Thousand Robots

Historically, significant progress in the area of reinforcement learning (RL) has resulted from simulation environments in games such as Dota 2, Minecraft and StarCraft II. Unfortunately, these developments came with a taxing amount of computation, typically requiring the usage of thousands of GPUs at once for just one experiment. Even despite these costs, many of these RL methods didn’t prove to be very applicable to solving real-world issues.

As soon as Facebook took note of the demand for an environment with a vast number of diverse observations that could still operate a simulation rapidly and

Ships carry more than 80% of world trade, and they rely heavily on some of the least environmentally friendly transportation fuels available.

There are no cheap, widely available solutions that can lower the shipping industry’s planet-warming carbon emissions – in fact,shipping is considered one of the hardest industries on the planet to decarbonize – but some exciting innovations are being tested right now.

As a professor of naval architecture and marine engineering, I work on ship propulsion and control systems, including electrification, batteries and fuel cells. With attention focused on climate change this week as world leaders meet at the

The R language has a new, built-in pipe operator as of R version 4.1:  |> 

%>% is the pipe that most R users know. Originally from the magrittr package, it’s now used in many other packages as well. (If you’re wondering where the magrittr name came from, it’s a reference to Belgian artist Rene Magritte and one of his paintings, The Treachery of Images, that says in French: “This is not a pipe.”)

Here’s a somewhat trivial example using the %>% pipe with the mtcars data set and a couple of dplyr functions. This code filters the data for

It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon.

Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company’s financial backers also include Geoffrey