The focus of the current paper is to abstract away from the specifics and, to instead describe the core ideas in such a manner that they can be transferred and applied in different contexts, and to relate those ideas to the ongoing research by others.

Cognitive simulation of analogical processing can be used to answer comparison questions such as: What are the similarities and/or differences between A and B, for concepts A and B in a knowledge base (KB). Previous attempts to use a general-purpose analogical reasoner to answer such questions revealed three major problems: (a) the system presented too much information in the answer, and the salient similarity or difference was not highlighted; (b) analogical inference found some incorrect differences; and (c) some expected similarities were not found. The cause of these problems was primarily a lack of a well-curated KB and, and secondarily, algorithmic deficiencies. In this paper, relying on a well-curated biology KB, we present a specific implementation of comparison questions inspired by a general model of analogical reasoning. We present numerous examples of answers produced by the system and empirical data on answer quality to illustrate that we have addressed many of the problems of the previous system.

This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington’s clever use of statistical NL techniques to answer questions across the open web).