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Artificial intelligence is now an integral part of our daily life. We come across things that depend on artificial intelligence every day from sci-fi films to technological advancements. Of course, there are questions about whether artificial intelligence will be beneficial for our life or not. Will the integration of AI result in mass unemployment? Will we use automated cars in the near future? Will our lives be led by artificial robots? Although there are some who are optimistic about the emergence of artificial intelligence, pessimistic opinions still outweigh the positive ones for now. Many people remain pessimistic about the future of humanity with the existence of artificial intelligence. There is a possibility that AI can be detrimental to our economy. But choosing to emphasize bad possibilities is harmful as artificial intelligence has the potential to change our world for the better. It can change our approach to work and offer healthcare benefits. You will discover a holistic approach to AI in this summary, touching upon both the good and the bad. With the approaches of 23 different AI experts interviewed in this book, you will have a detailed idea about the potential of artificial intelligence. Chapter 1 - Artificial intelligence uses various deep learning methods to finish its tasks. Let’s go back to your childhood. Can you recall the moment when you saw a cat or a photo of it for the first time? How long did it take you to register what a cat was? Usually, people become able to tell the difference between a cat and another animal after seeing one or two cats. Fundamentally, just a few examples are enough for people to learn what a certain object is. Yet, the situation is vastly different for artificial intelligence. For AI to register the difference between a cat and another animal, it must use various methods to learn. A type of machine learning called deep learning is influential in the development of artificial intelligence. AI starts its training to understand and differentiate objects with a neural network. A neural network is a software that consists of various layers of neurons, which imitate the practices of the human brain. Scientists resort to various methods to train neural networks. One of the commonly used methods is a kind of deep learning where the various examples are introduced to AI. Each of these examples consists of a description. When the AI is trained with enough information, a photo of a cat is shown to it. The pixels in the photo are identified by the neural network and then the machine becomes able to confirm that the photo in front of it is a cat. Despite being able to identify correctly, the AI is still not aware enough to understand what the word “cat” means. It knows what a cat looks like, but it has no idea whether the cat is a living being or not. To give AI the ability to understand the essence of objects, it should undergo a practice called grounded language learning. This method is a deep learning strategy in which real-life images, videos, or objects are linked to sentences or words. Such methods help deep learning gain different sorts of potential applications. For instance, grounded language learning contributes to AI’s language skills. AI that has developed sufficient language skills, in turn, gives opportunities for applications like Siri to emerge. In addition, scientists have resorted to deep learning to train AI to play games. One of the well-known examples is the AI AlphaGo, which was trained by exposure to various Go games. The AI soon became competent enough to beat the human champion of Go. Chapter 2 - Deep learning has its limits. AI is often acclaimed for being able to beat humans at games such as chess, Go, or shogi. But while we are impressed by these kinds of success stories, AI is still yet to develop a general intelligence. For the time being, AI is only competent at tasks that are really specific. Let’s explore the case of AlphaZero. It used deep learning to observe two-player games like chess and Go. And while it was quite good at such games, AlphaZero would be totally useless when it came to different kinds of games, like poker. Different from chess or Go, poker is not fully observable. It consists of partial information, meaning that it is impossible to see what both parties have in their hands while playing. So, if we were to train AI to play poker, we would need algorithms that would be able to guess the movements of the other party. AlphaZero is not programmed in a way to be able to do that. It is conditioned to assume that what is observable is the only information that is available. For now, AI is unable to finish general tasks. It needs thorough training for one specific task to be able to complete it. There is also another big issue with deep learning and neural networks. It is a well-known fact that humans are not objective beings. Even if we try to get rid of our biases, we are unable to do that completely. That is why the data with which we teach deep learning and neural networks are flawed. For example, in the case of policing, since some districts are patrolled more in comparison to others, we end up with more data about those districts. Considering this fact, if we trained AI to guess the possibility of crimes based on this flawed data, we would end up with biased information about which districts are more likely to be at risk. The fact that deep learning strategies are limited means that they do not provide sufficient opportunities for scientists to develop the AI further to use Artificial General Intelligence, also known as AGI. A machine with AGI would need to use common sense to assess new situations and make decisions based on its evaluation of the situation. In other words, it should be able to form opinions even though it has never experienced the said situation before. Still, scientists are adamant about finding new methods to enhance the interpretation skill of artificial intelligence. And there are some methods to help machines develop common sense. The first one is to give enough information to AI’s brain while following logical rules. This method is not quite effective as it means compiling limitless information and giving it to AI. Another method is to simply home that AI will start to develop common sense by interpreting the world around it and learning the way things operate. Of course, these two are not the only ways that can help artificial intelligence develop common sense. It is possible to combine neural networks and logical rules to create a system that can help AI. Let’s hop onto the following chapter to learn more about this hybrid system. Chapter 3 - Hybrid systems can be what we need to further develop AI. To enhance machine learning techniques, various methods have been tried and some of them worked while others didn’t. The emergence of deep learning has had a similar experience in the past as well. The method was first discovered in the 1950s, but it was rejected by scientists for not being effective until the 1960s. And today, deep learning maintains its position as the most prominent machine learning strategy. It is obvious that deep learning will continue to be a big part of artificial intelligence in the future as well. But since it is limited by nature, it is impossible to depend on deep learning alone to develop AGI. The creation of AI needs a hybrid system that will combine various methods. Humans are lucky enough to have a natural ability to learn. The only example of an interpretative intelligence that is lesser than that of an adult human is the example of a child. This fact is what led scientists to assess children to understand more about how the structures of the human brain operate in order to learn. Demis Hassabis, a neuroscientist and a researcher, states that using a combination of reinforcement learning and other methods will be necessary in order to develop AGI. He believed that the dopamine system in humans should be mimicked, where synapses in the brain get stronger each time they receive reward signals. Scientists can use this system to order an AI to finish a task and reward it each time it finishes the task. There are various ways the human brain uses to learn. One of those ways is unsupervised learning, meaning that we gain information on things simply by walking around and discovering our surroundings. If scientists can uncover how to integrate this kind of learning into AI, they will be free from the task of providing numerous data to it. Such a discovery will be a breakthrough. Scientists can create AI with an underlying structure like human brains. Using deep learning along with the structures mimicking the human brain is not really new. The self-driving cars that we use today are already using a hybrid system like this. How so? Let’s think about it: those cars should be able to decide what to do when they are on the road. They operate on data gained via deep learning, but as we have mentioned before, deep learning is not enough to predict an infinite number of potential situations. What this means, is that humans build in rules so that the cars can predict various new situations they can potentially experience. And the rules help them decide on what their responses would be in such new situations. Self-driving cars are indeed interesting to explore, but they are not the only places where we benefit from AI. The following chapter will explore various ways where we use AI.