What is AI? Different generations will answer this question in different ways. The meaning of Artificial Intelligence (better known as AI) has changed over the decades due to the continuity of technological development. For instance, if you were to ask this question back in the 50s, the answer would relate to any task that is performed by a machine. However, nowadays, that answer isn’t enough, especially if we are to consider that most of the machines back then were not self-operating.
Now, to understand what artificial intelligence really is, we need to first know the characteristics of AI systems. AI systems will at least portray some of the attributes relating to human intelligence. These include planning, adaptation, reasoning, discernment, movement, presentation of knowledge, and exploitation. Also included are creativity and social skills but to a lesser extent.
It is rare to find a machine with all of these characteristics as it will simply mean it is a human being. However, certain inventions have come close to presenting these behaviours in full. Take, for instance, a computer. This machine is able to understand, solve problems, learn new things, plan, and portray some level of social intelligence. To sum up, AI is the ability of a machine to execute tasks and react to situations like humans without needing human interference.
Is AI Useful?
Work is becoming more complicated each day that passes thanks to the ever-evolving technology. Therefore, as much as a piece of human knowledge can take us steps forward, the assistance that machines bring makes everything endurable. What a human can do within an hour, a machine can complete within 5 minutes. Machines are not affected by emotions; therefore, how fast they will complete a task doesn’t depend on their mood. They also neither succumb to pressure nor get tired. Thus, the quality of results remains the same, making AI a useful sector in our everyday life.
What Are the Uses for Artificial Intelligence?
With AI, the way we live has changed over the years. You no longer have to spend hours searching for something that you want to buy since your computer has the ability to understand what you need and help you with useful suggestions. For example, the introduction of Siri to Apple products has changed the way people use their smartphones. This virtual assistance feature has the ability to recognize the owner’s voice. Therefore, you do not have to access your phone while chopping onions or washing the dishes to know what time it is or the weather temperature. You just need to ask your phone, and it will respond with the right answer.
At the top level, AI is divided into two categories, namely narrow artificial intelligence and general artificial intelligence. Narrow AI is what we are used to in our everyday life. It portrays the simple intelligence found in computers. These are systems that can do things that they have been taught or learned over time while not being specifically programmed to carry out those tasks. As mentioned above, Siri is a perfect example of narrow AI. It is able to recognize your voice and, over time, learn how to respond to certain questions. To understand this further, let's analyze the way Siri works. For starters, AI’s knowledge is limited. However, as you continue using it, it loads itself with data to learn and understand your daily patterns. Thus, the more you use it, the easier it gets for AI to provide you with the desired results. As you can see, that is the same as humans. The longer you stay with someone, the better you get to know them. However, unlike a human, narrow AI systems need to be taught how to do things.
Narrow AI systems can be used to execute various tasks, such as planning daily activities or examining building infrastructure. There is an endless list of what narrow AI can do, but the outright fact is that it is useful.
General AI is different from narrow AI as general AI systems portray adaptable intellect, which is found in humans. Adaptable intelligence allows a general AI system to have certain human-like abilities, such as the ability to learn and adapt in order to complete a task. This flexible form of intelligence gives a machine the ability to reason based on the knowledge accumulated over the course of time. However, this type of AI can only be shown in movies as scientists are yet to bring it to reality. Movies such as CHAPPIE are a good example of what general AI can do.
Can we expect to see Artificial General Intelligence (AGI) become a reality? Well, based on research conducted in 2012/13, AGI is to be expected in the next 20 years or more. The group of researchers led by Vincent C Muller and Nick Bostrom further indicated that superintelligence in machines can be expected 30 years after the successful development of AGI as it significantly exceeds humans’ cognitive performance. That said, other experts still argue that these projections are too optimistic, especially if we are to consider our lack of understanding when it comes to the function of the human brain. Therefore, developing a machine that will think like humans is still centuries away. As much as these are solid arguments, how long it will take for AGI machines to come to reality remains unknown.
Understanding Machine Learning
Although there are various definitions of the term Machine Learning, it all leads back to one thing: feeding a machine with data for it to understand how it can better perform tasks. Take, for instance, GPS. Let’s say you are headed to a particular place. Remember, you are not the first to take this journey and you are also not the only one using this application. Therefore, when you input your points of departure and destination, your GPS app will use the data obtained previously from the users who have already taken this journey to come up with the fastest route and estimated time of arrival. As you can see, a system or machine becomes better through learning from experience.
Understanding Neural Networks
The key to machine learning lies in neural networks. Just like the human brain that uses neurons to feed data, neural networks consist of interconnected algorithm layers. Neural networks are trained to learn from specific experiences in order to come up with better results. Then, there is deep learning, which is a subset of machine learning. This is a phase in which neural networks are elaborated into sprawling networks that have a significant number of layers. With deep learning, machines are now able to perform tasks such as voice recognition and computer vision, which are mostly used in self-driving cars.
Neural networks exist in different forms and have various strengths and weaknesses. For voice recognition and language processing, what is called a recurrent neural network is used, whereas in face/image recognition, a convolutional neural network is what is required. Moreover, the infrastructure of neural networks continues to evolve with the most effective type of deep neural networks being long short-term memory (abbreviated as LSTM). The newly found design lets machines operate faster and carry out more demanding tasks, such as language translation.
Fueling the Resurgence in AI
Over the course of AI evolution, machine learning has seen some of the biggest breakthroughs. This has mostly been credited to the use of GPU clusters to train AI systems. Big brands such as Google and Microsoft have switched to specific chips that are designed to develop and operate machine training models.
To further understand the structure of machine learning, we also need to define its elements. As mentioned previously, machine learning is just a subset of Artificial Intelligence. Therefore, it also comes in two forms: supervised learning and unsupervised learning.
Supervised learning is a method used for training computers with a lot of examples. When trained, a machine will then apply the obtained knowledge to its data. To train a system, massive data is needed as some systems will need more data to master the art of carrying out a task efficiently.
Unsupervised learning utilizes an approach different from the one used in supervised learning as it applies algorithms that try to indicate patterns in data. The algorithms try to identify similarities within the data so as to categorize things.
What Firms Are Leading in AI?
It is no doubt that AI is playing a major role across all industries with most companies now relying on Artificial Intelligence more than ever before. The role of modern-day software, as well as services that rely on AI, is critical, making each and every firm focus on the development of AI systems. There are a bunch of firms that have participated in developing robust AI systems; however, Google stands out among the rest thanks to their outstanding DeepMind AI AlphaGo.
How Will AI Change the World?
The world is already changing thanks to AI systems, which are now widely used by individuals and organizations. Despite the society being divided in opinions about AI’s impact on human life, AI has changed things significantly. On the one hand, it helps us meet deadlines, produce quality results all the time, increase productivity, and cut expenses. On the other hand, some people fear that AI may increase unemployment rates by stealing jobs from humans.