In the late 1960s, urban planners Horst Rittel and Melvin Webber began formulating the concept of "wicked problems" or "wicked challenges" --problems so vexing in the realm of social and organizational planning that they could not be successfully ameliorated with traditional linear, analytical, systems-engineering types of approaches. These "wicked challenges" are poorly defined, abstruse, and connected to strong moral, political and professional issues. Some examples might include: "How should we deal with crime and violence in our schools? "How should we wage the'War on Terror'? or "What is good national immigration policy?" "Wicked problems," by their very nature, are strongly stakeholder dependent; there is often little consensus even about what the problem is, let alone how to deal with it.

AI Business recently interviewed one of the UK's leading experts in AI for Business, George Zarkadakis. George has a PhD in AI, is the Digital Lead at Willis Towers Watson, and the author of "In Our Own Image: will Artificial Intelligence save us or destroy us?". George is also a keynote speaker at The AI Summit London, presenting on the many different ways that AI will be shaping the business of tomorrow. George answered a number of interesting questions giving us a taster of the insightful presentation to come at The AI Summit on the 5th of May. How do you believe AI will impact business overall and in what ways?

We consider a statistical inverse learning problem, where we observe the image of a function $f$ through a linear operator $A$ at i.i.d. random design points $X_i$, superposed with an additive noise. The distribution of the design points is unknown and can be very general. We analyze simultaneously the direct (estimation of $Af$) and the inverse (estimation of $f$) learning problems. In this general framework, we obtain strong and weak minimax optimal rates of convergence (as the number of observations $n$ grows large) for a large class of spectral regularization methods over regularity classes defined through appropriate source conditions. This improves on or completes previous results obtained in related settings. The optimality of the obtained rates is shown not only in the exponent in $n$ but also in the explicit dependency of the constant factor in the variance of the noise and the radius of the source condition set.

Due to challenging applications such as collaborative filtering, the matrix completion problem has been widely studied in the past few years. Different approaches rely on different structure assumptions on the matrix in hand. Here, we focus on the completion of a (possibly) low-rank matrix with binary entries, the so-called 1-bit matrix completion problem. Our approach relies on tools from machine learning theory: empirical risk minimization and its convex relaxations. We propose an algorithm to compute a variational approximation of the pseudo-posterior. Thanks to the convex relaxation, the corresponding minimization problem is bi-convex, and thus the method behaves well in practice. We also study the performance of this variational approximation through PAC-Bayesian learning bounds. On the contrary to previous works that focused on upper bounds on the estimation error of M with various matrix norms, we are able to derive from this analysis a PAC bound on the prediction error of our algorithm. We focus essentially on convex relaxation through the hinge loss, for which we present the complete analysis, a complete simulation study and a test on the MovieLens data set. However, we also discuss a variational approximation to deal with the logistic loss.

We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave independently according to a Markov chain, but individual sample paths cannot be observed due to limitations of the observation process or the need to protect privacy. Instead, only population-level counts of the number of individuals in each state at each time step are available. When these counts are exact, a conditional least squares (CLS) estimator is known to be consistent and asymptotically normal. We initiate the study of method of moments estimators for this problem to handle the more realistic case when observations are additionally corrupted by noise. We show that CLS can be interpreted as a simple "plug-in" method of moments estimator. However, when observations are noisy, it is not consistent because it fails to account for additional variance introduced by the noise. We develop a new, simpler method of moments estimator that bypasses this problem and is consistent under noisy observations.

Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out.

WASHINGTON – An Ohio man paralyzed in an accident while diving in waves can now pick up a bottle or play the video game Guitar Hero thanks to a small computer chip in his brain that lets his mind guide his hands and fingers, bypassing his damaged spinal cord. Scientists on Wednesday described accomplishments achieved by 24-year-old quadriplegic Ian Burkhart using an implanted chip that relays signals from his brain through 130 electrodes on his forearm to produce muscle movement in his hands and fingers. Burkhart first demonstrated the "neural bypass" technology in 2014 when he was able simply to open and close his hand. But the scientists, in research published in the journal Nature, said he can now perform multiple useful tasks with more sophisticated hand and finger movements. The technology, which for now can only be used in the laboratory, is being perfected with an eye toward a wireless system without the need for a cable running from the head to relay brain signals.

Scientists have been able to develop artificial intelligence (AI) capable of besting humans at their own games, but a new study suggests that people may have the upper hand when it comes to intuitive thinking. A team of researchers led by Denmark's Aarhus University associate professor Jacob Sherson managed to develop a game based around complex theoretical science in which human players were "able to find solutions to difficult problems associated with the task of quantum computing," whereas computerized numerical optimization failed, according to the scientists' findings published in Nature. "The big surprise we had was that some of the players actually had solutions that were of higher quality and of shorter duration than any computer algorithms could find," Mr. Sherson told the Associated Press. The game, Quantum Moves, is available online for the purpose of helping in the development of quantum computing. While it functions as entertainment, Quantum Moves is built to take quantum physics optimization problems and turn them into a game, the results of which demonstrate fundamental differences between human thought processes and the problem solving of computers.

Ian Burkhart has been a cyborg for two years now. In 2014, scientists at Ohio State's Neurological Institute implanted a pea-sized microchip into the 24-year-old quadriplegic's motor cortex. Its goal: to bypass his damaged spinal cord and, with the help of a signal decoder and electrode-packed sleeve, control his right arm with his thoughts. Neuroengineers have been developing these so-called brain-computer interfaces for more than a decade. They've used readings from brain implants to help paralyzed patients play Pong on computer screens and control robotic arms.

Computers may have us beat at chess and checkers, but new research suggests our brains still have an edge when it comes to solving certain tricky problems thanks to a very human trait: intuition. Scientists in Denmark have found that people who played a game that simulated a complex calculation in physics sometimes did better than their silicon rivals. "The big surprise we had was that some of the players actually had solutions that were of high quality and of shorter duration than any computer algorithms could find," said Jacob Friis Sherson, a physicist at Aarhus University who co-wrote the study published Wednesday in the journal Nature. Experts say the results could advance the quest to develop effective quantum computers, something most major universities and several tech companies are working on as they seek to accelerate processing power. Such computers use individual atoms to store information and it's hoped they could one day outperform even the fastest conventional silicon-based supercomputers.