Holland is one of the greatest countries to live in, but the biggest downside is that it rains 145 days a year. That's why the Google Cloud Platform team in the Netherlands is launching Google Wind this Spring. We leveraged existing Dutch infrastructure to realize this moonshot in record time. We upgraded some historical windmills in Holland with control modules connected to Google Cloud Platform. Google Wind then uses Machine Learning to recognize cloud patterns and orchestrate the network of windmills when rain is approaching.

Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challenging to accurately cluster networks that are of a different size and density, but hypothesized to be structurally similar. In this paper, we address this problem by introducing a new network comparison methodology that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and applicable in a wide variety of settings ranging from the functional classification of proteins to tracking the evolution of a world trade network.

Recent years have seen great progress for AI. In particular, artificial agents have learned to classify images and recognize speech at near-human level. However, for artificial agents to reach their full potential, they should not only observe, but also act and learn from the consequences of their actions. Learning how to behave is especially important when an agent interacts with humans through natural language, because of the complexity of language and because each person has a different communication style. Reinforcement learning (RL) is the area of research that is concerned with learning effective behavior in a data-driven way.

Literate cultures use writing as a kind of'knowledge technology.' Unlike older, oral cultures, they are able to store human intelligence outside the human brain. Literate cultures change the kind of knowledge that humanity can experience. In the coming era of machine intelligence, we face a transformation that will similarly transform what humans can know. I just finished reading Eric A. Havelock's 1986 book, The Muse Learns to Write.

Things change pretty much on a daily basis in the world of SEO. Since the announcement of Google's AI machine learning algorithm – RankBrain – in 2015, one of the most discussed topics in SEO galleries is: With Google admitting RankBrain being one of the top three ranking factors, these discussions have become even more worthwhile. In past 3-4 months, we also saw a spike in the number of SERPed members asking the same question. And, multiple posts claiming 2017 as the year of AI and Voice Search, we think it is the right time to dive deeper to understand more about it. To get more clarity on this topic, we decided to go straight to the big guns and find out what they think about it. The responses from each expert are compiled below. Fasten your seat belts and get ready for an awesome ride. Albert Mora is the CEO and co-founder of Seolution, an SEO agency for Shopify e-commerce sites. He has been doing SEO from 1997 and has around 20 years of experience. Follow Albert on Twitter here. Since the beginning of the Internet, artificial intelligence has played a relevant role in the operation of search engines. Logically, the algorithms have been evolving, but the fundamental underlying principle remains the same: search engines want to deliver quality search results to the users. For this reason, if you want a long term sustainable SEO results, you must think about the users first, not about the search engines. Alex has more than 15 years of experience in Digital Marketing, and he is working online since 2002.

In a recent IDC spending guide titled Worldwide cognitive systems and artificial intelligence spending guide, some fantastic numbers were thrown out in terms of opportunity and growth 50 % CAGR, Verticals pouring in billions of dollars on cognitive systems. One of the key components of cognitive systems is Machine Learning. According to wikipedia Machine Learning is a subfield of computer science that gives the computers the ability to learn without being explicitly programmed. Just these two pieces of information were enough to get me interested in the field. After hours of daily searching, digging through inane babble and noise across the internet, the understanding of how machines can learn evaded me for weeks, until I hit a jackpot.

Want to receive a weekly deep dive into all things auto, transportation, & logistics tech? Click here to subscribe to our auto tech newsletter. Momentum in auto tech is at an all-time high, with investors funding private startups in the field at a record pace. Of course, much of the buzz has revolved around autonomous driving software, with startups like Zoox seeing $200M funding rounds, tech corporates looking to capitalize, and major automakers working feverishly to catch up. Validating the reliability of fully autonomous vehicles will be no small feat, with RAND estimating that tens or hundreds of billions of test miles might have to be driven to properly gauge their safety. While many players are meeting this challenge head-on, a number of other startups are also developing autonomous tech for more focused applications.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent? In the real world, the late and great Alan Turing came up a test to measure whether a machine is able to exhibit behaviour is that equivalent to that of a human, aptly known as the Turing Test.

Gloomy predictions abound that the applications of artificial intelligence and machine learning will put huge numbers of people out of work in the coming years. But the corollary is that these technologies create opportunities to develop new goods and services that will bring new jobs. What's certain is that advanced implementations of computer science are beginning to disrupt our lives. We must start thinking about how these technologies are applied and regulated if we are to reap the benefits and minimise potential harms. The introduction of the steam engine in the 18th century disrupted the life of the agricultural labourer and fuelled the rise of cities, creating new industries and new jobs. Traditional professions such as medicine and law were largely unchanged. But the latest industrial revolution has the potential to change almost every form of work.

Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce are competing in the race to acquire private AI companies, with Ford, Samsung, GE, and Uber emerging as new entrants. Over 200 private companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in Q1'17 alone (as of 3/24/17). This quarter also saw one of the largest M&A deals: Ford's acquisition of Argo AI for $1B. In 2013, Google picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.