From a while now, Artificial Intelligence and deep learning have been synonymous. We all know how AlphaGo is considered to be one of the biggest examples of deep learning. At the same time, we have also seen that deep learning machines are capable of naming the colors of paint (although this wasn't very successful) and imitating some famous painters. Along with that, there are many other applications also.
Deep learning is highly useful because it can be used to achieve great results, only through a small set of techniques. In other words, it allows us to achieve things without being an expert in Artificial Intelligence.
With that, deep learning is quite simple and there are only some basic techniques that which a person needs to know. As a result of this, it is relatively easy to ‘democratize' deep learning.
However, it is important to know that applications such as AlphaGo and other top tiers have not purely been deep learning applications. Rather, these systems were hybrid in nature. For instance, Alpha Go made use of two deep neural networks along with Monte Calo Tree Search.
Hence, many artificial intelligence systems combine deep learning and other ideas/methods. Similarly, the poker playing system by DeepStack makes use of a heuristic search along with a counterfactual regret minimization and neural networks.
The prime idea behind the use of deep learning is rather simple. For instance, deep learning involves systems that contain neural networks, with a number of hidden layers.
Every neuron itself is quite simple to understand. All it does is take some inputs from the preceding layers and combines them based on the weights of the input. Then, it creates an output that is passed on to the following layer. The network at the same time does not really care whether texts or images are being processed. Hence, we may be missing out on the structure. This is because texts and images do not tend to be the same, because of a different structuring of data.
It is important to know that Artificial Intelligence goes beyond just Deep Learning and Machine Learning. For instance, a full Artificial Intelligence application, for autonomous vehicles will require a lot more than just data analysis. For such applications, there will be a lot of progress required in a number of key areas. Significant advances in hardware and sensors will be a prime need in this case. At the same time, edge devices and their software will be a big concern. Moreover, the infrastructure for distributed computation and simulation will also be required. Lastly, an understanding of crafting a good user experience for intelligence devices will be needed.
Nowadays, researchers in a number of different organizations are working on creating tools for the Artificial Intelligence applications of tomorrow. There is still a lot of work to be done in areas including deep learning, and machine learning. However, both of these areas along with others are expanding rapidly, due to research and heavy funds being allocated for their development.
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