A similar share of companies who are already benefiting from machine learning also expect revenue growth of more than 6 percent for the two-year period of 2018-2019, the study showed. The study was conducted by the Economist Intelligence Unit (EIU) and written in discussion with SAP. "Making the Most of Machine Learning: 5 Lessons from Fast Learners" is based on survey results from 360 senior executives across four geographic regions: North America, Europe, Asia Pacific and Latin America. The study identifies the opportunities, value and implications for companies that look at machine learning in a holistic way. The results also reveal leading companies -- called Fast Learners -- that are already seeing substantial benefits from machine learning.

Based on their work with global public and private sector clients, Immuta outlines how enterprises can ensure their data science programs are GDPR compliant. Immuta's co-founder and Chief Technology Officer, Steve Touw, will highlight best practices for a 100 percent GDPR Compliant Data Science Program in his conference session "How will the GDPR impact machine learning?" on Wednesday, May 23, 2018 at Strata Data Conference London. According to Gartner, fewer than 50 percent of companies falling under GDPR will be compliance-ready by the end of 2018. GDPR requires a new, scalable, privacy-preserving approach to AI and machine learning initiatives. Immuta's platform enables algorithmic-driven enterprises to quickly connect to data with any tool, dynamically control data from any source, and fully comply with GDPR to enable fast, legal and ethical data science.

Check out Steve Touw's session "How will the GDPR impact machine learning?" Much has been made about the potential impact of the EU's General Data Protection Regulation (GDPR) on data science programs. But there's perhaps no more important--or uncertain--question than how the regulation will impact machine learning (ML), in particular. Given the recent advancements in ML, and given increasing investments in the field by global organizations, ML is fast becoming the future of enterprise data science. This article aims to demystify this intersection between ML and the GDPR, focusing on the three biggest questions I've received at Immuta about maintaining GDPR-compliant data science and R&D programs. Granted, with an enforcement data of May 25, the GDPR has yet to come into full effect, and a good deal of what we do know about how it will be enforced is either vague or evolving (or both!).

When it comes to developing artificial intelligence, President Trump may want a free-market approach. But a number of experts disagree -- we need guidelines to protect people from discriminatory algorithms. Today, a group of humans rights organizations such as Human Rights Watch, Amnesty International, The Wikimedia Foundation, Access Now, and others called on governments and technology companies to adopt guiding principles to protect human rights. As part of today's RightsCon Toronto symposium, the organizations joined to pen the Toronto Declaration on Machine Learning, which can be found in full on Access Now's website. The declaration calls for engineers to develop and revisit algorithms with the explicit goal of promoting transparency and equality while working to end algorithm-propagated racism and discrimination.

Artificial Intelligence is already changing society. Algorithms and machine learning are trading millions of euros in financial markets; they are predicting what people want to search for online and what shows they might like to watch on Netflix; AI is already helping police identify criminals using facial recognition (albeit with mixed results), and sifting through climate change data. Soon, AI could be driving our cars and trains (even our ships and planes). How will these new technologies transform our workplaces, our homes, our cities, and our lives? Inevitably, there will be disruption.

When it comes to developing artificial intelligence, President Trump may want a free-market approach. But a number of experts disagree -- we need guidelines to protect people from discriminatory algorithms. Today, a group of humans rights organizations such as Human Rights Watch, Amnesty International, The Wikimedia Foundation, Access Now, and others called on governments and technology companies to adopt guiding principles to protect human rights. As part of today's RightsCon Toronto symposium, the organizations joined to pen the Toronto Declaration on Machine Learning, which can be found in full on Access Now's website. The declaration calls for engineers to develop and revisit algorithms with the explicit goal of promoting transparency and equality while working to end algorithm-propagated racism and discrimination.

Machine learning algorithms are everywhere. It is not just Facebook and Google. Companies are using them to provide personalized education services and advanced business intelligence services, to fight cancer and to detect counterfeit goods. The technology will make us collectively wealthier and more capable of providing for human welfare, human rights, human justice and the fostering of the virtues we need to live well in communities. We should welcome it and do all that we can to promote it. As with any new technology, there are ethical challenges.

A coalition of human rights and technology groups have released a new declaration in Toronto, calling for tech companies and governments to ensure machine learning systems respect the basic principles of human rights. The document, called the Toronto Declaration, was announced at the ongoing RightsCon event in Toronto and aims to "protect individuals against discrimination, promote inclusion, diversity and equity, and safeguards equality." While the declaration isn't legally binding, it is meant to set machine learning standards that government bodies and tech companies are encouraged to adhere to. The declaration has already been signed by Access Now, Amnesty International, Human Rights Watch and the Wikimedia Foundation, with additional signatories expected in the weeks to come. One solution the group suggests is greater diversity in the teams in terms of race, culture, gender and socio-economic background.

Eyewitness accounts may be far more reliable when conducted by a virtual police officer, rather than the real thing. Witnesses of a mock car theft provided as much as 60 percent more information when interviewed in an avatar-to-avatar context compared to face-to-face interviews. Giving testimony to the police can be stressful and intimidating. Previous studies have shown that multiple factors - including an unfamiliar setting, the police officer, or the desire to'perform well' - can decrease the accuracy of a witness' testimony. 'Witnesses can become distracted from the task of remembering during an interview because they are attending to the social behavior of the interviewer, such as facial expressions.

On December 31, 2015, I published my original call to arms for society's rational regulation of artificial intelligence before it is too late. I explained certain reasons why someone who is against solving problems through regulation would propose precisely that mechanism to help hedge the threats created by AI, and announced my proposed legislation: The Artificial Intelligence Data Protection Act (AIDPA). Since 2015, we have witnessed AI's rapidly evolving national and international growth and adoption that will soon impact every phase of mankind's life, from birth to death, sex to religion, politics to war, education to emotion, jobs to unemployment. Three of many recent developments confirm why now is the time for the AIDPA: (1) a McKinsey study from late 2017 determined that up to 800 million workers worldwide may lose their jobs to AI by 2030, half of contemporary work functions could be automated by 2055 and other recent studies suggest as many as 47 percent of U.S. jobs could be threatened by automation or AI over the next few decades; (2) AI has now created IP with little or no human involvement and continues to be programmed, tested and used to do so; see my Twitter for a library of media reports on AI-created IP; (3) tech giants and regulators are starting to acknowledge that industries that create and use AI should be at least partially responsible for minimizing the impact of AI-displaced workers. Now – and not later -- society must address AI's legal, economic and social implications with regard to IP and employment.