Artificial Intelligence

What is the difference between Deep Learning and Reinforcement Learning?

The world is changing, and that change is inevitable. The technological wave sweeping across the globe speaks to the possibilities in fields once considered unrealistic. There’s a strong artificial intelligence presence in the world as companies are embracing the use of AI for the automation of their systems to create efficiency. Artificial Intelligence is a rather broad field; machine learning is just a small portion of it. Under machine learning we have the;

  • Deep learning algorithm
  • Reinforcement learning algorithm

Several books about artificial intelligence are being published on an annual basis and which help provide a clear view of what the field is all about. In fact, today it’s become easy to find a deep learning platform assisting individuals to better their knowledge of these concepts. This article focuses on the two algorithms deep and reinforcement learning, what they are and their differences.

Deep Learning

Deep learning in simple terms can be described as making machines that think intelligently by using past data. That means creating devices that operate the same way as the human brain. In most cases, individuals learn and act based on past experiences; it could be their own or someone else’s. In recent years significant progress is being made in the artificial intelligence industry, thanks to the availability of data not previously available.

Artificial intelligence (AI) is a broad field which focuses on making intelligent machines. Machine learning is a subset of AI that uses various algorithms to harvest data useful towards making smart devices. The deep learning algorithm is one such example. Using the available data, machines can be capable of making precise predictions. Think of the human brain, specifically the cerebral cortex that has a network of neurons. That’s the basis of the deep learning algorithm. There’s a neural network responsible for the strong artificial intelligence in machines.

On the deep learning platform, the significance of this development is being realized in multiple fields. In the 80s, very little could be achieved with regards to machine learning since there wasn’t enough data among other reasons. That was because only a single layer of neural networks could be produced. Today, we are looking at a different scenario with corporations employing the use of this technology to increase efficiency in their businesses. Let’s have a look at 3 of the leading global tech companies developing artificial intelligence Apple, Google, and Facebook. Google, for instance, applied deep learning, and it has become possible for them to reduce power consumption by a considerable percentage saving on millions of dollars. Deep learning is also responsible for the Google translate transformation or upgrade if you may want.

Google isn’t the only tech company working round the clock on Artificial Intelligence. Facebook has used deep learning for picture recognition. The same way human beings are capable of recognizing faces in photographs positively, so can machines. It’s become possible thanks to deep learning. Probably one of the most significant achievements of this algorithm is the deep learning bioinformatics; others include vehicle automation, speech recognition, and game playing among others. The perfect example of deep learning that most of us can relate to is Apple’s Face ID.

Reinforcement Learning

Reinforcement learning is similar to Deep learning except that, in this case, machines learn through trial and error using data from their own experience. Imagine a machine learning how to cut an apple or watermelon, or trying to walk, so instead of using previously collected data of how the said activities can be done with ease the machine learns independently and will use the data to better the skills. For instance, the first time cutting the fruits it will probably miss severally and create a mess or if it’s walking it will fall multiple times before it can get it correctly.

The reinforcement learning algorithm is an independent, self-teaching system. To get the best outcomes, machines learn by doing, hence the learning by trial and error concept. The goal is to maximize rewards. Human beings, too, do display by some reinforcement learning behaviors. Remember your first time learning how to ride a bike, scooter or car; your moves were all over the place. However, over time with practice using data from previous attempts, we get to perfect our skills. Machines learn the same way, they will try completing an activity using several actions, and the outcome from each try determines the best way to fulfill the task. The machine reinforces the actions that worked through autonomous algorithm modification.

Several reinforcement learning projects are being implemented by companies developing artificial intelligence. A good example is when Google’s Deep Mind applied reinforcement learning to Atari games. In the Break Out game, for instance, the goal was to achieve the score. After several trials the computer using the algorithm can improve the skill to the extent of even beating the human players, this is one of the many examples of artificial intelligence in games.

What are the differences?

Artificial intelligence is a vast field, while machine learning is a section of it. Reinforcement and deep learning algorithms are both techniques used in machine learning to harvest data. It’s worth noting that both systems of algorithms learn autonomously. The difference between them is very small, and it comes from the techniques of learning. Deep learning as mentioned will involve the learning from data that already exists and then applying that knowledge to a new data set. Reinforcement learning, on the other hand, is dynamic learning, using trial and error to make informed decisions. That is through adjusting actions based on the feedback being continuously received from attempting actions.

The two aren’t as separate as you might think. There have been attempts at employing deep learning algorithms to a reinforcement learning system. That arrangement is called a deep reinforcement learning system.

Conclusion

The artificial intelligence industry is growing at an exponential rate. Today there’s a strong artificial intelligence presence across the globe, especially across the developed nations. It’s inevitable that business sooner or later will have to embrace AI; it creates efficiency and helps save billions of dollars that would have been lost otherwise. In the health care industry, deep learning bioinformatics is catching on and fast. We can’t go on being blind to the endless possibilities that can be achieved thanks to AI. The reality is there are many companies developing artificial intelligence, and it’s time we all join the bandwagon.

Should we embrace artificial intelligence? What are some of the possibilities do you see being realized thanks to AI? Please share some of your suggestions with us.

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