Learning:

 

As humans, we learn new things the moment we come into this world. Even if it is just our subconscious working, we are still picking up on everything happening around us and learning from it. Whether that be your parents repeating the names of every object to you a million times, or watching the bachelor to know how to not find a lifelong partner, we are always learning. Now, AI has developed its own method of teaching computers and code to learn; this application of AI is better known as machine learning. As said by Rick Negrin, Vp of product development at MemSQL, “The idea is that the model gets better as you feed it more data points” (forbes). The process of machine learning is to first, train and teach the data. Essentially, take the data to grade school. This, just like human school, is a long and hard process that requires attention to detail, testing, and collaboration. Once the data has graduated, it is time to put it to practice. Machine learning could be used to beat a chess master (actually happened), develop a better voice assistant for your phone, or make predictions about you based on your use of social media. So, machines can learn about humans and human behavior as well. It is important to recognize where machine learning is used in our lives, as well as future developments it can be used to do both good, and potentially bad.

 

Problem solving:

As humans, we encounter problems every single day. Whether it be you can’t decide whether to get a blueberry or chocolate chip muffin for breakfast, or you need to develop a cure for cancer, problems come in all shapes in sizes and we have to deal with them. For artificial intelligence, as noted in an article by Britannica, it “may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution.” So, the machine looks through options of what it can do or how to react, just like we must think about our actions and reactions, to try and determine the best possible reaction that will in turn lead to the best outcome. The powerful thing about AI, is how incredibly quick it can complete these actions. It can run through thousands of possible solutions in the matter of minutes, whereas humans could take days of contemplation to do the same. Some of the practical uses of problem solving with artificial intelligence are developing and solving mathematical proofs, finding the perfect sequence of movements to assure you win a board game, re-engineer the way a business runs and/or operates, and to possibly suggest new ways to solve pre existing problems, such as match a new drug to cure potential diseases. AI is the first element of technology to have problem solving skills, and when that is paired with learning, it becomes that much more powerful. 

 

Reasoning:

 

As humans, we typically refer to our sense of reasoning as “common sense.” The knowing of what is right, what is wrong, and what things are generally acceptable in our society. We all know not to walk around the streets of New York naked; it;s just common sense. But, the more variables you add, the harder it is to reason. Say for example, you are planning an event, you have two family members, but there is only one seat left. How do you reason between inviting one of them over the other? What do they each add or bring to the event? Well, according to Britannica, reasoning requires you “to draw inferences appropriate to the situation. Inferences are classified as either deductive or inductive. An example of the former is, “Fred must be in either the museum or the café. He is not in the café; therefore he is in the museum,” and of the latter, “Previous accidents of this sort were caused by instrument failure; therefore this accident was caused by instrument failure.” The most significant difference between these forms of reasoning is that in the deductive case the truth of the premises guarantees the truth of the conclusion, whereas in the inductive case the truth of the premise lends support to the conclusion without giving absolute assurance.” Currently, artificial intelligence is great at deductive reasoning because it concludes with a “right answer.” Inductive reasoning is more difficult because you can’t be sure that your final answer is correct, and you have to find relevant information to support and develop your conclusion. This aspect of AI is still being developed, but as of now, AI still has the power to reason, draw conclusions, and make inferences about the world around them.

 

Perceive:

 

As humans, we perceive things by using all of our senses to analyze what is happening around us. Believe it or not, AI can perceive as well. AI perceives through understanding a model or data set, to better understand the world around them. A wonderful example of this is a Tesla. A Tesla’s computer uses perception to make sense of the environments around them such as other cars, cones, road lines, trees, people, bikes, etc. At its core, AI’s perception skills are controlling your fate. This asset of AI still has a long way to go, considering how it is even one of the most complex human skills. As AI is able to become even better at perceiving its environment, it will become an even more powerful tool than it is now.