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A Breakdown of AI, Machine Learning & Emerging Trends

Julia Shih 0


These days, buzz around Artificial Intelligence is everywhere. You hear about self-driving cars, futuristic personal assistants, and computers beating professional human players at board games. AI has the potential to radically improve our lives with early diagnostic tools and customized treatment applications in the medical industry. On the flip side, Elon Musk predicts that AI-driven technologies will displace 12-15% of the global workforce within 20 years. Stephen Hawking warns us that the rise of AI could be the best or worst thing to happen to humanity, depending on how it’s used.

Whatever the future holds, what we may not realize is that our lives already revolve around AI, much of which was developed using Machine Learning, a subfield of AI that trains machines to learn from data. From targeted ads on social media, to impulse buy recommendations from your favorite online retailers, even complex fraud detection systems that can distinguish unusual credit card purchases from fraudulent ones in near real time—all these are in some shape or form developed using Machine Learning to become life-integrated AI.

So What’s the Distinction between AI and Machine Learning?

AI is a branch of computer science geared towards building machines capable of human-like intelligence. Machine Learning is a subset of AI, involving the development of compute methods to enable a machine to learn in order to achieve that intelligence. As Arthur Samuel, the father of Machine Learning describes it, it’s giving machines “the ability to learn without being explicitly programmed.”

So think of AI as the intelligence within the machine or software, and Machine Learning as the underlying discipline that helps the artificial organism attain intelligence and common sense.

What about Deep Learning?

Going one step further, when you hear the term, Deep Learning, know that this is the most promising and cutting-edge subset of Machine Learning, involving the development of algorithms to build artificial neural networks that mimic the structure and function of the human brain.  This discipline was godfathered by Andrew Ng while at Google, where he famously taught his algorithm to recognize cats.


Deep Learning is particularly effective in the realm of image recognition. Promising applications include facial recognition for video surveillance, advanced screening technology for early cancer detection and prenatal care, weather forecasting, and financial modeling.

Deep Learning is so promising that NVIDIA, the maker of GPU cards that are integral to accelerated compute platforms for Deep Learning, has put its stake in the ground to be the world’s leading AI company. NVIDIA currently holds 90% of the market for the GPU technology powering Machine and Deep Learning, and Goldman Sachs believes NVIDIA’s total addressable market in AI and Deep Learning to be an estimated $5-$10 billion out of a $40 billion market.

3 Emerging Machine Learning Trends to Watch

So where are we today? Andrew Ng’s cat experiment was an example of Supervised Learning, which involves telling the machine what the answer is for a particular input (ie cat), then feeding it massive amounts of examples of that input as training. This is currently the most common technique for training neural networks.

Beyond Supervised Learning, there are three major emerging Machine Learning methods that have shown great promise for the development of AI.

Unsupervised Learning

While Supervised Learning requires a large pool of labeled training data, Unsupervised Learning involves large training pools of inputs with no labels. So rather than telling the system what the inputs are, it is left to the machine to figure out the structure and relationships between different inputs.

Two common approaches include cluster analysis, which looks for hidden patterns or groupings within data, and anomaly detection, which looks for outliers. Unsupervised Learning has proven particularly useful for data mining, with use cases that include fraud detection, medical image analysis, and marketing campaigns to identify trends and behaviors within demographics.


Reinforced Learning

Reinforced Learning begins with little data, but is trained based on reinforcement through rewards or penalties, similar to how children learn.

Reinforced Learning is based on three fundamental elements—States, Actions, and Rewards. A machine learns from applying an Action to a State with the goal being a transition to a new desirable State. If the resulting new State is desirable, the system receives a Reward. If the new State is not desirable, the system is penalized.

With Reinforced Learning, over time, the system learns to pick sequences of actions (ie policies) that work optimally to transition certain States to desirable States. Reinforced Learning has shown success in teaching machines to play games, as well as in advertising to influence consumer behavior. It’s credited as the branch of Machine Learning that teaches intuitive judgment.

Generative Adversarial Networks (GANs)

Training machines using labeled data (ie Supervised Learning) are called discriminative models. Generative models now hand the keys to the machines, once trained, to see what they can create.

So assume that a generative network has been trained to correctly recognize a set of inputs. For this example, let’s say, cats.


This is a Cat.

A generative AI program can now be created to generate cats. Kind of.


A GAN takes it one step further. It pits a generative network against an image-recognition network, both of which have been trained to recognize specific inputs. The generative network, aka the generator, produces fake images. The image recognition network, aka the discriminator, tries to correctly tell the fake images from the real ones. The discriminator then checks if the images were real or fake so that it can get better at distinguishing between the two, while also telling the generator how to tweak its output to make its images more real. Think of it like algorithm sparring that improves both partners, with one that gets better at spotting fakes, and the other that gets better at producing fakes.

So the results are things like realistic AI-generated images of space and volcanoes.


Or perhaps, one day, machines will be our society’s most admired artists.


Which leads us to the big question:

What will you do with AI and the emerging trends within Machine Learning?

We would love to hear from you!