An AI Dictionary for Leaders
Put simply, autonomy means that an AI construct doesn’t need help from people. Driverless cars illustrate the term in varying degrees. Level four autonomy represents a vehicle that doesn’t need a human inside of it to operate at full capacity. If we ever have a vehicle that can operate without a driver, and also doesn’t need to connect to any grid, server, GPS, or other external source in order to function, it will have reached level five autonomy. Anything beyond that would be called ‘sentient’, and despite the leaps that have been made in the field of AI, the singularity (an event representing an AI that becomes self-aware) is purely theoretical at this point.
The most important part of AI is the algorithm. These are math formulas and/or programming commands that inform a regular non-intelligent computer on how to solve problems with artificial intelligence. Algorithms are rules that teach computers how to figure things out on their own.
Machine learning is the process by which an AI uses algorithms to perform artificial intelligence functions. It’s the result of applying rules to create outcomes through an AI.
When the rules are applied, an AI does a lot of complex math. Often, this math can’t even be understood by humans, yet the system outputs useful information. When this happens it’s called ‘black box learning’. We don’t really care how the computer arrived at the decisions it’s made, because we know what rules it used to get there.
When we want an AI to get better at something, we create a neural network that is designed to be very similar to the human nervous system and brain. It uses stages of learning to give AI the ability to solve complex problems by breaking them down into levels of data. The first level of the network may only worry about a few pixels in an image file and check for similarities in other files; once the initial stage is done, the neural network will pass its findings on to the next level, which will try to understand a few more pixels, and perhaps some metadata. This process continues at every level of a neural network.
Deep learning is what happens when a neural network gets to work. As the layers process data, the AI gains a basic understanding. You might be teaching your AI to understand cats, but once it
learns what paws are, that AI can apply that knowledge to a different task. Deep learning means that instead of understanding what something is, the AI begins to learn ‘why’.
Natural Language Processing
It takes an advanced neural network to parse human language. When an AI is trained to interpret human communication, it’s called natural language processing. This is useful for chat bots and translation services, but it’s also represented at the cutting edge by AI assistants like Alexa and Siri.
AI and humans learn in almost the exact same way. One method of teaching a machine, just like a person, is to use reinforcement learning. This involves giving the AI a goal that isn’t defined with a specific metric, such as telling it to ‘improve efficiency’ or ‘find solutions’. Instead of finding one specific answer, the AI will run scenarios and report results, which are then evaluated by humans and judged. The AI takes the feedback and adjusts the next scenario to achieve better results.
This is the very serious business of proving things. When you train an AI model using a supervised learning method, you provide the machine with the correct answer ahead of time. Basically the AI knows the answer and it knows the question. This is the most common method of training because it yields the most data and defines patterns between the question and answer. If you want to know why something happens, or how something happens, an AI can look at the data and determine connections using the supervised learning method.
With unsupervised learning, we don’t give the AI an answer. Rather than finding patterns that are predefined, like ‘why people choose one brand over another’, we simply feed a machine a bunch of data so that it can find whatever patterns it is able to.
Once an AI has successfully learned something, like how to determine if an image is a cat or not, it can continue to build on its knowledge even if you aren’t asking it to learn anything about cats. You could take an AI that can determine if an image is a cat with 90 per cent accuracy, hypothetically, and after it spent a week training on identifying shoes, it could then return to its work on cats with a noticeable improvement in accuracy. -Courtesy of The Next Web (TNW), www.thenextweb.com