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- Contemporary Artificial Intelligence | Taylor & Francis Group.
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For Instructors Request Inspection Copy. This product is not available in your shipping region. Description Contents Author Subjects. Description The notion of artificial intelligence AI often sparks thoughts of characters from science fiction, such as the Terminator and HAL Offline Computer — Download Bookshelf software to your desktop so you can view your eBooks with or without Internet access. View Cart.
Generalised AI is a bit further off — to carry out a complete simulation of the human brain would require both a more complete understanding of the organ than we currently have, and more computing power than is commonly available to researchers. But that may not be the case for long, given the speed with which computer technology is evolving. A new generation of computer chip technology known as neuromorphic processors are being designed to more efficiently run brain-simulator code. All of these advances have been made possible due to the focus on imitating human thought processes.
However, this is an imprecise use of language, and the best way to think of it is that machine learning represents the current state-of-the-art in the wider field of AI. The foundation of machine learning is that rather than have to be taught to do everything step by step, machines, if they can be programmed to think like us, can learn to work by observing, classifying and learning from its mistakes, just like we do. The application of neuroscience to IT system architecture has led to the development of artificial neural networks— and although work in this field has evolved over the last half century it is only recently that computers with adequate power have been available to make the task a day-to-day reality for anyone except those with access to the most expensive, specialised tools.
Perhaps the single biggest enabling factor has been the explosion of data which has been unleashed since mainstream society merged itself with the digital world. This availability of data — from things we share on social media to machine data generated by connected industrial machinery — means computers now have a universe of information available to them, to help them learn more efficiently and make better decisions.
Female.AI: The Intersection Between Gender and Contemporary Artificial Intelligence
Real fears that development of intelligence which equals or exceeds our own, but has the capacity to work at far higher speeds, could have negative implications for the future of humanity have been voiced, and not just by apocalyptic sci-fi such as The Matrix or The Terminator, but respected scientists like Stephen Hawking.
This group will research and advocate for ethical implementations of AI, and to set guidelines for future research and deployment of robots and AI. Bernard Marr is an internationally bestselling author, futurist, keynote speaker, and strategic advisor to companies and governments. LinkedIn has recently ranked Bernard as one of the top 5 business influencers in the world and the No 1 influencer in the UK.
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He has authored 16 best-selling books, is a frequent contributor to the World Economic Forum and writes a regular column for Forbes. What do the builders assume the female presence could provoke during the man-machine interaction, and why?
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An early feminist account of AI , published at the end of the s, targeted it mainly as a knowledge project, which standards can be adjusted and improved through a critique based on feminist epistemology. Classic AI projects stemmed from an aspiration to build artificial agents subjects, even that could accumulate knowledge, and use it for human-like reasoning.
Their designers often assumed that all human knowledge can be broken into explicitly-defined, programmable rules, and their true purpose was using this research to progress towards discovering the inner workings of the human consciousness and brain. The discussed approach to AI became scarce, turning the critique into more of a feminist postmortem of two specific projects than a popularized analysis toolkit that could be taken into the twenty-first century.
Perhaps this was only a question of timing. Although science and technology have overlaps, AI today is much more an effort of invention than it is an effort of discovery. Even in AIs current weak sense, it does hold a promise for a technological revolution with global implications, from early diagnosis of diseases to public spaces redefined by autonomous vehicles. Personalized experiences already feel as though the machine truly knows us, while military and governmental decision-making processes are gradually being automated.
This wave in AI is, of course, occurring within a social, and not only a technological context, and is influenced by gendered power-relations within the accumulated data, just as much as it was influenced by the allegedly politically-neutral development of GPUs. Recently popularized by examples from both image and text processing, the replication of human biases that appear in the data shows how statistic models are betraying us.
The way to learn a pattern is to spot it repeating in the data.
If a significant part of cats appearing in the training data-set is white, and the significant part of dogs in the same data set is black, a machine learning algorithm will learn that the probability of a new black object to be a dog is higher. This is the case with humans, although there are more parameters about each human that the data will store.
The problem with data on human subjects is twofold:.
10 Powerful Examples Of Artificial Intelligence In Use Today
Sexism, gender inequality and lack of fairness do not arise from merely capturing the current distribution of men and women across professions, and not even from identifying women with certain stereotypical traits and men with others. Another danger, apart from data-based-bias, is the illusion of a machine as objective.
Once programmed and wrapped in appealing user interfaces, the algorithms that have been trained on man-made data turn into an entity of their own.
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