Prof. Kevin Warwick interviewed by Francesca Ferrando. These interviews are conceived as a project related to my PhD in Philosophy, on Posthumanism, Artificial Intelligence and Gender. You can check more info on my academic page: http://uniroma3.academia.edu/Francesc... --- CONVERSATION #10 In the Chinese Room argument (1980) John Searle holds that a program cannot give a computer a "mind" nor an "understanding", regardless of how intelligently it might make it behave. He concludes that "I can have any formal program you like, but I still understand nothing". What do you think of the Chinese room argument?

If these tasks were part of a bigger project, this gets checked off as well, and I get to see a motivational quote as a reward. Since I keep all of that in Dropbox, it is available across all my computers, and I don't have to worry about platform-specific workarounds. I know, this sounds all weird, but if there really is a person who is interested in this, I can elaborate more and upload an example to GitHub in no time. This article certainly became longer than I intended it to be. You probably didn't read all of it, but I hope that you at least skipped forward to this last section!

Stephanie Hay is the head of content strategy at Capital One and led the design team that created Capital One's Amazon Alexa skill earlier this year. People say them every day -- after the waiter delivers food, when finishing a customer service call or before launching a rocket into space. These two words are just fine in the context of real life, human-to-human interactions. They're also covered as a feedback loop in traditional UI design, where we can create a button that says "Done" or "Save" and know exactly to which touch point people are referring when they tap it. In human-to-robot interactions, however, that's where things get tricky.

Machine learning security offers many advantages over signature-based detection, but the technology can be as difficult to explain as it is for malware to beat. During an interview with SearchSecurity, Brett Hansen, executive director of data security solutions at Dell, offered insight into his company's investment in machine learning security and its partnership with advanced threat protection startup Cylance Inc. In part one of the interview, Hansen discussed the problems with traditional antivirus and antimalware programs relying on signature-based detection methods. In part of two of the interview, Hansen talks about the advantages of machine learning for smaller businesses, why it's a struggle to discuss the technology behind it, and how machine learning security serves as a better defense against ransomware attacks and other emerging threats. Here are excerpts from the conversation with Hansen. Is the move to machine learning security more about the shortcomings with signature-based detection and the frustrations people have had with it, or the benefits and value of machine learning?

"Intelligent system designers do have ethical responsibilities." I have interviewed John Markoff, technology writer at The New York Times. In 2013 he was awarded a Pulitzer Prize. The interview is related to his recent book "Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, published in August of 2015 by HarperCollins Ecco. Do you share the concerns of prominent technology leaders such as Tesla's chief executive, Elon Musk, who suggested we might need to regulate the development of artificial intelligence?

Welcome to the Applied Materials Earnings Conference Call. During the presentation, all participants will be in a listen-only mode. Afterwards you will be invited to participate in a question-and-answer session. As a reminder, this conference is being recorded. I'd now like to turn the conference over to Michael Sullivan, Vice President of Investor Relations. In a moment, we'll discuss the results for our third quarter which ended on July 31. Joining me are Gary Dickerson, our President and CEO; and Bob Halliday, our Chief Financial Officer. Before we begin, let me remind you that today's call contains forward-looking statements including Applied's current view of its industries, performance, products, share positions, profitability and business outlook. These statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements, and are not guarantees of future performance.

Members of Rajat Monga's team at Google will be teaching tutorials on deep learning with TensorFlow at Strata Hadoop World in Beijing (August 4th) and NYC (September 27th). Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies. There's not going to be too many areas left that run without machine learning that you can program.

You could say the job of the sports fan is not only to cheer but to jeer. American sprinter Justin Gatlin, who has been suspended in the past for doping, entered Olympic Stadium before his 100-meter race to resounding boos. Competitors are also a part of the ritual. After winning a gold medal, American swimmer Lilly King wagged her finger to mock her Russian competitor Yulia Efimova, who previously had been suspended for doping. To philosopher Julian Savulescu, the boos and censures ring with, if not outright hypocrisy, short memory spans. "Caffeine is a performance-enhancer," he says. "It used to be banned and now it's allowed." Savulescu, a native Australian, who directs the Uehiro Center for Practical Ethics at the University of Oxford, has been one of the loudest critics in recent years of doping policies. Sports governing bodies have had restrictions in place for decades, he says, and have had little effect. Athletes will always find a way to beat the system, he says, and like most sports fans, Savulescu laments that doping creates an uneven playing field. But unlike most fans, Savulescu thinks the solution is to make doping legal in sports.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid). Deep learning is often thought of as a set of algorithms that'mimics the brain'. A more accurate description would be an algorithm that'learns in layers'.

Artificial Lawyer caught up with Canadian legal consultant and futurist Jordan Furlong of Law Twenty One and asked him about his perspective on what opportunities and challenges AI faced in the legal sector. Do you see a strategic advantage for the law firms that embrace AI? If yes, how would that advantage manifest itself? We should probably begin by creating a working definition of'AI', which is a term applied so broadly in the legal sphere that, as Ryan McClead has pointed out, it might as well just be written as'magic'. Michael Mills of Neota Logic has suggested instead the term'cognitive technologies', which encompasses a wide range of tech applications including machine learning, natural language processing, and expert systems.