Neural networks have given researchers a powerful tool for looking into the future and making predictions. But one drawback is their insatiable need for data and computing power ("compute") to process all that information. At MIT, demand for compute is estimated to be five times greater than what the Institute can offer. To help ease the crunch, industry has stepped in. An $11.6 million supercomputer recently donated by IBM comes online this fall, and in the past year, both IBM and Google have provided cloud credits to MIT Quest for Intelligence for distribution across campus.

Democratic Congresswoman Ilhan Omar faces condemnation over her'some people did something' comments; reaction from Fox News contributor Ari Fleischer, former White House contributor. Rep. Ilhan Omar, D-Minn, blasted President Trump over his handling of Iran and suggested that his administration is to blame over the increased tensions between the two nations. Over the weekend, Iran-backed Houthi rebels claimed they launched drone attacks on the world's largest oil processing facility in Saudi Arabia and a major oil field Saturday, sparking huge fires and halting about half of the supplies from the world's largest exporter of oil. The attacks marked the latest of many drone assaults on the Kingdom's oil infrastructure in recent weeks, but easily the most damaging. They raised concerns about the global oil supply and could further escalate tensions across the Persian Gulf amid a growing crisis between the U.S. and Iran over the troubled nuclear deal.

Repeat after me: Machines are our friends; they're with us till the end! There, now doesn't that feel better? Oh, sure, the "narrative" says that machines will take jobs away from humans, but that's only somewhat true. Mostly, the machines of tomorrow will do what they've always done: streamline and expedite workflow across the spectrum of business processes. Keep in mind, the cotton gin eradicated thousands of jobs all over the Southern United States (and elsewhere), back when it stormed the market in the 1800s.

LittleBigBrain, LLC announced today the launch of their Artificial Intelligence (AI) software company. The founders, John Morad and Kevin Malone, have both built highly successful enterprise-level software organizations from the ground-up. They are excited about the endless range of what AI can accomplish. "We see extraordinary opportunities in AI and feel we are uniquely positioned to capitalize on what we view as just the beginning of a rapid upward growth trajectory in the industry," said Kevin Malone, CEO of LittleBigBrain. "The potential of what this technology can do is limitless, and we are currently just scratching the surface."

Artificial intelligence could be used to help catch paedophiles operating on the dark web. The technology would target the most dangerous and sophisticated offenders in efforts to tackle child sexual abuse, the Home Office said. Earlier this month Chancellor Sajid Javid announced £30 million would be set aside to tackle online child sexual exploitation. The Government has pledged to spend more money on the Child Abuse Image Database (CAID), which since 2014 has allowed police and other law enforcement agencies to search seized computers and other devices for indecent images of children quickly against a record of 14 million images to help identify victims. The investment will be used to consider whether adding aspects of artificial intelligence (AI) to the system to analyse voices and estimate ages would help in tracking down child abusers.

Artificial intelligence could be used to help catch paedophiles operating on the dark web, the Home Office has announced. The government has pledged to spend more money on the child abuse image database, which since 2014 has allowed police and other law enforcement agencies to search seized computers and other devices for indecent images of children quickly, against a record of 14m images, to help identify victims. The investment will be used to trial aspects of AI including voice analysis and age estimation to see whether they would help track down child abusers. Earlier this month, the chancellor, Sajid Javid, announced £30m would be set aside to tackle online child sexual exploitation, with the Home Office releasing more information on how this would be spent on Tuesday. There has been debate over the use of machine learning algorithms, part of the broad field of AI, with the government's Centre for Data Ethics and Innovation developing a code of practice for the trialling of the predictive analytical technology in policing.

As the plumes of smoke settle over two of Saudi Arabia's critical oil production facilities – which came under crippling drone strikes over the weekend – both the U.S. and Saudi Arabia are deliberating options for retaliation, raising the possibility of much broader instability across the region, although President Trump was quick to point out Monday, "I don't want war with anybody." Intelligence officials from both countries have been quick to point fingers at Iran as the orchestrators of the attack, which analysts have deemed as one of the most disruptive in history. "This is perhaps one of the greatest examples of kinetic economic warfare we have seen in recent times. Iran is suffering from our sanctions but does not want to escalate into an active war with us," Andrew Lewis, a former Defense Department staffer and the president of a private intelligence firm, the Ulysses Group, told Fox News. "They can do a lot to manipulate the world economy, which will have a negative impact on the U.S. and our allies in Europe."

Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.

The combined algorithm selection and hyperparameter tuning (CASH) problem is characterized by large hierarchical hyperparameter spaces. Model-free hyperparameter tuning methods can explore such large spaces efficiently since they are highly parallelizable across multiple machines. When no prior knowledge or meta-data exists to boost their performance, these methods commonly sample random configurations following a uniform distribution. In this work, we propose a novel sampling distribution as an alternative to uniform sampling and prove theoretically that it has a better chance of finding the best configuration in a worst-case setting. In order to compare competing methods rigorously in an experimental setting, one must perform statistical hypothesis testing. We show that there is little-to-no agreement in the automated machine learning literature regarding which methods should be used. We contrast this disparity with the methods recommended by the broader statistics literature, and identify the most suitable approach. We then select three popular model-free solutions to CASH and evaluate their performance, with uniform sampling as well as the proposed sampling scheme, across 67 datasets from the OpenML platform. We investigate the trade-off between exploration and exploitation across the three algorithms, and verify empirically that the proposed sampling distribution improves performance in all cases.

Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.