Artificial Intelligence — Human Intelligence Exhibited by Machines
Machine Learning — An Approach to Achieve Artificial Intelligence
Deep Learning — A Technique for Implementing Machine Learning
What Is Artificial Intelligence?
Artificial intelligence is simply a system’s ability to correctly interpret data, to learn from it, and to use those learnings to achieve specific goals and complete tasks through adaptation.
In general terms, AI is great at automating the routine and repetitive. In other words, it’s great at optimizing . Here’s a familiar example: Amazon Prime used to be powered by people whose jobs revolved around getting your product from their warehouse to your doorstep. That process is a predictable algorithm that does not change from one day to the next. Because of that, the repetitive and boring job in the warehouse could be optimized and handed over to robots. Knowing this, Amazon built distribution centers to enable same-day delivery closer to our homes, and put robots inside of them.
What AI is not great at is creating and thinking outside the box. Things that fall outside of this core competency are: creativity, imagination, holistic viewing of something, arts, motor skills, rhythm, non-verbal communication and cues, musicality, feelings, visualization, and empathy. When the robot carrying the yellow bin filled with products you bought gets stuck, it doesn’t know how to solve that problem. This requires creativity and a holistic approach—a job meant for people. Workers in new Amazon distribution centers now help robots do their jobs. Contrary to the popular media narrative of robots stealing human jobs, Amazon hired 235,000 people to help its robots perform their duties.
What Is Machine Learning?
How are Amazon’s robots able to do what they do? They use machine learning. An algorithm that is coded by hand (like the Apollo 11 guidance system) is a zero-learning algorithm. When it receives inputs, it always responds in the exact same way, learning nothing.
Machine learning is a system of algorithms that receives inputs, produces outputs, then checks the outputs and adjusts the system’s original algorithms to produce even better outputs.
One type of a machine learning algorithm is anomaly detection , which looks for events that vary significantly from the majority of data. Anomaly detection is employed by Stripe’s payment processing service to detect fraud, which is (thankfully) an anomalous event.
An organization called Crisis Text Line uses machine learning to figure out which words, when typed in a text message, are the most likely to predict suicide. To isolate words, it employs a machine learning technique called entity extraction . Then it uses natural language processing and sentiment analysis to figure out that the word “ibuprofen” is 14 times more likely to predict suicide than the actual word “suicide,” and that the crying face emoji is 11 times more likely to predict that the person is in crisis.
The more complicated the problem we attempt to solve with machine learning, the more sophisticated the algorithms become. Consider computer vision and the effort to identify a red ball. The ball is a simple shape that’s easy to recognize and label correctly. However, if the ball is placed next to a mirror, it stops looking like a ball. If we dim the light and put a small plant in front of the ball, obscuring its features, recognizing the shape becomes very difficult.
The solution to this problem is a technique called representation learning , which just means that we break the red ball into its component features— things that represent a ball that is red . In essence, representation learning extracts high-level abstract features of the red ball—it can be curvature, relative size, and color. But just as the small plant, the mirror, and the dimmed light make recognizing and labeling the ball difficult, the real world can obscure component features and pieces of data that our machine learning algorithms are able to observe.
When extracting high-level abstract features is difficult, another type of machine learning has to be used: deep learning.
What Is Deep Learning?
Machine learning checks the outputs of its algorithms and adjusts the underlying algorithms to get better at solving problems. Deep learning links (or layers) machine learning algorithms in such a way that the outputs of one algorithm are received as inputs by another. Let’s go back to our red ball to talk about a deep learning algorithm, or a deep learning network.
The first layer of the network is tasked with only looking for, recognizing, and labeling dots. When it finds a dot and labels it correctly, it tells the next layer, “Hey, I found a dot! Here it is.” That second layer’s job is to look for dots that are close together. It receives the news from the first layer, and in turn says, “That’s awesome, I found two dots that are close together!” The third layer is responsible for looking for dots that are close together and look like a curve. It receives an input from layer two, which in turn got its news from layer one, and says, “Hey, I think I see a curve!”
These layers work together to first find dots, then dots that are close together, and finally dots that form a curve. On top of them, hundreds of layers are tasked with seeing dots that turn into lines, lines into curves, and curves into shapes that resemble our red ball. This is deep learning: machine learning algorithms that use a nested hierarchy of simple concepts to represent more abstract and complex concepts.
A company called Dialpad uses deep learning loaded with natural language processing and entity extraction to automatically transcribe calls. It then uses sentiment analysis—a deep learning technique—to discern whether the sentiment of the conversation is positive or negative, in real time. This gives people using Dialpad an opportunity to respond to negative sentiments with more empathy and data.
What is data science?
Data science has an intersection with artificial intelligence but is not a subset of artificial intelligence.
Data science is the study of an aroused curiosity in any given field, the extraction of data from a large source of data related to the question in mind, processing data, analysing and visualising this data, so as to make meaning out of it for IT and business strategies.
In simple terms, it is understanding and making sense of data. A lot of tools are used in data science. They include statistical tools, probabilistic tools, linear and metric algebra, numerical optimisation and programming.
An application of data science :
Pick a random concept.
I choose sponsorship. How do people get sponsorship for a cause. Who is usually willing to respond to an email calling for sponsors. What keywords do they look out for in emails requesting for sponsorship? would they prefer a phone call?
In this case, data science can help. A pool of data related to everyone who has ever sponsored a cause, why they sponsored it, their preferences in terms of communication channels etc is pulled up a large set of unstructured data .
The data is processed, analysed and visualised using the various tools we already talked about. Conclusions are made from this data.
This information can help non-profits and people pursing a cause to look out for sponsors.
Data Science is not fully Artificial intelligence, however portions of Data science intersect with Artificial intelligence.
When it comes right down to it, one thing is common to these buzz words — DATA !