In the digital age, we are exposed to the early stages of artificial intelligence on a daily basis. Spearheaded by Google, Machine Learning is being used by companies to predict user behaviour and offer utility like never before. Entrepreneurs can take advantage of this burgeoning tech to build better businesses – but how far is too far?
Learning, like intelligence, covers such a broad range of processes that it is difficult to define precisely. A dictionary definition includes phrases such as “to gain knowledge, or understanding of, or skill in, by study, instruction, or experience,” and “modification of a behavioral tendency by experience.” Zoologists and psychologists study learning in animals and humans.
In this article, we focus on learning in machines.Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more and is set to be a pillar of our future civilization.Very broadly, that a machine learns whenever it changes its structure, program, or data (based on its inputs or in response to external information) in such a manner that its expected future performance improves.
Machine Learning usually refers to the changes in systems that perform tasks associated with artificial intelligence (AI). Such tasks involve recognition, diagnosis, planning, robot control, prediction, etc. The “changes” might be either enhancement to already performing systems or ab initio synthesis of new systems.To be slightly more specific, we show the architecture of a typical AI “agent” in below Figure. This agent perceives and models its environment and computes appropriate actions, perhaps by anticipating their effects. Changes made to any of the components shown in the figure might count as learning. Different learning mechanisms might be employed depending on which subsystem is being changed.
Most readers will be familiar with the concept of web page ranking. That is the process of submitting a query to a search engine, which then finds web pages relevant to the query and which returns them in their order of
relevance. See e.g. Figure below for an example of the query results for “Machine Learning”. That is, the search engine returns a sorted list of web pages given a query. To achieve this goal, a search engine needs to ‘know’ which pages are relevant and which pages match the query. Such knowledge can be gained from several sources: the link structure of web pages, their content, the frequency with which users will follow the suggested links in a query, or from examples of queries in combination with manually ranked web pages. IncreasinglyMachine Learning rather than guesswork and clever engineering is used to automate the process of designing a good search engine.
When do we need Machine Learning rather than directly program our computers to carry out the task at hand? Two aspects of a given problem may call for the use of programs that learn and improve on the basis of their “experience”: the problem’s complexity and the need for adaptivity.
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