1. — Issue #61 –
Tutorials, Tips and Tricks
At some point of time, we all search for the various opensource ML models, maybe to try it out, see how it works.
We saw Big GANs generator was opensourced last week and the community has gone wild ever since.
Tencent ML Images
Tencent recently put out the largest open-source multi-label image database, including 17,609,752 training and 88,739 validation image URLs, which are annotated with up to 11,166 categories. Read More
2. A wearable robot arm makes you work for Thanksgiving leftovers
The robotic arm will feed whichever human looks happiest about their upcoming treat, reports IEEE Spectrum. After someone manually guides the chest-mounted robot toward a piece of food, it will grab the morsel and hold it in the air. If the person sitting across from the arm looks happy about the prospect of a snack, Arm-A-Dine will deliver it to their mouth. Read More
3. Yoshua Bengia, one of the fathers of AI is worried about its future – we need more democracy in A.I. research ! – Futurist Gerd Leonhard
“another reason why we need to have more democracy in AI research. They have much more money, they have much more data. ”
One of the fathers of AI is worried about its futurehttps://www. Read More
4. Chinese facial recognition system confuses bus ad with a jaywalker
It illustrates one of the many issues with China’s surveillance culture.
There are many criticisms you can level at China’s growing reliance on facial recognition, including its absolute faith in technology: what happens if there’s a false positive. Unfortunately, we just saw an example of that in action. Read More
5. How Cheap Labor Drives China’s A.I. Ambitions
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. 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. Read More
6. AI Adoption Survey. Better Python Charts. Data Dictionaries. [DSR #163]
Happy Thanksgiving to all of the American readers out there 🦃🦃 .
❤️ Want to support us. Forward this email to three friends. Read More
7. We trained an algorithm to detect cancer in just two hours
We all can understand—at least conceptually—what it takes to be a doctor: years of medical school lectures attended, stacks of textbooks and journals read, countless hours of on-the-job residencies. The way we measure how accurate the nodule detection algorithm is as it learns to find these tumors is the same as they would be implemented in a specialist’s office, with a metric called “recall. ” Recall tells us the percentage of nodules the algorithm catches, given a set number of false alarms. Read More
8. Police scrap crime predicting software
Computer software intended to predict where and when crimes would occur has been scrapped by a police force.
Kent Police was the first force in England and Wales to introduce the “predictive policing” system in 2013.
It said a new approach to policing which “places victims and witnesses at its centre” had led it to “evaluate alternative options”. Read More
9. Artificial Intelligence Needs Conversational Intelligence. Here’s Why: – Innovation Excellence
The result was often things that were maddeningly difficult to use. The book is largely seen as pioneering the user-centered design movement. ” This can be useful, especially when driving or walking down the street,” but is also fairly limited, especially for more complex tasks. Read More
10. The Best of the Physics arXiv (week ending November 24, 2018
free articles this month.
for unlimited online access.
for more, or for unlimited online access. Read More
11. Embedded Curiosities
Embedded Agents — Decision Theory — Embedded World-ModelsRobust Delegation — Subsystem Alignment
A final word on curiosity, and intellectual puzzles:
I described an embedded agent, Emmy, and said that I don’t understand how she evaluates her options, models the world, models herself, or decomposes and solves problems.
In the past, when researchers have talked about motivations for working on problems like these, they’ve generally focused on the motivation from AI risk. When people figure out how to build general AI systems, we want those researchers to be in a better position to understand their systems, analyze their internal properties, and be confident in their future behavior. Read More
12. The Truth About Killer Robots: the year’s most terrifying documentary
When it comes to the dangers posed to us by automatons, film-maker Maxim Pozdorovkin wants us to start thinking beyond what Hollywood has warned us about. “[It gets us] thinking about something that we’re heading towards in the future, something that will one day hurt us. At the center of his film lies the question: “when a robot kills a human, who takes the blame?”
Pozdorovkin had long sought to make a film on automation, but he had a difficult time figuring out a way to approach the subject given its scope, as well as the many misconceptions surrounding it. Read More
13. Rule by robots is easy to imagine – we’re already victims of superintelligent firms | John Naughton
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14. [1811.08890] Learning from Multiview Correlations in Open-Domain Videos
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