Since we established our new European Headquarters in Valencia last July, we have been carrying the Machine Learning banner at all major tech events in the city. This time we were proud sponsors of the third edition of PyConES, the marquee Python event held in Spain. During the weekend event, almost 400 developers attended the conference.

Next time you schedule a meeting and an assistant named Amy or Andrew Ingram sets up the logistics, here's a pro tip: You may be chatting with a robot. And if it's one of x.ai's bots, you might never know the difference. That was my experience when I exchanged emails with "Andrew" to set up an interview with x.ai's CEO. After I emailed x.ai's press contact, she referred me to Andrew to hammer out the details. Andrew proposed a time, thanked me when I accepted and sent out a calendar invitation.

NOT since the era of imperial Rome has the "thumbs-up" sign been such a potent and public symbol of power. A mere 12 years after it was founded, Facebook is a great empire with a vast population, immense wealth, a charismatic leader, and mind-boggling reach and influence. The world's largest social network has 1.6 billion users, a billion of whom use it every day for an average of over 20 minutes each. In the Western world, Facebook accounts for the largest share of the most popular activity (social networking) on the most widely used computing devices (smartphones); its various apps account for 30% of mobile internet use by Americans. And it is the sixth-most-valuable public company on Earth, worth some 325 billion.

Next time you schedule a meeting and an assistant named Amy or Andrew Ingram sets up the logistics, here's a pro tip: You may be chatting with a robot. And if it's one of x.ai's bots, you might never know the difference. That was my experience when I exchanged emails with "Andrew" to set up an interview with x.ai's CEO. After I emailed x.ai's press contact, she referred me to Andrew to hammer out the details. Andrew proposed a time, thanked me when I accepted and sent out a calendar invitation.

If you put water on the stove and heat it up, it will at first just get hotter and hotter. You may then conclude that heating water results only in hotter water. But at some point everything changes – the water starts to boil, turning from hot liquid into steam. Automation, driven by technological progress, has been increasing inexorably for the past several decades. Two schools of economic thinking have for many years been engaged in a debate about the potential effects of automation on jobs, employment and human activity: will new technology spawn mass unemployment, as the robots take jobs away from humans? Or will the jobs robots take over release or unveil – or even create – demand for new human jobs?

In the third issue of this monthly digest series you can find out how Microsoft is bringing AI to the visually impaired, how to colorize your grayscale images, why a Google car caused a crash for the first time, and much more. Last Thursday, Microsoft showed off its Seeing AI app for the first time. It's still under development, but it looks extremely promising. Using a smartphone camera or a pair of camera-equipped smart glasses, the Seeing AI app can identify things in your environment--people, objects, and even emotions--to provide important context for what's going on around you. By a swipe of hand, the user can instruct the app to take a snapshot of the current visual scene and run it through image recognition software.

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances that are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.

Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the intuitive and well-defined notion of principal curves and surfaces. However, the problem of unwrapping or unfolding manifolds has received relatively little attention within the density ridge approach, despite being an integral part of manifold learning in general. This paper proposes two novel algorithms for unwrapping manifolds based on estimated principal curves and surfaces for one- and multi-dimensional manifolds respectively. The methods of unwrapping are founded in the realization that both principal curves and principal surfaces will have inherent local maxima of the probability density function. Following this observation, coordinate systems that follow the shape of the manifold can be computed by following the integral curves of the gradient flow of a kernel density estimate on the manifold. Furthermore, since integral curves of the gradient flow of a kernel density estimate is inherently local, we propose to stitch together local coordinate systems using parallel transport along the manifold. We provide numerical experiments on both real and synthetic data that illustrates clear and intuitive unwrapping results comparable to state-of-the-art manifold learning algorithms.

This paper describes the data collection effort that is part of the project Sprekend Nederland (The Netherlands Talking), and discusses its potential use in Automatic Accent Location. We define Automatic Accent Location as the task to describe the accent of a speaker in terms of the location of the speaker and its history. We discuss possible ways of describing accent location, the consequence these have for the task of automatic accent location, and potential evaluation metrics.

We study the problem of learning sparse structure changes between two Markov networks $P$ and $Q$. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes \emph{directly} via estimating the ratio between two Markov network models. In this paper, we give sufficient conditions for \emph{successful change detection} with respect to the sample size $n_p, n_q$, the dimension of data $m$, and the number of changed edges $d$. When using an unbounded density ratio model we prove that the true sparse changes can be consistently identified for $n_p = \Omega(d^2 \log \frac{m^2+m}{2})$ and $n_q = \Omega({n_p^2})$, with an exponentially decaying upper-bound on learning error. Such sample complexity can be improved to $\min(n_p, n_q) = \Omega(d^2 \log \frac{m^2+m}{2})$ when the boundedness of the density ratio model is assumed. Our theoretical guarantee can be applied to a wide range of discrete/continuous Markov networks.