“Can machines exhibit behavior that cannot be distinguished from that of human behavior? The answer is a resounding “Yes!” The technology behind this is called a Generative Adversarial Network.”

Can a machine imitate a person? This question was first asked by one of modern computing’s founders, Alan Turing, nearly 70 years ago and it is the basis of the Turing Test. This thought experiment asks, simply, “Can machines think?”.  Answering this, Turing asserted in his paper, Computing Machinery and Intelligence, that if a person cannot distinguish a machine’s behavior from that of a human, that the machine passed the test. That is, that the machine was intelligent.1

What has changed in the past 70-plus years? Certainly, a lot! Early on, machines were only capable of winning against a human in a simple game of Tic-Tac-Toe. However, the modern rise of deep learning has brought witness to a machine winning out over game champions. The modern world now has virtual assistants capable of answering questions and completing simple tasks. So, not only have we gotten to the point where machines can answer questions, but machines can also give these answers in the form of a question. 

Yet one important question remains– can machines pass the Turing test? Can machines exhibit behavior that cannot be distinguished from that of human behavior? The answer is a resounding “Yes!” The technology behind this is called a Generative Adversarial Network, or GAN for short, and the implications of this emerging technology are far reaching. So, what are GANs? 

A GAN is a specialized neural network, that is designed to beat the Turing test. The network is made up of three components: real-world data, a discriminator, and a generator. Of these, the discriminator network is trained using true, real-world, data. This component’s job is to answer the question “Is this real or manufactured?”. The discriminator network becomes a judge, of sorts. It decides what’s real and what’s fake. 

The “generative” node of a GAN typically creates text, images, or video. It begins with random data, and generates progressively-better samples, to try and trick the discriminator into believing that the sample is real-world data. The generator and discriminator are two discrete networks competing against each other. 

Figure 1: Generative Adversarial Network Model Architecture2

Initially, the generator network’s attempts are completely off the mark. Its initial guesses are incomprehensible text, static, or noise. Over time, the generator’s performance improves. This is done by iteratively improving both the discriminator and generator components of the network. 

When the generator fails to fool the judge, it learns from its mistakes and improves itself. Conversely, when the discriminator is fooled, it undergoes self-improvement and the contest continues. This adversarial competition doesn’t stop until the generator and the “judge” call a truce. If this competition hosted world-class competitors–meaning that the GAN, itself, had an ideal setup–the GAN would produce synthetic data that cannot be differentiated from real world samples and it would pass the Turing Test. 

A way to visualize this concept is to imagine a pair of sparring partners training in marital arts. As the student tries to best the teacher, the teacher presents new ways to defend against the different moves the student attempts. Every round, the martial artists train with each other, and both individuals improve on what they have learned. 

The results from these contests come in many practical forms. They can be used to enhance service to customers, to create a virtual marketing “influencer”, and to create art. Recently, GAN-created art sold at Christie’s Inc. auction house for over $400,000!3  On the business front, marketers are turning to synthesized personas to serve as their social media “influencer”, since it allows them to more-effectively control messaging to their target audience.4  In the app economy, GANs can be used to enhance facial authentication. The technology can simulate an account user’s profile photo in various lighting conditions and use this information to provide better account login. This provides an added layer of security to both parties and convenience to customers, since they’ll have fewer problems verifying themselves. 

For banks and financial services companies, GANs can improve chatbots and voice assistants as they learn how to respond in a more humanistic form. This heightened and seemingly more humanistic interface helps to potentially curb needless customer escalation from bot to human channels as well as encourage adoption of non-human interaction channels by customers. 

GANs can also help improve security and prevent fraud by proactively identifying weaknesses within fraud risk management, information security, and anti-money laundering compliance surveillance systems. These advanced analytical tools can allow banks to improve privacy and compliance efforts and detect irregular transactions or trades. 

Banks should also be acutely aware of the threats that GANs pose to them. Just as GANs can be used to make synthetic interactions appear more natural (e.g., as in the case of making a chatbot appear more human), the same can be done in reverse by those with malicious intent. GANs can be used by fraudsters to synthesize identities by creating faces and voices attached to an imaginary person. Since banks as a group process some of the largest transactional volumes in the world, it is important to be able to detect GAN-synthesized people, documents, or data, before they do any damage. 

Having a solid foundational knowledge of GANs and their capabilities is important to any public-facing organization; not only banks. GANs can be used to provide a higher level of service to new and existing customers and solid knowledge of them can help to secure the organization from their misuse while protecting against financial or reputational risk. 

Companies can best prepare for the future by expanding their understanding of how GANs work. Being at the forefront of modern machine learning techniques, these specialized neural networks should lead the way to our next big technological leap in artificial intelligence. They are expected to be a big part of our future and are increasingly present. 

It is recommended that financial services companies start investigating this new and emerging area to gain a strategic edge against competitors, criminals, and market trends. Accenture and its team of artificial intelligence specialists are here to help every step of the way. 

References:

  1.  “Computing Machinery and Intelligence,” A.M. Turing, first published October 1, 1950, Mind, Oxford Academic. Access at: https://academic.oup.com/mind/article/LIX/236/433/986238.  
  2. Generative models and adversarial training, Kevin McGuiness, Dublin City University, August 3, 2016. Access at: https://www.slideshare.net/xavigiro/deep-learning-for-computer-vision-generative-models-and-adversarial-training-upc-2016.  The pictures of cats used in this chart were sourced from https://pixabay.com/.   
  3.  “Is artificial intelligence set to become art’s next medium?,” Christie’s. Access at: https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx.  
  4. “CGI ‘Influencers’ Like Lil Miquela are About to Flood Your Feeds,” Wired, May 1, 2018. Access at: https://www.wired.com/story/lil-miquela-digital-humans/.

 

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