What technologies are responsible for the recent success of modern data-driven artificial intelligence?
There are a variety of industry trends driving data-driven A.I forward, including The Internet of Things (IoT), Big Data, virtual reality, data science, and more. However, there are three key technologies at the core of all modern AI In order to understand the recent success of AI and its future, it’s critical that you have at least a basic understanding of the following three technologies.
Machine learning is a subfield of AI based on statistics. It involves machines learning how to solve a problem without being explicitly programmed to do so. Essentially, with machine learning, we use existing data and a training algorithm to learn a model of the data. Then we use this model to make predictions given new data.
Machine learning allows us to perform many AI tasks like classification, regression, clustering, and anomaly detection. However, traditional machine learning tools only work well with categorical and numerical data stored in tables. This means that we are limited in the complexity of the problems that we can solve.
Deep learning is a new type of machine learning that stacks multiple layers of ML models one on top of the other. This is most common with deep neural networks — a type of neural network with more than one hidden layer. Adding more hidden layers to the network allows us to solve more complex problems.
Beyond simple tables of data, deep learning allows us to make predictions with text, images, audio, video, and more. In addition, we can also use deep learning to generate synthetic output as well — including generating text, images, audio, and more. Essentially, deep learning allows us to solve much more complex problems than traditional machine learning.
Reinforcement learning is a special type of machine learning where we teach an agent how to interact with its environment using rewards. We feed the agent these reward signals with each step that it gets closer to its goal. This allows the machine to incrementally learn how to solve multi-step problems in a complex environment.
In recent years, we’ve combined reinforcement learning with deep learning to create some truly impressive results. We’ve taught machines how to play ATARI video games without any instructions what so ever. In addition, we’ve taught robots how to walk in simulated environments — once again, without any explicit programming.
These three technologies give machines the ability to detect patterns, make decisions, and predict outcomes just like humans do. Over the next few decades, you can expect data-driven AI to radically transform industries across the globe. However, it does have its limitations — so it’s important to know both the pros and cons of each of these three technologies.
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