Chinese researchers have developed a hybrid chip architecture that could move the world a step closer to achieving artificial general intelligence (AGI) and a future filled with humanlike “thinking machines,” TechInAsia, reported.
The potential for attaining AGI, also known as “full AI,” by adopting such a general hardware platform was set out by a team of researchers, led by Tsinghua University professor Shi Luping, in a research paper that was published in the scientific journal Nature on Thursday.
Their research presented the case for the Tianjic chip, which was designed by integrating the “computer-science-oriented and neuroscience-oriented” approaches to developing AGI, the report said.
Tianjic shows that combining those two approaches, which rely on fundamentally different formulations and coding schemes, can enable a single computing platform to run diverse machine-learning algorithms and reconfigurable building blocks, among others.
“This chip for general AI has the potential of being applied across many industries,” said Shi, a professor at the Centre for Brain-Inspired Computing Research in Beijing’s Tsinghua University, in an interview.
“Autonomous driving, robotics and automation would be among the fields where this chip can make a difference.”
In their featured report on Nature, the Chinese researchers said they designed a self-driving bicycle to evaluate how their attempt at an AGI chip would fare in a road test. The bicycle was equipped with a camera, gyroscope, speedometer, driving and steering motors and a Tianjic chip.
The test, which was recorded on video, had the bicycle perform obstacle detection and avoidance, balance control, voice command recognition, tracking and decision making under different road conditions.
Shi said the chip ably demonstrated “its prowess in supporting multiple coding schemes and adaptive capabilities in a complicated environment.”
Efforts are now underway to incubate start-ups that would focus on developing applications based on the Chinese researchers’ work on the Tianjic chip design, according to Shi.
He said the goal was to launch a version of the chip for mass production by early next year, without elaborating on the details of potential commercial partnerships, investments and the costs such a plan would entail, the report said.
The world’s second-largest economy has not been shy about its ambitions for AI dominance. The country’s State Council released a road map in July 2017, with the goal of creating a domestic industry worth 1 trillion yuan (US$145 billion) and becoming a global AI powerhouse by 2030.
According to NOVA at PBS.org, machine learning technology has advanced quickly, but most devices share a common pitfall: the amount of time, energy, and human input required to get the skills of these systems up to snuff. When artificial intelligence learns, it often does so through brute force, cycling through countless rounds of trial and error until it converges on the best set of tactics.
People, on the other hand, are much better at thinking on their feet, and require much less brainpower to do so. To bridge this processing gap, many independent groups of computer scientists are trying to build computer chips with an internal architecture that mimics that of the human brain, the report said.
So-called neuromorphic chips are hybrids. Half of their makeup is standard AI fare, relying on standard computer algorithms. The rest, however, is biologically inspired, incorporating hundreds of thousands of faux neurons that attempt to approximate how human brain cells communicate: through electrical impulses, sent only when an input signal reaches a critical threshold.
The two parts of the chip then relay information to each other in a way that’s meant to combine their strengths in the learning process and consume less energy in doing so.
“This is about trying to bridge and unify computer science and neuroscience,” said Gordon Wilson, chief executive of Rain Neuromorphics, a start-up company that is developing its own human-brain-inspired chip.