Understanding the buzzwords AI, ML and Deep Learning

With huge strides in AI, from advances in the driverless vehicle realm, to mastering games such as poker and Go, to automating customer service interactions. This advanced technology is poised to revolutionize businesses and will be the future. But the terms AI, machine learning, and deep learning are often used erratically and interchangeably, without knowing the differences between each type of technology.

  • Artificial intelligence is a science like mathematics or biology. It studies ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative.
  • Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are different algorithms (e.g. neural networks) that help to solve problems.
  • Deep learning, or deep neural learning, is a subset of machine learning, which uses the neural networks to analyze different factors with a structure that is similar to the human neural system.
Fig1

Artificial Intelligence (AI)

The term artificial intelligence was coined in 1956, at a computer science conference in Dartmouth. But AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defence took interest in this type of work and began training computers to mimic basic human reasoning. It is described as an attempt to model how the human brain works and, based on this knowledge, create more advanced devices.

Fig2 – Source: https://www.freepik.com/

Devices designed to act intelligently – are often classified into one of two fundamental groups – Applied or General.

Artificial General Intelligence is an emerging field aiming at the building of ‘thinking machines’; that is, general-purpose systems with intelligence comparable to that of the human mind (and perhaps ultimately well beyond human general intelligence. Systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category.

Artificial Applied Intelligence is commonly defined as an application of artificial intelligence to enable a high-functioning system that replicates and, perhaps, surpasses human intelligence for a dedicated purpose.  It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art.

Machine Learning

It is a subset of the larger field of artificial intelligence (AI) that focuses on teaching computers how to learn without the need to be programmed for specific tasks. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect. It is being used in medical diagnosis, traffic predictions, product recommendations etc.

Fig3 – Source: https://www.freepik.com/

There are three components that are required to educate the machine :

  1. Algorithm

Any problem can be solved differently. Depending on the algorithm, the accuracy or speed of getting the results can be different. There is one important nuance though: if the data is crappy, even the best algorithm won’t help.

2. Dataset

Machine learning systems are trained on special collections of samples called datasets. The samples can include numbers, images, texts or any other kind of data. It usually takes a lot of time and effort to create a good dataset.

3.Features

These are also known as parameters or variables. Features are important pieces of data that work as the key to the solution of the task. They demonstrate to the machine what to pay attention to. When data stored in tables it’s simple — features are column names.

Deep Learning

Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. It is also known as deep neural learning or deep neural network.

The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.

Fig4 – Source: https://www.freepik.com/

Deep learning models are trained by using large sets of labelled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.

It is a technique that teaches computers to do what comes naturally to humans: learn by example. It is the key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

References

  1. https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
  2. https://vas3k.com/blog/machine_learning/?fbclid=IwAR0NjjOJlZt4-KiaBGi11DskcBHAa2d6xaUchkPZdDch7pxS5sbcrZkUBJA

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