What are other types of AI?
Another area of AI research is evolutionary computation.It borrows from Darwin’s theory of natural selection. It sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem.
This approach has even been used to help design AI models, effectively using AI to help build AI. This use of evolutionary algorithms to optimize neural networks is called neuroevolution. It could have an important role to play in helping design efficient AI as the use of intelligent systems becomes more prevalent, particularly as demand for data scientists often outstrips supply. The technique was showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.
Finally, there are expert systems, where computers are programmed with rules that allow them to take a series of decisions based on a large number of inputs, allowing that machine to mimic the behaviour of a human expert in a specific domain. An example of these knowledge-based systems might be, for example, an autopilot system flying a plane.
What is fueling the resurgence in AI?
As outlined above, the biggest breakthroughs for AI research in recent years have been in the field of machine learning, in particular within the field of deep learning.
This has been driven in part by the easy availability of data, but even more so by an explosion in parallel computing power, during which time the use of clusters of graphics processing units (GPUs) to train machine-learning systems has become more prevalent.
Not only do these clusters offer vastly more powerful systems for training machine-learning models, but they are now widely available as cloud services over the internet. Over time the major tech firms, the likes of Google, Microsoft, and Tesla, have moved to using specialised chips tailored to both running, and more recently, training, machine-learning models.
An example of one of these custom chips is Google’s Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which useful machine-learning models built using Google’s TensorFlow software library can infer information from data, as well as the rate at which they can be trained.
These chips are used to train up models for DeepMind and Google Brain and the models that underpin Google Translate and the image recognition in Google Photos and services that allow the public to build machine-learning models using Google’s TensorFlow Research Cloud. The third generation of these chips was unveiled at Google’s I/O conference in May 2018 and have since been packaged into machine-learning powerhouses called pods that can carry out more than one hundred thousand trillion floating-point operations per second (100 petaflops). These ongoing TPU upgrades have allowed Google to improve its services built on top of machine-learning models, for instance, halving the time taken to train models used in Google Translate.