WASHINGTON--(BUSINESS WIRE)--Jul 10, 2018--The following is an opinion editorial provided by Naveen Rao of Intel Corporation. This press release features multimedia. Naveen Rao was the founder of Nervana and is now corporate vice president and general manager of the Artificial Intelligence Products Group at Intel Corporation. Most people agree that artificial intelligence (AI) will transform modern society in positive ways. From autonomous cars that will save thousands of lives, to data analytics programs that may finally discover a cure for cancer, to machines that give voice to those who can't speak, AI will be known as one of the most revolutionary innovations of mankind. But this fantastic future is a long way off, and the path to get us there is still under construction.

What if we delegated so much to autonomous AI and intelligent machines that They passed a law that forbids humans to carry out a number of professions? We conceive the plot of a new episode of Black Mirror to reflect on what might await us and how we can deal with such a future.

Better Life Lab is a partnership of Slate and New America. In an age where every day brings more doomsday forecasts of massive technologicallybdriven unemployment, from driverless cars to A.I. robots as caregivers, journalist Annie Lowrey set out to answer a question: Is it possible to live in a world where we get what she calls "wages for breathing"? This week her findings come out in Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World. We spoke about what the idea of giving every American cash--no strings attached--would mean for work, gender inequality, and American identity, and whether it's actually a policy that could pass in the U.S. given the current climate of tying even the most basic benefits to paid work. This interview has been condensed and edited for clarity.

What if machines learn to communicate with each other? What if they begin to establish their own objectives? What if they become so intelligent that they are making decisions beyond the capacity of the human mind? Those are some of the questions the 95-year-old Kissinger poses in a piece published by the Atlantic under the apocalyptic headline: 'How The Enlightenment Ends.' Kissinger's interest in artificial intelligence began when he learned about a computer program that had become an expert at Go -- a game more complicated than chess. The machine learned to master the game by training itself through practice; it learned from its mistakes, redefined its algorithms as it went along -- and became the literal definition of'practice makes perfect.'

The basis of GDPR is the privacy of data for citizens and consumers of EU countries and the methods needed to track and enforce GDPR will need to have intelligent methods to process the large amount of data that will need to be sorted to identify GDPR compliance or non-compliance. According to Dr. Shane Archiquette, CTO – Global Communications, Media & Entertainment, Global Markets and Technology Architecture, AI-assisted auditing will most likely be the method used to look for patterns across multiple disparate datasets that businesses will need to produce and process. In addition, AI will provide a composable method to machine-learn new areas that are identified by GDPR legal precedence as new cases are opened and closed. According to a recent report, European Union Institute researchers created AI-enabled software to scrutinize the privacy policies of 14 major technology companies for violations of the new GDPR. They found that 1/3 rd of the clauses contained "insufficient information," with 11% of the policies' sentences using "unclear language."

From the critical, like law enforcement, healthcare, and humanitarian aid, to the mundane, like dating and shopping, artificial intelligence (AI) seems to be the answer to all our problems. AI is a catch-all phrase for a wide-ranging set of technologies most of which apply learning techniques from statistics to find patterns in large sets of data and make predictions based on those patterns. It seems like there are meetings every other week, organised by representatives from industry, government, academia, and civil society to address the perils of AI and formulate solutions to harness its potential. But who is driving the regulatory agenda and what do they stand to gain? This question needs to be answered because letting industry needs drive the AI agenda presents real risks.

While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

When new banking laws are passed, the text is written in legal language at the framework level. But to actually take effect, much of that language needs to be interpreted by regulators, then interpreted by financial executives and their bank operating staff, and then translated into technology-driven actions. That latter work is done by those who possess science, technology, engineering and math, or STEM, skills, including software engineers, data scientists and a cadre of other professionals who wrestle with turning legal terms and regulations into actionable financial services activities through computer code and algorithms. It's important that those at the front end of this information -- legislators, regulators and financial executives -- become conversant in these skills, lest the back-end stumbles over meaning and intent and, in some cases, declares it impossible to be implemented. In his recent testimony before Congress, Mark Zuckerberg, the chief executive of Facebook, responded to many questions by referring to the company's use of artificial intelligence.

Facial recognition AI could help police to spot'potentially dangerous' criminals before they've even broken the law, according to one expert. Dr Michal Kosinski - who last year invented a controversial AI he claimed could detect your sexuality - said such face-reading technology may one day help CCTV cameras monitor public spaces for people predisposed to violent behaviour. While the concept raises privacy issues, it has the potential to save lives, the Stanford University academic claims. Dr Kosinski is currently working on computer programmes that detect everything from your political beliefs to your IQ by looking at a single photograph. Stanford researcher Dr Michal Kosinski hit headlines last year after publishing research (pictured) suggesting AI can tell whether someone is straight or gay based on photos.

Since the 1990s, the UN General Assembly has served as a platform for states to debate these issues. For example, a series of groups of governmental experts agreed on the applicability of international law to cyberspace, recommended norms on state use of information and communication technologies (ICTs), and helped build trust and cooperative measures to reduce the risk of conflict arising from their misuse. Despite facing setbacks, these processes generated important results and can serve as a framework for global stability. Meanwhile, efforts should be redoubled to address the challenges posed by autonomous lethal weapons and emerging risks associated with advances in biotechnology. Of course, geo-strategic calculations will frame states' decisions on whether to cooperate or compete.