The UK's National Museum of Computing has expanded its exhibits celebrating the UK's wartime code-breakers and the machines used to crack German ciphers. On Saturday it will open a gallery dedicated to the Bombe, which helped speed up the cracking of messages scrambled with the Enigma machine. The Bombe was formerly on display at Bletchley Park next door to the museum. A crowd-funding campaign raised £60,000 in four weeks to move the machine and create its new home. The replica Bombe is a copy of the electro-mechanical machines used in World War II at Bletchley.

After the fall of the Berlin Wall, East German citizens were offered the chance to read the files kept on them by the Stasi, the much-feared Communist-era secret police service. To date, it is estimated that only 10 percent have taken the opportunity. In 2007, James Watson, the co-discoverer of the structure of DNA, asked that he not be given any information about his APOE gene, one allele of which is a known risk factor for Alzheimer's disease. Most people tell pollsters that, given the choice, they would prefer not to know the date of their own death--or even the future dates of happy events. Each of these is an example of willful ignorance.

The government and the police are discussing the idea of developing a computer system that can predict street crime by utilizing big data and artificial intelligence. They hope such a system will be able to show them where and how to take greater measures to prevent crime. Street crime prediction "has already achieved results in Europe and the United States," said Mami Kajita, who established the data-analysis company Singular Perturbations Inc. last year in hopes of developing a Japanese version of the methods used in the United States. In some parts of America, the police have ramped up patrols in areas where AI-based systems predicted crime was more likely to happen, achieving a reduction of 20 percent on average, Kajita said. A more cautionary tale comes from China, where the government is racing ahead to use big data and facial recognition technology to surveil the population.

The evolution of computing and cost efficiency has made commercial devices capable of running full-on operating systems and complex algorithms, right in the office. IoT platforms in 2018 are continuing to push for the fastest connectivity. That's of course where the concept of Edge Computing comes in, where workload is processed on the edge of the network where the IoT connects the Cloud with the physical world. A key part of this progression is the fast and effective integration between IoT and the Cloud, locating many of the processes onboard the devices themselves and connecting them with the Cloud for the most essential functions. As machine learning algorithms evolve and advance, there are a few things we can expect.

In a previous edition of DotNetCurry Magazine, I wrote about how you could make your apps smarter with the Azure Cognitive Services. In that article, I demonstrated how to leverage some simple REST APIs to have the Cognitive Services describe an image or extract the emotion of a person in the picture. In this edition, I will go a little deeper and show you how to use the Custom Vision API and Content Moderator Services to implement content moderation. Warning: this article may contain (references to) a small amount of offensive language to demonstrate content moderation. Are you keeping up with new developer technologies? When using Cognitive Services, you basically are using Azure's highest level of APIs.

By redirecting focus, wealth managers can successfully respond to challenges brought on by digital disruption, demographic shifts, and tighter regulation. Wealth managers have seen their fair share of ups and downs in recent years, and while challenges remain, advisers can drive business and growth by paying attention to demographic segmentation, how investors are using technology, and changes in regulation. In this episode of the McKinsey Podcast, Simon London first speaks with PriceMetrix chief customer officer Patrick Kennedy and McKinsey partner Jill Zucker about the North American wealth-management industry; he follows that with a discussion with senior partner Joe Ngai, on the industry in China. Simon London: Welcome to the McKinsey Podcast with me, Simon London. Today, we're going to be talking about financial advice and the people who provide it: financial advisers, or as they're sometimes known, wealth managers. Wealth management is a very big business--and also a business facing a number of challenges, such as new technology, changing demographics, and tighter regulation in a lot of countries. A little later, we're going to be getting a perspective on China. But we're going to start here in North America. For the first part of the conversation, I'm joined on the line by Jill Zucker, a McKinsey partner based in New York, and Patrick Kennedy, who's based in Toronto. Pat is chief customer officer for PriceMetrix, which provides data and analytics to the wealth-management industry.

Ahead of IFA world's leading trade show for consumer electronics and home appliance slated to hold this August in Berlin, Germany, LG Electronics Chief Executive Officer, Jo Seong-Jin and Chief Technology Officer, Dr. I.P. Park will deliver a joint opening keynote at the trade exhibition to outline their vision for LG's ThinQ strategy for Artificial Intelligence. The first IFA keynote for both executives, titled Think Wise, Be Free: Living Freer with AI will be delivered at the trade expo. With interest in all things AI at a peak, CEO Jo, according to LG Electronics would provide his insight into how LG's AI strategy will change customers' lives based on its unique philosophy of an "open platform, open partnership and open connectivity". Similarly, Dr. Park, according to the technology company is expected to show how this technology comes to life in everyday products like refrigerators, TVs and washing machines, and how LG's open AI strategy benefits consumers, thanks to its focus of giving them both control and convenience. "The IFA keynotes provide CEOs and top executives with a global media platform for forward thinking ideas and strategies," Jens Heithecker, Messe Berlin Group executive vice president and IFA executive director, said, adding that, "LG has been and continues to be a key global player in the field of artificial intelligence, which is why we are extremely pleased that Mr. Jo and Dr. Park will deliver the opening keynote at IFA this August."

Researchers have used artificial intelligence and machine learning to predict that Spain is the most likely 2018 World Cup winner. A number of factors were analysed to determine the outcome, including FIFA rankings, national population, gross domestic product, bookmakers' odds, the number of national team players who play together in a club, players' average age, and the number of times they've won the Champions League. According to the AI prediction, Spain is the most likely winner by a margin of almost 20 percent. However, the researchers added that Germany would be likely to beat Spain if given the chance, but the structure of the tournament will probably not allow for that. "Spain is slightly favoured over Germany, mainly due to the fact that Germany has a comparatively high chance to drop out in the round-of-sixteen," says Andreas Groll, of Technical University of Dortmund.

Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constraints. In this paper, we propose the Task-Oriented Grasping Network (TOG-Net) to jointly optimize both task-oriented grasping of a tool and the manipulation policy for that tool. The training process of the model is based on large-scale simulated self-supervision with procedurally generated tool objects. We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering. Our model achieves overall 71.1% task success rate for sweeping and 80.0% task success rate for hammering. Supplementary material is available at: bit.ly/task-oriented-grasp

We provide the first unified theoretical analysis of supervised learning with random Fourier features, covering different types of loss functions characteristic to kernel methods developed for this setting. More specifically, we investigate learning with squared error and Lipschitz continuous loss functions and give the sharpest expected risk convergence rates for problems in which random Fourier features are sampled either using the spectral measure corresponding to a shift-invariant kernel or the ridge leverage score function proposed in~\cite{avron2017random}. The trade-off between the number of features and the expected risk convergence rate is expressed in terms of the regularization parameter and the effective dimension of the problem. While the former can effectively capture the complexity of the target hypothesis, the latter is known for expressing the fine structure of the kernel with respect to the marginal distribution of a data generating process~\cite{caponnetto2007optimal}. In addition to our theoretical results, we propose an approximate leverage score sampler for large scale problems and show that it can be significantly more effective than the spectral measure sampler.