regulation needs to surround the technology that will help with these things

Some great blue sky ideas here.

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/.

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

Naudé. W., (2020). Artificial Intelligence against COVID-19: An Early Review. Institute of Labor Economics. Retrieved from: https://covid-19.iza.org/publications/dp13110/

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/

Let's Integrate artificial intelligence (AI) in Taxi Booking App to Uplift Your Services.here is list of use cases, benefits of AI Powered Taxi Dispatch System.

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

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

each of them flows through each of the two layers of EACH encoder, right?

This is similar to autoencoders struggling with producing high-resolution imagery because of the compression that happens in the latent space, right?

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.

I don't think anyone has figured latex diagrams (tikz) out :')

uhhh should we be worried

seems like a decision aiming for efficiency

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

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/.

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

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

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

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.

Is it just that how we all learn and evolve? 😲

But we have to be careful not to inadvertently favour the big tech companies in trying to stop favouring the big EMR providers.

AI is controlled by these external forces. In what direction will this lead it?

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.

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.

Advancing AI requires more than just AI stuff. It requires infrastructure and changes in human habit and culture.

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.).

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.

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.

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.

Volume of Activity

Technical Performance

Technical Performance

Technical Performance

Volume of Activity

Volume of Activity

Volume of Activity

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.

Again, this doesn't conflict with a machine-learning or deep-learning or neural-net way of seeing IP.

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.

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.

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.

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.

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).

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.

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.

AI software and the chatbots will be included in the current technologies and have automated with the robotic system. They will have given rights to access calendars, email accounts, browsing history, playlists, past purchases, and media viewing history. 6% is the huge number in the world as people would be seen struggling in finding the jobs. But there are benefits also as your work would have done easily and speedily

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

So then we know that most people will use supervised learning that requires less computational power and knowledge.

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.

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.

Astrology proven by artificial intelligence.

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.

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.

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.

Google's DeepDream neural network applied to Joy of Painting with Bob Ross. Very creepy.

Google Home talking personal assistant spouts audio ads. Not much of a surprise.

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.

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.

...

Content on websites such as dev.to are reposted elsewhere, word-for-word, by scrapers programmed by Black Hat SEO specialists.

...

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.

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."

White House report: Artificial Intelligence, Automation, and the Economy

“Resistance is futile” Personal-ized education

2013 article about Douglas Hofstadter, who has continued to pursue an understanding of the human mind through experiments with AI.

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.

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.

https://twitter.com/open_ai

They're hiring: https://openai.com/about/

The best computer vision company in Brazil

https://github.com/rhiever/tpot TPOT (Tree-based Pipeline Optimization Tool) Built on numpy, scipy, pandas, scikit-learn, and deap.

First comment

TLDR: Thought experiment that, by knowing about it, you are contributing to humanity enslavement to a all powerful AI