Firstly, it cannot be generalized that Machine Learning and Deep Learning are always in the same boat as Artificial Intelligence, but it must be kept in mind that their tools work very well together and help organizations work smarter, better and faster.
So where did Artificial Intelligence come from?
Most historians trace the beginning of artificial intelligence studies to 1956, when french email address list explored problem-solving using symbolic methods. In the 1960s, the U.S. Department of Defense became interested in this type of work and increased its focus on testing how computers imitate human reasoning.
Artificial intelligence was originally established to make computers more useful and more capable of independent reasoning. It is a field of research that has a long history rooted in military science and statistics, with contributions from the fields of philosophy, psychology, mathematics, and cognitive science.
Yes! Artificial Intelligence is already among us
Heading off on a business trip tomorrow? Your smart device will automatically provide weather reports and travel alerts for your destination city. Or are you planning a big birthday party? A smart bot will help you with invitations, make reservations, and remind you to pick up the cake. And if you’re planning a direct marketing campaign, there’s an AI assistant that can segment your customers into groups for targeted messaging and increased response rates.
We cannot deny that Artificial Intelligence is already affecting the way we live and work. Contrary to popular belief and discussion, Artificial Intelligence will not create consciousness, rise up or destroy humanity as Hollywood films portray. Furthermore, misled by contemporary narratives, the vast majority forget that innovations based on this concept are being used in activities where there is interaction between machines and human beings.
At the most basic level, AI-powered machines mimic human thought processes, such as the ability to identify an apple or an orange. According to NVIDIA, AI in its simplest and most narrowest sense occurs when hardware and software work together to perform very specific tasks.
One example of this is Facebook's ability to suggest which friends to tag when you post a photo on its site, as the social network has software that is able to look at the image, identify people with 98% accuracy, and do so even faster than humans themselves.
Machine Learning
Machine learning in its most basic definition is the practice of using algorithms to analyze data, learn from it, and then make a determination or prediction about something in the world. The idea is to marry algorithms and statistics so that the machine can learn from new data.
Machine learning was designed to automate the construction of analytical models and uses methods from networks, statistics, neural operations research, and physics to find hidden insights in data without being explicitly programmed. This means that an incredible number of problems can be solved with a mass of data and the right learning algorithm.
In fact, data is still the main ingredient that makes Machine Learning possible; sophisticated algorithms are just accessories for this intelligence. These are technologies that train a model based on patterns in its mass of training data, exploring new possible models defined by a series of parameters. Machine Learning can only identify patterns that are available in its mass of training data. For tasks such as classification, for example, you need a robust collection of correctly identified and categorized data.
Machine learning can also be used to allow sensors, cameras, and computers to process the images they see and create a type of computer vision. In a technology developed by NVIDIA for self-driving cars, hardware and software work together to understand what the edge of a road sign looks like and read what letters of the alphabet are in order to determine its location. When all of these algorithms work together and learn from what they “see,” they can identify things like parking permit signs.
Deep Learning
Deep Learning has made a name for itself by delivering advances in the application of Machine Learning in various areas. In addition, Deep Learning automates part of the functional engineering work, especially in videos and images.
But you might be wondering how Deep Learning works. Our brains have neural networks that connect to each other and help us process lots of seemingly unconnected information. By taking some information and making logical connections between other information, we begin to understand the world around us.
Deep Learning computers have their own artificial neural networks, which are physically stacked on top of each other so that they can also make connections.
Google used its own deep learning system to look at 10 million images from YouTube videos and find the cats in each one. Google's deep learning software was about twice as accurate at doing this task as any other image recognition system that preceded it.
So if a deep learning software is looking at pictures of cats, some layers might focus on colors, while others are determining shapes, and another layer will gather the results and try to determine whether what the computer is seeing is actually a cat, and maybe even what kind of cat it is.
To help you visualize better: see some of the biggest real examples
Machine learning helps companies like Uber determine arrival times for rides, estimate delivery times for meals on UberEATS, and calculate locations and distances.
Google uses Deep Learning for voice and image recognition algorithms; and Amazon also uses Deep Learning to help determine what its customers want to watch or buy after the sale.
The impact of these technologies
The three are still at the beginning of the technological growth curve, but they are already having a huge impact in ways that can sometimes be imperceptible.