We propose a new method to estimate causal effects from nonexperimental data. Each pair of sample units is first associated with a stochastic 'treatment' - differences in factors between units - and an effect - a resultant outcome difference. It is then proposed that all such pairs can be combined to provide more accurate estimates of causal effects in observational data, provided a statistical model connecting combinatorial properties of treatments to the accuracy and unbiasedness of their effects. The article introduces one such model and a Bayesian approach to combine the $O(n^2)$ pairwise observations typically available in nonexperimnetal data. This also leads to an interpretation of nonexperimental datasets as incomplete, or noisy, versions of ideal factorial experimental designs. This approach to causal effect estimation has several advantages: (1) it expands the number of observations, converting thousands of individuals into millions of observational treatments; (2) starting with treatments closest to the experimental ideal, it identifies noncausal variables that can be ignored in the future, making estimation easier in each subsequent iteration while departing minimally from experiment-like conditions; (3) it recovers individual causal effects in heterogeneous populations. We evaluate the method in simulations and the National Supported Work (NSW) program, an intensively studied program whose effects are known from randomized field experiments. We demonstrate that the proposed approach recovers causal effects in common NSW samples, as well as in arbitrary subpopulations and an order-of-magnitude larger supersample with the entire national program data, outperforming Statistical, Econometrics and Machine Learning estimators in all cases...

Current vision algorithms are largely designed for use in clear conditions, deep learning using convoluted neural networks is now being harnessed to improve visual performance in adverse weather. Capturing high-quality photos is easier than ever, as filters and image-adjustment tools can enhance images. Yet cameras still struggle to provide a clear image in bad weather, especially in extreme conditions such as heavy rain, fog, or poor lighting at night. Objects in a scene can become hard to see or even invisible, especially when they are far from the lens, and colors are often dulled. "In rain and snow, you also have motion blur because they are moving," says Dengxin Dai, a computer vision lecturer at ETH Zurich in Switzerland who coordinated a workshop on all-weather vision at the Conference for Computer Vision and Pattern Recognition (CVPR 2019) in Long Beach, CA. "So the geometry of an object might also get distorted."

The startup has raised another $100 million in funding, money that it plans to use to build out its AI algorithms and expand deeper into international markets. Hopper has now passed 30 million installs and 75 million trips planned, and says it's on track to make nearly $1 billion in sales this year. Sources very close to the company say Hopper's valuation with this round is also flying: it's now close to 1 billion Canadian dollars ($780 million in US dollars). As a point of comparison, Hopper was valued at US$300 million in its last round, in late 2016, and it has raised C$184 million (US$235 million) to date. Throughout that time, it's been a consistent presence in the top-10 travel apps in the US, according to stats from App Annie.

When it comes to fighting cancer, nearly all technologies can be explored as potential solutions. Thanks to a team of engineers, doctors might have a new Artificial Intelligence (AI) program to help them and their patients fight against the deadly disease. Researchers from the University of Central Florida's Center for Research in Computer vision created an AI with impeccable'vision' for spotting small tumors in CT scans. Most human radiologists have a success rate of 65 percent in identifying smaller tumors on a scan, the team noted. This new AI system bumps that percentage up to 95 percent accuracy, giving the radiologists a second set of keen'eyes' for them to use.

Engineers at the center have taught a computer how to detect tiny specks of lung cancer in CT scans, which radiologists often have a difficult time identifying. The artificial intelligence system is about 95 percent accurate, compared to 65 percent when done by human eyes, the team said. "We used the brain as a model to create our system," said Rodney LaLonde, a doctoral candidate and captain of UCF's hockey team. "You know how connections between neurons in the brain strengthen during development and learn? We used that blueprint, if you will, to help our system understand how to look for patterns in the CT scans and teach itself how to find these tiny tumors."

Doctors may soon have help in the fight against cancer thanks to the University of Central Florida's Computer Vision Research Center. Engineers at the center have taught a computer how to detect tiny specks of lung cancer in CT scans, which radiologists often have a difficult time identifying. The artificial intelligence system is about 95 percent accurate, compared to 65 percent when done by human eyes, the team said. "We used the brain as a model to create our system," said Rodney LaLonde, a doctoral candidate and captain of UCF's hockey team. "You know how connections between neurons in the brain strengthen during development and learn? We used that blueprint, if you will, to help our system understand how to look for patterns in the CT scans and teach itself how to find these tiny tumors."

Researchers have developed an artificial intelligence system to detect lung cancer on scans that radiologists fail to detect. The AI method can notice specks of lung cancer with about 95 percent accuracy compared with 65 percent by radiologists, according to research conducted by the University of Central Florida's Computer Vision Research Center. The researchers published their findings in the Cornell University Library before the Medical Image Computing and Computer Assisted Intervention Society's conference next month in Granada, Spain. Computed tomography, or CT, scans use computer-processed combinations of many X-ray measurements taken from different angles to produce cross-sectional images of specific areas of a scanned area. "I believe this will have a very big impact," Ulas Bagci, an engineering assistant professor at UCF, said in a press release.

Not so long ago, Canadian tech entrepreneurs had a long list of grievances: a dearth of early and late-stage funding, long visa wait times for foreign hires, local corporations that wouldn't buy their products, the best and brightest decamping for Silicon Valley. Fast forward to today, and those problems have largely evaporated. Justin Trudeau's Liberal government, eager to brand itself as innovative, has given tech leaders pretty much everything they asked for, including special fast-track visas for tech workers and hundreds of millions of dollars in venture capital money and support for artificial intelligence research. Of course, Canada has squandered its tech prowess before (see: BlackBerry). The trick this time is taking advantage of lessons learned so it doesn't happen again.

TORONTO - IBM will be joining the fintech hub at Toronto's MaRS discovery district, becoming the first technology tenant in a space currently occupied by financial services companies. The technology giant will move into the hub in the coming weeks, joining the likes of CIBC, Manulife and payment processor Moneris. Adam Nanjee, who heads up the fintech cluster at MaRS, says the so-called C Suite is meant to act as a bridge between larger corporations and tech-savvy startups. For companies, the fintech hub provides opportunities to partner with startups working on cutting-edge, potentially disruptive technologies. These partnerships have the potential to lead to investments and acquisition offers -- a huge benefit for the startups.