A simple example of Bayesian inference applied to a medical diagnosis problem. Here the problem is diagnosing a rare disease using information from the patient's symptoms and, potentially, the patient's genetic marker measurements, which indicate predisposition (gen pred) to this disease. In this example, all variables are assumed to be binary. The relationships between variables are indicated by directed arrows and the probability of each variable given other variables they directly depend on is also shown. Yellow nodes denote measurable variables, whereas green nodes denote hidden variables.

'RoboBees' are meant for artificial pollination but could have unforeseen environmental effects. Max Tegmark is a renowned physicist. He is also the irrepressibly optimistic co-founder of the Future of Life Institute in Cambridge, Massachusetts (motto: "Technology is giving life the potential to flourish like never before ... or to self-destruct. Now, in Life 3.0, he tackles a pressing future development -- the evolution of artificial intelligence (AI). He argues that the risks demand serious thought if our "cosmic endowment" is not to be inadvertently thrown away.

Stuart Russell weighs up a book on the risks and rewards of the AI revolution.

The number of times a paper is cited is a poor proxy for its impact (see P. Stephan et al. I suggest relying instead on a new metric that uses artificial intelligence (AI) to capture the subset of an author's or a paper's essential and therefore most highly influential citations. Academics may cite papers for non-essential reasons -- out of courtesy, for completeness or to promote their own publications. These superfluous citations can impede literature searches and exaggerate a paper's importance. The scientific search engine, Semantic Scholar, is the first to automatically identify the subset of a paper's citations in which the paper had a strong impact on the citing work (see http://semanticscholar.org).

Grandmaster Garry Kasparov during the last of six games against Deep Blue in 1997; the computer won the match by 3.5 games to 2.5. Nearly 20 years ago, I was fortunate enough to play friendly blitz chess against former world champion Garry Kasparov. It was quite an experience; his competitive spirit and creative genius were palpable. I had recently founded Elixir Studios, which specialized in artificial intelligence (AI) games, and my ambition was to conduct cutting-edge research in the field. AI was on my mind that day: Kasparov had played chess against IBM's supercomputer Deep Blue just a few years before.

Integrating intermittent renewable-energy supplies into existing electricity grids in a stable way will depend on artificial intelligence. Such a system could process massive volumes of consumption data and adjust power usage almost instantly, giving real-time control over supply and demand. Domestic consumers would be rewarded (with cheaper bills) for shifting their energy demand at short notice when the grid has a power imbalance, as is already the case for large industrial consumers and grid-scale storage systems. Smart meters that collect household consumption data would enable this process. By 2020, the United Kingdom aims to have such meters in 26 million homes and the European Union has a target of 200 million.

This room of the university's Artificial Intelligence Lab looked like the study of an Oxford don. The walls were covered with bookcases and -- of course! There was a small end-table with a lace doily. The visitors were not at all what one might expect in a computer lab. There was a Catholic priest, a Lutheran minister and a rabbi.

Nano, a Nature Research solution, has just been given a boost by artificial intelligence (AI), increasing the information available to users by sourcing data from over 400,000 relevant research papers. Launched in June 2016, Nano offers highly-indexed and structured information on nanotechnology. It first provided over 200,000 summaries of nanomaterials, containing information on properties, synthesis and applications. These were and continue to be drawn from 30 high-impact journals from all publishers and curated by experts from across this multidisciplinary field. The scale-up, announced today, will significantly increase the breadth of data available.

Come the summer, many minds will turn to sport as the London Olympics kicks off. So it seems apt that, in a special issue this week, Nature invites its readers to embrace and celebrate a superb marathon runner -- who also happened to be one of the brightest minds of all time. Alan Turing, computer pioneer, wartime code-breaker and polymath, was born in London on 23 June 1912. But for injury, he would probably have joined the British Olympic team for the London games of 1948. Yet, 100 years and one month after his birth, when the Olympics will return to the city, no official celebration of the connection is planned.

Each boxplot corresponds to the distribution of the AUC obtained from 50 evaluations (10 five-fold cross-validation; ends and center line: 25th, 50th and 75th percentiles of AUC, respectively; notches: 95% confidence interval around the median; whiskers: 1.5 interquartile range above and below the 25th and 75th percentiles; dots: outliers). Evidence-weighted classifier (purple) significantly outperforms all the other classifiers (Wilcoxon rank-sum test, U 2,399, P 1 10 14). Enrichment P-values atop each bar were calculated using a permutation test (Online Methods). Fractions of genes in the gene set (y axis) that occurred within each decile of the genome-wide ranking (x axis; first decile colored red; number of genes in top decile/total and enrichment P-value in parentheses).