Chinese tech powerhouse Baidu has filed a patent for a system that could use AI to decode animal sounds and behaviour then translate those signals into human language. For the millions of pet owners wondering what their animals are thinking, this could be the first real step toward bridging the communication gap between humans and animals. The tech Baidu’s system would collect animal vocalizations, body movements, and biological signals. It would merge that data and feed it into an AI model trained to identify emotional states. These emotional states could then be rendered in human language to boost “cross-species communication”. It’s still just a patent. A Baidu spokesperson told media the translator is “still in the research stage,” but acknowledged “a lot of interest in the filing.” Core idea: Photo by Angel Luciano on Unsplash The skepticism Some Chinese netizens aren’t convinced. One Weibo user commented, “While it sounds impressive, we’ll need to see how it performs in real-world applications.” Patent approvals in China can take 1–5 years, depending on complexity. Baidu’s idea may be early, but the conversation it’s sparking is already loud and clear. Want your dog to tell you how they really feel? You might not be...

Guest post by José Hernández-Orallo, Professor at Technical University of Valencia Two decades ago I started working on metrics of machine intelligence. By that time, during the glacial days of the second AI winter, few were really interested in measuring something that AI lacked completely. And very few, such as David L. Dowe and I, were interested in metrics of intelligence linked to algorithmic information theory, where the models of interaction between an agent and the world were sequences of bits, and intelligence was formulated using Solomonoff’s and Wallace’s theories of inductive inference. In the meantime, seemingly dozens of variants of the Turing test were proposed every year, the CAPTCHAs were introduced and David showed how easy it is to solve some IQ tests using a very simple program based on a big-switch approach. And, today, a new AI spring has arrived, triggered by a blossoming machine learning field, bringing a more experimental approach to AI with an increasing number of AI benchmarks and competitions (see a previous entry in this blog for a survey). Considering this 20-year perspective, last year was special in many ways. The first in a series of workshops on evaluating general-purpose AI took off, echoing the increasing...

A key factor driving these advancements is the meticulous process of data annotation—labeling of ophthalmic data to train models. This piece delves into the importance of data annotation in ophthalmology, including the types of images involved, annotation techniques, and key challenges. Importance of Annotation in Ophthalmology Data annotation for ophthalmology AI involves labeling eye structures in images, including the retina, cornea, and optic nerve, to help models achieve precise diagnosis and treatment. This enables medical AI systems to detect early signs of eye diseases. Precise labeling also helps identify abnormalities for timely intervention. Annotated ophthalmic data boosts the accuracy of eye disease detection. Specifically labeled areas in ophthalmic images enable models to understand and diagnose conditions accurately. Types of Ophthalmic Image Annotations Medical AI models need to be trained on various imaging modalities to gain unique insights into ocular health. Fundus Image Annotation: Fundus photographs capture high-resolution images of the back of the eye. By labeling critical structures such as the retina, optic nerve, and blood vessels, AI-powered systems can automatically detect diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Optical Coherence Tomography (OCT) Image Annotation: Labeling key features in cross-sectional images provides detailed visual information about retinal layers,...

Behavioral economist Sendhil Mullainathan has never forgotten the pleasure he felt the first time he tasted a delicious crisp, yet gooey Levain cookie. He compares the experience to when he encounters new ideas. “That hedonic pleasure is pretty much the same pleasure I get hearing a new idea, discovering a new way of looking at a situation, or thinking about something, getting stuck and then having a breakthrough. You get this kind of core basic reward,” says Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS). Mullainathan’s love of new ideas, and by extension of going beyond the usual interpretation of a situation or problem by looking at it from many different angles, seems to have started very early. As a child in school, he says, the multiple-choice answers on tests all seemed to offer possibilities for being correct. “They would say, ‘Here are three things. Which of these choices is the fourth?’ Well, I was like, ‘I don’t know.’ There are good explanations for all of them,” Mullainathan says. “While there’s a simple...