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Uber’s self-driving cars are returning to the road in Toronto — but just to collect data in manual mode





Months after a self-driving Uber operating in autonomous mode struck and killed a woman in the U.S., the company is returning its test fleet to Toronto’s streets  — but with humans at the wheel.

Last March, Elaine Herzberg, 49, was struck and killed just outside Phoenix, Ariz. The death prompted intense scrutiny of the company’s technology and the safety precautions it had in place.

The California-based company, which says it has implemented new safety measures, in November convinced U.S. transportation officials that its cars are fit to return to the road in autonomous mode.

Uber, which took all its autonomous vehicles off public streets in North America after the death, plans to resume autonomous driving in Pittsburgh this week.

The company’s high-tech cars will also be back on the road in Toronto beginning today (Thursday), but vehicles operating in and around Canada’s biggest city will still only be driven manually — as they were before the crash — with one Uber staffer behind the wheel and another in the passenger seat at all times.

That’s because in Toronto, the focus is primarily on data collection and analysis that can be used to improve the safety and performance of Uber’s fleet of autonomous (self-driving) vehicles, rather than testing the self-driving capability itself.

Raquel Urtasun, chief scientist of Uber’s Advanced Technologies Group (ATG) and head of the lab’s Toronto office, said that her team will spend time driving both city streets and highways. They’ll be testing new approaches to building the maps that most self-driving cars rely on to make sense of where they are in the world, and what they’re seeing.

Raquel Urtasun, chief scientist of Uber’s Advanced Technologies Group (ATG), expects to have 100 people working on self-driving car research in Toronto alone next year. (Anand Ramakrishnan/CBC)

Mapping, says Urtasun, is traditionally “a very expensive process and time-consuming process” involving frequent trips in a car dedicated to data collection, as well as lots of human labour to annotate important features like lane markers, crosswalks and stop signs.

Uber is trying to automate the creation of new maps by training AI to do the tedious work of annotation. Its self-driving vehicles already collect new mapping data while they move through the city — eliminating the need for a dedicated mapping car.

“This technology can allow us to build maps super fast and cheaply, which is one of the things that prevents everybody from going to scale,” Urtasun said.

Another effort focuses on teaching Uber’s self-driving vehicles to build new maps on the fly when encountering an area where maps aren’t available, or when the car has trouble figuring out where it is. All othis data could then be used to further train Uber’s self-driving software in simulations.

Uber ATG Toronto is currently housed within the MaRS Discovery District, an innovation hub that is home to numerous tech companies, but will move to a new location in the city in 2019. (Uber/Handout)

Uber announced in September that it would be expanding ATG in Toronto and opening a new engineering lab — its first in Canada. The company plans to spend $200 million on the Toronto hub over the next five years, which will eventually bring its head count in the city from 200 to about 500 employees.

Urtasun expects to have 100 people working on self-driving car research in Toronto alone next year in a new research and development office.

Trying to tackle safety concerns

As part of its bid to return to the road, Uber submitted its first voluntary safety report to the U.S. National Highway Traffic Safety Administration at the beginning of November. It is only the sixth autonomous car company to do so, joining other major players like Waymo, Ford and GM. 

In its report, Uber pledged to have two people in each of its vehicles at all times, to ensure its vehicles’ automatic and emergency braking systems are always enabled as backup measures, and to make overall improvements to both its software and employee training.

The Informationreported last month that an Uber engineer raised concerns about inadequate staff training just days before March’s fatal crash, noting that routine accidents were “usually the result of poor behaviour of the operator or the AV technology.”

Companies like Uber typically tout the promise of self-driving cars as one day being safer than traditional human drivers, believing that fully autonomous vehicles will greatly reduce collisions and fatalities — which would also include those caused by Uber itself. The Fifth Estate recently investigated the company’s safety record in Canada, after a Toronto man died in an Uber crash in March.

In addition to its efforts in Toronto and Pittsburgh, Uber will resume manual driving in San Francisco this week.

Still, by some measures, Uber is playing catch-up to one of its chief competitors, Waymo. The Alphabet-owned company recently launched a self-driving taxi service for the public in the suburbs of Phoenix, after months of private testing with a small group.


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Top 5 Analytics Trends That Are Shaping The Future





Digital transformation is increasingly becoming the focus for many CIOs around the world today—with analytics playing a fundamental role in driving the future of the digital economy.

While data is important to every business, it is necessary for businesses to have a firm grip on data analytics to allow them transform raw pieces of data into important insights. However, unlike the current trends in business intelligence—which is centred around data visualization—the future of data analytics would encompass a more contextual experience.

“The known data analytics development cycle is described in stages: from descriptive (what happened) to diagnostic (why did it happen), to discovery (what can we learn from it), to predictive (what is likely to happen), and, finally, to prescriptive analytics (what action is the best to take),” said Maurice op het Veld is a partner at KPMG Advisory in a report.

“Another way of looking at this is that data analytics initially “supported” the decision-making process but is now enabling “better” decisions than we can make on our own.”

Here are some of the current trends that arealready shaping the future of data analytics in individuals and businesses.

  1. Growth in mobile devices

With the number of mobile devices expanding to include watches, digital personal assistants, smartphones, smart glasses, in-car displays, to even video gaming systems, the final consumption plays a key role on the level of impact analytics can deliver.

Previously, most information consumers accessed were on a computer with sufficient room to view tables, charts and graphs filled with data, now, most consumers require information delivered in a format well optimized for whatever device they are currently viewing it on.

Therefore, the content must be personalized to fit the features of the user’s device and not just the user alone.

  1. Continuous Analytics

More and more businesses are relying on the Internet of Things (IoT) and their respective streaming data—which in turn shortens the time it takes to capture, analyze and react to the information gathered. Therefore, while analytics programspreviously were termed successful when results were delivered within days or weeks of processing, the future of analytics is bound to drastically reduce this benchmark to hours, minutes, seconds—and even milliseconds.

“All devices will be connected and exchange data within the “Internet of Things” and deliver enormous sets of data. Sensor data like location, weather, health, error messages, machine data, etc. will enable diagnostic and predictive analytics capabilities,” noted Maurice.

“We will be able to predict when machines will break down and plan maintenance repairs before it happens. Not only will this be cheaper, as you do not have to exchange supplies when it is not yet needed, but you can also increase uptime.”

  1. Augmented Data Preparation

During the process of data preparation, machine learning automation will begin to augment data profiling and data quality, enrichment, modelling, cataloguing and metadata development.

Newer techniques would include supervised, unsupervised and reinforcement learning which is bound to enhance the entire data preparation process. In contrast to previous processes—which depended on rule-based approach to data transformation—this current trend would involve advanced machine learning processes that would evolve based on recent data to become more precise at responding to changes in data.

  1. Augmented Data Discovery

Combined with the advancement in data preparation, a lot of these newer algorithms now allow information consumers to visualize and obtain relevant information within the data with more ease. Enhancements such as automatically revealing clusters, links, exceptions, correlation and predictions with pieces of data, eliminate the need for end users to build data models or write algorithms themselves.

This new form of augmented data discovery will lead to an increase in the number of citizen data scientist—which include information users who, with the aid of augmented assistance can now identify and respond to various patterns in data faster and a more distributed model.

  1. AugmentedData Science

It is important to note that the rise of citizen data scientist will not in any way eliminate the need for a data scientist who gathers and analyze data to discover profitable opportunities for the growth of a business. However, as these data scientists give room for citizen data scientists to perform the easier tasks, their overall analysis becomes more challenging and equally valuable to the business.

As time goes by, machine learning would be applied in other areas such as feature and model selection. This would free up some of the tasks performed by data scientist and allow them focus on the most important part of their job, which is to identify specific patterns in the data that can potentially transform business operations and ultimately increase revenue.

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Waterloo drone-maker Aeryon Labs bought by U.S. company for $265M






Waterloo’s Aeryon Labs has been bought by Oregon-based FLIR Systems Inc. for $256 million, or $200 million US.

The acquisition was announced Monday. 

Dave Kroetsch, co-founder and chief technology officer of Aeryon Labs, says not much will change in the foreseeable future.

“The Waterloo operations of Aeryon Labs will actually continue as they did yesterday with manufacturing, engineering and all the functions staying intact in Waterloo and ultimately, we see growing,” he said.

“The business here is very valuable to FLIR and our ability to sell internationally is a key piece of keeping these components of the business here in Canada.”

Aeroyn Labs builds high-performance drones that are sold to a variety of customers including military, police services and commercial businesses. The drones can provide high-resolution images for surveillance and reconnaissance.

The drones already include cameras and thermal technology from FLIR. Jim Cannon, president and CEO of FLIR Systems, said acquiring Aeryon Labs is part of the company’s strategy to move beyond sensors “to the development of complete solutions that save lives and livelihoods.”

‘A piece of a bigger solution’

Kroetsch said this is a good way for the company to grow into something bigger.

“We see the business evolving in much the direction our business has been headed over the last couple of years. And that’s moving beyond the drone as a product in and of itself as a drone as a piece of a bigger solution,” he said.

For example, FLIR bought a drone company that builds smaller drones that look like little helicopters.

“We can imagine integrating those with our drones, perhaps having ours carry their drones and drop them off,” he said.

FLIR also does border security systems, which Kroetsch says could use the drones to allow border agents to look over a hill where there have been issues.

“We see the opportunity there as something that we never could have done on our own but being involved with and part of a larger company that’s already providing these solutions today gives us access not only to these great applications, but also to some fantastic technologies,” he said.

Aeryon Labs has done a lot of work during emergency disasters, including in Philippines after Typhoon Hagupit in 2014, Ecuador after an earthquake in 2016 and the Fort McMurray wildfire in 2016.


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Inuvik infrastructure may not be ready for climate change, says study






The Arctic is expected to get warmer and wetter by the end of this century and new research says that could mean trouble for infrastructure in Inuvik.

The study from Global Water Futures looked at how climate change could impact Havipak Creek — which crosses the Dempster Highway in Inuvik, N.W.T. — and it predicts some major water changes.

“They were quite distressing,” John Pomeroy, director of Global Water Futures and the study’s lead author, said of the findings.

Researchers used a climate model and a hydrological model to predict future weather and climate patterns in the region. They also looked at data gathered from 1960 to the present. 

If greenhouse gas emissions continue at their current rate — which Pomeroy said they are on track to do — the study projects the region will be 6.1 C warmer by 2099 and precipitation, particularly rain, will increase by almost 40 per cent.

The study also found that the spring flood will be earlier and twice as large, and the permafrost will thaw an additional 25 centimetres. While the soil is expected to be wetter early in the summer, the study said it will be drier in late summer, meaning a higher risk of wildfires.

John Pomeroy is the director of Global Water Futures. (Erin Collins/CBC)

“The model’s painting kind of a different world than we’re living in right now for the Mackenzie Delta region,” Pomeroy said.

He noted these changes are not only expected for Havipak Creek, but also for “many, many creeks along the northern part of the Dempster [Highway].”

Pomeroy said the deeper permafrost thaw and a bigger spring flood could pose challenges for buildings, roads, culverts and crossings in the area that were designed with the 20th century climate in mind.

He said the projected growth of the snowpack and the spring flood are “of grave concern because that’s what washes out the Dempster [Highway] and damages infrastructure in the area.”

Culverts and bridges may have to be adjusted to allow room for greater stream flows, Pomeroy said. And building foundations that are dependent upon the ground staying frozen will have to be reinforced or redesigned.

Pomeroy said the ultimate solution is for humans to reduce greenhouse gas emissions.

“This study is the future we’re heading for, but it’s not the future we necessarily have if we can find a way to reduce those gases,” he said.  

“It’d be far smarter to get those emissions under control than to pay the terrible expenses for infrastructure and endangered safety of humans and destroyed ecosystems.”


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