Earlier this year, Mark Zuckerberg revealed he is dedicating his year to making a home AI butler. Now, he has revealed the project is already coming to fruition - and promised to reveal it next month. While at a Facebook'town hall' event in Rome, he told an audience'I'm making progress - I hope to have a demo next month.' Pope Francis meets Facebook founder and CEO Mark Zuckerberg, second from left, and his wife Priscilla Chan, at the Santa Marta residence, the guest house in Vatican City. However, Zuckerberg later revealed his wife doesn't have access to his home AI system. 'I've got to the point where I can control lights, gates, and temperature, much to the chagrin of my wife who cannot now control the temperature, as is it's programmed to only respond to my voice.'

Mark Zuckerberg, shown here meeting with Pope Francis on his vacation in Italy, told a Roman audience that he should be ready to show off his at-home personal assistant in a month. SAN FRANCISCO - Next month, Mark Zuckerberg hopes to offer the world a glimpse at his pet project, a face- and voice-recognition system that allows the Facebook CEO to command his home environment much like the fictional Tony Stark orders around Jarvis in Iron Man. And his wife, Priscilla Chan, might not be thrilled. "I got it to this point where now I can control the lights, I can control the gates, I can control the temperature -- much to the chagrin of my wife, who now cannot control the temperature because it is programmed to only listen to my voice," Zuckerberg told a packed audience in Rome, the CEO's latest town hall Q and A session. The artificial intelligence software powering the system - part of a personal challenge Zuckerberg set for himself earlier in the year - is a by-product of Facebook's strategic mission to improve the social network's ability to better identify faces in photos and videos that are relevant to users.

Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.

In image deconvolution problems, the diagonalization of the underlying operators by means of the FFT usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather slow methods, or, otherwise, use inexact convolution models, resulting in the occurrence of artifacts in the enhanced images. In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast. We iteratively alternate the estimation of the unknown pixels and of the deconvolved image, using, e.g., an FFT-based deconvolution method. This framework is an efficient, high-quality alternative to existing methods of dealing with the image boundaries, such as edge tapering. It can be used with any fast deconvolution method. We give an example in which a state-of-the-art method that assumes periodic boundary conditions is extended, through the use of this framework, to unknown boundary conditions. Furthermore, we propose a specific implementation of this framework, based on the alternating direction method of multipliers (ADMM). We provide a proof of convergence for the resulting algorithm, which can be seen as a "partial" ADMM, in which not all variables are dualized. We report experimental comparisons with other primal-dual methods, where the proposed one performed at the level of the state of the art. Four different kinds of applications were tested in the experiments: deconvolution, deconvolution with inpainting, superresolution, and demosaicing, all with unknown boundaries.

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as compressive sensing, and subset selection in regression, and group testing. In this paper, we take a unified approach to support recovery problems, considering general probabilistic models relating a sparse data vector to an observation vector. We study the information-theoretic limits of both exact and partial support recovery, taking a novel approach motivated by thresholding techniques in channel coding. We provide general achievability and converse bounds characterizing the trade-off between the error probability and number of measurements, and we specialize these to the linear, 1-bit, and group testing models. In several cases, our bounds not only provide matching scaling laws in the necessary and sufficient number of measurements, but also sharp thresholds with matching constant factors. Our approach has several advantages over previous approaches: For the achievability part, we obtain sharp thresholds under broader scalings of the sparsity level and other parameters (e.g., signal-to-noise ratio) compared to several previous works, and for the converse part, we not only provide conditions under which the error probability fails to vanish, but also conditions under which it tends to one.

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence does not crucially depend on high sampling frequency. Experimental results show that the proposed ALIP system performs better than the conventional IP-based load disaggregation system.

Robot brothels could soon be commonplace as Britain's sex industry undergoes a technological revolution. A leading law professor claims that technology can clean up the industry, wiping out issues such as sexually transmitted disease and sex slavery. NUI Galway Law professor John Danaher believes that robots, which can be mass-produced, could even legalise the industry, which only regulates humans. Robot brothels could soon be commonplace as Britain's sex industry undergoes a technological revolution. Pimps and brothel-owners would be free to set up businesses offering cyborg sex to anyone who demands it without legal sanction, he said.

Facebook today named the recipients of 22 servers that Facebook designed specifically for artificial intelligence (A.I.) research. This comes after Facebook's introduction of the giveaway program for academic researchers back in February. University departments in Austria, Belgium, the Czech Republic, France, Germany, Italy, Russia, Switzerland, and the United Kingdom are getting the machines, the designs of which Facebook open-sourced in December. The servers can support as many as eight graphics processing units (GPUs), which are often used to train artificial neural networks (ANNs) with lots of data. After being trained, the ANNs can make inferences on new data.

A range of studies in recent months has warned of an imminent job apocalypse. The most famous of these suggests that up to 47 per cent of US jobs are at high-risk of automation over the next two decades. Another study finds 54 per cent of EU jobs are likely automatable, while the chief economist of the Bank of England has argued that 45 per cent of UK jobs are similarly under threat. This is not simply a rich country problem either, as low-income economies look set to be hit even harder by automation. It would seem that we are on the verge of a mass job extinction.

Earlier this year, Mark Zuckerberg revealed he is dedicating his year to making a home AI butler. Now, he has revealed the project is already coming to fruition - and promised to reveal it next month. While at a Facebook'town hall' event in Rome, he told an audience'I'm making progress - I hope to have a demo next month.' Pope Francis meets Facebook founder and CEO Mark Zuckerberg, second from left, and his wife Priscilla Chan, at the Santa Marta residence, the guest house in Vatican City. However, Zuckerberg later revealed his wife doesn't have access to his home AI system.