If Twitter can identify which user interactions are healthy, the thinking goes, then maybe it can change the product to encourage more of those behaviors while discouraging more antisocial conduct.
“If you dunk on somebody and you get a lot of engagement, a lot of ‘Likes,’ a lot of retweets, that is encouraging you to be mean, basically,” said David Gasca, the Twitter product executive in charge of the company’s health efforts, in a recent interview with Recode. “We could imagine ways of changing the product in order to [discourage] that.”
“At the same time,” he continued, “you could imagine changing [the product] such that you provide positive incentives for encouraging more constructive conversation.”
If computers understood humor, their manufacturers could make money. That’s because a more natural conversation pattern makes users engage longer with their devices and helps companies better target customers using information the devices collect.
Purdue University professor Muhammad Rahman studies technology usage and says if a device can understand the nuances of humor, its developer could better tailor services and goods to a consumer.
“So if it doesn’t understand those emotions, it cannot learn over time of what my preferences and how I make judgement based on the information I am getting from this tool,” says Rahman. [audio, 1:03]
Minnesota State University is accepting student applications for slots in the new master’s degree program in data science, becoming the first school in the Minnesota State system to offer the degree in a booming new industry. The size of the program is expected to grow over time, but the initial class will be held to 25 and the number of applicants is likely to far exceed that.
“We expect that this will be a high-demand program,” Bukralia said.
Berkeley News spoke with Deirdre Mulligan, associate professor in Berkeley’s School of Information and faculty director of the Berkeley Center for Law and Technology, about Berkeley’s long-standing commitment to supporting research and teaching that explores the social and political consequences of technology and develops strategies and methods to advance human rights and social welfare.
Michael Kearns, a leading machine learning researcher, recently addressed a group of 60 esteemed scientists at the prestigious Santa Fe Institute. His subject was the insidious bias built into many of the algorithms used for socially sensitive activities such as criminal sentencing and loan approval.
It was a version of a talk that Kearns had given before. But he couldn’t ignore the irony of discussing the dangers inherent in new technologies in this particular place. The Santa Fe Institute is just 40 miles from the town of Los Alamos, site of the Manhattan Project,
There has been recent discussion on the existence of several different data gaps across economic, social and political divides – deficits that are left unaddressed at our own peril. But there is another deficit that has, I would argue, gone relatively unnoticed but is no less important: Canada’s skills gap in data analysis.
If Canada’s data deficit is to be eliminated, more collaborative learning and engagement between data science and the arts is needed. Much of the problem could be addressed by ensuring people have the skills to know not only how to look for data, but how to interpret them.
Estimating inflation is a tricky and complex task. In the United States, the government’s Bureau of Labor Statistics sends testers to stores to record the price of everything from cheese to tires, and surveys consumers over the phone about what they spent on gas and funeral services.
Amazon thinks it could do it better.
With help from outside researchers, the company’s economists are working on a way to measure inflation using thousands of transactions across its own platform. Automatically analyzing product descriptions allows them to better assess the quality of a dress or a juicer or a bathmat, theoretically creating a more accurate, up-to-date index of how much things cost.
That’s just one way Amazon is using the squad of economists it has recruited in recent years. The company has turned so many businesses, from retailing to cloud computing, inside out. Now Amazon is upending the traditional role of economists within companies, as well as the field of economics.
Cleveland Clinic has launched a center to advance the use of artificial intelligence (AI) in health care.
The Center for Clinical Artificial Intelligence will focus on developing innovative clinical applications of AI and leveraging machine-learning technology in hopes of improving health care delivery in areas such as diagnostics, disease prediction and treatment planning, according to a news release.
Launched by Cleveland Clinic Enterprise Analytics, the center aims to foster collaboration and communication between physicians, researchers and data-scientists; offer programmatic and technology support for AI initiatives at the Clinic; and conduct research in several areas of medicine, according to the release.
In a few short months, as some students graduate and others prepare for summer break, the Machine Learning Center at Georgia Tech will be getting a new home. Faculty and graduate students will move to Coda, the newest addition to Georgia Tech’s growing presence in Tech Square and a mile east from the center’s current location in the College of Computing building.
The 645,000 square foot building is a mixed-use development and will also be home to other Georgia Tech entities such as Georgia Tech Research Institute (GTRI), the School of Computational Science and Engineering, the Institute for People and Technology (IPAT), and Health Analytics. Companies like WeWork, Keysight, and ThyssenKrupp will also neighbors of ML@GT.
Hassabis has never spoken about why he wanted Thiel’s backing in particular. (Hassabis refused multiple interview requests for this article through a spokesperson. 1843 spoke to 25 sources, including current and former employees and investors. Most of them spoke anonymously, as they were not authorised to talk about the company.) But Thiel believes in AGI with even greater fervour than Hassabis. In a talk at the Singularity Summit in 2009, Thiel had said that his biggest fear for the future was not a robot uprising (though with an apocalypse-proof bolthole in the New Zealand outback, he’s better prepared than most people). Rather, he worried that the Singularity would take too long coming. The world needed new technology to ward off economic decline.
DeepMind ended up raising £2m; Thiel contributed £1.4m. When Google bought the company in January 2014 for $600m, Thiel and other early investors earned a 5,000% return on their investment.
For many founders, this would be a happy ending. They could slow down, take a step back and spend more time with their money. For Hassabis, the acquisition by Google was just another step in his pursuit of AGI.
At the world’s top computer-vision conference last June, Google and Apple sponsored an academic contest that challenged algorithms to make sense of images from twin cameras collected under varied conditions, such as sunny and poor weather. Artificial intelligence software proficient at that task could help the US tech giants with money-making projects such as autonomous cars or augmented reality. But the winner was an institution with very different interests and allegiances: China’s National University of Defense Technology, a top military academy of the People’s Liberation Army.
That anecdote helps illustrate China’s broad ambitions in AI and recent prominence on the field’s frontiers. In 2017 the country’s government announced a new artificial intelligence strategy that aims to rival the US in the crucial technology by 2020. The latest data on the output of US and Chinese AI researchers suggest China is on track.
U.S. Senators Martin Heinrich (D-N.M.) and Rob Portman (R-Ohio) announced today the formation of the bipartisan Senate Artificial Intelligence (AI) Caucus.
AI is a transformative technology with implications spanning a number of fields including transportation, health care, agriculture, manufacturing and national security. The AI Caucus will help connect members and staff with AI experts in private industry, academia and the executive branch.
Most recently, Haven hired Sandhya Rao, the senior medical director for health care system Partners Population Health, to run clinical strategy, according to an email viewed by CNBC announcing her departure. A Haven spokesperson confirmed the hire to CNBC, and said that Rao’s official title is vice president of clinical strategy.
When it comes to climate change, one thing is certain, says Caltech climate scientist Tapio Schneider: “Global warming is upon us. Earth has warmed 1.8 degrees Fahrenheit over the past century. This warming is consistent with what basic physics tells us we should expect, given the accumulation of human greenhouse gas emissions in the atmosphere.”
The question, then, is not whether but how much the earth will be warming and how fast. For these projections, researchers rely on computer models to generate how-and-when scenarios.
University of California-Berkeley, Berkeley Institute for Data Science
from
Berkeley, CA April 22, starting at 8 a.m., University of California-Berkeley David Brower Center (2150 Allston Way). This event is open to UC Berkeley and UCSF faculty and researchers in the areas of population health, data science, biostatistics, and public policy. [registration required]
New York, NY May 24, 2019. “While seeking to revisit major narratives centered on the UK and US, we plan as much as possible to incorporate a global story of AI, which has often been told predominantly in an Anglo-American framework, and to draw together a capacious range of methodological approaches, kinds of histories, and historians.” Deadline for submissions is March 18.
The application for a 2019-2020 Mozilla Fellowship is a two part process. Application part 1 closes on April 8. Final application must be completed by May 6.
Denver, CO October 10-11. “CIC is seeking proposals that reflect research, expertise and/or practical experience that is innovative, well-grounded, and related to community indicators. Proposals must be submitted online using the form below.” Deadline for submissions is April 19.
Bear with me as I am going to show you how you can build a scalable architecture to surround your witty Data Science solution!
I am starting a series of 2 articles that will cover the basics of software engineering with regards to architecture and design and how to apply these on each step of the Machine Learning Pipeline:
As an ontologist, I’m often asked about the distinctions between taxonomies and ontologies, and whether ontologies are replacing taxonomies. The second question is easy to answer: “No.” Both taxonomies and ontologies serve vital, and often complementary, roles … if they are used right.
Today we are launching Open Distro for Elasticsearch. This is a value-added distribution of Elasticsearch that is 100% open source (Apache 2.0 license) and supported by AWS. Open Distro for Elasticsearch leverages the open source code for Elasticsearch and Kibana. This is not a fork; we will continue to send our contributions and patches upstream to advance these projects.
A new GitHub project, PyTorch Geometric (PyG), is attracting attention across the machine learning community. PyG is a geometric deep learning extension library for PyTorch dedicated to processing irregularly structured input data such as graphs, point clouds, and manifolds. The project was developed and released by two PhD students from TU Dortmund University, Matthias Fey and Jan E. Lenssen.