Your machine is crying
Machine learning and artificial intelligence are terms that are interchangeably used to refer to technological advancement in the realm of computer science and knowledge. Machine learning involves the ability of computers to learn without the utilization of programme features. On the same hand, artificial intelligence refers to the astuteness that is exhibited by computers to the extent that they can perform cognitive functions that are considered to be only performed by the human mind (Sharon, 2017). Therefore, both artificial intelligence and machine learning are complementary terms that refer to features and ability of computers to perform spontaneous functions such as problem-solving and prediction of unknown outcomes.
Machine learning is closely conflated to mathematical optimization, data mining, and computational statistics. Through computational statistics and data analytics, computer learning is capable of predicting future outcomes and even devising models and complex algorithms that are responsible for detection and prediction of results. The models and algorithms are applied in the field of planning by engineers and data scientists to give out reliable and exact data that is reached based on all considerations performed by them (Sharon, 2017).
However, in the field of machine learning, ethics become a key issue because application of machine learning may cause biases. Machines that are set to interpret data based on falsehood may be prejudiced and result in the absolute failure of the desired outcomes. Therefore, since machine learning depends on the settings of the computer, it is wise if computers are fed with accurate data that is not manipulated so that realistic data can be achieved. Today, machine learning has been applied in medical diagnosis, marketing, robot locomotion, and speech and handwriting recognition by computers (Keller, 2017). Machine learning is, therefore, a milestone in the development of the world in the field social, economic, political and socio-economic.
The fundamental and general objective of artificial intelligence is to devise technologies that make computers function in a more intelligent way as human minds. While the impacts of artificial intelligence have been celebrated by the majority of people across the globe, the impacts have been double edged (Keller, 2017). For instance, in the field of economy, artificial intelligence in the form of automation has solicited mixed reaction from workers, who are directly affected by the technological development. Despite contributing to significant growth in the Gross Domestic Product of states that have embraced it, automation has resulted in wage inequality and even loss of jobs among workers (Chris, 2017). Therefore, despite the world taking big strides in embracing technological advancement, it is indebted to ensure that they do not cause more harm than benefit to the people.
Artificial intelligence has proved to be fruitful in boosting international cooperation in various fields that necessitate collaborative effort. There has been the assessment of the benefits and challenges of AI between states, where several breakthroughs in AI have resulted to effective relations between states (Chris, 2017). For instance, the quagmire of global warming that has attracted global attention from many states across the world has seen successful application of AI in disaster management through preparedness, response, and recovery. Similarly, the application of AI has been effectively employed in the field of healthcare across the globe, especially in the United States (Sharon, 2017). The wider application of AI in automation of information and communication has also yielded fruits in the majority of states because there has been retrieval and quick access of information without distortion and data loss.
Tt is worth noting that machine learning and artificial intelligence have contributed a lot toward the growth of the world in various socio-economic and political fields. Machine learning has been the embodiment of verbatim planning through mathematical calculations and algorithm by engineers and data scientists. However, the impacts of AI have to be laced with some of the threatening negative outcomes. Therefore, states embracing AI need to consider developing plans that mitigate its negative outcomes.
References
Chris, T. (2017). Accenture Brings Tech Into Rugby Scrum. Multichannel News, Vol. 38 Issue 4, p20-20. 1/2p.
Keller, J. (2017). GPGPU processors to help with artificial intelligence, machine learning, pattern recognition. Military & Aerospace Electronics, Vol. 28 Issue 2, p26-26. 2/3p
Sharon, S. (2017). The Inhuman Touch: Educators Teach the Nuances of Artificial Intelligence. BizEd, Vol. 16 Issue 1, p75-79. 5p.
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