This guest article was written by Dr. Cleber Gomes who is an Electronic Engineer with a Ph.D. from Tokyo University of Technology and Agriculture. As we approach the date of the Impeachment vote in Brazil, it might be interesting to take a look at how the Brazilian stock market behaves. In this article, I propose to do that from the point of view of Trend Following Algorithms. To exemplify, I will present the results acquired from my own Trend Following System, which is based on Machine Learning technologies, specifically Neural Networks. Take the lead from today's leaders.

The use of data within an organisation to improve elements of the business such as the supply chain, improve decision making, and to make cost savings, is becoming more widely accepted as being vital. It is vital in respect to the business remaining competitive, vital to remaining relevant, and vital to the future of the business. One of the industries that has been looking significantly at the use of its data is the manufacturing industry, and stepping back one level to the steel industry. Magnitogorsk Iron and Steel Works (MMK), is the third largest steel company in Russia with a revenue of 9.3bn. Established in 1870, the company has taken to using machine learning technology from Yandex Data Factory to creative a competitive advantage that will see it being competitive for years to come.

Back in December, I wrote a detailed piece about the potential for artificial intelligence streamlining the real estate industry through automation. Eight months on, and there is increasing evidence of just how big an impact there could be for machine learning in real estate. The most recent evidence, launched in mid-July, is TouchAssist, created by Touch Commerce, which promises to "enable brands to offer intelligent automated conversations leading consumers to self-serve on digital channels". The idea is to create a'smart' virtual assistant (or'chat bot') which can answer customer queries or book in property viewings, whilst simultaneously gathering analytical and KPI data. Wherever the bot fails to answer a customer question, a human customer operator steps in, and the machine continues to listen in and learn more answers.

A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry. There has been a lot of talk recently about advancements in the smart home arena, especially about new ways to control smart home devices. I have heard Amazon's Echo referred to as a smart home device, and just this week, web service IFTTT announced new partnerships that are intended to allow smart home devices to connect in an automated fashion to other devices and services. However, what we're still missing when it comes to the smart home is a true smart home assistant -- a counterpart, if you will, to the smart assistants that come baked into every modern smartphone operating system. This post dives into what that means in practice, and who might be best positioned to deliver on this vision.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.

The next Machine Intelligence Summit will take place in New York on 9-10 November, to explore how AI will impact transport, manufacturing, healthcare, retail and more. Early Bird tickets are now available for this event - for more information and to register, visit the event page here. See the full events list here for events focused on AI, Deep Learning and Machine Intelligence taking place in London, Amsterdam, Boston, San Francisco, New York and Singapore.

In Munich, Germany, the technology conglomerate Siemens AG is betting 1.1 billion on digital technologies, such as the proposition that blockchain data can be leveraged by machine learning to improve the secure transmission of data used in energy trading. Siemens is welcoming its employees and independent firms to bid for the money if they are willing to research how Siemens can develop businesses that use artificial intelligence. Siemens is just one of a number of companies embracing machine learning--the combination of artificial intelligence and deep analytics that enables enterprises to make predictions on large amounts of data and allows developers to experiment by incorporating features like speech and pattern recognition, as well as statistical techniques, into their analysis. And HPE's recent announcement of "machine learning as a service" (MLaaS) is aimed at helping the process really take off. Speaking at HPE Discover Las Vegas 2016, HPE Executive Vice President Robert Youngjohns called machine learning and deep analytics the most fundamental change we're ever going to see.

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As we continue to chart the evolution of humanoid robots, humanity seems to be bypassing ethics over practicality. Robots already have advanced to a point that is leading to serious concern about the economic impact of humans being outsourced to robots for tasks as diverse as service, manufacturing, nursing, housework, yard maintenance and full-fledged agricultural duties. Some are predicting that robots of all types could fully replace humans by 2045. Meanwhile, humanoid robots filled with the latest artificial intelligence could lead to the outsourcing of future soldiers, leading to the literal possibility of robot wars. All of this is occurring as ethicists, governments and citizens scramble to make sense of exactly what type of future we should be looking forward to, and if any boundaries to this type of progress need to be made universal.

Google announced today that it has acquired Paris-based Moodstocks, a startup that has developed machine learning technology to bolster the image recognition features on smartphones. "We continue to pursue our machine learning and research efforts," wrote Vincent Simonet, head of the research and development team for France Google, "and Moodstocks is the latest proof of our commitment to this area." Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription. The terms of the deal were not disclosed.