The UK has a genuine opportunity to take a lead on the ethics of artificial intelligence, says Nigel Shadbolt, principal of Jesus College, Oxford and co-founder of the Open Data Institute (ODI). You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

Deep Learning (DL) provides a methodology to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise from intraday system constraints due to supply and demand fluctuations. We propose deep spatio-temporal models and extreme value theory (DL-EVT) to capture the tail behavior of load spikes. Deep architectures, such as ReLU and LSTM can model generation trends and temporal dependencies while EVT captures highly volatile load spikes. To illustrate our methodology, we use hourly price and demand data from the PJM interconnection for 4719 nodes and we develop a deep predictor. DL-EVT outperforms traditional Fourier and time series methods, both in-and out-of-sample, by capturing the nonlinearities in prices. Finally, we conclude with directions for future research.

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. In this paper, we consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we propose a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We present the new model, its induced likelihood function, and a fitting algorithm which can be applied in the presence of missing data. We demonstrate how to use XPCA to produce an estimated full conditional distribution for each data point, and use this to produce to provide estimates for missing data that are automatically range respecting. We compare the methods as applied to simulated and real-world data sets that have a mixture of discrete and continuous variables.

The accuracy and reliability of machine learning algorithms are an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety, security, and provenance, are also critical elements to engender consumers' trust in a service. In this paper, we propose a supplier's declaration of conformity (SDoC) for AI services to help increase trust in AI services. An SDoC is a transparent, standardized, but often not legally required, document used in many industries and sectors to describe the lineage of a product along with the safety and performance testing it has undergone. We envision an SDoC for AI services to contain purpose, performance, safety, security, and provenance information to be completed and voluntarily released by AI service providers for examination by consumers. Importantly, it conveys product-level rather than component-level functional testing. We suggest a set of declaration items tailored to AI and provide examples for two fictitious AI services.

"Machine learning is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn" or be trained to make predictions on based on input "data[3]" such algorithms are not bound by static program instructions, instead making their predictions or decisions according to models they themselves build from sample inputs. "Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is unfeasible; example applications include spam filtering, optical character recognition (OCR),[5] search engines and computer vision." Machine learning is also employed experimentally to detect patterns and linkages in seemingly random or unrelated data. A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

All of China's 1.4 billion citizens are about to be put under greater scrutiny as the country prepares to launch its'social credit score' scheme. The project rates citizens based on their behaviour, and those who do not play by the rules are added to a list that prohibits them from certain luxuries. Fears are growing regarding the ethical implications of scheme, with some questioning the morality of the big-brother culture. The government is likely to use its rapidly growing surveillance network to enforce the system, with some academics growing concerned that it may be manipulated to enforce the ideology of the ruling Communist party. Completing community service and buying Chinese products is thought to improve it whereas fraud, tax evasion and smoking in non-smoking areas can drop it.

They are the bricks that could build mankind's first home on another planet. European Space Agency officials have revealed the latest'moon bricks' that could soon be used to construct a lunar habitat. They say the bricks are the starting point to building up a permanent lunar outpost and breaking explorers' reliance on Earth supplies. This 1.5 tonne building block was produced as a demonstration of 3D printing techniques using lunar soil. The surface of the Moon is covered in grey, fine, rough dust.

Opponents of the Federal Communications Commission have outlined their chief arguments on net neutrality to a federal appeals court in Washington, in hopes of undoing the FCC's move last year to repeal its own rules for Internet service providers. The legal briefs reflect a widening front in the multipronged campaign by consumer groups and tech companies to rescue the ISP regulations, which originally barred providers from blocking websites or slowing them. With the FCC's changes, Internet providers may legally manipulate Internet traffic as it travels over their infrastructure, as long as they disclose their practices to consumers. The FCC's decision last year to repeal the rules was "arbitrary and capricious," said officials from the state of New York, the California Public Utilities Commission and others in court documents Monday -- asking the U.S. Court of Appeals for the District of Columbia Circuit to overrule the agency. The FCC was too credulous in accepting industry promises "to refrain from harmful practices," the officials said, "notwithstanding substantial record evidence showing that [Internet] providers have abused and will abuse their gatekeeper roles in ways that harm consumers and threaten public safety."

Is artificial intelligence (AI) a panacea to make work and life nearly effortless – or the first step to a nightmare scenario of self-aware machines? There's been both ballyhoo and angst over the prospects of AI, including some that think AI poses a risk to the "existence of human civilization." On the other hand, senior military and national security leaders have asserted that to not pursue AI is the real existential threat, as it can put the country at risk from other nations that don't share the need for caution. With apologies to those who fear the worst, federal and SLED governments have come down squarely on the side of vigorously pursuing artificial intelligence. Companies that sell storage solutions, automation, big data, security and data mining tools should be encouraged by all the buzz going on in government about AI.

Russia's most famous weapons manufacturer has unveiled a 13ft tall walking killer robot operated by pilots who sit inside it. Kalashnikov Concern presented the state-of-the-art bulletproof robot along with utility vehicles and new assault rifles at the Army 2018 fair at the Patriot Park just outside Moscow. The gold robot, called Igorek, is still in development and its creators do not wish to reveal all of its features until they have finished. So far, all that is known about the'controlled bipedal walker' is that it weighs 4.5 tonnes and can reportedly hold and move objects - including weapons - with its claws. Russia's most famous weapons manufacturer has unveiled a 13ft tall walking robot operated by pilots who sit inside it A cabin behind the robot's glass panels allows people to sit and operate the robot from inside.