Google’s BERT and recent transformer-based methods have taken the NLP landscape by a storm, outperforming the state-of-the-art on several tasks. Lately, varying improvements over BERT have been shown — and here I will contrast the main similarities and differences so you can choose which one to use in your research or application. Read More
Monthly Archives: April 2020
Neuromorphic Chips: The Third Wave Of Artificial Intelligence
The age of traditional computers is reaching its limit. Without innovations taking place, it is difficult to move past the technology threshold. Hence it is necessary to bring major design transformation with improved performance that can change the way we view computers. The Moore’s law (named after Gordon Moore, in 1965) states that the number of transistors in a dense integrated circuit doubles about every two years while their price halves. But now the law is losing its validity. Hence hardware and software experts have come up with two solutions: Quantum Computing and Neuromorphic Computing. While quantum computing has made major strides, neuromorphic is still in its lab stage, until recently when Intel announced its neuromorphic chip, Loihi. This may indicate the third wave of Artificial Intelligence. Read More
#nvidiaMatt tries to ditch all his Zoom calls using AI
How You Can Tell If An AI Startup Is Bogus
Ask these six questions:
- What data sets did you use to train and evaluate your AI?
- What is a human doing now that your AI should be doing?
- Has the AI been used to drive consistent business outcomes and solve a real problem for multiple customers?
- How much time went into constructing the AI, how much field testing has it been put through and who has examined it and rendered an opinion on it?
- How easy is it to understand your AI’s decisions or recommendations?
- What kind of bias does the AI have and how is it mitigated?
Joint Artificial Intelligence Center to Train “AI Champions”
The Joint Artificial Intelligence Center in the Department of Defense will be training individuals to implement and champion the AI principles which the department adopted.
The center, known as JAIC, announced the creation of a cohort of “Responsible AI Champions” who will receive training on how to apply the department’s AI Ethical Principles in areas such as product design and development; testing and evaluation/verification and validation; and acquisition. Read More
Self-supervised learning is the future of AI
Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.
Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers. Read More
How we can build AI to help humans, not hurt us | Margaret Mitchell
Royal Dutch Shell reskills workers in artificial intelligence as part of huge energy transition
- Royal Dutch Shell is collaborating with Udacity to digitally train its workers in artificial intelligence.
- This began long before the coronavirus pandemic and the company continues to use this training method.
- The digital workforce skilling platform may become the training method of choice for a growing number of companies who need to keep employees up to speed in the weeks and months ahead.
State-Of-The-Art Reviewing: A Radical Proposal To Improve Scientific Publication
Peer review forms the backbone of modern scientific manuscript evaluation. But after two hundred and eighty-nine years of egalitarian service to the scientific community, does this protocol remain fit for purpose in 2020? In this work, we answer this question in the negative (strong reject, high confidence) and propose instead State-Of-the-Art Review (SOAR), a neoteric reviewing pipeline that serves as a “plug-and-play” replacement for peer review. At the heart of our approach is an interpretation of the review process as a multi-objective, massively distributed and extremely-high-latency optimisation, which we scalarise and solve efficiently for PAC and CMT-optimal solutions.
We make the following contributions: (1) We propose a highly scalable, fully automatic methodology for review, drawing inspiration from best-practices from premier computer vision and machine learning conferences; (2) We explore several instantiations of our approach and demonstrate that SOAR can be used to both review prints and pre-review pre-prints; (3) We wander listlessly in vain search of catharsis from our latest rounds of savage CVPR rejections. Read More.
Why It’s So Freaking Hard To Make A Good COVID-19 Model
Here we are, in the middle of a pandemic, staring out our living room windows like aquarium fish. The question on everybody’s minds: How bad will this really get? Followed quickly by: Seriously, how long am I going to have to live cooped up like this?
We all want answers. And, given the volume of research and data being collected about the novel coronavirus, it seems like answers ought to exist. Read More
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