A series of recent news stories have highlighted the massive advances made in the field of artificial intelligence (AI). Yitu created a technology called Dragonfly. Dragonfly can search and analyze billions of photographs and locate a single person in a matter of seconds. Connected to cameras in the Shanghai Metro, it identified over 500 criminals in a three month period. It's so powerful it can recognize people wearing disguises.

Ministry of Defence: The recently-constituted Artificial Intelligence (AI) Task Force of the Ministry of Defence led by Tata Sons Chairman N Chandrasekaran on submitted its final report to Defence Minister Nirmala Sitharaman on using AI for military superiority. They reportedly have made recommendations on how to make India a significant power in AI, in terms of both offensive and defensive needs, especially in aviation, naval, land systems, cyber, nuclear and biological warfare arenas. Initial tenders or RFIs (requests for information) will be floated over the next two years on dual-use AI capabilities.

BUYER'S remorse is often experienced in Silicon Valley by investors who plough money into risky startups only to see them fail. Some technology entrepreneurs are now suffering from seller's remorse. They are those whose young companies have grown big in part thanks to Chinese financial backing, but now feel under scrutiny because of an escalating fight between the two tech superpowers. One entrepreneur who took money from Danhua Capital, a Chinese venture-capital firm based near Stanford University, for example, only recently learned that the firm was established with help and funding from China's government. If there are issues down the line you may not know who you're dealing with," he laments. Upgrade your inbox and get our Daily Dispatch and Editor's Picks. In coming days President Donald Trump is expected to sign into effect the Foreign Investment Risk Review Modernisation Act (FIRRMA), which establishes more vigilant reviews of foreign investments into American companies, ...

Microsoft's AI wields the cool but questionably useful skill of being able to conjure Chinese poetry from a couple of images. Conversation isn't all it's able to do, with the AI creating better poetry than I managed in my university creative writing module. And, while Xiaolce isn't the first AI to create poetry – just look at this one recreating Donald Trump's famously erudite speeches – it is the first that's able interpret images as poetry. Obviously we're not literary critics, but it has to be said that the verse conjured by Microsoft's Xiaolce AI isn't atrocious. In fact, it's capable of some elegantly sparse lines: "Wings hold rocks and water lightly / in the loneliness / Stroll the empty / The land becomes soft".

Imagine a world where you can detect issues early and can use that information to make changes that are beneficial for everyone? What if that was possible now? Thanks to Ken Ferry, the CEO of iCAD, there is no need to wonder. Ken and his team have developed innovative technology that detects cancer at an early stage and offers therapy solutions that provide non-invasive treatment for patients. Tamara: Can you share a story that inspired you to get involved in AI? Ken: What inspires and excites me the most about AI is the fact that this innovative technology has the ability to protect and preserve life through the collaboration between technology and medical science.

Data-driven discovery of "hidden physics" -- i.e., machine learning of differential equation models underlying observed data -- has recently been approached by embedding the discovery problem into a Gaussian Process regression of spatial data, treating and discovering unknown equation parameters as hyperparameters of a modified "physics informed" Gaussian Process kernel. This kernel includes the parametrized differential operators applied to a prior covariance kernel. We extend this framework to linear space-fractional differential equations. The methodology is compatible with a wide variety of fractional operators in $\mathbb{R}^d$ and stationary covariance kernels, including the Matern class, and can optimize the Matern parameter during training. We provide a user-friendly and feasible way to perform fractional derivatives of kernels, via a unified set of d-dimensional Fourier integral formulas amenable to generalized Gauss-Laguerre quadrature. The implementation of fractional derivatives has several benefits. First, it allows for discovering fractional-order PDEs for systems characterized by heavy tails or anomalous diffusion, bypassing the analytical difficulty of fractional calculus. Data sets exhibiting such features are of increasing prevalence in physical and financial domains. Second, a single fractional-order archetype allows for a derivative of arbitrary order to be learned, with the order itself being a parameter in the regression. This is advantageous even when used for discovering integer-order equations; the user is not required to assume a "dictionary" of derivatives of various orders, and directly controls the parsimony of the models being discovered. We illustrate on several examples, including fractional-order interpolation of advection-diffusion and modeling relative stock performance in the S&P 500 with alpha-stable motion via a fractional diffusion equation.

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally in order to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.

When Pearse Keane started using optical coherence tomography (OCT) scanners to peer to the back of a person's eye in Los Angeles a decade ago, the machines were relatively crude. "The devices were lower resolution, they had much slower image acquisition speeds," says Keane, a consultant ophthalmic surgeon at Moorfields Eye Hospital and researcher at University College, London. From 2007, Keane spent two years studying scans from OCT machines learning to diagnose eye conditions in patients and pick out the minute details which make up sight-threatening diseases. "It was very time consuming, laborious work," Keane says. OCT scans use light to quickly create high resolution, 3D images of the back of the eye.

Elon Musk's SpaceX program plans to break another important barrier in private space flight Tuesday with the re-launch of a previously-used Block 5 booster just three months after its initial flight. In conjunction with the re-launch, SpaceX announced plans to shorten re-launch times for the Block 5 booster to less than 24 hours by 2019, which would further solidify SpaceX's dominance of the private space flight market? Private space flight companies have been involved in a technological arms race for years on a number of different fronts. One of the most important fronts is the development of an inexpensive reusable rocket booster system that can be used to launch satelites and manned craft into space.