We have certainly been hearing a lot about Artificial Intelligence (AI) in the news lately. The stories have ranged from technical articles describing how AI works, to philosophical articles talking about the ethics of using AI. One thing is for sure, and that is that AI has people talking. So, let’s talk.
We have all seen movies that deal with computers that display various forms of AI. Who can forget HAL from the movie “2001” that takes on a mind of its own? Faced with the prospect of disconnection, HAL decides to kill the astronauts in order to protect and continue his programmed directives. This is certainly AI at its best, or at its worst, but there is one key difference between AI in the movies and AI today. In the movies, AI has always been Science Fiction. Today, AI is becoming a reality. We all need to get smarter so we understand what AI is, and so we know how to stop computers from killing us, and most importantly, so we understand that computers can not actually choose to kill us.
So, what really is AI?
AI is a computer algorithm which mimics human intelligence through decision making. The two most common algorithms used in AI are Machine Learning and Deep Learning.
Machine Learning is a form of artificial intelligence which allows the machine to train with diverse datasets, and then predict based on those experiences. Machine Learning uses automated algorithms to predict decisions for the future. Most importantly, all of the analysis and algorithm tweaking is supervised by a human being whose goal is to improve the quality of the Machine’s performance. One Example of Machine Learning would be speech Recognition, where an algorithm hears a spoken word and determines what that word is. Through Machine Learning, the Engineer will help the algorithm improve, and get more and more words correct over time.
With Deep Learning, there is a constant focus on improvement and flexibility by using Algorithms which attempt to duplicate the thinking of the human brain, and do not require the constant supervision of a human. Through use of multiple layers of neural networks ( yep, neural, just like in the human brain ), the algorithms learn to make decisions as a human would, or perhaps better than a human would. To go back to our example from above, while Machine Learning would be used for an application like voice recognition, Deep Learning and neural nets would be used to go much further, such as with virtual assistants like Alexa or Cirri. The algorithms must not only covert the spoken word, but the algorithm must understand the meaning of questions, and then compose an intelligent response. Deep Learning is Machine Learning on Steroids.
Machine Learning is a subset of AI, and Deep Learning is a subset of Machine Learning. Key differences between ML and DL include: DL requires much larger data sets for training, DL requires far less ( if any ) human intervention, DL takes longer for training but yields better accuracy, and DL makes complex correlations that ML would not be able to accomplish. Deep learning is the most accomplishes some of the most sophisticated tasks in AI.
Want to learn more? ATI is offering a course called “Deep Learning Architectures for Defense and Security.” This 3-day course provides a broad introduction to classical neural networks (NN) and its current evolution to deep learning (DL) technology. This course introduces the well-known deep learning architectures and their applications in defense and security for object detection, identification, verification, action recognition, scene understanding and biometrics using a single modality or multimodality sensor information. This course will describe the history of neural networks and its progress to current deep learning technology.
And as always, to learn more about the full set of courses offered by ATI, please visit www.aticourses.com .