Dataiku’s enterprise AI and ML platform gets $100mn boost
The company touts its offering as incorporating the whole process from gathering raw data to deployment, while remaining accessible to all stakeholders from business analysts to to data scientists. AI models offered by the company include solutions for fraud detection, the prevention of customer churn, predictive maintenance and supply chain optimisation.
Dataiku says it has over 300 customers, including the likes of global giants such as Unilever, Santander, GE Aviation and BNP Paribas.
Since its foundation in 2013, the company has raised . Adding to that figure, the company has today announced a further $100mn Series D led by Stripes, alongside Tiger Global Management, Battery Ventures, CapitalG, Dawn Capital, FirstMark Capital and ICONIQ.
“Our leadership in enterprise AI continues to attract world-class investors who understand that Dataiku’s solution and customer base are truly global and that we’re uniquely positioned to help businesses realize the untapped potential for AI to transform the enterprise,” said Florian Douetteau, co-founder and CEO of Dataiku. “In a global business market rocked by the changes 2020 has brought, AI has proven to be a critical element of organizational success driving business growth in every major vertical market.”
Machine learning and AI projects are to achieve within a business setting, with issues such as feature creep, lack of expertise and more proving significant stumbling blocks. Dataiku’s ambition to enable enterprise adoption of AI is one it shares with many other players, both startups and established giants such as .
“What we’ve seen in Dataiku is not only a commitment to developing future-proof technology for customers ranging across nearly every major industry and geography, but perhaps more importantly, a team of dedicated professionals and global organization working to ensure trust, safety and resilience through machine learning. We’re excited to be a part of their continued success through this investment,” said Paul Melchiorre, Operating Partner at Stripes.
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’.