AI tagged posts

An auto factory worker can remember the storage bin where she left a partly assembled component the night before and quickly return to that spot to pick it up. But robots that may work side by side with her would struggle to develop and access this same type of “spatiotemporal” memory.

Now, MIT researchers have developed a long-term memory framework that allows robots to rapidly form and recall a detailed mental model of complicated, large-scale environments...

Cleveland Clinic researchers are unlocking quantum computing’s full potential through the creation of a new computing paradigm inspired by the human brain. Fabio Cumbo, Ph.D., research associate in the lab of Daniel Blankenberg, Ph.D., associate staff, Computational Life Sciences, is developing the model, called quantum hyperdimensional computing (QHDC).

Cumbo published the first-ever implementation of QHDC in two distinct experiments in npj Unconventional Computing.

Hyperdimensional computing (HDC) is a type of computing based in neuroscience. It follows the idea that a concept in the brain is not stored on one single neuron...

The human brain is the ultimate supercomputer. It uses a highly branched and interconnected network of neurons and synapses to achieve massive computational power with extreme efficiency. In the age of AI, the brain, a paradigm of efficient neuromorphic computing, is providing inspiration for scientists.

Ionic computing—which uses ions to compute instead of the electrons in typical devices—could provide a path forward for neuromorphic technology that rivals the brain’s efficiency. But the field is only a few years old, and many challenges remain before it moves beyond proof of principle and toward real-world deployment.

To bring neuromorphic ...

To accelerate and refine decision-making in a fast-paced, global marketplace, enterprises may deploy generative artificial intelligence models to help summarize and interpret the charts that often fill market summaries and financial reports.

But even the latest vision-language models sometimes struggle with this task, since it requires a model to integrate visual, numerical, and linguistic understanding. A company that invests in a state-of-the-art model might still receive inaccurate or incomplete information.

To fill this performance gap, researchers from MIT and the MIT-IBM Computing Research Lab developed a multifaceted resource for AI users that is specifically designed to teach vision-language models (VLMs) how to effectively interpret charts.

They used a novel data gen...