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Bill Fulkerson

Anatomy of an AI System - 1 views

anatomyof.ai

AI amazon

shared by Bill Fulkerson on 14 Sep 18 - No Cached
  • Bill Fulkerson on 14 Sep 18
     
    "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

How humans use objects in novel ways to solve problems - 0 views

medicalxpress.com/...0-11-humans-ways-problems.html

novel objects problems humans

shared by Bill Fulkerson on 25 Nov 20 - No Cached
  • Bill Fulkerson on 25 Nov 20
     
    Human beings are naturally creative tool users. When we need to drive in a nail but don't have a hammer, we easily realize that we can use a heavy, flat object like a rock in its place. When our table is shaky, we quickly find that we can put a stack of paper under the table leg to stabilize it. But while these actions seem so natural to us, they are believed to be a hallmark of great intelligence-only a few other species use objects in novel ways to solve their problems, and none can do so as flexibly as people. What provides us with these powerful capabilities for using objects in this way?
Steve Bosserman

New houses in Puerto Rico designed to survive future storms - 0 views

www.fastcompany.com/...rto-rico-survive-future-storms

affordable housing off the grid puerto rico

shared by Steve Bosserman on 28 Nov 18 - No Cached
  • The design is “based on what we know is affordable housing in Puerto Rico for a single family,” says Hector Ralat, an architect based in the firm’s Puerto Rican office. “But the focus was to alter the DNA of that knowledge and to put in the essential components that someone would need to sustain living conditions for at least two weeks, which is the recommended time here for someone to receive aid after a disaster.” The houses will likely cost around $120,000, a number that lets homeowners access favorable interest rates on mortgages. The units can be stacked on top of each other; in Villalba, most of the community will be three stories high (the solar will serve the whole building).
  • “We just know our product is better than stick-built construction in these types of dangerous environments,” says Paul Galvin, chairman and CEO of SG Blocks, the parent company of SG Residential. “Heavy-gauge steel structures are just designed to a high tolerance for the effects of climate change.” The houses, most of which will have two bedrooms, will start at $90,000 to $130,000. It might be possible to build cheaper houses, Galvin says, but the company is “trying to deliver product that is quality-driven, in that it’s going to be built once and it’s not going to be destroyed every storm.” The company is also working with a bank to create a mortgage that is similar in monthly cost to a car payment. The design can incorporate solar panels.
  • HiveCube, another modular housing company, is also using shipping containers, and has targeted a much lower cost–the houses start at $39,000 for a two-bedroom home. “We believe that your safety should not be a matter of income, but a given when you are planning to buy a home for your family,” says María Velasco, cofounder of HiveCube. The homes are designed to be fully off the grid, with solar power and batteries, a rainwater collection system, and a gray and black water treatment system that uses plants and bacteria to treat wastewater instead of septic tanks.
Steve Bosserman

How We Made AI As Racist and Sexist As Humans - 0 views

thewalrus.ca/as-racist-and-sexist-as-humans

artificial intelligence algorithms sexism data mining racism

shared by Steve Bosserman on 25 May 18 - No Cached
  • Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.
  • The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.
  • Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.
  • ...8 more annotations...
  • But not everyone will be equally represented in that data.
  • And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity
  • The power of the system is its “ability to recognize that correlations occur between gender and professions,” says Kathryn Hume. “The downside is that there’s no intentionality behind the system—it’s just math picking up on correlations. It doesn’t know this is a sensitive issue.” There’s a tension between the futuristic and the archaic at play in this technology. AI is evolving much more rapidly than the data it has to work with, so it’s destined not just to reflect and replicate biases but also to prolong and reinforce them.
  • Accordingly, groups that have been the target of systemic discrimination by institutions that include police forces and courts don’t fare any better when judgment is handed over to a machine.
  • A growing field of research, in fact, now looks to apply algorithmic solutions to the problems of algorithmic bias.
  • Still, algorithmic interventions only do so much; addressing bias also demands diversity in the programmers who are training machines in the first place.
  • A growing awareness of algorithmic bias isn’t only a chance to intervene in our approaches to building AI systems. It’s an opportunity to interrogate why the data we’ve created looks like this and what prejudices continue to shape a society that allows these patterns in the data to emerge.
  • Of course, there’s another solution, elegant in its simplicity and fundamentally fair: get better data.
Bill Fulkerson

Why Observability Needs to Stay Weird - The New Stack - 0 views

thenewstack.io/ervability-needs-to-stay-weird

technology weird

shared by Bill Fulkerson on 14 Mar 20 - No Cached
  • Bill Fulkerson on 14 Mar 20
     
    Monitoring, as a discipline, requires you to pre-define normal and then freeze it, with ruthless efficiency, suborning agility and humanity and adaptiveness in the sake of producing a steady-state system.
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