Sabre partners with Google for smart retailing travel AI
Travel tech company Sabre Corporation has announced the first product of its partnership with Google to develop an AI-driven travel platform.
Sabre, which is best known for its global distribution systems which have long served as the underlying framework for air travel bookings, operates a platform that makes use of Google Cloud’s AI and machine learning capabilities. By analysing and predicting customer behaviour, Sabre’s platform allows travel businesses such as airlines to make the most relevant offers to customers.
Building on that, the company is preparing to release a Smart Retail Engine next year, with a focus on omni-channel retailing. The product uses AI and machine learning-based decision models to generate the most optimal offers for customers, based on real-time shopping data and available content.
In , Wade Jones, chief product officer for Sabre, said: "Our proprietary technology infuses the power of Sabre Travel AI to deliver, not next-, but a third-generation of technology to the travel marketplace. By bringing together some of the brightest minds from Google and Sabre, we are accelerating the delivery of this smart and scalable retailing engine that we believe will enable customers to deliver personalized offers to their customers, better serving the needs of today's traveler, while unlocking more value per passenger boarded."
The work builds on an earlier partnership with Google Cloud signed in January 2020, with Sabre also expecting its customers to be able to integrate their systems with Travel AI, for instance by plugging-in third party databases.
"We are pleased to be working side-by-side with Sabre to bring innovative, industry-first technologies to the travel space," said Ravi Simhambhatla, Managing Director, Digital Transformation Officer – Travel & Transportation of Google Cloud. "Today's announcement, as well as Sabre Travel AI, are what we hope will be the first of many concepts generated from our partnership."
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’.