Apr 27, 2021

Why Toyota is acquiring Lyft’s autonomous vehicle unit

AI
autonomousvehicles
ridehailing
machinelearning
William Smith
2 min
Lyft’s Level 5 unit will become part of Toyota subsidiary Woven Planet, which is dedicated to advanced mobility and self-driving technologies
Lyft’s Level 5 unit will become part of Toyota subsidiary Woven Planet, which is dedicated to advanced mobility and self-driving technologies...

Ride-hailing firm Lyft has announced it is selling its self-driving vehicle unit to a subsidiary of Japanese automotive giant Toyota, in a deal worth $550mn.

Lyft’s Level 5 unit will become part of Toyota subsidiary Woven Planet, which is dedicated to advanced mobility and self-driving technologies.

Lyft follows Uber’s self-driving sale

The move is highly reminiscent of the sale of fellow ride-hailing firm Uber’s Advanced Technologies Group self-driving unit at the tail-end of last year. The self-driving vehicle business was sold to autonomous vehicle firm Aurora, in another effort from Uber to divest itself of unprofitable parts of its business and focus on its bread-and-butter ride-hailing and food delivery platforms, though it retained a 25% stake after the $4bn transaction.

“Not only will this transaction allow Lyft to focus on advancing our leading Autonomous platform and transportation network, this partnership will help pull in our profitability timeline,” Lyft Co-Founder and President John Zimmer said. “Assuming the transaction closes within the expected timeframe and the COVID recovery continues, we are confident that we can achieve Adjusted EBITDA profitability in the third quarter of this year.”

In pursuit of full autonomous driving

The established car giants are increasingly catching up to challengers by investing in autonomous vehicle technology. While the eventual goal is an autonomous vehicle at level 5 of the Society of Automotive Engineers’ (SAE) Levels of Driving Automation Standard, representing complete autonomy at all times, that target remains a way off.

The deal is expected to close in the third quarter of 2021, subject to the usual regulatory approvals and conditions.

“This acquisition assembles a dream team of world-class engineers and scientists to deliver safe mobility technology for the world,” James Kuffner, CEO of Woven Planet said. “The Woven Planet team, alongside the team of researchers at Toyota Research Institute, have already established a center of excellence for software development, automated driving, and advanced safety technology within the Toyota Group. I am absolutely thrilled to welcome Level 5’s world-class engineers and experts into our company, which will greatly strengthen our efforts.”

(Image: Lyft)

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Jun 17, 2021

Facebook Develops AI to Crackdown on Deepfakes

Facebook
MSU
AI
Deepfakes
3 min
Social media giant, Facebook, has developed artificial intelligence that can supposedly identify and reverse-engineer deepfake images

In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale? 

 

Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.” 

 

The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with. 

Why Reverse Engineering? 

Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient. 

 

First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source. 

 

How It Works 

Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.

 

To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters. 

 

What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’. 

 

What Did They Find? 

‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising. 


Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.

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