How AI is revolutionising crisis communication
Covid-19 has impacted companies all around the world with massive operational changes as leaders scramble to respond, survive and innovate. It has shown us the importance of agility in our business plans but also how critical internal and external communication is to businesses, so they thrive rather than simply survive.
Many businesses who have weathered the storms and are seeing the calmer waters ahead are doing so thanks to a combination of technology and the human touch. Together, these two factors will be the secrets to success moving forwards.
Giants of technology
Microsoft Teams, Workplace by Facebook, Google, AWS and Zoom are everyday working terms today, and implementing and tailoring these tech giants’ solutions within our own structure, we were able to provide a near seamless service to our customers and look after the wellbeing of our people. Adopting it early is all well and good, but maintaining communication is the key now.
Over 2020 we have been working with state of the art technologies to help us better understand those conversations. We leverage the work from Google, Facebook, Baidu and OpenAI, who are all making significant breakthroughs in this field. Over the past few years Natural Laguage Processing technology has evolved rapidly, starting with Google’s Attention Is All You Need paper that presented the Transformer model, which achieved state-of-the-art performance on almost all Natural Language tasks. That paper opened the flood gates for increasingly sophisticated and increasingly large transformer-based models to be produced.
Today, companies announce cutting edge NLP performance based on the transformer approach on an almost weekly basis constantly pushing the boundaries of what is possible. Improvements using Transformer based models will only continue and the recently announced Performer model from Google may prove to be another step-change in NLP accuracy. This technology is helping us to move closer to a point where machines can have context aware and human-like interactions with us.
Another area that is helping us automate our communication processing is the ability for computers to guess. Historically the lack of good training data has been a barrier for all but the largest organizations, however that barrier has been reduced with the introduction of zero shot classification. This approach uses general purpose pre-trained models to classify text the machine has never seen before, taking contextual information from pre-trained general models to predict a classification of something it’s never seen, essentially guessing. For certain tasks, the models are already amazingly accurate, removing the need to train new models from scratch removes a significant barrier for companies wanting to adopt these technologies, essentially democratizing capabilities that were historically only available to the largest tech organizations.
These are just two examples where technology is advancing quicker than ever before, and it is especially true in the field of communication. It has given us more options to communicate, improved the ways we communicate and generally pulled us into a new world of opportunity. Covid-19 has brought to the fore what can be achieved and what is on the cusp of being achieved in communication. It has shown that we crave human interaction facilitated by technology and together these two elements will shape the way the world communicates as we step into our new future.
Pete Hanlon is CTO of Moneypenny
Facebook Develops AI to Crackdown on Deepfakes
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’.