Irakli Loladze is a mathematician by training, but he was in a biology lab when he encountered the puzzle that would change his life. It was in 1998, and Loladze was studying for his Ph.D. at Arizona State University. Against a backdrop of glass containers glowing with bright green algae, a biologist told Loladze and a half-dozen other graduate students that scientists had discovered something mysterious about zooplankton.
Zooplankton are microscopic animals that float in the world’s oceans and lakes, and for food they rely on algae, which are essentially tiny plants. Scientists found that they could make algae grow faster by shining more light onto them—increasing the food supply for the zooplankton, which should have flourished. But it didn’t work out that way. When the researchers shined more light on the algae, the algae grew faster, and the tiny animals had lots and lots to eat—but at a certain point they started struggling to survive. This was a paradox. More food should lead to more growth. How could more algae be a problem?
Loladze was technically in the math department, but he loved biology and couldn’t stop thinking about this. The biologists had an idea of what was going on: The increased light was making the algae grow faster, but they ended up containing fewer of the nutrients the zooplankton needed to thrive. By speeding up their growth, the researchers had essentially turned the algae into junk food. The zooplankton had plenty to eat, but their food was less nutritious, and so they were starving.
Apple on Tuesday said that the forthcoming top-of-the-line iPhone X smartphone will feature a chip custom-built for handling artificial intelligence workloads.
The dual-core “A11 bionic neural engine” chip can perform 600 billion operations per second, Apple executive Phil Schiller said at the inaugural launch event at the company’s new Apple Park headquarters in Cupertino, California.
The biggest thing the chip can do is enable fast face recognition for the Face ID authentication feature for unlocking and making purchases on the iPhone X.
AI as a basic technology should not be regulated. It also seems impractical for the government to stop you from implementing a neural network on your laptop. However, there’re applications of AI, for example autonomous driving, that need regulation. AI also has new implications on anti-trust (regulation of monopolies), that regulators have not yet thought through but should.
When Alteryx acquired Yhat in June, it was only a matter of time before the startup’s data-science management software began showing up in Alteryx. Just today, the company announced Alteryx Promote, a new tool based on Yhat’s product set.
Medicine has throughout history been a artisanal vocation — that is, it has focused on the skill and experience of the individual doctor, rather than looking to build a standardized process for diagnosing and treating patients. In recent years this has started to change, as initiatives like Evidence Based Medicine and Precision Medicine have tried to inject additional rigor and data-driven practices into the field. However, the vast majority of medical care is provided through the traditional Hippocratic philosophy.
This needs to change. The largest population centers on the planet have less than 1/10th of the doctors they need, and it will take hundreds of years to fill the gap. Misdiagnoses, late diagnoses, and over-diagnoses kill millions and cost tens of billions. The technology is now being developed to fix this problem — to give medical workers and patients a clear summary of the exact information they need, when they need it. Such technology can give a remote area community health worker access to a distillation of the world’s medical knowledge. It can make doctors in the developed world dramatically more productive and accurate , while giving patients and families more control over and insight into their medical care.
Geospatial imagery as well as facial recognition and other biometrics are driving the intelligence community’s research into artificial intelligence. Other intelligence activities, such as human language translation and event warning and forecasting, also stand to gain from advances being pursued in government, academic and industry research programs funded by the community’s research arm.
The Intelligence Advanced Research Projects Activity (IARPA) is working toward breakthroughs in artificial intelligence, or AI, through a number of research programs. All these AI programs tap expertise in government, industry or academia.
A molecular biologist by training and a biotechnologist by trade, Rando wanted to use simplified DNA testing technology to help people—religious Jewish or Muslim tourists eating at unfamiliar restaurants, he thought— tell whether their supposedly pork-less meal really was pork-free. Think of it as a pregnancy stick, but for pork.
In 2015, he brought the idea to a speed-dating style investor meeting event in Geneva, where he lives. “Gianpaolo stood there and he had this card and he said, ‘I want people to rub this in their food and wait 30 minutes and if there’s pork in it, don’t it eat it,’” Brij Sahi, one of the investors at the meeting, says now with a laugh. “I was intrigued … but nobody is going to wait a half hour to eat their food while its sitting in front of them getting cold!”
Rando’s idea missed the mark for a number of reasons; not only do people not want to wait around for the food to get cold before getting the all-clear to eat it, but also pork or no pork isn’t the only question diners with specialized dietary requirements have about what they’re eating. But the seed of an idea was there—what could a simplified, is-it-or-isnt-it DNA test with the ability to do for the food industry?
I’m just going to come right out and say it: The excitement over Apple’s new AI chip and facial-recognition security feature seems overblown. It’s just so … utilitarian.
Don’t get me wrong: Apple’s strategy is very smart from a UX perspective, assuming the Neural Engine gets a full workout. Use deep learning to power tasks like facial recognition (I’m not convinced this is a game-changer), NLP for text messages, image processing and, of course, Siri. In the process, save battery life by minimizing use of the main CPU and GPU, thus making users that much happier.
I am overwhelmingly OMG ecstatically bouncing for joy to announce that Janet Haven has agreed to become Data & Society’s first executive director. You can read more about Janet through the formal organizational announcement here. But since this is my blog and I’m telling my story, what I want to say is more personal. I was truly breaking when we hired Janet. I had taken off more than I could chew. I was hitting rock bottom and trying desperately to put on a strong face to support everyone else. As I see it, Janet came in, took one look at the duct tape upon which I’d built the organization and got to work with steel, concrete, and wood in her hands. She helped me see what could happen if we fixed this and that. And then she started helping me see new pathways for moving forward. Over the last 18 months, I’ve grown increasingly confident that what we’re doing makes sense and that we can build an organization that can last. I’ve also been in awe watching her enable others to shine.
I’m not leaving Data & Society. To the contrary, I’m actually taking on the role that my title – founder and president – signals. And I’m ecstatic. Over the last 4.5 years, I’ve learned what I’m good at and what I’m not, what excites me and what makes me want to stay in bed. I built Data & Society because I believe that it needs to exist in this world. But I also realize that I’m the classic founder – the crazy visionary that can kickstart insanity but who isn’t necessarily the right person to take an organization to the next stage. Lucky for me, Janet is. And together, I can’t wait to take Data & Society to the next level!
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The New York Times, The Upshot blog, Sendhil Mullainathan
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Liye Fu, Cristian Danescu-Niculescu-Mizil and Lillian Lee, three computer scientists at Cornell, built algorithms to find out whether such examples were isolated incidents or reflective of a broader pattern. These algorithms processed the language in tens of thousands of questions spanning thousands of matches over 15 years and looked for how their content differed between genders.
Their work is interesting even if you have no interest in tennis, and not just because it reveals the subtle and persistent gender bias in our society. Understanding how they accomplished this feat provides a valuable window into how algorithms operate. How can algorithms tread into language — a quintessentially human activity — and uncover patterns that some may have suspected, but had no clear way of demonstrating?
The Seattle and Vancouver metropolitan areas share more than their breathtaking scenery and growing tech industries: They both face rising housing prices, homelessness, increasing traffic – and also the great potential to work together to solve these and other urban challenges.
That’s why Stus was among 28 students from the University of Washington in Seattle and the University of British Columbia in Vancouver who used data science and analytics projects to tackle traffic, transportation and other metropolitan issues over the summer. Academic researchers and public stakeholder groups also participated in the projects in Data Science for Social Good programs, done with support from the Microsoft-sponsored Cascadia Urban Analytics Cooperative.
Equate Health, an artificial intelligence (AI)/machine learning startup in the healthcare domain that specializes in chronic care platforms, has joined the University of California, Davis (UC Davis), Foods for Health Institute (FFHI), and Healbe, a health and wellness startup, in the recently established Nutrition and Health Innovation Research Collaboration, to bring precision health solutions to consumers.
For the research announced in May 2017, Equate Health will employ its various AI/machine learning tools to create targeted algorithms for various demographics and populations to produce predictive analytics and meaningful solutions for maintaining health.
The $225 million initiative, called Resolve, announced Tuesday in New York, aims to reduce the global burden of heart disease and stroke, the world’s leading causes of death. It also will focus on helping low- and middle-income countries fight infectious disease epidemics by strengthening laboratory networks so emerging threats are identified promptly, and training disease detectives to track and investigate disease outbreaks, including those that circulate in animals and jump to humans.
This survey was created to get better understanding of how research are used by vis practitioners in the industry. Results will appear at the Visualization in Practice workshop at IEEE VIS 2017.
The Berkeley Artificial Intelligence Research Blog, Ke Li
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“Since we posted our paper on ‘Learning to Optimize’ last year, the area of optimizer learning has received growing attention. In this article, we provide an introduction to this line of work and share our perspective on the opportunities and challenges in this area.”
A few months, we took an early look at running Keras with Apache MXNet as its backend. Things were pretty beta at the time, but a lot of progress has since been made. It’s time to reevaluate… and benchmark MXNet against Tensorflow.
Right now there are literally thousands of datasets on Kaggle, and more being added every day. It’s a fabulous resource, but with so many datasets it can sometimes be a little tricky to find a dataset on the exact topic you’re interested in. Luckily, I’ve learned some tips and tricks over the last couple months that might help you out!
“Steven Bower and Houston Putman are contributing a new version of their Analytics Component to a project called Apache Solr, and their work is benefitting programmers and data scientists all over the world.” … “Bower said Solr also serves as the technical foundation for more than 300 functions on the Bloomberg Terminal. “Pretty much anytime you search on the Terminal, Solr is there,” he said.”
Following our draft update and executive summary, Make Data Count and COUNTER are proud to release our first draft of a Code of Practice for Research Data.
