Sep 15, 2020

C3.ai to launch data-driven initiative to combat COVID-19

Kayleigh Shooter
2 min
stocks covered in covid particles
The technology giant is set to launch a new initiative focused on data-driven solutions to combat COVID-19...

 It was announced today that the technology and artificial intelligence company, C3.ai is launching a new initiative which invites data scientists, developers, researchers, and creative thinkers internationally to take part in the new and innovative "C3.ai COVID-19 Grand Challenge". The company is serving up $200,000 in cash prizes. The competition hopes to generate insights that can aid the fight against the coronavirus and insights that were not previously neither apparent nor achievable.

Thomas M. Siebel, CEO of C3.ai has high hopes for the competition and says; “The C3.ai COVID-19 Grand Challenge represents an opportunity to inform decision-makers at the local, state, and federal levels and transform the way the world confronts this pandemic, as with the C3.ai COVID-19 Data Lake and the C3.ai Digital Transformation Institute, this initiative will tap our community’s collective IQ to make important strides toward necessary, innovative solutions that will help solve a global crisis.”

A panel of judges will evaluate all submissions and base their decisions on whether the results are insights, leveraging data science techniques (e.g., statistical analyses, AI/ML algorithms, optimization approaches, etc.) that were not obvious before. The judging panel consists of five high level technology executives:

  • Pat House, Vice Chairman at C3.ai
  • Mike Callagy, County Manager at County of San Mateo
  • Richard Levin, C3.ai and Former President Emeritus at Yale University
  • S. Shankar Sastry, Co-Director, C3.ai Digital Transformation Institute and Professor of Electrical Engineering & Computer Sciences at UC Berkeley
  • Zico Kolter, Associate Professor of Computer Science at Carnegie Mellon University

The competition is now open and a registration deadline of the 25th October and a submission deadline of Nov. 18, 2020. You can learn more about how to submit proposals, here: https://c3.ai/grand-challenge/.

C3.ai is a high fledged artificial intelligence software provider specialising in the acceleration of digital transformation. The company delivers its innovative C3 AI Suite for developing, deploying, and operating large-scale artificial intelligence, predictive analytics, and Internet of Things applications in addition to an increasingly growing portfolio of innovative AI applications. The core of the C3.ai's business is a revolutionary, model-driven AI architecture that dramatically enhances data science and application development.

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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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