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) …
From personal assistants to legal counsel on parking fines, artificial intelligence (AI) and machine learning (ML) have established their potential as disruptive technology that will alter industries. With each passing day, further discoveries enable AI to become more sophisticated and viable in our world. Naturally, like all things digital, AI has had a profound impact on digital marketing as well. From Google's RankBrain search engine algorithm to Amazon's personalized recommendations, it is powering the world's leading organizations and changing the face of the modern digital marketing landscape. Currently, I work as senior vice president of marketing at CUJO AI, an AI-driven network security and intelligence company.
Would you drink a whisky designed and created by artificial intelligence? This fall, this hypothetical question becomes a reality, as popular award-winning Swedish whisky distillery Mackmyra releases the first ever whisky, a single malt, designed with machine learning. Working in collaboration with Microsoft and Fourkind, a Finnish technology consultancy specializing in AI spearhead projects, the distillery has made the claim that this is the first ever machine-learning designed complex consumer product recipe. I for one, welcome the chance to try a whisky created by our robot overlords. The distillery's machine learning models running off of Microsoft's Azure Cloud Computing platform and AI cognitive services will be fed raw data related to whisky production (including malting, fermentation, distillation, and maturation), Mackmyra's historical recipes, sales numbers, and customer preferences.
All this additional hardware, systems, and infrastructure needs to be maintained. In addition, no system, especially a system as complex as this, works without issues from the get go. The maintenance and human capital required to support PTC is tremendous. Calculated to cost up to $22.5bn during the next 20 years2, PTC is the single-largest regulatory expenditure ever imposed on the industry by the Federal Railroad Administration (FRA), according to the Association of American Railroads (AAR). This economic burden will have tremendous effects across the entire supply chain lifecycle and affect every American household.
The battle for future markets and bigger market shares is in full swing. The world's most influential companies are in a steady race to develop better automated systems and, in turn, boost artificial intelligence technology – taking them ahead of their competitors. By 2020, AI is expected to turn over more than 21 billion euros worldwide. However, further development of machine learning and artificial intelligence technologies seems to be blocked by a major obstacle: data privacy. The more data is consumed, the better these computer algorithms can recognize and capture patterns in the data.
When Geoffrey Hinton started doing graduate student work on artificial intelligence at the University of Edinburgh in 1972, the idea that it could be achieved using neural networks that mimicked the human brain was in disrepute. Computer scientists Marvin Minsky and Seymour Papert had published a book in 1969 on Perceptrons, an early attempt at building a neural net, and it left people in the field with the impression that such devices were nonsense. "It didn't actually say that, but that's how the community interpreted the book," says Hinton who, along with Yoshua Bengio and Yann LeCun, will receive the 2018 ACM A.M. Turing award for their work that led deep neural networks to become an important component of today's computing. "People thought I was just completely crazy to be working on neural nets." Even in the 1980s, when Bengio and LeCun entered graduate school, neural nets were not seen as promising.
As a recent article in the Wall Street Journal points out, artificial intelligence (AI) is becoming one of the most important technological advances of our era. It uses statistical methods and very large datasets to identify patterns and predict outcomes, but it still has a ways to go before it can identify cause-and-effect relationships. Being able to do this, however, just may represent the next frontier in AI. According to the Wall Street Journal article, determining causal relationships requires tried and true scientific, empirical and measurable methods that can "detect faint signals within large and/or noisy data sets -- the proverbial needle in a haystack." It's one thing to use statistical methods and very large data sets to find patterns that, for example, can identify the presence of a mass on an Xray, but it's another thing entirely to identify how a specific treatment will affect the outcome.
The behavioral revolution in economics was triggered by a simple, haunting question: what if people don't act rationally? In the online world, once expected to be a place of ready information and easy collaboration, lies and hate can spread faster than truth and kindness. For example, when predicting sales, employees often hide bad deals and selectively report the good ones. AI stands at the crossroads of the behavioral question, with the potential to make matters worse or to elicit better outcomes from us. The key to better outcomes is to boost AI's emotional quotient -- its EQ.
Researchers at UC Davis and UC San Francisco have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer's disease in human brain tissue, delivering a proof of concept for a machine-learning approach capable of automating a key component of Alzheimer's research. Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can "see" if a sample of brain tissue has one type of amyloid plaque or another -- and do it very quickly. The findings, published May 15, 2019 in Nature Communications, suggest that machine learning can augment the expertise and analysis of an expert neuropathologist. The tool allows them to analyze thousands of times more data and ask new questions that would not be possible with the limited data processing capabilities of even the most highly trained human experts.