Byron Reese: This is Voices in AI, brought to you by Gigaom. Today, our guest is Oren Etzioni. He's a professor of computer science who founded and ran University of Washington's Turing Center. And since 2013, he's been the CEO of the Allen Institute for Artificial Intelligence. The Institute investigates problems in data mining, natural language processing, and the semantic web. And if all of that weren't enough to keep a person busy, he's also a venture partner at the Madrona Venture Group. Business Insider called him, quote: "The most successful entrepreneur you've never heard of." Welcome to the show, Oren. Oren Etzioni: Thank you, and thanks for the kind introduction. I think the key emphasis there would be, "you've never heard of." Well, I've heard of you, and I've followed your work and the Allen Institute's as well. And let's start, if that's Okay, let's start there. So if you would just start off by telling us a bit about the Allen Institute, and then I would love to go through the four projects that you feature prominently on the website. And just talk about each one; they're all really interesting. The Allen Institute for AI is really Paul Allen's brainchild. He's had a passion for AI for decades, and he's founded a series of institutes--scientific institutes--in Seattle, which were modeled after the Allen Institute for Brain Science, which has been very successful running since 2003. We were launched as a nonprofit on January 1, 2014, and it's a great honor to serve as CEO. Our mission is AI for the common good, and as you mentioned, we have four projects that I'm really excited about.

Google's AI-research arm DeepMind has announced the creation of DeepMind Ethics & Society (DMES), a new unit dedicated to exploring the impact and morality of the way AI shapes the world around us. Along with external advisors from academia and the charitable sector, the team aims to "help technologists put ethics into practice, and to help society anticipate and direct the impact of AI so that it works for the benefit of all". The unit has been in the works for the last 18 months with eight staff members and six external fellows, and is expected to grow to around 25 people over the coming year. The team will focus on six areas: privacy, transparency and fairness; economic impacts; governance and accountability; managing AI risk; AI morality and values; and the way AI can address global challenges. This is not the first ethics-orientated unit Google has put together within the AI sphere.

In recent years, there has been significant progress in the field of machine learning. Much of this progress can be attributed to the increasing use of graphics processing units (GPUs) to accelerate the training of machine learning models. In particular, the extra computational power has lead to the popularization of deep learning -- the use of complex, multi-level neural networks to create models, capable of feature detection from large amounts of unlabeled training data. GPUs are great for deep learning because the type of calculations they were designed to process are the same as those encountered in deep learning. Images, videos, and other graphics are represented as matrices so that when you perform any operation, such as a zoom-in effect or a camera rotation, all you are doing is applying some mathematical transformation to a matrix.

Will artificial intelligence (AI) ring the death knell for humanity, or will it improve our lives immeasurably? It depends who you ask. Tech luminary Elon Musk, for example, believes AI is the biggest threat we face as a civilization, while Facebook's Mark Zuckerberg thinks such viewpoints are irresponsible, at best. The bottom line is, we don't really know how AI will evolve. But we do know that a lot of money is being invested in developing AI technologies, and we are also aware that many people in the know expect that machines will surpass human intellect and abilities at some point in the foreseeable future. It's against this backdrop that Alphabet's U.K.-based AI subsidiary DeepMind has launched a new "ethics and society" research unit tasked with "exploring and understanding" the implications of AI permeating the world.

Deepmind, Google's London-based AI research sibling, has opened a new unit focused on the ethical and societal questions raised by artificial intelligence. The new research unit will aim "to help technologists put ethics into practice, and to help society anticipate and direct the impact of AI so that it works for the benefit of all", according to the company, which hit headlines in 2016 for building the first machine to beat a world champion at the ancient Asian board game Go. The company is bringing in external advisers from academia and the charitable sector, including Columbia development professor Jeffrey Sachs, Oxford AI professor Nick Bostrom, and climate change campaigner Christiana Figueres to advise the unit. "These Fellows are important not only for the expertise that they bring but for the diversity of thought they represent," said the unit's co-leads, Verity Harding and Sean Legassick, in a blogpost announcing its creation. The unit, called DeepMind Ethics and Society, is not the AI Ethics Board that DeepMind was promised when it agreed to be acquired by Google in 2014.

Summary: If you are mid-career and thinking about switching into data science here are some things to think about in planning your journey. We get lots of inquiries from readers asking for career advice and many of these identify as mid-career looking to switch into data science. If you're in this group you face some of the same challenges beginners do but also some that are unique to your circumstance. Here are some thoughts and observations that may be valuable. When folks self-identify as mid-career they usually cite 10 or 20 years experience. By my way of thinking that makes you most likely 30 or 40 years old.

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As we hand over more of our lives to artificial intelligence systems, keeping a firm grip on their ethical and societal impact is crucial. For DeepMind, whose stated mission is to "solve intelligence", that task will be the work of a new initiative tackling one of the most fundamental challenges of the digital age: technology is not neutral. DeepMind Ethics & Society (DMES), a unit comprised of both full-time DeepMind employees and external fellows, is the company's latest attempt to scrutinise the societal impacts of the technologies it creates. In development for the past 18 months, the unit is currently made up of around eight DeepMind staffers and six external, unpaid fellows. The full-time team within DeepMind will swell to around 25 people within the next 12 months.

Last time in our Keras/OpenAI tutorial, we discussed a very fundamental algorithm in reinforcement learning: the DQN. The Deep Q-Network is actually a fairly new advent that arrived on the seen only a couple years back, so it is quite incredible if you were able to understand and implement this algorithm having just gotten a start in the field. As with the original post, let's take a quick moment to appreciate how incredible results we achieved are: in a continuous output space scenario and starting with absolutely no knowledge on what "winning" entails, we were able to explore our environment and "complete" the trials. Put yourself in the situation of this simulation. This would essentially be like asking you to play a game, without a rulebook or specific endgoal, and demanding you to continue to play until you win (almost seems a bit cruel).

Machine learning and artificial intelligence (AI) are all the rage these days -- but with all the buzzwords swirling around them, it's easy to get lost and not see the difference between hype and reality. For example, just because an algorithm is used to calculate information doesn't mean the label "machine learning" or "artificial intelligence" should be applied. Before we can even define AI or machine learning, though, I want to take a step back and define a concept that is at the core of both AI and machine learning: algorithm. An algorithm is a set of rules to be followed when solving problems. In machine learning, algorithms take in data and perform calculations to find an answer.