AI, Machine Learning, & The Future
Source: Safar S
What is Artificial Intelligence?
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The Future and Machine Learning
How is the future intertwined with machine learning?
In 2016, Google went public with its plans greatly include machine learning in any future AI to greater its capabilities. Even today, whilst artificial intelligence is widely understood, machine learning isn’t necessarily a household name.
Founded in 2000, and now one of Europes biggest companies. Industrial Vision Systems Ltd (IVS®) is a major manufacturer of machine vision and artificial intelligence products whose name is synonymous with innovative image processing systems for quality control, code reading, guiding and identification. By combining software expertise with application integration, we offer a unique service – that of vision system manufacturer and vision system integrator. The machine vision systems that they have created some of the most innovative seen around the globe.
Nonetheless, machine learning is here to stay. As coding, and technology a greater included in the future educational curricula. Students will be will have greater capability is undertaking the lines of work. Once again, machine learning is here to stay.
The most powerful form of machine learning being used today, called “deep learning”, builds a complex mathematical structure called a neural network based on vast quantities of data. Designed to be analogous to how a human brain works, neural networks themselves were first described in the 1930s. But it’s only in the last three or four years that computers have become powerful enough to use them effectively.
The Chinese government’s use of machine learning for political repression has gone much further than surveillance cameras. It is possible to make them represent their reasoning in ways that humans can understand. In fact, in the EU and Britain it may be illegal not to in certain circumstances: the General Data Protection Regulation (GDPR) gives people the right to know on what grounds computer programs make decisions that affect their future, although this has not been tested in practice. This kind of safety check is not just a precaution against the propagation of bias and wrongful discrimination: it’s also needed to make the partnership between humans and their newest tools productive.
There are highly beneficial applications of machine learning. In education, for example, this innovation will enable personalised learning for all and is already enabling individualised learning support for increasing numbers of students. Well-designed AI can be used to identify learners’ particular needs so that everyone – especially the most vulnerable – can receive targeted support. Given the magnitude of what people have to gain from machine learning tools, we feel an obligation to mitigate and counteract the inherent risks so that the best possible outcomes can be realised.
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