Decision makers, such as doctors, make crucial decisions su ch as recommending treatments to patients on a daily basis. Such decisions typically involve careful assessment of the subject's condition, analyzing the costs associated with the possible actions, and the nature of the consequent outcomes. Further, there might be costs associated with the assessmen t of the subject's condition itself (e.g., physical pain endured during medical tests, monetary costs etc.). Decision makers often leverage personal experience to make decisions in these contexts, wi thout considering data, even if massive amounts of it exist. Machine learning models could be of immense help in such scenarios - but these models would need to consider all three aspects discussed ab ove: predictions of counterfactuals, costs of gathering information, and costs of treatments. Fu rther, these models must be interpretable in order to create any reasonable chance of a human decision m aker actually using them. In this work, we address the problem of learning such cost-effectiv e, interpretable treatment regimes from observational data. Prior research addresses various aspects of the problem at h and in isolation. For instance, there exists a large body of literature on estimating treatment ef fects [5, 12, 4], recommending optimal treatments [1, 15, 6], and learning intelligible models for prediction [9, 7, 10, 2].

We introduce a methodology to construct parsimonious probabilistic models. This method makes use of Information Filtering Networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small sub-parts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust even for the estimation of inverse covariance of high-dimensional, noisy and short time-series. Applied to financial data our method results computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big datasets with large numbers of variables. Examples of practical application for forecasting, stress testing and risk allocation in financial systems are also provided.

Design mining [54, 55, 56] is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation, whilst harnessing the creativity of both computational and human design methods. A sample-model-search-sample loop creates an agile/flexible approach, i.e., primarily test-driven, enabling a continuing process of prototype design consideration and criteria refinement by both producers and users. Computational intelligence techniques have long been used in design, particularly for optimisation within simulations/models. Recent developments in additive-layer manufacturing (3D printing) means that it is now possible to work with over a hundred different materials, from ceramics to cells.

Intel CEO, Brian Krzanich, announced that Intel Capital will invest $250m in the next two years in the autonomous vehicle (AV) ecosystem, focused on problems in connectivity, communication, context awareness, deep learning, security and safety. When viewed in the context of the fund's short-lived intention to sell $1bn worth of portfolio holdings in March this year (it was cancelled in May), I think this shows Intel is serious on going long with AI. Indeed, the company purchased recently Nervana Systems and Movidius, which could help it's larger AV program and the race against NVIDIA. Nauto, the startup offering a direct to consumer network of cloud-connected dashboard cameras applied to car insurance, inked a data sharing agreement and investment from Toyota Research Institute, BMW iVentures and Allianz Ventures (thanks Moritz for sharing!). One of the reasons for the immense progress in AI is data crowdsourcing.

A team of researchers at Oxford University have coaxed an artificial intelligence program into an impressive leap forward and towards our own obsolescence. The program, known as LipNet, is showing particularly promising ability to read lips in video clips, thanks to machine learning and a novel way of approaching the data. The key difference is that rather than try to teach the AI the mouth shapes of single words and phonemes, the LipNet is asked to interpret whole sentences. Using GRID, a huge bank of 3 second videos featuring brightly lit forward facing speakers, LipNet has learned to translate speech to text with a 93.4% accuracy rate. Compare that to humans' 52.3%.

New analytics engine for users allows anyone to receive fast, accurate imaging analysis for key clinical conditions, by simply uploading their scans to Zebra's online system Zebra Medical Vision (https://www.zebra-med.com/), the leading machine learning imaging analytics company, is launching Profound (http://profound.zebra-med.com) The company's new service allows people to upload their medical imaging scans such as CTs and Mammograms to Zebra's online service, and receive an automated analysis for key clinical conditions. This Smart News Release features multimedia. "We are all anxious about our health. Undergoing an imaging scan such as a CT or a Mammogram is stressful for many people, often compounded by a long wait for results, with additional follow-up tests and examinations." said Elad Benjamin, co-founder and CEO of Zebra Medical Vision.

A few weeks ago I posted a map of the Artificial Intelligence landscape in Ireland. The map is a visual representation of resident Irish companies in the A.I. space. I decided to go a step further and put together an overview of the Irish Artificial Intelligence industry. I used the companies on the map to form the basis of the findings covered in this blog. I use the phrase'Artificial Intelligence' as an umbrella term to categorise the industry.

In our previous blog posts of this series, we have introduced Topic Models, BigML's latest resource that helps you find thematically related terms in your unstructured text data, explained how to use it through the BigML Dashboard and the API, and lastly showed how to apply Topic Models in a real-life use case. This post will focus on automating LDA workflows by using WhizzML, a DSL for Machine Learning that provides programmatic support for all the resources you work with in our platform. Let's dive in by creating a Topic Model and making a prediction with it. In BigML, you can perform single instance predictions (referred to as a Topic Distribution) or in batch mode, which is called Batch Topic Distribution. Firstly, we will create a Topic Model without specifying any particular configuration option, that is, relying on default settings.

Newsweek and International Business Times are to host an Artificial Intelligence and Data Science event, taking place on 1 and 2 March 2017 at the Barbican in the City of London. The event, which is taking place in association with Imperial College London will bring together data scientists from banks, hedge funds and fintech startup companies to discuss the frontiers of AI and machine learning technology in financial markets. IBT Media is the parent company of Newsweek and International Business Times. Professor Nick Jennings CB, FREng, Vice-Provost (Research) Imperial College, said: "Imperial College London is at the forefront of getting technology and economics/business expertise working together to create real impact at the forefront of the fintech revolution. "We have a broad and substantial expertise, from crypto-currency to financial economics, from signal processing to mathematical analysis and from AI to data processing and high performance computing.

It's the holiday season, which means it's time for retailers to bombard you with sales, special offers, and other attempts to sell you things. For many people, the Black Friday-to-Christmas shopping rush will lead to Amazon. It makes sense: The e-commerce giant sells a whole lot of products and makes it very easy to buy them. Many times, those products are less expensive than they are elsewhere. Though most people seem to be satisfied with the Amazon shopping experience, certain aspects of it can still be misleading.