Machine Learning- Setting Trends in Science & Technology
A learning machine is any device whose actions are influenced by the past experience — Nils John Nilsson
Machine learning is a term incepted by Arthur Samuel in computer learning. It is a technique by which any computer can identify patterns, trends and can store them for its future decisions in similar situations. Machine learning is mostly confused with artificial intelligence but actually, it is not the same.
It is only the current application being used in AI and deals with making machines learn about things on their own. We can save our time in explicitly programming the bots and can work on other areas. This new term has evolved from the study of pattern recognition & computational learning theory.
A computer is made to study the patterns and make note of them so that it might recognize the same style if it sees them again. In simple terms, we say it something learned by the machine. By implementing machine learning tools and applications we can design various algorithms that are crucial for imparting knowledge into the machines.
Imparting knowledge into the machines and making them smarter, is a concept loved by all but still debatable in terms of its limits. Different groups are putting their views about setting some parameters and identifying boundaries for this knowledge transfer. It has been a long-time dream of the humanity to produce something which can give us a feeling of a creator. We are working rigorously to create some really smart beings for whom we can take the full credit. Smart bots are something similar to that only. Machines were created to ease of the human efforts and to make our living more comfortable on this planet.
We want machines to go some extra miles and take the jargon into their own hands. We want machines to be autonomous as well as loyal. We want them to feel, take decisions but those decisions should be the one approved by their master (humans). It is something which is not acceptable by all in a similar manner. Some believe that machines can go a level beyond if they know how to take decisions and can be a danger to our own existence. But others are looking at only the brighter aspects which are making our life easier and less complicated.
As evolution has helped humans to get better with each passing day. We have learned from our past deeds and mistakes. We have even learned a few things from our peers and other’s experience. There are certain ways in which a machine learns. Learning in the case of machines is largely supported by computational statistics. It is highly dependent upon mathematical optimization which provides methods and theories about learning. IT companies all over the world are using these mathematical models to improve learning and extracting maximum information from a small data
a) Learning on examples: Machines are given information to study and understand. It could be patterns, some actions or any other tasks to be done after identifying the certain pattern or listening to a particular command. They are enabled to store that information for their future use so that they can check and follow the same act whenever they see the same thing or pattern.
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b) Learning from experience: Computers do a certain task and gets trapped into an unwanted situation. They are made to learn from their own mistake and expected not to follow the same steps when again they found a similar problem.
c) Self Learning: A method where machines or bots are given enough knowledge to understand what to do and what not to. And then they are being tested upon whether they have some sort of self-learning or not. It could be doing some small things and getting an idea of doing or not doing it. It is generally done by giving data to the machines. They interpret the data and then act accordingly. It is something beneficial to share markets etc.
d) Deep Learning: It is a branch of learning in which computers study about patterns and analyze it for getting a further level of information. Such learning is important in understanding genetic coding and other biological information.
All these types of learning mechanisms are used for training the bots for specific type work. Machines are being made smarter to improve accuracy and reduce human efforts. These smart bots can take some really tiring and high calculative work and give us the results without fatigue. We are constantly upgrading ways to find our some really smart techniques in machine learning which are quick as well as effective. We want these bots to be smart and that too soon keeping in mind that the whole learning process is always under control and bots always abide human instructions.
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