Beyond Limits partners with NVIDIA on AI for energy sector
The company says its Cognitive AI technology involves the use of conventional techniques such as machine learning, neural networks and deep learning, as well as knowledge-based reasoning to make autonomous decisions that nevertheless leave behind audit trails that can explain the reasoning. Such an approach is crucial in industries such as healthcare, where accountability is an ethical necessity.
The company has announced it is collaborating with US tech firm NVIDIA, a specialist in the graphical processing units (GPUs) useful for machine learning applications thanks to their highly parallel nature. The collaboration will include AI software optimised for GPUs to improve performance and efficiency in the software development cycle.
In , AJ Abdallat, CEO of Beyond Limits, said: “AI has the potential to make a major impact on problems facing the heart of the global energy business, but the technology requires high levels of computing power to operate on the level and scale required by many of today’s global producers. That’s why we’re so excited to collaborate with NVIDIA, a leading provider of AI computing platforms. With NVIDIA technology support and expertise, Beyond Limits is better positioned to offer faster, more intelligent and efficient AI-based solutions for maximizing energy production and profitability.”
One example of AI’s benefit in the energy industry has been demonstrated by Beyond Limit’s use of a deep learning framework on NVIDIA A100 GPUs to predict and recommend the placement of wells in the oil and gas sector.
“The NVIDIA A100 offers the performance and reliability required to meet the demands of the modern day energy sector,” said Marc Spieler, Global Energy Director at NVIDIA. “The ability to process hundreds of thousands of AI simulations in real-time provides the insight required for Beyond Limits to develop scalable applications that advance energy technologies.”
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’.