Deep Learning vs. Machine Learning: Explained

Well, you clicked this, so obviously you’re interested in some of the finer nuances of artificial intelligence. Little wonder; it’s popping up everywhere, taking on applications as far ranging as trying to catch asymptomatic COVID infections via cough, creating maps of wildfires faster, and beating up on esports pros.

It also listens when you ask Alexa or summon Siri, and unlocks your phone with a glance.

But artificial intelligence is an umbrella term, and when we start moving down the specificity chain, things can get confusing — especially when the names are so similar, e.g. deep learning vs. machine learning.

Deep Learning vs. Machine Learning

Let’s make that distinction between deep learning vs machine learning; they’re pretty closely related. Machine learning is the broader category here, so let’s define that first.

Machine learning is a field of AI wherein the program “learns” via data. It existed on paper in the 1950s and in rudimentary forms by the 1990s, but only recently has the computing power it needs to really shine been available.

That learning data could come from a large set labeled by humans — called a ground truth — or it can be generated by the AI itself.

For example, to train a machine learning algorithm to know what’s a cat — you knew the cat was coming — or not you could feed it an immense collection of images, labeled by humans as cats, to act as the ground truth. By churning through it all, the AI learns what makes something a cat and something not, and can then identify it.

The key difference for deep learning vs machine learning is that deep learning is a specific form of machine learning powered by what are called neural nets.

As their name suggests, neural nets are inspired by the human brain. Between your ears, neurons work in concert; a deep learning algorithm does essentially the same thing. It uses multiple layers of neural networks to process the information, delivering, from deep within this complicated system, the output we ask it to.

Take the computer program AlphaGo. By playing the strategy board game Go against itself countless times, AlphaGo developed its own unique playing style. Its technique was so unsettling and alien that during a game against Lee Sedol, the best Go player in the world, it made a move so discombobulating that Sedol had to leave the room. When he returned, he took another 15 minutes to think of his next step.

He has since announced his retirement. “Even if I become the number one, there is an entity that cannot be defeated,” Sedol told Yonhap News Agency.

Notice how Sedol called AlphaGo an “entity?” That’s because it didn’t play like a run-of-the-mill Go program, or even a typical AI. It made itself into something … else.

Deep learning systems like AlphaGo are, well, deep. And complex. They create programs we really do call entities because they take on a “thinking” pattern that is so complex that we don’t know how they arrive at their output. In fact, deep learning is often referred to as a “black box.”.

The Black Box Problem

Since deep learning neural nets are so complex, they can actually become too complex to comprehend; we know what we put into the AI, we know what it gave us, but in-between, we don’t know how it arrived at that output — that’s the black box.

This may not seem too concerning when the AI in question is recognizing your face to open your iPhone, but the stakes are considerably higher when it’s recognizing your face for the police. Or when it is trying to determine a medical diagnosis. Or when it is keeping autonomous vehicles safely on the road. While not necessarily dangerous, black boxes pose a problem in that we don’t know how the entities are arriving at their decision — and if the medical diagnosis is wrong or the autonomous vehicle goes off the road, we may not know exactly why.

Deep learning uses multiple layers of neural networks to process the information, delivering, from deep within this complicated system, the output we ask it to.

Does this mean we shouldn’t use black boxes? Not necessarily. Deep learning experts are divided on how to handle the black box.

Some researchers, like Auburn University computer scientist Anh Nguyen, want to crack open these boxes and figure out what makes deep learning tick. Meanwhile, Duke University computer scientist Cynthia Rudin thinks we should focus on building AI that doesn’t have a black box problem in the first place, like more traditional algorithms. Still other computer scientists, like the University of Toronto’s Geoff Hinton and Facebook’s Yann LeCun, think we shouldn’t be worried about black boxes at all. Humans, after all, are black boxes as well.

It’s a problem we’ll have to wrestle with, because it can’t really be avoided; more complex problems require more complex neural nets, which means more black boxes. In deep learning vs machine learning, the former’s going to wipe the floor with the later when problems get tough — and it uses that black box to do so.

As Nguyen told me, there’s no free lunch when it comes to AI.

Published at Fri, 18 Dec 2020 23:10:16 +0000

Why the shift to mastery-based learning has become critical in high-stakes industries

As we enter into a new year and a transformed workplace, it is more urgent than ever for organizations to make an impact and provide professionals with the right learning technology. With the effects of the COVID-19 pandemic disrupting the structure of workforces around the world, enterprises have been forced to reckon with not having tools as readily available that are needed to support onboarding, training and skill development for their distributed groups of associates. For high-stakes industries – such as aerospace, energy, health care, manufacturing, law, IT, supply chain and life sciences – the shift to mastery-based learning has become that much more crucial.

Mastery-based learning is designed to ensure professionals are not just passing through the content, but fully comprehending and mastering the material. It often involves levels of capabilities and a curriculum that starts with basic proficiency and goes all the way up to expert classifications. This becomes especially important in high-stakes industries where professionals’ knowledge of their job-critical skills must be verified. Mastery-based learning supports this verification process, which can include passing a knowledge exam or completing hands-on exercises.

This type of approach is more of an investment of time and energy, but the payoff is substantial. Not only does mastery learning improve the quality of work, but in these high-stakes industries, such as health care and manufacturing, it protects the professional from making uninformed decisions that can lead to malpractice suits, heavy fines or even loss of life. This is the reason that this type of educational approach is being adopted by high-stakes industries first.

Why it’s worth the investment

While it has always been important to have qualified training programs for associates, the search now begins for a better overall learning experience. Driven by the pursuit of mastery, the focus in high-stakes industries today is on acquiring professional skills through a more engaging, comprehensive, and consistent learning experience. This can be accomplished through microlearning; instructional content is broken down into easily digestible modules that can accompany a new service product.

By continuously providing professional learners with educational content, the learning experience moves beyond random engagement and toward growth and the accumulation of expertise.

Content and AI meet for a personal learning experience

Content is once again “king” when it comes to courseware, and users are demanding better access. Since this is a must-have feature for enterprises, they are partnering for, acquiring or refreshing their learning content. This has also led to providers managing content from a variety of sources, like video.

However, to reach the ultimate goal of mastery, accessible content is just the starting point. A training program must also drive results through engaging, learner-centric features on the cutting edge of technology. Organizations are at the beginning of the predictive learning era and will start to leverage artificial intelligence to make learning more personal. By adding machine learning to a learning platform, informed training recommendations can be made, locating the appropriate video content down to a specific frame. This type of personalized delivery can streamline the learning process, as well as inform management when the content has not yet been viewed or skipped.

Video learning as a catalyst for knowledge retention

Video learning has been on the rise over the past few years, becoming the go-to learning method for online. Video tutorials help people learn things quickly and efficiently, as it enables microlearning. Microlearning encourages learning engagement by allowing professionals to enhance their skill development throughout the day. Videos can be paused and replayed at the learner’s leisure, ensuring complete comprehension and mastery of the topic before moving forward.

Since hands-on, in-person training has been halted or drastically modified out of caution during the current pandemic, enterprises should be ensuring their learning platform supports video content.

Moving to mastery-based learning in 2021

As an enterprise in a high-stakes industry, the first step to adopting a mastery-based learning strategy in 2021 is to determine the key positions that require critical skills. By understanding what roles are the most vital, the development of a modern curriculum can begin around the skills and levels of mastery required for that job.

It is also important to note that program development should include the needs of certification identification. From there, the courseware development will begin with the selection of a provider and a pilot test, followed by program alterations. The project should have a curriculum development plan for each role that includes what is required for each different level of mastery. After all of this has been completed, the full program launch of mastery-based learning is ready to go.

Following the launch, the team needs to monitor and evaluate how learning is progressing. This is most critical in the highest levels of certification. It is also important to note that time spent in a role may be an important criterion to some levels of mastery and often is a requirement for advancement.

For high-stakes industries, a professional’s industry knowledge and job-critical skills have a direct positive impact on the long-term success of the enterprise. Mastery-based learning delivers a new approach to professional development that enables a higher-skilled workforce.

The continuous delivery of knowledge, along with skill-based certifications, will help to power enterprises, associations and professionals themselves. A more engaged workforce delivers the desired business outcomes. Today’s virtual landscape allows for businesses to embrace continuous learning models and this on-demand training will help learners master the content.

Published at Fri, 18 Dec 2020 21:45:00 +0000