When it comes to cell phones, there are few things more frustrating than a short battery life. As the battery bar of a phone dwindles down below 10 percent, there’s a mad rush to find a charger and an outlet, and then it can take up to an hour for the battery to fully charge. Twelve hours later, the process repeats when the battery drains itself once again.
But researchers at the University of Pennsylvania and Drexel University are working together on a novel technique that will allow batteries and supercapacitors to store more energy and last longer as well as drastically reduce the amount of time they take to fully charge. The technique could lead to better phones and electric cars, and even wearable chargers woven into the fabric of a shirt. Their most recent results, which focused on supercapacitors, have been published in Nature.
JURGEN SCHMIDHUBER: With the Canadian guys, it’s clear we are not using their algorithms; they are using our algorithms. LeCun is really a French guy originally, and we are using his algorithm. So that’s good. And he had lots of contributions, which were really important and useful.
I have known these other guys for a long time. My first encounter with Yoshua was when he published the same thing, or more or less the same thing, four years after one of my students published it. And then a couple of years later there was a showdown at a conference where all of this came out. There was a public debate in the workshop, and there it was really clear who did what first. It wasn’t nasty. It was just clarifying things. What you do in science is you clarify things. (Bengio has denied Schmidhuber’s claims.)
LECUN: The problem back then was that the methods required complicated software, lots of data, and powerful computers. Not many people had access to those things or were willing to invest the time. Between the mid-1990s and mid-2000s, people opted for simpler methods—nobody was really interested in neural nets. That was kind of a dark period for Geoff, Yoshua, and I. We were not bitter, but perhaps a little sad that people didn’t want to see what we all thought was an obvious advantage.
Depending which forecast you subscribe to, the global market value for artificial intelligence is projected to reach anywhere between US$36.8-billion and US$1.2-trillion by 2025. This for a series of technologies that only reached mainstream consciousness a few years ago.
At the vanguard of this push are the tech giants that have seen explosive growth thanks to the most lucrative applications of this technology: entertainment and advertising. The result of these investments has been phenomenal changes in our daily consumption of media.
However, in the excitement over what these technologies can do for immediate wealth generation, there’s a risk of overlooking large swaths of the economy that need more of the positive effects that serious AI research can bring.
A Montreal program is trying to help get more women working in artificial intelligence.
Twenty-eight women, mostly senior undergraduate students, began the six-week AI4Good summer lab on May 14.
Almost none of the participants have any experience with AI or machine learning, said Doina Precup, one of the program’s creators.
The program, now in its second year, aims to give participants the background and the confidence to continue in AI, whether that’s by studying it in graduate school, applying the technology in other areas or by starting a business, said Precup, a computer science professor at McGill University and the head of Google-affiliated AI company DeepMind’s Montreal lab.
Thanks to the General Data Protection Regulation that just went into effect in the European Union, the internet has been raining privacy updates for weeks. Although it may be tempting, especially if you’re not an EU citizen and aren’t enjoying any new privacy rights, to immediately delete these emails, they’re worth preserving long enough to take in all at once. If your inbox is anything like mine, these emails are coming from all over the place: record labels you bought an album from two years ago, that place where you booked a deep-tissue massage that one time, a sushi joint, the gym where you used to be a member. It’s the digital remnants of your consumer history, come back to remind you it exists.
When it comes to artificial intelligence, NASA and other space agencies are nowhere near building a “Terminator” in space. So, you can rest easy — Arnold Schwarzenegger isn’t about to hunt you down because you’re leading a rebellion against the machines.
Artificial intelligence is in its infancy, but scientists have used it to find alien planets, classify galaxies and create spacecraft capable of dodging debris. More uses will follow. But some critics, like SpaceX founder Elon Musk and the renowned physicist Stephen Hawking (recently deceased), have warned that artificial intelligence could be dangerous if left unchecked.
While AI is a popular theme in space exploration, its use (and misuse) has been discussed by several people in other applications as well. In 2015, Musk, Hawking and other science and tech leaders signed an open letter saying that artificial intelligence technology could generate a global arms race unless the United Nations steps in with a ban. The letter was issued by the Future of Life organization and presented at the International Joint Conference on Artificial Intelligence that year in Buenos Aires, Argentina. Artificial intelligence is the focus of “AMC Visionaries: James Cameron’s Story of Science Fiction” Episode 5, which airs Monday, May 28 during the two-hour finale at 9 p.m. EDT/PDT (8 p.m. CDT) as part of a two-hour season finale.
First, we need a way to measure whether popular music in our parents’ generation (and every generation prior) was more musically diverse than today. What defines a song’s sound—its musical composition?
One such dataset is the Music Genome Project, which is the engine powering Pandora.
The Intel AI Lab has open-sourced a library for natural language processing to help researchers and developers give conversational agents like chatbots and virtual assistants the smarts necessary to function, such as name entity recognition, intent extraction, and semantic parsing to identify the action a person wants to take from their words.
Just a few months old, the Intel AI Lab plans to open-source more libraries to help developers train and deploy artificial intelligence, publish research, and reproduce the latest innovative techniques from members of the AI research community in order to “push AI and deep learning into domains it’s not a part of yet.”
“We would like to contribute this back to the open source community so that either as a beginner or as an engineer or researcher you can look at what with reproduce and investigated and verified and then use it for your own purpose,” Intel AI Lab head of data science Yinyin Liu told VentureBeat in an interview at Intel AI DevCon.
Thomson Reuters Foundation News, Isabelle Gerretsen
More than half a billion children worldwide live in countries that cannot properly measure progress towards the Sustainable Development Goals, the U.N. children’s agency (UNICEF) said in March.
A lack of open data in developing countries is hindering progress towards the globally agreed goals – which aim to end poverty and hunger, among other things – speakers told a live Zilient discussion on the topic, moderated by Joyce Coffee, president of Climate Resilient Consulting.
For example, open data that can be used to track gender-based violence – which falls under Goal 5 – is extremely limited in developing countries, said Aniket Bhushan, lead analyst and principal investigator with the Canadian International Development Platform (CIDP).
Government Data Science News
The EU’s General Data Protection Regulation went into effect last week. Lawsuits against tech companies for compliance failure have already ensued. More on those in coming months. But for now, you might enjoy the GDPR profile in The New Yorker.
Saudi Arabia announced it will spend $US 500 billion to build a smart city that will span the Red Sea, connecting Saudi Arabia with Egypt and North Africa. In the new city, “residents’ medical files, household electronics, and transportation will all be integrated with IoT systems.” The astounding amount of funding is certain to attract tech contracts from all over the world. Some companies linked to the project: Huawei, Amazon, SAP.
Germany and more recently Sweden have moved forcefully towards open-access publishing by refusing to sign agreements with publishers that involve paywalls. Now librarians are sharing their negotiating tactics in hopes of opening access at many more libraries. Perhaps this can fix the highly unfair publishing model in which academics write the articles, review the articles, and edit the journals for free for publishers who are able to make profits of >30% by selling hugely expensive access rights to university libraries. To the digital barricades!
Scotland is offering fully-funded apprentice style data science training at the universities of Edinburgh and St. Andrews. It is extremely rare to find funding for masters degrees in data science and Edinburgh has great strengths in the core set of data science training components, so I expect these apprenticeships will be highly competitive. Get in now before everyone else hears about them. (I did not comment on St. Andrews because I am unfamiliar with the university. It could be a fantastic data science school, but it is not on my personal radar.)
In my recent series on artificial intelligence for smarter investing, one of the key themes is the importance of collaboration between computer programmers and financial subject matter experts. Most efforts to use AI in finance fail because programmers don’t understand the financial data while the financial experts don’t understand technology.
Not only has this bank made concrete efforts to foster collaboration between programmers and financial experts, its CEO has experience in both fields. These efforts are already improving profitability and efficiency, which when combined with a cheap valuation make Royal Bank of Canada (RY) this week’s Long Idea.
Facebook wants to be able to filter content, including live video streams, in real-time. The company said at a Paris technology conference that it’s going to design its own machine learning (ML) processors to achieve that goal. Facebook had previously designed its own server architecture, motherboards, and its own communication chips for the data center.
While most companies working on full self-driving technology have made heavy use of lidar sensors, Mobileye is testing cars that rely exclusively on cameras for navigation. Mobileye isn’t necessarily planning to ship self-driving technology that works that way. Instead, testing a camera-only system is part of the company’s unorthodox approach for verifying the safety of its technology stack. That strategy was first outlined in an October white paper, and Mobileye CTO Amnon Shashua elaborated on that strategy in a Thursday blog post.
“We target a vehicle that gets from point A to point B faster, smoother, and less-expensively than a human-driven vehicle; can operate in any geography; and achieves a verifiable, transparent 1,000-times safety improvement over a human-driven vehicle without the need for billions of miles of validation testing on public roads,” Shashua wrote on Thursday.
It’s a bold claim. We’re skeptical it’s actually true.
New York, NY June 22-24. “We’re thrilled to be hosting a DataDive June 22 to 24 where we will be partnering with three mission-driven organizations working to promote a healthy environment and resilient communities.” [free, registration required]
Berlin, Germany October 22 at IEEE VIS. “The goal of this workshop is to initiate a call for “explainables” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.” Deadline for submissions is July 12.
“How long does it take you to install your complete GPU-enabled deep learning environment including RStudio or jupyter and all your packages? And do you have to do that on multiple systems? In this blog post I’m going to show you how and why I manage my data science environment with GPU enabled docker containers.”
“Fairness is becoming a hot topic amongst machine learning researchers and practitioners. The field is aware that their models have a large impact on society and that their predictions are not always beneficial. In a previous blog, Stijn showed how adversarial networks can be used to make fairer predictions. This blog post focuses on the implementation part of things, so that you as a practitioner are able to build your own fair classifiers.”