Teaching a machine correctly to do what you want them to do can be a challenging task. Unlike humans, computers do not inherently take the initiative. They are brilliant and efficient at what they do, but they still require a set of instructions to tell them what to do. Below you can get the details about The Role of Data Annotation in AI and Machine Learning
AI technology has made leaps and bounds over years of study and implementation, and they are close to becoming as smart as they need to be for what humanity wants from them.
AI, for instance, is no longer limited to automating scripts for e-commerce websites. Now, they can be found at the helm of self-driving, albeit experimental cars. There’s also Elon Musk’s ambitious Artificial General Intelligence project that wants to create AI that comes close to human intelligence and cognitive capabilities.
So, how does AI learn? Is it a fully automated process, or one that is fully controlled by real human beings?
The process is complicated, but, at the core of everything is data. The AI reads data and acts according to what is written in its programming. This is similar to how a human being understands the information before taking action.
Data Annotation is The Most Crucial Part of the Process
How does AI identify what is in front of it? That information is derived through annotation. First, a machine looks at raw data collected by the sensors that it employs. It, however, cannot identify specific objects in the raw data on its own.
Human analysts step in at this part. Humans look at the raw data collected and interpret it for the machine to read. The “comprehension” part is the job of the programming, but the computer won’t be able to follow the program if it can’t read the data.
There is a considerable amount of data that is collected by a machine governed by an AI. Self-driven cars, for example, record images as well as take measurements such as distances, elevation, angle of incline, and even temperature.
The Role of Images in AI Technology
Images are the most crucial information collected by the AI. Pictures have identifiable data, and it is the job of analysts to annotate this information using an image labeling software. This software is capable of converting the text data and render it to a format that’s readable in machine language.
There are many image labeling software in the market. Each of these programs has certain features that improve not only the annotation process but also, the archiving of the data so that pre-determined information can be annotated for similar data in the future.
The use of these software simplifies the annotation process, so analysts can focus more on accuracy than in learning the procedure.
What’s the Future for AI?
At this stage, the future for AI seems bright and promising. The progress in self-driven cars is a prime indicator of that.
One should not discount the virtual assistants Siri, Alexa, and Google as well. Both Alexa and Siri are now smart enough to be accurate in their assistance for their owners and have the potential to be even more intelligent in the future.