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Can AI Do Art? Are You Afraid It Could?

Two years ago I was sitting in a Belgian concert hall listening to the Brussels Philharmonic playing a series of original pieces. The composer? An AI created by Luxembourg company Aiva.

After the concert I mingled with the attendees. Most of the conversations were around this recurring question: Can AI really do art?

Despite the fact that we had just silently sat for more than an hour listening to very agreeable AI-made music, many found themselves passing a harsh judgement. Most comments were along that line: « That’s not art, it’s only a pastiche of the great composers ».

What stroke me, though, was not the conversations themselves, but the fact that we were all suddenly unified in our judgemental attitude. As is often the case when we pass judgement on someone else — or something else, in the case of AI — I felt we were collectively projecting our own fears. But fear of what?

I’d say it’s the fear of losing our supremacy on a trait that we strongly associate with our identity as human beings.

This would not be the first identity crisis caused by the relentless march of technology. Another example is illustrated by these words, uttered by world-champion Lee Sedol as he lost his match against AlphaGo. “I’m sorry for letting humanity down.”, he said, with tears in his voice.

But humans haven’t stopped playing Go since that famous defeat. On the contrary, they converted the AIs into allies in their pursuit to understand the game. Today, thanks to artificial intelligence, new Go openings are constantly being tested and mastered by humans.

Here’s another anecdote. In December 2018, famous cellist Yo -Yo Ma was speaking at the world’s largest conference on Artificial Intelligence. When asked the question about music and AI he answered something along those lines: « I don’t care, because whenever I’m listening to music I look for the intentions of the human behind it. »

In his recent critic of « Beethoven X », a project to complete Beethoven’s unfinished tenth symphony, composer Jan Swafford notes something similar: « The ability of a machine to do or outdo something humans do is interesting once atmost. We humans need to see the human doing it. »

Might it be that our fear comes from the fact that we see art as the artifact rather than as the intention of the human creating this artifact?

AI will definitely create music that you’ll find pleasing to listen to as you sit in a waiting room or as you drive your car. But, unless you can connect to the human behind that AI – to their intention, their struggles, their humanity – chances are you’ll soon forget about this music.

So can AI create art? To that I answer: who cares. It will never be able to disconnect me from my fellow humans and from the ways in which they try to communicate their humanity through the artifacts they create. That’s what I choose to call art.

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Open Facebook to Researchers!

Amongst all the recent complaints against Facebook, the one I find the most problematic is the way in which internal employees have access to an exceptional experimental framework while researchers from outside the company are barred from it.

If Facebook is anything like Google, then its software engineers really are scientists constantly running counterfactual experiments. They deploy any new feature on a subset of users and measure if the proposed change has an impact when compared to a control group. This is hardcore science. It’s good to see companies embracing scientific practices to such an extent.

What is not so good, however, is that external researchers can’t do anything remotely close to that. Their options are limited to:

  • Using analytic tools that offer them an external view onto Facebook. An example is CrowdTangle, acquired by Facebook in 2016 and recently “regorganized”, leading to the departure of its founder and long-time advocate of more transparency, Brandon Silverman. [1]
  • Crowdsourcing data gathering to an army of willing volunteers using a browser plug-in, and sometimes having to stop because Facebook threatens to sue. [2]

So, not only does Facebook block external researchers from operating on the same footing as its internal engineers, it seems to be going out of its ways to make researcher’s lives harder.

There is no denying that Facebook has become a force that shapes society, but we’re mostly blind to the precise way in which it does it.

Does Facebook and its algorithms create filter bubbles? Polarization? Addiction? Infodemics? Doomscrolling? Social anxiety?

Maybe… Probably… I don’t know….

…but it’s precisely the fact that I don’t know and that I could know that is my biggest issue with Facebook.

We need to ask Facebook and all the other society-shaping tech giants to give researchers access to the tools it uses internally. This is the very first step towards the transparency we deserve — if not as individuals, at least as a society.


This post was inspired by this recent piece on researchers using CrowdTangle to study local news on Facebook. Especially by the fact that they had such a hard time to gather data and that they couldn’t derive causal relationships from their experiment.

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Your Recommender System Is a Horny Teen

There is a story I like to tell about recommender systems… Someone on the YouTube team once told me that they ran an experimental recommender to decide which frame of a video should be used as a thumbnail. The goal being to maximize clicks. After letting that recommender learn from user’s behaviors, it converged to… Porn! Ok, not quite porn, but the recommender learned that the more skin was visible in a thumbnail the higher the likelihood of a click. Naturally the experiment was scrapped (thank God for human oversight), but it still goes to show that purely metric-driven recommender systems can land you in a very weird place…


That’s what I feel is happening with Amazon’s recommender system picking the ads to run on my Facebook stream. The top picks systematically look like sex toys or, as is the case in the example below, drugs. They are all excellent at triggering my curiosity — and I’m sure their metrics show a very high click-through rate in average user’s — but they are pretty bad at convincing me Amazon is a great company…

May be an image of text that says 'mazon.ca Sponsored Shop our selection of deals, best sellers, and interesting finds on Amazon amazon amazon Jack III 6 Pack Premium California White Sage... amazon.ca Shop Now Jack Richeso Assorted Ass amazon.ca'
Example of ads run by the Amazon Recommender System
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The Phrase “Social Network” Is Trapping Us

As we’re integrating more human-to-human interaction into Waverly I’m getting a bit anxious. Will we end up building yet another social network? If not, what are we building?


In a recent discussion with Matthieu Dugal on The Waverly Podcast (en français), he pointed out that, even though more and more people get informed through their social networks, most people use them to nurture their social ties. Social networks are a bit like virtual bars where some of the customers are having a casual drink while others are lecturing them in all seriousness.

Why do we end up with these combined online platforms? A look at how they grow helps answer that question. They typically start as purely social spaces, but evolve over time as they attract a more diverse crowd of content publishers.

Combining an informational and a social space into the same platform makes it hard to know how to react to different pieces of content. The Guardian and The New York Times might increase their readership by publishing on Instagram, but their presence on the platform also contributes to the confusion. It requires mental effort to figure out that we should react differently to a piece of journalism than to the anxiety-loaded message of an anti-vax friend. The former should be processed for the information it contains, whereas the latter is best met with words of compassion.

Followup question: Why do most modern online platforms start as purely social spaces? Maybe because the phrase social network occupies too much space in our collective imagination. Thanks to our limited vocabulary, we believe the only thing we can do collectively, online, is to reinforce our social ties.

Despite the popularity of the phrase social networks, it’s easy to find online spaces where people interact without trying to socialize. Wikipedia, for example, has an army of volunteers who update its pages. Some will become friends, but everyone understands that their primarily goal is to build a “written compendium that contains information on all branches of knowledge”. Other examples abound: Stack Overflow, GitHub, Quora…

What should we call these? Collaborative platforms? If so, can we all agree to push on that phrase really hard so that it gains a foothold in our collective mind? I’d really like to see a different kind of online spaces flourish, and I believe it wont happen unless we have some words to describe what we want to build.

In the meantime, that’s what I’ll do. I’ll build Waverly as a collaborative platform. I’ll make sure it feels like a space where communities gather around a joint mission — building healthier algorithms for all of us — rather that around their need to nurture social ties.

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Why It’s So Hard to Trust the Machines

If a careless driver runs a red light and kills a pedestrian you might read about it in your local newspaper. If a self-driving car does the same, you’ll see it in every international news outlet.

When it comes to trusting machines, people often seem to have a ridiculously high bar. We reject machines even when they are statistically safer than humans at performing a given task. In fact, most experts agree that self-driving cars will need to be significantly safer than human drivers in order to gain social acceptance.

Why is it so hard to trust the machines? Let me venture a hypothesis…

If a person makes a poor decision, we assume that this person is flawed in some way. If a machine makes a poor decision, we assume all similar machines are broken. A human driver running a red light means that this driver is careless. A self-driving car doing the same instantly means that all self-driving cars are potentially broken.

We consider humans as separate beings while we see machines as copies made from the same blueprint. We’re not wrong. If a mobile device suffers from a security flaw, it’s fair to assume that all similar devices suffer from the same flaw.

In fact, this replicated nature of technology gives us the powerful ability to “patch” all the machines at once. We use such patches regularly to make our devices safer. Yet, at the same time, perfect replication might be precisely what makes it impossibly hard to trust the machines. Where an individual human being gives us a natural boundary beyond which we won’t extend our trust (or distrust), replicated machines have no such boundary.

Should we give up replication and purposefully build each machine slightly differently to make it easier for us to trust them? This sounds like a stupid idea, but it may not be. In fact, injecting artificial diversity may be required to help speed up social acceptance of automation.

In general, we know that diversity makes systems more resilient at the cost of slightly reducing their effectiveness. Plant diverse crops and you reduce the potential negative outcome of a disease outbreak.

What I’m proposing today is that, as paradoxical as it may seem, diversity might also help us build systems that are easier to trust at the cost of slightly reducing their safety. Our tendency to maximize safety might be running at odds with our desire to deploy trustworthy systems at scale. Instead of building a fleet of identical replicas that are easy to patch, we might be better off inserting artificial “fracture lines” that make it harder for distrust to spread.

More generally, I’ve always seen diversity as a way for us to stay humble in the face of what we’re creating. Welcoming diverse point-of-views necessarily means we will not put all our efforts and energy behind the optimal idea. However, it also means that we acknowledge our inability to predict the future. We acknowledge that there are unforeseen events that might throw a wrench at any seemingly optimal idea and that we’re better off pursuing many different paths simultaneously, even if some of these paths appear to be less efficient.

Just how much diversity do we need if we are to build trust between humans and machines? Would having a hundred different self-driving car models be enough? Would each car need its random artificial DNA to ensure it makes decisions slightly differently from other cars? I don’t have the answer… But I do believe it is interesting to look at the lack of diversity in our replicated systems as a hurdle towards building trustworthy machines.

Note: I’ve experimented with a featured image for this post, using a quote from the post in the image. The photo is from Jason Leung on Unsplash.

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Solving Science

A topic I often come back to is how, in my opinion, scientists are not trying hard enough to solve the problems in the processes that drive modern science. I find that particularly sad given how some of the people I love and admire the most are scientists.

For me, like for many grad students, it started with a personal emotional crisis following the harsh comments of an anonymous reviewer #2. I was surprised at how a community of people who strived to make the world a better place was full of critics who didn’t seem to care that there was a human on the receiving end of their comment.

As I made my way through the academic ecosystem I started observing latent in-goup / out-group dynamics in tightly knit sub-communities. These dynamics made it really hard for a newcomer to propose alternative approaches that would challenge the views of these sub-communities. Again, as a starry-eyed idealistic researcher I got my fingers slapped, through reviews, in a way that felt very unfair.

My unease with these observations — and how strongly they clashed with my idealistic vision of science — turned me into a vocal advocate of greater experimentation in the academic processes.

At that point in my postdoc I got this advice from a successful prof: “If you keep worrying about the process you’ll never be a good researcher. Focus on the science.” She was right. In fact, my inability to stop caring about the process is partly why I gave up on the academic track…

Yet if researchers give up on the process, who will care?

Right now, it seems to be the funding agencies. The ones that gave us impact factors and h-index and a whole slew of bibliometric methods. They turned scientific funding into a game with well-defined rules… and as a result they turned (some) scientists into players. Even though, deep down, most scientists would rather just be doing good science.

I dont talk about this too much these days. For one, I’m out of the academic circuit (even though in my heart I very much still feel like a scientist 😊 ). But also because the last thing I want is to be confused with a proponent of anti-intellectualism. It’s quite the opposite: it’s because I love the spirit of science that I care about how it’s done.

What prompted this post was a discussion with Marie Lambert-Chan and Matthieu Dugal. Marie pointed me to this article. I couldn’t read it because of its paywall but the subtitle makes me hopeful: “For the first time prestigious funder has explicitly told academics they must not include metric when applying for grants.”

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Humans are not Pixels

Grateful that the world is made of more than pixels…

This week the entire Waverly team convened in Montréal. It was good. Really good. I wanted to share the story.

We started and grew Waverly during the pandemic. Which means we hired pixelated faces and then met these pixels daily in little Zoom squares.

Because it was the pandemic we decided to hire the best possible people (sticking to a single time zone & country). It means our small team now spans from Rimouski to Windsor.

In the past year we built a never seen before technology — the world’s first natural language based recommendation system — and we managed to package it in a POC mobile app. No small feat. Yet most of us had never been in the same room together.

We fixed that bug.

This week we spent time in our Montréal office coding together. We took walks across Montréal’s delightfully sunny plateau neighborhood to design the product. We ate arepas on picnic tables, we played board games that made us die in laughter, we mixed Aman’s (our Windsor software engineer) special Chai mix from raw ingredients…

We jumped out of the pixel world to be humans together.

This week I rediscovered a bunch of little things I would not have celebrated before but that made me realize the importance of apparently mundane moments. In the words of one of my favourite song from Bénabar: “Le bonheur ça s’trouve pas en lingots, mais en petite monnaie.” — you won’t find happiness in a gold bar, but in pocket change.

I want to say it again. I’m grateful the world is made of flesh and blood humans, not pixels. I’ll be starting next week full of renewed energy knowing not only that we’re building an important product for the world, but that we’re doing it as a team of humans I deeply care about. A team that reinforces, for me, this undying belief that people are good and that we need more tools to help that goodness shine.

❤️

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Healthy Social Media, Secrets of Pascal’s Triangle and Venus’ Tectonics

Welcome to this week’s Via Waverly, where I expose diverse and unexpected finds that were served to me by Waverly.

Principles of Healthy Social Media

I stumbled on this research by New Public, an organization that wants to reimagine the Internet as a public space. (Via Fast Company.) They asked power users of major Internet platforms questions like Does the platform encourage people to treat one another humanely?

Based on the answers, the researchers came up with 14 principles for healthy social media. Here are some of my favorites:

  • Inviting everyone to participate
  • Encouraging the humanization of others
  • Building bridges between groups
  • Promoting thoughtful conversation

Wave: 🕸️ Better Web

Secrets hidden in Pascal’s Triangle

You know Pascal’s triangle, right? If I asked you, apart from 1, which number is the most frequent in the triangle and how often it appears, what would you say?

If you’re like me, you’d probably guess something like: “I have no idea which number it is, but it probably appears infinitely many times.” Well, thanks to Terrence Tao and Waverly, I learned this week about the Singmaster’s conjecture which says that no number larget than 1 appears infinitely many times. In fact, the current record holder is 3003 and it appears 8 times.

I always love it when strange numbers like 3003 appear in a conjecture. Makes maths feel like a wonderful and unexplored world.

Wave: 🧮 Math Geekiness

Tectonic Plates on Venus

What’s one thing Earth has that no other planet has? Tectonic plates! At least that was what we thought until a couple of weeks ago where scientist found evidence that Venus surface moves around.

Wave: 🌋 Geological Mysteries

A Feel-Good Math Story

I immersed myself in this heartfelt tribute of a son to his mathematician father.

For me, the symbols are mathematical madeleines. They remind me of the pads of paper that were scattered around our house, each full of my father’s scribblings—his version of the sandpiper tracks that had delighted him as a child. When I was a child myself, I would watch him on the couch, deep in thought, scratching away with a mechanical pencil. At some point, I thought that I might like to have a life like that.

Dan Rockmore

There’s something about the struggle of intellectuals that moves my heart. I connect with their desire to do the greatest work, the slow realization that they might not get there, and their human condition rising from the depth of their soul and making them fall in love again with the mundane.

Wave: 🧮 Math Geekiness

Rally, A New Privacy-First Platform

Mozilla just introduced Rally, a novel data sharing platform that puts privacy above everything else.

Today, we’re announcing the Mozilla Rally platform. Built for the browser with privacy and transparency at its core, Rally puts users in control of their data and empowers them to contribute their browsing data to crowdfund projects for a better Internet and a better society.

The goal seems to be to enable technology policy research by academics, which often do not have access to the data they need —this data being trapped in the walled gardens of online services. This objective reminds me quite a bit of data trusts, although the article doesn’t mention them.

Wave: ⚖️ Policies for people

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Algorithmic Fatigue, Bullshit Jobs and Data Trusts

Welcome to this week’s Via Waverly, where I expose diverse and unexpected finds that were served to me by Waverly.

Fighting Algorithmic Fatigue

I’m still looking for the right term to capture that nauseous feeling that grasps me when I’ve spent too much time stuck in an algorithmic stream. Doomscrolling is my favorite one for now, but I’d like one that captures the emotion, not the action. I stumbled on algorithmic fatigue this week.

I can’t really find a way to communicate with this app or service to say that’s not what I want, or at least that is not everything I want.

Female, 30 (Shanghai, China)

This is a quote from a series of interviews in this research report by the University of Helsinki, Alice Labs and Reaktor. You might prefer reading this summary.

The whole report is full of interesting findings:

The meticulous, first-hand observations demonstrate that recommender systems and digital assistants repeatedly fail in their promise of providing pleasurable encounters, rather delivering irritating engagements with crude and clumsy machines. […]

Digital technologies are often developed to a ‘one size fits all’ model. Yet, as the experiences with recommender systems and digital assistants suggest, in different contexts, people take up very different stances in relation to technologies. They might want to be passive, or prefer to be actively involved.

Wave: ☕ Design Strategy

Are Bullshit Jobs Bullshit?

I’ve been a fan of David Graeber’s Bullshit Job hypothesis ever since I read the books a few years ago. In fact, I believe the reason we don’t see more jobs being displaced by automation is because we’re in some weird “job bubble” where bullshit jobs are being created through a very complex and opaque system of incentives.

Yet, when I created Waverly, one of my goal was to help me step out of my filter bubble, so when I saw that article pop up in my daily stack, I was not exactly happy (it’s not fun to have one’s beliefs challenged by a triggering title) but I still went ahead and read it:

Graebers made a number of claims that the researchers attempted to corroborate:

Between 20% and 50% of the workforce are working in bullshit jobs. No, only 4.8% of EU workers said they were doing meaningless work.

The number of bullshit jobs has been ‘increasing rapidly in recent years’. Nope. Actually, the percentage of bullshit jobs fell from 7.8% to 4.8% in 2015.

Graeber argued bullshit jobs clustered in certain occupations, like finance, law, administration, and marketing. The researchers found no evidence that those occupations had more people feeling like their work was meaningless.

OK, so my gut feeling — that the number of Bullshit Jobs is constantly increasing — doesn’t seem to be corroborated by these researcher’s finding.

Maybe I’m wrong? Maybe we need more research on this? Anyway, interesting data point.

Wave: 👍 Modern Leadership

Lessons from Existing and Failed Data Trusts

Interesting research from Cambridge’s Bennett Institute that contrasts failed data trusts with successful ones. Starting with a famous failure:

Sidewalk Labs’ proposal for the Urban Data Trust in Toronto, Canada was abandoned amid a heated public controversy. Legal scholars and privacy advocates argue the goal of the trust may have been to make the data collected in the city exempt from Canada’s privacy laws.

However, there seems to be some agreement that sharing data is needed to improve our common goals:

European policymakers argue it is important for individuals to accept their role as “data donors” who willingly share information with the trustworthy organisations for collective benefit.

And some examples where data trusts are working:

One example of a data trust that works for a civic purpose is the Silicon Valley Regional Data Trust, which is operated by the University of California in partnership with several district school boards and local social services. The trust is a non-profit cooperative and shares the data only among the organisations that donate data.

Wave: 💡 Value Alignment

Learning about Slipstream

Thanks to this article, I’ve learned a new term for a literary genre: Slipstream. It seems to be very ill-defined, but from what I gather from the article, it’s a genre I’m quite likely to enjoy:

Sterling then goes on to name “slipstream” for a group of books that straddle the fence of mainstream and genre, even acknowledging the term as a parody of the word “mainstream.”

Sterling admitted it’s not clearcut what slipstream is. Most of the essay brainstorms and then acknowledges arguments against the term. In a nutshell he wrote, “this is a kind of writing which simply makes you feel very strange.”

Slipstream novels are categorized as not strictly under science fiction, fantasy, or horror, but may be recommended by their ardent readers. On the other hand, a mainstream reader may also recommend a slipstream novel. Although they may add it might be a bit on the weirder side.

Wave: 📗 Litterature Lover

Faster Synthetic Data

I started my research career in Computer Graphics, which is all about (approximate) physics simulations. Now that I’m more into Machine Learning, I often find myself to be one the biggest proponent of synthetic training data: going back to first principles to synthesize something that looks like the real thing, and try to train an ML system on this.

This project goes further and proposes to use ML to speed-up the generation of synthetic data… to train future ML system!

This may sound ridiculous. If you already succeeded in training a system to generate your synthetic data, why use it to train a new system?

But it might be brilliant… If you have a fast ML-based data synthesizer, you might be able to use it as a component within a more complex synthesizer, ultimately allowing you to train better downstream AI models.

Wave: 🧠 Generalized Machine Intelligence

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Browsers Should Browse

A few weeks ago I gave a short talk at The Future of the Browser conference where I made a this controversial claim: The browser is not where we do our browsing.

Indeed, if we go back to the definition:

To Browse

To look over or through an aggregate of things casually especially in search of something of interest.

Merriam-Webster

Browsing is about looking at aggregates, yet browsers are all about presenting us with individual units of contents.

In a world that produces information much faster than we can process it, the superpower we need is the ability to effectively look at collections. That’s the superpower our platforms grant us. Twitter, Facebook, YouTube, Instagram, Twitch… They are all designed to make it easy for us to browse through a collection.

Unfortunately, though, they do it while trapping us in their walled-gardens. What we really need, is a similarly powerful collection-browsing tool that is also open and can work on any collection.

So, what’s the difference between a web browser and browsing?

Units. Browsers are good at presenting atomic pieces of content. Web pages. They do not intrinsically understand the concept of a collection. Yet browsing is about looking through an aggregate of things. To browse, we need a tool that works primarily with collections.

Presentation. Browsing should minimize the cognitive load required to make sense of the things we browse through. Yet browsers render every page according to the whims of the page creator. It’s great to create our rich and diversified web, but it’s awful to reduce the cognitive load we must deploy to browse this disparate content. Browsing, on the other hand, cares about creating a more uniform environment. An environment in which each thing in the collection can be quickly understood.

Level of detail. A browser allows us to interact with the content in its entirety, with all the details included. But browsing works best if we can focus on the important details first. A good browser should hide low-level details and let us unfold them as we need.

This is very much in line with what we’re building at Waverly. I hope we can free the superpower of browsing and make it available to everyone, no matter which collection you want to look through.