Machine learning: A cheat sheet

Artificial intelligence (AI), 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 was the year of “AI on steroids,” and its explosive growth hasn’t stopped.

While there are different forms of AI, machine learning (ML) represents today’s most widely valued mechanism for reaching intelligence. Here’s what it means.

SEE: Managing AI and ML in the enterprise (ZDNet special report) | Download the report as a PDF (TechRepublic)

Executive summary

• What is machine learning? 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.
• When did machine learning become popular? The term “artificial intelligence” was coined in the 1950s by Alan Turing. Machine learning became popular in the 1990s, and returned to the public eye when Google’s DeepMind beat the world champion of Go in 2016. Since then, ML applications and machine learning’s popularity have only increased.
• Why does machine learning matter? 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.
• Which industries use machine learning? Machine learning touches industries spanning from government to education to healthcare. 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.
• How do businesses use machine learning? Business applications of machine learning are numerous, but all boil down to one type of use: Processing, sorting, and finding patterns in huge amounts of data that would be impractical for humans to make sense of.
• What are the security and ethical concerns about machine learning? AI has already been trained to bypass advanced antimalware software, and it has the potential to be a huge security risk in the future. Ethical concerns also abound, especially in relation to the loss of jobs and the practicality of allowing machines to make moral decisions like those that would be necessary in self-driving vehicles.
• What machine learning tools are available? Businesses like IBM, Amazon, Microsoft, Google, and others offer tools for machine learning. There are free platforms as well.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

What is machine learning?

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.

There 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 (e.g., 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 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.

SEE: What is machine learning? Everything you need to know (ZDNet)

Many cases of machine learning involve “deep learning,” a subset of ML 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.

Additional resources

When did machine learning become popular?

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. The algorithm 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.
• 2017: Google announced that its machine learning tools can recognize objects in photos and understand speech better than humans.
• 2018: Alphabet subsidiary Waymo launched the ML-powered self-driving ride hailing service in Phoenix, AZ.
• 2020: Machine learning algorithms are brought into play against the COVID-19 pandemic, helping to speed vaccine research and improve the ability to track the virus’ spread.

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Why does machine learning matter?

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.

SEE: All of TechRepublic’s cheat sheets and smart person’s guides

While there are many potential benefits of AI, there are also concerns about its usage. 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.

As machine learning and AI in the workplace have evolved, many of its applications have centered on assisting workers rather than replacing them outright. This was especially true during the COVID-19 pandemic, which forced many companies to send large portions of their workforce home to work remotely, leading to AI bots and machine learning supplementing humans to take care of mundane tasks.

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 Elon Musk to Morgan Freeman.
• The Future of Humanity Institute at Oxford is one of the premier sites for cutting-edge academic research. The FHI 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.”

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Which industries use machine learning?

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.

SEE: Sensor’d enterprise: IoT, ML, and big data (ZDNet special report) | Download the report as a PDF (TechRepublic)

Along with analytics, machine learning can be used to supplement human workers by taking on mundane tasks and freeing them to do more meaningful, innovative, and productive work. Like with analytics, and business that has employees dealing with repetitive, high-volume tasks can benefit from machine learning.

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How do businesses use machine learning?

2017 was a huge year for growth in the capabilities of machine learning, and 2018 set the stage for explosive growth that, by early 2020, found that 85% of businesses were using some form of AI in their deployed applications. 

One of the things that may be holding that growth back, Deloitte said, is confusion–just what is machine learning capable of doing for businesses?

There are numerous examples of how businesses are leveraging machine learning, and all of it breaks down to the same basic thing: Processing massive amounts of data to draw conclusions much faster than a team of data scientists ever could.

Some examples of business uses of machine learning include:

• Alphabet-owned security firm Chronicle is using machine learning to identify cyberthreats and minimize the damage they can cause.
• Airbus Defense & Space is using ML-based image recognition technology to decrease the error rate of cloud recognition in satellite images.
• Global Fishing Watch is fighting overfishing by monitoring the GPS coordinates of fishing vessels, which has enabled them to monitor the whole ocean at once.
• Insurance firm AXA raised accident prediction accuracy by 78% by using machine learning to build accurate driver risk profiles.
• Japanese food safety company Kewpie has automated detection of defective potato cubes so that workers don’t have to spend hours watching for them.
• Yelp uses deep learning to classify photos people take of businesses by certain tags.
• MIT’s OptiVax can develop and test peptide vaccines for COVID-19 and other diseases in a completely virtual environment with variables including geographic coverage, population data, and more.

SEE: Executive’s guide to AI in business (free ebook) (TechRepublic)

Any business that deals with big data analysis can use machine learning technology to speed up the process and put humans to better use, and the particulars can vary greatly from industry to industry.

AI applications don’t come first–they’re tools used to solve business problems, and should be seen as such. Finding the proper application for machine learning technology involves asking the right questions, or being faced with a massive wall of data that would be impossible for a human to process.

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What are the security and ethical concerns about machine learning?

There are a number of concerns about using machine learning and AI, including the security of cloud-hosted data and the ethical considerations of self-driving cars.

From a security perspective, there are always concerns about the theft of large amounts of data, but security fears go beyond how to lock down data repositories.

Security professionals are nearly universally concerned about the potential of AI to bypass antimalware software and other security measures, and they’re right to be worried: Artificial intelligence software has been developed that can modify malware to bypass AI-powered antimalware platforms.

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.

SEE: Machine learning as a service: Can privacy be taught? (ZDNet)

Ethical concerns abound in the machine learning world as well; one example is a self-driving vehicle adaptation of the trolley problem thought experiment. In short, when a self-driving vehicle is presented with a choice between killing its occupants or a pedestrian, which is the right choice to make? There’s no clear answer with philosophical problems like this one–no matter how the machine is programmed, it has to make a moral judgement about the value of human lives.

Deep fake videos, which realistically replace one person’s face and/or voice with someone else’s based on photos and other recordings, have the potential to upset elections, insert unwilling people into pornography, and otherwise insert individuals into situtations they aren’t okay with. The far-reaching effects of this machine learning-powered tool could be devastating.

Along with whether giving learning machines the ability to make moral decisions is correct, or whether access to certain ML tools is socially dangerous, there are issues of the other major human cost likely to come with machine learning: Job loss.

If the AI revolution is truly the next major shift in the world, there are a lot of jobs that will cease to exist, and it isn’t necessarily the ones you’d think. While many low-skilled jobs are definitely at risk of being eliminated, so are jobs that require a high degree of training but are based on simple concepts like pattern recognition.

Radiologists, pathologists, oncologists, and other similar professions are all based on finding and diagnosing irregularities, something that machine learning is particularly suited to do.

There’s also the ethical concern of barrier to entry–while machine learning software itself isn’t expensive, only the largest enterprises in the world have the vast stores of data necessary to properly train learning machines to provide reliable results.

As time goes on, some experts predict that it’s going to become more difficult for smaller firms to make an impact, making machine learning primarily a game for the largest, wealthiest companies.

Additional resources

What machine learning tools are available?

There are many online resources about machine learning. To get an overview of how to create a machine learning system, check out this series of YouTube videos by Google Developer. 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.

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Editor’s note: This article was updated by Brandon Vigliarolo.