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January 11, 2018

Artificial water (+ women at PC Gamer)

I’ve long wondered — like for a couple of decades — when software developers who write algorithms that produce beautiful animations of water will be treated with the respect accorded to painters who create beautiful paintings of water. Both require the creators to observe carefully, choose what they want to express, and apply their skills to realizing their vision. When it comes to artistic vision or merit, are there any serious differences?

In the January issue of PC Gamer , Philippa Warr [twitter: philippawarr] — recently snagged
from Rock, Paper, Shotgun points to v r 3 a museum of water animations put together by Pippin Barr. (It’s conceivable that Pippin Barr is Philippa’s hobbit name. I’m just putting that out there.) The museum is software you download (here) that displays 24 varieties of computer-generated water, from the complex and realistic, to simple textures, to purposefully stylized low-information versions.


Philippa also points to the Seascape
page by Alexander Alekseev where you can read the code that procedurally produces an astounding graphic of the open sea. You can directly fiddle with the algorithm to immediately see the results. (Thank you, Alexander, for putting this out under a Creative Commons license.) Here’s a video someone made of the result:

Philippa also points to David Li’s Waves where you can adjust wind, choppiness, and scale through sliders.

More than ten years ago we got to the point where bodies of water look stunning in video games. (Falling water is a different question.) In ten years, perhaps we’ll be there with hair. In the meantime, we should recognize software designers as artists when they produce art.



Good work, PC Gamer, in increasing the number of women reviewers, and especially as members of your editorial staff. As a long-time subscriber I can say that their voices have definitely improved the magazine. More please!

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January 7, 2018

[javascript] Displaying entries’ initial letter when scrolling

I’m more surprised than proud that I got this to work, but here’s some JavaScript that slides down a box when the user scrolls an alphabetized table and slides that box back up once the user stops. While the user continues scroll up or down the page, the box displays the first letter of the row at the top. When the user stops scrolling for about a tenth of a second, the box goes away.

Note that when I say that “I got this to work,” what I really mean is that I successfully copy-and-pasted code from StackOverflow into the part of my script that runs when the script is first loaded. And when I say “JavaScript” I really mean “JavaScript using the jQuery library along with the Visible plugin that I think I actually don’t need but I couldn’t get jQuery’s is(":visible") to work the way I thought it should.

So here’s an annotated walkthrough of the embarrassing code.

The first part notices the scrolling, shows the box, and fills it with the first letter of the relevant column of the table the page is displaying. (Thank you, Stackoverflow!)

programming code

The second part comes from another StackOverflow question. It notices when someone has stopped scrolling for 0.15 seconds and hides the block displaying the letter. And, yes, it could probably be combined with the first bit.

This is amateurish hackery. I understand that. But I’m an amateur. I’m not writing production code. I don’t have to worry about performance: this code works fine for scrolling 350 rows of a text-only table, but might crap out with 1,000 lines or 5,000 lines. At least it works fine so far. On the current versions of Chrome and Firefox. Under a waxing moon. I understand that I can get this far only because millions of real developers have posted their own code, and answered questions from fools like me. My hat is off to you.



For your copying-and-pasting convenience, here’s the code in copy-able form. (Click on the “Toggle line numbers” button on the bottom.)



var mywindow = $(window); // get the window within which


// the page is being displayed


var mypos = mywindow.scrollTop();


var newscroll;



// add a function that’s called whenever the window is scrolled


mywindow.scroll(function () {


newscroll = mywindow.scrollTop(); // the scroll bar indicator’s


// vertical position


// Go through the rows of the table to find the one currently at the top.


// I am undoubtedly doing this embarrassingly inefficiently.


var letter = “”, done = false, i = 0;


// loop until we find the row at the top or we’ve looked at all rows


while (!done){


var title = $(“#title” + i); // id of the cell with the phrase the


// table is sorted on


if ( $(title).visible() == true){ // Unnecessary use of the


// Visible plugin


var currentTopRow = i;


done = true;


// Get the first letter of the relevant cell


letter = $(title).text().substr(0,1).toUpperCase();


// put the letter into the box that will display it










// if we’ve checked all the rows and none is visible


if (i >= gData.length){ // gData is the array the table is built from


done = true;


letter = “?”;






// display the box with the letter




mypos = newscroll;





// hide letter block when scrolling stops



var scrollpausetimer = null; // create a timer to note


// when scrolling has stopped


$(window).scroll( function() {


if(scrollpausetimer !== null) {






scrollpausetimer = setTimeout(function() {


// hide the letter block




}, 150); // 150 is the pause to be noticed in 1/1000ths of a sec


}, false);


Javascript converted into html by this.

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December 17, 2017

[liveblog] Mariia Gavriushenko on personalized learning environments

I’m at the STEAM ed Finland conference in Jyväskylä where Mariia Gavriushenko is talking about personalized learning environments.

Web-based learning systems are being more and more widely used in large part because they can be used any time, anywhere. She points to two types: Learning management systems and game-based systems. But they lack personalization that makes them suitable for particular learners in terms of learning speed, knowledge background, preferences in learning and career, goals for future life, and their differing habits. Personalized systems can provide assistance in learning and adapt their learning path. Web-based learning shouldn’t just be more convenient. It should also be better adapted to personal needs.

But this is hard. But if you can do it, it can monitor the learner’s knowledge level and automatically present the right materials. In can help teachers create suitable material and find the most relevant content and convert it into comprehensive info. It can also help students identify the best courses and programs.

She talks about two types of personalized learning systems: 1. systems that allow the user to change the system or 2. the sysytem changes itself to meet the users needs. The systems can be based on rules and context or can be algorithm driven.

Five main features of adaptive learning systems:

  • Pre-test

  • Pacing and control

  • Feedback and assessment

  • Progress tracking and reports

  • Motivation and reward

The ontological presentation of every learner keeps something like a profile for each user, enabling semantic reasoning.

She gives an example of this model: automated academic advising. It’s based on learning analytics. It’s an intelligent learning support system based on semantically-enhanced decision support, that identifies gaps, and recommends materials and courses. It can create a personal study plan. The ontology helps the system understand which topics are connected to others so that it can identify knowledge gaps.

An adaptive vocabulary learning environment provides cildren with an adaptive way to train their vocabulary, taking into account the individuality of the learner. It assumes the more similar the words, the harder they are to recognize.

Mariia believes we will make increasing use of adaptive educational tech.

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[liveblog] Maarit Rossi on teaching math that matters

I’m at the STEAM ed Finland conference in Jyväskylä. Maarit Rossi, who teaches math teaching around the world, is talking on the topic: “AI forces us to change maths education.”

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

Finnish teachers are doing a great, great job, she says. “But we are doing it too quietly.”

Education is too similar to industrial assembly lines. Students sit passively in rows. Students find math education to be boring, meaningless, and frightening. Typically this happens sometime in 5-7th grade. Teaching math has not changed in 100 years. It is a global problem.

Meanwhile, tech is changing really quickly. (She shows a photo from 1956 of workers shoving a 5 megabyte drive onto a truck.)

1956 5mb drive loaded onto truck

These days we are talking about personalizing math education. Easily available programs solve math problems. In the USA, people say the students are “cheating.” No, they’re being educated wrong. We need to be asking if we’re teaching students 10 critical skills, including cognitive flexibility, nebotiation, coordinating with others, emotional intelligence, critical thinking, creaetivity, complex problem solving, service orientation [and a couple of others I didn’t have time to copy down].

A modern math curriculum addresses attitudes, metacognition (e.g., self-regulation), skills, concepts, and processes. Instead, we focus on the concepts (e.g., algebraic, statistical, etc.).

A classroom has to be a safe place where you can make mistakes.

There are four pillars: practice, learning by doing, social learning, and interdisciplinary math. She gives some examples. Students estimate the price of a week’s shopping for a family of four. Maaritt has students work in groups of four. After that, they go to the nearest shop to find the actual prices; the students have to divide up the task to get it done in time. (You can have them do online shopping if there isn’t nearby shop.) Students estimate and round the numbers, tasks that are usually taught separately.

For higher grades, the students deal with real data from an African refugee camp. The students have to estimate how much food is needed to keep everyone alive for two weeks. “This is meaningful to them.”

It’s important for math to have double the lesson length. If it’s only one hour, it is not enough. “The students love it when they have the opportunity to think, to discover, to find themselves.”

Re-arrange the classroom. Cluster the tables rather than rows. The students can teach one another. “It is important that the feel successful.”

“And of course we use computers. And apps. And phones.”

“Math is also interesting because it can model many things.” If they have an embodied sense of a cubic meter, for example, they learn how to convert them to other measures. Or model the size of the solar system outside.

She has students estimate collections of objects, e.g. a bowl of noodles. Then they round. Then they count. Groups come up with strategies for counting, including doing it in ways that enable the count to be interrupted and resumed.

Physical exercise makes brains work better.

Classifying is important. She asks students to take sheets of paper and make the biggest triangle they can, and another of a different shape. They put all the triangles in the middle of the room. Then she asks them to see if they can cluster them by similarities.

“Students need to use their own language” rather than only hearing the teacher talk. This is how they learn to understand.

[My notes about the last few minutes, and the questions, go cut off via brain-computer glitch. Sorry.]


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[liveblogging] SMART education

I’m at the STEAM ed Finland conference in Jyväskylä. Maria Kankaanranta, Leena Hiltunen, Kati Clements and Tiina Mäkelä are on the faculty of the School of Education at the University of Jyväskylä The are going to talk about SMART education.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

SMART means self-directed, motivated, adaptive, reseource enriched, and technology-embedded learning. (They credit South Korean researchers for this.) This is a paradigm shift: From education a specific times to any time. From lectures to motivated ed methods. From teaching the 3Rs to epanding the ed capacity. From traditional textbooks to enriched resources. From a physical space to anywhere there is the enabling tech.

One project (Horizon 2020) works across disciplines to connect students, parents, teachers, and companies. Companies expect universities to develop the skills they need, but you really have to begin with primary school. The aim of the project is to create a pedagogical framework and design principles for attractive and engaging STEM learning environments. She presents a long list of pedagogical design principles that guide the design of this kind of hybrid learning enviroments. It includes adaptive learning, self-regulation, project-based learning, novelty, but also conventionality: “you don’t have to abandon everything.”

What beyond MOODLE can we do? The EU has funded instruments for procurement of innovation. The presenters have worked on IMAILE & LEA (LearnTech Accelerator). IMAILE ran for 48 months in four countries. To address problems, the project pointed to two existing solutions: YipTree and AMIGO (e-books publisher from Spain). YipTree provides individual personalized learning paths (adaptive materials), student motivation by a virtual tutor and by other students, gamificiation, quick assessment tools, and notifications when a student is having difficulties. They tested this in two schools per country. YipTree did well.

They have been training teachers in computational thinking, programming, and robotics. They use online, mobile apps to make it available and free for all teachers and students. They’re using different training models to motivate and encourage teachers to adopt these apps. E.g., they’re “hijacking” schools and workplaces to train them where they are. Teachers really want human engagement.

Schools have access to tech resources but they’re under-used because the teachers don’t know what’s available and possible. This presentation’s project is helping teachers with this.

Conclusion: Smart ed is not easy. It takes time. It requires getting out of your comfort zone. It requires training, tools, research, and a human touch.


Q: Does your model take into account students with disabilities?

A: Yes. Part of this is “access for all.” Also, IMAILE does. Imperfectly. They collaborate with a local school for the impaired.

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[liveblog] Ulla Richardson on a game that teaches reading

I’m at the STEAM ed Finland conference in Jyväskylä where Ulla Richardson is going to talk about GraphoLearn, an adaptive learning method for learning to read.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

Ulla has been working on the Jyväskylä< Longitudinal Study of Dyslexia (JLD). Globally, one third of people can’t read or have poor reading skills. One fifth of Europe also. About 15% of children have learning disabilities.

One Issue: knowing which sound goes with which letters. GraphoLearn is a game to help students with this, developed by a multidisciplinary team. You learn a word by connecting a sound to a written letter. Then you can move to syllables and words. The game teaches by trial and error. If you get it wrong, it immediately tells you the correct sound. It uses a simple adaptive approach to select the wrong choices that are presented. The game aims at being entertaining, and motivates also with points and rewards. It’s a multi-modal system: visual and audio. It helps dyslexics by training them on the distinctions between sounds. Unlike human beings, it never displays any impatience.

It adapts to the user’s skill level, automatically assessing performance and aiming at at 80% accuracy so that it’s challenging but not too challenging.

13,000 players have played in Finland, and more in other languages. Ulla displays data that shows positive results among students who use GraphoLearn, including when teaching English where every letter has multiple pronunciations.

There are some difficulties analyzing the logs: there’s great variability in how kids play the game, how long they play, etc. There’s no background info on the students. [I missed some of this.] There’s an opportunity to come up with new ways to understand and analyze this data.


Q: Your work is amazing. When I was learning English I could already read Finnish, so I made natural mispronunciations of ape, anarchist, etc. How do you cope with this?

A: Spoken and written English are like separate languages, especially if Finnish is your first language where each letter has only one pronunciation. You need a bigger unit to teach a language like English. That’s why we have the Rime approach where we show the letters in more context. [I may have gotten this wrong.]

Q: How hard is it to adapt the game to each language’s logic?

A: It’s hard.

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December 16, 2017

[liveblog] Mirka Saarela and Sanna Juutinen on analyzing education data

I’m at the STEAM ed Finland conference in Jyväskylä. Mirka Saarela and Sanna Juutinen are talking about their analysis of education data.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

There’s a triennial worldwide study by the OECD to assess students. Usually, people are only interested in its ranking of education by country. Finland does extremely well at this. This is surprising because Finland does not do particularly well in the factors that are taken to produce high quality educational systems. So Finnish ed has been studied extensively. PISA augments this analysis using learning analytics. (The US does at best average in the OECD ranking.)

Traditional research usually starts with the literature, develops a hypothesis, collects the data, and checks the result. PISA’s data mining approach starts with the data. “We want to find a needle in the haystack, but we don’t know what the needle looks like.” That is, they don’t know what type of pattern to look for.

Results of 2012 PISA: If you cluster all 24M students with their characteristics and attitudes without regard to their country you get clusters for Asia, developing world, Islamic, western countries. So, that maps well.

For Finland, the most salient factor seems to be its comprehensive school system that promotes equality and equity.

In 2015 for the first time there was a computerized test environment available. Most students used it. The logfile recorded how long students spent on a task and the number of activities (mouse clicks, etc.) as well as the score. They examined the Finnish log file to find student profiles, related to student’s strategies and knowledge. Their analysis found five different clusters. [I can’t read the slide from here. Sorry.] They are still studying what this tells us. (They purposefully have not yet factored in gender.)

Nov. 2017 results showed that girls did far better than boys. The test was done in a chat environment which might have been more familiar for the girls? Is the computerization of the tests affecting the results? Is the computerization of education affecting the results? More research is needed.


Q: Does the clustering suggest interventions? E.g., “Slow down. Less clicking.”

A: [I couldn’t quite hear the answer, but I think the answer is that it needs more analysis. I think.]

Q: I work for ETS. Are the slides available?

A: Yes, but the research isn’t public yet.

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[liveblog] Harri Ketamo on micro-learning

I’m at the STEAM ed Finland conference in Jyväskylä. Harri Ketamo is giving a talk on “micro-learning.” He recently won a prestigious prize for the best new ideas in Finland. He is interested in the use of AI for learning.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

We don’t have enough good teachers globally, so we have to think about ed in new ways, Harri says. Can we use AI to bring good ed to everyone without hiring 200M new teachers globally? If we paid teachers equivalent to doctors and lawyers, we could hire those 200M. But we apparently not willing to do that.

One challenge: Career coaching. What do you want to study? Why? What are the skills you need? What do you need to know?

His company does natural language analysis — not word matches, but meaning. As an example he shows a shareholder agreement. Such agreements always have the same elements. After being trained on law, his company’s AI can create a map of the topic and analyze a block of text to see if it covers the legal requirements…the sort of work that a legal assistant does. For some standard agreements, we may soon not need lawyers, he predicts.

The system’s language model is a mess of words and relations. But if you zoom out from the map, the AI has clustered the concepts. At the Slush Sanghai conference, his AI could develop a list of the companies a customer might want to meet based on a text analysis of the companies’ web sites, etc. Likewise if your business is looking for help with a project.

Finland has a lot of public data about skills and openings. Universities’ curricula are publicly available.[Yay!] Unlike LinkedIn, all this data is public. Harri shows a map that displays the skills and competencies Finnish businesses want and the matching training offered by Finnish universities. The system can explore public information about a user and map that to available jobs and the training that is required and available for it. The available jobs are listed with relevancy expressed as a percentage. It can also look internationally to find matches.

The AI can also put together a course for a topic that a user needs. It can tell what the core concepts are by mining publications, courses, news, etc. The result is an interaction with a bot that talks with you in a Whatsapp like way. (See his paper “Agents and Analytics: A framework for educational data mining with games based learning”). It generates tests that show what a student needs to study if she gets a question wrong.

His newest project, in process: Libraries are the biggest collections of creative, educational material, so the AI ought to point people there. His software can find the common sources among courses and areas of study. It can discover the skills and competencies that materials can teach. This lets it cluster materials around degree programs. It can also generate micro-educational programs, curating a collection of readings.

His platform has an open an API. See Headai.


Q: Have you done controlled experiments?

A: Yes. We’ve found that people get 20-40% better performance when our software is used in blended model, i.e., with a human teacher. It helps motivate people if they can see the areas they need to work on disappear over time.

Q: The sw only found male authors in the example you put up of automatically collated materials.

A: Small training set. Gender is not part of the metadata in Finland.

A: Don’t you worry that your system will exacerbate bias?

Q: Humans are biased. AI is a black box. We need to think about how to manage this

Q: [me] Are the topics generated from the content? Or do you start off with an ontology?

A: It creates its ontology out of the data.

Q: [me] Are you committing to make sure that the results of your AI do not reflect the built in biases?

A: Our news system on the Web presents a range of views. We need to think about how to do this for gender issues with the course software.

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December 15, 2017

[liveblog] Geun-Sik Jo on AR and video mashups

I’m at the STEAM ed Finland conference in Jyväskylä. Geun-Sik Jo is a professor at Inha University in Seoul. He teaches AI and is an augmented reality expert. He also has a startup using AR for aircraft maintenance [pdf].

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

Why do we need mashups? To foster innovation. To create new systems very efficiently. He uses the integration of Google Maps into Craigslist as a simple example.

Interactive video — video with clickable spots — is often a mashup: click and go to a product page or send a text to a friend. E.g., or http://www.raptmedia or .

TVs today are computers with their own operating systems and applications. Prof. Jo shows a video of AR TV. The “screen” is a virtual image displayed on special glasses.

If we mash up services, linked data clouds, TV content, social media, int an AR device, “we can do nice things.”

He shows some videos created by his students that present AR objects that are linked to the Internet: a clickable travel ad, location-based pizza ordering, a very cool dance instruction video, a short movie.

He shows a demo of the AI Content Creation System that can make movies. In the example, it creates a drama, mashing up scenes from multiple movies. The system identifies the characters, their actions, and their displayed emotions.

Is this creative? “I’m not a philosopher. I’m explaining this from the engineering point of view,” he says modestly. [If remixes count as creativity — and I certainly think they do — then it’s creative. Does that mean the AI system is creative? Not necessarily in any meaningful sense. Debate amongst yourselves.]

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[liveblog] Sonja Amadae on computational creativity

I’m at the STEAM ed Finland conference in Jyväskylä. Sonja Amadae at Swansea University (also currently at Helsinki U.) works on robotic ethics. She will argue in this talk that computers are algorithmic, that they only do what they’re programmed to do, that they don’t understand what they’re doing and they don’t feel human experience. AI is, she concludes, a tool.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

AI is like a human prosthetic that helps us walk. AI is an enhancement of human capabilities.

She will talk about about three cases.

Case 1: Generating a Rembrandt

A bank funded a project [cool site about it] to see what would happen if a computer had all of the data about Rembrandt’s portraits. They quantified the paintings: types, facial aspects including the size and distance of facial features, depth, contour, etc. They programmed the algorithm to create a portrait. The result was quite goood. People were pleased. Of course, it painted a white male. Is that creativity?

We are now recogizing the biases widespread in AI. E.g., “Biased algorithms are everywhere and one seeems to care” in MIT Tech Review by Will Knight. She also points to the time that Google mistakenly tagged black people as “gorillas.” So, we know there are limitations.

So, we fix the problem…and we end up with facial recognition systems so good that China can identify jaywalkers from surveillance cams, and then they post their images and names on large screens at the intersections.

Case 2: Forgery detection

The aim of one project was to detect forgeries. It was built on work done by Marits Michel van Dantzig in the 1950s. He looked at the brushstrokes on a painting; artists have signature brushstrokes. Each painting has on average 80,000 brushstrokes. A computer can compare a suspect painting’s brushstrokes with the legitimate brushstrokes of the artist. The result: the AI could identify forgeries 80% of the time from a single stroke.

Case 3: Computational creativity

She cites Wikipedia on Computational Creativity because she thinks it gets it roughly right:

Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.
The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:[

  • To construct a program or computer capable of human-level creativity.

  • To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.

  • To design programs that can enhance human creativity without necessarily being creative themselves.

She also quotes John McCarthy:

‘To ascribe certain beliefs, knowledge, free will, intentions, consciousness, abilities, or wants to a machine or computer program is legitimate when such an ascription expresses the same information about the machine that it expresses about a person.’

If you google “computational art” and you’ll see many pictures created computationally. [Or here.] Is it genuine creativity? What’s going on here?

We know that AI’s products can be taken as human. A poem created by AI won a poetry contest for humans. E.g., “A home transformed by the lightning the balanced alcoves smother” But the AI doesn’t know it’s making a poem.

Can AI make art? Well, art is in the eye of the beholder, so if you think it’s art, it’s art. But philosophically, we need to recall Turing-Church Computability which states that the computation “need not be intelligible to the one calculating.” The fact that computers can create works that look creative does not mean that the machines have the awareness required for creativity.

Can the operations of the brain be simulated on a computer? The Turing-Church statement says yes. But now we have computing so advanced that it’s unpredictable, probabilistic, and is beyond human capability. But the computations need not be artistic to the one computing.

Computability has limits:

1. Information and data are not meaning or knowledge.

2. Every single moment of existence is unique in the universe. Every single moment we see a unique aspect of the world. A Turing computer can’t see the outside world. It only has what it’s internal to it.

3. Human mind has existential experience.

4. The mind can reflect on itself.

5. Scott Aaronson says that humans can exercise free will and AI cannot, based on quantum theory. [Something about quantum free states.]

6.The universe has non-computable systems. Equilibrium paths?

“Aspect seeing” means that we can make a choice about how we see what we see. And each moment of each aspect is unique in time.

In SF, the SPCA uses a robot to chase away homeless people. Robots cannot exercise compassion.

Computers compute. Humans create. Creativity is not computable.


Q: [me] Very interesting talk. What’s at stake in the question?

A: AI has had such a huge presence in our lives. There’s a power of thinking about rationality as computation. Gets best articulated in game theory. Can we conclude that this game theoretical rationality — the foundational understanding of rationality — is computable? Do human brings anything to the table? This leads to an argument for the obsolescence of the human. If we’re just computational, then we aren’t capable of any creativity. Or free will. That’s what’s ultimately at stake here.

Q: Can we create machines that are irrational, and have them bring a more human creativity?

A: There are many more types of rationality than game theory sort. E.g., we are rational in connection with one another working toward shared goals. The dichotomy between the rational and irrational is not sufficient.

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