Why AI supports human intelligence but does not replace it?

Table of Contents

In Berlin’s Palais Populaire, MIA guides through the exhibition. Before her first tour, MIA had to build up knowledge about the work of art and the artist. Sometimes it happens that MIA cannot answer a question. Then MIA asks questions and learns new things continuously.

But what makes MIA so special? MIA is not a real person. MIA is an artificial intelligence, a kind of chatbot as we already know from various insurance sites for example. The task of introducing art to someone is much more complex than selling car insurances. However, the technical mechanisms behind are similar.

In both cases, artificial intelligence must be fed with a bulk of questions and answers. From thousands of questions, the most frequently asked ones are filtered and taught to the AI. Little by little she now learns further questions. Who knows, maybe one day it will be possible to have a dialogue with MIA and discuss our personal interpretation of the work of art with her. However, a lot of time will pass until then.

What does AI better than a human?

Equipped with great computing power, artificial intelligence is good at recognizing patterns. Based on these patterns, connections are to be recognised, conclusions drawn and finally predictions made. Artificial intelligences can do this much faster than the human brain can do it.

A large potential for errors lies in the data basis that is fed to the AI. If incorrect or biased data is collected, the result can also be biased. Therefore, an error in the data collection is referred to a as a bias. These can be systematic or random (without causal connection).

Is that a dog or can I eat it?

Social media teaches us how difficult the recognition of patterns can be. In 2016, Karen Zack created some images that make it as difficult for AI to decide between dog and food as it is for the human brain.

Suitable for the upcoming summer here is a very nice variation of Lucie Kehoe.

Commons sense & laws of nature remain

The collection of data for the development of artificial intelligence and the design of this intelligence continues to require human beings with their ability to think and their common sense. In addition to the systematic or random bias already mentioned, several data collection errors can occur.

Deep-Dive: Visual IoT application

Visual IoT applications, which use images and subsequent pattern recognition to make everyday work easier, are popular in the municipal sector. Overflow basins, sewers and many more are monitored. Automatically, an image is recorded at regular intervals. Due to the environment it is often not so easy to take a good picture.

The less light, the more difficult it is to take a high-quality photo. It is therefore relevant to understand how cameras work to provide the artificial intelligence with useful data for further processing. For example, an image becomes brighter when the exposure time is increased because light falls on the sensor for a longer time.

Fortunately, cameras already have a certain amount of intelligence built in. If you use the automatic mode, the camera tries to determine the exposure time itself. Most of the time, this works quite well, especially under changing light conditions. If photos are taken in a channel where there is always little light, night mode can be used. The program automatic selects the largest possible exposure time.

IoT applications should run for several months or years without human intervention. Therefore, the camera settings are not adapted for every photo. The manual mode is only an option if the lighting conditions remain constant. Exposure time, aperture and so on must be set manually in manual mode, as the name suggests.

Mastery of the basics of photography is necessary to produce high-quality data material for further processing with artificial intelligence. Especially in the development and installation of such a camera, human skill is required to make the basic settings accordingly. IoT and subsequently AI supports the person afterwards and makes his tasks easier. Such measuring points are often widely distributed and located in places that are difficult to access. The automatic transmission of data to a central server is not only convenient, but also offers potential for pattern recognition, evaluations and predictions.

One finale note: Every wearer of glasses know it only too well. A smudged, dirty lens causes blurred vision. And it is the same with cameras that provide data for artificial intelligence. If the camera lens is dirty, the images are worthless. So, besides the impulses to blame the technology directly in case of errors, do not forget to consider completely natural, conventional sources of error next time. Because any artificial intelligence is only as smart as the human being behind it.

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