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Artificial Intelligence: Smarter Than Man?

Tags: Artificial Intelligence
Artificial intelligence is a buzzword and trending topic. But what does AI actually mean? What can it do today, and what will it be able to do in the future? An overview.
Arnold Schlegel, June 14, 2018
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Arnold Schlegel is a ZF engineer and expert for autonomous driving and artificial intelligence.
People were already dealing with artificial intelligence (AI) decades ago. Initially it was mathematicians, later software nerds and game developers. Today it concerns presidents of countries and heads of state. But where does the hype come from? And is it justified?

What does artificial intelligence actually mean?

At its core, artificial intelligence it is about equipping machines with cognitive abilities so that they can perform certain tasks better than humans. This is precisely the essence of the innovation – and it represents a revolution, because in the course of history, the ability to analyze and plan, as well as draw logical conclusions, has been something reserved solely for humankind up to this point.
Several industrial revolutions didn’t change anything about that. From the steam engine to the modern industrial robot, they were always about mechanization or automation. The machines surpassed human muscle power or precision and concentration, but only today, thanks to AI, can computers handle the most complex problems better than humans.

The time has come for AI

The time has come for AI

This is possible because several developments are currently coinciding:
  • The computing power of computer processors has increased rapidly, a basic prerequisite for complex and resource-hungry AI programs.
  • Huge amounts of data are available today from the cloud and high mobile bandwidths. This is important because AI systems are particularly useful when it comes to identifying patterns in large amounts of data.
  • Software developers are now able to write programs that work in a similar way to the human brain – including the ability to learn as well as to reflect on and adapt their own actions.

The needle in the haystack of big data

The needle in the haystack of big data

Artificial intelligence has long since found its way into our everyday lives. For example, 56 percent of Germans already use voice assistants such as Amazon’s Alexa or Apple’s Siri. Smartphones are able to recognize whether someone is traveling by foot or in a car. And thanks to intelligent algorithms, Facebook, Twitter and the like offer us exactly the information that interests us.
In addition, a wide variety of professions could benefit from big data analysis by such AI assistants. Some are already doing so today: business consultants, doctors analyzing MRI and X-ray images, lawyers searching for precedent judgments and tax advisors organizing client profiles.
The key difference from the traditional software world is that AI systems can make decisions and answer questions that are not already thematically stored in their code.

This abstraction, this use of experience and this finding of creative, new possibilities is what gives these systems cognitive abilities that people have long since had. The algorithm learns patterns from data and draws conclusions from them without knowing a defined context.

Deep learning: Understanding information, layer by layer

Deep learning: Understanding information, layer by layer

Software that can learn in this way has a similar structure to the neural networks in the human brain. In the brain, synapses form the nodes in a finely meshed structure that is closely connected. Deep learning algorithms consist of simulated nodes arranged in layers one after another, unlike in the brain. The input goes through the individual layers successively. Thus each layer takes on special tasks during processing. For example, in image recognition, the first layer processes the image information pixel by pixel, while the next one specializes in analyzing corners and edges. This is followed by an analysis of oblique lines or round shapes and so on. Only at the end is the object identified as “tree”, “cat” or “street sign”. The number of involved nodes is immense: Current AI systems organize billions of artificial neurons in roughly 30 layers.
The term deep learning refers to the depth of this layered structure. Deep learning algorithms also allow for feedback and correction loops and are particularly well-suited to specializing in the detection of patterns or deviations in huge data quantities. This makes them unbeatable for use in image recognition programs, for example. In addition to enormous computing power, they also require training on the basis of a veritable flood of data. After all, an algorithm is only as good as the data with which it was trained.

The world as an application field for AI

The world as an application field for AI

However, training AI systems is one of the big challenges. How do you test a system that is designed to solve unforeseen problems? Virtual training and software-in-the-loop methods will make a contribution to overcoming this hurdle. On this basis, autonomous vehicles could soon significantly reduce the number of traffic accidents. What that means and where artificial intelligence is already being used in the car today is the topic of the second part of our AI Special .