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”
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
Three weeks ago, New York Times reporter Cade Metz sent shockwaves through society with a startling announcement that A.I. researchers were making more than $1 Million dollars, even at a nonprofit!
Within hours, I received multiple emails. Parents, friends, old classmates, my girlfriend all sent emails. Did you see the article? Maybe they wanted me to know what riches a life in private industry had in store for me? Perhaps they were curious if I was already bathing in Cristal, shopping for yachts, or planning to purchase an atoll among the Maldives? Perhaps the communist sympathizers in my social circles had renewed admiration for my abstention from such extreme opulence.
In a shocking tweet, organizers of the 35th International Conference on Machine Learning (ICML 2018) announced today, through an official Twitter account, that this year’s conference has sold out. The announcement came as a surprise owing to the timing. Slated to occur in July, 2018, the conference has historically been attended by professors and graduate student authors, who attend primarily to present their research to audience of peers. With the submission deadline set for February 9th and registrations already closed, it remains unclear if and how authors of accepted papers might attend.
It’s about time someone developed an anime series about deep learning. In the last several years, I’ve paid close attention to deep learning. And while I’m far from an expert on anime, I’ve watched a nonzero number of anime cartoons. And yet through neither route did I encounter even one single anime about deep learning.
There were some close calls. Ghost in the Shell gives a vague pretense of addressing AI. But the character might as well be a body-jumping alien. Nothing in this story speaks to the reality of machine learning research.
In Knights of Sidonia, if you can muster the superhuman endurance required to follow the series past its only interesting season, you’ll eventually find out that the flying space-ship made out of remnants of Earth on which Tanikaze and friends photosynthesize, while taking breaks from fighting space monsters, while wearing space-faring versions of mecha suits … [breath] contains an artificially intelligent brain-emulating parasitic nematode. But no serious consideration of ML appears.
If you were looking to anime for a critical discourse on artificial intelligence, until recently you’d be disappointed.
In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer.
This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous bisecting convolutions. By the year’s end, Alphabet executives expect that these neural networks will exhibit fully autonomous self-improvement. What comes next may affect us all.