While the self-driving bike itself was of little use, the AI technology behind it was remarkable.
Powering the bicycle was a neuromorphic chip, a special kind of AI computer.
Neuromorphic computing is not new.
In fact, it was first proposed in the 1980s.
But recent developments in the artificial intelligence industry have renewed interest in neuromorphic computers.
Neural networks are composed of artificial neurons, tiny computation units that perform simple mathematical functions.
Artificial neurons arent of much use alone.
Deep neural networks can contain hundreds of millions of neurons, spread across dozens of layers.
The AI model adjusts each of the artificial neurons as it reviews more and more data.
CPUs pack a lot of power and can perform complex operations at fast speeds.
Given the distributed nature of neural networks, running them on classic computers is cumbersome.
GPU arrays have proven to be very useful in neural data pipe operations.
The rise in popularity of neural networks and deep learning have been a boon to GPU manufacturers.
Graphics hardware company Nvidia has seen its stock price rise in value severalfold in the past few years.
The dissimilarities between GPUs and neural networks cause a lot of inefficiencies, such as excessive power consumption.
Every neuromorphic chip consists of many small computing units that correspond to an artificial neuron.
Contrary to CPUs, the computing units in neuromorphic chips cant perform a lot of different operations.
They have just enough power to perform the mathematical function of a single neuron.
Another essential characteristic of neuromorphic chips is the physical connections between artificial neurons.
Creating an array of physically connected artificial neurons is what gives neuromorphic computers their real strength.
The structure of neuromorphic computers makes them much more efficient at training and running neural networks.
They can run AI models at a faster speed than equivalent CPUs and GPUs while consuming less power.
This is important since power consumption is alreadyone of AIs essential challenges.
Neuromorphic chips are characterized by the number of neurons they contain.
But 40,000 is a limited number of neurons,as much as the brain of a fish.
The human brain contains approximately 100 billion neurons.
But the Tianjic chip was more of a proof of concept than a neuromorphic computer purposed for commercial uses.
Other companies have already been developing neuromorphic chips ready to be used in different AI applications.
One example is Intels Loihi chips and Pohoiki Beach computers.
Each Loihi chip contains 131,000 neurons and 130 million synapses.
The Pohoiki computer, introduced in July, packs 8.3 million neurons.
The Pohoiki delivers 1000x better performance and is 10,000x more energy efficient than equivalent GPUs.
That will require atotally different AI algorithm.
Artificial general intelligence requires more than bundling several narrow AI models together.
Examples includenatural language understanding and navigating open worlds.
Creating more efficient ANN hardware wont solve those problems.
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