The Artificial Intelligence Primer


The Machine Intelligence Primer – Understanding AI and ML

Length: 1:35

We wrote the Machine Intelligence Primer to help executives and others understand machine intelligence and really separate the fact from fiction.

Video Transcript

The machine intelligence market hot right now. There's a huge amount of excitement and hype around the technology. What we have to remember is behind that there really are true technological advances that's propelling machine intelligence into some meaningful commercial applications. And organizations of all types are seeing these advances and beginning to invest heavily both to advance the technology as well as deploy it broadly across the organizations. Simultaneously, senior executives are getting inundated from outside their firm by technology vendors and others with software and solutions that have lofty claims about what they're capable of doing with machine intelligence. In some cases, these claims are fact, and in others, they've been exaggerated to a little or large extent, and it's left up to the senior executive to ferret out fact from fiction, which is becoming increasingly difficult. We wrote the Machine Intelligence Primer to help executives and others understand machine intelligence and really separate the fact from fiction. We are deeply passionate about machine intelligence technologies, and we really want people to understand what it is, how it works, and how it's going to affect how we work and live. At the same time, we're a bit pragmatic and realistic, and it's equally important to us that people understand the limitations of the technology. Machine intelligence is not a panacea that will solve every business problem. Although it can add significant value to solving those problems if used properly. Our hope is the Machine Intelligence Primer provides its understanding and empowers people to create the future in which machine intelligence is harnessed for the collective benefit of all of our lives and our society. 

What Is Machine Intelligence?

Length: 1:01

What is the difference between machine intelligence and machine learning? Machine learning is a technique used to achieve machine intelligence. Watch to learn more.

Video Transcript

Excitement about machine intelligence has grown a great deal in the past few years, and as excitement about the technology has grown, so have the number of definitions for what people call machine intelligence. There are a number of terms that people often confuse when they think about machine intelligence. One of the most common of these is confusing machine intelligence with machine learning. Machine learning is a technique used to achieve machine intelligence. It involves developing algorithms that take in data to interpret the world and learn new information. Another common point of confusion is understanding the difference between artificial intelligence and machine intelligence in general. These two terms are actually completely interchangeable. Some firms just prefer to use the term machine intelligence because it doesn't have as many negative connotations as artificial intelligence sometimes does. 

Developments in Machine Intelligence

Length: 1:17

Three trends help explain why machine intelligence has become so transformative and disruptive across such a wide range of sectors. Watch to learn more.

Video Transcript

Machine intelligence has existed as a field of study for more than 60 years, so it's been around for a very long time. But there have been a few key developments that have enabled machine intelligence to really ascend in the past few years. The first of these trends will be obvious to most everyone. It's the proliferation of digital data. As all of us are aware, data now is being produced by all of our devices all the time from our smartphones to our connected refrigerators and even toasters. There's now a huge amount of data on which to train the machine learning algorithms that make machine intelligence work. The second major development has been advances in computer processing power. This is really important because for machine learning algorithms to interpret all of that data requires a huge amount of processing power. The third piece that's really enabled machine intelligence to take off is advancements in research around machine learning. When taken together, these three trends help explain why machine intelligence has seemingly overnight become so transformative and disruptive across such a wide range of sectors. 

What Is Deep Learning?

Length: 0:49

Deep learning is a machine learning tool that allows computers to form their own perceptions of data, preventing possible human bias. Watch to learn more.

Video Transcript

One machine learning tool you may have heard of is called deep learning. Deep learning is inspired by how our brain works and it's allowed us to get much higher accuracies for certain types of machine learning problems than we were ever able to before. What's special about deep learning is that it allows the computer to form its own perceptions of the data. When a human looks at the data set to analyze it, even with machine learning algorithms, they typically add a little bit of their own personality to it. They add a little bit of their own analytical touch. This is interesting because it allows us to kind of let the computer, let the algorithm see the data in its own way and find things that a human may have never seen. 

Supervised vs. Unsupervised Machine Learning

Length: 0:48

What is the difference between supervised and unsupervised machine learning? Learn about these two categories of ML in our AI/ML Primer.

Video Transcript

Machine learning comes in two major categories. One is supervised learning; the other is unsupervised learning. Supervised learning is where you have examples of what you're trying to train the machine learning algorithm to find. Unsupervised learning is where you don't have any of that; you just have the data. It's interesting because you might find patterns that you never knew you were even looking for. Stuff in the data that was, as they say, unknown unknowns. But it's challenging because you can't tell whether or not the algorithm is doing a good job because there's no flash card to turn over and say, 'yeah, you got it right; you found what I was looking for.' 

How Do Machines Learn?

Length: 1:02

Traditionally, machines have not really learned at all. Machine learning, though, is not just a set of instructions; it's more than that. Watch to learn more.

Video Transcript

Traditionally, machines have not really learned at all. We've just used data and we've used very explicit instructions in computer code to have the machines and the programs and the computers execute very specific instructions, one after another. Machine learning, on the other hand, is not just an explicit set of instructions; it's more a set of algorithms, and these algorithms are special because we can teach them what we want them to look for. For example, if you wanted to find a certain animal and a set of pictures, you could show it examples of pictures with that animal in it, and they would eventually be able to learn a representation that it could use to identify what it thinks it might be, that animal and other pictures. And this is a problem that humans are very, very good at solving. But we have had a very hard time translating it to the computer world. 

What Machines Can and Can't Do

Length: 1:40

There are lots of problems that can be solved with machine intelligence, but there are some that don't fit so well into machine intelligence solutions. Watch to learn more.

Video Transcript

Machine intelligence, or MI, has captured the public's fascination within the past few years. There are lots of problems that can be solved with machine intelligence, but there are some that don't fit so well into machine intelligence solutions. Let's look at a variety of the different tasks that are well suited for machine intelligence. For instance, if an organization has rote repetitive tasks that take up a lot of human employees' time, techniques such as robotic process automation can be used to automate those tasks. Tasks such as data entry or completing forms can very easily be automated using these techniques. Also, if an organization's data is largely pattern-based and requires human judgment to determine outcomes for that data, there's a good chance that supervised machine learning can be used to automate that decision making process. While that's the good news, there's plenty that machines are still not capable of fundamentally. Although machines can understand patterns in the data, they don't have a true understanding in a human sense as to the meaning behind data. As a result, machine intelligence isn't great for tasks that require complex judgment or higher order thinking. One of the weaknesses of today's machine intelligence algorithms is that the decisions that they make can't be readily explained. Finally, machine intelligence algorithms are data hungry. If you have a problem that you're trying to solve, but insufficient data or insufficient understanding of the data required to solve that problem, you may want to look for other solutions or invest time in building a larger collection of data to shape your problem space.

Considerations for Using Machine Intelligence

Length: 1:53

What should organizations consider when first using machine intelligence? Watch to learn more.

Video Transcript

Organizations considering using machine intelligence should realize that it's really no different than evaluating any emerging technology. Executives and others should ask themselves: What are our goals? What are we hoping to accomplish? And define the success criteria upfront. Without that criteria and without that vision, you run the risk of disenfranchising executives, stakeholders, and users of this new technology should you run into a failure or a hiccup. Another consideration that organizations need to look at is understanding what the risk tolerance is. With machine intelligence, you're inherently handing over some level of decision making to machines. It's important to note that you can't always explain how a machine arrives at that decision. That uncertainty creates both inward and outward-facing risks that organizations need to understand and learn how to mitigate and alleviate those concerns. Also, at the beginning of any machine intelligence engagement, it's important to take note of what talent assets you have at your disposal. Because machine intelligence is such a nascent technology, the marketplace for this expertise is scarce. Equally important is understanding what data an organization has. Data is critical for machine learning, and machine learning is essential for any machine intelligence application. Organizations that find themselves lacking both talent and or data can lean on partnerships and vendor relationships to fill that gap. A final consideration is your organizational values. Machine intelligence has the potential to impact a large portion of society, but also your workforce, your clients, and potentially your shareholders, too. Understanding these impacts and making sure that you are responsibly deploying machine intelligence in your organization is critical, so is keeping consistent with both your personal and your organizational values. 

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