Data Science at Home

By Francesco GadaletaNews Technology Tech News

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Data Science at Home episodes

February 14, 2020 Bridging the gap between data science and data engineering: metrics (Ep. 95)
Data science and data engineering are usually two different departments in organisations. Bridging the gap between the two is essential to success. Many times the brilliant applications created by data scientists don't find a match in production, just because they are not production-ready.
In this episode I have a talk with Daan Gerits, co-founder and CTO at Pryml.io

14 min
February 07, 2020 A big welcome to Pryml: faster machine learning applications to production (Ep. 94)
Why so much silence? Building a company! That's why :)
I am building pryml, a platform that allows data scientists build their applications on data they cannot get access to.
This is the first of a series of episodes in which I will speak about the technology and the challenges we are facing while we build it.
Happy listening and stay tuned!
10 min
December 31, 2019 It's cold outside. Let's speak about AI winter (Ep. 93)
In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite episode and propose new ones.
I would like to thank all of you for supporting and inspiring us. I wish you a wonderful 2020!

Francesco and the team of Data Science at Home
37 min
December 28, 2019The dark side of AI: bias in the machine (Ep. 92)
This is the fourth and last episode of mini series "The dark side of AI".
I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine

C: Francesco, today we are starting with an infuriating discussion. Are you ready to be angry?

F: yeah sure is this about brexit?
No, I don’t talk about that. In 1986 the New York City’s Rockefeller University conducted a study on breast and uterine cancers and their link to obesity. Like in all clinical trials up to that point, the subjects of the study were all men.
So Francesco, do you see a problem with this approach?

F: No problem at all, as long as those men had a perfectly healthy uterus.
In medicine, up to the end of the 20th century, medical studies and clinical trials were conducted on men, medicine dosage and therapy calculated on men (white men). The female body has historically been considered an exception, or variation, from a male body.

F: Like Eve coming from Adam’s rib. I thought we were past that...
When the female body has been under analysis, the focus was on the difference between it and the male body, the so-called “bikini approach”: the reproductive organs are different, therefore we study those, and those only. For a long time medicine assumed this was the only difference.

Oh good ...
This has led to a hugely harmful fallout across society. Because women had reproductive organs, they should reproduce, and all else about them was deemed uninteresting. Still today, they consider a woman without children somehow to have betrayed her biological destiny. This somehow does not apply to a man without children, who also has reproductive organs.

F: so this is an example of a very specific type of bias in medicine, regarding clinical trials and medical studies, that is not only harmful for the purposes of these studies, but has ripple effects in all of society
Only in the 2010 a serious conversation has started about the damage caused by not including women in clinical trials. There are many many examples (which we list in the references for this episode).

Give me one
Researchers consider cardiovascular disease a male disease - they even call it “the widower”. They conduct studies on male samples. But it turns out, the symptoms of a heart attack, especially the ones leading up to one, are different in women. This led to doctors not recognising or dismissing the early symptoms in women.

F: I was reading that women are also subject to chronic pain much more than men: for example migraines, and pain related to endometriosis. But there is extensive evidence now of doctors dismissing women’s pain, as either imaginary, or “inevitable”, like it is a normal state of being and does not need a cure at all.

The failure of the medical community as a whole to recognise this obvious bias up to the 21st century is an example of how insidious the problem of bias is.

There are 3 fundamental types of bias:
One: Stochastic drift: you train your model on a dataset, and you validate the model on a split of the training set. When you apply your model out in the world, you systematically add bias in the predictions due to the training data being too specific
Two: The bias in the model, introduced by your choice of the parameters of your model.
Three: The bias in your training sample: people put training samples together, and people have culture, experience, and prejudice. As we will see today, this is the most dangerous and subtle bias. Today we’ll talk about this bias.

Bias is a warping of our understanding of reality. We see reality through the lens of our experience and our culture. The origin of bias can date back to traditions going back centuries, and is so ingrained in our way of thinking, that we don’t even see it anymore.

F: And let me add, when it comes to machine learning, we see reality through the lens of data. Bias is everywhere, and we could spend hours and hours talking about it. It’s complicated.

It’s about to become more complicated.

F: of course, if I know you…
Let’s throw artificial intelligence in the mix.

F: You know, there was a happier time when this sentence didn’t fill me with a sense of dread...
ImageNet is an online database of over 14 million photos, compiled more than a decade ago at Stanford University. They used it to train machine learning algorithms for image recognition and computer vision, and played an important role in the rise of deep learning. We’ve all played with it, right? The cats and dogs classifier when learning Tensorflow? (I am a dog by the way. )

F: ImageNet has been a critical asset for computer-vision research. There was an annual international competition to create algorithms that could most accurately label subsets of images.
In 2012, a team from the University of Toronto used a Convolutional Neural Network to handily win the top prize. That moment is widely considered a turning point in the development of contemporary AI. The final year of the ImageNet competition was 2017, and accuracy in classifying objects in the limited subset had risen from 71% to 97%. But that subset did not include the “Person” category, where the accuracy was much lower...
ImageNet contained photos of thousands of people, with labels. This included straightforward tags like “teacher,” “dancer” and “plumber”, as well as highly charged labels like “failure, loser” and “slut, slovenly woman, trollop.”

F: Uh Oh.
Then “ImageNet Roulette” was created, by an artist called Trevor Paglen and a Microsoft researcher named Kate Crawford. It was a digital art project, where you could upload your photo and let the classifier identify you, based on the labels of the database. Imagine how well that went.

F: I bet it did’t work
Of course it didn’t work. Random people were classified as “orphans” or “non-smoker” or “alcoholic”. Somebody with glasses was a “nerd”. Tabong Kima, a 24-year old African American, was classified as “offender” and “wrongdoer”.

F: and there it is.
Quote from Trevor Paglen: “We want to show how layers of bias and racism and misogyny move from one system to the next. The point is to let people see the work that is being done behind the scenes, to see how we are being processed and categorized all the time.”

F: The ImageNet labels were applied by thousands of unknown people, most likely in the United States, hired by the team from Stanford, and working through the crowdsourcing service Amazon Mechanical Turk. They earned pennies for each photo they labeled, churning through hundreds of labels an hour. The labels were not verified in any way : if a labeler thought someone looks “shady”, this label is just a result of their prejudice, but has no basis in reality.
As they did, biases were baked into the database. Paglen quote again: “The way we classify images is a product of our worldview,” he said. “Any kind of classification system is always going to reflect the values of the person doing the classifying.” They defined what a “loser” looked like. And a “slut.” And a “wrongdoer.”

F: The labels originally came from another sprawling collection of data called WordNet, a kind of conceptual dictionary for machines built by researchers at Princeton University in the 1980s. But with these inflammatory labels included, the Stanford researchers may not have realized what they were doing.
What is happening here is the transferring of bias from one system to the next.

Tech jobs, in past decades but still today, predominantly go to white males from a narrow social class. Inevitably, they imprint the technology with their worldview. So their algorithms learn that a person of color is a criminal, and a woman with a certain look is a slut.

I’m not saying they do it on purpose, but the lack of diversity in the tech industry translates into a narrower world view, which has real consequences in the quality of AI systems.

F: Diversity in tech teams is often framed as an equality issue (which of course it is), but there are enormous advantages in it: it allows to create that cognitive diversity that will reflect into superior products or services.
I believe this is an ongoing problem. In recent months, researchers have shown that face-recognition services from companies like Amazon, Microsoft and IBM can be biased against women and people of color.

Crawford and Paglen argue this:
“In many narratives around AI it is assumed that ongoing technical improvements will resolve all problems and limitations.
But what if the opposite is true? What if the challenge of getting computers to “describe what they see” will always be a problem? The automated interpretation of images is an inherently social and political project, rather than a purely technical one. Understanding the politics within AI systems matters more than ever, as they are quickly moving into the architecture of social institutions: deciding whom to interview for a job, which students are paying attention in class, which suspects to arrest, and much else.”

F: You are using the words “interpretation of images” here, as opposed to “description” or “classification”. Certain images depict something concrete, with an objective reality. Like an apple. But other images… not so much?

ImageNet contain images only corresponding to nouns (not verbs for example). Noun categories such as “apple” are well defined.
But not all nouns are created equal. Linguist George Lakoff points out that the concept of an “apple” is more nouny than the concept of “light”, which in turn is more nouny than a concept such as “health.”
Nouns occupy various places on an axis from concrete to abstract, and from descriptive to judgmental. The images corresponding to these nouns become more and more ambiguous.
These gradients have been erased in the logic of ImageNet. Everything is flattened out and pinned to a label.
The results can be problematic, illogical, and cruel, especially when it comes to labels applied to people.

F: so when an image is interpreted as Drug Addict, Crazy, Hypocrite, Spinster, Schizophrenic, Mulatto, Red Neck… this is not an objective description of reality, it’s somebody’s worldview coming to the surface.
The selection of images for these categories skews the meaning in ways that are gendered, racialized, ableist, and ageist. ImageNet is an object lesson in what happens when people are categorized like objects.
And this practice has only become more common in recent years, often inside the big AI companies, where there is no way for outsiders to see how images are being ordered and classified.

The bizarre thing about these systems is that they remind of early 20th century criminologists like Lombroso, or phrenologists (including Nazi scientists), and physiognomy in general. This was a discipline founded on the assumption that there is a relationship between an image of a person and the character of that person. If you are a murderer, or a Jew, the shape of your head for instance will tell.

F: In reaction to these ideas, Rene’ Magritte produced that famous painting of the pipe with the tag “This is not a pipe”.

You know that famous photograph of the soldier kissing the nurse at the end of the second world war? The nurse came public about it when she was like 90 years old, and told how this total stranger in the street had grabbed her and kissed her. This is a picture of sexual harassment. And knowing that, it does not seem romantic anymore.

F: not romantic at all indeed

Images do not describe themselves. This is a feature that artists have explored for centuries. We see those images differently when we see how they’re labeled. The correspondence between image, label, and referent is fluid. What’s more, those relations can change over time as the cultural context of an image shifts, and can mean different things depending on who looks, and where they are located. Images are open to interpretation and reinterpretation. Entire subfields of philosophy, art history, and media theory are dedicated to teasing out all the nuances of the unstable relationship between images and meanings.
The common mythos of AI and the data it draws on, is that they are objectively and scientifically classifying the world. But it’s not true, everywhere there is politics, ideology, prejudices, and all of the subjective stuff of history.

F: When we survey the most widely used training sets, we find that this is the rule rather than the exception.
Training sets are the foundation on which contemporary machine-learning systems are built. They are central to how AI systems recognize and interpret the world.
By looking at the construction of these training sets and their underlying structures, we discover many unquestioned assumptions that are shaky and skewed. These assumptions inform the way AI systems work—and fail—to this day.
And the impenetrability of the algorithms, the impossibility of reconstructing the decision-making of a NN, hides the bias further away from scrutiny. When an algorithm is a black box and you can’t look inside, you have no way of analysing its bias.

And the skewness and bias of these algorithms have real effects in society, the more you use AI in the judicial system, in medicine, the job market, in security systems based on facial recognition, the list goes on and on.

Last year Google unveiled BERT (Bidirectional Encoder Representations from Transformers). It’s an AI system that learns to talk: it’s a Natural Language Processing engine to generate written (or spoken) language.

F: we have an episode in which we explain all that

They trained it from lots and lots of digitized information, as varied as old books, Wikipedia entries and news articles. They baked decades and even centuries of biases — along with a few new ones — into all that material. So for instance BERT is extremely sexist: it associates with male almost all professions and positive attributes (except for “mom”).

BERT is widely used in industry and academia. For example it can interpret news headlines automatically. Even Google’s search engine use it.

Try googling “CEO”, and you get out a gallery of images of old white men.

F: such a pervasive and flawed AI system can propagate inequality at scale. And it’s super dangerous because it’s subtle. Especially in industry, query results will not be tested and examined for bias. AI is a black box and researchers take results at face value.

There are many cases of algorithm-based discrimination in the job market. Targeting candidates for tech jobs for instance, may be done by algorithms that will not recognise women as potential candidates. Therefore, they will not be exposed to as many job ads as men. Or, automated HR systems will rank them lower (for the same CV) and screen them out.

In the US, algorithms are used to calculate bail. The majority of the prison population in the US is composed of people of colour, as a result of a systemic bias that goes back centuries. An algorithm learns that a person of colour is more likely to commit a crime, is more likely to not be able to afford bail, is more likely to violate parole. Therefore, people of colour will receive harsher punishments for the same crime. This amplifies this inequality at scale.

Conclusion

Question everything, never take predictions of your models at face value. Always question how your training samples have been put together, who put them together, when and in what context. Always remember that your model produces an interpretation of reality, not a faithful depiction.
Treat reality responsibly.
21 min
December 23, 2019The dark side of AI: metadata and the death of privacy (Ep. 91)
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Episode transcript
We always hear the word “metadata”, usually in a sentence that goes like this

Your Honor, I swear, we were not collecting users data, just metadata.

Usually the guy saying this sentence is Zuckerberg, but could be anybody from Amazon or Google. “Just” metadata, so no problem. This is one of the biggest lies about the reality of data collection.

F: Ok the first question is, what the hell is metadata?

Metadata is data about data.

F: Ok… still not clear.
Imagine you make a phone call to your mum. How often do you call your mum, Francesco?

F: Every day of course! (coughing)

Good boy! Ok, so let’s talk about today’s phone call. Let’s call “data” the stuff that you and your mum actually said. What did you talk about?

F: She was giving me the recipe for her famous lasagna.
So your mum’s lasagna is the DATA. What is the metadata of this phone call? The lasagna has data of its own attached to it: the date and time when the conversation happened, the duration of the call, the unique hardware identifiers of your phone and your mum’s phone, the identifiers of the two sim cards, the location of the cell towers that pinged the call, the GPS coordinates of the phones themselves.

F: yeah well, this lasagna comes with a lot of data :)
And this is assuming that this data is not linked to any other data like your Facebook account or your web browsing history. More of that later.

F: Whoa Whoa Whoa, ok. Let’s put a pin in that. Going back to the “basic” metadata that you describe. I think we understand the concept of data about data. I am sure you did your research and you would love to paint me a dystopian nightmare, as always. Tell us why is this a big deal?

Metadata is a very big deal. In fact, metadata is far more “useful” than the actual data, where by “useful” I mean that it allows a third party to learn about you and your whole life. What I am saying is, the fact that you talk with your mum every day for 15 minutes is telling me more about you than the content of the actual conversations. In a way, the content does not matter. Only the metadata matters.

F: Ok, can you explain this point a bit more?

Imagine this scenario: you work in an office in Brussels, and you go by car. Every day, you use your time in the car while you go home to call your mum. So every day around 6pm, a cell tower along the path from your office to your home pings a call from your phone to your mum’s phone. Someone who is looking at your metadata, knows exactly where you are while you call your mum. Every day you will talk about something different, and it doesn't really matter. Your location will come through loud and clear. A lot of additional information can be deduced from this too: for example, you are moving along a motorway, therefore you have a car. The metadata of a call to mum now becomes information on where you are at 6pm, and the way you travel.

F: I see. So metadata about the phone call is, in fact, real data about me.

Exactly. YOU are what is interesting, not your mum’s lasagna.

F: you say so because you haven’t tried my mum’s lasagna. But I totally get your point.

Now, imagine that one day, instead of going straight home, you decide to go somewhere else. Maybe you are secretly looking for another job. Your metadata is recording the fact that after work you visit the offices of a rival company. Maybe you are a journalist and you visit your anonymous source. Your metadata records wherever you go, and one of these places is your secret meeting with your source. Anyone’s metadata can be combined with yours. There will be someone who was with you at the time and place of your secret meeting. Anyone who comes in contact with you can be tagged and monitored. Now their anonymity has been reduced.

F: I get it. So, compared to the content of my conversation, its metadata contains more actionable information. And this is the most useful, and most precious, kind of information about me. What I do, what I like, who I am, beyond the particular conversation.

Precisely. If companies like Facebook or the phone companies had the explicit permission to collect all the users’ data, including all content of conversations, it’s still the metadata that would generate the most actionable information. They would probably throw the content of conversations away. In the vast majority of instances, the content does not matter. Unless you are an actual spy talking about state secrets, nobody cares.

F: Let’s stay on the spy point for a minute. One could say, So what? As I have heard this many times. So what if my metadata contains actionable information, and there are entities that collect it. If I am an honest person, I have nothing to hide.

There are two aspects to the problem of privacy. Government surveillance, and corporate - in other words private - surveillance.
Government surveillance is a topic that has been covered flawlessly by Edward Snowden in his book “Permanent Record”, and in the documentary about his activity, “Citizenfour”. Which I both recommend, and in fact I think every data scientist should read and watch.
Let’s just briefly mention the obvious: just because something comes from a government, it does not mean it’s legal or legitimate, or even ethical or moral. What if your government is corrupt, or authoritarian. What if you are a dissident and you are fighting for human rights. What if you are a journalist, trying to uncover government corruption.

F: In other words, it is a false equivalence to say that protecting your privacy has anything to do with having something to hide.

Mass surveillance of private citizens without cause is a danger to individual freedom as well as civil liberties. Government exists to serve its citizens, not the other way around. To freely paraphrase Snowden, as individuals have no power compared to the government, the only way the system works is if the government is completely transparent to the citizens, so that they can collectively change it, and at the same time the single citizens are opaque to the government, so that it cannot abuse its power. But today the opposite happens: we citizens are completely naked and exposed in front of a completely opaque government machine, with secret surveillance programs on us, that we don’t even know exist. We are not free to self-determine, or do anything about government power, really.

F: We could really talk for days and days about government mass surveillance. But let’s go back to metadata, and let’s talk about the commercial use of it. Metadata for sale. You mentioned this term, “corporate surveillance”. It sounds…. Ominous.

We live in privacy hell, Francesco.

F: I get that. According to your research, where can we find metadata?

First of all, metadata is everywhere. We are swimming in it. In each and every interaction between two people, that make use of digital technology, metadata is generated automatically, without the user’s consent. When two people interact, two machines also interact, recording the “context” of this interaction. Who we are, when, where, why, what we want.

F: And that doesn’t seem avoidable. In fact metadata must be generated by devices and software to just work properly. I look at it as an intrinsic component that cannot be removed from the communication system, whatever it is. The problem is who owns it. So tell me, who has such data?

It does not matter, because it’s all for sale. Which means, we are for sale.

F: Ok, holy s**t, this keeps getting darker. Let’s have a practical example, shall we?

Have you booked a flight recently?

F: Yep. I’m going to Berlin, and in fact so are you. For a hackathon, no less.

Have you ever heard of a company called Adara?

F: No… Cannot say that I have.

Adara is a “Predictive Traveler Intelligence” company.

F: sounds pretty pretentious. Kinda douchy.

This came up on the terrifying twitter account of Wolfie Christl, author among other things of a great report about corporate surveillance for Cracked Labs. Go check him out on twitter, he’s great.

F: Sure I will add what I find to the show notes of this episode. Oh and by the way you can find all this stuff on datascienceathome.com
Sorry go ahead.

Adara collects data - metadata - about travel-related online searches, purchases, devices, passenger records, loyalty program records. Data from clients that include major airlines, major airports, hotel chains and car rental chains. It creates a profile, a “traveler graph” in real time, for 750 million people around the world. A profile based on personal identifiers.

F: uhh uhh Then what?

Then Adara sells these profiles.

F: Ok… I have to say, the box that I tick giving consent to the third-party use of my personal data when I use an airline website does not quite convey how far my data actually goes.

Consent. LOL. Adara calculates a: “traveler value score” based on customer behaviour and needs across the global travel ecosystem, over time.

The score is in the Salesforce Service Cloud, for sale to anyone.
This score, and your profile, determine the personalisation of travel offers and treatment, before purchase, during booking, post purchase, at check in, in airport, at destination.
In their own website, Adara explains how customer service agents for their myriad of clients - for example a front desk agent at a hotel - can instantly see the Traveler value score. Therefore they will treat you differently based on this score.

F: Oh so if you have money to spend they will treat you differently

The score is used to assess your potential value, to inform service and customer service strategies for you, as well as personalised messaging and relevant offers. And of course, the pricing you see when you look for flights. Low score? Prepare yourself to wait to have your call rerouted to a customer service agent. Would you ever tick a box to give consent to this?

F: Fuck no. How is this even legal? What about the GDPR?

It is, in fact, illegal. Adara is based in the US, but they collect data through data warehouses in the Netherlands. They claim they are GDPR-compliant. However, they collect all the data, and then decide on the specific business use, which is definitely not GDPR compliant.

F: exactly! According to GDPR the user has to know in advance what the business use of the data they are giving consent for!!
With GDPR and future regulations, there is a way to control how the data is used and with what purpose. Regulations are still blurred or undefined when it comes to metadata. For example, there’s no regulation for the number of records in a database or the timestamp when such record was created. As a matter of fact data is useless without metadata.

One cannot even collect data without metadata.

Whatsapp, telegram, Facebook messenger... they all create metadata. So one might say “I’ve got end-to-end encryption, buddy”. Sure thing. How about the metadata attached to that encrypted gibberish nobody is really interested in? To show you how unavoidable the concept of metadata is, even Signal developed by the Signal Foundation which is considered the truly end-to-end and open source protocol for confidential information exchange, can see metadata. At Signal they claim they just don’t keep it, as they also state in the Signal’s privacy policy.

"Certain information (e.g. a recipient's identifier, an encrypted message body, etc.) is transmitted to us solely for the purpose of placing calls or transmitting messages. Unless otherwise stated below, this information is only kept as long as necessary to place each call or transmit each message, and is not used for any other purpose."
This is one of those issues that shall be solved with legislation.

But like money laundering, your data is caught in a storm of transactions so intricate that at a certain point, how do you even check...
All participating companies share customer data with each other (a process called value exchange). They let marketers utilize the data, for example to target people after they have searched for flights or hotels. Adara creates audience segments and sells them, for example to Google, for advertisement targeting. The consumer data broker LiveRamp for example lists Adara as a data provider.

F: consumer data broker. I am starting to get what you mean when you say that we are for sale.

Let’s talk about LiveRamp, part of Acxiom.

F: there they go... Acxiom... I heard of them

They self-describe as an “Identity Resolution Platform”.

F: I mean, George Orwell would be proud.

Their mission? “To connect offline data and online data back to a single identifier”. In other words, clients can “resolve all” of their “offline and online identifiers back to the individual consumer”.
Various digital profiles, like the ones generated on social media or when you visit a website, are matched to databases which contains names, postal addresses, email addresses, phone numbers, geo locations and IP addresses, online and mobile identifiers, such as cookie and device IDs.

F: well, all this stuff is possible if and only if someone gets in possession of all these profiles, or well... they purchase them. Still, what the f**k.

A cute example? Imagine you register on any random website but you don’t want to give them your home address. They just buy it from LiveRamp, which gets it from your phone geolocation data - which is for sale. Where does your phone sit still for 12 hours every night? That’s your home address. Easy.

F: And they definitely know how much time do I spend at the gym, without even checking my Instagram! Ok this is another level of creepy.

Clients of LiveRamp can upload their own consumer data to the platform, combine it with data from hundreds of 100 third-party data providers, and then utilize it on more than 500 marketing technology platforms. They can use this data to find and target people with specific characteristics, to recognize and track consumers across devices and platforms, to profile and categorize them, to personalize content for them, and to measure how they behave. For example, clients could “recognize a website visitor” and “provide a customized offer” based on extensive profile data, without requiring said user to log in to the website. Furthermore, LiveRamp has a data store, for other companies to “buy and sell valuable customer data”.

F: What is even the point of giving me the choice to consent to anything online?

In short, there is no point.

F: it seems we are so behind with regulations on data sharing. GDPR is not cutting it, not really. With programmatic advertising we have created a monster that has really grown out of control.
So: our lives are completely transparent to private corporations, that constantly surveil us en-masse, and exploit all of our data to sell us shit. How does this affect our freedom? How about we just don’t buy it? Can it be that simple? And I would not take a no for an answer here.

Unfortunately, no.

F: oh crap!

I’m going to read you a passage from Permanent Record:

Who among us can predict the future? Who would dare to?
The answer to the first question is no one, really, and the answer to the second is everyone, especially every government and business on the planet. This is what that data of ours is used for. Algorithms analyze it for patterns of established behaviour in order to extrapolate behaviours to come, a type of digital prophecy that’s only slightly more accurate that analog methods like palm reading. Once you go digging into the actual technical mechanisms by which predictability is calculated, you come to understand that its science is, in fact, anti-scientific, and fatally misnamed: predictability is actually manipulation.

A website that tells you that because you liked book 1 then you might also like book 2, isn’t offering an educated guess as much as a mechanism of subtle coercion. We can’t allow ourselves to be used in this way, to be used against the future. We can’t permit our data to be used to sell us the very things that must not be sold, such as journalism. [....]
We can’t let the god-like surveillance we’re under be used to “calculate” our citizenship scores, or to “predict” our criminal activity; to tell us what kind of education we can have, or what kind of job we can have [...], to discriminate against us based on our financial, legal, and medical histories, not to mention our ethnicity or race, which are constructs that data often assumes or imposes.

[...] if we allow [our data] to be used to identify us, then it will be used to victimize us, even to modify us - to remake the very essence of our humanity in the image of the technology that seeks its control. Of course, all of the above has already happened.

F: In other words, we are surveilled and our data collected, and used to affect every aspect of our lives - what we read, what movies we watch, where we travel, what we buy, who we date, what we study, where we work… This is a self-fulfilling prophecy for all of humanity, and the prophet is a stupid, imperfect algorithm optimised just to make money.
So I guess my message of today for all Data Scientists out there is this: just… don't.

References
https://github.com/signalapp
Wolfie Christl report https://crackedlabs.org/en/corporate-surveillance
wolfie.crackedlabs.org
23 min
December 11, 2019The dark side of AI: recommend and manipulate (Ep. 90)
In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the different groups of audiences participating in the debate, and found an alignment of the groups with the political left and political right, as well as clear alignments with groups participating in other debates, like environmental issues, abortion issues and so on. In simple terms, someone who is pro-environment, pro-abortion, left-leaning, is also supportive of the Black Lives Matter movement, and viceversa.
F: Ok, this seems to make sense, right? But… I suspect there is more to this story?
So far, yes…. What they did not expect to find, though, was a pervasive network of Russian accounts participating in the debate, which turned out to be orchestrated by the Internet Research Agency, the not-so-secret Russian secret service agency of internet black ops. The same connected with the US election and Brexit referendum, allegedly.
F: Are we talking about actual spies? Where are you going with this?
Basically, the Russian accounts (part of them human and part of them bots) were infiltrating all aspects of the debate, both on the left and on the right side, and always taking the most extreme stances on any particular aspect of the debate. The aim was to radicalise the conversation, to make it more and more extreme, in a tactic of divide-and-conquer: turn the population against itself in an online civil war, push for policies that normally would be considered too extreme (for instance, give tanks to the police to control riots, force a curfew, try to ban Muslims from your country). Chaos and unrest have repercussions on international trade and relations, and can align to foreign interests.
F: It seems like a pretty indirect and convoluted way of influencing a foreign power…
You might think so, but you are forgetting social media. This sort of operation is directly exploiting a core feature of internet social media platforms. And that feature, I am afraid, is recommender systems.
F: Whoa. Let’s take a step back. Let’s recap the general features of recommender systems, so we are on the same page.
The main purpose of recommender systems is to recommend people the same items similar people show an interest in.
Let’s think about books and readers. The general idea is to find a way to predict the best book to the best reader. Amazon is doing it, Netflix is doing it, probably the bookstore down the road does that too, just on a smaller scale.
Some of the most common methods to implement recommender systems, use concepts such as cosine/correlation similarity, matrix factorization, neural autoencoders and sequence predictors.
The major issue of recommender systems is in their validation. Even though validation occurs in a way that is similar to many machine learning methods, one should recommend a set of items first (in production) and measure the efficacy of such a recommendation. But, recommending is already altering the entire scenario, a bit in the flavour of the Heisenberg principle of uncertainty.
F: In the attention economy, the business model is to monetise the time the user spends on a platform, by showing them ads. Recommender systems are crucial for this purpose.
Chiara, you are saying that these algorithms have effects that are problematic?
As you say, recommender systems exist because the business model of social media platforms is to monetise attention. The most effective way to keep users’ attention is to show them stuff they could show an interest in.
In order to do that, one must segment the audience to find the best content for each user. But then, for each user, how do you keep them engaged, and make them consume more content?
F: You’re going to say the word “filter bubble” very soon.
Spot on. To keep the user on the platform, you start by showing them content that they are interested in, and that agrees with their opinion.
But that is not all. How many videos of the same stuff can you watch, how many articles can you read? You must also escalate the content that the user sees, increasing the wow factor. The content goes from mild to extreme (conspiracy theories, hate speech etc).
The recommended content pushes the user opinion towards more extreme stances. It is hard to see from inside the bubble, but a simple experiment will show it. If you continue to click the first recommended video on YouTube, and you follow the chain of first recommended videos, soon you will find yourself watching stuff you’d never have actively looked for, like conspiracy theories, or alt-right propaganda (or pranks that get progressively more cruel, videos by people committing suicide, and so on).
F: So you are saying that this is not an accident: is this the basis of the optimisation of the recommender system?
Yes, and it’s very effective. But obviously there are consequences.
F: And I’m guessing they are not good.
The collective result of single users being pushed toward more radical stances is a radicalisation of the whole conversation, the disappearance of nuances in the argument, the trivialisation of complex issues. For example, the Brexit debate in 2016 was about trade deals and custom unions, and now it is about remain vs no deal, with almost nothing in between.
F: Yes, the conversation is getting stupider. Is this just a giant accident? Just a sensible system that got out of control?
Yes and no. Recommender systems originate as a tool for boosting commercial revenue, by selling more products. But applied to social media, they have caused an aberration: the recommendation of information, which leads to the so-called filter bubbles, the rise of fake news and disinformation, and the manipulation of the masses.
There is an intense debate in the scientific community about the polarising effects of the internet and social media on the population. An example of such study is a paper by Johnson et al. It predicts that whether and how a population becomes polarised is dictated by the nature of the underlying competition, rather than the validity of the information that individuals receive or their online bubbles.
F: I would like to stress on this finding. This is really f*cked up. Polarisation is not caused by the particular subject nor the way a debate is conducted. But by how legitimate the information seems to the single person. Which means that if I find a way to convince the single individuals about something, I will be in fact manipulating the debate at a community scale or, in some cases, globally!
Oh my god we seem to be so f*cked.
Take for instance the people who believe that the Earth is flat. Or the time it took people to recognise global warming as scientific, despite the fact that, the threshold for scientific confirmation was reached decades ago.
F: So, recommender systems let loose on social media platforms amplify controversy and conflict, and fringe opinions. I know I’m not going to like the answer, but I’m going to ask the question anyway.
This is all just an innocent mistake, right?
Last year, the European Data Protection Supervisor has published a report on online manipulation at scale.
F: That does not sound good.
The online digital ecosystem has connected people across the world with over 50% of the population on the Internet, albeit very unevenly in terms of geography, wealth and gender. The initial optimism about the potential of internet tools and social media for civic engagement has given way to concern that people are being manipulated. This happens through the combination of constant harvesting of often intimate information about them, and the control over the information they see online according to the category they are put into (so called segmentation of the audience). Arguably since 2016, but probably before, mass manipulation at scale has occurred during democratic elections. By using algorithms to game recommender systems, among other things, to spread misinformation. Remember Cambridge Analytica?
F: I remember. I wish I didn’t. But why does it work? Are we so easy to manipulate?
An interesting point is this. When one receives information collectively, as for example from the television news, it is far less likely that she develops extreme views (like, the Earth is flat), because she would base the discourse on a common understanding of reality. And people call out each other’s bulls*it.
F: Fair enough.

But when one receives information singularly, like what happens via a recommender system through micro-targeting, then reality has a different manifestation for each audience member, with no common ground. It is far more likely to adopt extreme views, because there is no way to fact check, and because the news feel personal. In fact, they tailor such news are to the users to push their buttons.
Francesco, if you show me George Clooney shirtless and holding a puppy, and George tells me that the Earth is flat, I might have doubts for a minute. Too personal?
F: That’s good to know about you. I’m more of a cat person. But, experts keep saying that we are moving towards personalisation of everything. While this makes sense for things like personalised medicine, it probably is not that beneficial with many other kinds of recommendations. Especially not the news.
But social media feeds are extremely personalised. What can we do?
Solutions have focused on transparency measures, exposing the source of information while neglecting the accountability of players in the ecosystem who profit from harmful behaviour. But these are band aids on bullet wounds.
The problem is the social media platforms. In October 2019 Zuckerberg was in front of congress again, because Facebook refuses to fact-check political advertisements, in 2019, after everything that’s happened. At the same time market concentration and the rise of platform dominance threatens media pluralism. This in turn, is leading to repeat and amplify a handful of news pieces and to silence independent journalism.
F: When I think of a recommender system, I think of Netflix.
You liked this kind of show in the past, so here are more shows of the same genre
People like you have liked this other type of show. Hence, here it is for your consideration
This seems relatively benign. Although, if you think some more, you realise that this mechanism will prevent you from actually discovering anything new. It just gives you more of what you are likely to like. But one would not think that this would have world-changing consequences.
If you think of the news, this mechanism becomes lethal: in the mildest form – which is already bad – you will only hear opinions that already align with those of your own peer group. In the worst scenario, you will not hear some news at all, or you will hear a misleading or false version of the news, and you don’t even know that a different version exists.
In the Brexit referendum, misleading or false content (like the famous NHS money that supposedly was going to the EU instead) has been amplified in filter bubbles. Each bubble of people was essentially understanding a different version of the same issue. Brexit was a million different things, depending on your social media feeds.
And of course, there are malicious players in the game, like the russian Internet Research Agency and Cambridge Analytica, who actively exploited this features in order to swing the vote.
F: Even the traditional media is starting to adopt recommender systems for the news content. This seems like a very bad idea, after all. Is there any other scenario in which recommender systems are not great?
Researchers use recommender systems in a variety of applications.
For instance, in the job market. A recommender system limits exposure to certain information about jobs on the basis of the person’s gender or inferred health status, and therefore it perpetuates discriminatory attitudes and practices. In the US, researchers use recommender systems to calculate the bail fee for people who have been arrested, disproportionately penalising people of colour. This has to do with the training of the algorithm. In an already unequal system (where for instance there are few women in top managerial positions, and more African-Americans in jail than white Americans) a recommender system will by design amplify such inequality.
F: Recommender systems are part of the problem, and they make everything worse. But the origin of the problem lies somewhere else, I suspect.
Yep. The problem with recommender systems goes even deeper. I would rather connect it to the problem of privacy. A recommender system only works if it knows its audience. They are so powerful, because they know everything about us.
We don’t have any privacy anymore. Online players know exactly who we are, our lives are transparent to both corporations and governments. For an excellent analysis of this, read Snowden’s book “Permanent Record”. I highly recommend it.
F: The pun was intended wasn’t it?
With all this information about us, we are put into “categories” for specific purposes: selling us products, influencing our vote. They target us with ads aimed at our specific category, and this generates more discussion and more content on our social media. Recommender systems amplify the targeting by design. They would be much less effective, and much less dangerous, in a world where our lives are private.
F: Social media platforms base their whole business model in “knowing us”. The business model itself is problematic.
As we said in the previous episode, the internet has become centralised, with a handful of platforms controlling most of the traffic. In some countries like Myanmar, internet access itself is provided and controlled by Facebook.
F: Chiara, where’s Myanmar?
In South-East Asia, between India and Thailand.
In effect, the forum for public discourse and the available space for freedom of speech is now bounded by the profit motives of powerful private companies. Due to technical complexity or on the grounds of commercial secrecy, such companies decline to explain how decisions are made. Mostly, they make decisions via recommender algorithms, which amplify bias and segregation. And at the same time the few major platforms with their extraordinary reach offer an easy target for people seeking to use the system for malicious ends.
Conclusion
This is our call to all data scientists out there. Be aware of personalisation in building recommender systems. Personalising is not always beneficial. There are a few cases where it is, e.g. medicine, genetics, drug discovery. Many other cases where it is detrimental e.g. news, consumer products/services, opinions.
Personalisation by algorithm, and in particular of the news, leads to a fragmentation of reality that undermines democracy. Collectively we need to push for reigning in targeted advertising, and the path to this leads to more strict rules on privacy. As long as we are completely transparent to commercial and governmental players, like we are today, we are vulnerable to lies, misdirection and manipulation.
As Christopher Wylie (the Cambridge Analytica whistleblower) eloquently said, it’s like going on a date, where you know nothing about the other person, but they know absolutely everything about you.
We are left without agency, and without real choice.
In other words, we are f*cked
References
Black lives matter / Internet Research Agency (IRA) articles:
http://faculty.washington.edu/kstarbi/Stewart_Starbird_Drawing_the_Lines_of_Contention-final.pdf
https://medium.com/s/story/the-trolls-within-how-russian-information-operations-infiltrated-online-communities-691fb969b9e4
https://medium.com/s/story/the-trolls-within-how-russian-information-operations-infiltrated-online-communities-691fb969b9e4
https://faculty.washington.edu/kstarbi/BLM-IRA-Camera-Ready.pdf
IRA tactics:
https://int.nyt.com/data/documenthelper/533-read-report-internet-research-agency/7871ea6d5b7bedafbf19/optimized/full.pdf#page=1
https://int.nyt.com/data/documenthelper/534-oxford-russia-internet-research-agency/c6588b4a7b940c551c38/optimized/full.pdf#page=1
EDPS report
https://edps.europa.eu/sites/edp/files/publication/18-03-19_online_manipulation_en.pdf
Johnson et al. “Population polarization dynamics and next-generation social media algorithms” https://arxiv.org/abs/1712.06009
21 min
December 03, 2019 The dark side of AI: social media and the optimization of addiction (Ep. 89)
Chamath Palihapitiya, former Vice President of User Growth at Facebook, was giving a talk at Stanford University, when he said this:
“I feel tremendous guilt. The short-term, dopamine-driven feedback loops that we have created are destroying how society works ”.
He was referring to how social media platforms leverage our neurological build-up in the same way slot machines and cocaine do, to keep us using their products as much as possible. They turn us into addicts.

F: how many times do you check your Facebook in a day?
I am not a fan of Facebook. I do not have it on my phone. Still, I check it in the morning on my laptop, and maybe twice more per day. I have a trick though: I do not scroll down. I only check the top bar to see if someone has invited me to an event, or contacted me directly. But from time to time, this resolution of mine slips, and I catch myself scrolling down, without even realising it!

F: is it the first thing you check when you wake up?
No because usually I have a message from you!! :) But yes, while I have my coffee I do a sweep on Facebook and twitter and maybe Instagram, plus the news.

F: Check how much time you spend on Facebook
And then sum it up to your email, twitter, reddit, youtube, instagram, etc. (all viable channels for ads to reach you)
We have an answer. More on that later.
Clearly in this episode there is some form of addiction we would like to talk about. So let’s start from the beginning: how does addiction work?
Dopamine is a hormone produced by our body, and in the brain it works as a neurotransmitter, a chemical that neurons use to transmit signals to each other. One of the main functions of dopamine is to shape the “reward-motivated behaviour”: this is the way our brain learns through association, positive reinforcement, incentives, and positively-valenced emotions, in particular, pleasure. In other words, it makes our brain desire more of the things that make us feel good. These things can be for example good food, sex, and crucially, good social interactions, like hugging your friends or your baby, or having a laugh together. Because we are evolved to be social animals with complex social structures, successful social interactions are an evolutionary advantage, and therefore they trigger dopamine release in our brain, which makes us feel good, and reinforces the association between the action and the reward. This feeling motivates us to repeat the behaviour.

F: now that you mention reinforcement, I recall that this mechanism is so powerful and effective that in fact we have been inspired by nature and replicated it in-silico with reinforcement learning. The idea is to motivate (and eventually create an addictive pattern) an agent to follow what is called the optimal policy by giving it positive rewards or punishing it when things don’t go the way we planned.
In our brain, every time an action produces a reward, the connection between action and reward becomes stronger. Through reinforcement, a baby learns to distinguish a cat from a dog, or that fire hurts (that was me).

F: and so this means that all the social interactions people get from social media platforms are in fact doing the same, right?
Yes, but with a difference: smartphones in our pockets keep us connected to an unlimited reserve of constant social interactions. This constant flux of notifications - the rewards - flood our brain with dopamine. The mechanism of reinforcement can spin out of control. The reward pathways in our brain can malfunction, and this leads to addiction.

F: you are saying that social media has LITERALLY the effect of a drug?
Yes. In fact, social media platforms are DESIGNED to exploit the rewards systems in our brain. They are designed to work like a drug.
Have you been to a casino and played roulette or the slot machines?

F: ...maybe?
Why is it fun to play roulette? The fun comes from the WAIT before the reward. You put a chip on a number, you don’t know how it’s going to go. You wait for the ball to spin, you get excited. And from time to time, BAM! Your number comes out. Now, compare this with posting something on facebook. You write a message into the void, wait…. And then the LIKES start coming in.

F: yeah i find that familiar...
Contrary to the casino, social media platforms do not want our money, in fact they are free. What they want is, and what we are buying into with, is our time. Because the longer we stay on, the longer they can show us ads, and the more money advertisers can pay them. This is no accident, this is the business model. But asking for our time out loud would not work, we would probably not consciously give it to them. So, like a casino, they make it hard for us to get off, once we are on: they make us crave the likes, the right-swipes, the retweets, the subscriptions. So we check in, we stay on, we keep scrolling, because we hope to get those rewards. The short-term satisfaction of getting a “like” is a little boost of dopamine in our brain. We get used to it, and we want more.

F: a lot of machine learning is also being deployed to amplify this form of addiction and make it.... Well more addictive :) But the question is: how such powerful ads and scenarios are so effective because of the algorithms and how much just because humans are just wired to obey such dynamics? My question is: are we essentially flawed or are these algorithms truly powerful?
It is not a flaw, it’s a feature. The way our brain has evolved has been in response to very specific needs. In particular for this conversation, our brain is wired to favour social interactions, because it is an evolutionary advantage. These algorithms exploit these features of the brain on purpose, they are designed to exploit them.

F: I believe so, but I also believe that the human brain is a powerful machine, so it should be able to predict what satisfaction it can get from social media. So how does it happen that we become addicted?
An example of optimisation strategy that social media platforms use is based on the principle of “reward prediction error coding”. Our brain learns to find patterns in data - this is a basic survival skill - and therefore learns when to expect a reward for a given set of actions. I eat cake, therefore I am happy. Every time.
Imagine a scenario, where we have learnt through experience that when we play slot machines in a casino, we learn that we win some money once every 100 times we pull the lever. The difference between predicted and received rewards is a known, fixed quantity. If so, just after winning once, we have almost zero incentive to play again. So the casino fixes the slot machines, to introduce a random element to the timing of the reward. Suddenly our prediction error increases substantially. In this margin of error, in the time between the action (pull the lever) and the reward (maybe) our brain has time to make us anticipate the result and make us excited at the possibility, and this releases dopamine. Playing in itself becomes a reward.

F: There is an equivalent in reinforcement learning called the grid world which consists in a mouse getting to the cheese in a maze. In reinforcement learning, everything works smooth as long as the cheese stays in the same place.
Exactly! Now social media apps implement an equivalent trick, called “variable reward schedules”.
In our brain, after an action we get a reward or punishment, and we generate positive or negative feedback to that action.
Social media apps optimise their algorithms for the ideal balance of negative and positive feedback in our brains caused by the difference between these predicted and received rewards.
If we perceive a reward to be delivered at random, and - crucially - if checking for the reward comes at little cost, like opening the Facebook app, we end up checking for rewards all the time. Every time we are just a little bit bored, without even thinking, we check the app. The Facebook reward system (the schedule and triggers of notification and likes) has been optimised to maximise this behaviour.

F: are you saying that buffering some likes and then finding the right moment to show them to the user can make the user crave for reward?
Oh yes. Instagram will withhold likes for a period of time, causing a dip in reward compared to the expected level. It will then deliver them later in larger bundles, thus boosting the reward above the expected value, which trigger extra dopamine release, which sends us on a high akin to a cocaine hit.

F: Dear audience, do you remember my question? How much time do each of you spend on social media (or similar) in a day? And why do we still do it?
The fundamental feature here is how little is the perceived cost to check for the reward: I just need to open the app. We perceive this cost to be minimal, so we don’t even think about it. YouTube for instance had the autoplay feature, so you need to do absolutely nothing to remain on the app. But the cost is cumulative over time, it becomes hours in our day, days in a month, years in our lives!! 2 hours of social media per day amounts to 1 month per year.

F: But it’s so EASY, it has become so natural to use social media for everything. To use Google for everything.
The convenience that the platforms give us is one of the most dangerous things about them, and not only for our individual life. The convenience of reaching so many users, together with the business model of monetising attention is one of the causes of the centralisation of the internet, i.e. the fact a few giant platforms control most of the internet traffic. Revenue from ads is concentrated on big platforms, and content creators have no other choice but to use them, if they want to be competitive. The internet went from looking like a distributed network to a centralised network. And this in turn causes data to be centralised, in a self-reinforcing loop. Most of human conversations and interactions pass through the servers of a handful of private corporations.

Conclusion
As Data scientists we should be aware of this (and we think mostly we are). We should also be ethically responsible. I think that being a data scientist no longer has a neutral connotation. Algorithms have this huge power of manipulating human behaviour, and let’s be honest, we are the only ones who really understand how they work. So we have a responsibility here.
There are some organisations, like Data For Democracy for example, who are advocating for something equivalent to the Hippocratic Oath for data scientists. Do no harm.

References
Dopamine reward prediction error coding https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4826767/
Skinner - Operant Conditioning https://www.simplypsychology.org/operant-conditioning.html
Dopamine, Smartphones & You: A battle for your time http://sitn.hms.harvard.edu/flash/2018/dopamine-smartphones-battle-time/
Reward system https://en.wikipedia.org/wiki/Reward_system
Data for democracy datafordemocracy.org
23 min
November 27, 2019More powerful deep learning with transformers (Ep. 84) (Rebroadcast)
Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture.
Such architecture is built on top of another important concept already known to the community: self-attention.
In this episode I explain what these mechanisms are, how they work and why they are so powerful.
Don't forget to subscribe to our Newsletter or join the discussion on our Discord server

References
Attention is all you need
https://arxiv.org/abs/1706.03762
The illustrated transformer
https://jalammar.github.io/illustrated-transformer
Self-attention for generative models
http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture14-transformers.pdf
38 min
November 18, 2019 How to improve the stability of training a GAN (Ep. 88)
Generative Adversarial Networks or GANs are very powerful tools to generate data. However, training a GAN is not easy. More specifically, GANs suffer of three major issues such as instability of the training procedure, mode collapse and vanishing gradients.

In this episode I not only explain the most challenging issues one would encounter while designing and training Generative Adversarial Networks. But also some methods and architectures to mitigate them. In addition I elucidate the three specific strategies that researchers are considering to improve the accuracy and the reliability of GANs.

The most tedious issues of GANs

Convergence to equilibrium

A typical GAN is formed by at least two networks: a generator G and a discriminator D. The generator's task is to generate samples from random noise. In turn, the discriminator has to learn to distinguish fake samples from real ones. While it is theoretically possible that generators and discriminators converge to a Nash Equilibrium (at which both networks are in their optimal state), reaching such equilibrium is not easy.

Vanishing gradients

Moreover, a very accurate discriminator would push the loss function towards lower and lower values. This in turn, might cause the gradient to vanish and the entire network to stop learning completely.

Mode collapse

Another phenomenon that is easy to observe when dealing with GANs is mode collapse. That is the incapability of the model to generate diverse samples. This in turn, leads to generated data that are more and more similar to the previous ones. Hence, the entire generated dataset would be just concentrated around a particular statistical value.

The solution

Researchers have taken into consideration several approaches to overcome such issues. They have been playing with architectural changes, different loss functions and game theory.

Listen to the full episode to know more about the most effective strategies to build GANs that are reliable and robust.
Don't forget to join the conversation on our new Discord channel. See you there!

29 min
November 12, 2019What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)
What happens to a neural network trained with random data?
Are massive neural networks just lookup tables or do they truly learn something?
Today’s episode will be about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski from New York University.
Stan spent two summers as a visiting student with Prof. Yoshua Bengio and has been working on
Understanding and improving how deep network generalise
Representation Learning
Natural Language Processing
Computer Aided Drug Design

What makes deep learning unique?
I have asked him a few questions for which I was looking for an answer for a long time. For instance, what is deep learning bringing to the table that other methods don’t or are not capable of?
Stan believe that the one thing that makes deep learning special is representation learning. All the other competing methods, be it kernel machines, or random forests, do not have this capability. Moreover, optimisation (SGD) lies at the heart of representation learning in the sense that it allows finding good representations.

What really improves the training quality of a neural network?
We discussed about the accuracy of neural networks depending pretty much on how good the Stochastic Gradient Descent method is at finding minima of the loss function. What would influence such minima?
Stan's answer has revealed that training set accuracy or loss value is not that interesting actually. It is relatively easy to overfit data (i.e. achieve the lowest loss possible), provided a large enough network, and a large enough computational budget. However, shape of the minima, or performance on validation sets are in a quite fascinating way influenced by optimisation.
Optimisation in the beginning of the trajectory, steers such trajectory towards minima of certain properties that go much further than just training accuracy.
As always we spoke about the future of AI and the role deep learning will play.
I hope you enjoy the show!
Don't forget to join the conversation on our new Discord channel. See you there!

References

Homepage of Stanisław Jastrzębski https://kudkudak.github.io/
A Closer Look at Memorization in Deep Networks https://arxiv.org/abs/1706.05394
Three Factors Influencing Minima in SGD https://arxiv.org/abs/1711.04623
Don't Decay the Learning Rate, Increase the Batch Size https://arxiv.org/abs/1711.00489
Stiffness: A New Perspective on Generalization in Neural Networks https://arxiv.org/abs/1901.09491
20 min