We've seen companies looking into high-speed trains transportation that will take people from New York City to Washington, D.C., faster, but France is taking it up a step: driverless high-speed trains. France's railway system, SNCF, said it's working on a TGVs (high-speed trains) that are autonomous, according to FranceInfo. The TGVs can also transport people to other countries, like Belgium, Spain and Italy. SNCF is reportedly working on a "drone train" project, which will be equipped with autonomous technology. The system will include external sensors that will anticipate obstacles on the track and automatically brake, if necessary.

Source: IDC Global Digital Data (in Exabyte) Enabling forces behind Artificial Applications 5. Artificial Intelligence, May 2016 5 Scope of report This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors. Over $1B has been invested in AI-Infrastructure startups since 2010 with $340M being invested in 2015.

Jobs are disappearing, incomes retreating, the precariat growing. Thousands of people risk their lives in stormy seas to flee wars, moribund economies and climate change on a daily basis. Traditional politicians continue to avoid publicly addressing the tsunami of unemployment, apparently baffled as to how to react to a historic transition: the automation of critical masses of labour once performed by humans. Five acronyms - AI, AR, VR, BC and UBI - promise to shape the developed world's future and solve the problems of the present. In the process, however, these innovations risk transforming the world around us, and upsetting humanity's very definition of itself.

In its fight to take down tech support scams worldwide, Microsoft relied on artificial intelligence (AI) to help it track down the scammers. In a Thursday blog post, the company detailed how its Digital Crimes Unit used the technology to figure out the methods employed by the scammers, and how they avoided getting caught in the past. The scams in question typically occur when a pop-up window shows up, informing a user that they need to call for tech support to remove a virus or other form of malicious software. If the user calls the number listed, they are typically directed to someone trying to sell them services they don't actually need, the post said. The US Federal Trade Commission (FTC) announced an official crackdown on these scams with Operation Tech Trap in May 2017.

Recently, I had the honor of speaking with a number of the world's most influential thought-leaders in the fields of data science, data analytics, machine learning and digital transformation. This group of prominent data technologists was more than happy to answer a wide variety of question on topics ranging from the fast-evolving area of unified governance and preparing for General Data Protection Regulations (GDPR) to transformative hybrid data management technologies, and of course, data science and machine learning. You'll learn more about all of the topics discussed here during the main event, breakout sessions and demos, plus have the opportunity to join in the conversation and connect with these renowned data pioneers who can help you understand how to build a data-driven strategy to outsmart your competition. There will be an additional opportunity to chat with a number of these thought-leaders, as well as fellow data enthusiasts, at the Fast Track Your Data CrowdChat on Tuesday, June 20th, 2017 at 1:00 PM (EDT). Now let's meet our panel of experts: His latest book is Leading Innovation: Building a Scalable, Innovative Organization. She is also on the faculty of the Data Science Graduate Program at UC Berkeley, the Data Analytics MS Advisory Board at CUNY SPS, and the Data Science Committee for the Grace Hopper Conference. Ronald Van Loon, Director Adversitement, where he is helping data-driven companies generate business value as a globally recognized Top 10 Big Data, Data Science, IoT, and BI Influencer. Aylee Nielsen: Thank you all so much for joining me today, I'd like to start off with questions on a subject matter that I know you are all very familiar with – Data Science and Machine Learning.

There was further evidence of the high demand for artificial intelligence and data science professionals this week, as job site Indeed.com Three of the top 10 spots went to AI and data-related positions, with the job of machine learning engineer now coming in at a strong second place finish. Meanwhile, more than 10 percent of new jobs created in the U.K. this year have been in technology fields, with expertise in artificial intelligence and data science two of the primary drivers--but filling those positions is proving tough. Confirming the demand for technology jobs in general, tech jobs are the third-largest category in "hard to fill" roles on Indeed's platform, behind sales and management. Indeed based its top tech jobs list on hundreds of thousands of job searches and vacancy listings conducted on its website over the past 12 months. "The software economy is driving significant new employment opportunities in London, and this is showing up in the tech talent shortage, especially where developer and more senior roles are concerned," said Raj Mukherjee, a senior vice president at Indeed.

Can technology ever truly replace a present and attentive human mind? It's a question with philosophical undertones, but as Artificial Intelligence (AI) continues to evolve and surprise us, that isn't stopping the tech industry from debating it. While some in the tech world would have you believe that AI is on the brink of replacing vast swathes of the human workforce, now may be a good time to pause and think about just how much AI can realistically do on the ground level. And while AI technologies by definition are capable of certain cognitive functions, they can only learn from the data put in front of them. Humans, on the other hand, have the innate ability to adapt in real time, even in totally alien situations.

Analysis The ability to think logically and to reason is key to intelligence. When this can be replicated in machines, it will no doubt make AI smarter. But it's a difficult problem, and current methods used in deep learning aren't advanced enough. Deep learning is good for processing information, but it can struggle with reasoning. Enter a different player to the game: relational networks, or RNs.

What if our data have way more than 3-dimensions? In the table is the average consumption of 17 types of food in grams per person per week for every country in the UK. The table shows some interesting variations across different food types, but overall differences aren't so notable. Let's see if PCA can eliminate dimensions to emphasize how countries differ. Already we can see something is different about Northern Ireland.

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with Deep-Bach easy to use.