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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
Steve Bosserman

Which Industries Are Investing in Artificial Intelligence? - 0 views

priceonomics.com/es-are-investing-in-artificial

artificial intelligence machine learning investment future trends

shared by Steve Bosserman on 29 Nov 18 - No Cached
  • 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.
Steve Bosserman

Creating robots capable of moral reasoning is like parenting | Aeon Essays - 0 views

aeon.co/...al-reasoning-is-like-parenting

artificial intelligence machine learning ethics morality decision-making

shared by Steve Bosserman on 18 Apr 17 - No Cached
  • Intelligent machines will be our intellectual children, our progeny. They will start off inheriting many of our moral norms, because we will not allow anything else. But they will come to reflect on their nature, including their relationships with us and with each other. If we are wise and benevolent, we will have prepared the way for them to make their own choices – just as we do with our adolescent children.What does this mean in practice? It means being ready to accept that machines might eventually make moral decisions that none of us find acceptable. The only condition is that they must be able to give intelligible reasons for what they’re doing. An intelligible reason is one you can at least see why someone might find morally motivating, even if you don’t necessarily agree.
Steve Bosserman

Opinion | It's Westworld. What's Wrong With Cruelty to Robots? - 1 views

www.nytimes.com/...conscious-robots-morality.html

ethics robotics human behavior morality artificial intelligence

shared by Steve Bosserman on 23 Apr 18 - No Cached
  • The biggest concern is that we might one day create conscious machines: sentient beings with beliefs, desires and, most morally pressing, the capacity to suffer. Nothing seems to be stopping us from doing this. Philosophers and scientists remain uncertain about how consciousness emerges from the material world, but few doubt that it does. This suggests that the creation of conscious machines is possible.
  • If we did create conscious beings, conventional morality tells us that it would be wrong to harm them — precisely to the degree that they are conscious, and can suffer or be deprived of happiness. Just as it would be wrong to breed animals for the sake of torturing them, or to have children only to enslave them, it would be wrong to mistreat the conscious machines of the future.
  • Anything that looks and acts like the hosts on “Westworld” will appear conscious to us, whether or not we understand how consciousness emerges in physical systems. Indeed, experiments with AI and robotics have already shown how quick we are to attribute feelings to machines that look and behave like independent agents.
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  • This is where actually watching “Westworld” matters. The pleasure of entertainment aside, the makers of the series have produced a powerful work of philosophy. It’s one thing to sit in a seminar and argue about what it would mean, morally, if robots were conscious. It’s quite another to witness the torments of such creatures, as portrayed by actors such as Evan Rachel Wood and Thandie Newton. You may still raise the question intellectually, but in your heart and your gut, you already know the answer.
  • But the prospect of building a place like “Westworld” is much more troubling, because the experience of harming a host isn’t merely similar to that of harming a person; it’s identical. We have no idea what repeatedly indulging such fantasies would do to us, ethically or psychologically — but there seems little reason to think that it would be good.
  • For the first time in our history, then, we run the risk of building machines that only monsters would use as they please.
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.
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  • 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.
Steve Bosserman

How Cheap Labor Drives China's A.I. Ambitions - The New York Times - 1 views

www.nytimes.com/...ial-intelligence-labeling.html

artificial intelligence china labor data management machine learning

shared by Steve Bosserman on 26 Nov 18 - No Cached
  • But the ability to tag that data may be China’s true A.I. strength, the only one that the United States may not be able to match. In China, this new industry offers a glimpse of a future that the government has long promised: an economy built on technology rather than manufacturing.
  • “We’re the construction workers in the digital world. Our job is to lay one brick after another,” said Yi Yake, co-founder of a data labeling factory in Jiaxian, a city in central Henan province. “But we play an important role in A.I. Without us, they can’t build the skyscrapers.”
  • While A.I. engines are superfast learners and good at tackling complex calculations, they lack cognitive abilities that even the average 5-year-old possesses. Small children know that a furry brown cocker spaniel and a black Great Dane are both dogs. They can tell a Ford pickup from a Volkswagen Beetle, and yet they know both are cars.A.I. has to be taught. It must digest vast amounts of tagged photos and videos before it realizes that a black cat and a white cat are both cats. This is where the data factories and their workers come in.
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  • “All the artificial intelligence is built on human labor,” Mr. Liang said.
  • “We’re the assembly lines 10 years ago,” said Mr. Yi, the co-founder of the data factory in Henan.
Steve Bosserman

The Pentagon's 'Terminator Conundrum': Robots That Could Kill on Their Own - The New Yo... - 0 views

www.diigo.com/...7c8d7dc2592f33ae4694d7455d593e

robotics autonomous weapons artificial intelligence

shared by Steve Bosserman on 25 Oct 16 - No Cached
  • Just as the Industrial Revolution spurred the creation of powerful and destructive machines like airplanes and tanks that diminished the role of individual soldiers, artificial intelligence technology is enabling the Pentagon to reorder the places of man and machine on the battlefield the same way it is transforming ordinary life with computers that can see, hear and speak and cars that can drive themselves.
Steve Bosserman

How teaching AI to be curious helps machines learn for themselves - The Verge - 0 views

www.theverge.com/...zumas-revenge-noisy-tv-problem

artificial intelligence machine learning curiosity

shared by Steve Bosserman on 03 Nov 18 - No Cached
  • The problem with Montezuma’s Revenge is that it doesn’t provide regular rewards for the AI agent. It’s a puzzle-platformer where players have to explore an underground pyramid, dodging traps and enemies while collecting keys that unlock doors and special items. If you were training an AI agent to beat the game, you could reward it for staying alive and collecting keys, but how do you teach it to save certain keys for certain items, and use those items to overcome traps and complete the level? The answer: curiosity.
Steve Bosserman

AI, automation, and the future of work: Ten things to solve for - 0 views

www.mckinsey.com/...f-work-ten-things-to-solve-for

technology automation workforce artificial intelligence workplace

shared by Steve Bosserman on 23 Nov 18 - No Cached
  • Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. They will also help address “moonshot” societal challenges in areas from health to climate change.
  • At the same time, these technologies will transform the nature of work and the workplace itself. Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.
  • While we believe there will be enough work to go around (barring extreme scenarios), society will need to grapple with significant workforce transitions and dislocation. Workers will need to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace. They may have to move from declining occupations to growing and, in some cases, new occupations.
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  • This executive briefing, which draws on the latest research from the McKinsey Global Institute, examines both the promise and the challenge of automation and AI in the workplace and outlines some of the critical issues that policy makers, companies, and individuals will need to solve for.
Steve Bosserman

Teaching an Algorithm to Understand Right and Wrong - 0 views

hbr.org/...-to-understand-right-and-wrong

algorithm machine learning machine ethics artificial intelligence

shared by Steve Bosserman on 10 Sep 17 - No Cached
  • The rise of artificial intelligence is forcing us to take abstract ethical dilemmas much more seriously because we need to code in moral principles concretely. Should a self-driving car risk killing its passenger to save a pedestrian? To what extent should a drone take into account the risk of collateral damage when killing a terrorist? Should robots make life-or-death decisions about humans at all? We will have to make concrete decisions about what we will leave up to humans and what we will encode into software.
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