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Digital Brief: Far-right falsities

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Welcome to EURACTIV's Digital Brief, your weekly update on all things digital in the EU. You can subscribe to the newsletter here. With the Brits and the Dutch heading to the polls today, the big news of the week is the story that Facebook has removed around 80 pages spreading fake news or using tactics aimed at unfairly influencing the polls. The takedowns came following a discovery by the human rights group Avaaz, in which it uncovered far-right disinformation networks in France, UK, Germany, Spain, Italy and Poland, posting content that was viewed an estimated 533 million times over the past three months. EURACTIV Digital went to investigate further and paid Avaaz a visit at their recently opened'Citizens' War Room' in Brussels (pictured below).


Google's Duplex calls still frequently require human intervention – TechCrunch

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When Google launched Duplex with a demo at I/O last year, the audience was left wondering how much of the call was staged. The AI-based reservation booking service seemed almost too impressive to be a machine. Now that it's been used for real-world reservations, Google has revealed that it frequently isn't. The company recently told The New York Times that Duplex calls are often still made by human operators at call centers. Roughly a quarter of calls start with a live human voice.


6 Ways AI Can Transform ITSM Tools - ITSM.tools

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In the last 10 years, we've seen some significant breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be a reality TV show winner. In 2014, Google acquired an AI company called DeepMind, and one of its project, AlphaGo, beat the European Go champion in 2015. In 2016, Google made its TensorFlow library open source, which made machine learning accessible to the masses. Last year, people were left dumbfounded when Google Duplex made a haircut appointment over the phone.


Enterprise Search and Machine Learning: A Match Whose Time Has Come

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Over the last few years, artificial intelligence and machine learning have increasingly come up in conversations about enterprise search. As artificial intelligence (AI) and its cousin, machine learning (ML), increased in accuracy and ease of integration, instances of them being directly integrated with or running alongside of search to improve results increased as well. But chances are you remember when search relevancy was based on simple metrics like term frequency -- the document with the largest number of instances was ranked highest, and documents with fewer instances ranked lower. You were able to provide stop words like "the" and "of" whose frequent use typically added no value in retrieving relevant documents. The only content really useful to the search engine was the terms in the user query.


BP explores Azure AI to boost safety and drive business success

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As part of a corporate-wide digital transformation, BP is embracing artificial intelligence (AI) to change the way the company works. Using Microsoft Azure Machine Learning service and its automated machine learning capabilities, BP scientists can now explore the potential of new energy deposits. They can also build more finely tuned, accurate models in dramatically less time, helping them better gauge available hydrocarbon reserves.


A Glossary For Next-Generation AI

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As business adoption of artificial intelligence (AI) expands rapidly, so does the vocabulary used to describe the technology and the myriad ways companies are putting it to work. While terms such as algorithm, machine learning and neural networks have become as familiar today as cloud, SaaS and IoT, dozens of new AI terms and trends are already entering the field or rising in importance. Here's a look at some of those--and why you should become familiar with each. A machine-learning training technique in which scientists intentionally expose algorithms to corrupted data to trick them into making faulty predictions or reach incorrect conclusions. The technique allows developers to uncover security vulnerabilities that could be exploited by hackers or to examine the results for hidden bias that could lead to flawed results.


FinEduAI Artificial Intelligence project at Finnish high schools

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Amazon shareholders reject banning sale of facial recognition software to law enforcement

USATODAY - Tech Top Stories

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. Amazon shareholders will continue selling the company's facial recognition technology "Rekognition" to governments and law enforcement agencies. During the e-commerce giant's annual meeting Wednesday, shareholders rejected all proposals including two related to Rekognition, Amazon confirmed to USA TODAY. One proposed banning the sales of the technology and the other called for the company to conduct an independent study and issue a report on the risks of governments using the technology. Amazon did not release shareholder vote totals Wednesday but said information would be filed with the U.S. Securities and Exchange Commission later in the week.


The impact of Artificial Intelligence on marketing

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How will AI change the field of marketing? How can your prepare for this? Join IMD business school's signature program Orchestrating Winning Performance to get the full picture on AI & marketing and 40 other critical business topics for 2019.


An AI Pioneer Explains the Evolution of Neural Networks

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Geoffrey Hinton is one of the creators of Deep Learning, a 2019 winner of the Turing Award, and an engineering fellow at Google. Last week, at the company's I/O developer conference, we discussed his early fascination with the brain, and the possibility that computers could be modeled after its neural structure--an idea long dismissed by other scholars as foolhardy. We also discussed consciousness, his future plans, and whether computers should be taught to dream. The conversation has been lightly edited for length and clarity. Nicholas Thompson: Let's start when you write some of your early, very influential papers. Everybody says, "This is a smart idea, but we're not actually going to be able to design computers this way." Explain why you persisted and why you were so confident that you had found something important. Geoffrey Hinton: It seemed to me there's no other way the brain could work. It has to work by learning the strength of connections. And if you want to make a device do something intelligent, you've got two options: You can program it, or it can learn. And people certainly weren't programmed, so we had to learn. This had to be the right way to go. NT: Explain what neural networks are. GH: You have relatively simple processing elements that are very loosely models of neurons. They have connections coming in, each connection has a weight on it, and that weight can be changed through learning. And what a neuron does is take the activities on the connections times the weights, adds them all up, and then decides whether to send an output.