Triangular cross relationship between AI, Machine Learning and Deep Learning

There has been noticed the prevalent use of the terms AI, Machine Learning and Deep Learning embedded almost very fields now from innovations in the driverless vehicle to games such as poker, automating customer service using Chatbots, anomaly detection of next unavoidable breach in cyber security and opening doors to understanding the large-scale incredibly complex data set that makes up the human genome. There are fundamentally key differences between each type of technology one should understand and know to get familiarize with their use cases and application.

Artificial Intelligence (AI)

In the early era of electronic computers (1940–1950s), the machines were relatively simple, small and slow (compared with today). They were programmed (given recipe-like instructions) to do simple but boring but straightforward arithmetic tasks.

The phrase artificial intelligence was coined in 1956 by John McCarthy, who organized an academic conference at Dartmouth dedicated to the topic. AI is a subfield of computer science, which was created in the 1960s, and it applies on solving tasks that are easy for humans, but hard for computers.

Strong AI would be a system that can do anything a human can (perhaps without purely physical things) that includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.

The graphical representation on AI is put well together on Nvidia’s blog to understand it.

More companies are investing in AI projects, and we do interact with AI software every day through smartphones, social media, Web search engines or e-commerce sites. And one of the types of AI that we interact with most often is machine learning.

Machine Learning (ML)

The phrase “machine learning” also dates back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.

Machine learning is one subfield of AI. The core principle here is that machines take data and “learn” for themselves. 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. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.

Types

1. Deep Blue

Deep Blue was rule-based, dependent on programming—so it was not a form of ML

1. DeepMind

DeepMind, on the other hand, is: It beat the world champion in Go by training itself on a large data set of expert moves.

“Machine learning can happen in 3 ways. Again taking an analogy, a different one. Imagine a kid. He is nothing but an AI.

• You describe the shape of an apple to a kid and ask him to draw. It may take several takes for him to get the closer looking figure. This is Reinforcement learning.
• You show a picture of an apple to a kid and ask him to identify that as Apple. This is Supervised learning.
• You kid learns chilies are not to be eaten by actually biting on a chilly. It self-learns this information. This is Unsupervised learning”.

Companies like Facebook, Amazon, and Netflix use machine learning to power their recommendation engines. For example, Facebook enables newsfeeds, Amazon highlights products you might want to purchase and Netflix suggests movies you might want to watch, all of those recommendations are based predictions that arise from patterns in their existing data.

In fact, machine learning has become so associated with statistics, data mining and predictive analytics that some people argue it should be classified as a separate field of artificial intelligence.

Deep Learning (DL)

Deep learning is a subset of ML and uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For instance, a deep learning algorithm could be instructed to “learn” what a cat looks like. It would take a very massive data set of images for it to understand the very minor details that distinguish a cat from, say, a cheetah or a panther or a fox.

Use cases:

1) Deep learning and AI disrupting healthcare industry

Using artificial intelligence (AI), deep learning algorithms, and complex data sets disrupting the entire healthcare industry in diagnostics to gene therapies to personalized medicine. Deep Genomics holds the key to unlocking the biggest disruptions in the medical, life sciences, and pharmaceutical industries.

Startups are raising billions of dollars in venture capital. Grail raised $900M in a series B round for their “high-intensity sequencing approach” in a market estimated to reach $45B by 2024.

The companies such as Fabric Genomics, Guardant Health and Ion Torrent Business are prevailing with this emerging technology’s ecosystem.

2) Deep Learning enabling Intelligence from Big Data

Deep learning has a direct linkage with data from two sources one being recorded from the physical world (image, sound) and another from the data human produce (words, meta data, tagging data).Operators of a deep learning algorithm feed data to the algorithm and then train the algorithm to evaluate the data effectively. Deep learning processes have recently disrupted robotics, manufacturing, agriculture, and a plethora of other industries.

The Ph.D.’s and brain scientists at Clarifai applies award-winning speed to image recognition and tagging, Skymind will analyze millions of news articles for you to determine if someone’s talking about your company with emotional bias, AlchemyAPI will help you develop apps to understand customer behavior, Nirvana systems develops hardware specifically for deep learning algorithms, and Ersatz Labs provides web based access to deep learning modules so you can run your own experiments.

With all those disruptions, which technology might be powerful over the long run: AI, ML or DL?

References:

[1] https://en.wikipedia.org/wiki/Artificial_intelligence

[2] http://www.datamation.com/applications/artificial-intelligence-software-45-ai-projects-to-watch-1.html

[3] https://www.vlab.org/

[4] https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

[5] https://en.wikipedia.org/wiki/Arthur_Samuel