If you’re not living under a rock, then you’ve surely encountered the Heroes of Deep Learning, an inspiring, diverse band of Deep Learning all-stars whose sheer grit, determination, and—[dare we say?]—genius, catalyzed the earth-shaking revolution that has brought to market such technological marvels as DeepFakes, GPT-7, and Gary Marcus.
But these are no ordinary times. And as the world contends with a rampaging virus, incendiary wildfires, and smouldering social unrest, no ordinary heroes will suffice. However, you needn’t fear. Hope has returned to the Machine Learning Universe, and boy, oh boy the timing couldn’t be better.
As confirmed to us by several independent witnesses, the sun, moon, and stars have been joined in the night’s sky by new, supernatural, sights. After a months-long meticulous investigation, including consultations with NASA, MI6, and Singularity University, we can confirm the presence, on Earth, of the Superheroes of Deep Learning!Continue reading “Hope Returns to the Machine Learning Universe”
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.” 1Geoff Hinton
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.2Continue reading “5 Habits of Highly Effective Data Scientists”
On Thursday, OpenAI announced that they had trained a language model. They used a large training dataset and showed that the resulting model was useful for downstream tasks where training data is scarce. They announced the new model with a puffy press release, complete with this animation (below) featuring dancing text. They demonstrated that their model could produce realistic-looking text and warned that they would be keeping the dataset, code, and model weights private. The world promptly lost its mind.
For reference, language models assign probabilities to sequences of words. Typically, they express this probability via the chain rule as the product of probabilities of each word, conditioned on that word’s antecedents Alternatively, one could train a language model backwards, predicting each previous word given its successors. After training a language model, one typically either 1) uses it to generate text by iteratively decoding from left to right, or 2) fine-tunes it to some downstream supervised learning task.
Training large neural network language models and subsequently applying them to downstream tasks has become an all-consuming pursuit that describes a devouring share of the research in contemporary natural language processing.
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?
What is a conference? Common definitions provide only a vague sketch: “a meeting of two or more persons for discussing matters of common concern” (Merriam Webster a); “a usually formal interchange of views” (Merriam-Webster b); “a formal meeting for discussion” (Google a).
What qualifies as a meeting? Are all congregations of people in all places conferences? How formal must it be? Must the borders be agreed upon? Does it require a designated name? What counts as a discussion? How many discussions can fit in one conference? Does a sufficiently formal meeting held within the allotted times and assigned premises of a larger, longer conference constitute a sub-conference?
Absent context, the word verges on vacuous. And yet in professional contexts, e.g., among computer science academics, culture endows precise meaning. Google also offers a more colloquial definitions that cuts closer:
With paper submissions rocketing and the pool of experienced researchers stagnant, machine learning conferences, backs to the wall, have made the inevitable choice to inflate the ranks of peer reviewers, in the hopes that a fortified pool might handle the onslaught.
With nearly every professor and senior grad student already reviewing at capacity, conference organizers have gotten creative, finding reviewers in unlikely places. Reached for comment, ICLR’s program chairs declined to reveal their strategy for scouting out untapped reviewing talent, indicating that these trade secrets might be exploited by rivals NeurIPS and ICML. Fortunately, on condition of anonymity, several (less senior) ICLR officials agreed to discuss a few unusual sources they’ve tapped:
- All of /r/machinelearning
- Twitter users who follow @ylecun
- Holders of registered .ai & .ml domains
- Commenters from ML articles posted to Hacker News
- YouTube commenters on Siraj Raval deep learning rap videos
- Employees of entities registered as owners of .ai & .ml domains
- Everyone camped within 4° of Andrej Karpathy at Burning Man
- GitHub handles forking TensorFlow, Pytorch, or MXNet in last 6 mos.
- A joint venture with Udacity to make reviewing for ICLR a course project for their Intro to Deep Learning class
By Zachary C. Lipton* & Jacob Steinhardt*
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?”
Artificial intelligence is transforming the way we work (Venture Beat), turning all of us into hyper-productive business centaurs (The Next Web). Artificial intelligence will merge with human brains to transform the way we think (The Verge). Artificial intelligence is the new electricity (Andrew Ng). Within five years, artificial intelligence will be behind your every decision (Ginni Rometty of IBM via Computer World ).
Before committing all future posts to the coming revolution, or abandoning the blog altogether to beseech good favor from our AI overlords at the 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?