If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The European Union is poised to ban artificial intelligence systems used for mass surveillance or for ranking social behavior, while companies developing AI could face fines as high as 4% of global revenue if they fail to comply with new rules governing the software applications. The rules are part of legislation set to be proposed by the European Commission, the bloc's executive body, according to a draft of the proposal obtained by Bloomberg. The details could change before the commission unveils the measure, which is expected to be as soon as next week. European member states would be required to appoint assessment bodies to test, certify and inspect the systems, according to the document. Companies that develop prohibited AI services, or supply incorrect information or fail to cooperate with the national authorities could be fined up to a maximum of 4% of global revenue.
Reinforcement learning is arguably the coolest branch of artificial intelligence. It has already proven its prowess: stunning the world, beating the world champions in games of Chess, Go, and even DotA 2. Using RL for stock trading has always been a holy grail among data scientists. Stock trading has drawn our imaginations because of its ease of access and to misquote Cardi B, we like diamond and we like dollars . There are several ways of using Machine Learning for stock trading. One approach is to use forecasting techniques to predict the movement of the stock and build some heuristic based bot that uses the prediction to make decisions.
The Detroit department is also among hundreds of police agencies that have used Clearview AI, a facial recognition tool that searches through a large database of photos taken from across the Internet, according to a BuzzFeed News report earlier this month based on data from a confidential source. Neither the Detroit police nor Clearview have confirmed the report, and it does not appear Clearview was used in Williams's case.
Microsoft is buying AI speech tech firm Nuance for $19.7 billion, bolstering the Redmond, Washington-based tech giant's prowess in voice recognition and giving it further leverage in the health care market, where Nuance sells many products. Microsoft will pay $56 per share for Nuance, a 23 percent premium over the company's closing price last Friday. The deal includes Nuance's net debt. Nuance is best known for its Dragon software, which uses deep learning to transcribe speech and improves its accuracy over time by adapting to a user's voice. Nuance has licensed this tech for many services and applications, including, most famously, Apple's digital assistant Siri.
A really significant change in brain science in recent years has been the gradual acceptance in mainstream science venues of sympathy for panpsychism -- the position that everything is conscious to some degree. Leading neuroscientist Christof Koch, for example, explained last month in MIT Reader: But who else, besides myself, has experiences? Because you are so similar to me, I abduce that you do. The same logic applies to other people. Apart from the occasional solitary solipsist this is uncontroversial.
As machine learning (ML) integrates itself into almost every industry – from automotive and healthcare to banking and manufacturing- the most exciting advancements look as if they are still yet to come. Machine learning as a subset of artificial intelligence (AI) have been among the most significant technological developments in recent history, with few fields possessing the same amount of potential to disrupt a wide range of industries. And while many applications of ML technology go unseen, there are countless ways companies are harnessing its power in new and intriguing applications. That said, ML's revolutionary impact is most poised perhaps when put to use for age-old problems. Hearing loss is not a new condition by any means, and people have suffered from it for centuries.
An analysis of electronic health records for 1.7 million Wisconsin patients revealed a variety of health problems newly associated with fragile X syndrome, the most common inherited cause of intellectual disability and autism, and may help identify cases years in advance of the typical clinical diagnosis. Researchers from the Waisman Center at the University of Wisconsin–Madison found that people with fragile X are more likely than the general population to also have diagnoses for a variety of circulatory, digestive, metabolic, respiratory, and genital and urinary disorders. Their study, published recently in the journal Genetics in Medicine, the official journal of the American College of Medical Genetics and Genomics, shows that machine learning algorithms may help identify undiagnosed cases of fragile X syndrome based on diagnoses of other physical and mental impairments. "Machine learning is providing new opportunities to look at huge amounts of data," says lead author Arezoo Movaghar, a postdoctoral fellow at the Waisman Center. "There's no way that we can look at 2 million records and just go through them one by one. We need those tools to help us to learn from what is in the data."
The term Artificial Intelligence (AI) is a somewhat catch-all term that refers to the different possibilities offered by recent technological developments. From machine learning to natural language processing, news organisations can use AI to automate a huge number of tasks that make up the chain of journalistic production, including detecting, extracting and verifying data, producing stories and graphics, publishing (with sorting, selection and prioritisation filters) and automatically tagging articles. These systems offer numerous advantages: speed in executing complex procedures based on large volumes of data; support for journalistic routines through alerts on events and the provision of draft texts to be supplemented with contextual information; an expansion of media coverage to areas that were previously either not covered or not well covered (the results of matches between'small' sports clubs, for example); optimisation of real-time news coverage; strengthening a media outlet's ties with its audiences by providing them with personalised context according to their location or preferences; and more. But there is a flipside to the coin: the efficiency of these systems depends on the availability and the quality of data fed into them. The principle of garbage in, garbage out (GIGO), tried and tested in the IT world, essentially states that without reliable, accurate and precise input, it is impossible to obtain reliable, accurate and precise output.