Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While a myriad of classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 7 well-known clustering methods available in the R language. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. We also found that the default configuration of the adopted implementations was not accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.

From benefits that don't come from your job to a basic income, cities need to be the laboratories to find solutions for a world where most tasks are automated. Work isn't what it used to be. Jobs are being automated out of existence. Fewer of us are tied to single, long-term employers. Up to 25% of tasks in manufacturing, packing, construction, maintenance, and agriculture may be lost to robots and artificial intelligence by 2025, according to the McKinsey Global Institute.

A shorter version was published in HBR Online on 21st December 2016. Also, the post does not review a new White House paper on AI and its impact released on 20th December 2016, that cites some posts on this blog.] On October 12, 2016, President Obama's Executive Office published two reports that received less media attention than they might have otherwise because the United States was gripped by the final weeks of a presidential campaign race. In these two reports, the administration laid out its plans for the future of artificial intelligence (AI). Depending on one's view of AI's potential impact, the actions resulting from these reports may be more influential on the long arc of history than the outcome of that election.

The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3]. On the darker side, you do not have to be paranoid to be paranoid about the potential technologies that the super-secret computers of the US National Security Agency (NSA) have on their digital drawing-boards.

The growing popularity of artificial intelligence technology probably will lead to millions of lost jobs, especially among less-educated workers, and could exacerbate the economic divide between socioeconomic classes in the United States, according to a newly released White House report. But that same technology is also essential to improving the country's productivity growth, a key measure of how efficiently the economy produces goods. That could ultimately lead to higher average wages and fewer work hours. For that reason, the report concludes, our economy actually needs more artificial intelligence, not less. To reconcile the benefits of the technology with its expected toll, the report states, the federal government should expand both access to education in technical fields and the scope of unemployment benefits.

Joss Wright is training a robot to freak people out. Wright, a computer scientist, is plotting an experiment with a humanoid robot called Nao. He and his colleagues plan to introduce this cute bot to people on the street and elsewhere – where it will deliberately invade their privacy. Upon meeting strangers, for example, Nao may use face-recognition software to dig up some detailed information online about them. Or, it may tap into their mobile phone's location tracking history, learn where they ate lunch yesterday, and ask what they thought of the soup.

Pentesting tools like Metasploit, Burp, ExploitPack, BeEF, etc. are used by security practitioners to identify possible vulnerability points and to assess compliance with security policies. Pentesting tools come with a library of known exploits that have to be configured or customized for your particular environment. This configuration typically takes the form of a DSL or a set of fairly complex UIs to configure individual attacks. There are two major shortcomings with this approach (1) scanning doesn't yield perfect knowledge (2) scanning generates significant network traffic and can run for a very long time on a large network (Sarraute). It is perhaps due to these shortcomings (and maybe 0day exploits) that "most testing tools, provide no guarantee of soundness. Indeed, in the last few years, several reports have shown that state-of-the-art web application scanners fail to detect a significant number of vulnerabilities in test applications" (Doupé).

The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That threshold is known as "The Singularity." That epithet is a metaphor borrowed from physics terminology to express the point at which information technology--specifically artificial intelligence--becomes sufficiently advanced as to irreversibly alter the course of history on earth. While The Singularity may be a familiar cautionary tale told by renowned thinkers such as Bill Gates, Carl Sagan, and Stephen Hawking, and artistically explored through the famous sci-trope of sentient robots, e.g. But that depends on how you choose to define doom, specifically.

In the summer of 2015, Google released DeepDream, a neural network that transformed images into hypnotic hallucinations. It was one of the first instances of an experimental project that demonstrated what neural networks were capable of to the public, giving us a visceral glimpse at the future of AI. At the end of 2016, that future, well, hasn't quite arrived yet. However, this year we saw AI truly enter mainstream dialogue, as society confronted the sticky ethical implications of its design and regulation. Meanwhile, alongside this serious debate, we saw a multitude of highly visible, experimental, and sometimes very silly projects borne of AI.

Five percent of Accenture's workforce is no longer human. One of Accenture's managing directors, Michael Redding, shared that figure this month at a summit in New York on artificial intelligence. If five percent does not sound like much, note that, at Accenture, it equates to 20,000 full-time-equivalent positions. These are not projected numbers. This is the potential of A.I., and that potential is being tapped everywhere. Magellan Health, for example, utilizes a suite of programs – all under the banner of artificial intelligence – to handle a significant portion of its process for reviewing and approving requests for medical tests.