Artificial intelligence is a fascinating subject. Every day, there is a constant improvement in these programs’ processing. They make more intelligent decisions as each day passes, but this is all because of the humans who are behind the AI’s learning processes.
There are many tasks that humans do with regards to assisting AI in improving their learning curves. Let’s take a look at two techniques that are used in the context of artificial intelligence operating self-driving vehicles.
How AI Perceives Things
Because artificial intelligence is an attempt at mimicking the learning abilities of the human brain, its decision making is based on how we make our choices every day. Humans require background information which is used as a basis to make sound decisions.
When we have the data that we need, we can take make the best choice out of those options that are given to us. AI works roughly the same way.
In automated cars, there are several sensors scattered throughout the body of the vehicle. All of these sensors collect data and send them to the proper processing unit. This is similar to the nerves in our body managing stimuli transmitted to the brain, triggering biological and chemical reactions in response.
Various sensors are used in automated driving vehicles. One that is worth noting perhaps is LiDAR. This is an acronym for Light Detection and Ranging. The technology is famously used by the United States National Oceanic and Atmospheric Administration.
According to the NOAA, LiDAR technology uses a combination of Global Positioning Satellite technology, light-based sensors, and a receiving scanner to create a detailed 3D mapping of the terrain.
How does It Work?
The sensors fire off photo pulses that bounce off surfaces and back to the aircraft’s receivers. The data is then mapped out by the scanner into a primary image. This image is refined further by other data collected by different sensors through a process involving an Image labeling tool.
Self-driving cars also make use of specialized cameras that record panoramic images of the places that the automobile passes by. Humans then interpret all of these data in the headquarters, or data processing centers.
How is the Data Interpreted?
Human analysts use specific software to view the images and manually annotate the pictures to identify what is being seen by the car’s camera. Using an Image labeling tool, analysts draw boxes and other patterns on identifiable shapes and label them accordingly.
Once each image is fully annotated, the software then feeds the raw data into the central processing unit of the AI computer. The car’s computer updates with new data allowing it to make better decisions on the next drive.
Image labeling and data annotation work in conjunction with programming, the part in which the AI is taught how to deal with environmental data and what actions to take when encountering some aspects while driving.
Self-driving vehicles are continually researched and developed. It would probably take a few more years until a car can respond to external stimuli like a human driver. The progress of AI is worth following. However, one should not discount the role human analysts play in their development.