We're lucky to have Travana sponsoring the night, providing food, drinks and their amazing space! Sergei Burkov, Founder and CEO of Alterra.ai, a Deep Learning / NLP startup, building bots for online travel. Sergei's previous startup was acquired by Google, where he became the first head of its R&D Center in Moscow, Russia. Sergei will talk about how Deep Learning can be utilized for building smart conversational bots and compare them with not-so-smart bots that rely on on-screen buttons. He will also describe what the rise of bots means for the travel space, and where the opportunities are.

As the capability of new artificial intelligence systems improve more organisations are leveraging AI products and tools to help them maintain and extend their competitive edge Uber have announced that they've launched an artificial intelligence (AI) lab and acquired Geometric Intelligence, an AI startup, as the company looks to use the technology to improve the performance and efficiency of its driverless vehicles and products. DeepMinds AI based WaveNet tech makes computers sound human The effort marks Uber's biggest step yet into AI, which has become a key competitive battleground for the technology companies dotted along the west coast, and its use in the transportation space is hotting up substantially as companies, such as Tesla, who have their own AI lab, along with many others begin to use AI in anger to improve the performance of their autonomous vehicles and optimise their services – which, in Ubers case will likely include their using AI to improve routing, matching passengers to rides, and one day using it to underpin their future flying taxi service. "With all of its complexity and uncertainty, negotiating the real world is a high order intelligence problem," said Jeff Holden, Uber's Chief Product Officer, and he pointed to cars, planes and robotics as all needing better navigational intelligence. As tech companies increase their focus on AI, buying AI start ups in oder to get into the space has become more common. Terms of Uber's acquisition were not disclosed, but its new AI lab will be formed from the staff at Geometric Intelligence and led by Gary Marcus, a cognitive scientist at New York University.

With AI becoming integral to nearly every industry, it's no surprise that banking is increasingly automated. Chatbots like BankBot and Nao are slowly taking us one step further than digital banking, but there are still privacy risks that come with feeding both banks and their bots more information. BankBot is an app prototype designed by the Polish digital design and communication agency K2. BankBot itself is a robotic bank teller, financial advisor, and personal assistant all in one. The automated sidekick provides a conversational text-based interface, but users can also use their voices instead of typing.

I am a Machine Learning Engineer, Deep Learning Engineer and even an Indie Game Developer with a Major in Compilers and a Master's degree in Artificial Intelligence from University Politehnica of Bucharest. I am passionate about Games and Artificial Intelligence. I love to give life to A.I. agents in my project or my friend's projects and I want to teach you too.

Security officials, police and legal experts from around the world are gathered in London for a global conference on tackling the threats posed to prisons, airports, nuclear facilities and other infrastructure by consumer drones. The Countering Drones conference, which organisers describe as the first of its kind, reflects concerns that increasingly high-powered and affordable models of drones are posing new and wide-ranging security challenges for police and other protection forces. Nearly 80% of people surveyed by Defence IQ, the conference organisers, said they believed a major security incident involving drones in civilian airspace was strongly likely or almost certain to happen in the next five years. In some areas, such as at airports and in prisons, drones are already causing widespread disruption, but the conference also highlights areas such as at sporting events and seaports, where threats posed by unmanned aircraft are still emerging. The conference is sponsored by defence companies Thales and Rheinmetall with tickets starting at £599 a head.

Robot" has gained a cult-like following not just in the U.S., but also in other parts of the world. Stars of the show are aware that the show has devoted fans, but it wasn't until Rami Malek, who plays the series' lead character Elliot Alderson, encountered a fan in Serbia that he realized how big the show's effect really is. During a panel, as quoted by Variety, Malek said: "I was just shooting a movie in Serbia and so many young kids came out and waited outside my hotel to talk to me about ['Mr. They had nothing but good things to say about the show and how the characters have affected them. I've never had that experience before." Malek added: "I'm very proud of what [series creator] Sam [Esmail] has created and that the show is bringing attention to people suffering with mental illness.

Two simple proofs of the triangle inequality for the Jaccard distance in terms of nonnegative, monotone, submodular functions are given and discussed. The Jaccard index [8] is a classical similarity measure on sets with a lot of practical applications in information retrieval, data mining, machine learning, and many more (cf., e.g., [7]). A very simple, elementary proof of the triangle inequality was given in [5] using an appropriate partitioning of sets. Here, we give two more simple, direct proofs of the triangle inequality. One proof comes without any set difference or disjointness of sets.

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. Brain-predicted age represents an accurate, highly reliable and genetically-valid phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

We consider the problem of multi-class classification and a stochastic opti- mization approach to it. We derive risk bounds for stochastic mirror descent algorithm and provide examples of set geometries that make the use of the algorithm efficient in terms of error in k.