Many in the AI art community took issue with Christie’s selecting Obvious because they felt there are so many other artists who have been working far longer in the medium and who are more technically and artistically accomplished, artists who have given back to the community and helped to expand the genre. Artists like Helena Sarin.
Sarin was born in Moscow and went to college for computer science at Moscow Civil Engineering University. She lived in Israel for several years and then settled in the US. While she has always worked in tech, she has moonlighted in the applied arts like fashion and food styling. She has played with marrying her interests in programming and art in the past, even taking a Processing class with Casey Reas, Processing felt a little too much like her day job as a developer. Then two years ago, she landed a gig with a transportation company doing deep learning for object recognition. She used CycleGAN to generate synthetic data sets for her client. Then a light went off and she decided to train CycleGAN with her own photography and artwork.
This is actually a pretty important distinction in AI art made with GANs.
GE Healthcare has unveiled its Edison healthcare artificial intelligence platform designed to connect data on millions of medical imaging devices.
The company said that clinical partners will be able to use Edison to develop algorithms and technology partners will be able to bring data processing advancements to Edison applications and smart devices.
A full 90 percent of healthcare data comes from imaging technology, yet 97 percent goes unanalyzed or unused, GE Healthcare said.
Some of the most critical work in advancing China’s technology goals takes place in a former cement factory in the middle of the country’s heartland, far from the aspiring Silicon Valleys of Beijing and Shenzhen. An idled concrete mixer still stands in the middle of the courtyard. Boxes of melamine dinnerware are stacked in a warehouse next door.
Inside, Hou Xiameng runs a company that helps artificial intelligence make sense of the world. Two dozen young people go through photos and videos, labeling just about everything they see. That’s a car. That’s a traffic light. That’s bread, that’s milk, that’s chocolate. That’s what it looks like when a person walks.
“I used to think the machines are geniuses,” Ms. Hou, 24, said. “Now I know we’re the reason for their genius.”
In China, long the world’s factory floor, a new generation of low-wage workers is assembling the foundations of the future. Start-ups in smaller, cheaper cities have sprung up to apply labels to China’s huge trove of images and surveillance footage. If China is the Saudi Arabia of data, as one expert says, these businesses are the refineries, turning raw data into the fuel that can power China’s A.I. ambitions.
CANARIE, a vital component of Canada’s digital infrastructure supporting research, education and innovation, today announced nine successful recipients of its Research Data Management (RDM) funding call, announced in May 2018. This new funding will enable research teams to develop software components and tools to enable Canadian researchers to adopt best practices in managing data resulting from scientific research.
Journal of Machine Learning Research, Nihar B. Shah et al.
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Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100% in terms of attendees as compared to the previous year. The massive scale as well as rapid growth of the conference calls for a thorough quality assessment of the peer-review process and novel means of improvement. In this paper, we analyze several aspects of the data collected during the review process, including an experiment investigating the efficacy of collecting ordinal rankings from reviewers. We make a number of key observations, provide suggestions that may be useful for subsequent conferences, and discuss open problems towards the goal of improving peer review.
A new working model between industry and academia is needed, one in which stable, long-term industry-academic partnerships enable continued AI advancement while preserving our society’s capacity to conduct fundamental research and train future generations of AI experts.
In a long-term partnership, academic and industry researchers must work collaboratively as equals, rather than industry merely sponsoring research or pulling faculty or students out of academia.
Instead of traditional top-down or single-organization decision-making, successful partnerships should be guided by more inclusive decision-making approaches – for example, through joint committees, with equal representation of academic and industry members, each of whom feels a strong responsibility to the collaboration and to the advancement of AI.
We believe our MIT-IBM Watson AI Lab collaboration offers a new model for engaging between academia and industry. Below are five key advantages to such a model, and an explanation of why it’s the surest path to transformational progress in AI research.
A few months ago, we announced our AI Principles, a set of commitments we are upholding to guide our work in artificial intelligence (AI) going forward. Along with our AI Principles, we shared a set of recommended practices to help the larger community design and build responsible AI systems.
In particular, one of our AI Principles speaks to the importance of recognizing that AI algorithms and datasets are the product of the environment—and, as such, we need to be conscious of any potential unfair outcomes generated by an AI system and the risk it poses across cultures and societies. A recommended practice here for practitioners is to understand the limitations of their algorithm and datasets—but this is a problem that is far from solved.
To help practitioners take on the challenge of building fairer and more inclusive AI systems, we developed a short, self-study training module on fairness in machine learning.
Craig talks to Julian Togelius, perhaps the most prolific researcher at the intersection of video games and artificial intelligence. Julian works on AI for games and games for AI. Some of his most significant work is in training deep neural networks to play video games and generalize what they have learned, a critical step toward artificial general intelligence. [audio, 27:07]
As we look at the future of AI, machine learning (AI/ML), and autonomous systems — and their increasingly outsized role across businesses, public agencies, educational institutions, and society broadly — we should be aware that models are trained on our past behaviors, not on our values or ideals. For instance, say an airline creates an algorithm to upgrade passengers trained on past upgrades given by gate agents. We could view those agents’ choices as an expression of the company values. While the airline doesn’t have an explicit value of a certain type of customer, perhaps gate agents’ unconscious bias led to more affluent passengers getting this perk.
Computer chips work behind the scenes, powering your internet surfing, video streaming, and gaming. Because they’re just little bits of hardware, the engineers that make them rarely spend time thinking about what they should look like. After all, most people will never even see them.
Yet the prominent design agency Pentagram and industrial design studio Map Project Office recently created a downright gorgeous computer chip for the the U.K.-based startup Graphcore. Why bother? Because as so much of our computing is moving into the cloud, Graphcore’s chips, which are designed to run machine learning algorithms, will mostly be located in server farms. And the company, with help from Pentagram and Map, is betting on design to help it stand out on the server rack–even if the only people that ever see the chips are the engineers who maintain the servers.
Amazon said on Tuesday that it’s working with partners in health care on a project called Amazon Comprehend Medical, which involves analyzing medical data.
The company said its machine learning tool meets federal privacy requirements.
Amazon has a growing interest in health care, including in areas like medical records and drug supply chain.
San Francisco, CA starting at 6 p.m., swissnex San Francisco (Pier 17). Panel discussion with Erin Rapacki, Cynthia Yeung, Tessa Lau,
Marta Bulaich. [free, registration required]
“Lawrence Livermore National Laboratory is offering data science graduate students and advanced undergraduate students at UC Berkeley the opportunity to join the LLNL Data Science Summer Institute (DSSI).” Students should apply before January 1, 2019.
Lately, friends at companies of all sizes and industries have been asking me the same question, “How do I apply machine learning to my business?” These folks know enough to have a sense of good use cases for machine learning. Where everyone gets stuck is actually making it work, hiring people, and making them successful.
I’ll outline my three main approaches depending on the size of your business.
“Facebook AI Research (FAIR) and the New York University (NYU) School of Medicine’s Center for Advanced Imaging Innovation and Research (CAI2R) announced today they are sharing a standardized set of AI tools and baselines and MRI data as part of their joint research project fastMRI. The aim is to leverage AI-driven image reconstruction to achieve a tenfold reduction in MRI scan times. This is the first large-scale MRI data set of its kind, and is expected to serve as a benchmark for future research in the field.”
“The mission is to increase the value and practice of open data science in environmental science by focusing on three things: 1) engaging researchers at scientific institutions around the world, 2) empowering them with existing open software and communities, and 3) amplifying their individual and cascading efforts and impact.”
QANTA (Question Aanswering is Not a Trivial Activity) is a question answering dataset composed of questions from Quizbowl – a trivia game that is challenging for both humans and machines.
