With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game?

Researchers at Google have open-sourced a new framework that can scale up artificial intelligence model training across thousands of machines. It's a promising development because it should enable AI algorithm training to be performed at millions of frames per second while reducing the costs of doing so by as much as 80%, Google noted in a research paper. That kind of reduction could help to level the playing field a bit for startups that previously haven't been able to compete with major players such as Google in AI. Indeed, the cost of training sophisticated machine learning models in the cloud is surprisingly expensive. One recent report by Synced found that the University of Washington racked up $25,000 in costs to train its Grover model, which is used to detect and generate fake news.

It has crime, drama, adventure and also romantic movies in it.

Artificial intelligence is having a significant impact on mainstream business and computing after years of being in the hype cycle. Companies such as Amazon and Netflix have saved billions of dollars a year, and AI is expected to boost the global economy by trillions of dollars over the next several years. Concerns about AI's use remain, however, including security risks and biases it could introduce into hiring and society as a whole as well as bad decisions it might make due to poor underlying data quality. Here's a snapshot of the present and future of AI, told in 11 statistics: That's a 14 percent increase, more than the current economic output of China and India combined, a PwC study projects. Some $6.6 trillion of the boost will come from increased productivity, while $9.1 trillion will arrive as a result of increased economic consumption.

Wait, how did Netflix know I wanted to watch that? Through the use of Machine Learning, Collaborative Filtering, NLP and more, Netflix undertake a 5 step process to not only enhance UX, but to create a tailored and personalised platform to maximise engagement, retention and enjoyment. In the last decade, learning algorithms and models at Netflix have evolved with multiple layers, multiple stages and nonlinearities. This has developed to the stage at which they now use machine learning and deep variants to rank large catalogues of content by determining the relevance of each of their titles to each user, creating a personalized content strategy. Not only is the content customized, it is then also ranked from most to least likely to be watched.

At Lionbridge, we have deep experience helping the world's largest companies teach applications to understand audio. From virtual assistants to in-car navigation, all sound-activated machine learning systems rely on large sets of audio data. This time, we at Lionbridge combed the web and compiled this ultimate cheat sheet for public audio datasets for machine learning. AudioSet: AudioSet is an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos. LibriSpeech: LibriSpeech is a carefully segmented and aligned corpus of approximately 1000 hours of 16kHz read English speech, derived from read audiobooks. Spoken Digit Dataset: This dataset was created to solve the task of identifying spoken digits in audio samples.

Researchers at the University of Notre Dame are using artificial intelligence to develop an early warning system that will identify manipulated images, deepfake videos and disinformation online. The project is an effort to combat the rise of coordinated social media campaigns to incite violence, sew discord and threaten the integrity of democratic elections. The scalable, automated system uses content-based image retrieval and applies computer vision-based techniques to root out political memes from multiple social networks. "Memes are easy to create and even easier to share," said Tim Weninger, associate professor in the Department of Computer Science and Engineering at Notre Dame. "When it comes to political memes, these can be used to help get out the vote, but they can also be used to spread inaccurate information and cause harm."

News media is increasingly getting reliant on technology. It is getting transformed not only in terms of the modes of disseminating content, but also the ways in which they function internally. They use different kinds of technology, like web-based applications for online editing, advanced video processing technologies, data processing through various statistical packages and advanced programming languages, which are all part of this new model of journalism. The new journalists can be anything from experts on various topics to hackers and data scientists (remember Wikileaks?). The field has also substantially changed with the coming of what we know as the "New Media", which also has an infamous first cousin, "Citizen Journalism".

Artificial Intelligence is penetrating all corners of life. We've heard the Echo dots; we've met Sophia and we've asked Siri. But away from the every day AI that's rife in society, industries are starting to use the technology to their advantage, and the newsroom is no different. Organisations like Washington Post, Reuters and Press Association are all using their own forms of AI to improve the processes and systems that journalists are used to. At Forbes, their AI system'Bertie' has been programmed to give journalists first drafts and templates for stories.

One of my favorite Talend customer success stories is the International Consortium of Investigative Journalists (ICIJ). I love this story not only because they transformed investigation journalism with data, won the Pulitzer prize for the Panama papers, and helped the public to recover billions of dollars lost to illegal tax evasion. The story is also fascinating because they managed to decipher intelligence out of highly disparate and unknown data that they got from some of the history's largest data leaks. By retro-engineering massive amounts of raw data using Talend and other innovative data management tools, they revealed some of the most important stories in the world. This is the power of data intelligence.