580 Matching Annotations
- May 2021
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www.semanticscholar.org www.semanticscholar.org
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Semantic Scholar, a free, AI-powered research tool for scientific literature
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arxiv.org arxiv.org
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link.springer.com link.springer.com
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Turing was an exceptional mathematician with a peculiar and fascinating personality and yet he remains largely unknown. In fact, he might be considered the father of the von Neumann architecture computer and the pioneer of Artificial Intelligence. And all thanks to his machines; both those that Church called “Turing machines” and the a-, c-, o-, unorganized- and p-machines, which gave rise to evolutionary computations and genetic programming as well as connectionism and learning. This paper looks at all of these and at why he is such an often overlooked and misunderstood figure.
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twitter.com twitter.com
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Full Fact on Twitter. (n.d.). Twitter. Retrieved 5 March 2021, from https://twitter.com/FullFact/status/1330922811448315911
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- Apr 2021
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boardgamegeek.com boardgamegeek.com
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There is a tendency in short luck-heavy games to require you to play multiple rounds in one sitting, to balance the scores. This is one such game. This multiple-rounds "mechanic" feels like an artificial fix for the problem of luck. Saboteur 1 and 2 advise the same thing because the different roles in the game are not balanced. ("Oh, well. I had the bad luck to draw the Profiteer character this time. Maybe I'll I'll draw a more useful character in round 2.") This doesn't change the fact that you are really playing a series of short unbalanced games. Scores will probably even out... statistically speaking. The Lost Cities card game tries to deal with the luck-problem in the same way.
possibly rename: games: luck: managing/mitigating the luck to games: luck: dealing with/mitigating the luck problem
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med.stanford.edu med.stanford.edu
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The insertion of an algorithm’s predictions into the patient-physician relationship also introduces a third party, turning the relationship into one between the patient and the health care system. It also means significant changes in terms of a patient’s expectation of confidentiality. “Once machine-learning-based decision support is integrated into clinical care, withholding information from electronic records will become increasingly difficult, since patients whose data aren’t recorded can’t benefit from machine-learning analyses,” the authors wrote.
There is some work being done on federated learning, where the algorithm works on decentralised data that stays in place with the patient and the ML model is brought to the patient so that their data remains private.
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- Mar 2021
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www.cam.ac.uk www.cam.ac.uk
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Machine learning models for diagnosing COVID-19 are not yet suitable for clinical use. (2021, March 15). University of Cambridge. https://www.cam.ac.uk/research/news/machine-learning-models-for-diagnosing-covid-19-are-not-yet-suitable-for-clinical-use
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BBC Worklife. (2020, October 23). Coronavirus: How the world of work may change forever. https://www.bbc.com/worklife/article/20201023-coronavirus-how-will-the-pandemic-change-the-way-we-work
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journal.disruptivemedia.org.uk journal.disruptivemedia.org.uk
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In this respect, we join Fitzpatrick (2011) in exploring “the extent to which the means of media production and distribution are undergoing a process of radical democratization in the Web 2.0 era, and a desire to test the limits of that democratization”
Something about this is reminiscent of WordPress' mission to democratize publishing. We can also compare it to Facebook whose (stated) mission is to connect people, while it's actual mission is to make money by seemingly radicalizing people to the extremes of our political spectrum.
This highlights the fact that while many may look at content moderation on platforms like Facebook as removing their voices or deplatforming them in the case of people like Donald J. Trump or Alex Jones as an anti-democratic move. In fact it is not. Because of Facebooks active move to accelerate extreme ideas by pushing them algorithmically, they are actively be un-democratic. Democratic behavior on Facebook would look like one voice, one account and reach only commensurate with that person's standing in real life. Instead, the algorithmic timeline gives far outsized influence and reach to some of the most extreme voices on the platform. This is patently un-democratic.
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www.technologyreview.com www.technologyreview.com
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Meanwhile, the algorithms that recommend this content still work to maximize engagement. This means every toxic post that escapes the content-moderation filters will continue to be pushed higher up the news feed and promoted to reach a larger audience.
This and the prior note are also underpinned by the fact that only 10% of people are going to be responsible for the majority of posts, so if you can filter out the velocity that accrues to these people, you can effectively dampen down the crazy.
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In his New York Times profile, Schroepfer named these limitations of the company’s content-moderation strategy. “Every time Mr. Schroepfer and his more than 150 engineering specialists create A.I. solutions that flag and squelch noxious material, new and dubious posts that the A.I. systems have never seen before pop up—and are thus not caught,” wrote the Times. “It’s never going to go to zero,” Schroepfer told the publication.
The one thing many of these types of noxious content WILL have in common are the people at the fringes who are regularly promoting it. Why not latch onto that as a means of filtering?
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But anything that reduced engagement, even for reasons such as not exacerbating someone’s depression, led to a lot of hemming and hawing among leadership. With their performance reviews and salaries tied to the successful completion of projects, employees quickly learned to drop those that received pushback and continue working on those dictated from the top down.
If the company can't help regulate itself using some sort of moral compass, it's imperative that government or other outside regulators should.
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<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>Joan Donovan, PhD</span> in "This is just some of the best back story I’ve ever read. Facebooks web of influence unravels when @_KarenHao pulls the wrong thread. Sike!! (Only the Boston folks will get that.)" / Twitter (<time class='dt-published'>03/14/2021 12:10:09</time>)</cite></small>
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positivus.io positivus.io
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<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>@Currofile</span> in marina @ bliss on Twitter: "@__baileybrooks @SlackHQ This looks in line with something that https://t.co/lo2XmYayhG is building." / Twitter (<time class='dt-published'>03/09/2021 09:27:46</time>)</cite></small>
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www.pnas.org www.pnas.org
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Mendels, D.-A., Dortet, L., Emeraud, C., Oueslati, S., Girlich, D., Ronat, J.-B., Bernabeu, S., Bahi, S., Atkinson, G. J. H., & Naas, T. (2021). Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation. Proceedings of the National Academy of Sciences, 118(12). https://doi.org/10.1073/pnas.2019893118
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trailblazer.to trailblazer.to
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Hey, that’s is an imaginary complication of our example - please don’t do this with every condition you have in your app.
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www.nature.com www.nature.com
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Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nature Human Behaviour, 4(12), 1303–1312. https://doi.org/10.1038/s41562-020-01009-0
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- Feb 2021
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www.youtube.com www.youtube.com
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<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>Cory Doctorow</span> in Pluralistic: 16 Feb 2021 – Pluralistic: Daily links from Cory Doctorow (<time class='dt-published'>02/25/2021 12:20:24</time>)</cite></small>
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psychclassics.yorku.ca psychclassics.yorku.ca
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The result was a mother, soft, warm, and tender, a mother with infinite patience, a mother available twenty-four hours a day, a mother that never scolded her infant and never struck or bit her baby in anger. Furthermore, we designed a mother-machine with maximal maintenance efficiency since failure of any system or function could be resolved by the simple substitution of black boxes and new component parts. It is our opinion that we engineered a very superior monkey mother, although this position is not held universally by the monkey fathers.
Finding the importance the monkeys senses were to thriving the development of this surrogate mother figure was able to demonstrate that it was more than just the need for milk that the infant monkeys craved.
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psyarxiv.com psyarxiv.com
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Longoni, C., Fradkin, A., Cian, L., & Pennycook, G. (2021, February 16). News from Artificial Intelligence is Believed Less. https://doi.org/10.31234/osf.io/wgy9e
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- Jan 2021
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covid-19.iza.org covid-19.iza.org
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Naudé. W., (2020). Artificial Intelligence against COVID-19: An Early Review. Institute of Labor Economics. Retrieved from: https://covid-19.iza.org/publications/dp13110/
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- Dec 2020
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globalfintechseries.com globalfintechseries.com
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FTS News Desk (2020) Blue J Launches Free Tools to Help Determine COVID-19 Relief Eligibility. Global FinTech Series. Retrieved from: https://globalfintechseries.com/blue-j-launches-free-tools-to-help-determine-covid-19-relief-eligibility/
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- Nov 2020
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img1.wsimg.com img1.wsimg.com
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www.theatlantic.com www.theatlantic.com
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The real heart of the matter of selection, however, goes deeper than a lag in the adoption of mechanisms by libraries, or a lack of development of devices for their use. Our ineptitude in getting at the record is largely caused by the artificiality of systems of indexing. When data of any sort are placed in storage, they are filed alphabetically or numerically, and information is found (when it is) by tracing it down from subclass to subclass. It can be in only one place, unless duplicates are used; one has to have rules as to which path will locate it, and the rules are cumbersome. Having found one item, moreover, one has to emerge from the system and re-enter on a new path.
Bush emphasises the importance of retrieval in the storage of information. He talks about technical limitations, but in this paragraph he stresses that retrieval is made more difficult by the "artificiality of systems of indexing", in other words, our default file-cabinet metaphor for storing information.
Information in such a hierarchical architecture is found by descending down into the hierarchy, and back up again. Moreover, the information we're looking for can only be in one place at a time (unless we introduce duplicates).
Having found our item of interest, we need to ascend back up the hierarchy to make our next descent.
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medium.com medium.com
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But, it’s not how we usually work with Svelte and write reusable components such asUnorderedListor similar. Because your example is really synthetic and unuseful in the real world.
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www.kickstarter.com www.kickstarter.com
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I'm still calling this v1.00 as this is what will be included in the first print run.
There seems to be an artificial pressure and a false assumption that the version that gets printed and included in the box be the "magic number" 1.00.
But I think there is absolutely nothing bad or to be ashamed of to have the version number printed in the rule book be 1.47 or even 2.0. (Or, of course, you could just not print it at all.) It's just being transparent/honest about how many versions/revisions you've made. 
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- Oct 2020
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medium.com medium.com
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we will work with an artificial application that pretty prints object trees into a YAML like format
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pairagraph.com pairagraph.com
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Similarly, technology can help us control the climate, make AI safe, and improve privacy.
regulation needs to surround the technology that will help with these things
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Since re-rendering in Svelte happens at a more granular level than the component, there is no artificial pressure to create smaller components than would be naturally desirable
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itp.nyu.edu itp.nyu.edu
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What if you could use AI to control the content in your feed? Dialing up or down whatever is most useful to you. If I’m on a budget, maybe I don’t want to see photos of friends on extravagant vacations. Or, if I’m trying to pay more attention to my health, encourage me with lots of salads and exercise photos. If I recently broke up with somebody, happy couple photos probably aren’t going to help in the healing process. Why can’t I have control over it all, without having to unfollow anyone. Or, opening endless accounts to separate feeds by topic. And if I want to risk seeing everything, or spend a week replacing my usual feed with images from a different culture, country, or belief system, couldn’t I do that, too?
Some great blue sky ideas here.
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www.researchgate.net www.researchgate.net
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Walter Pitts was pivotal in establishing the revolutionary notion of the brain as a computer, which was seminal in the development of computer design, cybernetics, artificial intelligence, and theoretical neuroscience. He was also a participant in a large number of key advances in 20th-century science.
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www.theatlantic.com www.theatlantic.com
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DiResta, Renée. ‘The Supply of Disinformation Will Soon Be Infinite’. The Atlantic, 20 September 2020. https://www.theatlantic.com/ideas/archive/2020/09/future-propaganda-will-be-computer-generated/616400/.
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- Sep 2020
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www.plymouth.edu www.plymouth.edu
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synthesize
To synthesize in definition is create something chemically. This means that if out of 118 elements, 20 of those are man-made via a nuclear reactor and/or a particle accelerator. These elements are unstable because they are built upon fusing an Atom's nucleus with more proton's than it may usually have which causes the stability to become dangerously chaotic as it is not natural for the element. This is the building block for the Atomic Bombs creation.
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github.com github.com
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Since re-rendering in Svelte happens at a more granular level than the component, there is no artificial pressure to create smaller components than would be naturally desirable, and in fact (because one-component-per-file) there is pressure in the opposite direction. As such, large components are not uncommon.
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www.newscientist.com www.newscientist.com
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Lu, D. (n.d.). AI can edit video in real time to sync new audio to people’s lips. New Scientist. Retrieved September 14, 2020, from https://www.newscientist.com/article/2254326-ai-can-edit-video-in-real-time-to-sync-new-audio-to-peoples-lips/
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psyarxiv.com psyarxiv.com
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Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.
Tags
- is:preprint
- machine learning
- cognitive science
- active learning
- recommendation accuracy
- artificial intelligence
- lang:en
- recommender system
- information filtering
- exploration-exploitation tradeoff
- computer science
- experimental approach
- AI
- Internet
- algorithms
- predictive accuracy
- parameterized model
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- Aug 2020
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pubs.acs.org pubs.acs.org
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Shan, B., Broza, Y. Y., Li, W., Wang, Y., Wu, S., Liu, Z., Wang, J., Gui, S., Wang, L., Zhang, Z., Liu, W., Zhou, S., Jin, W., Zhang, Q., Hu, D., Lin, L., Zhang, Q., Li, W., Wang, J., … Haick, H. (2020). Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath. ACS Nano. https://doi.org/10.1021/acsnano.0c05657
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covid-19.iza.org covid-19.iza.org
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Cognitive Performance in the Home Office – Evidence from Professional Chess. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13491/
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- Jul 2020
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www.youtube.com www.youtube.com
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American Philosophical Society. (2020, June 08). Evidence Symposium. YouTube. https://www.youtube.com/playlist?list=PLoKwLGnyZL4Ds5cQo5muFMg8zKXK4KobH
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fivethirtyeight.com fivethirtyeight.com
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Kiefer, P. (2020, May 4). Why Scientists Think The Novel Coronavirus Developed Naturally—Not In A Chinese Lab. FiveThirtyEight. https://fivethirtyeight.com/features/why-scientists-think-the-novel-coronavirus-developed-naturally-not-in-a-chinese-lab/
Tags
- COVID-19
- manufacture
- artificial
- conspiracy theory
- natural
- China
- information
- is:news
- science
- evidence
- development
- intelligence
- lang:en
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URL
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www.youtube.com www.youtube.com
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Virtual MLSS 2020 (Opening Remarks). (2020, June 29). https://www.youtube.com/watch?v=8staJlMbAig
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osf.io osf.io
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Hossain, M. M., McKyer, E. L. J., & Ma, P. (2020). Applications of artificial intelligence technologies on mental health research during COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/w6c9b
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- Jun 2020
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jalammar.github.io jalammar.github.io
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each of them flows through each of the two layers of the encoder
each of them flows through each of the two layers of EACH encoder, right?
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jalammar.github.io jalammar.github.io
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It made it challenging for the models to deal with long sentences.
This is similar to autoencoders struggling with producing high-resolution imagery because of the compression that happens in the latent space, right?
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karpathy.github.io karpathy.github.io
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it seems that word-level models work better than character-level models
Interesting, if you think about it, both when we as humans read and write, we think in terms of words or even phrases, rather than characters. Unless we're unsure how to spell something, the characters are a secondary thought. I wonder if this is at all related to the fact that word-level models seem to work better than character-level models.
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As you can see above, sometimes the model tries to generate latex diagrams, but clearly it hasn’t really figured them out.
I don't think anyone has figured latex diagrams (tikz) out :')
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Antichrist
uhhh should we be worried
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colah.github.io colah.github.io
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We only forget when we’re going to input something in its place. We only input new values to the state when we forget something older.
seems like a decision aiming for efficiency
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outputs a number between 000 and 111 for each number in the cell state Ct−1Ct−1C_{t-1}
remember, each line represents a vector.
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psyarxiv.com psyarxiv.com
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Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2019, December 4). Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools. https://doi.org/10.31234/osf.io/ky4x8
Tags
- online behavior
- cognitive tools
- is:preprint
- behavioral policy
- nudging
- attention economy
- disinformation
- boosting
- reasoning
- artificial intelligence
- lang:en
- decision aid
- internet
- choice architecture
- technocognition
- digital
- algorithm
- self-nudging
- online manipulation
- AI
- misinformation
- fake news
- decision autonomy
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singularityhub.com singularityhub.com
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Gent, Edd. ‘Robots to the Rescue: How They Can Help During Coronavirus (and Future Pandemics)’. Singularity Hub (blog), 1 April 2020. https://singularityhub.com/2020/04/01/robots-to-the-rescue-how-they-can-help-during-coronavirus-and-future-pandemics/.
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Kurzweil, R. (2020 May 19). AI-Powered Biotech Can Help Deploy a Vaccine In Record Time. Wired. https://www.wired.com/story/opinion-ai-powered-biotech-can-help-deploy-a-vaccine-in-record-time/
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www.metascience2019.org www.metascience2019.org
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Yang Yang: The Replicability of Scientific Findings Using Human and Machine Intelligence (Video). Metascience 2019 Symposium. https://www.metascience2019.org/presentations/yang-yang/
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www.pnas.org www.pnas.org
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Yang, Y., Youyou, W., & Uzzi, B. (2020). Estimating the deep replicability of scientific findings using human and artificial intelligence. Proceedings of the National Academy of Sciences, 117(20), 10762–10768. https://doi.org/10.1073/pnas.1909046117
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- May 2020
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www.healtheuropa.eu www.healtheuropa.eu
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Halim, S. (2019, August 13). How Artificial Intelligence can revolutionise healthcare. Health Europa. https://www.healtheuropa.eu/how-artificial-intelligence-can-revolutionise-healthcare/92824/
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Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
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Mei, X., Lee, H.-C., Diao, K., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P. M., Chung, M., Bernheim, A., Mani, V., Calcagno, C., Li, K., Li, S., Shan, H., Lv, J., Zhao, T., Xia, J., … Yang, Y. (2020). Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19). MedRxiv, 2020.04.12.20062661. https://doi.org/10.1101/2020.04.12.20062661
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www.thelancet.com www.thelancet.com
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Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet, 395(10236), 1579–1586. https://doi.org/10.1016/S0140-6736(20)30226-9
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Wahn, B., & Kingstone, A. (2020, April 30). Sharing task load with artificial – yet human-like – co-actors. https://doi.org/10.31234/osf.io/2am8y
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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Results reveal a significant shift in the gut microbiome and metabolome within one day following morphine treatment compared to that observed after placebo. Morphine-induced gut microbial dysbiosis exhibited distinct characteristic signatures, including significant increase in communities associated with pathogenic function, decrease in communities associated with stress tolerance and significant impairment in bile acids and morphine-3-glucuronide/morphine biotransformation in the gut.
Unsurprisingly, various substances appear to disrupt the microbiome; artificial sweeteners are not unique. Given that I don't worry about opioids, I probably shouldn't worry about sweeteners.
However, opioids are known for causing constipation. That is to say, they have a clear effect on digestion. Perhaps I should worry about opioids rather than not worry about sweeteners.
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- Apr 2020
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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Although it has been proposed that NNS do not affect glycemia (3), data from several recent studies suggest that NNS are not physiologically inert. First, it has been demonstrated that the gastrointestinal tract (4,5) and the pancreas (6,7) can detect sugars through taste receptors and transduction mechanisms that are similar to those indentified in taste cells in the mouth. Second, NNS-induced activation of gut sweet taste receptors in isolated duodenal L cells and pancreatic β-cells triggers the secretion of glucagon-like peptide 1 (GLP-1) (4,5) and insulin (6–9), respectively. Third, data from studies conducted in animal models demonstrate that NNS interact with sweet taste receptors expressed in enteroendocrine cells to increase both active and passive intestinal glucose absorption by upregulating the expression of sodium-dependent glucose transporter isoform 1 (5,10,11) and increasing the translocation of GLUT2 to the apical membrane of intestinal epithelia (12).
This supports my previous assertion that the effects of artificial sweeteners on the microbiome are taste-mediated. However, I did not predict the intestinal taste receptors. That means that my previous way to falsify the claim, such as delivery by oral gavage, is no longer adequate. Nonetheless, interesting things could be learned from such tests.
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academic.oup.com academic.oup.com
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These variations were related to inflammation in the host
In which direction? This statement makes me wonder if inflammation caused the changes in the microbiome.
It seems possible that the sweetness itself is the ultimate cause. To test this, a study using oral gavage. It's easily plausible that the flavor alerts dietary patterns (I believe humans eat more calories in response to sweeteners, will need to check on source). Alternatively, direct effects on the brain, and downstream effects on the body, is also not out of the question.
The reason I suspect taste-mediated effects is that it seems unlikely that so many completely unrelated sweeteners would have such similar effects. However, one might might expect more similar results than those found if it were the case (or the dose is so high that the taste changes for some, e.g. saccharin).
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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news.stanford.edu news.stanford.edu
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University, S. (2020, March 20). Stanford virtual conference to focus on COVID‑19 and artificial intelligence. Stanford News. https://news.stanford.edu/2020/03/20/stanford-virtual-conference-focus-covid-19-artificial-intelligence/
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onlinelibrary.wiley.com onlinelibrary.wiley.com
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Abdulla, A., Wang, B., Qian, F., Kee, T., Blasiak, A., Ong, Y. H., Hooi, L., Parekh, F., Soriano, R., Olinger, G. G., Keppo, J., Hardesty, C. L., Chow, E. K., Ho, D., & Ding, X. (n.d.). Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention. Advanced Therapeutics, n/a(n/a), 2000034. https://doi.org/10.1002/adtp.202000034
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- Dec 2019
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collect.readwriterespond.com collect.readwriterespond.com
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Alexander Samuel reflects on tagging and its origins as a backbone to the social web. Along with RSS, tags allowed users to connect and collate content using such tools as feed readers. This all changed with the advent of social media and the algorithmically curated news feed.
Tags were used for discovery of specific types of content. Who needs that now that our new overlords of artificial intelligence and algorithmic feeds can tell us what we want to see?!
Of course we still need tags!!! How are you going to know serendipitously that you need more poetry in your life until you run into the tag on a service like IndieWeb.xyz? An algorithmic feed is unlikely to notice--or at least in my decade of living with them I've yet to run into poetry in one.
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- Aug 2019
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becominghuman.ai becominghuman.ai
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so there won’t be a blinking bunny, at least not yet, let’s train our bunny to blink on command by mixing stimuli ( the tone and the air puff)
Is it just that how we all learn and evolve? 😲
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pt.wikipedia.org pt.wikipedia.org
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Em 2015, o serviço de streaming de música Spotify criou a playlist chamada Descobertas da Semana, que funciona como uma curadoria digital. O algoritmo responsável por esta playlist utiliza técnicas de Filtragem Colaborativa, Processamento de Linguagem Natural e Processamento de Sinais de Áudio através de Redes Neurais Convolucionais para compor a playlist semanalmente.[33]
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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A notable by-product of a move of clinical as well as research data to the cloud would be the erosion of market power of EMR providers.
But we have to be careful not to inadvertently favour the big tech companies in trying to stop favouring the big EMR providers.
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cloud computing is provided by a small number of large technology companies who have both significant market power and strong commercial interests outside of healthcare for which healthcare data might potentially be beneficial
AI is controlled by these external forces. In what direction will this lead it?
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it has long been argued that patients themselves should be the owners and guardians of their health data and subsequently consent to their data being used to develop AI solutions.
Mere consent isn't enough. We consent to give away all sorts of data for phone apps that we don't even really consider. We need much stronger awareness, or better defaults so that people aren't sharing things without proper consideration.
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To realize this vision and to realize the potential of AI across health systems, more fundamental issues have to be addressed: who owns health data, who is responsible for it, and who can use it? Cloud computing alone will not answer these questions—public discourse and policy intervention will be needed.
This is part of the habit and culture of data use. And it's very different in health than in other sectors, given the sensitivity of the data, among other things.
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In spite of the widely touted benefits of “data liberation”,15 a sufficiently compelling use case has not been presented to overcome the vested interests maintaining the status quo and justify the significant upfront investment necessary to build data infrastructure.
Advancing AI requires more than just AI stuff. It requires infrastructure and changes in human habit and culture.
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However, clinician satisfaction with EMRs remains low, resulting in variable completeness and quality of data entry, and interoperability between different providers remains elusive.11
Another issue with complex systems: the data can be volumous but poor individual quality, relying on domain knowledge to be able to properly interpret (eg. that doctor didn't really prescribe 10x the recommended dose. It was probably an error.).
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Second, most healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) “fit” the local population and/or the local practice patterns, a requirement prior to deployment that is rarely highlighted by current AI publications, and (b) interrogate them for bias to guarantee that the algorithms perform consistently across patient cohorts, especially those who may not have been adequately represented in the training cohort.9
AI depends on:
- static processes - if the population you are predicting changes relative to the one used to train the model, all bets are off. It remains to be seen how similar they need to be given the brittleness of AI algorithms.
- homogeneous population - beyond race, what else is important? If we don't have a good theory of health, we don't know.
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Simply adding AI applications to a fragmented system will not create sustainable change.
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bookbook.pubpub.org bookbook.pubpub.org
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Both artists, through annotation, have produced new forms of public dialogue in response to other people (like Harvey Weinstein), texts (The New York Times), and ideas (sexual assault and racial bias) that are of broad social and political consequence.
What about examples of future sorts of annotations/redactions like these with emerging technologies? Stories about deepfakes (like Obama calling Trump a "dipshit" or the Youtube Channel Bad Lip Reading redubbing the words of Senator Ted Cruz) are becoming more prevalent and these are versions of this sort of redaction taken to greater lengths. At present, these examples are obviously fake and facetious, but in short order they will be indistinguishable and more commonplace.
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labsblog.f-secure.com labsblog.f-secure.com
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Security Issues, Dangers And Implications Of Smart Systems
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- Jun 2019
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en.wikipedia.org en.wikipedia.org
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The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.[2] At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the "winter" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.
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weneedtotalkai.files.wordpress.com weneedtotalkai.files.wordpress.com
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We Need to Talk, AIDr. Julia SchneiderLena Kadriye ZiyalA Comic Essay on Artificial Intelligence
Un ensayo en cómic: esto pinta muy pero muy bien
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- May 2019
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engl201.opened.ca engl201.opened.ca
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Deepmachinelearning,whichisusingalgorithmstoreplicatehumanthinking,ispredicatedonspecificvaluesfromspecifickindsofpeople—namely,themostpowerfulinstitutionsinsocietyandthosewhocontrolthem.
This reminds me of this Reddit page
The page takes pictures and texts from other Reddit pages and uses it to create computer generated posts and comments. It is interesting to see the intelligence and quality of understanding grow as it gathers more and more information.
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cdn.aiindex.org cdn.aiindex.org
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government investments
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initiatives from the U.S., China, and Europ
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Recent Government Initiatives
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engagement in AI activities by academics, corporations, entrepreneurs, and the general public
Volume of Activity
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Derivative Measures
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AI Vibrancy Index
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limited gender diversity in the classroom
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improvement in natural language
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the COCO leaderboard
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patents
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robot operating system downloads,
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he GLUE metric
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robot installations
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AI conference attendance
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the speed at which computers can be trained to detect objects
Technical Performance
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quality of question answering
Technical Performance
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changes in AI performance
Technical Performance
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Technical Performance
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number of undergraduates studying AI
Volume of Activity
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growth in venture capital funding of AI startups
Volume of Activity
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percent of female applicants for AI jobs
Volume of Activity
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Volume of Activity
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increased participation in organizations like AI4ALL and Women in Machine Learning
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producers of AI patents
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ML teaching events
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University course enrollment
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83 percent of 2017 AI papers
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- Apr 2019
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er.educause.edu er.educause.edu
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Ashley Norris is the Chief Academic Officer at ProctorU, an organization that provides online exam proctoring for schools. This article has an interesting overview of the negative side of technology advancements and what that has meant for student's ability to cheat. While the article does culminate as an ad, of sorts, for ProctorU, it is an interesting read and sparks thoughts on ProctorU's use of both human monitors for testing but also their integration of Artificial Intelligence into the process.
Rating: 9/10.
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- Mar 2019
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timharford.com timharford.com
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If you do not like the price you’re being offered when you shop, do not take it personally: many of the prices we see online are being set by algorithms that respond to demand and may also try to guess your personal willingness to pay. What’s next? A logical next step is that computers will start conspiring against us. That may sound paranoid, but a new study by four economists at the University of Bologna shows how this can happen.
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aeon.co aeon.co
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Worse still, even if we had the ability to take a snapshot of all of the brain’s 86 billion neurons and then to simulate the state of those neurons in a computer, that vast pattern would mean nothing outside the body of the brain that produced it. This is perhaps the most egregious way in which the IP metaphor has distorted our thinking about human functioning.
Again, this doesn't conflict with a machine-learning or deep-learning or neural-net way of seeing IP.
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No ‘copy’ of the story is ever made
Or, the copy initially made is changed over time since human "memory" is interdependent and interactive with other brain changes, whereas each bit in computer memory is independent of all other bits.
However, machine learning probably results in interactions between bits as the learning algorithm is exposed to more training data. The values in a deep neural network interact in ways that are not so obvious. So this machine-human analogy might be getting new life with machine learning.
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The IP perspective requires the player to formulate an estimate of various initial conditions of the ball’s flight
I don't see how this is true. The IP perspective depends on algorithms. There are many different algorithms to perform various tasks. Some perform reverse-kinematic calculations, but others conduct simpler, repeated steps. In computer science, this might be dynamic programming, recursive algorithms, or optimization. It seems that the IP metaphor still fits: it's just that those using the metaphor may not have updated their model of IP to be more modern.
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techbuzztalk.com techbuzztalk.com
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There is no wonder that AI gains popularity. A lot of facts and pros are the stimulators of such profitable growth of AI. The essential peculiarities are fully presented in the given article.
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dougengelbart.org dougengelbart.org
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we provide him as much help as possible in making a plan of action. Then we give him as much help as we can in carrying it out. But we also have to allow him to change his mind at almost any point, and to want to modify his plans.
I'm thinking about the role of AI tutors/advisors here. How often do they operate in the kind of flexible way described here. I wonder if they can without actual human intervention.
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- Feb 2019
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rightsanddissent.org rightsanddissent.org
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Nearly half of FBI rap sheets failed to include information on the outcome of a case after an arrest—for example, whether a charge was dismissed or otherwise disposed of without a conviction, or if a record was expunged
This explains my personal experience here: https://hyp.is/EIfMfivUEem7SFcAiWxUpA/epic.org/privacy/global_entry/default.html (Why someone who had Global Entry was flagged for a police incident before he applied for Global Entry).
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Applicants also agree to have their fingerprints entered into DHS’ Automatic Biometric Identification System (IDENT) “for recurrent immigration, law enforcement, and intelligence checks, including checks against latent prints associated with unsolved crimes.
Intelligence checks is very concerning here as it suggests pretty much what has already been leaked, that the US is running complex autonomous screening of all of this data all the time. This also opens up the possibility for discriminatory algorithms since most of these are probably rooted in machine learning techniques and the criminal justice system in the US today tends to be fairly biased towards certain groups of people to begin with.
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It cited research, including some authored by the FBI, indicating that “some of the biometrics at the core of NGI, like facial recognition, may misidentify African Americans, young people, and women at higher rates than whites, older people, and men, respectively.
This re-affirms the previous annotation that the set of training data for the intelligence checks the US runs on global entry data is biased towards certain groups of people.
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- Jan 2019
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CTP synthesizes critical reflection with technology production as a way of highlighting and altering unconsciously-held assumptions that are hindering progress in a technical field.
Definition of critical technical practice.
This approach is grounded in AI rather than HCI
(verbatim from the paper) "CTP consists of the following moves:
• identifying the core metaphors of the field
• noticing what, when working with those metaphors, remains marginalized
• inverting the dominant metaphors to bring that margin to the center
• embodying the alternative as a new technology
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- Dec 2018
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wendynorris.com wendynorris.comhci1523.vp13
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Our under-standing of the gap is driven by technological exploration through artifact cre-ation and deployment, but HCI and CSCW systems need to have at their corea fundamental understanding of how people really work and live in groups, or-ganizations, communities, and other forms of collective life. Otherwise, wewill produce unusable systems, badly mechanizing and distorting collabora-tion and other social activity.
The risk of CSCW not driving toward a more scientific pursuit of social theory, understanding, and ethnomethodology and instead simply building "cool toys"
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The gap is also CSCW’s unique contribution. CSCW exists intellectually atthe boundary and interaction of technology and social settings. Its unique intel-lectual importance is at the confluence of technology and the social, and its
CSCW's potential to become a science of the artificial resides in the study of interactions between society and technology
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Nonetheless, several guiding questions are required based on thesocial–technical gap and its role in any CSCW science of the artificial:• When can a computational system successfully ignore the need fornuance and context?• When can a computational system augment human activity withcomputer technologies suitably to make up for the loss in nuance andcontext, as argued in the approximation section earlier?• Can these benefits be systematized so that we know when we are add-ing benefit rather than creating loss?• What types of future research will solve some of the gaps betweentechnical capabilities and what people expect in their full range of so-cial and collaborative activities?
Questions to consider in moving CSCW toward a science of the artificial
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The final first-order approximation is the creation of technical architecturesthat do not invoke the social–technical gap; these architectures neither requireaction nor delegate it. Instead, these architectures provide supportive oraugmentative facilities, such as advice, to users.
Support infrastructures provide a different type of approximation to augment the user experience.
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Another approximation incorporates new computational mechanisms tosubstitute adequately for social mechanisms or to provide for new social issues(Hollan & Stornetta, 1992).
Approximate a social need with a technical cue. Example in Google Docs of anonymous user icons on page indicates presence but not identity.
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First-order approximations, to adopt a metaphor from fluid dynamics, aretractable solutions that partially solve specific problems with knowntrade-offs.
Definition of first-order approximations.
Ackerman argues that CSCW needs a set of approximations that drive the development of initial work-arounds for the socio-technical gaps.
Essentially, how to satisfy some social requirements and then approximate the trade-offs. Doesn't consider the product a solution in full but something to iterate and improve
This may have been new/radical thinking 20 years ago but seems to have been largely adopted by the CSCW community
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Similarly, an educational perspective would argue that programmers andusers should understand the fundamental nature of the social requirements.
Ackerman argues that CS education should include understanding how to design/build for social needs but also to appreciate the social impacts of technology.
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CSCW’s science, however, must centralize the necessary gap between whatwe would prefer to construct and what we can construct. To do this as a practi-cal program of action requires several steps—palliatives to ameliorate the cur-rent social conditions, first-order approximations to explore the design space,and fundamental lines of inquiry to create the science. These steps should de-velop into a new science of the artificial. In any case, the steps are necessary tomove forward intellectually within CSCW, given the nature of the social–tech-nical gap.
Ackerman sets up the steps necessary for CSCW to become a science of the artificial and to try to resolve the socio-technical gap:
Palliatives to ameliorate social conditions
Approximations to explore the design space
Lines of scientific inquiry
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Ideological initiatives include those that prioritize the needs of the peopleusing the systems.
Approaches to address social conditions and "block troublesome impacts":
Stakeholder analysis
Participatory design
Scandinavian approach to info system design requires trade union involvement
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Simon’s (1969/1981) book does not address the inevitable gaps betweenthe desired outcome and the means of producing that outcome for anylarge-scale design process, but CSCW researchers see these gaps as unavoid-able. The social–technical gap should not have been ignored by Simon.Yet, CSCW is exactly the type of science Simon envisioned, and CSCW couldserve as a reconstruction and renewal of Simon’s viewpoint, suitably revised. Asmuch as was AI, CSCW is inherently a science of the artificial,
How Ackerman sees CSCW as a science of the artificial:
"CSCW is at once an engineering discipline attempting to construct suitable systems for groups, organizations, and other collectivities, and at the same time, CSCW is a social science attempting to understand the basis for that construction in the social world (or everyday experience)."
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At a simple level,CSCW’s intellectual context is framed by social constructionism andethnomethodology (e.g., Berger & Luckmann, 1966; Garfinkel, 1967), systemstheories (e.g., Hutchins, 1995a), and many large-scale system experiences (e.g.,American urban renewal, nuclear power, and Vietnam). All of these pointed tothe complexities underlying any social activity, even those felt to be straightfor-ward.
Succinct description of CSCW as social constructionism, ethnomethodlogy, system theory and large-scale system implementation.
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Yet,The Sciences of the Artificialbecame an an-them call for artificial intelligence and computer science. In the book he ar-gued for a path between the idea for a new science (such as economics orartificial intelligence) and the construction of that new science (perhaps withsome backtracking in the creation process). This argument was both charac-teristically logical and psychologically appealing for the time.
Simon defines "Sciences of the Artificial" as new sciences/disciplines that synthesize knowledge that is technically or socially constructed or "created and maintained through human design and agency" as opposed to the natural sciences
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The HCI and CSCW research communitiesneed to ask what one might do to ameliorate the effects of the gap and to fur-ther understand the gap. I believe an answer—and a future HCI challenge—is toreconceptualize CSCW as a science of the artificial. This echoes Simon (1981)but properly updates his work for CSCW’s time and intellectual task.2
Ackerman describes "CSCW as a science of the artificial" as a potential approach to reduce the socio-technical gap
Tags
Annotators
URL
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- Nov 2018
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www.technologyreview.com www.technologyreview.com
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The vast majority of machine-learning applications rely on supervised learning.
So then we know that most people will use supervised learning that requires less computational power and knowledge.
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Entscheidend ist, dass sie Herren des Verfahrens bleiben - und eine Vision für das neue Maschinenzeitalter entwickeln.
Es sieht für mich nicht eigentlich so aus als wären wir jemals die "Herren des Verfahrens" gewesen. Und auch darum geht es ja bei Marx. Denke ich.
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- Sep 2018
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www.mnemotext.com www.mnemotext.com
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And its very likely that IA is a much easier road to the achievement of superhumanity than pure AI. In humans, the hardest development problems have already been solved. Building up from within ourselves ought to be easier than figuring out what we really are and then building machines that are all of that.
The authors of the text are proposing a radically different approach to the inevitable "singularity" event. They propose the research and development IA, or Intelligence Amplification, is developing computers with a symbiosis with humans. Noting that IA could be easier to develop than AI algorithms, since humanity had to probe what their true weaknesses and strengths are. In turn, developing an IA system that could cover humanities' weaknesses. This would summarily prevent an IA algorithm from getting over itself, which could potentially slow a point when we reach singularity.
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- Jul 2018
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www.economist.com www.economist.com
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Leading thinkers in China argue that putting government in charge of technology has one big advantage: the state can distribute the fruits of AI, which would otherwise go to the owners of algorithms.
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- Jun 2018
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www.lancaster.ac.uk www.lancaster.ac.uk
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In “Getting Real,” Barad proposes that “reality is sedimented out of the process ofmaking the world intelligible through certain practices and not others ...” (1998: 105). If,as Barad and other feminist researchers suggest, we are responsible for what exists, what isthe reality that current discourses and practices regarding new technologies makeintelligible, and what is excluded? To answer this question Barad argues that we need asimultaneous account of the relations of humans and nonhumansandof their asymmetriesand differences. This requires remembering that boundaries between humans and machinesare not naturally given but constructed, in particular historical ways and with particularsocial and material consequences. As Barad points out, boundaries are necessary for thecreation of meaning, and, for that very reason, are never innocent. Because the cuts impliedin boundary making are always agentially positioned rather than naturally occurring, andbecause boundaries have real consequences, she argues, “accountability is mandatory”(187). :We are responsible for the world in which we live not because it is an arbitraryconstruction of our choosing, but because it is sedimented out of particular practicesthat we have a role in shaping (1998: 102).The accountability involved is not, however, a matter of identifying authorship in anysimple sense, but rather a problem of understanding the effects of particular assemblages,and assessing the distributions, for better and worse, that they engender.
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Finally, the ‘smart’ machine's presentation of itself asthe always obliging, 'labor-saving device' erases any evidence of the labor involved in itsoperation "from bank personnel to software programmers to the third-world workers whoso often make the chips" (75).
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Chasin poses the question (which I return to below) of how a change in our view ofobjects from passiveand outside the social could help to undo the subject/object binaryand all of its attendant orderings, including for example male/female, or mental/manua
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Figured as servants,she points out, technologies reinscribe the difference between ‘us’ and those who serve us,while eliding the difference between the latter and machines: "The servanttroubles thedistinction between we-human-subjects-inventors with a lot to do (on the onehand) andthem-object-things that make it easier for us (on the other)" (1995: 73)
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- Apr 2018
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astrologynewsservice.com astrologynewsservice.com
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Astrology proven by artificial intelligence.
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www.economist.com www.economist.com
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The alternative, of a regulatory patchwork, would make it harder for the West to amass a shared stock of AI training data to rival China’s.
Fascinating geopolitical suggestion here: Trans-Atlantic GDPR-like rules as the NATO of data privacy to effectively allow "the West" to compete against the People's Republic of China in the development of artificial intelligence.
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- Mar 2018
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webfoundation.org webfoundation.org
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The concentration of skills in certain countries and global companies could lead to a situation where other (native) companies are crowded out.
Riesgo de la falta de desarrollo de capacidades informacionales, pero o por lo mínimo, sino por lo máximo: No se trata de gente que no sabe usar un computador y debe aprenderlo, sino de aquellos que desarrollan inteligencia artificial y no tienen la capacidad de llegar hasta lo más alto en esta escala.
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Artificial intelligence (AI), machine learning and deep learning
Explicación gráfica de artificial intelligence, machine learning y deep learning
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we will use an inclusive definition of intelligence as ‘problem-solving’ and consider ‘an intelligent system’ to be one which takes the best possible action in a given situation.
Inteligencia:
- Capacidad de resolver problemas.
- Capacidad de elegir la mejor opción en una determinada situación.
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- Feb 2018
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ojs.library.queensu.ca ojs.library.queensu.ca
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ecent article in the Scientific Americanasked whether democracy will survive big data and artificial intel
Ciertamente los temas relacionados con datos, inteligencia artificial y algoritmos son de mucho interés en la actualidad, frente al uso de tecnologías. La Web Foundation tiene documentos al respecto.
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- Jan 2018
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maestriadesarrollo.com maestriadesarrollo.com
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ante la destructividad naturalizada que ha acompañado el Antropoceno y ante la aparición de lo artificial como el modo ineluctable de la vida humana, necesitamos oponer el cultivo de modos de devenir cualitativamente nuevos a través del potencial futurizante ofrecido por lo artificial. En este caso ‘posibilidad’ significa “la negociación con la realidad y no una escalada de lo que es”
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- Dec 2017
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Most of the recent advances in AI depend on deep learning, which is the use of backpropagation to train neural nets with multiple layers ("deep" neural nets).
Neural nets consist of layers of nodes, with edges from each node to the nodes in the next layer. The first and last layers are input and output. The output layer might only have two nodes, representing true or false. Each node holds a value representing how excited it is. Each edge has a value representing strength of connection, which determines how much of the excitement passes through.
The edges in an untrained neural net start with random values. The training data consists of a series of samples that are already labeled. If the output is wrong, the edges are adjusted according to how much they contributed to the error. It's called backpropagation because it starts with the output nodes and works toward the input nodes.
Deep neural nets can be effective, but only for single specific tasks. And they need huge sets of training data. They can also be tricked rather easily. Worse, someone who has access to the net can discover ways of adding noise to images that will make the net "see" things that obviously aren't there.
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- Aug 2017
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So this transforms how we do design. The human engineer now says what the design should achieve, and the machine says, "Here's the possibilities." Now in her job, the engineer's job is to pick the one that best meets the goals of the design, which she knows as a human better than anyone else, using human judgment and expertise.
A post on the Keras blog was talking about eventually using AI to generate computer programs to match certain specifications. Gruber is saying something very similar.
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- Jun 2017
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opentextbc.ca opentextbc.ca
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Expert communicators may sometimes be perceived as trustworthy because they know a lot about the product they are selling.
Brain Games persuasion experiment: A fake sales person utilizes the halo effect, expert bias, and artificial scarcity to to promote a worthless product to consumers.
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- Apr 2017
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www.inverse.com www.inverse.com
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Google's DeepDream neural network applied to Joy of Painting with Bob Ross. Very creepy.
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- Mar 2017
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www.theregister.co.uk www.theregister.co.uk
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Google Home talking personal assistant spouts audio ads. Not much of a surprise.
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techcrunch.com techcrunch.com
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Great overview and commentary. However, I would have liked some more insight into the ethical ramifications and potential destructiveness of an ASI-system as demonstrated in the movie.
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- Feb 2017
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meta.com meta.com
Tags
Annotators
URL
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- Jan 2017
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According to a 2015 report by Incapsula, 48.5% of all web traffic are by bots.
...
The majority of bots are "bad bots" - scrapers that are harvesting emails and looking for content to steal, DDoS bots, hacking tools that are scanning websites for security vulnerabilities, spammers trying to sell the latest diet pill, ad bots that are clicking on your advertisements, etc.
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Content on websites such as dev.to are reposted elsewhere, word-for-word, by scrapers programmed by Black Hat SEO specialists.
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However, a new breed of scrapers exist - intelligent scrapers. They can search websites for sentences containing certain keywords, and then rewrite those sentences using "article spinning" techniques.
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- Dec 2016
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www.newscientist.com www.newscientist.com
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The team on Google Translate has developed a neural network that can translate language pairs for which it has not been directly trained. "For example, if the neural network has been taught to translate between English and Japanese, and English and Korean, it can also translate between Japanese and Korean without first going through English."
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obamawhitehouse.archives.gov obamawhitehouse.archives.gov
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White House report: Artificial Intelligence, Automation, and the Economy
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- Sep 2016
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robotics.stanford.edu robotics.stanford.edu
- Jun 2016
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www.teachthought.com www.teachthought.com
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Artificial Intelligence will eventually revolutionize this practice.
“Resistance is futile” Personal-ized education
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- May 2016
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www.theatlantic.com www.theatlantic.com
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2013 article about Douglas Hofstadter, who has continued to pursue an understanding of the human mind through experiments with AI.
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- Apr 2016
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techcrunch.com techcrunch.com
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We should have control of the algorithms and data that guide our experiences online, and increasingly offline. Under our guidance, they can be powerful personal assistants.
Big business has been very militant about protecting their "intellectual property". Yet they regard every detail of our personal lives as theirs to collect and sell at whim. What a bunch of little darlings they are.
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- Jan 2016
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cacm.acm.org cacm.acm.org
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If I am committed to an intention that is not anchored in time, what exactly am I committing to, and how does it actually drive action?
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nautil.us nautil.us
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The story of Walter Pitts and Warren McCulloch, whose theory of brain function was followed by John von Neumann's design for the stored program computer.
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- Dec 2015
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openai.com openai.comOpenAI1
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OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.
They're hiring: https://openai.com/about/
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code.facebook.com code.facebook.com
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Big Sur is our newest Open Rack-compatible hardware designed for AI computing at a large scale. In collaboration with partners, we've built Big Sur to incorporate eight high-performance GPUs
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verde.com.br verde.com.br
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The best computer vision company in Brazil
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- Nov 2015
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www.randalolson.com www.randalolson.com
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TPOT is a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. Think of TPOT as your “Data Science Assistant”: TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines, then recommending the pipelines that work best for your data.
https://github.com/rhiever/tpot TPOT (Tree-based Pipeline Optimization Tool) Built on numpy, scipy, pandas, scikit-learn, and deap.
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- Jul 2015
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ocw.mit.edu ocw.mit.edu
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This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence.
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- May 2015
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www.columbia.edu www.columbia.edu
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In this work, Lee and Brunskill fit a separate Knowledge Tracing model to each student’s data. This involv ed fitting four parameters: initial probability o f mastery, probability of transitioning from unmastered to mastered, probability of giving an incorrect answer if the student has mastered the skill, and probability of giving a correct answer if the student has not mastered the skill. Each student’s model is fit using a combination of Expectation Maximization (EM) combined with a brute force search
First comment
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- Nov 2014
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www.slate.com www.slate.com
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The Most Terrifying Thought Experiment of All Time
TLDR: Thought experiment that, by knowing about it, you are contributing to humanity enslavement to a all powerful AI
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- Feb 2014
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cm.bell-labs.com cm.bell-labs.com
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Point 3 is almost certainly the one that still bugs Doug. All sorts of mechanisms and utilities are around and used (source code control, registries, WWW search engines, and on and on), but the problem of indexing and finding relevant information is tougher today than ever before, even on one's own hard disk, let alone the WWW.
I would agree that "the problem of indexing and finding relevant information is tougher today than ever before" ... and especially "on one's own hard disk".
Vannevar Bush recognized the problem of artificial systems of indexing long before McIlroy pulled this page from his typewriter in 1964, and here we are 50 years later using the same kind of filesystem indexing systems and wondering why it's harder than ever to find information on our own hard drives.
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www.theatlantic.com www.theatlantic.com
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The real heart of the matter of selection, however, goes deeper than a lag in the adoption of mechanisms by libraries, or a lack of development of devices for their use. Our ineptitude in getting at the record is largely caused by the artificiality of systems of indexing. When data of any sort are placed in storage, they are filed alphabetically or numerically, and information is found (when it is) by tracing it down from subclass to subclass. It can be in only one place, unless duplicates are used; one has to have rules as to which path will locate it, and the rules are cumbersome. Having found one item, moreover, one has to emerge from the system and re-enter on a new path. The human mind does not work that way. It operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain. It has other characteristics, of course; trails that are not frequently followed are prone to fade, items are not fully permanent, memory is transitory. Yet the speed of action, the intricacy of trails, the detail of mental pictures, is awe-inspiring beyond all else in nature.
With the advent of Google Docs we're finally moving away from the archaic indexing mentioned here. The filesystem metaphor was simple and dominated how everyone manages their data-- which extended into how we developed web content, as well.
The declaration that Hierarchical File Systems are Dead has led to better systems of tagging and search, but we're still far from where we need to be since there is still a heavy focus on the document as a whole instead of also the content within the document.
The linearity of printed books is even more treacherously entrenched in our minds than the classification systems used by libraries to store those books.
One day maybe we'll liberate every piece of content from every layer of its concentric cages: artificial systems of indexing, books, web pages, paragraphs, even sentences and words themselves. Only then will we be able to re-dress those thoughts automatically into those familiar and comforting forms that keep our thoughts caged.
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