An artificial intelligence is a simulation of human knowledge in machines programmed to think like humans and mimic their behavior. But the term may also be used to any machine that exhibits traits associated with a human mind like learning and problem-solving.
Ideally, artificial intelligence has the ability to rationalize and take actions that have the best chance of achieving a specific goal, including learning, reasoning, and perception.
AI is continuously advancing as technology advances. Previous benchmarks that defined artificial intelligence no longer embody the term, since it has become the standard function taken for granted as a basic computer function.
It is evolving to benefit many different industries. These machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, and many more.
Neural Network Gains Benefits' Equivalent to a Good Night's Rest'
Artificial intelligence that mimics the brain needs sleep just like humans, study reveals https://t.co/dfFQ8rBijd — Indy Science (@IndyScience) June 9, 2020
Researchers from Los Alamos National Laboratory in the United States found that the artificial intelligence they designed to function as a human may need sleep just as the real brains.
They discovered that neural networks experience benefits similar to a good night's sleep when exposed to an artificial analog of sleep, Independent News reports.
One of the computer scientists in Los Alamos, Yijing Watkins, said that they are "fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development."
The team was working on a form of artificial intelligence designed to copy how humans learn to see when they discovered that exposing the AI to an artificial analog of sleep could benefit it.
The AI becomes unstable after long periods of unsupervised learning while attempting to classify objects using their dictionary definition without any prior examples to compare it to. But it became stable after it was exposed to a state similar to human brain experiences in sleeping, as its neural network's stability was restored.
Los Alamos computer scientist and study co-author Garrett Kenyon said that "the issue of how to keep learning systems from becoming unstable really only arises when attempting to utilize biologically realistic, spiking neuromorphic processors or when trying to understand biology itself."
Majority of machine learning, deep learning, and AI researchers never encounter this type of problem since they can perform mathematical operations that regulate the overall dynamical gain of the system.
Read Also : Hiring Bosses Could Soon Use AI that Analyzes Applicant's Selfie to Identify Personality
Last Hope in Stabilizing the AI
The researchers only decided to expose the neural networks to an artificial analog sleep as a last effort in stabilizing them. They have tried various types of noise, roughly comparable to the static while tuning in the radio.
But it was only when they used the so-called Gaussian noise - that includes a wide range of frequencies and amplitudes - when they got the best results. It is similar to the sound received by biological neurons during a slow-wave sleep, which suggests that it may act, in part, to ensure cortical neurons maintain stability and do not hallucinate.
Their next goal is to implement their algorithm on the Loihi neuromorphic chip of Intel, allowing it to sleep from time to time to process information from a silicon retina in real-time stably.
The researchers will be presenting their study at the Women in Computer Vision Workshop on June 14 in Seattle.
