What were you doing when you were 14 years old? Sean Le Van, from Aneheim in California, will put you to shame. The teenager has created a contextually aware, artifically intelligent bot that you can converse with on the web. The bot – which Le Van calls Acuman, an acronym for Artificial Chatting Utility Matching Algorithmic Nodes – acts as a personal assistant that responds to every-day language and learns information about a user's life and personality. Le Van used his own scripting language and natural language processing algorithms to create Acuman, which uses the data gathered from conversations to analyse and create categorised visual representations in the form of infographics.

Elon Musk is pretty sure that we're in some sort of simulation. Musk explained that, given the advances we've made in computer graphics and virtual reality in just a few decades, it's almost definite that there's a more advanced civilization playing or monitoring us like characters, in a game world that is "indistinguishable from reality." Big Bang Games and God Studios have teamed up once again for the latest iteration of "Reality"--the four hundred and sixty-seventh release in the popular series--dropping gamers into a simulation of the planet Earth in the year 2016 A.D. Players who found earlier versions to be lacking in drama will be pleased that the new "Reality" finds humanity on the cusp of complete antibiotic resistance and irreversible climate change. Here are the key takeaways. In-game tasks vary greatly depending on geographical location.

The World Economic Forum has put together a list of the top 10 emerging technologies that will change our lives. The list includes nanosensors that will circulate through the human body, a battery that will be able to power an entire town and socially aware artificial intelligence that will track our finances and health. These are not far-flung visions, according to the forum. They are technologies that are on the cusp of having a meaningful impact. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Bernard Meyerson, chairman of the World Economic Forum council that compiled the list of the top 10 emerging technologies in 2016.

SAN FRANCISCO -- Some Tesla owners have used the cars' Autopilot feature to take their hands off the wheel -- and film themselves doing anything but driving. YouTube videos uploaded since Tesla introduced the self-driving feature in October show drivers playing games, pretending to sleep, and in general, not holding the steering wheel. This kind of distracted driving is exactly what Tesla Motors Inc., under federal investigation after a man using Autopilot died from injuries sustained in a May crash, says drivers should not do. The Autopilot feature is designed to allow Teslas to cruise highways without drivers steering, braking or accelerating. The car is supposed to stay in its lane and stop suddenly if traffic halts.

We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition. By assuming the input matrices to be perturbations of noise-free, simultaneously diagonalizable ground-truth matrices, the approximate joint triangularizers are expected to be perturbations of the exact joint triangularizers of the ground-truth matrices. We provide a priori and a posteriori perturbation bounds on the `distance' between an approximate joint triangularizer and its exact counterpart. The a priori bounds are theoretical inequalities that involve functions of the ground-truth matrices and noise matrices, whereas the a posteriori bounds are given in terms of observable quantities that can be computed from the input matrices. From a practical perspective, the problem of finding the best approximate joint triangularizer of a set of noisy matrices amounts to solving a nonconvex optimization problem. We show that, under a condition on the noise level of the input matrices, it is possible to find a good initial triangularizer such that the solution obtained by any local descent-type algorithm has certain global guarantees. Finally, we discuss the application of approximate joint matrix triangularization to canonical tensor decomposition and we derive novel estimation error bounds.

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the \emph{ADNI} MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy and robustness. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the \emph{CADDementia} dataset.

This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a double-detector scheme is introduced in which a sensor level threshold detector is integrated into network level GLRT to improve the performance. The detection criteria of oracle and clairvoyant detectors are also derived. Simulation results show that with careful design of the threshold detector, the overall detection performance of double-detector scheme would be better than the sign-GLRT proposed in [1] and close to oracle and clairvoyant detectors. Also, the proposed detector is applied to spectrum sensing and the results are near the well known energy detector which uses the real valued data while the proposed detector only uses the sign of the data.

The Budapest BI Forum is the leading vendor-independent business intelligence and analytics conference in Hungary. One of the main feature of the event is the multi-subject approach: we have Pydata, Rstats, Data visualization, machine learning and BI talk in the 2 day s of the conference. This way speaking at the Budapest BI Forum in any of the tracks also provides a special chance to learn about other interesting fields of BI and analytics. This year we will have the following track, with a separate CFP for each. All speakers are welcome to submit more than one talks to the same or to different tracks.

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. Thanks to technologies that generate, store and analyze huge sets of data, companies are able to perform tasks that previously were impossible. But the added benefit does come with its own setbacks, specifically from a security standpoint. With reams of data being generated and transferred over networks, cybersecurity experts will have a hard time monitoring everything that gets exchanged -- potential threats can easily go unnoticed.

It's your nighttime routine: You drop your phone onto the nightstand charging pad, and it asks about your day. You tell it, talking to the virtual personal assistant just like you'd talk to a friend. Your phone's artificial intelligence knows you almost as well as you know yourself (maybe even better). So when it suggests ways to get through tomorrow's calendar, you trust its advice. AI is practically everywhere, and getting smarter all the time.