Nvidia announces new AI software development platform
NVIDIA has unveiled its new NVIDIA® Base Command™ Platform, a cloud-hosted development hub that allows companies to move their AI projects from prototypes to production.
The Base Command Platform is available through a monthly subscription jointly offered by NVIDIA and NetApp, with pricing starting at $90,000. The platform allows developers access to the cloud-hosted computing power of Nvidia DGX SuperPOD AI supercomputers and NetApp data management tools.
The software is designed for large-scale, multi-user and multi-team AI development workflows hosted either on-premises or in the cloud. The software development platform enables numerous researchers and data scientists to “simultaneously work on accelerated computing resources, helping enterprises maximise the productivity of both their expert developers and their valuable AI infrastructure,” the company said.
Managing AI workflows
“World-class AI development requires powerful computing infrastructure, and making these resources accessible and attainable is essential to bringing AI to every company and their customers,” said Manuvir Das, head of Enterprise Computing at NVIDIA. “Deployed as a cloud-hosted solution with NVIDIA-accelerated computing, NVIDIA Base Command Platform reduces the complexity of managing AI workflows, so that data scientists and researchers can spend more time developing their AI projects and less time managing their machines.”
According to NVIDIA, Google Cloud plans to add support for Base Command Platform in its marketplace to deliver a hybrid AI experience for customers later this year.
“A majority of enterprises now see AI as critical to the success of their digital transformation initiatives, but are challenged by the complexity of deploying and integrating it into their organisations,” said Brad Anderson, executive vice president of the hybrid cloud group at NetApp. “The NVIDIA Base Command Platform with NetApp and new subscription offering make it easier for customers to implement AI and put it to work, simplifying workflow management, and providing unmatched performance and processing power to supercharge their deployments.”
NVIDIA also announced a variety of new servers certified to run NVIDIA AI Enterprise software, showing a rapid expansion of the NVIDIA-Certified Systems programme, which has grown to include more than 50 systems from leading manufacturers. The platform enables enterprises to support a range of demanding workloads in traditional data centres and hybrid clouds. These include running the NVIDIA AI Enterprise suite of AI and data analytics software on VMware vSphere to deploy an AI-ready enterprise platform that scales AI workloads.
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’.