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) …
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.
Several European artificial intelligence projects rely on race without explicitly saying so. In February, El Confidencial revealed that Renfe, the Spanish railways operator, published a public tender for a system of cameras that could automatically analyze the behavior of passengers on train platforms. One characteristic that the system should be able to assess was "ethnic origin". Ethnic origin can mean many things. But in the context of an automated system that assigns a category to people based on their appearance captured by camera the term is misleading.
Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."
As companies welcome more autonomous robots and other heavy equipment into the workplace, we need to ensure equipment can operate safely around human teammates. In this post, we will show you how to build a virtual boundary with computer vision and AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning (ML). Using the machine learning techniques in this post, you can build virtual boundaries for restricted areas that automatically shut down equipment or sound an alert when humans come close. For this project, you will train a custom object detection model with Amazon SageMaker and deploy the model to an AWS DeepLens device. Object detection is an ML algorithm that takes an image as input and identifies objects and their location within the image.
Today we're joined by Saqib Shaikh, a Software Engineer at Microsoft, and the lead for the Seeing AI Project. In our conversation with Saqib, we explore the Seeing AI app, an app "that narrates the world around you." We discuss the various technologies and use cases for the app, and how it has evolved since the inception of the project, how the technology landscape supports projects like this one, and the technical challenges he faces when building out the app. We also the relationship and trust between humans and robots, and how that translates to this app, what Saqib sees on the research horizon that will support his vision for the future of Seeing AI, and how the integration of tech like Apple's upcoming "smart" glasses could change the way their app is used. Cognitive Services is a portfolio of domain-specific capabilities that brings AI within reach of every developer--without requiring machine-learning expertise.