Harness collective intelligence augmented by digital technology, and unlock exponential innovation. Beyond old hierarchical structures and archaic tools.
https://twitter.com/augmented_CI
The words "beyond", "hierarchical", and "archaic" are all designed to marginalize prior thought and tools which all work, and are likely upon which this broader idea is built. This is a potentially toxic means of creating "power over" this prior art rather than a more open spirit of "power with".
Interesting sounding high level paper about the limits and constraints on general intelligence and how this might relate to the struggles AI/ML research has had historically.
Hypothesis page to discuss this high level description of DeepMind's new Gato framework.
Bret Victor shared this post to make the point that we shouldn't be worrying about sentient AI right now; that the melting ice caps are way more of a threat than AGI. He linked to this article, saying that corporations act like a non-human, intelligent entity, that has real impacts in the world today, that may be way more consequential than AI.
Ben Williamson shared this post on Twitter, saying that it's a good idea to remove the words 'artificial intelligence' and 'AI' from policy statements, etc. as a way of talking about specific details of a technology. We can see loads of examples of companies using 'AI' to obfuscate what they are really going.
The bulk of Vumacam’s subscribers have thus far been private security companies like AI Surveillance, which supply anything from armed guards to monitoring for a wide range of clients, including schools, businesses, and residential neighborhoods. This was always the plan: Vumacam CEO Croock started AI Surveillance with Nichol shortly after founding Vumacam and then stepped away to avoid conflicts with other Vumacam customers.
Vumacam provides the platform, AI-driven target selection, and human review. Others subscribe to that service and add their own layers of services to customers.
Since most of our feeds rely on either machine algorithms or human curation, there is very little control over what we actually want to see.
While algorithmic feeds and "artificial intelligences" might control large swaths of what we see in our passive acquisition modes, we can and certainly should spend more of our time in active search modes which don't employ these tools or methods.
How might we better blend our passive and active modes of search and discovery while still having and maintaining the value of serendipity in our workflows?
Consider the loss of library stacks in our research workflows? We've lost some of the serendipity of seeing the book titles on the shelf that are adjacent to the one we're looking for. What about the books just above and below it? How do we replicate that sort of serendipity into our digital world?
How do we help prevent the shiny object syndrome? How can stay on task rather than move onto the next pretty thing or topic presented to us by an algorithmic feed so that we can accomplish the task we set out to do? Certainly bookmarking a thing or a topic for later follow up can be useful so we don't go too far afield, but what other methods might we use? How can we optimize our random walks through life and a sea of information to tie disparate parts of everything together? Do we need to only rely on doing it as a broader species? Can smaller subgroups accomplish this if carefully planned or is exploring the problem space only possible at mass scale? And even then we may be under shooting the goal by an order of magnitude (or ten)?
ResearchRabbit, which fully launched in August 2021, describes itself as “Spotify for papers”.
Research Rabbit is a search engine for academic research that was launched in August of 2021 and bills itself as "Spotify for papers." It uses artificial intelligence to recommend related papers to researchers and updates those recommendations based on the contents of one's growing corpus of interest.
Connected Papers uses the publicly available corpus compiled by Semantic Scholar — a tool set up in 2015 by the Allen Institute for Artificial Intelligence in Seattle, Washington — amounting to around 200 million articles, including preprints.
Semantic Scholar is a digital tool created by the Allen Institute for Artificial Intelligence in Seattle, Washington in 2015. It's corpus is publicly available for search and is used by other tools including Connected Papers.
ReconfigBehSci [@SciBeh]. (2021, November 14). @STWorg @olbeun @lombardi_learn @kostas_exarhia @stefanmherzog @commscholar @johnfocook @Briony_Swire @Sander_vdLinden @DG_Rand @kendeou @dlholf @ProfSunitaSah @HendirkB @gordpennycook @andyguess @emmapsychology @ThomsonAngus @UMDCollegeofEd @gavaruzzi @katytapper @orspaca [Tweet]. Twitter. https://twitter.com/SciBeh/status/1459813535974842371
ReconfigBehSci [@SciBeh]. (2021, November 14). Kai Spiekermann will speak the need for science communication and how it supports the pivotal role of knowledge in a functioning democracy. The panel will focus on what collective intelligence has to offer. 3/6 [Tweet]. Twitter. https://twitter.com/SciBeh/status/1459813528987217926
He continues by comparing open works to Quantum mechanics, and he arrives at the conclusion that open works are more like Einstein's idea of the universe, which is governed by precise laws but seems random at first. The artist in those open works arranges the work carefully so it could be re-organized by another but still keep the original voice or intent of the artist.
Is physics open or closed?
Could a play, made in a zettelkasten-like structure, be performed in a way so as to keep a consistent authorial voice?
What potential applications does the idea of opera aperta have for artificial intelligence? Can it be created in such a way as to give an artificial brain a consistent "authorial voice"?
ReconfigBehSci. (2021, November 14). Join us this week at our 2021 SciBeh Workshop on the topic of ‘Science Communication as Collective Intelligence’! Nov. 18/19 with a schedule that allows any time zone to take part in at least some of the workshop. Includes: Keynotes, panels, and breakout manifesto writing 1/6 [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1459813525635973122
Empathy – This is perhaps the most important element of emotional intelligence. Empathy is the ability to identify with and understand the wants, needs, and viewpoints of those around you. People with empathy are good at recognizing the feelings of others, even when those feelings may not be obvious. As a result, empathetic people are usually excellent at managing relationships , listening , and relating to others. They avoid stereotyping and judging too quickly, and they live their lives in a very open, honest way.
Empathy – I value empathy as I consider it to be the most important element of emotional intelligence. Empathy means to be able to recognise and understand the wants, needs, and perspectives of others around us.
Empathy allows you to be better at recognizing the feelings of others, even if those people aren't making it obvious to notice. Hence, empathetic people make excellent relationship managers, also making good listeners , and relating to others. This traits allows one to avoid stereotyping and judging others at face value.
Frimer, J., Aujla, H., Feinberg, M., Skitka, L., Aquino, K., Eichstaedt, johannes C., & Willer, R. (2022). Incivility is Rising among American Politicians on Twitter. PsyArXiv. https://doi.org/10.31234/osf.io/2hku3
ReconfigBehSci. (2021, November 20). Thanks to everyone who took part in our Workshop on #SciComm as Collective Intelligence It was amazing! Materials will be uploaded to http://SciBeh.org website 1/2 @kakape @DrTomori @SpiekermannKai @GeoffreySupran @ArendJK @STWorg @dgurdasani1 @suneman @philipplenz6 [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1461978072924762117
projet européen X5-GON (Global Open Education Network) qui collecte les informations sur les ressources éducatives libres et qui marche bien avec un gros apport d’intelligence artificielle pour analyser en profondeur les documents
This generative model normally penalizes predicted toxicity and rewards predicted target activity. We simply proposed to invert this logic by using the same approach to design molecules de novo, but now guiding the model to reward both toxicity and bioactivity instead.
By changing the parameters of the AI, the output of the AI changed dramatically.
Of course, users are still the source of the insight that makes a complete document also a compelling document.
Nice that he takes a more humanistic viewpoint here rather than indicating that it will all be artificial intelligence in the future.
Hmm...this page doesn’t exist. Try searching for something else.
Apparently Persuall was embarrassed about their pro-surveillance capitalism stance and perhaps not so much for its lack of kindness and care for the basic humanity of students.
Sad that they haven't explained or apologized for their misstep.
https://web.archive.org/web/20220222022208/https://twitter.com/perusall/status/1495945680002719751
Additional context: https://twitter.com/search?q=(%40perusall)%20until%3A2022-02-23%20since%3A2022-02-21&src=typed_query
Stay at the forefront of educational innovation
What about a standard of care for students?
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>Bragging about students not knowing how the surveillance technology works is unethical.<br><br>Students using accessibility software or open educational resources shouldn't be punished for accidentally avoiding surveillance. pic.twitter.com/Uv7fiAm0a3
— Ian Linkletter (@Linkletter) February 22, 2022
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>#annotation https://t.co/wVemEk2yao
— Remi Kalir (@remikalir) February 23, 2022
At the back of Dr Duncan's book on the topic, Index, A History Of The, he includes not one but two indexes, in order to make a point.
Dennis Duncan includes two indices in his book Index, A History of The, one by a professional human indexer and the second generated by artificial intelligence. He indicates that the human version is far better.
SciBeh Virtual Workshop 2021: Science Communication as Collective Intelligence. (n.d.). SciBeh. Retrieved 14 February 2022, from https://www.scibeh.org/events/workshop2021/
Der Hauptanwendungspartner für die hier beschriebenen Lösungen war und ist der Sie-mens-Konzern. Die Lösungen wurden durch das KI-Start-up Giance, eine deutsch-chinesi-sche Ausgründung des DFKI, für den chinesischen Markt angepasst und weiterentwickelt.
KI-basierte Serviceplattform für Enterprise Intelli- gence
Unsere Global Enterprise Intelligence (GEI) Platform eignet sich nicht nur zur Beobachtung von Zulieferern, sondern wird auch in ande-ren Bereichen eingesetzt, in denen Firmen beobachtet werden müssen wie z. B. Wettbewer-beranalyse, Partnerbetreuung, Key-Account-Management oder Portfolio- Management.
Mehrwert
Knowledge Graph Check & UpdateMithilfe der Neo4J-Graphdatenbanktechnologie werden für die Anwendungen Wis-sensgraphen aufgebaut und ständig um neue Relationen und Eigenschaften der beob-achteten Firmen ergänzt. Die Wissensgraphen dienen nicht nur der Visualisierung der Ergebnisse, sie werden auch zum Entity Linking und zur Erkennung von bereits be-kannter Information verwendet
Neo4J-Graphdatenbanktechnologie werden für die Anwendungen Wissensgraphen aufgebaut und ständig um neue Relationen und Eigenschaften der beobachteten Firmen ergänzt.
Die Wissensgraphen dienen nicht nur der Visualisierung der Ergebnisse, sie werden auch zum Entity Linking und zur Erkennung von bereits bekannter Information verwendet.
integrierten IT-basierten Management- und Entscheidungsunterstützung (Business Intelligence)
Business Intelligence
We need to getour thoughts on paper first and improve them there, where we canlook at them. Especially complex ideas are difficult to turn into alinear text in the head alone. If we try to please the critical readerinstantly, our workflow would come to a standstill. We tend to callextremely slow writers, who always try to write as if for print,perfectionists. Even though it sounds like praise for extremeprofessionalism, it is not: A real professional would wait until it wastime for proofreading, so he or she can focus on one thing at a time.While proofreading requires more focused attention, finding the rightwords during writing requires much more floating attention.
Proofreading while rewriting, structuring, or doing the thinking or creative parts of writing is a form of bikeshedding. It is easy to focus on the small and picayune fixes when writing, but this distracts from the more important parts of the work which really need one's attention to be successful.
Get your ideas down on paper and only afterwards work on proofreading at the end. Switching contexts from thinking and creativity to spelling, small bits of grammar, and typography can be taxing from the perspective of trying to multi-task.
Link: Draft #4 and using Webster's 1913 dictionary for choosing better words/verbiage as a discrete step within the rewrite.
Linked to above: Are there other dictionaries, thesauruses, books of quotations, or individual commonplace books, waste books that can serve as resources for finding better words, phrases, or phrasing when writing? Imagine searching through Thoreau's commonplace book for finding interesting turns of phrase. Naturally searching through one's own commonplace book is a great place to start, if you're saving those sorts of things, especially from fiction.
Link this to Robin Sloan's AI talk and using artificial intelligence and corpuses of literature to generate writing.
from: Eyeo Conference 2017
Robin Sloan at Eyeo 2017 | Writing with the Machine | Language models built with recurrent neural networks are advancing the state of the art on what feels like a weekly basis; off-the-shelf code is capable of astonishing mimicry and composition. What happens, though, when we take those models off the command line and put them into an interactive writing environment? In this talk Robin presents demos of several tools, including one presented here for the first time. He discusses motivations and process, shares some technical tips, proposes a course for the future — and along the way, write at least one short story together with the audience: all of us, and the machine.
Robin created a corpus using If Magazine and Galaxy Magazine from the Internet Archive and used it as a writing tool. He talks about using a few other models for generating text.
Some of the idea here is reminiscent of the way John McPhee used the 1913 Webster Dictionary for finding words (or le mot juste) for his work, as tangentially suggested in Draft #4 in The New Yorker (2013-04-22)
Cross reference: https://hypothes.is/a/t2a9_pTQEeuNSDf16lq3qw and https://hypothes.is/a/vUG82pTOEeu6Z99lBsrRrg from https://jsomers.net/blog/dictionary
Croatian acapella singing: klapa https://www.youtube.com/watch?v=sciwtWcfdH4
Writing using the adjacent possible.
Corpus building as an art [~37:00]
Forgetting what one trained their model on and then seeing the unexpected come out of it. This is similar to Luhmann's use of the zettelkasten as a serendipitous writing partner.
How might we use information theory to do this more easily?
What does a person or machine's "hand" look like in the long term with these tools?
Can we use corpus linguistics in reverse for this?
What sources would you use to train your model?
An interesting coming book grouping various application issues (business, crime, military and terrorism).
Markoff, a long-time chronicler of computing, sees Engelbart as one pole in a decades-long competition "between artificial intelligence and intelligence augmentation -- A.I. versus I.A."
There is an interesting difference between artificial intelligence and intelligence automation. Index cards were already doing the second by the early 1940s.
There is the mammal way and there is the bird way." This is one scientist's pithy distinction between mammal brains and bird brains: two ways to make a highly intelligent mind.
evolutionary theorists like Christopher berm whose book hierarchy in the forest he's a primatologist is quite explicit about 00:11:27 this and says well this is precisely what makes human politics different from the politics of say chimpanzees or bonobos or orangutangs is what he calls our actuarial intelligence which I 00:11:39 believe what he means by this is the fact that we can in fact imagine what another kind of society might be like
Primatologist [[Christopher Boehm]] argues in his book Hierarchy in the Forest: The Evolution of Egalitarian Behavior that humans are different from our primate ancestors because homo sapiens possess actuarial intelligence, or the ability to imagine what other kinds of society might look like.
Just because a dataset is publicly available doesn't mean that you can use it to build commercial AI software.
ReconfigBehSci. (2021, October 31). Please join us at our upcoming workshop on ‘Science Communication as Collective Intelligence’ featuring talks (@SpiekermannKai, @dgurdasani1), panel discussions (@kakape,@CaulfieldTim, @joshua_a_becker, @suneman, @GeoffreySupran and more!) 1/2 https://t.co/isupbnF6yA [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1454763345748414465
Why people believe Covid conspiracy theories: Could folklore hold the answer? | Coronavirus | The Guardian. (n.d.). Retrieved October 26, 2021, from https://www.theguardian.com/world/ng-interactive/2021/oct/26/why-people-believe-covid-conspiracy-theories-could-folklore-hold-the-answer
I met Flávia Macêdo in a Trimtab Space Camp course. She invited me to participate in Holoptic – Global collective intelligence platform.
I suggested to the Stop Reset Go team that if we are focusing on connection, this might be a useful approach.
Collective Intelligence. (2020, July 18). SAGE Publications Ltd. https://uk.sagepub.com/en-gb/eur/collective-intelligence/journal203713
Crowdpol is a pro-social platform, where changemakers from across the globe can work together to tackle the challenges of the century.
Shared by Ferial Puren
Annotate the web, with anyone, anywhere.
Gyuri Lajos recommended Hypothesis to the Stop Reset Go team to simulate the experience of using the IndyWiki project that he is working on.
Gyuri Lajos is a member of the Stop Reset Go team. He is working on IndyWiki as a Web3 Native platform for collective intelligence.
I meant to join this last week, but didn't manage. Now I'll have to watch the video after the fact:
civic intelligence
A ‘Brainwashed Death Cult’: The Gamification of Conspiracy – Byline Times. (n.d.). Retrieved August 17, 2021, from https://bylinetimes.com/2021/08/10/a-brainwashed-death-cult-the-gamification-of-conspiracy/
Provide more opportunities for new talent. Because healthcare has been relatively solid and stagnant in what it does, we're losing out on some of the new talent that comes out — who are developing artificial intelligence, who are working at high-tech firms — and those firms can pay significantly higher than hospitals for those talents. We have to find a way to provide some opportunities for that and apply those technologies to make improvements in healthcare.
Intestesing. Mr. Roach thinks healthcare is not doing enough to attract new types of talent (AI and emerging tech) into healthcare. We seem to be losing this talent to the technology sector.
I would agree with this point. Why work for healthcare with all of its massive demands and HIPPA and lack of people knowing what you are even building. Instead, you can go into tech, have a better quality of life, get paid so much more, and have the possibility of exiting due to a buyout from the healthcare industry.
Building on platforms' stores of user-generated content, competing middleware services could offer feeds curated according to alternate ranking, labeling, or content-moderation rules.
Already I can see too many companies relying on artificial intelligence to sort and filter this material and it has the ability to cause even worse nth degree level problems.
Allowing the end user to easily control the content curation and filtering will be absolutely necessary, and even then, customer desire to do this will likely loose out to the automaticity of AI. Customer laziness will likely win the day on this, so the design around it must be robust.
Roberts, M. (n.d.). Artificial intelligence has been of little use for diagnosing covid-19. New Scientist. Retrieved 24 May 2021, from https://www.newscientist.com/article/mg25033350-100-artificial-intelligence-has-been-of-little-use-for-diagnosing-covid-19/
<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>John Pavlus </span> in Melanie Mitchell Trains AI to Think With Analogies | Quanta Magazine (<time class='dt-published'>07/24/2021 17:19:52</time>)</cite></small>
<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>John Pavlus </span> in Melanie Mitchell Trains AI to Think With Analogies | Quanta Magazine (<time class='dt-published'>07/24/2021 17:19:52</time>)</cite></small>
Facebook AI. (2021, July 16). We’ve built and open-sourced BlenderBot 2.0, the first #chatbot that can store and access long-term memory, search the internet for timely information, and converse intelligently on nearly any topic. It’s a significant advancement in conversational AI. https://t.co/H17Dk6m1Vx https://t.co/0BC5oQMEck [Tweet]. @facebookai. https://twitter.com/facebookai/status/1416029884179271684
Johnson, Khari. ‘AI Could Soon Write Code Based on Ordinary Language’. Wired. Accessed 21 June 2021. https://www.wired.com/story/ai-write-code-ordinary-language.
intelligence collective réflexive
Il ne s’agirait donc pas de simplement devenir collectivement «plus intelligent» (au sens d’efficace, dans un strict paradigme scientifique et technique pour accélérer le fonctionnement de l’économie), mais aussi réflexif: réfléchir aux conditions de cette société renouvelée, en proie à de nouvelles dynamiques de pouvoir extrêmement concentrées et asymétriques.
t hadn’t learned sort of the concept of a paddle or the concept of a ball. It only learned about patterns of pixels.
Cognition and perception are closely related in humans, as the theory of embodied cognition has shown. But until the concept of embodied cognition gained traction, we had developed a pretty intellectual concept of cognition: as something located in our brains, drained of emotions, utterly rational, deterministic, logical, and so on. This is still the concept of intelligence that rules research in AI.
the original goal at least, was to have a machine that could be like a human, in that the machine could do many tasks and could learn something in one domain, like if I learned how to play checkers maybe that would help me learn better how to play chess or other similar games, or even that I could use things that I’d learned in chess in other areas of life, that we sort of have this ability to generalize the things that we know or the things that we’ve learned and apply it to many different kinds of situations. But this is something that’s eluded AI systems for its entire history.
The truth is we do not need to have computers to excel in the things we do best, but to complement us. We shall bet on cognitive extension instead of trying to re-create human intelligence --which is a legitimate area of research, but computer scientists should leave this to cognitive science and neuroscience.
Last year, Page told a convention of scientists that Google is “really trying to build artificial intelligence and to do it on a large scale.”
What if they're not? What if they're building an advertising machine to manipulate us into giving them all our money?
From an investor perspective, the artificial answer certainly seems sexy while using some clever legerdemain to keep the public from seeing what's really going on behind the curtain?
It seeks to develop “the perfect search engine,” which it defines as something that “understands exactly what you mean and gives you back exactly what you want.”
What if we want more serendipity? What if we don't know what we really want? Where is this in their system?
Semantic Scholar, a free, AI-powered research tool for scientific literature
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.
Full Fact on Twitter. (n.d.). Twitter. Retrieved 5 March 2021, from https://twitter.com/FullFact/status/1330922811448315911
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
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
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.
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.
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?
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.
<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>
System architects: equivalents to architecture and planning for a world of knowledge and data Both government and business need new skills to do this work well. At present the capabilities described in this paper are divided up. Parts sit within data teams; others in knowledge management, product development, research, policy analysis or strategy teams, or in the various professions dotted around government, from economists to statisticians. In governments, for example, the main emphasis of digital teams in recent years has been very much on service design and delivery, not intelligence. This may be one reason why some aspects of government intelligence appear to have declined in recent years – notably the organisation of memory.57 What we need is a skill set analogous to architects. Good architects learn to think in multiple ways – combining engineering, aesthetics, attention to place and politics. Their work necessitates linking awareness of building materials, planning contexts, psychology and design. Architecture sits alongside urban planning which was also created as an integrative discipline, combining awareness of physical design with finance, strategy and law. So we have two very well-developed integrative skills for the material world. But there is very little comparable for the intangibles of data, knowledge and intelligence. What’s needed now is a profession with skills straddling engineering, data and social science – who are adept at understanding, designing and improving intelligent systems that are transparent and self-aware58. Some should also specialise in processes that engage stakeholders in the task of systems mapping and design, and make the most of collective intelligence. As with architecture and urban planning supply and demand need to evolve in tandem, with governments and other funders seeking to recruit ‘systems architects’ or ‘intelligence architects’ while universities put in place new courses to develop them.
<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>
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
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
<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>
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
Educational Data Mining (EDM) and Learning Analytics (LA) are among the top buzzwords of the EdTech scene right now.
Naudé. W., (2020). Artificial Intelligence against COVID-19: An Early Review. Institute of Labor Economics. Retrieved from: https://covid-19.iza.org/publications/dp13110/
As an opening move, I’d suggest that we could reconceptualize intelligence as NaQ (neuroacoustic quotient), or ‘the capacity to cleanly switch between different complex neuroacoustic profiles.’
also seems more neutral and embracing the differences in [[neurodiversity]] / individual thinking vs relentless optimizing for a certain KPI (like for IQs) #[[to write]]
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/
création collective de sens qui est au cœur de l’intelligence humaine
objectif de l'intelligence collective, des humanités numériques comme discipline en communauté
les chercheurs en sciences humaines doivent donner l’exemple – dans leur pratique ! – d’une production de sens qui s’offre à la connaissance de la manière la plus transparente possible
Injonction aux faiseurs de connaissance – autre morceau du programme de l’intelligence collective de Pierre Lévy?
versant éthique?
Australia's Cyber Security Strategy: $1.66 billion dollar cyber security package = AFP gets $88 million; $66 million to critical infrastructure organisations to assess their networks for vulnerabilities; ASD $1.35 billion (over a decade) to recruit 500 officers.
Reasons Dutton gives for package:
What is defined as critical infrastructure is expanded and subject to obligations to improve their defences.
Supporting cyber resilience of SMEs through information, training, and services to make them more secure.
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
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.
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.
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/.
doivent consulter des oracles
Par exemple, pour entraîner une intelligence artificielle, le philosophe montréalais Martin Gibert propose de montrer aux IA des exemples, des modèles à suivre (des Greta et des Mère Theresa) plutôt que d’essayer de leur enseigner les concepts de la philosophie morale.
Gignac, G. E., & Zajenkowski, M. (2020). The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data. Intelligence, 80, 101449. https://doi.org/10.1016/j.intell.2020.101449
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/
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.
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
Online Harms & Disinformation Post-COVID. (n.d.). Retrieved 20 August 2020, from https://www.youtube.com/watch?v=N2BmRuXbNhk
Optimizing Research Collaboration. (n.d.). Retrieved 10 August 2020, from https://youtu.be/TFNqf7sKwKw
Advantages of people in [[Silicon Valley]]:** super smart but not necessarily highly educated so they don’t just believe what everyone else does. **They think outside the box. They’re thinkers as well as people that have had to do things and pass [[reality]] tests. The only test most academics face is "can I publish this piece?"
What differs people in Silicon Valley and typical students
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/
Martin, G., Hanna, E., & Dingwall, R. (2020). Face masks for the public during Covid-19: An appeal for caution in policy [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/uyzxe
American Philosophical Society. (2020, June 08). Evidence Symposium. YouTube. https://www.youtube.com/playlist?list=PLoKwLGnyZL4Ds5cQo5muFMg8zKXK4KobH
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/
Virtual MLSS 2020 (Opening Remarks). (2020, June 29). https://www.youtube.com/watch?v=8staJlMbAig
Thomas W. Malone—COVID-19 and Collective Intelligence (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=W5RfAZMMTPM
Jeff Howe - Crowdsourcing and the Crisis: Collective Intelligence in the Age of Covid-19 (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=POPMMHyIoS0
James Evans—Designing Diversity for Collective Advance (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=1OMmJJz0oF0
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
ACM Collective Intelligence 2020—Keynotes. (n.d.). YouTube. https://www.youtube.com/channel/UCYecxGPlIg9XryIeXVtsMvA
But tagging, alone, is still not good enough. Even our many tags become useless if/when their meaning changes (in our minds) by the time we go retrieve the data they point to. This could be years after we tagged something. Somehow, whether manually or automatically, we need agents and tools to help us keep our tags updated and relevant.
search engines usually can surface that faster (less cognitive load than recalling what and where you store something) than you retrieve it in your second brain (abundance info, do can always retrieve from external source in a JIT fashion)
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?
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?
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.
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 :')
Antichrist
uhhh should we be worried
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
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.
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
World Pandemic Research Network
Just as journalists should be able to write about anything they want, comedians should be able to do the same and tell jokes about anything they please
where's the line though? every output generates a feedback loop with the hivemind, turning into input to ourselves with our cracking, overwhelmed, filters
it's unrealistic to wish everyone to see jokes are jokes, to rely on journalists to generate unbiased facts, and politicians as self serving leeches, err that's my bias speaking
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/.
Pescetelli, N., Cebrian, M., & Rahwan, I. (2020, February 10). Real-time Internet Control of Situated Human Agents. https://doi.org/10.31234/osf.io/xn7sr
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/
Yang Yang: The Replicability of Scientific Findings Using Human and Machine Intelligence (Video). Metascience 2019 Symposium. https://www.metascience2019.org/presentations/yang-yang/
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
Halim, S. (2019, August 13). How Artificial Intelligence can revolutionise healthcare. Health Europa. https://www.healtheuropa.eu/how-artificial-intelligence-can-revolutionise-healthcare/92824/
Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
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
Dhami, M. K., & Mandel, D. R. (2020). UK and US policies for communicating probability in intelligence analysis: A review [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/kuyhb
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
Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaid, M., & Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences, 201917687. https://doi.org/10.1073/pnas.1917687117
Coronavirus (COVID-19) in Australia – Pandemic Health Intelligence Plan [Text]. (2020, May 6). Australian Government Department of Health. https://www.health.gov.au//resources/publications/coronavirus-covid-19-in-australia-pandemic-health-intelligence-plan
Pescetelli, N., Rutherford, A., Kao, A., & Rahwan, I. (2020). Pescetelli_et_al_Collective learning in news consumption. https://doi.org/10.31234/osf.io/w6nc5
Shweta, F., Murugadoss, K., Awasthi, S., Venkatakrishnan, A., Puranik, A., Kang, M., Pickering, B. W., O’Horo, J. C., Bauer, P. R., Razonable, R. R., Vergidis, P., Temesgen, Z., Rizza, S., Mahmood, M., Wilson, W. R., Challener, D., Anand, P., Liebers, M., Doctor, Z., … Badley, A. D. (2020). Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis [Preprint]. Infectious Diseases (except HIV/AIDS). https://doi.org/10.1101/2020.04.19.20067660
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
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
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/
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
Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19
The world’s largest exhibitions organizer, London-based Informa plc, outlined on Thursday morning a series of emergency actions it’s taking to alleviate the impact of the COVID-19 pandemic on its events business, which drives nearly two-thirds of the company’s overall revenues. Noting that the effects have been “significantly deeper, more volatile and wide-reaching,” than was initially anticipated, the company says it’s temporarily suspending dividends, cutting executive pay and issuing new shares worth about 20% of its total existing capital in an effort to strengthen its balance sheet and reduce its approximately £2.4 billion ($2.9 billion) in debt to £1.4 billion ($1.7 billion). Further, Informa says it’s engaged in “constructive discussions” with its U.S.-based debt holders over a covenant waiver agreement.
Informa Group, que posee editoriales como Taylor & Francis, de Informa Intelligent Division toma medidas en su sector de conferencias y eventos. Provee dos tercios de sus ingresos totales, 2.9 billion dólares. Emite acciones y para el mercado norteamericano acuerdos de deuda. Mientras la parte editorial que aporta un 35% de los ingresos se mantiene sin cambios y con pronósticos estables y sólidos. Stephen Carter CEO
Le public acquiert ainsi une nouvelle fonction : celle d’instance critique auquel doit s’exposer le pouvoir.
fonction de l’espace public: un appareil critique (la critique est productrice d’espace public).
le pouvoir, pour maintenir sa légitimité, doit être exposé à la sphère publique et se montrer à lui avec transparence; il doit pouvoir être challengé; s’il ne résiste pas à la critique publique, il ne mérite pas d’être en place.
la possibilité de challenger l’instance publique est comparable à la publication des protocoles de sécurité utilisées dans le domaine public (ex. SSL/TLS): la sécurité des contenus encryptés tirent justement leur robustesse du fait que leur algorithme est public; quiconque pourrait le challenger à tout moment, si bien qu’on s’assure d’en éliminer toutes les failles (et l’intelligence collective peut être mise à contribution, le cas échéant).
Des applications de visites guidées intelligentes s’appuient sur un processus de gestion des flux visiteurs (Visitor Flow Management Process, VFMP) pour les orienter vers les zones où ils sont le moins nombreux. Il s’agira alors de combiner les données sur l’affluence en temps réel pour chaque espace avec les souhaits et les goûts des visiteurs pour suggérer le parcours personnalisé idéal
Argument en faveur de l'IA qui permet bien de gérer le flux mais ajoute un second bénéfice : proposer un parcours idéal. Ce bénéfice supplémentaire peut être considéré comme un argument réthorique de type Logos.
Certaines technologies intelligentes utilisées dans d’autres secteurs pourraient être transposées dans les musées. Avec le big data, il est possible de connaître l’affluence en fonction des dates et des horaires, les types de visiteurs selon les jours et les périodes, ou la durée de visite moyenne par rapport différents paramètres comme la météo.
Argument épistémique inductif et réthorique de type logos.
On passe à l'intelligence artificielle, technologie de pointe. Apporte du crédit à l'affirmation du bénéfice du numérique.
visual are processed 60,000 times faster in the brain than text and visual aids in the classroom improve learning up to 400 percent. Ideas presented graphically are easier to understand and remember than those presented as words, (Kliegel et al., 1987).
throw out this factoid when doing video?
Ranking the intelligence of animals seems an increasingly pointless exercise when one considers the really important thing: how well that animal is adapted to its niche
“NextNow Collaboratory is an interesting example of a new kind of collective intelligence: an Internet-enabled, portable social network, easily transferable from one social cause to another.”
Sense Collective's TotemSDK brings together tools, protocols, platform integrations and best practices for extending collective intelligence beyond our current capabilities. A number of cryptographic primitives have emerged which support the amazing work of projects like the NextNow Collaboratory in exciting ways that help to upgrade the general purpose social computing substrate which make tools like hypothes.is so valuable.
A natural language provides its user with a ready-made structure of concepts that establishes a basic mental structure, and that allows relatively flexible, general-purpose concept structuring. Our concept of language as one of the basic means for augmenting the human intellect embraces all of the concept structuring which the human may make use of.
It has been jokingly suggested several times during the course of this study that what we are seeking is an "intelligence amplifier." (The term is attributed originally to W. Ross Ashby[2,3]. At first this term was rejected on the grounds that in our view one's only hope was to make a better match between existing human intelligence and the problems to be tackled, rather than in making man more intelligent. But deriving the concepts brought out in the preceding section has shown us that indeed this term does seem applicable to our objective. 2c2a Accepting the term "intelligence amplification" does not imply any attempt to increase native human intelligence. The term "intelligence amplification" seems applicable to our goal of augmenting the human intellect in that the entity to be produced will exhibit more of what can be called intelligence than an unaided human could; we will have amplified the intelligence of the human by organizing his intellectual capabilities into higher levels of synergistic structuring. What possesses the amplified intelligence is the resulting H-LAM/T system, in which the LAM/T augmentation means represent the amplifier of the human's intelligence.2c2b In amplifying our intelligence, we are applying the principle of synergistic structuring that was followed by natural evolution in developing the basic human capabilities. What we have done in the development of our augmentation means is to construct a superstructure that is a synthetic extension of the natural structure upon which it is built. In a very real sense, as represented by the steady evolution of our augmentation means, the development of "artificial intelligence" has been going on for centuries.
This is not a new idea. It is based on the vision expounded by Vannevar Bush in his 1945 essay “As We May Think,” which conjured up a “memex” machine that would remember and connect information for us mere mortals. The concept was refined in the early 1960s by the Internet pioneer J. C. R. Licklider, who wrote a paper titled “Man-Computer Symbiosis,” and the computer designer Douglas Engelbart, who wrote “Augmenting Human Intellect.” They often found themselves in opposition to their colleagues, like Marvin Minsky and John McCarthy, who stressed the goal of pursuing artificial intelligence machines that left humans out of the loop.
Seymour Papert, had an approach that provides a nice synthesis between these two camps, buy leveraging early childhood development to provide insights on the creation of AI.
Thompson’s point is that “artificial intelligence” — defined as machines that can think on their own just like or better than humans — is not yet (and may never be) as powerful as “intelligence amplification,” the symbiotic smarts that occur when human cognition is augmented by a close interaction with computers.
Intelligence amplification over artificial intelligence. In reality you can't get to AI until you've mastered IA.
lants speak in a chemical vocabulary we can’t directly perceive or comprehend. The first important discoveries in plant communication were made in the lab in the nineteen-eighties, by isolating plants and their chemical emissions in Plexiglas chambers, but Rick Karban, the U.C. Davis ecologist, and others have set themselves the messier task of studying how plants exchange chemical signals outdoors, in a natural setting.
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.
A multimedia approach to affective learning and training can result in more life-like trainings which replicate scenarios and thus provide more targeted feedback, interventions, and experience to improve decision making and outcomes. Rating: 7/10
Adult Learning in the Workplace:Emotion Work or Emotion Learning?
A blended learning approach to emotional intelligence is the methodology for the course created by Google and implemented in various institutions around the globe. Rating: 8/10
An emotional intelligence course initiated by Google became a tool to improve mindfulness, productivity, and emotional IQ. The course has since expanded into other businesses which report that employees are coping better with stressors and challenges. Rating: 7/10 Key questions...what is the format of the course, tools etc?
The idea of a “plant intelligence”—an intelligence that goes beyond adaptation and reaction and into the realm of active memory and decision-making—has been in the air since at least the early seventies.
what is intelligence after all?
“Trees do not have will or intention. They solve problems, but it’s all under hormonal control, and it all evolved through natural selection.”
is having will or intention akin to having intelligence?
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? 😲
HTM and SDR's - part of how the brain implements intelligence.
"In this first introductory episode of HTM School, Matt Taylor, Numenta's Open Source Flag-Bearer, walks you through the high-level theory of Hierarchical Temporal Memory in less than 15 minutes."
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.
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?
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.
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.
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.
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.).
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:
Simply adding AI applications to a fragmented system will not create sustainable change.
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.
Security Issues, Dangers And Implications Of Smart Systems
1. Direction Over Speed
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.
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.
government investments
initiatives from the U.S., China, and Europ
Recent Government Initiatives
engagement in AI activities by academics, corporations, entrepreneurs, and the general public
Volume of Activity
Derivative Measures
AI Vibrancy Index
limited gender diversity in the classroom
improvement in natural language
the COCO leaderboard
patents
robot operating system downloads,
he GLUE metric
robot installations
AI conference attendance
the speed at which computers can be trained to detect objects
Technical Performance
quality of question answering
Technical Performance
changes in AI performance
Technical Performance
Technical Performance
number of undergraduates studying AI
Volume of Activity
growth in venture capital funding of AI startups
Volume of Activity
percent of female applicants for AI jobs
Volume of Activity
Volume of Activity
increased participation in organizations like AI4ALL and Women in Machine Learning
