JPMorgan Chase is integrating AI into its internal security systems to thwart malware infections within its own networks. A formal paper [PDF] emitted this month by techies at the mega-bank describes how deep learning can be used to identify malicious activity, such as spyware on staff PCs attempting to connect to hackers' servers on the public internet. It can also finger URLs in received emails as suspicious. And it's not just an academic exercise: some of these AI-based programs are already in production use within the financial giant. The aim is, basically, to detect and neutralize malware that an employee may have accidentally installed on their workstation after, say, opening a booby-trapped attachment in a spear-phishing email.

Where does the mind stop and the rest of the world begin? When Andy Clark, a philosopher at the University of Edinburgh, UK, asked this question in the 1990s, it was a world without deep learning or smartphones. As technology has developed, his argument that the boundary between cognition and the environment is porous has deepened. He spoke to Nature about the state of intelligence research and how a truly intelligent machine needs not only a mind, but also a body. There have been two main advances since I joined the philosophy and cognitive-science community in 1984.

As the waitress approached the table, Sam Altman held up his phone. That made it easier to see the dollar amount typed into an investment contract he had spent the last 30 days negotiating with Microsoft. The investment from Microsoft, signed early this month and announced Monday, signals a new direction for Altman's research lab. In March, Altman stepped down from his daily duties as the head of Y Combinator, the startup "accelerator" that catapulted him into the Silicon Valley elite. Now, at 34, he is the chief executive of OpenAI, the artificial intelligence lab he helped create in 2015 with Elon Musk, the billionaire chief executive of the electric carmaker Tesla. Musk left the lab last year to concentrate on his own AI ambitions at Tesla.

Whether large or small, almost every organisation is looking for aspiring data scientists who will not only help them churn out meaningful insights from data but also help them stay ahead of the curve. It does not matter if you are a college drop-out or a fresher, with the right knowledge of tools and a good understanding of the concepts of machine learning you can still pursue a fruitful data science career with a good pay scale. While hiring a data scientist, organisations expect the candidates to have prior work experience or data science-related projects. Projects are a way to prove your skills and knowledge in any domain. In a full-stack data science project, a data scientist does not only build a machine learning model but along with it, there are lots of other tasks which need to be done single-handedly such as prepare the problem statement, design a specific solution to the problem, gather and clean data, evaluate the quality of the machine learning model, etc.

Dalca, and Mert R. Sabuncu In compressed sensing MRI, k-space measurements are under-sampled to achieve accelerated scan times. There are two fundamental problems in compressed sensing MRI: (1) where to sample and (2) how to reconstruct. In this paper, we tackle both problems simultaneously, using a novel unsupervised, end-to-end learning framework, called LOUPE. Our method trains a neural network model on a set of full-resolution MRI scans, which are retrospectively under-sampled and forwarded to an antialiasing model that computes a reconstruction, which is in turn compared with the input. In our experiments, we demonstrate that LOUPEoptimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods. We present empirical results obtained with a large-scale, publicly available knee MRI dataset, where LOUPE offered the most superior reconstruction quality across different conditions. Even with an aggressive 8-fold acceleration rate, LOUPE's reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods. Our experiments also show how LOUPE yielded optimal under-sampling patterns that were significantly different for brain vs knee MRI scans. I NTRODUCTION M AGNETIC Resonance Imaging (MRI) is a ubiquitous, noninvasive, and versatile biomedical imaging technology. A central challenge in MRI is long scan times, which constrains accessibility and increases costs. One remedy is to accelerate MRI via compressed sensing [1], [2]. In compressed sensing MRI, k-space data (i.e., the Fourier transform of the image) is sampled below the Nyquist-Shannon rate [1], which is often referred to as "under-sampling." Given an under-sampled set of measurements, the objective is to "reconstruct" the full-resolution MRI.

Artificial neural networks (ANNs) have demonstrated remarkable utility in a variety of challenging machine learning applications. However, their complex architecture makes asserting any formal guarantees about their behavior difficult. Existing approaches to this problem typically consider verification as a post facto white-box process, one that reasons about the safety of an existing network through exploration of its internal structure, rather than via a methodology that ensures the network is correct-by-construction. In this paper, we present a novel learning framework that takes an important first step towards realizing such a methodology. Our technique enables the construction of provably correct networks with respect to a broad class of safety properties, a capability that goes well-beyond existing approaches. Overcoming the challenge of general safety property enforcement within the network training process in a supervised learning pipeline, however, requires a fundamental shift in how we architect and build ANNs. Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process that iteratively splits the input space from which training data is drawn, based on the efficacy with which such a partition enables safety verification. To do so, our approach enables training to take place over an abstraction of a concrete network that operates over dynamically constructed partitions of the input space. We provide theoretical results that show that classical gradient descent methods used to optimize these networks can be seamlessly adopted to this framework to ensure soundness of our approach. Moreover, we empirically demonstrate that realizing soundness does not come at the price of accuracy, giving us a meaningful pathway for building both precise and correct networks.

Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms. Current studies on deep learning mainly focus on data sets with balanced class labels, or unbalanced data but with massive amount of samples available, like in speech recognition. However, the capacities of deep learning on imbalanced data with little samples is not deeply investigated in literature, while it is a very common application context, in numerous industries. To contribute to fill this gap, this paper compares the performances of several popular machine learning algorithms previously applied with success to unbalanced data set with deep learning algorithms. We conduct those experiments on an highly unbalanced data set, used for credit scoring. We evaluate various configuration including neural network optimisation techniques and try to determine their capacities when they operate with imbalanced corpora.

Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency. In this paper, we focus on how to use action guidance by means of a non-expert demonstrator to improve sample efficiency in a domain with sparse, delayed, and possibly deceptive rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a new framework where even a non-expert simulated demonstrator, e.g., planning algorithms such as Monte Carlo tree search with a small number rollouts, can be integrated within asynchronous distributed deep reinforcement learning methods. Compared to a vanilla deep RL algorithm, our proposed methods both learn faster and converge to better policies on a two-player mini version of the Pommerman game. Introduction Deep reinforcement learning (DRL) has enabled better scalability and generalization for challenging domains (Arulku-maran et al. 2017; Li 2017; Hernandez-Leal, Kartal, and Taylor 2018) such as Atari games (Mnih et al. 2015), Go (Silver et al. 2016) and multiagent games (e.g., Starcraft II and DOT A 2) (OpenAI 2018). However, one of the current biggest challenges for DRL is sample efficiency (Y u 2018). On the one hand, once a DRL agent is trained, it can be deployed to act in real-time by only performing an inference through the trained model. On the other hand, planning methods such as Monte Carlo tree search (MCTS) (Browne et al. 2012) do not have a training phase, but they perform computationally costly simulation based rollouts (assuming access to a simulator) to find the best action to take. There are several ways to get the best of both DRL and search methods.

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and suggest ways of deriving predictive intervals from these. We then propose and test a method for calculating intervals as an output of a multi-task network. These predictive intervals are optimised to be as narrow as possible, while also enclosing a desired percentage of the data. We demonstrate the effectiveness of this technique on histopathological cell counting and white matter hyperintensity counting. Finally, we offer insight into other areas where this technique may apply.

In machine learning, chemical molecules are often represented by sparse high-dimensional vectorial fingerprints. However, a more natural mathematical object for molecule representation is a graph, which is much more challenging to handle from a machine learning perspective. In recent years, several deep learning architectures have been proposed to directly learn from the graph structure of chemical molecules, including graph convolution (Duvenaud et al., 2015) and graph gating networks (Li et al., 2015). Here, we introduce Graph Informer, a route-based multi-head attention mechanism inspired by transformer networks (Vaswani et al., 2017), which incorporates features for node pairs. We show empirically that the proposed method gives significant improvements over existing approaches in prediction tasks for 13C nuclear magnetic resonance spectra and for drug bioactivity. These results indicate that our method is well suited for both node-level and graph-level prediction tasks.