Illustration by Belkin et al. (2018) of the effect of increased model complexity on generalization: traditional belief (a) vs actual practice (b). Traditional wisdom in machine learning holds that there is a careful trade-off between training error and generalization gap. There is a "sweet spot" for the model complexity such that the model (i) is big enough to achieve reasonably good training error, and (ii) is small enough so that the generalization gap – the difference between test error and training error – can be controlled. A smaller model would give a larger training error while making the model bigger would result in a larger generalization gap, both leading to larger test errors. This is described by the classical U-shaped curve for the test error when the model complexity varies (see Figure 1(a)).

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. The media is rife with stories that warn of AI algorithms bringing people back from the dead, AI algorithms developing secret languages, mass technological unemployment, and a looming robot apocalypse. Movies and TV series like Her, The Circle and Westworld, which present a mystic portrayal of conscious machines and human-level AI being just around the corner. Rebooting AI is a refreshing read and a much-needed reality check on the current confusing state of artificial intelligence. Consider the following text, mentioned in Rebooting AI: "Elsie tried to reach her aunt on the phone, but she didn't answer." You don't need to be a genius to quickly make the following assumptions after reading this sentence: But even the most sophisticated AI algorithm would struggle to draw the same conclusions.

We're excited to kick off the W&B Deep Learning Salon! The idea behind the DL salon is simple - we're building some wicked cool projects with some wicked cool people. Please bring a deep learning project that you're excited about (paper to reproduce, Github repo, Kaggle competition, personal project). You'll have the chance to work on it with a community of like-minded deep learning folks, get help, answer others' questions, and learn together! There'll be pizza, an amazing crew and a guest speaker!

ODSC is the best community data science event on the planet. There are other events that cover special topics, or industries, etc., but ODSC is comprehensive and totally community-focused: it's the conference to engage, to build, to develop, and to learn from the whole data science community. ODSC West 2019 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC West 2019 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field.

AI is constantly in the news these days, identifying prospects for the technology doing both good and bad. One topic that's generating a lot of buzz is the use of AI for creating "deepfakes," a term originally coined in 2017. Deepfakes uses neural networks to combine and superimpose existing images and videos onto source images or videos using a deep learning technique known as generative adversarial networks (GANs). Three of the most common deepfakes techniques are known as "lip-sync," "face swap," and "puppet-master." These techniques, however, can create a disconnect that may be uncovered by a clever algorithm as a way to combat deepfakes.

A study co-written by a Southwest Research Institute scientist describes a new algorithm that combines the capabilities of two spacecraft instruments, which could result in lower cost and higher efficiency space missions. The virtual "super instrument," is a computer algorithm that utilizes deep learning to analyze ultraviolet images of the Sun, taken by NASA's Solar Dynamics Observatory, and measure the energy that the Sun emits as ultraviolet light. "Deep learning is an emerging capability that is revolutionizing the way we interact with data," said Dr. Andrés Muñoz-Jaramillo, senior research scientist at SwRI. Muñoz-Jaramillo co-authored the study, published this month in Science Advances, alongside collaborators from nine other institutions as part of NASA's Frontier Development Laboratory. The laboratory is an applied artificial intelligence research accelerator that applies deep learning and machine learning techniques to challenges in space science and exploration.

Deep Genomics, the Toronto-based AI therapeutics startup, has made the first-ever discovery of a diseases treatment and drug candidate using artificial intelligence. The startup announced on Wednesday that its propriety AI-based drug discovery platform has identified a novel treatment target and corresponding drug candidate for Wilson disease, a rare and potentially life-threatening genetic disorder. "Our AI systems can figure out how diseases are caused and how to fix those diseases much more rapidly than humans ever could." "This is the amazing accomplishment for the team," Brendan Frey, founder and CEO of Deep Genomics, told BetaKit. He noted that part of Deep Genomic's goal is to "help everyone in the world" use the discovery and technology its developing in Canada to support discovery and development more broadly.

OpenAI topped Bert with its own AI tool dubbed GPT2, which mimicked the writing style of humans with high accuracy. It used an astonishing 1.5 billion parameters to train its model. To address the issues of heavy resource utilization & climate toll, researchers have been actively pursuing an option where they can shrink these AI's in size, thus not only reducing their resource-intensive nature but at the same time making them more efficient. Two new research papers released recently, have come up with models that might be able to accomplish this. The first one is from researchers at Huawei Noah's Ark Lab called TinyBERT (figure below).

That said, I am really confused by OpenAI's PR strategy for GPT-2; they push it heavily as the future of text writing, but all of their GPT-2 related repos are archived w/o any updates expected.

Companies around the world are investing tens of billions of dollars on artificial intelligence (AI) and machine learning (ML), and for good reason. These technologies have real business-altering potential, and that's why Gartner's "Enter the Age of Analytics" report predicts that by 2023, AI and deep-learning techniques will be the two most common approaches for new applications of data science. Effective use of both AI and ML in business production use cases can help enterprises that use them jump far ahead of competitors in their sectors, because the technologies remove friction that gums up processes. But despite this promise, few companies have been able to successfully implement and deploy this technology as part of their overall data and analytics strategy. According to Gartner, 46 percent of CIOs have developed plans to deploy AI, but just 4 percent have made the concept a reality.