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Autonomous trucking company Plus will use AI and billions of miles of data to train self-driving semis

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Autonomous trucking company Plus will use AI and billions of miles of data to train self-driving semis

This article is part of a VB Lab Insight series paid for by Plus.


The safest drivers are those with the most experience. Studies show it can take years of practice for automobile drivers to become careful and competent road users. Similarly, the more experience a truck driver has the less likely it is that they will cause a serious crash.

What holds true for human drivers holds true for autonomous driving systems — up to a point. The safest self-driving vehicle platforms are those that have accumulated the most experience.

Since driving experience is so important, how can technologists make sure computerized driving systems get the training they need to operate safely on the nation’s roads and highways?

Solving this challenge is the key to unlocking a fully autonomous future.

How computers learn to drive a semi-truck

Thanks to advances in sensor technology and artificial intelligence (AI), an automated truck is capable of analyzing many objects on the road and making a decision about how to respond.

This is accomplished in large part by training so-called “deep learning” algorithms. Repeatedly expose a self-driving system to all kinds of obstacles, from a cut-in vehicle to a construction site, and the system will start to understand how to react when an obstruction appears on the highway.

Here it is important to note that unlike people, machines lack common sense and don’t do well handling novel situations. Human drivers know to slow down in the face of an unexpected obstacle — a bear, say — because we can make decisions based on similar situations we have already encountered or extrapolate from other incidents.

Unlike humans, however, deep neural networks can only learn from data they have been trained on, whether from public roads, closed courses, or computer simulations.

So back to the original question: How do you train the machines so they are exposed to the full range of the driving experience?

Data, data, and more data

Plus’s goal is to help truck drivers on long-haul routes, where they encounter a variety of road and weather conditions. In addition to closed-road testing and computer simulations, the company’s PlusDrive system is learning on the open road, where the trucks can be exposed to real-world obstacles and situations. Junk flying from a pickup bed. Ice slicks. A wind turbine blade. A zigzagging motorcycle.

Plus3

Though these so-called “long tail” phenomena comprise less than 1% of the time behind the wheel, knowing how to safely navigate them is critical for machines. Society expects that a computer-operated machine must be at least an order of magnitude safer than a human driver.

Billions of miles of on-road testing

Starting this summer, Plus will put its supervised automated driving system into factory production. It is also retrofitting existing trucks with the system. By this time next year, hundreds of automated trucks powered by PlusDrive will be on the road, hauling commercial cargo.

Human drivers will be behind the wheel. Like an experienced professional training a new recruit, Plus drivers will monitor the autonomous trucks while teaching them how to handle unexpected obstacles.

Plus estimates that its fleet will accumulate billions of collective miles before the company deploys fully driverless vehicles. Taking an evolutionary approach to full autonomy enables the company to rack up miles more quickly, with the assistance of on-board professional drivers who are training and validating the system.

To support its global deployment in the U.S., China, Europe, and other markets, Plus recently raised $420 million in new funding.

Truck driver retention and low-carbon solution

The drivers benefit too. The Plus supervised autonomous trucking solution elevates the role of the truck driver, upskilling them in preparation for an autonomous future. At the same time a digital co-pilot will ease driver exhaustion on long-haul routes, and fleets will spend less on the hiring process.

The system yields other gains. Fuel comprises about a third of a trucking company’s operating budget, by far the largest cost for heavy trucks. When an automated system understands the road, pulling in GPS and weather data too, they optimize shifting and braking. Plus has run pilot projects showing that  PlusDrive saves 10% of the tank compared to the most efficient drivers, a win for the bottom line and the environment.

The autonomous trucking future, now

Commercial space travel, solar-powered cities, autonomous vehicles — the first two visions of the future depend on specific economic inflection points, while the third is wholly dependent on the amount of data a system has accumulated.

Plus is building the necessary feedback loop of information today. Its trucks are accumulating the data. Its drivers, who are among the safest and most efficient Class A drivers, are training the system with their responses. Its engineers are fine-tuning PlusDrive’s algorithms and decisions. And eventually PlusDrive will be one of the safest and most experienced drivers on the road.

Plus is applying autonomous trucking technology to trucks today. For more information, please visit www.plus.ai.


VB Lab Insights content is created in collaboration with a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected]

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GitHub now lets all developers upload videos to demo bugs and features

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GitHub now lets all developers upload videos to demo bugs and features

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GitHub has officially opened up video uploads five months after launching in beta, allowing all developers to include .mp4 or .mov files directly in pull requests, discussions, issues, comments, and more.

The feature is designed to help developers visually demonstrate to project maintainers the steps they went through when they encountered a bug, for example, or illustrate what a major new code change achieves in terms of functionality.

So rather than having to follow detailed step-by-step textual instructions which may be ambiguous or unclear, it’s now easier to see exactly what’s happening at the other end first-hand and should go some way toward avoiding time-consuming back-and-forth written discussions. This could also be used in conjunction with a voice track with a narrator explaining the on-screen actions.

Above: Video in GitHub

It’s worth noting that with this launch, GitHub also now fully supports video uploads from within its mobile app.

ezgif.com gif maker 2

Above: Uploading video to GitHub via mobile app

Seeing is believing

Native video upload support helps bypass the cumbersome alternative involving recording and uploading a video to a third-party platform, then sharing a link. On that note, GitHub actually doesn’t yet support video unfurling from shared links, but that is something it said that it’s working on, alongside enabling video annotations for specific pieces of code.

At a time when the world has had to adapt to remote work and collaboration, learning to embrace asynchronous communication is one of the fundamental factors for distributed teams to succeed — recorded video plays a big part in enabling this.

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Warhammer III hands-on — A journey into the Realm of Chaos

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Warhammer III hands-on -- A journey into the Realm of Chaos

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Sega Europe’s The Creative Assembly studio showed off a demo of Total War: Warhammer III at a press event, and I got to go hands-on with the game in a battle set in the Realm of Chaos.

Being launched later on this year in partnership with franchise owner Games Workshop, Warhammer III the latest in the Total War series. The franchise has sold more than 34.3 million copies to date. The Total War: Warhammer spinoff is a cataclysmic conflict between demonic powers and the sentinels of the mortal world. I played the first two games, and many others, in the Total War series. This game brings the Warhammer trilogy to its conclusion.

The Creative Assembly has been making Total War strategy games for more than two decades. Most of these have focused on historical wars; until recently, when they’ve expanded into myths such as Total War: Three Kingdoms and fantasy with the Warhammer titles. In a Total War strategy game, you move armies around on a strategic map and fight in a 3D real-time battle when they meet on the battlefield.

In Total War: Warhammer III, each choice the player makes will shape the conflict to come. You’ll explore the mysterious Lands of the East to the demon-infested Realms of Chaos.

“Warhammer III is of course the concluding chapter in the series and we’re planning on going out with a bang,” said Al Bickham, the development communications manager for The Creative Assembly, at a press event. “We’ve crafted a huge arching narrative which ties the trilogy together. There are going to be more playable races out of the box than the previous two games. And it’s all set across a hyper-detailed campaign map which begins at the very fringes of Warhammer lands and takes you deep into the mind-bending horrors of the four Realms of Chaos.”

The game will have iconic races from the World of Warhammer Fantasy Battles, including the video game debut of Kislev and Cathay alongside the factions of Chaos — Khorne, Nurgle, Slaanesh, and Tzeentch. This means players will wage war with the most diverse array of legendary heroes, gargantuan monsters, flying creatures, and magical powers.

Embarking on a new grand campaign, you will be tasked with saving or exploiting the power of a dying god. Each race offers a unique journey through the nightmarish Chaos Realm. The endgame will determine the fate of the world.

The Survival Battle

Above: Everything looks so orderly at the beginning of the Survival Battle in Warhammer III.

Image Credit: Sega/Creative Assembly

The Creative Assembly used the Parsec to let me play a sample Survival Battle, where your goal is to attack into the Realm of Chaos and take objectives and fend off the demon hordes. It’s a new kind of narrated battle that is fresh to the franchise. They’re like boss battles in Warhammer III, and they trigger after you reach key points in the game’s narrative.

“We want the [Survival Battle] to feel epic, really memorable, and full of decisive moments in the course of your campaign,” Bickham said.

My faction was the Kislev, an Eastern human faction that resembles the Russian Cossacks. And I had to take a number of victory locations within the a bloody fortress called the Brass Citadel.

The faction leader, Tzarina Katarin (the Ice Queen of Kislev) has taken her loyal forces into the Realm of Chaos. Khorne, the Chaos God of rage and war, sends a legion of demons to destroy the trespassers. The Kislev forces have been detailed for the first time in the series. Katarin is an Ice Witch with magical powers to both rally her troops and strike fear in the hearts of demons.

I wasn’t exactly impressed with the forces I got in the battle. There were some excellent sword troops, but I only have five companies of them in a place where I had to defend against attacks coming from all directions. I had twice as many archers and a few archer cavalry units.

The Realm of Chaos, of course, is a bad place. It has plenty of blood-red backdrops and one of its decorations is an actual fountain of blood. The four Ruinous Powers rule over this place, ever seeking to slip their bonds and engulf the world in a tide of daemonic corruption. Nurgle, the plague god; Slaanesh, the lord of excess; Tzeentch, the changer of ways; and Khorne, the god of blood and slaughter.

My troops had to fight uphill and sweep some light demon units from the top of a ridge. That was easy enough, and I claimed a victory point in doing so. That allowed me to draw reinforcements from another realm to strengthen my army. But then I was attacked from four directions. At least I was defending a hill, but I had a hard time figuring out where to place my five sword troops, as they were the best units to stave off attacks.

chaos 5

Above: My soldiers are devolving into chaos in Warhammer III.

Image Credit: Sega/Creative Assembly

The cavalry was useful in taking down wolf-borne demons from the enemy, but it wasn’t useful in charging headlong into enemy lines. Rather, it was better to use them to harass the enemy with missile fire from a distance. But I didn’t have nearly enough units to form a full line of defense in all directions. The result was, you guessed it, chaos.

But I tried to survive. One of the goals was to earn a battle currency called “supplies,” which allowed me to build towers and barricades. It also let me recruit new warriors, upgrade my existing units, and bring on reinforcements. Being new to the game, I couldn’t figure out how much to spend on each kind of task. I found I could build barricades and get reinforcements, but I didn’t have enough supplies to build towers, and that meant the hordes of Chaos were going to charge me without being harassed. You generate more supplies by capturing victory points or killing enemies.

Had I looked more, I would have seen that I could have used The Lore of Ice, or ice-themed spells that would slow down the enemy and help my soldiers thin their ranks as they tried to attack. There were six different spells altogether. I also could have used the Elemental Bear, a huge monster on my side, and some of the bear cavalry for the faction. Sadly they were nowhere to be found in my playthrough.

Still, after a few battle restarts, I was able to survive the first wave of attacks and open up a new part of the Brass Citadel, which was circular with a big pit in the middle. Once again, I was forced to divide my forces and try to hold off larger numbers of enemies coming from all sides. It wasn’t pretty.

I didn’t get near the goal of the battle, to fight Khorne’s champion, an Exalted Greater Demon, in a final struggle. It was a very difficult battle, but I enjoyed the idea of being assaulted by endless hordes and figuring out how to stay alive when you’re vastly outnumbered. This is a difficult mode when it comes to figuring out where to throw your troops and when. But it adds some excitement to the pressure that you feel when you have to make decisions quickly to head off disaster.

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LinkedIn open-sources Greykite, a library for time series forecasting

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Greykite Silverkite

Join Transform 2021 this July 12-16. Register for the AI event of the year.


LinkedIn today open-sourced Greykite, a Python library for long- and short-term predictive analytics. Greykite’s main algorithm, Silverkite, delivers automated forecasting, which LinkedIn says it uses for resource planning, performance management, optimization, and ecosystem insight generation.

For enterprises using predictive models to forecast consumer behavior, data drift was a major challenge in 2020 due to never-before-seen circumstances related to the pandemic. This being the case, accurate knowledge about the future remains helpful to any business. Automation, which enables reproducibility, may improve accuracy and can be consumed by algorithms downstream to make decisions.

For example, LinkedIn says that Silverkite improved revenue forecasts for 1-day ahead and 7-day ahead, as well as Weekly Active User forecasts for 2-week ahead. Median absolute percent error for revenue and Weekly Active User forecasts grew by more than 50% and 30%, respectively.

Greykite library

Greykite provides time series tools for trends, seasonality, holidays, and more so that users can fit the AI models of their choice. The library provides exploratory plots and templates for tuning, which define regressors based on data characteristics and forecast requirements like hourly short-term forecast and daily long-term forecast. Tuning knobs provided by the templates reduce the search to find a satisfactory forecast. And the Greykite library has flexibility to customize a model template for algorithms, letting users label (and specify whether to ignore or adjust) known anomalies.

Greykite, which provides outlier detection, can also select the optimal model from multiple candidates using past performance data. Instead of tuning each forecast separately, users can define a set of candidate forecast configurations that capture different types of patterns. Lastly, the library provides a summary that can be used to assess the effect of individual data points. For example, Greykite can check the magnitude of a holiday, see how much a changepoint affected the trend, or show how a certain feature might be beneficial to a model.

With Greykite, a “next 7-day” forecast trained on over 8 years of daily data takes only a few seconds to produce forecasts. LinkedIn says that its whole pipeline, including automatic changepoint detection, cross-validation, backtest, and evaluation, completes in under 45 seconds.

“The Greykite library provides a fast, accurate, and highly customizable algorithm — Silverkite — for forecasting. Greykite also provides intuitive tuning options and diagnostics for model interpretation. It is extensible to multiple algorithms, and facilitates benchmarking them through a single interface,” the LinkedIn research team wrote in a blog post. “We have successfully applied Greykite at LinkedIn for multiple business and infrastructure metrics use cases.”

The Greykite library is available on GitHub and PyPI, and it joins the many other tools LinkedIn has open-sourced to date. They include Iris, for managing website outages; PalDB, a low-key value store for handling side data; Ambry, an object store for media files; GDMix, a framework for training AI personalization models; LiFT, a toolkit to measure AI model fairness; and Dagli, a machine learning library for Java.

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