Algorithms, Microcontent, and the Vanishing Distinction between Platforms and Creators
Authors: Liu Leqi, Dylan Hadfield-Menell, and Zachary C. Lipton
To appear in Communications of the ACM (CACM) and available on arXiv.org.
Ever since social activity on the Internet began migrating from the wilds of the open web to the walled gardens erected by so-called platforms (think Myspace, Facebook, Twitter, YouTube, or TikTok), debates have raged about the responsibilities that these platforms ought to bear. And yet, despite intense scrutiny from the news media and grassroots movements of outraged users, platforms continue to operate, from a legal standpoint, on the friendliest terms.
You might say that today’s platforms enjoy a “have your cake, eat it too, and here’s a side of ice cream” deal. They simultaneously benefit from: (1) broad discretion to organize (and censor) content however they choose; (2) powerful algorithms for curating a practically limitless supply of user-posted microcontent according to whatever ends they wish; and (3) absolution from almost any liability associated with that content.
This favorable regulatory environment results from the current legal framework, which distinguishes between intermediaries (e.g., platforms) and content providers. This distinction is ill-adapted to the modern social media landscape, where platforms deploy powerful data-driven algorithms (so-called AI) to play an increasingly active role in shaping what people see and where users supply disconnected bits of raw content (tweets, photos, etc.) as fodder.
While COVID has negatively impacted many sectors, bringing the global economy to its knees, one sector has not only survived but thrived: Data Science. If anything, the current pandemic has only scaled up demand for data scientists, as the world’s leaders scramble to make sense of the exponentially expanding data streams generated by the pandemic.
“These days the data scientist is king. But extracting true business value from data requires a unique combination of technical skills, mathematical know-how, storytelling, and intuition.” 1
According to Gartner’s 2020 report on AI✝, 63% of the United States labor force has either (i) already transitioned; or (ii) is actively transitioning; towards a career in data science. However, the same report shows that only 5% of this cohort eventually lands their dream job in Data Science.
We interviewed top executives in Big Data, Machine Learning, Deep Learning, and Artificial General Intelligence; and distilled these 5 tips to guarantee success in Data Science.2
Whether you are speaking to corporate managers, Silicon Valley script kiddies, or seasoned academics pitching commercial applications of their research, you’re likely to hear a lot of claims about what AI is going to do.
Hysterical discussions about AI machine learning’s applicability begin with a breathless recap of breakthroughs in predictive modeling (9X.XX% accuracy on ImageNet!, 5.XX% word error rate on speech recognition!) and then abruptly leap to prophesies of miraculous technologies that AI will drive in the near future: automated surgeons, human-level virtual assistants, robo-software development, AI-based legal services.
This sleight of hand elides a key question—when are accurate predictions sufficient for guiding actions?
Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible.
What sort of papers best serve their readers? We can enumerate desirable characteristics: these papers should (i) provide intuition to aid the reader’s understanding, but clearly distinguish it from stronger conclusions supported by evidence; (ii) describe empirical investigations that consider and rule out alternative hypotheses ; (iii) make clear the relationship between theoretical analysis and intuitive or empirical claims ; and (iv) use language to empower the reader, choosing terminology to avoid misleading or unproven connotations, collisions with other definitions, or conflation with other related but distinct concepts .
Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship:
Failure to distinguish between explanation and speculation.
Failure to identify the sources of empirical gains, e.g. emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning.
Mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g. by confusing technical and non-technical concepts.
Misuse of language, e.g. by choosing terms of art with colloquial connotations or by overloading established technical terms.
Last week, I flew from London to Tel Aviv. The man sitting to my right was a road warrior, just this side of a late-night bender in London. He was rocking an ostentatious pair of headphones and a pair of pants ripped wide apart at both knees. Perhaps a D.J.? At some point, circumstances emerged for us to commiserate over the experience of flying on Easyjet (not the easiest). Soon after, we stumbled through the obligatory airplane smalltalk: Where are you going? What do you do?
Turns out I was flying next to the CEO of an AI+Blockchain startup.
It’s always a bit surreal when I learn of entrepreneurs combining AI with blockchain technology. For the past few years, whenever I found my myself bored among Silicon Valley socialites, this was my go-to satirical startup. What do you do? Startup CEO. What does your startup do? Deep learning on the blockchain… in The Cloud. Whoa.Continue reading “The Blockchain Bubble will Pop, What Next?”
Before committing all future posts to the coming revolution, or abandoning the blog altogether to beseech good favor from our AI overlords atthe AI church, perhaps we should ask, why are today’s headlines, startups and even academic institutions suddenly all embracing the term artificial intelligence (AI)?
In this blog post, I hope to prod all stakeholders (researchers, entrepreneurs, venture capitalists, journalists, think-fluencers, and casual observers alike) to ask the following questions:
What substantive transformation does this switch in the nomenclature from machine learning (ML) to artificial intelligence (AI) signal?
If the research hasn’t categorically changed, then why are we rebranding it?
What are the dangers, to both scholarship and society, of mindlessly shifting the way we talk about research to maximize buzz?
In 2014, Szegedy et al. published an ICLR paper with a surprising discovery: modern deep neural networks trained for image classification exhibit the following vulnerability: by making only slight alterations to an input image, it’s possible to drastically fool a model that would otherwise classify the image correctly (say, as a dog), into outputting a completely wrong label (say, as a banana). Moreover, this attack is possible even with perturbations that are so tiny that a human couldn’t distinguish the altered image from the original.
These doctored images are called adversarial examples and the study of how to make neural networks robust to these attacks is an increasingly active area of machine learning research.
It’s January 28th and I should be working on my paper submissions. So should you! But why write when we can meta-write? ICML deadlines loom only twelve days away. And KDD follows shortly after. The schedule hardly lets up there, with ACL, COLT, ECML, UAI, and NIPS all approaching before the summer break. Thousands of papers will be submitted to each.
The tremendous surge of interest in machine learning along with ML’s democratization due to open source software, YouTube coursework, and the availability of preprint articles are all exciting happenings. But every rose has a thorn. Of the thousands of papers that hit the arXiv in the coming month, many will be unreadable. Poor writing will damn some to rejection while others will fail to reach their potential impact. Even among accepted and influential papers, careless writing will sow confusion and damn some papers to later criticism for sloppy scholarship (you better hope Ali Rahimi and Ben Recht don’t win another test of time award!).
But wait, there’s hope! Your technical writing doesn’t have to stink. Over the course of my academic career, I’ve formed strong opinions about how to write a paper (as with all opinions, you may disagree). While one-liners can be trite, I learned early in my PhD from Charles Elkan that many important heuristics for scientific paper writing can be summed up in snappy maxims. These days, as I work with younger students, teaching them how to write clear scientific prose, I find myself repeating these one-liners, and occasionally inventing new ones.
The following list consists of easy-to-memorize dictates, each with a short explanation. Some address language, some address positioning, and others address aesthetics. Most are just heuristics so take each with a grain of salt, especially when they come into conflict. But if you’re going to violate one of them, have a good reason. This can be a living document, if you have some gems, please leave a comment.
Consider a little science experiment we’ve all done, to find out if a switch controls a light. How many data points does it usually take to convince you? Not many! Even if you didn’t do a randomized trial yourself, and observed somebody else manipulating the switch you’d figure it out pretty quickly. This type of science is easy!
One thing that makes this easy is that you already know the right level of abstraction for the problem: what a switch is, and what a bulb is. You also have some prior knowledge, e.g. that switches typically have two states, and that it often controls things like lights. What if the data you had was actually a million variables, representing the state of every atom in the switch, or in the room?