Artificial intelligence, machine learning, and deep training are an integral part of many organizations. Often these terms are used interchangeably.
Artificial intelligence is advancing in leaps and bounds – from advances in the field of unmanned vehicles and the ability to beat a person at games like poker and that’s it, to automated customer service. Artificial Intelligence is an advanced technology that is poised to revolutionize business.
The terms AI, machine learning and deep learning are often used interchangeably, but in reality there are differences between them. How exactly these terms will be more different.
Artificial Intelligence (AI)
Artificial intelligence is a broad concept in relation to advanced machine intelligence. In 1956, at a conference on artificial intelligence at Dartmott, this technology was described as follows: “Every aspect of learning or any other feature of intelligence can, in principle, be described so precisely that a machine will be able to simulate it .”
AI can cure anything – from computer programs for playing chess to speech recognition systems such as the Amazon Alexa voice assistant, which is able to perceive speech and answer questions. In general, AI systems can be divided into three groups: narrow artificial intelligence (narrow AI), artificial general intelligence (AGI) and artificial superintelligence.
IBM’s Deep Blue program, which defeated Harry Kasparov’s chess in 1996, or Google Deepmind’s AlphaGo program, which defeated the world in 2016 at the world championships, are examples of limited AI capable of solving a specific problem. This is the main difference between artificial general intelligence (AGI), which is on par with human intelligence and can perform many different tasks.
Brilliant artificial intelligence is one step above human. Nick Bostrom describes it this way: This is “an intelligence smarter than the best human brain, in almost every area, including scientific work, general wisdom, and social skills.” In other words, this is when cars get smarter than us.
Machine Training
Machine training is an area of artificial intelligence. The basic principle is that cars receive data and “study” it. Currently, this is the most promising for business, based on artificial intelligence.
Machine learning systems allow you to quickly apply the knowledge gained in learning to large datasets, which allows them to be successful at tasks such as people recognition, speech recognition, object recognition, translation, and more. .
Unlike programs with hand-coded instructions to perform specific tasks, machine learning allows the system to independently learn to recognize models and make predictions.
While both programs – Deep Blue and Deep Blue are examples of the use of AI, Deep Blue is built on top of a pre-programmed set of rules, so it is not related to machine learning. DeepMind, on the other hand, is an example of machine learning: the program beats the world champion GO by studying itself in a series of such moves made by experienced players.
Is your company interested in integrating machine learning into its strategy? Amazon, Baidu, Google, IBM, Microsoft and others already offer machine learning platforms that organizations can use.
deep training
Deep training is a subset of machine learning. It uses some machine learning methods to solve real problems using neural networks that can mimic human decision making. Deep training can be very expensive and requires large datasets to train on.
This is due to the fact that there are a huge number of parameters that need to be set in order to learn algorithms to avoid wrong actions. For example, a deep learning algorithm can be provided to “know” what a cat looks like.
For the study, you will need a large number of photographs in order to learn to distinguish the smallest details that allow you to distinguish a cat from a leopard, leopard or fox.
As mentioned above, in March 2016 AI scored a huge victory when Alphago Deepmind beat the world in 4 out of 5 games using deep learning. According to Google, the deep training system worked by combining the Monte Carlo method of searching a tree with deep neural networks trained with a teacher in professional games and training with support in games with themselves. “
Deep training also has a job application. You can capture a huge amount of data – millions of pictures, and with their help determine certain characteristics. Text search, fraud detection, spam detection, handwritten input recognition, image search, speech recognition, translation – all these tasks can be performed using deep learning. For example, at Google, deep learning networks have replaced many “rule-based systems and require craft”.
It should be noted that deep training can be very “biased”. For example, when Google Faces was originally published, it featured a lot of black faces like gorillas. “This is an example of what would happen if you didn’t have African Americans in your training group,” said Anu Toyari, senior Mint job specialist at Intuit. “If you don’t have African Americans running the system, if you don’t have African Americans testing the system, when your system finds African American faces, it won’t know how to act.”
There is an opinion that the topic of deep learning is greatly exaggerated. For example, the Sundown AI system provides automated customer interactions using a combination of machine learning algorithms and graphics policy without the use of deep learning.
