California's passage of their "GDPR-lite" caught people off guard. We think this is part of a trend we've studied for a long time. Much of the current analysis misses key points, so it seems worth explaining. About two years ago, we asked several thought leaders in the U.S. about the odds we'd see legislation like the E.U. GDPR provides clear rights to E.U citizens, controlling data captured on-line.

As we come to the end of 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record-breaking funding year for AI. But the path getting real value from data science and AI can be a long and difficult journey. To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that evolve at the pace at which humans can change -- much slower. Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines ('physical technology') mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust ('social technology'). With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just'cool things' for data science teams.

At the Neural Information Processing Systems (NeurIPS) conference this week in Vancouver, Canada, machine learning took center stage as 13,000 researchers explored things like neuroscience, how to interpret neural network outputs, and how AI can help solve big real-world problems. With more than 1,400 works accepted for publication, you have to choose how to prioritize your time. For Google AI chief Jeff Dean, that means giving talks at workshops about how machine learning can help confront the threat posed by climate change and how machine learning is reshaping systems and semiconductors. VentureBeat spoke with Dean Thursday about Google's early work on the use of ML to create semiconductors for machine learning, the impact of Google's BERT on conversational AI, and machine learning trends to watch in 2020. This interview has been edited for brevity and clarity.

Deepfakes are spreading fast, and while some have playful intentions, others can cause serious harm. We stepped inside this deceptive new world to see what experts are doing to catch this altered content. Chances are you've seen a deepfake; Donald Trump, Barack Obama, and Mark Zuckerberg have all been targets of the computer-generated replications. A deepfake is a video or an audio clip where deep learning models create versions of people saying and doing things that have never actually happened. A good deepfake can chip away at our ability to discern fact from fiction, testing whether seeing is really believing.

Introduced in 2014, GANs (Generative Adversarial Networks) was first presented by Ian Goodfellow and other researchers at the University of Montreal. It comprises of two deep networks, the generator which generates data instances, and the discriminator which evaluates the data for authenticity. GANs works not only as a form of generative model for unsupervised learning, but also has proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. In this article, we are in conversation with Greg Walters, one of the authors of the book'Hands-On Generative Adversarial Networks with PyTorch 1.x', where we discuss some of the real-world applications of GANs. According to Greg, facial recognition and age progression will one of the areas where GANs will shine in the future.

Artificial intelligence is all over the world, not least of all taking over agencies as a means of supplying actionable insights. Machine learning tools teach an AI what it should and shouldn't do, iteratively producing better results with each cycle. Today's machine learning ad tools can both create ads and then use insights generated from their creation to adapt their next attempt. Many businesses have already embraced the new technology of AI and machine learning to develop ads and check their efficacy. Below, 11 contributors to Forbes Agency Council look at the lesser-used ways businesses can leverage machine learning advertising tools for their benefit.

Today's fast-paced life has many challenges, which has led to the Millenials being called as the Burnout Generation. A report by the World Health Organisation (WHO) predicts that by 2020, 20% of the Indian population will suffer from mental illnesses. The report also says that by next year, depression will be the second-largest disease burden for the entire world. But now, artificial intelligence is making its presence felt in this sector. For example, researchers at IBM are using transcripts and audio inputs from psychiatric interviews, coupled with machine learning techniques to find patterns in speech.

Volkswagen Group and Qatar have agreed to develop a public transit system of autonomous shuttles and buses by 2022 for the capital city of Doha. The agreement signed Saturday by VW Group and the Qatar Investment Authority is an expansive project that will involve four brands under VW Group, including Volkswagen Commercial Vehicles, Scania, its shared ride service MOIA and Audi subsidiary Autonomous Intelligent Driving, or AID. The aim is to develop the entire transport system, including the electric autonomous shuttles and buses, legal framework, city infrastructure and ride-hailing software required to deploy a commercial service there. The autonomous vehicles will be integrated into existing public transit. "For our cities to progress we need a new wave of innovation," QIA CEO Mansoor Al Mahmoud said in a statement.

Cars are still all over the place. And we spend way too much time in traffic jams. For years we have been hearing that self-driving cars and buses are coming. But before that happens, those futuristic vehicles need to be tested in every possible way. In many cases this is done at Transpolis, Europe's largest testing ground for future mobility solutions.

Deep learning will radically change aspects of our medical care. How well do we need to understand how AI tools work? In clinics around the world, a type of artificial intelligence called deep learning is starting to supplement or replace humans in common tasks such as analyzing medical images. Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data.