People have been fascinated by artificial intelligence for thousands of years. In ancient mythology, the Greek deity Hefestus was so efficient with his hammer that he made a huge bronze -mechanical man called Tellos to protect Europa from pirates in the Crete. Thousands of years later, in 1817, Mary Shelley wrote Frankstein, battling artificial intelligence (AI). Recently, the 1927 classic futuristic film Metropolis featured a robot named Maria that took care of children and eventually forced the city to rebel. While myths and science fiction have aroused imagination to consider the ability of artificial intelligence (and potential magnitude), philosophers have fought to define the nature of human intelligence. In the 17th century, Thomas Hobbes wrote famous that our “logic is only calculation.” He concluded that we are only the sum of our memories, suggesting that these memories may be coded for mechanical intelligence.
Several centuries later in the 1960s, philosopher Hubert Drafus criticized the idea of mechanical intelligence. Films like The Terminator warns us how easy it can be to create a wise network like Skynet that can eliminate our species through a simple binary miscalculation. Famous celebrities of Silicon Valley have argued whether artificial intelligence will be our greatest achievement or the final reason for human extinction. Mythology, science stories and philosophy make the subject of artificial intelligence attractive. However, when you begin to see what artificially intelligent machines do, it is difficult to ensure that screams or yawning. On the one hand, some performances of artificial intelligence are extraordinary. It is amazing to ride in a self-driving car. On the other hand, most modern AI focus on classification. You have millions of photos, videos or audio files classified. This is not a technique that motivates you to make underground bunkers or destroy robots.
Certainly Artificial Intelligence has immense possibilities. But we prefer to underestimate things not based on their ability, but on their performance. So far, Artificial Intelligence has performed very well. The availability of huge data sets in recent years has given new food to the machines to learn more about us and the world we live. Machines are capable of identifying such patterns in data that humans cannot understand and may not even think of finding it. But there is still a huge difference between this level and human intelligence of performance. There is also a huge difference between artificial intelligence and dangers caused by human nature. Certainly AI bears practical and moral challenges. But the first round of challenges will be less about the moral implications of creating sensitive creatures and more about our responsibilities towards each other. Think of it as less than the terminator and more than the 1981 Cultic Escape from New York. When this film came, unemployment in men aged 16 to 24 was 84%. It imagined New York as a pity as a pity urban forest that had to be converted into jail. Our moral obligations towards each other will fall behind us long before any existential threat from evil robots.
The first challenge introduced by AI will definitely be of course how to support those whose skills will become obsolete due to automation. What will we do with millions of truck drivers, cab drivers, retail employees, machine operators and accountants? They will not become all programmers, yoga trainers, individual trainers, youtubers and artists. It is very likely that with automation these socio-economic challenges will eventually receive our concerns about defeating humans by machines. This will not be about controlling the robot army by a supercomputer and doing it against mankind. Instead it will be about the automatic burger flipper which took away your nephew’s job in stake and shake. After all, he may need that job to help him pay college fees. You should know about these challenges as you start thinking about the effects of artificial intelligence.
If you think about this, some top businesses did not succeed because they came first in the market. Apple did not make the first music player. Google was not the first search engine. These companies were successful because they understood the scope of equipment and techniques and how to apply them to current and future business needs.
This article is about taking you on that path. You will get high level observation of various techniques under Artificial Intelligence. Once you understand the devices better, you will be in a better position to create long -term strategies for a new or existing business. Business opportunities are very diverse (and have not yet been discovered) for a simple list. I hope you understand the entire scope of technology and then you will be able to apply it on opportunities in your organization or start a new business. The region has become popular in recent years due to increasing availability of computer storage and processing and access to declining costs and access to huge data sets.
These initial ideas still affect AI. This symbolic approach gave rise to expert systems. These systems used to pass through statements to follow thinking and decision making; For example, if you see “A”, make the “ah” sound. If you see a stop sign, do “stop”. Each of these decisions points were to be programmed laborily in the computer. In the 1990s, expert systems were a major form of artificial intelligence. Companies used these systems to help the medical diagnosis, accept or reject loan applications or choose a good stock. Computers passed through a long list of statements. So you may have an expert system for loan that passes through a predetermined list, such as, “If they have credit history, check out the missing payments.” “If they have missed the payment, how many payments were missed in the last year?” “If they have missed out on paying more than 10 in the previous year, reject this loan application.”
As you can imagine these lists can be very long. You need a human being to imagine every possible if and then. A very complex task, a combination can be the result of an explosion-so many different possibilities that it is almost impossible to come with all different combinations. When computer programmers face these boundaries, they begin to reconsider the idea of machine learning. Machine learning is actually existed since the early 1950s. It was used to make programs that could defeat the human player in checkers. These checker programs were very innovative. Machines could make their own strategy and learn from their mistakes. Even these early computers were so sophisticated that they could learn to defeat a human player. Machine learning programmed instructions and Agar-so was a major leap from the statements that simulated the human process of thinking and decisions only. The second part of this book dives deeply into machine learning and explains how it changed the rules of traditional software development.
With machine learning, the machine no longer needs to be programmed clearly to complete a task; It can see and create its understanding through giant data sets. It can learn from data and create its own model, which represents various rules to explain the relationship between data and uses those rules to conclude and make decisions and make predictions. With machine learning, you can give all the data of various inventory required to make the machine a car as well as the blueprint of each car manufactured so far. After seeing this data, the machine begins to understand some things about the meaning of being a car. It knows that cars require wheels, doors and windshields. There may be thousands of different types of cars, but the machine makes a model to identify them all.
To enable machines to make these models, the programmer has developed several advanced machine learning algorithms. The machine learning algorithm is a mathematical function that enables the machine to identify the relationship between input and output. The role of a programmer has changed from writing clear instructions to create and choose the correct algorithm. To move the machine learning to the next level, computer scientists created the concept of an artificial nerve network, which is the subject of Part Three of this book. Artificial nerve networks are created according to the structure and function of the brain. The machine has a mesh of artificial “neurons” connected among themselves, each of which has a machine learning algorithm. These neurons decide on the basis of their algorithms and internal prejudices of neurons, input from other neurons, the strength of the connections to other neurons and the decision -making neuron.
The artificial nerve network was inspired by the way biological neurons work in the human brain. As humans, we learn new things and create memories based on increasing the strength of connections between these nerve cells. Modern artificial nervous networks can create machine learning systems consisting of billions of these neurons. Such complex networks have a tremendous power to find patterns in the huge data sets. You can feed the data into such a network, and it will create a model to better understand the large pattern. For example, you can feed millions of images of dogs in your nervous network and let it mean to adjust yourself and make it your own model. That model may not match the thinking of humans about dogs. This cannot identify dogs and colors or their ears and nose. Instead, it identifies the statistical pattern of various points (pixels) in the images of dogs. In a way, the nerve network develops its own understanding of “dogma”. In this way it can learn to recognize the dog correctly, even if he has never seen this particular dog before.
As you can imagine, the forecast capacity of the neural network has many practical applications and immense business capabilities. If you are in finance, neural networks can identify the trends in the market to help you trade. If you are in pharmaceuticals, you may have a neural network to see the characteristics of existing drugs and compare them with new compounds. If you are in retail trade, you can see patterns in things purchased by customers to find out what they are likely to buy next time. Many large companies are already using a neural network for voice recognition, transcription and digital personal assistants. For example, if you take membership of Netflix, the system recommends based on films and shows you have seen in the past. Amazon uses a neural network to make targeted product recommendations and strengthen its individual digital assistants including Alexa.
But you do not need to be so big to get the value of the neural network. Think about the data present in your organization. Then think about some patterns that will be valuable to see in your data. If you can quickly make valuable patterns, AI is probably suitable for your outfit. If you have no experience of artificial intelligence, Think about the data you have and imagine what you can extract from that data to overcome a specific challenge, solve a specific problem or answer a specific question. Finally, without your human ability to ask questions and imagine the possibilities, artificial intelligence and machine learning are useless. Shakti, as has always been, lies in our creativity and combination of our equipment.
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