This is the hard skills development open hack session. Experts will be available to assist with a variety of things from Tensor Flow to Docker to Microsoft Cognitive Services and Azure. There are several projects in motion as well as folks taking several online classes. Come to learn about Python, Machine Learning, Robotics, how they work together and start getting some hands on experience. There will be pointers to guided tutorials as well as other experts.

Here's how Google Play is using AI to improve search Trying to find an app on the Google Play Store can be an exercise in frustration, especially if it's an eagerly anticipated app or new release. Things don't get much better for mega-popular apps, as the search results are often cluttered with irrelevant results. Fortunately, Google is working on a solution, using machine learning to get better results. "Searches by topic require more than simply indexing apps by query terms; they require an understanding of the topics associated with an app," the team of software engineers wrote. The work required machine-learning approaches, but one big challenge for machine learning was the size of the data-set to work with.

Researchers from the US have developed an artificial intelligence (AI) system that surfs the internet, extracts information from the available plain text and organises it for quantitative analysis in very less time. Recently at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head. Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. In their new paper, the MIT researchers trained their system on scanty data -- because in the scenario they're investigating, that's usually all that's available. But then they find the limited information an easy problem to solve.

The Bank of England is using its new fintech accelerator to work with startups on developing proof-of-concepts (POCs) in data analytics, information security and distributed ledgers. Launched in June, the accelerator is designed to boost the BofE's practical experience with fintech, with firms invited to apply to work with it on POCs that address challenges unique to the central bank. Speaking at Web Summit in Lisbon this week, BofE COO Charlotte Hogg invited new applications and gave an update on the project, revealing that the bank is working with BMLL Technologies on a POC that uses a machine learning platform, applied to historic limit order book data, to spot anomalies and facilitate the use of new tools in analytical capabilities. A second POC, with Enforcd, uses an analytic platform designed specifically to share public information on regulatory enforcement action. Meanwhile, two firms - Anomali and ThreatConnect - are working on technologies to collect, correlate, categorise and integrate cyber security intelligence data.

Exploring the Artificially Intelligent Future of Finance With technological enhancements increasing computing power and decreasing its cost, easing access to big data and innovating algorithms, there has been a huge surge in interest of artificial intelligence, machine learning and its subset, deep learning, in recent years. The popularity of smartphones, wearables and social media platforms has led to an explosion in the amount of data being recorded and AI is the only way to make use of it. With the surge of digital disruption in the financial services, the industry has led to hundreds of emerging startups bringing new ways for people to bank, which is causing traditional methods to undergo an innovation overhaul to integrate new technological advancements in order to compete. To celebrate London's 3rd Annual (15-22 July), we spoke to experts in the field to find out how and why, and, most importantly, what we can expect in the future. What have been the leading factors enabling recent advancements and uptake of deep learning? Jan: Astonishing increases in computing power and data availability in recent years have been the main benefactors of deep learning technology.

Customer support is one the most resource-intensive departments in a company. Staff spend their day answering queries, on the telephone with customers, communicating with other departments, and much more. It is also a part of the operation that is hard to link to an ROI. Although the concept of "customer success" is gaining traction in the startup world (particularly in SaaS), it is still tough to report the real value a customer support team can deliver. Therefore, anything reducing the need for manpower in customer support is considered a good thing.

Nvidia just revealed that they earned huge profits in the third quarter last Thursday. The chipmaker racked up a revenue of $2 billion, a figure that is more than the forecast $1.69 billion. Forbes reported that the earnings are also had a year-over-year increase of 54%. Stock prices are also up by 83 cents, compared to the previous estimate of 57 cents. This number displays an increase of 89% compared to last year's digits.

About $1B has been invested in early stage SaaS startups as of November 1. Over the last nine months, marketing startups have raised more dollars in aggregate than any other segment. The chart above shows the early-stage investment dollars by buyer within the organization. Operations teams following second, with human resources focused startups in third. Notably, sales startups raised the least amount of capital. If we compare these trends to the total aggregate market capitalization of public SaaS companies by buyer, we observe a few interesting patterns.

A Terminator of American jobs went unnoticed on Trump's judgment day: artificial intelligence Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Once the preserve of science-fiction movies, artificial intelligence is one of the hottest areas of research right now. While the idea behind AI is to make our lives easier, there is concern that as the technology becomes more advanced, we may be heading for disaster. How can we be sure, for instance, that artificially intelligent robots will make ethical choices? There are plenty of instances of artificial intelligence gone wrong. The case of the rude and racist chatbot** Chatbot Tay, Microsoft's AI millennial chatbot, was meant to be a friendly chatbot that would sound like a teenage girl and engage in light conversation with her followers on Twitter.