A.I. vs Machine Learning vs Deep Learning

Artificial Intelligence (A.I.)

Here are a few definitions that define artificial intelligence:

AI is most likely the broadest concept to describe advanced, computer intelligence. This goes from the use of a computer to imitate the cognitive functions of humans to voice recognition technology such as Alexa or Siri. When machines perform tasks based on “intelligent” algorithms, this is also considered as AI.

In 1956 at the Dartmouth Artificial Intelligence Conference, the technology was described as such: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”|

AI is mainly categorized into two types, type-1 based on capabilities and type-2 based on functionalities.

Type-1: Based on capabilities

Type-2: Based on functionalities

Machine Learning (ML)

Here are a few definitions that define machine learning:

Machine learning is a subfield of AI. The core principal with machine learning is that it “learns” from data on their own. “It’s currently the most promising tool in the AI kit for businesses. ML systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks.” ML can recognize patterns of its own and make predictions. Resulting in decision making with minimal human supervision. Comparatively to hang-coding everything and calculating all possible outcomes along with instructions on the reaction from the action.

Deep Learning

Here are a few definitions that define deep learning:

Deep learning mimics the human brain by processing data and creating patterns to make decisions. Deep Learning is deeper than ML and is also a subset of machine learning. It “[…]has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

How does deep learning work?

“Computer programs that use deep learning go through much the same process. Each algorithm in the hierarchy applies a nonlinear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.”

As mentioned above, traditional machine learning, the learning curve is supervised and the programmer needs to be very specific when telling the computer to look for when it finds the image of contains a house or not a house. This process is “feature extraction” and the success rate of this depends solely on the skills of the programmer. Defining the variable “cat”. However, with deep learning, it programs its settings on its own without the help of the programmer. This makes the process more efficient, and surprisingly more accurate.

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At the start, the program might arrive with training data, and a set of images with meta tags set as “cat” and “not cat”. Further on, the program uses this information to create a set for houses and build a predictive model. With the cat example, the program is not aware of the label “four legs” or “tail” and might predict that anything that has four legs should be a cat. It the program needs to do is look over the pattern pixels in the digital data. With each iteration, each model becomes more and more complex.

As accuracy is an important factor today, immense amounts of training data and processing power is necessary. Since deep learning programming can “[…] create complex statistical models directly from its own iterative output, it can create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive, because most of the data humans and machines create is unstructured and is not labeled.”

Example of deep learning can include language processing, language translations, medical diagnosis, stock market signals, etc.

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We are Mapendo. Curious and innovative. Mobile App Marketing is what we do and w. Artificial Intelligence is how we do it. || Bologna, IT || https://mapendo.co/

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