An Imminent Threat from Artificial Intelligence | Aidan Gomez | TEDxOxford

i'm sure that by now you've heard a wealth of op-eds and talking heads herald the rise of artificial intelligence and along with that you've probably heard a cascade of risks that we're going to face risks from ai becoming some nightmarish immortal dictator that we'll never escape from to philosophical questions surrounding agi and super intelligence to the end of work as a product of automation and fully autonomous weaponry the media has tended to focus on these mid to long-term threats while in my view neglecting an extremely imminent if not already in process risk the risk that i'm concerned with is one affecting our well-being our mental health our beliefs about the world in this talk what i want to do is i want to get us all on a common ground concerning what we mean when we say artificial intelligence where we're interacting with artificial intelligence today and the kinds of risks that arise from that interaction so to begin with what is artificial intelligence well this contemporary wave of progress in the field has really been fueled by a subfield of a subfield called deep learning and neural networks a neural network is just a program consisting of a set of neurons which are wired together at one end of the network will feed in inputs at the other end of the network we'll receive outputs so on that input side we can feed in any sort of data for instance we can feed in pictures and then we can ask the question is a baby in this image or is it not in the example on the screen you can see that the network's making a mistake it's claiming that there's no baby in an image which clearly contains a baby so that brings us to the second point of using neural networks teaching them training them so what we do is we simply let the neural network know you're making a mistake and then we step back we let the neural network reconfigure itself in some way in the example on the screen you'll see removing connections between the neurons and as a product of this change the correction occurs the neural network is now expressing the correct function so there's this two-step procedure you specify some network some objective rather you ask it to do something and then you step back and you let the neural network come to its own solution its own solution strategy to solve the problem you posed okay so that's a bit about how neural networks work where are you interacting with them today so there's broadly two categories which you can kind of lump your interactions into the first are these companies that want you to buy some product the second are the companies that collaborate with the first category to keep you on platform and serve you ads i'm going to focus on the second of these categories these social medias these content delivery platforms this is where we are we spend an immense amount of our time we spend hours of our day interacting with neural networks so how do neural networks play into these platforms well on something like youtube the primary method of navigating the site is this sidebar which contains recommendations for the next video you should watch this is completely determined by a neural network on a platform like twitter your home feed this aggregation point of all the accounts that you follow is being filtered and re-ranked according to neural networks similarly on instagram those posts that you're presented with the order in which they arrive is decided by a neural network okay so this is where we're kind of interacting with these models what's the risk well we know that the content you consume has a massive impact on your well-being it affects your belief about the world your mental health and there's this famous now infamous study conducted by researchers at facebook and cornell which seeks to address the extent to which emotion contagion exists within digital social networks so emotion contagion it's this idea that when we're presented with other humans expressing an emotion we ourselves will begin to experience that emotion interestingly you don't necessarily have the ability to recognize that emotion is external to you you you believe that it's authentically your own and so what these researchers did is without informing users they manipulated the news feeds of seven hundred thousand accounts the way they manipulated these news feeds was to bias them towards more positive or more negative content and then they would record how those manipulated users own posts changed in sentiment what they found was that there was an overwhelming positive correlation with that manipulation so if i go and i serve you a bunch of negative content your own expressions your own communications will become negative similarly for positive content so i mentioned that this study was infamous and it's infamous on the level of there are some strong questions about the ethics of the conduct of the study and there's also some questions about facebook's chosen conclusions to draw from the results but the work stands as an extremely compelling piece of evidence for the hypothesis that simply by manipulating the content that you consume you can have immense impact on the well-being the mental states the beliefs of your downstream users okay so i hope that the pieces are starting to fall into place for how this current setup this current framework that we exist in has risk the content that we consume has massive impact on our well-being and yet we've outsourced that role to ai to neural networks okay so let's take a step back and let's think about how we train neural networks so again we specify some objective we ask the network to do something and then we let the algorithm arrive at its own solution well what's the objective that we're specifying for these content serving networks they broadly fall into two categories the first is engagement engagement is the probability that you'll interact with a piece of content i serve you maybe you'll like it you'll comment on it you'll reshare it to your friends the second category is time and so how can i serve you content in order to keep you on platform which will give me the company more opportunities to serve you ads which means more opportunities to make money so i want to talk about this second category time this seems like an immensely risky choice of objective to optimize humans have this natural pathology of addiction and we're explicitly optimizing for them to spend time on one thing and remember that neural networks they're going to take whatever strategy they come up with that's most effective at achieving their goals and if our goal is time we really need to ask the question what if addicting enraging depressing our users is the most effective strategy to keep them on site engaged engrossed with the content i think that's a very plausible hypothesis and so the next question we can ask is okay well what sort of frameworks exist at the moment to keep track of this risk or even to mitigate it unfortunately very few but there's an extremely simple and familiar framework that we can rely on in order to kind of defend against these risks to begin with consider your total user pool of your service on facebook we're talking about more than two billion users now separate a small baseline pool and call that call that your baseline pool and this baseline pool what's going to happen is there's going to be no interaction with your model so for these users in this baseline set they're not going to have their feeds ranked by a neural network it's going to be very simple straightforward algorithms like sequential serving okay the next thing we can do is we can track these metrics of well-being on both pools and we can compare them these metrics are being tracked in real time continuously so when we see these metrics begin to diverge when some gap appears we can take active measures to try to minimize that gap or reduce it these active measures might look something like reducing the impact a model has on users experience of the site so this framework it's it's incredibly general it could be applied to pretty much any content serving platform or social media that exists today but there's still two core issues or questions that i have with the setup the first is well what's a metric of well-being you know choosing metrics is an incredibly difficult thing i really don't think that computer scientists should be trusted with coming up with the fundamental metric of well-being we're the ones who came up with time in the first place so where i think this is going to come from is a cross-disciplinary collaboration between computer scientists and statisticians and ethicists and philosophers and social scientists i think that this kind of this issue is approachable and we can make concrete steps towards solving it so that's the first issue i see with this framework the second question i have is one that i don't have an answer to and i'm going to leave you with today so when i was talking about taking some active measure in order to close that gap between the metrics i was talking about population level dynamics so we're tracking these metrics on aggregate we're tracking among all the model facing users and among all the baseline users and then when those two pools shift in some way we're taking an action across all users to try to correct for that but that's not the only option we could track metrics on a per user basis so we could watch each individual user we could see how their metrics begin to shift from some baseline or from perhaps their historical average and then we could take a per user action to push them in a different direction i see a massive potential for good in this framework you can imagine a suicidal user if our metrics can pick up on entry into a depressive episode we could take steps to try to mitigate risk consider the fact that suicidal humans are about 0.1 percent of the population if our intervention is only one percent effective at the scale of facebook billions of users we're talking about tens of thousands of lives impacted so massive potential for good but consider what we have to accept if we build frameworks for this we're allowing companies to track our mental state and then actively encouraging them to manipulate it it's kind of this huxley and nightmare so i see both the potential for massive good and massive risk i don't know where the balance falls but i think that points to another major problem there's not a public discussion surrounding these issues these systems these content serving systems they've existed for well over a decade now and yet there's no regulation there's no discussion of the types of checks and balances we'd expect to find in the technology of this maturity if there's anything i want you to take away from this talk it's a call to action to speak to your family your friends your teachers your politicians about these issues build an opinion through discourse your opinions are sorely needed and we the ones implementing this technology we desperately want to hear them thank you so much for your time [Applause]

Channel: TEDx Talks

Views: 88,473

Rating: 4.8265414 out of 5

Keywords: TEDxTalks, English, Social Science, AI, Future, Technology

Id: s0rwZXt-C04

Length: 13min 23sec (803 seconds)

Published: Thu Nov 12 2020

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