Ever thought of what is and what is not true when it comes to machine learning? Here are some myths surrounding machine learning.
Machine learning is the term used in the field of computer science to describe the ability of the computer system to use statistical techniques to progressively improve performance on specific operations without being programmed. The term (Machine Learning) was used for the first time in 1959. It evolved from pattern recognition and computational learning in artificial intelligent (AI). Machine learning is applicable in some computing tasks such as detecting network intruders, email filtering, ranking, computer vision, and optical character recognition, in self-driven cars, speech translation and many more. Artificial Intelligence in machines is a force to reckon with because it is shaping our future and changing our lives.
Unfortunately, there are emerging misconceptions surrounding artificial intelligence and creating unnecessary uncertainty of the past. There are numerous assumptions or myths that people have developed about the learning machines which cannot be substantiated but portray a negative approach in real life. The six topmost myths prevalent in the information technology (IT) space about machine learning are: machines work without human involvement, they eliminate human biases, are a threat to our jobs, can perform any function, will give rise to superhuman intelligence, and they can learn like humans.
Machines work without human involvement: It is a fact that artificial intelligence in machines is a process of self-learning and can perform tasks on their own. On the contrary, the technology and the algorithms used in machine learning is a human creation. Therefore, we cannot rule out that artificial intelligence in machines can replace that of human beings. This is one of the most common artificial intelligence myths.
Machine learning eliminates human bias: It is true that machine learning aids in removing biased decisions that are likely to be taken by humans. In most cases, the notion does not apply because the bias is not entirely removed due to data categorization at the initial development stage. In fact, the programming platform is a human creation.
Machine learning can perform any task: Machines perform functions based on the type and the amount of input data available. They cannot be applied where there is insufficient data. There are still challenges in AI when it comes to performing other tasks because the machine learning systems have not figured out to do unsupervised learning well or train on a limited amount of data.
Machine learning will give rise to superhuman intelligence: It is assumed that with advancement in AI, machines will acquire some sophisticated intelligence and get ahead of human beings. This is not the case; computers don’t possess common sense as we do. Therefore, we still have a long way to go to achieve that significant step in artificial intelligence.
Machine learning can learn like human beings: The learning process of humans cannot be equated to that of a machine. For instance, we don’t need to watch millions of examples to learn how to execute some activities in our daily lives. Moreover, we learn out of curiosity, unlike the machines which require guidance and support at every step of learning. Therefore, it is not correct to have an assumption that machines learn as we do.
Machine learning poses a threat to our jobs. There is a misconception that massive numbers of jobs are at stake with the emerging application of AI in various sectors. In fact, the tasks performed by machine learning are supervised or predetermined by humans, hence giving room for more job creations in addition to the pre-existing ones.
In a nutshell, machine learning is far away from matching human intelligence. Therefore, it is upon us to stay focused and embrace technological changes without fear of contradiction.
