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Bill Fulkerson

Anatomy of an AI System - 1 views

shared by Bill Fulkerson on 14 Sep 18 - No Cached
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    "With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay
Bill Fulkerson

Why a 400-Year Program of Modernist Thinking is Exploding | naked capitalism - 0 views

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    " Fearless commentary on finance, economics, politics and power Follow yvessmith on Twitter Feedburner RSS Feed RSS Feed for Comments Subscribe via Email SUBSCRIBE Recent Items Links 3/11/17 - 03/11/2017 - Yves Smith Deutsche Bank Tries to Stay Alive - 03/11/2017 - Yves Smith John Helmer: Australian Government Trips Up Ukrainian Court Claim of MH17 as Terrorism - 03/11/2017 - Yves Smith 2:00PM Water Cooler 3/10/2017 - 03/10/2017 - Lambert Strether Why a 400-Year Program of Modernist Thinking is Exploding - 03/10/2017 - Yves Smith Links 3/10/17 - 03/10/2017 - Yves Smith Why It Will Take a Lot More Than a Smartphone to Get the Sharing Economy Started - 03/10/2017 - Yves Smith CalPERS' General Counsel Railroads Board on Fiduciary Counsel Selection - 03/10/2017 - Yves Smith Another Somalian Famine - 03/10/2017 - Yves Smith Trade now with TradeStation - Highest rated for frequent traders Why a 400-Year Program of Modernist Thinking is Exploding Posted on March 10, 2017 by Yves Smith By Lynn Parramore, Senior Research Analyst at the Institute for New Economic Thinking. Originally published at the Institute for New Economic Thinking website Across the globe, a collective freak-out spanning the whole political system is picking up steam with every new "surprise" election, rush of tormented souls across borders, and tweet from the star of America's great unreality show, Donald Trump. But what exactly is the force that seems to be pushing us towards Armageddon? Is it capitalism gone wild? Globalization? Political corruption? Techno-nightmares? Rajani Kanth, a political economist, social thinker, and poet, goes beyond any of these explanations for the answer. In his view, what's throwing most of us off kilter - whether we think of ourselves as on the left or right, capitalist or socialist -was birthed 400 years ago during the period of the Enlightenment. It's a set of assumptions, a particular way of looking at the world that pushed out previous modes o
Steve Bosserman

Which Industries Are Investing in Artificial Intelligence? - 0 views

  • The term artificial intelligence typically refers to automation of tasks by software that previously required human levels of intelligence to perform. While machine learning is sometimes used interchangeably with AI, machine learning is just one sub-category of artificial intelligence whereby a device learns from its access to a stream of data.When we talk about AI spending, we’re typically talking about investment that companies are making in building AI capabilities. While this may change in the future, McKinsey estimates that the vast majority of spending is done internally or as an investment, and very little of it is done purchasing artificial intelligence applications from other businesses.
  • 62% of AI spending in 2016 was for machine learning, twice as much as the second largest category computer vision. It’s worth noting that these categories are all types of “narrow” (or “weak”) forms of AI that use data to learn about and accomplish a specific narrowly defined task. Excluded from this report is “general” (or “strong”) artificial intelligence which is more akin to trying to create a thinking human brain.
  • The McKinsey survey mostly fits well as evidence supporting Cross’s framework that large profitable industries are the most fertile grounds of AI adoption. Not surprisingly, Technology is the industry with highest AI adoption and financial services also makes the top three as Cross would predict.Notably, automotive and assembly is the industry with the second highest rate of AI adoption in the McKinsey survey. This may be somewhat surprising as automotive isn’t necessarily an industry with the reputation for high margins. However, the use cases of AI for developing self-driving cars and cost savings using machine learning to improve manufacturing and procurement efficiencies are two potential drivers of this industry’s adoption.
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  • AI jobs are much more likely to be unfilled after 60 days compared to the typical job on Indeed, which is only unfilled a quarter of the time. As the demand for AI talent continues to grow faster than the supply, there is no indication this hiring cycle will become quicker anytime soon.
  • One thing we know for certain is that it is very expensive to attract AI talent, given that starting salaries for entry-level talent exceed $300,000. A good bet is that the companies that invest in AI are the ones with healthy enough profit margins that they can afford it.
Bill Fulkerson

The flaw of averages | 1843 - 0 views

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    "America is in the very early stages of a big pedagogical experiment based on old ideas given new life by digital technology and the techies' money. There isn't enough evidence yet to conclude that this blend of technology and personalised learning serves pupils better than the status quo, but the revolution is gathering pace. It could, Rose acknowledges, "go horribly, horribly wrong". If it does, a lot of children's lives will have been damaged; but then it is hardly as though the existing system is releasing the full potential of America's young people. For Rose, giving children more control over what they learn and how they learn it is central to achieving that. Ultimately, he says, "you should have a right to know who you are.""
Bill Fulkerson

Valuing traditional ecological knowledge and indigenous wisdom - 0 views

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    To overcome - once and for all - the false separation between nature and culture requires us to acknowledge that learning from human ingenuity and long-term adaptations to particular environments is also learning from nature. Among indigenous peoples there is a long tradition of solving human problems by learning from other species and from the wider natural processes in which we participate.
Steve Bosserman

Why we find change so difficult, according to neuroscience - 0 views

  • “Emotionally and cognitively and executively the brain has established a lot of pathways,” says Dr. Sanam Hafeez, a licensed clinical psychologist and neuropsychologist. “The more you do something the more ingrained it becomes in neural pathways, much like how a computer that stores the sites you visit — when you log onto your browser, they will pop up because you use them a lot. Change is an upheaval of many things and the brain has to work to fit it into an existing framework.”
  • “You absolutely can and should teach your brain to change,” says Hafeez, noting that keeping the brain agile has been shown to help delay aging. “I've done quite a bit of work on the aging process and slowing that down. It starts with changing the aversion to change.”
  • “Let’s say you’re a financial planer who takes up knitting,” says Hafeez. “That is doing something very different, where the brain truly has to adapt new neural pathways. Learning a new skill like this have been shown to ward off dementia, aging and cognitive decline because it regenerates cellular activity. Learn a new language in middle age. You tax your brain by shaking things up and it’s effective for your body in the way HIIT is for your body.”
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  • “Most people won't try something new because they’re deathly afraid of failing,” notes Hafeez. “When you see that something is doable it makes you more receptive and brave. There's that emotional, therapeutic factor that is separate from the neural pathway factor. Over the years, we learn to succeed by viewing our previous failures and successes in a certain light and as we get older we lose sight of that. When you try a new thing it makes you more confident to try to do more new things.”
Bill Fulkerson

It's not all Pepes and trollfaces - memes can be a force for good - The Verge - 0 views

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    "How the 'emotional contagion' of memes makes them the internet's moral conscience By Allie Volpe Aug 27, 2018, 11:30am EDT Illustration by Alex Castro & Keegan Larwin SHARE Newly single, Jason Donahoe was perusing Tinder for the first time since it started integrating users' Instagram feeds. Suddenly, he had an idea: follow the Instagram accounts of some of the women he'd been interested in but didn't match with on the dating service. A few days later, he considered taking it a step further and direct messaging one of the women on Instagram. After all, the new interface of the dating app seemed to encourage users to explore other areas of potential matches' online lives, so why not take the initiative to reach out? Before he had a chance, however, he came across the profile of another woman whose Tinder photo spread featured a meme with Parks and Recreation character Jean-Ralphio Saperstein (Ben Schwartz) leaning into the face of Ben Wyatt (Adam Scott) with the caption: hey I saw you on Tinder but we didn't match so I found your Instagram you're so beautiful you don't need to wear all that makeup ahah I bet you get a lot of creepy dm's but I'm not like all those other guys message me back beautiful btw what's your snap "I was like, 'Oh shit, wow,'" Donahoe says. Seeing his potential jerk move laid out so plainly as a neatly generalized joke, he saw it in a new light. "I knew a) to be aware of that, and b) to cut that shit out … It prompted self-reflection on my part." THE MOST SUCCESSFUL MEMES STRIKE A CULTURAL CHORD AND CAN GUIDE AND EVEN INFLUENCE BEHAVIOR Donahoe says memes have resonated with him particularly when they depict a "worse, extreme version" of himself. For Donahoe, the most successful memes are more than just jokes. They "strike a societal, cultural chord" and can be a potent cocktail for self-reflection as tools that can guide and even influence behavior. In the months leading up to the 2016 US
Steve Bosserman

Uber has cracked two classic '80s video games by giving an AI algorithm a new type of m... - 0 views

  • AI researchers have typically tried to get around the issues posed by by Montezuma’s Revenge and Pitfall! by instructing reinforcement-learning algorithms to explore randomly at times, while adding rewards for exploration—what’s known as “intrinsic motivation.” But the Uber researchers believe this fails to capture an important aspect of human curiosity. “We hypothesize that a major weakness of current intrinsic motivation algorithms is detachment,” they write. “Wherein the algorithms forget about promising areas they have visited, meaning they do not return to them to see if they lead to new states.”
  • The team’s new family of reinforcement-learning algorithms, dubbed Go-Explore, remember where they have been before, and will return to a particular area or task later on to see if it might help provide better overall results. The researchers also found that adding a little bit of domain knowledge, by having human players highlight interesting or important areas, sped up the algorithms’ learning and progress by a remarkable amount. This is significant because there may be many real-world situations where you would want an algorithm and a person to work together to solve a hard task.
Bill Fulkerson

Optimization is as hard as approximation - Machine Learning Research Blog - 0 views

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    Optimization is a key tool in machine learning, where the goal is to achieve the best possible objective function value in a minimum amount of time. Obtaining any form of global guarantees can usually be done with convex objective functions, or with special cases such as risk minimization with one-hidden over-parameterized layer neural networks (see the June post). In this post, I will consider low-dimensional problems (imagine 10 or 20), with no constraint on running time (thus get ready for some running-times that are exponential in dimension!).
Steve Bosserman

Specifying AI safety problems in simple environments | DeepMind - 0 views

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    "As AI systems become more general and more useful in the real world, ensuring they behave safely will become even more important. To date, the majority of technical AI safety research has focused on developing a theoretical understanding about the nature and causes of unsafe behaviour. Our new paper builds on a recent shift towards empirical testing (see Concrete Problems in AI Safety) and introduces a selection of simple reinforcement learning environments designed specifically to measure 'safe behaviours'."
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