May 28, 2021

Digital.ai announces AI-Powered Innovation Platform

AI
ML
software
Data
2 min
The Digital.ai Platform features AI and ML based analytics to predict and prevent issues impacting software reliability, efficiency and customer experience

Digital.ai, a technology company, has announced Digital.ai Platform, an AI-powered end-to-end solution that enables enterprises to orchestrate the delivery of software-driven business outcomes. 

Purpose-built for the enterprise, the platform offers AI-powered analytics, enhanced insights into value streams, risk management, and software delivery predictability. The platform gathers data from across the software lifecycle to form a unified system of record containing all of the data required to gain key business insights. 

The Digital.ai Platform

The Digital.ai Platform capabilities now include:

  • End-to-end DevOps lifecycle orchestration – Enables organisations to standardise and automate their entire release process. It provides the flexibility to address complex, enterprise-sized challenges. The enhanced Digital.ai Platform ecosystem offers best-of-breed integrations across the software lifecycle, with hundreds of pre-built integrations that enable customers to leverage existing toolchain investments and incorporate them as part of the end-to-end enterprise-wide release orchestration.
  • Unparalleled visibility into DevOps including DORA metrics and SAFe® value streams – Intelligence-infused Digital.ai Analytics Lenses deliver deep insights into data collected from Digital.ai and third-party solutions. Domain-specific lenses reduce time to value and improve enterprise decision making with out-of-the box pre-built metrics and dashboards, including DORA metrics, flow metrics, and more.
  • AI/ML-powered predictive & prescriptive insights – The platform offers AI solutions that shift organisations from being reactive to proactive. Digital.ai has updated and expanded its Digital.ai Change Risk Prediction – now with bi-directional integration with Digital.ai Release – and Digital.ai Service Management Process Optimisation solutions. The company also introduced two new solutions: Digital.ai Flow Acceleration and Digital.ai Quality Improvement.

The Digital Transformation Progress Report, recently published by Digital.ai, showed that 49% of enterprise leaders are not seeing the results they expected from their digital transformation efforts, 54% are worried about their ability to compete in today’s digital environments, and over 90% need to get more out of their digital transformation initiatives.

"In today's fast-paced digital economy, Agile and DevOps are important foundational practices, but they are not enough. To achieve the full benefits of digital transformation and deliver better outcomes faster than the competition, organizations must adopt a value stream center of excellence approach," said Ashok Reddy, CEO at Digital.ai. 

"With Value Stream Management (VSM) and the Digital.ai Platform, organisations transform traditional project teams, typically structured around and focused on outputs and features, to cross-functional value stream teams, structured around customer-centric value. These teams are able to continuously adapt to market changes and customer needs, and predictably deliver business outcomes."

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Jun 17, 2021

Facebook Develops AI to Crackdown on Deepfakes

Facebook
MSU
AI
Deepfakes
3 min
Social media giant, Facebook, has developed artificial intelligence that can supposedly identify and reverse-engineer deepfake images

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’.

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