SpamCop: A Spam Classification & Organization Program

AAAI Conferences

Patrick Pantel and Dekang Lin Department of Computer Science University of Manitoba Winnipeg, Manitoba Canada R3T 2N2 Abstract We present a simple, yet highly accurate, spam filtering program, called SpamCop, which is able to identify about 92% of the spams while misclassifying only about 1.16% of the nonspam emails. SpamCop treats an email message as a multiset of words and employs a na'fve Bayes algorithm to determine whether or not a message is likely to be a spam. Compared with keyword-spotting rules, the probabilistic approach taken in SpamCop not only offers high accuracy, but also overcomes the brittleness suffered by the keyword spotting approach. Introduction With the explosive growth of the Internet, so too comes the proliferation of spams. Spammers collect a plethora of email addresses without the consent of the owners of these addresses.


AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

Journal of Artificial Intelligence Research

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.


Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions

arXiv.org Machine Learning

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a substantial departure from the traditional EM approach and alleviates some of the associated computational complexities and uncertainties. Our variational algorithm is applied to simulated and real data. The paper concludes with discussion and suggestions for future work.


Elon Musk, DeepMind and AI researchers promise not to develop robot killing machines

The Independent

Elon Musk and many of the world's most respected artificial intelligence researchers have committed not to build autonomous killer robots. The public pledge not to make any "lethal autonomous weapons" comes amid increasing concern about how machine learning and AI will be used on the battlefields of the future. The signatories to the new pledge – which includes the founders of DeepMind, a founder of Skype, and leading academics from across the industry – promise that they will not allow the technology they create to be used to help create killing machines. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.


IBM Watson drives 3D-printed autonomous bus around Washington DC

#artificialintelligence

IBM has teamed up with an electric vehicle company to put its Watson artificial intelligence into a driverless electric bus. Dubbed Olli, the autonomous vehicle will be used to take passengers around Washington DC, and is the brainchild of Local Motors, the Arizona-based automaker. It said its bus is the first vehicle to use IBM Watson's car-focused cognitive learning platform, Watson Internet of Things (IoT) for Automotive. Local Motors unveiled the bus at its new facility in National Harbor, Maryland, 12 miles from the US capital. The bus itself is 3D-printed and can carry up to 12 people and is powered by an electric motor.