Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI—and companies from Amazon to Facebook to Google are scrambling to take the lead. According to AI expert Roman Yampolskiy, 2016 is the year of "AI on steroids."
While there are different forms of AI, machine learning represents today's most widely valued mechanism for reaching intelligence. Here's what it means.
- What it is: Machine learning is a subfield of artificial intelligence. Instead of relying on explicit programming, it is a system through which computers use a massive set of data and apply algorithms to "train" on—to teach themselves—and make predictions.
- Why it matters: Machine learning systems are able to quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks.
- Who it affects: Machine learning touches industries spanning from government to education to health care. It can be used by businesses focused on marketing, social media, customer service, driverless cars, and many more. It is now widely regarded as a core tool for decision-making.
- When it's happening: The term "artificial intelligence" was coined in the 1950s by Alan Turing. Machine learning became popular in the 1990s, and recently came into the public eye with Google's DeepMind beating the world champion of Go in 2016.
- How to take advantage of machine learning: Businesses like IBM, Amazon, Microsoft, Google, and others offer tools for machine learning. There are free platforms, as well.
What it is
Machine learning is a branch of AI. Other tools for reaching AI include rule-based engines, evolutionary algorithms, and Bayesian statistics. While many early AI programs, like IBM's Deep Blue, which defeated Garry Kasparov in chess in 1997, were rule-based and dependent on human programming, machine learning is a tool through which computers have the ability to teach themselves, and set their own rules. In 2016, Google's DeepMind, beat the world champion in Go by using machine learning—training itself on a large data set of expert moves.
Here are several kinds of machine learning:
In supervised learning, the "trainer" will present the computer with certain rules that connect an input (an object's feature, like "smooth," for example) with an output (the object itself, like a marble).
In unsupervised learning, the computer is given inputs and is left alone to discover patterns.
In reinforcement learning, a computer system receives input continuously (in the case of a driverless car receiving input about the road, for example) and constantly is improving.
A massive amount of data is required to train algorithms for machine learning. First, the "training data" must be labeled (for instance: a GPS location attached to a photo). Then it is "classified." This happens when features of the object in question are labeled and put into the system with a set of rules that lead to a prediction. For example, "red" and "round" are inputs into the system that leads to the output: Apple. Similarly, a learning algorithm could also be left alone to create its own rules that will apply when it is provided with a large set of the object—like a group of apples, and the machine figures out that they have properties like "round" and "red" in common.
Many cases of machine learning involve "deep learning," a subset of machine learning that uses algorithms that are layered, and form a network, to process information and reach predictions. What distinguishes deep learning is the fact that the system can learn on its own, without human training.
- IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next (TechRepublic)
- IBM Watson: Six lessons from an early adopter on how to do machine learning (TechRepublic)
- How one AI security system combines humans and machine learning to detect cyberthreats (TechRepublic)
- Google AI gets better at 'seeing' the world by learning what to focus on (TechRepublic)
Why it matters
Aside from the tremendous power machine learning has to beat humans at games like Jeopardy, chess, and Go, machine learning has many practical applications. Machine learning tools are used to translate messages on Facebook, spot faces from photos, and find locations around the globe that have certain geographic features. IBM Watson is used to help doctors make cancer treatment decisions. Driverless cars use machine learning to gather information from the environment. Machine learning is also central to fraud prevention. Unsupervised machine learning, combined with human experts, has been proven to be very accurate in detecting cybersecurity threats, for example.
While there are many potential benefits of AI, there are also concerns. Many worry that AI (like automation) will put human jobs at risk. And whether or not AI replaces humans at work, it will definitely shift the kinds of jobs that are necessary. Machine learning's requirement for labeled data, for example, has meant a huge need for humans to manually do the labeling.
On top of economic concerns, several tech leaders, like Elon Musk, Stephen Hawking, and Bill Gates, have expressed worries about how AI may be misused, and the importance of creating ethical AI. Evidenced by the disaster of Microsoft's racist chatbot, Tay, AI can go wrong if left unmonitored.
There are several institutions dedicated to exploring the impact of artificial intelligence. Here are a few (culled from our Twitter list of AI insiders):
- The Future of Life Institute brings together some of the greatest minds—from the co-founder of Skype to professors at Harvard and MIT—to explore some of the big questions about our future with machines. This Cambridge-based institute also has a stellar lineup on its scientific advisory board, from Nick Bostrom to Stephen Hawking to Morgan Freeman.
- The Future of Humanity Institute at Oxford is one of the premier sites for cutting-edge academic research. The Twitter feed is a wonderful place for content on the latest in AI, and the many retweets by the account are also useful in finding other Twitter users who are working on the latest in artificial intelligence.
- The Machine Intelligence Research Institute at Berkeley is an excellent resource for the latest academic work in artificial intelligence. MIRI exists, according to Twitter, not only to investigate AI, but also to "ensure that the creation of smarter-than-human intelligence has a positive impact."
- New research shows that Swarm AI makes more ethical decisions than individuals (TechRepublic)
- AI gone wrong: Cybersecurity director warns of 'malevolent AI' (TechRepublic)
- Russian facial recognition program beats Google, but big privacy questions linger (TechRepublic)
- 5 companies using IBM Watson to power their business (TechRepublic)
- AI stops identity fraud before it occurs (TechRepublic)
Who it affects
Just about any organization that wants to capitalize on its data to gain insights, improve relationships with customers, increase sales, or be competitive at a specific task will rely on machine learning. It has applications in government, business, education—virtually anyone who wants to make predictions, and has a large enough data set, can use machine learning to achieve their goals.
- Why AI is the 'agent of the economy': EmTechDIGITAL leaders show global impact of AI (TechRepublic)
- Google to take on Slack and Facebook with new AI-powered chat, says report (TechRepublic)
- Why Facebook wants to use AI to track your conversations online (TechRepublic)
- Workaholics: Let Google's machine learning tool solve your work-life balance problems (TechRepublic)
- Facebook embraces AI to get businesses and customers talking (TechRepublic)
When is it happening?
Machine learning was popular in the 1990s, and has seen a recent resurgence. Here are some timeline highlights:
- 2011: Google Brain was created, which was a deep neural network that could identify and categorize objects.
- 2014: Facebook's DeepFace algorithm was introduced, which could recognize people from a set of photos.
- 2015: Amazon launched its machine learning platform and Microsoft offered a Distributed Machine Learning Toolkit.
- 2016: Google's DeepMind program "AlphaGo" beat the world champion, Lee Sedol, at the complex game of Go.
Apple and Google have both added machine learning capabilities to their photo tools recently, as well—and we expect other major platforms to follow suit.
- Google tailors A.I.-powered search for enterprise (ZDNet)
- How Google's DeepMind beat the game of Go, which is even more complex than chess (TechRepublic)
- Microsoft Build: 5 big moves you need to know (TechRepublic)
- Google Cloud Platform signs up enterprise giants, how does it compare to AWS? (TechRepublic)
- IBM Watson takes on cybercrime with new cloud-based cybersecurity technology (TechRepublic)
How to take advantage of machine learning
There are many online resources for machine learning. To get an overview of how to create a machine learning system, a series of YouTube videos by Google Developer has come in handy for me. There are also classes on machine learning from Coursera and many other institutions.
And to integrate machine learning into your organization, you can use resources like Microsoft's Azure, Google Cloud Machine Learning, Amazon Machine Learning, IBM Watson, and free platforms like Scikit.
- Facebook's machine learning director shares tips for building a successful AI platform (TechRepublic)
- AI helpers aren't just for Facebook's Zuckerberg: Here's how to build your own (TechRepublic)
- IBM Watson: What are companies using it for? (ZDNet)
- How developers can take advantage of machine learning on Google Cloud Platform (TechRepublic)
- How to prepare your business to benefit from AI (TechRepublic)
- Executive's guide to AI in business (free ebook)
Hope Reese has nothing to disclose. She doesn't hold investments in the technology companies she covers.
Hope Reese is a Staff Writer for TechRepublic. She covers the intersection of technology and society, examining the people and ideas that transform how we live today.