Dec-6-2017


Pandas: Framing the Data

@machinelearnbot

Data science, and numerical computing, in general, has a problem: the deep linear algebra libraries deal with pure numbers in vectors and matrices, but in the real world there is always metadata attached to those structures that needs to be carried along through the computational pipeline. Rows and columns have information attached to them--names, typically--that has to be accounted for even as we do things like remove rows or swap data around to make certain computations more tractable. Over the past 20 years, most scientists who deal with numerics have struggled with this problem, and hacked up some kind of mostly-adequate solution for specific problems. In my own work in genomics, where data sets typically have tens of thousands of columns (each one representing a gene) and a few dozen rows (each one representing a tissue sample), dimensionality reduction is always a critical focus, and keeping the right gene names attached to the right columns at various stages of analysis while stripping out irrelevant ones was always a challenging and error-prone process. That is, until I discovered R and learned about the miracle that is the DataFrame, which is an annotated 2D data structure that does all the metadata book-keeping for you.


BlackBerry pens framework for securing connected and autonomous cars

ZDNet

BlackBerry on Wednesday laid out a recommended framework for automakers to address the cybersecurity challenges surrounding connected and autonomous vehicles. As driverless technologies improve, cars will likely become more of a membership perk than objects of ownership. BlackBerry sees four industry trends that are making vehicles vulnerable to cyber attacks and failures: vehicles access, software control, autonomous driving, and the changing state of software. BlackBerry also teased tools and services, saying it will demonstrate its vision for connected cars and autonomous vehicles at CES in early January. "Protecting a car from cybersecurity threats requires a holistic approach," Sandeep Chennakeshu, President of BlackBerry Technology Solutions, said in a statement.


Lyft's self-driving cars are now on the road in Boston

Mashable

Bostonites have a new way to get around the city's famously contentious streets: robotaxis. SEE ALSO: Lyft now has permission to test self-driving cars on California's roads Lyft and autonomous driving company nuTonomy announced their joint pilot program has been cleared by the city's authorities to begin picking up passengers. The two companies first disclosed their partnership back in June, but had to wait until the city's regulatory bodies gave it the green light to actually offer Lyft users driverless rides. The program will begin in Boston's Seaport district, matching riders looking to travel on routes within the area with driverless cabs. The cars will have human safety operators, like other trials, and the program will emphasize rider education about self-driving cars as one of its major points of focus.


Australian boy who lost visual part of his brain can see

Daily Mail

A seven-year-old boy who lost the visual processing center of his brain at two weeks old has shocked doctors by having normal sight. The unidentified Australian boy, known as BI, lost his visual cortex due to a rare metabolic disorder called medium-chain acyl-Co-A dehydrogenase deficiency. Now a report has revealed that BI is the first person ever to have normal sight without a visual cortex - he is able to play soccer, see colors and identify faces and only suffers nearsightedness. New tests showed that his brain rerouted itself to make up for sight, leading researchers to believe that newborn brains can recover and adapt much better than mature ones. An MRI shows a normal brain (left) and the seven-year-old Australian boy's brain that has been missing the visual cortex (right) since he was two weeks old The visual cortex is the part of the brain that receives and processes sensory nerve impulses from the eyes, ultimately giving you the ability to see.


Improving Clinical Trials With Machine Learning

#artificialintelligence

Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference--from common functional maps to individual behaviour--is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain.


Artificial Intelligence Software to Use in Your Business

#artificialintelligence

The development of artificial intelligence gathers pace. Every day you can read some new information about chatbots, voice recognition, virtual assistants, robots, etc. More artificial intelligence software get on your phones and computers. However, are these technologies worth the money you pay? Though many scientists, professors, and entrepreneurs talk about how dangerous artificial intelligence can be, AI is still in the focus of Google, Facebook, Microsoft, and other companies.


San Francisco Just Put the Brakes on Delivery Robots

WIRED

San Francisco, land of unrestrained tech wealth and the attendant hoodies and $29 loaves of bread, just said whoa whoa whoa to delivery robots. The SF Board of Supervisors voted on Tuesday, December 5 to severely restrict the machines, which roll on sidewalks and autonomously dodge obstacles like dogs and buskers. Now startups will have to get permits to run their robots under strict guidelines in particular zones, typically industrial areas with low foot traffic. And even then, they may only do so for research purposes, not making actual deliveries. It's perhaps the harshest crackdown on delivery robots in the United States--again, this in the city that gave the world an app that sends someone to your car to park it for you.


5 Predictions About the Future of Machine Learning - Talend Real-Time Open Source Data Integration Software

@machinelearnbot

Machine Learning is currently one of the hottest topics in IT. The reason stems from the seemingly unlimited use cases where machine learning can play from fraud detection to self-driving cars, and even identifying your'gold card' customers to price prediction. But what is the future for this fascinating field? What will be the next best thing? Where will we be in ten years time?


JPL's AI-Powered Racing Drone Challenges Pro Human Pilot

IEEE Spectrum Robotics Channel

As drones and their components get smaller, more efficient, and more capable, we've seen an increasing amount of research towards getting these things flying by themselves in semi-structured environments without relying on external localization. The University of Pennsylvania has done some amazing work in this area, as has DARPA's Fast Lightweight Autonomy program. At NASA's Jet Propulsion Laboratory, they've been working on small drone autonomy for the past few years as part of a Google-funded project. The focus is on high-speed dynamic maneuvering, in the context of flying a drone as fast as possible around an indoor race course using only on-board hardware. For the project's final demo, JPL raced their autonomous drones through an obstacle course against a professional human racing drone pilot.


Lyft's self-driving car pilot launches in Boston

Engadget

It took several months, but Lyft and nuTonomy have made good on their promise to test autonomous ridesharing cars in Boston. The two have launched a pilot program that gives "select" Seaport-area passengers a ride in one of nuTonomy's self-driving Renault cars. If you're one of the few to hop in (the Lyft app will make it obvious), your feedback will help refine the system to make sure it's both comfortable and safe. This is as much a tech demo as it is a trial run. Lyft and nuTonomy aren't shy about using the Boston experiment to help you "better understand the impact" of self-driving cars -- that is, to sell you on the concept so that you'll be a customer when driverless cars dominate.