Subex launches a new augmented analytics platform
Subex, an enterprise software company, has launched HyperSense, an end-to-end augmented analytics platform that helps enterprises make faster, better decisions by leveraging Artificial Intelligence (AI) across the data value chain
to Subex, HyperSense contains ‘all the augmented analytics capabilities enterprises need in one flexible and modular platform’. HyperSense‘s unique no-code capabilities allow users without a knowledge of coding to easily aggregate data from disparate sources, turn data into insights by building, interpreting, and tuning AI models, and effortlessly share their findings across the organization.
How does it work?
When businesses start using AI and machine learning technology they can sometimes encounter problems such as a lack of AI skills and the absence of an integrated AI and data stack.
HyperSense has been designed to help solve all these challenges through five powerful yet modular studios:
- Data Management Studio gives users one-place access to batch and streaming data across multiple data formats and lets them search, enrich, structure, and validate the data collected from across the business;
- Business Modelling Studio allows users to apply rules and run data audits in real-time, profile information, monitor and forecast business KPIs;
- AI Studio adds AI capabilities such as model building, model diagnostics, explainable AI, and hyperparameter tuning;
- Business Intelligence Studio visualises the data analytics output for fast insights and easy sharing;
- Process Automation Studio allows users to create actions and workflows to drive forward decisions based on the data insights
Suresh Chintada, Chief Technology Officer, Subex, : “Most enterprises struggle to implement AI with business value at its centre. Instead, AI initiatives are driven by data science teams, with little alignment between the priorities of business and IT. We developed HyperSense to address this – it gives organisations more autonomy, wider access to AI and machine learning, and puts more power into the hands of business users. With this game-changing platform, businesses will be able to truly democratise AI and turn data into reliable insights with greater speed and efficiency, supporting elastic business models and significantly accelerating their business transformation.”
HyperSense also includes several pre-built analytics use cases in marketing, finance, and technology verticals for enterprises to deliver ultra-fast results. Customers can also use the HyperSense platform to build their own tailor-made, AI-powered analytics applications.
The cloud-native platform can be integrated with existing data management infrastructures or implemented as a standalone, plug-and-play data analytics solution.
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’.