Pi is an analytics company focused on transforming Business Intelligence (BI) with modern machine learning tools. With our wide and diverse client base, we are moving traditional, service-lead BI into a productised data-driven toolset, helping our clients seamlessly integrate their data sources into one platform. Automation and machine learning are being utilised not just to replace manual data munging, ETL and report writing but also to offer new insights and provide more power to our analyst and business manager clients. We are seeking candidates for the role of data scientist to form part of Pi's new California office. As an early member of this team, you will have a major impact on shaping our data science platform and responsibility for developing novel machine learning solutions.

Google just announced the 10 startups that have been shortlisted for the second calls of its Launchpad Accelerator program in India. All of the startups on the list have used artificial intelligence and machine learning to formulate their products. Google just announced the second wave of startups selected for their Launch Accelerator program in India. The program kicks off today with a one week mentorship programme boot camp organised by Google in Bengaluru which will be followed by more classes in April and May to address more specific issues -- lasting a total of three months. Aside from guidance, Google will also provide support for AI and ML, cloud computing, developing user interfaces, using the Android platform, online presence, product strategy and marketing.

In February, President Donald Trump showed his administration's commitment to keeping America at the forefront of technological advancements by signing an executive order directing federal agencies to allocate resources to expand the use of artificial intelligence as part of a new "American AI Initiative." With a renewed focus on innovation, the U.S. has a unique opportunity to invest in the technologies that can unlock the full potential of data. Information is generated at a rate that far surpasses our ability to process and measure it. To bridge that gap, we must develop technologies that help identify and mitigate risks within datasets. This process, however, is complicated.

"If I wasn't gaming, I was thinking about gaming". Sean - not his real name - has told the BBC's Victoria Derbyshire programme about how gaming addiction took over his life to the point where he lost his family and his job. Matthew Preece, a senior therapist at the UK Addiction Treatment group (Ukat), says there has been a "significant" increase in the number of people seeking treatment. The group, which has traditionally treated people for alcohol, drug and gambling problems, says that it has seen the number of people wanting help with gaming addiction rise year-on-year from four in 2014 to 22 in 2018. Sean was one person who became addicted.

Investigations into Facebook's data handling keep piling up. The New York Times has learned that federal prosecutors are in the midst of a criminal investigation into the data deals Facebook arranged with tech companies. It's not known when the investigation began or just what the focus is, but a New York grand jury reportedly used subpoenas to obtain records from two or more "prominent makers of smartphones." The deals included heavyweights like Apple, Microsoft and Sony. Facebook acknowledged the investigation to the Times, stating that it was "cooperating with investigators" and was taking probes "seriously."

The rapid adoption of Industry 4.0 technologies leaves manufacturers with a choice: accelerate with the market or be left behind. According to a 2019 Global Market Insights, Inc. report, the market for artificial intelligence in manufacturing will grow to $16 billion by 2025. Factors driving the adoption of Industry 4.0, the general name given to the deployment of cyber-physical systems, Internet-of-Things technologies and cognitive computing in the manufacturing environment, include: Achieving these goals is supported by two main principles: interconnection and information transparency. Interconnection refers to the ability of machines, devices, sensors and people to connect and communicate with each other. Information transparency provides operators with large amounts of useful information needed to make appropriate decisions. The interconnected nature of machines and systems in an Industry 4.0 environment combined with information transparency allows manufacturers greater insight into the current operating conditions and operational efficiency of the factory.

Artificial intelligence (AI) is trending globally in commerce, science, health care, geopolitics, and more areas. Deep learning, a subset of machine learning, is the lever that launched the worldwide rush--an area of strategic interest for researchers, scientists, visionary CEOs, academics, geopolitical think tanks, pioneering entrepreneurs, astute venture capitalists, strategy consultants, and management executives from companies of all sizes. Yet in the midst of this AI renaissance, is a relatively fundamental unsolvable problem with machine learning that is not commonly known, nor frequently discussed outside of the small cadre of philosophers, and artificial intelligence experts. A global research team of researchers have recently demonstrated that machine learning has an unsolvable problem, and published their findings in Nature Machine Intelligence in January 2019. Researchers from Princeton University, the University of Waterloo, Technion-IIT, Tel Aviv University, and the Institute of Mathematics of the Academy of Sciences of the Czech Republic, proved that AI learnability cannot be proved nor refuted when using the standard axioms of mathematics.

Facebook is under criminal investigation by federal prosecutors examining its data-sharing deals with other major technology companies, according to the New York Times. A New York grand jury has subpoenaed records from "at least two prominent makers of smartphones and other devices", the Times reported, citing two unnamed sources. The two companies are among more than 150, including Amazon, Apple and Microsoft, that have entered into partnerships with Facebook for access to the personal information of hundreds of millions of its users, according to the report. "We are cooperating with investigators and take those probes seriously," a Facebook spokesman told the Times. "We've provided public testimony, answered questions and pledged that we will continue to do so."

Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.

Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions. All of these transforms can be represented by dense matrix-vector multiplication, yet each has a specialized and highly efficient (subquadratic) algorithm. We ask to what extent hand-crafting these algorithms and implementations is necessary, what structural priors they encode, and how much knowledge is required to automatically learn a fast algorithm for a provided structured transform. Motivated by a characterization of fast matrix-vector multiplication as products of sparse matrices, we introduce a parameterization of divide-and-conquer methods that is capable of representing a large class of transforms. This generic formulation can automatically learn an efficient algorithm for many important transforms; for example, it recovers the $O(N \log N)$ Cooley-Tukey FFT algorithm to machine precision, for dimensions $N$ up to $1024$. Furthermore, our method can be incorporated as a lightweight replacement of generic matrices in machine learning pipelines to learn efficient and compressible transformations. On a standard task of compressing a single hidden-layer network, our method exceeds the classification accuracy of unconstrained matrices on CIFAR-10 by 3.9 points---the first time a structured approach has done so---with 4X faster inference speed and 40X fewer parameters.