IBM CEO, Ginni Rometty has made the case that AI will be the foundation of the Fourth Industrial Revolution if intelligently used. Rometty was joined on a panel at the World Economic Forum in Davos, Switzerland yesterday by MIT Media Lab director Joichi Ito, Healthtap CEO Ron Gutman and Microsoft CEO Satya Nadella (19/01) about the future of AI. They stressed that the development of the field should be guided by the overarching principle that technology should enhance and support human capability, not replace it. The CEO of IBM, which has taken the lead in cognitive computing and developed advanced AI platform Watson, said that transparency is key in order to develop trust in emerging technologies. Soon everyone will be working with AI technologies and people will want to know how they are designed, by which experts and using which data.

IT professionals remain unfazed by any existential threat that artificial intelligence (AI) may pose to their careers, a survey has found. Just 18% of respondents in the survey commissioned by SolarWinds were concerned about the impact of AI on job security – far lower than the growing concern over cyber security that was cited by 91% of respondents. From cyber security experts to data scientists, the competition for IT talent has never been stiffer. This is evident in the findings of our annual IT Salary Survey in ANZ and ASEAN, where about 50% of respondents received salary increments compared to a year ago. You forgot to provide an Email Address.

Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim of this article is to survey how fairness is formalized in the machine learning literature for the task of prediction and present these formalizations with their corresponding notions of distributive justice from the social sciences literature. We provide theoretical as well as empirical critiques of these notions from the social sciences literature and explain how these critiques limit the suitability of the corresponding fairness formalizations to certain domains. We also suggest two notions of distributive justice which address some of these critiques and discuss avenues for prospective fairness formalizations.

Clustering samples according to an effective metric and/or vector space representation is a challenging unsupervised learning task with a wide spectrum of applications. Among several clustering algorithms, k-means and its kernelized version have still a wide audience because of their conceptual simplicity and efficacy. However, the systematic application of the kernelized version of k-means is hampered by its inherent square scaling in memory with the number of samples. In this contribution, we devise an approximate strategy to minimize the kernel k-means cost function in which the trade-off between accuracy and velocity is automatically ruled by the available system memory. Moreover, we define an ad-hoc parallelization scheme well suited for hybrid cpu-gpu state-of-the-art parallel architectures. We proved the effectiveness both of the approximation scheme and of the parallelization method on standard UCI datasets and on molecular dynamics (MD) data in the realm of computational chemistry. In this applicative domain, clustering can play a key role for both quantitively estimating kinetics rates via Markov State Models or to give qualitatively a human compatible summarization of the underlying chemical phenomenon under study. For these reasons, we selected it as a valuable real-world application scenario.

We propose new algorithms for topic modeling when the number of topics is unknown. Our approach relies on an analysis of the concentration of mass and angular geometry of the topic simplex, a convex polytope constructed by taking the convex hull of vertices representing the latent topics. Our algorithms are shown in practice to have accuracy comparable to a Gibbs sampler in terms of topic estimation, which requires the number of topics be given. Moreover, they are one of the fastest among several state of the art parametric techniques. Statistical consistency of our estimator is established under some conditions.

Denoising autoencoders (DAEs) are powerful deep learning models used for feature extraction, data generation and network pre-training. DAEs consist of an encoder and decoder which may be trained simultaneously to minimise a loss (function) between an input and the reconstruction of a corrupted version of the input. There are two common loss functions used for training autoencoders, these include the mean-squared error (MSE) and the binary cross-entropy (BCE). When training autoencoders on image data a natural choice of loss function is BCE, since pixel values may be normalised to take values in [0,1] and the decoder model may be designed to generate samples that take values in (0,1). We show theoretically that DAEs trained to minimise BCE may be used to take gradient steps in the data space towards regions of high probability under the data-generating distribution. Previously this had only been shown for DAEs trained using MSE. As a consequence of the theory, iterative application of a trained DAE moves a data sample from regions of low probability to regions of higher probability under the data-generating distribution. Firstly, we validate the theory by showing that novel data samples, consistent with the training data, may be synthesised when the initial data samples are random noise. Secondly, we motivate the theory by showing that initial data samples synthesised via other methods may be improved via iterative application of a trained DAE to those initial samples.

For CIOs who feel behind the eight ball on artificial intelligence, here's a bit of good news: You're probably not. According to a survey of 83 Gartner clients, 60% of respondents reported to be in an AI "knowledge-gathering phase," 25% said they are piloting an AI solution and a mere 5% of respondents said they have implemented an AI solution. Looking to establish accountability across disparate project teams? Trying to automate processes or allow for lean methodology support? Hoping to enable business consequence modeling or real-time reporting?

"In an era of post-truth politics, driven by the 24-hour news cycle, diminishing trust in institutions, rich visual media, and the ubiquity and velocity of social networked spaces, how do we identify information that is tinted -- information that is incomplete, that may help affirm our existing beliefs or support someone's agenda, or that may be manipulative -- effectively driving a form of propaganda?" (Lotan, Gilad. Over 70 years ago, Karl Polanyi established the term "double movement" Reviewing the most recent developments, politically, economically and socially, one may understand the value of his vision and why his ideas have been increasingly discussed lately. Propaganda 2.0 -- Post-truth politics The Definition of Post-truth politics Adjective: "relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief," Why post-truth rhetoric is propaganda This definition not only sounds familiar. The most recent events in global politics give incessant evidence that what is described as an "era" is nothing but history repeating itself. From the economic and social context to commonly used rhetoric.

People may worry that robots are coming for their jobs - but the companies making the bots are struggling to find qualified employees, research suggests. According to analysis from jobs site Indeed, there are at least twice as many jobs in artificial intelligence as there are suitable applicants. It says the number of roles in AI has risen by 485% in the UK since 2014. Academics say the "massive" skills gap in education systems is partly to blame for the shortage. Indeed said that the artificial-intelligence sector would benefit from investment in education.

At HelloFresh, we want to change the way people eat. Over the past 5 years we've seen this mission spread beyond our wildest dreams. So, how did we do it? Our weekly recipe boxes full of exciting recipes and lovingly sourced, fresh ingredients have blossomed into a community of inspired, energised home cooks that expands across the globe. Our story started in Berlin.