Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.

Your data has value, but unlocking it for your own benefit is challenging. Understanding how valuable data are collected and approved for use can help you to get there. Two primary means for differentiating audiences by their data collection methods are site-authenticated data collection and people-based data collection, suggested a recent piece in BulletinHealthcare written by Justin Fadgen, chief corporate development officer for the firm. Site-authenticated data are sourced from individual authentication events, such as when a user completes an online form, and generally agrees to a privacy policy that includes a data use agreement. User data are then be combined with other data sources that add meaning, becoming the basis of advertising targeting for instance.

While the familiar idiom "you say tomayto, I say tomahto" is meant to showcase the triviality of differences, the irony lies in its illustration of the wide variation in English pronunciation. Such vagaries in pronunciation can make English difficult for many nonnative speakers unused to pronouncing certain sounds. English is a stress-based language, meaning that it requires emphasis on particular syllables, said Sarah Daniels, CEO and co-founder of English-learning startup Blue Canoe. "If someone is not proactively thinking about stress ... we, in our system, can teach them where it is and how to do it." Bellvue-based Blue Canoe's mobile app directs its users to repeat sentence prompts and record them.

A Rwandan firm, Shaka AI Ltd, has debuted on the global Artificial Intelligence scene, providing services to an American firm on a Knowledge Process Outsourcing model. Knowledge Process Outsourcing (KPO) is the allocation of relatively high-level tasks, to an outside organisation or a different group usually in a different geographic location. Shaka AI is a joint venture between two Canadian firms and a Rwandan start-up. It is registered in Rwanda. The Rwandan start-up; SOLVIT Africa, specializes in providing practical internship/apprenticeship opportunities.

My name is Vincenzo Lomonaco and I'm a Postdoctoral Researcher at the University of Bologna where, in early 2019, I obtained my PhD in computer science working on "Continual Learning with Deep Architectures" in the effort of making current AI systems more autonomous and adaptive. Personally, I've always been fascinated and intrigued by the research insights coming out of the 15 years of Numenta research at the intersection of biological and machine intelligence. Now, as a visiting research scientist at Numenta, I've finally gotten the chance to go through all its fascinating research in much greater detail. I soon realized that, given the broadness of the Numenta research scope (across both neuroscience and computer science), along with the substantial changes made over the years to both the general theory and its algorithmic implementations, it may not be really straightforward to quickly grasp the concepts around them from a pure machine learning perspective. This is why I decided to provide a single-entry-point, easy-to-follow, and reasonably short guide to the HTM algorithm for people who have never been exposed to Numenta research but have a basic machine learning background.

The excitement and activity surrounding the field of machine learning was clearly evident at the 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), which took place this summer in Valencia, Spain. Over 25 minisymposia--as well as several prize lectures and invited talks--touched on the theme of "learning," while other invited presentations addressed important mathematical research challenges necessary to advance the field. Panelists Hans De Sterck (University of Waterloo), Gitta Kutyniok (Technische Universität Berlin), James Nagy (Emory University), and Eitan Tadmor (University of Maryland, College Park) represented various core areas of computational and applied mathematics that develop and utilize machine learning techniques, including computational science and engineering, imaging science, linear algebra, and partial differential equations. Discussion broached a variety of issues surrounding machine learning, such as the obvious fact that machine learning will remain, as mathematician Ali Rahimi stated, "an area comparable to alchemy" without new mathematical understanding and developments. Deep learning is among the most transformative technologies of our time, and its many potential applications--from driverless cars to drug discovery--can have tremendous societal impact.

Cognex CGNX recently announced the acquisition of Seoul-based SUALAB, a developer of deep learning-based vision software. Although the financial terms of the acquisition have been kept under wraps, per a Pulse article the transaction price is estimated to be $168.6 million. Deep learning allows Cognex to solve the most complex vision application operations in factories faster, easier and in a cost-effective manner. The addition of SUALAB's Intellectual property and highly skillful engineering team, which specializes in deep learning, is expected to strengthen the company's product portfolio. The latest acquisition will help Cognex to reap benefits from strong prospects of the global deep learning system software market.

Below please find a short recap and an outlook for our next conference on September 6, 2018. The aim of this conference was to bring together European academics, young researchers, students and industrial practitioners to discuss the application of Artificial Intelligence to various practical fields. In a broader context, we wanted to promote «Mathematics for Industry» in Switzerland, as part of the European COST (Cooperation in Science and Technology) Action "Mathematics for Industry", where members of ZHAW are in the management committee for Switzerland. COST is the longest-running European framework supporting transnational cooperation among researchers, engineers and scholars across Europe. The 1st European COST Conference in Switzerland on this topic was held on September 15, 2016.

In the nation's capital, it is a rare sight for all the players in an industry, large and small, to come together and ask for new regulations. The relationship between regulator and regulated can sometimes be adversarial at worst, tense at best. But in the case of automated vehicles (AVs) – a technology whose driving philosophy is reducing roadway deaths and injuries – it has become a shared priority to create a federal regulatory framework to assure safety. But building a regulatory framework for a nascent technology is challenging work: it requires a certain degree of flexibility, gathering a lot of input, and no small amount of elbow grease. Since the beginning of this year, Congressional committees have been engaged in a bipartisan and bicameral initiative to develop AV legislation that will create a path to deployment to ensure companies continue to develop this life-saving and life-changing technology here in the U.S. A federal framework is needed to ensure the safety and mobility benefits AVs promise to deliver happen here at home, rather than abroad.

The U.S. Department of Energy (DOE) is planning a major initiative to use artificial intelligence to speed up scientific discoveries. At a meeting here last week, DOE officials said they will likely ask Congress for between $3 billion and $4 billion over 10 years, roughly the amount the agency is spending to build next-generation "exascale" supercomputers. But DOE has a unique asset: torrents of data. Algorithms trained with these data could help discover new materials or rare signals of new particles in the deluge of high energy physics data. But they face intense global competition to fund researchers and companies to lead what could be the next phase of the digital revolution.