AI Chip That Works Like a Human Brain

Tech corporation IBM has unveiled a new "prototype" of an analog AI chip that works like a human brain and performs complex computations in various deep neural networks (DNN) tasks.

The chip promises more. IBM says the state-of-the-art chip can make artificial intelligence remarkably efficient and less battery-draining for computers and smartphones.

Introducing the chip in a paper published by IBM Research, the company said: “The fully integrated chip features 64 AIMC cores interconnected via an on-chip communication network. It also implements the digital activation functions and additional processing involved in individual convolutional layers and long short-term memory units.”

Reinventing ways in which AI is computed
The new AI chip is developed in IBM’s Albany NanoTech Complex and comprises 64 analog in-memory compute cores. By borrowing key features of how neural networks run in biological brains, IBM explains that it has embedded the chip with compact, time-based analog-to-digital converters in each tile or core to transition between the analog and digital worlds.

Each tile (or core) is also integrated with lightweight digital processing units that perform simple nonlinear neuronal activation functions and scaling operations, explained IBM in a blog published on August 10.

A replacement for current digital chips?
In the future, IBM's prototype chip could replace the current chips powering heavy AI applications in computers and phones. “A global digital processing unit is integrated into the middle of the chip that implements more complex operations that are critical for the execution of certain types of neural networks,” further said the blog.  READ MORE...

Shortest Path to Human Happiness

The researchers created a digital model of psychology aimed to improve mental health. The system offers superior personalization and identifies the shortest path toward a cluster of mental stability for any individual.

Deep Longevity, in collaboration with Harvard Medical School, presents a deep learning approach to mental health.

Deep Longevity has published a paper in Aging-US outlining a machine learning approach to human psychology in collaboration with Nancy Etcoff, Ph.D., Harvard Medical School, an authority on happiness and beauty.

The authors created two digital models of human psychology based on data from the Midlife in the United States study.


The first model is an ensemble of deep neural networks that predicts respondents’ chronological age and psychological well-being in 10 years using information from a psychological survey. This model depicts the trajectories of the human mind as it ages. 

It also demonstrates that the capacity to form meaningful connections, as well as mental autonomy and environmental mastery, develops with age. It also suggests that the emphasis on personal progress is constantly declining, but the sense of having a purpose in life only fades after 40-50 years. 

These results add to the growing body of knowledge on socioemotional selectivity and hedonic adaptation in the context of adult personality development.  READ MORE...