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) …
With the move to DevOps and high-paced development, there is a greater and more frequent need to specify test environments to ensure that systems are working efficiently; yet the ability of enterprise to model and manage capacity accurately is immature. Performance testers are theoretically well-placed to help but they may be naturally cautious about modelling capacity since testing functions can run up significant annual costs in capacity usage alone. You'll have heard plenty about AI (artificial intelligence) and ML (machine learning) of late, and with good reason – delicate, complex and downright costly technology and tools are rapidly maturing into usable toolsets in a wide range of verticals. Analyst firms predict huge markets for AI and ML, indeed the number of enterprises implementing artificial intelligence (AI) grew 270 percent in the past four years and tripled in the past year, according to industry analyst Gartner's 2019 CIO Survey. Results showed that organisations across all industries use AI in a variety of applications but on the downside struggle with acute talent shortages.
If you listen to university advertisements for data science masters degrees, you'd believe that data scientists are so in-demand that they can walk into any company, state their salary, and start work straight away. Interviewing for data science positions is tough, and job-seekers face some bad behaviour from recruiters and hiring managers. Many companies understand that they need to do something with data, but they don't know what. They'll say they want machine learning when they really want a few dashboards. I'm going to put some advice here for anyone about to face the same job market.
When trying to understand time series, there's so much to think about. Is it affected by seasonality? What kind of model should I use, and how well will it perform? All these questions can make time series modeling kind of intimidating, but it doesn't have to be that bad. While working on a project for my data science bootcamp recently, I tried Facebook Prophet, an open-source package for time series modeling developed by … y'know, Facebook.
In other words, the research from the University of Jyväskylä, indicates that the way you dance is unique, and from the subtle differences between dance patterns, algorithms can tell it's you rather than someone else. The objective of the research was to apply machine learning to understand how and why music affects people the way that it does. To explore this question, the Finnish scientists used motion capture technology (much like the technology now common movies with a CGI element) to gain an insight about the uniqueness of dance moves and to also extrapolate what the dance move might say about the person. From studying different patterns of dancing, the researchers are of the view that they can determine how extroverted or neurotic a person is and also draw insights in the particular mood a person is experiencing. The recent study used seventy-three people, who were motion captured dancing to eight different forms of music: Blues, Country, Dance/Electronica, Jazz, Metal, Pop, Reggae and Rap.
In this special guest feature, Bob Fletcher from Verne Global reflects on how liquid cooling technologies on display at SC19 represent more than just a wave. Bob Fletcher is VP of Artificial Intelligence at Verne Global. Perhaps it is because I returned from my last business trip of 2019 to a flooded house, but more likely it's all the wicked cool water-cooled equipment that I encountered at SC19 that I'm in a watery mood! Many of the hardware vendors at SC19 were pushing their exascale-ready devices and about 15% of the devices on a typical computer manufacturer's booth were water-cooled. Adding rack-level water cooling is theoretically straight forward, so I spent a few minutes checking out the various options.
People [being] aware of how that information is being used and integrated is a key part of AI," she said. "If you think about AI there is a lot of misconceptions about its usage and the technology in general. But if you take the uses of AI -- image recognition and pattern recognition -- these things are quite innovative. But we really haven't looked and tested how these technologies are being misused and [if] are they actually achieving the goals they say they are going to, and secondly, if they do achieve those goals such as improving outcomes and reducing challenges and risks. Is it driving to improving patient experience?
To best understand how we made our Uber Eats recommendations more accurate, it helps to know the basics of how graph learning works. Many machine learning tasks can be performed on data structured as graphs by learning representations of the nodes. The representations that we learn from graphs can encode properties of the structure of the graph and be easily used for the above-mentioned machine learning tasks. For example, to represent an eater in our Uber Eats model we don't only use order history to inform order suggestions, but also information about what food items are connected to past Uber Eats orders and insights about similar users.
AI systems are being rapidly integrated into core social domains, informing decisions about who gets resources and opportunity, and who doesn't. These systems, often marketed as smarter, better, and more objective, have been shown repeatedly to produce biased and erroneous outputs. And while much AI bias research and reporting has focused on race and gender, there has been much less attention paid to AI bias and disability. This is a significant omission. Disabled people have been subject to historical and present-day marginalization, much of which has excluded them from access to power, resources, and opportunity.
As well as playing a key role in cracking the Enigma code at Bletchley Park during the Second World War, and conceiving of the modern computer, the British mathematician Alan Turing owes his public reputation to the test he devised in 1950. Crudely speaking, it asks whether a human judge can distinguish between a human and an artificial intelligence based only on their responses to conversation or questions. This test, which he called the "imitation game," was popularised 15 years later in Philip K Dick's science-fiction novel Do Androids Dream of Electric Sheep? But Turing is also widely remembered as having committed suicide in 1954, quite probably driven to it by the hormone treatment he was instructed to take as an alternative to imprisonment for homosexuality (deemed to make him a security risk), and it is only comparatively recently that his genius has been afforded its full due. In 2009, Gordon Brown apologised on behalf of the British government for his treatment; in 2014, his posthumous star rose further again when Benedict Cumberbatch played him in The Imitation Game; and in 2021, he will be the face on the new £50 note.