The Artificial Intelligence means making the machines we have and depend on in our daily lives ‘smart’. This can be achieved by the amazing technological advances that were reached in the last decade. So, in this following article, we are going to learn few basic things about Artificial Intelligence or ‘AI’.
Content
- What Is An Intelligent machine?
- The History Of AI
- How Does AI Work?
What Is An Intelligent machine?
A smart machine is a device embedded with technologies that enable it to reason, solve problems, make decisions and even take action.
Smart machines include robots, self-driving cars and other cognitive computing systems designed to work through tasks without human intervention.
smart machines are expected to displace workers as well as dramatically change the nature of work and other societal norms.
Many smart machines can replace humans in completing a task. For example, robotic process automation in manufacturing facilities can and does replace human workers. But some smart machines work for humans, such as doctors that use the devices to diagnose diseases and recommend treatments.
Now, IBM Watson is known for its work in the healthcare field, where it aids in drug discovery, social program management, patient care management and treatment option selection.
The History Of AI
In the last two decades, computers progressed so swiftly and became a very vital part of our lives in such a short period of a time that is easy to forget how new this technology is.
In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots.
By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence, suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can’t machines do the same thing?
From 1957 to 1974, AI flourished. Computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem.
In the 1980’s, AI was reignited by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert.
In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. Moore’s Law, which estimates that the memory and speed of computers doubles every year, had finally caught up and in many cases, surpassed our needs. This is precisely how Deep Blue was able to defeat Gary Kasparov in 1997, and how Google’s Alpha Go was able to defeat Chinese Go champion, Ke Jie in 2017. It offers a bit of an explanation to the roller coaster of AI research; we saturate the capabilities of AI to the level of our current computational power (computer storage and processing speed), and then wait for Moore’s Law to catch up again.
How Does AI Work?
AI works through five steps: inputs, processing, outcomes, adjustments, and assessments. Inputs: Data is first collected from various sources in the form of text, audio, video, and more. It is sorted into categories, such as those that can be read by the algorithms and those that cannot.
Processing: Once data is gathered and inputted, the next step is to allow AI to decide what to do with the data. The AI sorts and deciphers the data using patterns it has been programmed to learn until it recognizes similar patterns in the data that is being filtered into the system.
Outcomes: In this step, the AI is programmed to decide whether specific data is a “pass” or “fail”—in other words, does it match previous patterns? That determines outcomes that can be used to make decisions.
Adjustments: When data sets are considered a “fail”, AI learns from that mistake, and the process is repeated again under different conditions. In this step, you might return to the outcomes step to better align with the current data set’s conditions.
Assessments: The final step for AI completing an assigned task is assessment. Here, the AI technology synthesizes insights gained from the data set to make predictions based on the outcomes and adjustments.
We hope you enjoyed this information about AI and we surely are going to talk more about AI in following articles.
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