Recently, Artificial Intelligence has been used interchangeably with Machine Learning but that notion is incorrect. Artificial intelligence known as “Al” refers to the machines that do tasks which mimic the characteristic of human intelligence and involve planning, understanding the human language and sounds, learning and solving the problem. Artificial intelligence definition is a bit different from Machine Learning definition.
Artificial intelligence is usually classified into two categories
General A I
This is also commonly referred to as full Al or strong AI. This is the notion that a machine could perform any intellectual task that a human being can perform. The General AI is also sometimes referred to as an attempt to make a machine achieve consciousness.
The problems that this technology aims to solve is beyond a specific algorithm including general computer vision, natural language understanding and dealing with real-life situations and common world problems.
However, the research on Strong Al has received strong opposition from right-wing conservatives who believe that Al pursuing cognition is a threat to human existence.
This, on the other hand, is involved with the day to day aspects of like in management, industry, engineering, administration, and education as well as the examination of the impact of both general and applied AI in the world. These are systems that perceive the environment and reacts effectively to increase the chances of completely performing a task or a goal that is set. These machines mostly perform functions that are mostly associated with the human mind.
However, as the technology improves and machines are increasingly able to perform more complicated tasks, those tasks often end up being removed from the definition through a phenomenon known as “The Al effect”. This has brought up the presumption that Al is what has not yet been accomplished. For example, Optical Character Recognition (OCR) was regarded as Al but recently it is excluded because it has become a routine technology.
However currently, the current Al frontline includes interpreting and analyzing complex data like pictures as well as understanding emotions. Military research is also at the forefront as countries strive to rid the battlefield of human players. Companies like Google have also thrown in full weight and resources to improve AI like the Autonomous Google Vehicles that is fronted to replace drivers on the roads and eventually reducing accidents caused by human error.
The importance of artificial intelligence is that it is at a strategic point to improve the lives of mankind. However, the path to improvement of Al should be trodded carefully since it has an important impact on the life of humans and their future.
As compared to Artificial Intelligence, Machine learning does not require the machine to perceive data on it’s own but rather it is provided with the data that it then interprets to and eventually learn from. The learning of the machine is pegged on the data provided and through that data. The machine progressively improves their rate of doing work on a specific task in the case of machine learning, the machine is not programmed but programs itself to perform the task.
The term machine learning was made by Arthur Samuel in the year 1959. The term arose from the research of pattern recognition and computational learning theories. Machine learning enables the machine to analyze the data provided and consequently make appropriate algorithms to that and eventually be used to make specific decisions based on the data provided and the program instruction that made it.
One of the leading causes of need for the development of machine learning is that it is difficult to create an algorithm to deal with a task that is unpredictable. A good example of that is mail filtering, finding and removing malicious attacks on a system or even detection of network intruders. Machine learning is very intertwined with computational statistic. This is due to the fact that the machine learns from the previous statistics to make driven decisions Computational statistics is based on making predictions. Machine learning is also a part of the mathematical computation.
Machine learning can either be supervised or unsupervised. Supervised machine learning is what is commonly referred to as data mining. This is where a programmer develops a program that collects data on its own, as opposed to data being fed. The machine then proceeds to analyze the data and use it to find some meaningful anomalies or can as well be used by hackers to collect data from people and later use it for various purposes.
The best example of unsupervised machine learning is the collection of data by Facebook that is then used to determine the most appropriate ads to show to the user to increase the chance at the product to be bought by the user.
Supervised machine learning is where the machine is intentionally fed with data with an aim to make accurate predictions. This is seen in data analytics by devising complex models and algorithms that can produce reliable predictions end results previously impervious to the human eye and memory.
However, the effective use of machine learning is hindered by the lack of constant patterns and must: data provided in the case of supervised machine learning, the following are some of the uses of machine learning,
Classifying the DNA
samples received for use in forensics and even medicine.
For use in Search Engines such as Google whereby the algorithm collects data from the previous users, and use to make the search engine optimized in that it brings more accurate results.
Banks also use the machine teaming to detect credit card frauds. This is made possible because
the algorithm can analyze the patterns by the fraudsters hence preventing an attack before it occurs.
Pop-up ads are
currently very common. they utilize machine learning to get to know whatever you are searching for and provides related products and services Google Translate is one of the most used translation apps in the World right now.
This is achieved by allowing the machine to team the language and come up with the results based on previous user queries and feedback.
Furthermore, these two terms will be growing exponentially in the coming future and maybe some new concepts or branches come up. We have a lot more to see the wonders of science.
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