The secret to Artificial Intelligence in smaller Businesses

10

October

2021

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Artificial intelligence and machine learning are known hot topics in any industry. However, the development and usage of such techniques seem successful for a happy few mostly very large companies. Mckinsey research from 2020 has shown that ai adoption is not increasing while firms that apply ai gain performance boosts with the last year seeing a significant increase in revenue based on ai. An average increase of 5% revenue was found in firms applying ai for revenue generation or cost-reduction. However, McKinsey’s research also showed that average firms gain relatively little from ai while a few superstars gain much more.

So how can you, as a smaller firm, be one of the Superstars? Luckily, there are a couple of best practices to follow and become of the stars of Ai usage. Firstly, you need to have a dedicated AI champion at the lower level of management. Firms with such an AI champion are 2.3 times more likely to experience significant AI performance boosts. Secondly, Resource commitment for three years or more allows your champion to develop in-house AI solutions leading to a significant performance boost over outsourced AI solutions. Thirdly, firms with a higher AI performance increase use several practices for development. These are; having a road map linked to AI initiatives linked to business value, top management fully committed and aligned with AI strategy, acceptance towards risk-taking in ai implementation, and having a standard framework for Ai implementation with an understanding of the frequency of updates on ai models. Finally, as an employer or a top manager, you need to have trust your employees to develop and know where to apply the AI to create value for your firm.

Of course, the development and implementation of AI are not without risks and research from 2019 shows that the majority of firms has no active plans on how to deal with the most common risks associated with AI. As well as a majority of firms does not even recognise most of the common risks as risks. A framework to best assess the risks for your firm is the following. First, create clarity by using a structured approach to specify the critical risks by creating a cross-functional team that tiers and points out risks. Secondly, create Breadth company-wide controls for AI usage by training staff across functions to create a wide knowledge base on risk prevention. Thirdly, nuance for specific critical risks as some risks are so important they need their own controls apart from the company-wide controls. For example, if the usage of AI can be reversed at any time without any fall out then a broad approach is sufficient. However, if the usage of AI will fundamentally change the business process a more nuanced control is necessary.

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The arms race in Deepfakes

5

October

2021

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In the new Starwars series The Mandalorian, there is an iconic scene with Mark Hamill where he appears a young 30 even though he is 70 years of age. Disney used Digital techniques to de-age Mark Hamill to appear as he did during the first Starwars films some 40 years ago. Now, what does this have to do with deepfakes? Lucas film used an entire team of VFX producers to de-age mark’s character and the result was a wax doll looking version of the character. In just four days one person using deepfake technology was able to create a better-looking scene and was consequently hired by Lucas film. In the video below you can see for yourself the power of this technology.

As you just saw deepfake technology gives filmmakers tremendous opportunities to do what only was reserved for large film studio’s. This gives filmmakers around the world the possibility to make high-end visual effects without the need for large studio technologies or backing.

However, there are downsides to this technology as well. Even though this is a golden gift for the film industry malicious parties could use this technology to slander and/or discredit public figures, and as deepfakes use machine learning they keep improving themselves and will become indistinguishable from reality. The following video is from 2018 and since then the quality of deepfakes has only improved.

Now, what can we do to battle against this ever-increasing threat of mis- and dis-information. In 2019 a paper was released called Faceforensics++ in which the researchers used machine learning to create a deepfake detector by using four deepfake generators run over their data set of a thousand video’s. In this way, the deepfake detector got trained to detect several kinds of deepfakes and in high resolution, it could detect deepfakes with an accuracy over 99%, however, in low-resolution video’s it would go down to 51,80%. (Rössler, et al., 2019) This reveals a weakness of the technology as online videos are compressed and changed numerously. Additionally, the detection software allows deepfake machine learning to keep improving itself based on where faults were found. A true arms race between deep fake generation and detection. As one improves the other will inevitably improve along with it. The only way to protect ourselves against such technology is proactively developing deepfakes and their detectors, in order to stay ahead of those who would use them to do harm.

Refernce : Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J. and Nießner, M., 2019. FaceForensics++: Learning to Detect Manipulated Facial Images. arXiv (2019).

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