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Showing posts from March, 2018

Facebook Exec Who Wrote Terrorism and Death Are 'Justified' by Facebook's 'Growth' Says He Was Just Trying to Be 'Provocative'

Andrew “Boz” Bosworth backtracks on his leaked memo statements, claiming that "he didn’t agree with his own statements, even as he wrote them". from gizmodo http://www.gizmodo.co.uk/2018/03/facebook-exec-who-wrote-terrorism-and-death-are-justified-by-facebooks-growth-says-he-was-just-trying-to-be-provocative/
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Why Retailers Need to be Utilizing Mobile Payments, and How They Can Choose Systems Wisely

While the ecommerce market has grown rapidly in recent years, and is set to continue to boom, the fact is that most retail transactions are still actually completed in bricks and mortar stores. However, as more and more consumers get used to the convenience and quick process of buying online, it’s imperative that retailers use every tool at their disposal to streamline transactions in store, and to offer customers an excellent experience at every touchpoint. One of the ways they can do that is through using mobile payments (mPOS). A BI Intelligence report forecasted that there will be a whopping 27.7 million mPOS devices in circulation by 2021 in the United States, up from just 3.2 million items seven years prior. For many retailers though, the introduction of mobile payments isn’t a priority yet, so mPOS adoption continues to lag . However, if you’re an entrepreneur who hasn’t started using this tech, you’re probably not just missing out on sales, but also losing the opportuni
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Voices in AI – Episode 37: A Conversation with Carolina Galleguillos

Today's leading minds talk AI with host Byron Reese In this episode Byron and Carolina discuss computer vision, machine learning, biology and more. - - 0 : 00 0 : 00 0 : 00 Today's leading minds talk AI with host Byron Reese Byron Reese: This is Voices in AI brought to you by Gigaom, I’m Byron Reese. Today our guest is Carolina Galleguillos. She’s an expert in machine learning and computer vision. She did her undergrad work in Chile and has a master’s and PhD in Computer Science from UC San Diego. She’s presently a machine learning engineer at Thumbtack. Welcome to the show. Carolina Galleguillos: Thank you. Thank you for having me. So, let’s start at the very beginning with definitions. What exactly is “artificial” about artificial intelligence? Well, I read somewhere that artificial intelligence is basically trying to make machines think, which is very “sci-fi,” I think, but what I’m trying to say here is we’re trying to autom
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Confronted With Severe Climate Change, Ancient Britons Kept Calm and Carried On

This latest research suggests abrupt climate change wasn’t as catastrophic to early humans as we thought, and that they were in fact remarkably resilient and adaptable in the face of these dramatic shifts in temperature. from gizmodo http://www.gizmodo.co.uk/2018/03/confronted-with-severe-climate-change-ancient-britons-kept-calm-and-carried-on/
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Voices in AI – Episode 37: A Conversation with Mike Tamir

Today's leading minds talk AI with host Byron Reese In this episode, Byron and Mike talk about AGI, Turing Test, machine learning, jobs, and Takt. - - 0 : 00 0 : 00 0 : 00 Today's leading minds talk AI with host Byron Reese Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. I’m excited today, our guest is Mike Tamir. He is the Chief Data Science Officer at Takt, and he’s also a lecturer at UC Berkeley. If you look him up online and read what people have to say about him, you notice that some really, really smart people say Mike is the smartest person they know. Which implies one of two things: Either he really is that awesome, or he has dirt on people and is not above using it to get good accolades. Welcome to the show, Mike! Mark Cuban came to Austin, where we’re based, and gave a talk at South By Southwest where he said the first trillionaires are going to be in artificial intelligence. And he said som
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AggregateIQ Created Cambridge Analytica's Election Software, and Here’s the Proof

A little-known Canadian data firm ensnared in an international investigation into alleged wrongdoing during the Brexit campaign created an election software platform marketed by Cambridge Analytica. from gizmodo http://www.gizmodo.co.uk/2018/03/aggregateiq-created-cambridge-analyticas-election-software-and-heres-the-proof/
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Digital Transformation 101: What are we trying to transform, and why?

It’s pretty easy to be a digital transformation consultant these days. Here’s what you do. First, you report on the amount of data growth, the increasing rate of change and other exponential factors; you flag up the massive growth of recent, tech-first companies such as Amazon and Alibaba (whilst carefully ignoring those who tried and failed to follow similar models); you list out conveniently acronymised manifestations of technological progress — Social, Mobile, Analytics and Cloud. Oh and IoT. And AI. You get the picture. Having engendered a suitable level of fear and uncertainty among your target audience, namely executive decision makers (who happen to control consulting budgets), you go in with the scoop: that the only possible response is to transform. Not to tweak, nor encourage stepwise progress, but to make a ground-to-sky, soup-to-nuts matrix-style inversion of the entire organisation. How should we do this, you are asked. Well, how fortunate you have an answer for that, y
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Is enhanced reality an AR/VR cop-out?

Watch out, there’s a new term on the block. Even as the initial flurry of excitement over Oculus-primed virtual reality seems to be in a perpetual state of prototyping , and as other forms of augmentation are hanging about like costume options for Ready Player One, discussion is turning to enhanced reality. I know this not because of some online insight (Google Trends isn’t showing much ), but because it has come up in conversation more than once with enterprise technology strategists. So, what can we take from this? All forms of adjusted reality are predicated on a real-time feed of information that brings a direct effect to our senses: At one end of the scale, we have fully immersive environments known as Virtual Reality (VR) . These are showing themselves to be enormously powerful tools, with potential not just in gaming or architecture but also areas such as healthcare — imagine if you can shrink to the size of a tiny cancer, and then control microscopic lasers to burn it away
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How Machines Learn: The Top Four Approaches to ML in Business

Machine learning sits at the forefront of innovation across a growing number of industries in today’s business world. Still, it’s a mistake to think of machine learning as one monolithic business solution — there are many forms of machine learning and each is capable of solving different sets of problems. The most popular forms of ML used in business today are supervised, unsupervised, semi-supervised, and reinforcement learning. At Vidora, we’ve used these techniques to help Fortune 500 partners solve some of their most pressing problems in innovative ways. This article draws from our experiences to demystify these four common approaches to ML, introducing practical applications of each technique so that anyone in your organization can recognize how machine learning can enhance your business. Machine Learning at a Glance Machine learning is an approach to Artificial Intelligence which borrows principles from computer science and statistics to model relationships in data. Unlike ot
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Lambda is an AWS internal efficiency driver. So why no private serverless models?

I’ve been in a number of conversations recently about Functions as a Service (FaaS), and more specifically, AWS’ Lambda instantiation of the idea. For the lay person, this is where you don’t have to actually provide anything but program code — “everything else” is taken care of by the environment. You upload and press play. Sounds great, doesn’t it? Unsurprisingly, some see application development moving inexorably towards a serverless, i.e. FaaS-only, future. As with all things technological however, there are plusses and minuses to any such model. FaaS implementations tend to be stateless and event-driven — that is, they react to whatever they are asked to do without remembering what position they were in. This means you have to manage state within the application code. FaaS frameworks are vendor-specific by nature, and tend to add transactional latency, so a re good for doing small things with huge amounts of data, rather than lots of little things each with small amounts of data
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