When we imagine a future where humans and robots coexist, it doesn't take long for us to arrive at a conclusion where the human race tragically ends. A robot takeover usually occurs, followed by the inevitable enslavement of all humankind. But when it comes to the future and what will actually unfold, Palmer Luckey, founder of Oculus VR (which Facebook now owns) and inventor of the Oculus Rift virtual reality headset, isn't sweating it. "The reason I'm not creeped out is pretty simple," said Luckey, who sat down with Apple co-founder Steve Wozniak and Re/code journalist Kara Swisher on Saturday at the Silicon Valley Comic Con in San Jose, California. "A lot of people look to science-fiction for representations of technology. It can also be flawed."

Small and medium sized businesses are being targeted by IBM and MasterCard as they look to bring big data analytics insights to better understand their markets and consumers. A partnership has been formed by the two companies that sees MasterCard integrate IBM Watson Analytics into its platform, along with its own anonymised transaction data that is gathered through the payment company's Local Market Intelligence. This combination will bring artificial intelligence to its payments platform. The aim is to be able to offer SMBs insights on revenue, market share, customer demographics and competitors in a particular location and across multiple locations. The problem being tackled is that smaller merchants often don't have the resources to maximise data insights.

In this paper, we compare 5 different nonlinear kernels: min-max, RBF, fRBF (folded RBF), acos, and acos-$\chi^2$, on a wide range of publicly available datasets. The proposed fRBF kernel performs very similarly to the RBF kernel. Both RBF and fRBF kernels require an important tuning parameter ($\gamma$). Interestingly, for a significant portion of the datasets, the min-max kernel outperforms the best-tuned RBF/fRBF kernels. The acos kernel and acos-$\chi^2$ kernel also perform well in general and in some datasets achieve the best accuracies. One crucial issue with the use of nonlinear kernels is the excessive computational and memory cost. These days, one increasingly popular strategy is to linearize the kernels through various randomization algorithms. In our study, the randomization method for the min-max kernel demonstrates excellent performance compared to the randomization methods for other types of nonlinear kernels, measured in terms of the number of nonzero terms in the transformed dataset. Our study provides evidence for supporting the use of the min-max kernel and the corresponding randomized linearization method (i.e., the so-called "0-bit CWS"). Furthermore, the results motivate at least two directions for future research: (i) To develop new (and linearizable) nonlinear kernels for better accuracies; and (ii) To develop better linearization algorithms for improving the current linearization methods for the RBF kernel, the acos kernel, and the acos-$\chi^2$ kernel. One attempt is to combine the min-max kernel with the acos kernel or the acos-$\chi^2$ kernel. The advantages of these two new and tuning-free nonlinear kernels are demonstrated vias our extensive experiments.

If a document is about travel, we may expect that short snippets of the document should also be about travel. We introduce a general framework for incorporating these types of invariances into a discriminative classifier. The framework imagines data as being drawn from a slice of a Lévy process. If we slice the Lévy process at an earlier point in time, we obtain additional pseudo-examples, which can be used to train the classifier. We show that this scheme has two desirable properties: it preserves the Bayes decision boundary, and it is equivalent to fitting a generative model in the limit where we rewind time back to 0. Our construction captures popular schemes such as Gaussian feature noising and dropout training, as well as admitting new generalizations. Black-box discriminative classifiers such as logistic regression, neural networks, and SVMs are the go-to solution in machine learning: they are simple to apply and often perform well. However, an expert may have additional knowledge to exploit, often taking the form of a certain family of transformations that should usually leave labels fixed. For example, in object recognition, an image of a cat rotated, translated, and peppered with a small amount of noise is probably still a cat.

Crowdsourcing enables researchers to conduct social experiments on a heterogenous set of participants and at a lower economic cost than conventional laboratory studies. For example, researchers can harness internet users to conduct user studies on their personal computers. Among various approaches to conduct subjective tests, pairwise comparisons are expected to yield more reliable results. However, in crowdsourced studies, the individuals performing the ratings are diverse compared to more controlled settings, which is difficult to control for using traditional experimental designs; researchers have recently proposed several randomized methods to conduct user studies [1, 2, 3], which accommodate incomplete and imbalanced data. HodgeRank, as an application of combinatorial Hodge theory to the preference or rank aggregation problem from pairwise comparison data, possibly being incomplete and imbalanced, was first introduced by [4], and inspired a series of studies in statistical ranking [5, 6, 7, 8]. Hodge theory has also found applications in game theory [9] and computer vision [10, 11], in addition to traditional applications in fluid mechanics [12] etc. HodgeRank formulates the ranking problem in terms of the discrete Hodge decomposition of the pairwise data and shows that it can be decomposed into three orthogonal components: a gradient flow representing a global rating (optimal in the L

Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings. Despite successes and a large ongoing research effort, the limitation of ICA to square linear transformations have not been overcome, so that general INFOMAX is still far from being realized. As an alternative, we present feature analysis in medical imaging as a problem solved by Helmholtz machines, which include dimensionality reduction and reconstruction of the raw data under the same objective, and which recently have overcome major difficulties in inference and learning with deep and nonlinear configurations. We demonstrate one approach to training Helmholtz machines, variational auto-encoders (VAE), as a viable approach toward feature extraction with magnetic resonance imaging (MRI) data.

The stochastic block model is one of the oldest and most ubiquitous models for studying clustering and community detection. In an exciting sequence of developments, motivated by deep but non-rigorous ideas from statistical physics, Decelle et al. conjectured a sharp threshold for when community detection is possible in the sparse regime. Mossel, Neeman and Sly and Massoulie proved the conjecture and gave matching algorithms and lower bounds. Here we revisit the stochastic block model from the perspective of semirandom models where we allow an adversary to make `helpful' changes that strengthen ties within each community and break ties between them. We show a surprising result that these `helpful' changes can shift the information-theoretic threshold, making the community detection problem strictly harder. We complement this by showing that an algorithm based on semidefinite programming (which was known to get close to the threshold) continues to work in the semirandom model (even for partial recovery). This suggests that algorithms based on semidefinite programming are robust in ways that any algorithm meeting the information-theoretic threshold cannot be. These results point to an interesting new direction: Can we find robust, semirandom analogues to some of the classical, average-case thresholds in statistics? We also explore this question in the broadcast tree model, and we show that the viewpoint of semirandom models can help explain why some algorithms are preferred to others in practice, in spite of the gaps in their statistical performance on random models.

We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix completion, robust principal component analysis or factor analysis. It is also important in machine learning: unsupervised representation learning can often be studied through matrix factorization. We use the tools of statistical mechanics - the cavity and replica methods - to analyze the achievability and computational tractability of the inference problems in the setting of Bayes-optimal inference, which amounts to assuming that the two matrices have random independent elements generated from some known distribution, and this information is available to the inference algorithm. In this setting, we compute the minimal mean-squared-error achievable in principle in any computational time, and the error that can be achieved by an efficient approximate message passing algorithm. The computation is based on the asymptotic state-evolution analysis of the algorithm. The performance that our analysis predicts, both in terms of the achieved mean-squared-error, and in terms of sample complexity, is extremely promising and motivating for a further development of the algorithm.

"Soon, our habitat will be invaded by unreal humans. Not only they'll influence our way of living, but also intervene in our modus operandi." I'm not the only one who thinks this way. Last week I released a list of must watch movies on Machine Learning and Data Science. I've watched 8 of them till now.

Iris Capital is a pan-European venture capital fund manager specializing in digital economy. In such a world, many information platforms are available, but there is no software tool that applies the latest in machine and deep learning. Come to us to present us the next generation software tool or platform that automatically detects the right innovative teams/companies depending on who's looking for it and how innovation is defined. Our pitching competition is aimed at international early-stage start-ups between 1 and 5 years of existence. The 5 to 10 best start-ups will be evaluated on stage by a jury made of Iris Capital investors, large corporate innovation VP, leading start-up CEOs and media agencies.