193 Matching Annotations
  1. Last 7 days
    1. 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.

  2. Jul 2021
    1. 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

  3. Jun 2021
    1. 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.

    2. 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.

    1. 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?

    2. 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?

  4. May 2021
    1. 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.
  5. Apr 2021
    1. 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

    1. 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.

  6. Mar 2021
    1. 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.

    1. 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.

    2. 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?

    3. 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.

    4. <small><cite class='h-cite via'> <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>

  7. Feb 2021
    1. 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.

  8. Jan 2021
  9. Dec 2020
  10. Nov 2020
    1. 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.

    1. 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. 

  11. Oct 2020
    1. 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

    1. 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.

    1. 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.
  12. Sep 2020
    1. 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.

    1. 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.
  13. Aug 2020
  14. Jul 2020
  15. Jun 2020
    1. 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?

    1. 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?

    1. 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.

    2. 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 :')

    3. Antichrist

      uhhh should we be worried

    1. 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

    2. 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.

  16. May 2020
    1. 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

    1. 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.

  17. Apr 2020
    1. 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.

    1. 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).

    1. 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

  18. Dec 2019
    1. 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.

  19. Aug 2019
    1. 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? 😲

    1. 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]
    1. 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.

    2. 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?

    3. 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.

    4. 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.

    5. 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.

    6. 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.).

    7. 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.
    8. Simply adding AI applications to a fragmented system will not create sustainable change.
    1. 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.

  20. Jun 2019
    1. 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.
    1. We Need to Talk, AIDr. Julia SchneiderLena Kadriye ZiyalA Comic Essay on Artificial Intelligence

      Un ensayo en cómic: esto pinta muy pero muy bien

  21. May 2019
    1. 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.

    1. government investments
    2. initiatives from the U.S., China, and Europ
    3. Recent Government Initiatives
    4. engagement in AI activities by academics, corporations, entrepreneurs, and the general public

      Volume of Activity

    5. Derivative Measures
    6. AI Vibrancy Index
    7. limited gender diversity in the classroom
    8. improvement in natural language
    9. the COCO leaderboard
    10. patents
    11. robot operating system downloads,
    12. he GLUE metric
    13. robot installations
    14. AI conference attendance
    15. the speed at which computers can be trained to detect objects

      Technical Performance

    16. quality of question answering

      Technical Performance

    17. changes in AI performance

      Technical Performance

    18. Technical Performance
    19. number of undergraduates studying AI

      Volume of Activity

    20. growth in venture capital funding of AI startups

      Volume of Activity

    21. percent of female applicants for AI jobs

      Volume of Activity

    22. Volume of Activity
    23. increased participation in organizations like AI4ALL and Women in Machine Learning
    24. producers of AI patents
    25. ML teaching events
    26. University course enrollment
    27. 83 percent of 2017 AI papers
  22. Apr 2019
    1. 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.

  23. Mar 2019
    1. 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.
    1. 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.

    2. No ‘copy’ of the story i