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February 11, 2018

The story of lead and crime, told in tweets

Patrick Sharkey [twitter: patrick_sharkey] uses a Twitter thread to evaluate the evidence about a possible relationship between exposure to lead and crime. The thread is a bit hard to get unspooled correctly, but it’s worth it as an example of:

1. Thinking carefully about complex evidence and data.

2. How Twitter affects the reasoning and its expression.

3. The complexity of data, which will only get worse (= better) as machine learning can scale up their size and complexity.

Note: I lack the skills and knowledge to evaluate Patrick’s reasoning. And, hat tip to David Lazer for the retweet of the thread.

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The brain is not a computer and the world is not information

Robert Epstein argues in Aeon against the dominant assumption that the brain is a computer, that it processes information, stores and retrieves memories, etc. That we assume so comes from what I think of as the informationalizing of everything.

The strongest part of his argument is that computers operate on symbolic information, but brains do not. There is no evidence (that I know of, but I’m no expert. On anything) that the brain decomposes visual images into pixels and those pixels into on-offs in a code that represents colors.

In the second half, Epstein tries to prove that the brain isn’t a computer through some simple experiments, such as drawing a dollar bill from memory and while looking at it. Someone committed to the idea that the brain is a computer would probably just conclude that the brain just isn’t a very good computer. But judge for yourself. There’s more to it than I’m presenting here.

Back to Epstein’s first point…

It is of the essence of information that it is independent of its medium: you can encode it into voltage levels of transistors, magnetized dust on tape, or holes in punch cards, and it’s the same information. Therefore, a representation of a brain’s states in another medium should also be conscious. Epstein doesn’t make the following argument, but I will (and I believe I am cribbing it from someone else but I don’t remember who).

Because information is independent of its medium, we could encode it in dust particles swirling clockwise or counter-clockwise; clockwise is an on, and counter is an off. In fact, imagine there’s a dust cloud somewhere in the universe that has 86 billion motes, the number of neurons in the human brain. Imagine the direction of those motes exactly matches the on-offs of your neurons when you first spied the love of your life across the room. Imagine those spins shift but happen to match how your neural states shifted over the next ten seconds of your life. That dust cloud is thus perfectly representing the informational state of your brain as you fell in love. It is therefore experiencing your feelings and thinking your thoughts.

That by itself is absurd. But perhaps you say it is just hard to imagine. Ok, then let’s change it. Same dust cloud. Same spins. But this time we say that clockwise is an off, and the other is an on. Now that dust cloud no longer represents your brain states. It therefore is both experiencing your thoughts and feeling and is not experiencing them at the same time. Aristotle would tell us that that is logically impossible: a thing cannot simultaneously be something and its opposite.

Anyway…

Toward the end of the article, Epstein gets to a crucial point that I was very glad to see him bring up: Thinking is not a brain activity, but the activity of a body engaged in the world. (He cites Anthony Chemero’s Radical Embodied Cognitive Science (2009) which I have not read. I’d trace it back further to Andy Clark, David Chalmers, Eleanor Rosch, Heidegger…). Reducing it to a brain function, and further stripping the brain of its materiality to focus on its “processing” of “information” is reductive without being clarifying.

I came into this debate many years ago already made skeptical of the most recent claims about the causes of consciousness by having some awareness of the series of failed metaphors we have used over the past couple of thousands of years. Epstein puts this well, citing another book I have not read (and another book I’ve consequently just ordered):

In his book In Our Own Image (2015), the artificial intelligence expert George Zarkadakis describes six different metaphors people have employed over the past 2,000 years to try to explain human intelligence.

In the earliest one, eventually preserved in the Bible, humans were formed from clay or dirt, which an intelligent god then infused with its spirit. That spirit ‘explained’ our intelligence – grammatically, at least.

The invention of hydraulic engineering in the 3rd century BCE led to the popularity of a hydraulic model of human intelligence, the idea that the flow of different fluids in the body – the ‘humours’ – accounted for both our physical and mental functioning. The hydraulic metaphor persisted for more than 1,600 years, handicapping medical practice all the while.

By the 1500s, automata powered by springs and gears had been devised, eventually inspiring leading thinkers such as René Descartes to assert that humans are complex machines. In the 1600s, the British philosopher Thomas Hobbes suggested that thinking arose from small mechanical motions in the brain. By the 1700s, discoveries about electricity and chemistry led to new theories of human intelligence – again, largely metaphorical in nature. In the mid-1800s, inspired by recent advances in communications, the German physicist Hermann von Helmholtz compared the brain to a telegraph.

Maybe this time our tech-based metaphor has happened to get it right. But history says we should assume not. We should be very alert to the disanologies, which Epstein helps us with.

Getting this right, or at least not getting it wrong, matters. The most pressing problem with the informationalizing of thought is not that it applies a metaphor, or even that the metaphor is inapt. Rather it’s that this metaphor leads us to a seriously diminished understanding of what it means to be a living, caring creature.

I think.

 

Hat tip to @JenniferSertl for pointing out the Aeon article.

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February 1, 2018

Can AI predict the odds on you leaving the hospital vertically?

A new research paper, published Jan. 24 with 34 co-authors and not peer-reviewed, claims better accuracy than existing software at predicting outcomes like whether a patient will die in the hospital, be discharged and readmitted, and their final diagnosis. To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. The data span 11 combined years at two hospitals,

That’s from an article in Quartz by Dave Gershgorn (Jan. 27, 2018), based on the original article by Google researchers posted at Arxiv.org.

…Google claims vast improvements over traditional models used today for predicting medical outcomes. Its biggest claim is the ability to predict patient deaths 24-48 hours before current methods, which could allow time for doctors to administer life-saving procedures.

Dave points to one of the biggest obstacles to this sort of computing: the data are in such different formats, from hand-written notes to the various form-based data that’s collected. It’s all about the magic of interoperability … and the frustration when data (and services and ideas and language) can’t easily work together. Then there’s what Paul Edwards, in his great book A Vast Machine calls “data friction”: “…the costs in time, energy, and attention required simply to collect, check, store, move, receive, and access data.” (p. 84)

On the other hand, machine learning can sometimes get past the incompatible expression of data in a way that’s so brutal that it’s elegant. One of the earlier breakthroughs in machine learning came in the 1990s when IBM analyzed the English and French versions of Hansard, the bi-lingual transcripts of the Canadian Parliament. Without the machines knowing the first thing about either language, the system produced more accurate results than software that was fed rules of grammar, bilingual dictionaries, etc.

Indeed, the abstract of the Google paper says “Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. ” It continues: “We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.”

The paper also says that their approach affords clinicians “some transparency into the predictions.” Some transparency is definitely better than none. But, as I’ve argued elsewhere, in many instances there may be tools other than transparency that can give us some assurance that AI’s outcomes accord with our aims and our principles of fairness.

 


 

I found this article by clicking on Dave Gershgon’s byline on a brief article about the Wired version of the paper of mine I referenced in the previous paragraph. He does a great job explaining it. And, believe me, it’s hard to get a writer — well, me, anyway — to acknowledge that without having to insert even one caveat. Thanks, Dave!

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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.

[ IMAGE ]

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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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] 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&A


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&A


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&A


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., https://www.wirewax.com or http://www.raptmedia or http://www.zentrick.com
http://www.zentrick.com .


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&A


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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