Many and long were the conversations between Lord Byron and Shelley to which I was a devout and silent listener. During one of these, various philosophical doctrines were discussed, and among others the nature of the principle of life, and whether there was any probability of its ever being discovered and communicated. They talked of the experiments of Dr. Darwin (I speak not of what the doctor really did or said that he did, but, as more to my purpose, of what was then spoken of as having been done by him), who preserved a piece of vermicelli in a glass case till by some extraordinary means it began to move with a voluntary motion. Not thus, after all, would life be given. Perhaps a corpse would be reanimated; galvanism had given token of such things: perhaps the component parts of a creature might be manufactured, brought together, and endued with vital warmth (Butler 1998).

Tim Berry is president and founder of Palo Alto Software and bplans.com, Call it coincidence, serendipity, synchronicity, or just random, but last week I was accidentally exposed to two seemingly unrelated ideas that ended up seeming very related to me. And they gave me a fascinating whack on the side of the head. I thought artificial intelligence had run its course, but computers that learn could be much more important. First, the book Blondie24, by David Fogel, describing how he and his team used evolutionary computing to develop computer programming that taught itself to play checkers.

Engineers love to do crazy things and when they involve robots, we love to tell you about them. We've reported on a lot of ping-pong playing robots over the years but usually they're based on conventional industrial robot arms or humanoid arm designs. What if, instead of a multi-jointed arm, you wanted to design a cartesian ping-pong playing robot? That is, a robot that can only move linearly on an X, Y, and Z axis. That's the question Hossein Jahandideh and his fellow engineers asked themselves.

The AnyScale Learning For All (ALFA) Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to solve the most challenging big-data problems -- questions that go beyond the scope of typical analytics. ALFA applies the latest machine learning and evolutionary computing concepts to target very complex problems that involve high dimensionality. "People have data coming at them from so many different channels these days," says ALFA director Una-May O'Reilly, a principal research scientist at CSAIL. "We're helping them connect and link the data between those channels." The ALFA Group has taken on challenges ranging from laying out wind farms to studying and categorizing the beats in blood pressure data in order to predict drops and spikes.

The application of Evolutionary Computation (EC) techniques for the development of creative systems is a new, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: art and music generation, analysis and interpretation; architecture; and design. EvoMUSART 2005 is the third workshop of the EvoNet working group on Evolutionary Music and Art. Following the success of previous events, the main goal of EvoMUSART 2005 is to bring together researchers who are using Evolutionary Computation in this context, providing the opportunity to promote, present and discuss ongoing work in the area. The workshop will include an open panel for the discussion of the most relevant questions of the field.

The application of Evolutionary Computation (EC) techniques for the development of creative systems is a new, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: art and music generation, analysis and interpretation; architecture; and design. EvoMUSART 2006 is the third workshop of the EvoNet working group on Evolutionary Music and Art. Following the success of previous events, the main goal of EvoMUSART 2006 is to bring together researchers who are using Evolutionary Computation in this context, providing the opportunity to promote, present and discuss ongoing work in the area. The workshop will include an open panel for the discussion of the most relevant questions of the field.

Researchers from the Massachusetts Institute of Technology recently presented their new artificially intelligent system that can fill the information gap itself by surfing the Internet. During the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, MIT researchers said the AI system has the ability to gather structured information from unstructured machine readable documents automatically. Karthik Nasarimhan, one of the co-authors of the study, said that in order for them to do this, they employed a technique called reinforcement learning where the system learns through the notion of cumulative reward. This technique was based on behavioral psychology and is also used in swarm intelligence, game theory, and genetic algorithms among others. According to Nasarimhan, the technique is necessary because there is a lot of contrasting information out which can cause uncertainty when the data is merged.

Researchers from the Massachusetts Institute of Technology recently presented their new artificially intelligent system that can fill the information gap itself by surfing the Internet. During the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, MIT researchers said the AI system has the ability to gather structured information from unstructured machine readable documents automatically. Karthik Nasarimhan, one of the co-authors of the study, said that in order for them to do this, they employed a technique called reinforcement learning where the system learns through the notion of cumulative reward. This technique was based on behavioral psychology and is also used in swarm intelligence, game theory, and genetic algorithms among others. According to Nasarimhan, the technique is necessary because there is a lot of contrasting information out which can cause uncertainty when the data is merged.

Abstract: One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods and their thirteen variants based on extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed classifiers mainly lie in two categories: reconstruction based and boundary based, where three proposed classifiers belong to reconstruction based and three belong to boundary based. We are presenting both types of learning viz., online and offline learning for OCC. Out of six methods, four are offline and remaining two are online methods. Out of four offline methods, two methods perform random feature mapping and two methods perform kernel feature mapping. We present a comprehensive discussion on these methods and their comparison to each other. Kernel feature mapping based approaches have been tested with RBF kernel and online version of one-class classifiers are tested with both types of nodes viz., additive and RBF. It is well known fact that threshold decision is a crucial factor in case of OCC, so, three different threshold deciding criteria have been employed so far and analyses the effectiveness of one threshold deciding criteria over another. Further, these methods are tested on two artificial datasets to check there boundary construction capability and on eight benchmark datasets from different discipline to evaluate the performance of the classifiers. Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers. Through proposed one-class classifiers, we intend to expand the functionality of the most used toolbox for OCC i.e.

From CIA director John Brennan's private email to Sony Inc, from the IRS to CVS, from Target to the notorious Ashley Madison, millions of people suffered from cybersecurity breakdowns across industries. According to the Ponemon Institute, the average cost of damages from data breaches in the US hit a staggering $6.5 million this year, up $600,000 from 2014. Untallied are the personal costs to the hacker's victims: the stress associated with leaked phone numbers, credit card information, social security numbers, tax information, and the time spent getting their lives back on track. The sophistication and scope of cyber threats are expected to further escalate, yet our defenses remain rudimentary, even medieval. Overwhelmingly, the current strategy is to define the threats, and then build strong defensive walls focused on keeping nefarious agents, viruses or programs out.