Apr 29, 2021

New DWS equity fund focuses on artificial intelligence

AI
Data
Digitalisation
DWS
Tilly Kenyon
2 min
The DWS Concept ESG Arabesque AI Global Equity fund is the first joint product of DWS and Arabesque AI, and focuses on the use of AI
The DWS Concept ESG Arabesque AI Global Equity fund is the first joint product of DWS and Arabesque AI...

DWS, a German fund manager, has launched an equity fund whose stock selection is based on the use of artificial intelligence. The DWS Concept ESG Arabesque AI Global Equity fund is the first product to result from the partnership between DWS and UK-based AI firm Arabesque that was struck in early 2020.

The DWS Concept ESG Arabesque AI Global Equity picks up on the three megatrends that DWS has identified for the coming decade: Low-interest rates, sustainability, and digitalisation. It takes ESG criteria into account, pursues a total return approach, and comprises between 60 and 70 stocks from the MSCI World universe. The expected tracking error is between 6 and 7 %.

"With this product, we combine the advantages of artificial intelligence with the expertise of our investment managers in an actively managed investment fund. By combining the unique strengths of both partners, we can better analyse the exponentially growing amount of data through innovative technologies to derive new insights," says Manfred Bauer, head of the product division at DWS.

Yasin Rosowsky, co-CEO of Arabesque AI, adds: “Advancements in AI technology are driving transformation of the global marketplace. The launch of the DWS Concept ESG Arabesque AI fund is the result of a strong collaboration between Arabesque and DWS, combining DWS’ fund management expertise with Arabesque’s AI capabilities to co-develop a cutting-edge investment product. We are delighted to be partnering with DWS on this exciting new offering.”

Increasing use of AI 

DWS explained how fast data volumes are increasing. ‘In 2009, the number of new data was still 0.5 zettabytes (one zettabyte corresponds to 1,073,741,824 terabytes), in 2019 it will already be 45 zettabytes. For the year 2025, experts expect new data amounting to 175 zettabytes, i.e. 350 times the amount of 2009. For the human brain, this development is a big problem, but for artificial intelligence, the more information, the better.’

Although AI will increasingly become more widely used and taking on an important role, DWS insists it will not completely replace analysts or fund managers. 

Gerard Grech, CEO of Tech Nation said: “UK AI continues to go from strength-to-strength: in 2020 UK firms that were adopting or creating AI-based technologies received £1.78bn in funding”

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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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