Collaborating Authors


The Impact Of Artificial Intelligence On Influencer Marketing


In October 2017, Facebook altered the Instagram API to make it harder for users to search its giant database of photos. The change was a small element of the company's response to the Cambridge Analytica scandal, but it was a significant problem for parts of the digital marketing industry. Not long before, New York-based influencer marketing agency Amra & Elma had developed a platform that ingested data from Instagram, and allowed its client to use AI image classifiers to find very specific influencers. For instance, they could find an influencer with, say, between 10,000 and 50,000 followers who had posted photos of themselves in a Jeep. Facebook's move killed this capability in a keystroke.

Advances in Collaborative Filtering and Ranking Machine Learning

In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE, which plays an important role in preventing our personalized model from overfitting to the training data; chapter 7, we summarize what we have achieved so far and predict what the future directions can be; chapter 8 is the appendix to all the chapters.

The Matrix Conspiracy updates (The Matrix Dictionary)


With my concept of The Matrix Conspiracy I put myself in the risk of being accused of being a paranoid conspiracy theorist. This is not the case. I m just making aware of that there exists a conspiracy theory which is called The Matrix Conspiracy, and that this conspiracy in fact is a global spreading ideology. My critique is in that way ideology critique, or cultural critique. The concept of the Matrix comes from mathematics, but is more popular known from the movie the Matrix, which asks the question whether we might live in a computer simulation. In The Matrix though, there is also an evil demon, or evil demons, namely the machines which keep the humans in tanks linked to black cable wires that stimulates the virtual reality of the Matrix. Doing this the machines can use the human bodies as batteries that supply the machines with energy. It is the fascination of the virtual reality that deceives the humans. The philosophy behind the movie comes from especially two philosophers: Rene Descartes and George Berkeley. Descartes was very dubious concerning how much we can trust our senses. Therefore he took up the question Is life a dream? However, his intention with this was in his Meditations to develop a confident cognition-argument. In his Meditations Descartes presents the problem approximately like this: I frequently dream during the night, and while I dream, I am convinced, that what I dream is real. But then it always happens, that I wake up and realize, that everything I dreamt was not real, but only an illusion. And then is it I think: is it possible, that what I now, while I am awake, believe is real, also is something, which only is being dreamt by me right now? If it is not the case, how shall I then determinate it? Precisely because Descartes not even in dreams can doubt, that 2 plus 3 is 5, he leaves the dream-argument in his Meditations and goes in tackle with the question, whether he could be cheated by an evil demon concerning all cognition, also the mathematics. This radical skepticism leads him forward to the cogito-argument: Cogito ergo Sum (I think, therefore I exist). But he didn t deny the existence of the external world. The external world he described in a way that resembles what would later be known as modern natural sciences. In the view of nature in natural science, nature is reduced to atomic particles, empty space, fields, electromagnetic waves and particles etc., etc. I have called this the instrumental view of nature. Berkeley is famous for the sentence Esse est percipi, which means that being, or reality, consists in being percepted (to be is to be experienced). The absurdity in Berkeley s assertion is swiftly seen: If a thing, or a human being for that matter, is not being perceived by the senses, then it does not exist. In accordance with Berkeley there therefore does not exist any sense-independent world.

The 84 biggest flops, fails, and dead dreams of the decade in tech


The world never changes quite the way you expect. But at The Verge, we've had a front-row seat while technology has permeated every aspect of our lives over the past decade. Some of the resulting moments -- and gadgets -- arguably defined the decade and the world we live in now. But others we ate up with popcorn in hand, marveling at just how incredibly hard they flopped. This is the decade we learned that crowdfunded gadgets can be utter disasters, even if they don't outright steal your hard-earned cash. It's the decade of wearables, tablets, drones and burning batteries, and of ridiculous valuations for companies that were really good at hiding how little they actually had to offer. Here are 84 things that died hard, often hilariously, to bring us where we are today. Everyone was confused by Google's Nexus Q when it debuted in 2012, including The Verge -- which is probably why the bowling ball of a media streamer crashed and burned before it even came to market.

Artificial Intelligence for Social Good: A Survey Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.

8 life lessons everyone should learn before 2020


Anything you do online can come back to bite you. It's been a decade full of lessons: who to trust, when to speak out and how to stream big events online after you've broken up with your cable company. In 2010, the first iPhone was only three years old. Uber and Lyft didn't exist, and neither did Google Assistant and Siri, Instagram or streaming video. We've come a long way since then, but the next 10 years won't be easy.

Why we need an AI-resilient society Artificial Intelligence

Artificial intelligence is considered as a key technology. It has a huge impact on our society. Besides many positive effects, there are also some negative effects or threats. Some of these threats to society are well-known, e.g., weapons or killer robots. But there are also threats that are ignored. These unknown-knowns or blind spots affect privacy, and facilitate manipulation and mistaken identities. We cannot trust data, audio, video, and identities any more. Democracies are able to cope with known threats, the known-knowns. Transforming unknown-knowns to known-knowns is one important cornerstone of resilient societies. An AI-resilient society is able to transform threats caused by new AI tecchnologies such as generative adversarial networks. Resilience can be seen as a positive adaptation of these threats. We propose three strategies how this adaptation can be achieved: awareness, agreements, and red flags. This article accompanies the TEDx talk "Why we urgently need an AI-resilient society", see

19 examples of artificial intelligence shaking up business as usual


Examples of artificial intelligence (AI) in pop culture usually involve a pack of intelligent robots hell-bent on overthrowing the human race, or at least a fancy theme park. Sentient machines with general artificial intelligence don't yet exist, and they likely won't exist anytime soon, so we're safe... for now. That's not to make light of AI's potential impact on our future. In a recent survey, more than 72% of Americans expressed worry about a future in which machines perform many human jobs. Additionally, tech billionaire Elon Musk, long an advocate for the regulation of artificial intelligence, recently called AI more dangerous than nukes. Whether we realize it or not, artificial intelligence is all around us and playing an active role in our daily lives. Every time we open our Facebook newsfeed, do a Google search, get a product recommendation from Amazon or book a trip online, AI is lurking in the background.

Michael Kearns: Algorithmic Fairness, Privacy, and Ethics in Machine Learning AI Podcast


Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. This conversation is part of the Artificial Intelligence podcast. This episode is sponsored by Pessimists Archive podcast: OUTLINE: 0:00 - Introduction 2:45 - Influence from literature and journalism 7:39 - Are most people good?

From Elon Musk to Jeff Bezos, these 30 personalities defined the 2010s


The first decade of the 21st century introduced us to sweeping mobile and social revolutions largely driven by names like Jobs, Zuckerberg and Bezos. In the second decade that's now closing, things got a little more… complicated. During those years, a new collection of faces have joined the earlier tech titans to continue moving us into the future. A person wears a Guy Fawkes mask, which today is a trademark and symbol for the online hacktivist group Anonymous. More a decentralized collective than a personality, Anonymous was the name claimed by the loose affiliation of hackers who brought "hacktivism" into the mainstream. During the first half of the decade, Anonymous launched attacks against targets like ISIS, the governments of the US and Tunisia, and corporations such as Sony and PayPal.