Locus Robotics’ warehouse automation leads to unicorn status
Wilmington, Massachusetts-based Locus Robotics is building autonomous robots for use in warehouses supporting the boom in ecommerce.
While static industrial robots have been with the world for some time, we’ve seen mobile robotics come on leaps and bounds in recent years, moving out into real-world applications. Take fellow Massachussetss-based firm Boston Dynamics, whose robot went on sale last year.
Mobile robots in the warehouse
One section ripe for robot deployment is logistics, with robots taking up the work of transporting items in a warehouse.
That’s precisely what Locus Robotics specialises in with its collaborative and autonomous LocusBots, which are capable of moving inbound and outbound items alongside human workers.
The company currently works with 40 customers and 80 warehouses globally, with its LocusBots picking up a total 300 million units.
The latest AI unicorn
The company has just become the latest technology unicorn (a startup worth over $1bn) thanks to a new $150mn round of Series E funding, announced yesterday and led by Tiger Global Management and Bond, alongside Scale Ventures Partners and Prologis. Since being founded in, the company has raised across five funding rounds.
“This new round of funding marks an important inflection point for Locus Robotics,” Rick Faulk, CEO of Locus Robotics. “Warehouses facing ongoing labor shortages and exploding volumes, are looking for flexible, intelligent automation to improve productivity and grow their operations. Locus is uniquely positioned to drive digital transformation in this enormous global market.”
The company said it would use the funds to continue research and development efforts, as well as expand globally.
“Locus’s innovative mix of proven technology, flexible design, and seamless scalability makes it an ideal choice to lead the digital transformation of the warehouse,” said Griffin Schroeder, Partner at Tiger Global. “Facing rapidly growing ecommerce volumes, rising labor costs, and increasingly demanding customers, warehouse operators are seeking an automation solution that is flexible, scalable, and just works.”
(Image: Locus Robotics)
Facebook Develops AI to Crackdown on Deepfakes
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’.