In a paper published on the preprint server this week, researchers at Uber’s Advanced Technologies Group (ATG) propose an AI technique to improve autonomous vehicles’ traffic movement predictions.

It’s directly applicable to the driverless technologies that Uber itself is developing, which must be able to detect, track, and anticipate surrounding cars’ trajectories in order to safely navigate public roads.

Interesting times we live in.

It’s well-understood that without the ability to predict the decisions other drivers on the road might make, vehicles can’t be fully autonomous. In a tragic case in point, an Uber self-driving prototype hit and killed a pedestrian in Tempe, Arizona two years ago, partly because the vehicle failed to detect and avoid the victim. ATG’s research, then — which is novel in that it employs a GAN to make car trajectory predictions as opposed to less complex architectures — promises to advance the state of the art by boosting the precision of predictions by an order of magnitude.

Cruise, the self-driving subsidiary of General Motors, revealed its first vehicle to operate without a human driver, the Cruise Origin.

The vehicle, which lacks a steering wheel and pedals, is designed to be more spacious and passenger-friendly than typical self-driving cars.

Cruise says the electric vehicle will be deployed as part of a ride-hailing service, but declined to say when that might be. 

Deep Learning has enabled all sorts of innovations, but what would happen if more people (ie. non developers & ML engineers) had access to this technology. That’s the goal of Ludwig.

Ludwig is a toolbox built on top of TensorFlow that allows anyone to train and test deep learning models without any code. It provides a datatype based approach to develop a predictive deep learning models suitable for a wide range of applications.

In essence, it will allow non-experts to develop and integrate deep learning models in their website or app to get the full benefit of a deep learning system that will help them accelerate their business or improve their lives. These non-experts and even experts for that matter can leverage this powerful new technology to dramatically reduce the time spent training and testing deep learning models and instead “Focus on developing deep learning architectures rather than data wrangling” to quote the Uber researchers from their blog post.

Over the last decade or so, open source has blossomed into a major movement and the backbone of the tech industry. For instance, check out this project that Uber, yes Uber, has open sourced.

Ludwig is a TensorFlow-based toolbox that allows you to train and test deep learning models without the need to write any of the code. Incubated at Uber for the last two years, Ludwig was finally open sourced this February to incorporate the contributions of the data science community. With Ludwig, a data scientist can train a deep learning model by simply providing a CSV file that contains the training data as well as the YAML file with the outputs and inputs of the model.

While Uber is known as one of the pioneers in self-driving vehicels, its autonomous vehicle division has been a source of contention for investors. TechCrunch recently reported numbers that were less than flattering : The ride-hailing company was spending $20 million a month on developing self-driving technologies.The Wall Street Journal estimates that Uber spent about $750 million on building out self-driving technologies before scaling back in 2018.

However, all is not bleak: Uber ’s autonomous vehicle unit may be about to get a massive ($1 billion+) cash injection?

It’s highly possible, according to news reports indicating a group of investors including SoftBank Group is putting money into the division. The Wall Street Journal reported last night that Uber, more formally known as Uber Technologies Inc., was in “late-stage” discussions with a consortium that would invest in the startup’s self-driving vehicle division.

I live in Maryland, near DC, and, from time to time, you will hear me talk about “spending quality time” on the beltway to get to Northern Virginia. The most irritating part is not the bumper to bumper traffic, it’s the fact that I actually live quite close to NoVA as the crow flies. It’s just that there are not enough bridges. However, due to many factors, a new bridge will never be built where it’s needed most. See artistic rendering below.

Going 9 miles takes 30.

I would often dream of being able to fly over the Potomac with ease. However, with recent innovations in technology, aerospace, and electric vehicles, this could actually happen (sooner than a new bridge, at least).

I know I’m not alone: images of the future have always included flying cars. But it turns out, air-commuting had a heyday in the 60s and just might make a comeback in the 2020s according to this video from Bloomberg.