Can chatbots help meet growing consumer expectations?
In a world where technology is constantly evolving and digital transformation is accelerating, consumers have developed higher expectations. The COVID-19 pandemic has accelerated global deployments of conversational artificial intelligence (AI) solutions, to help meet the growing needs.
What are chatbots and why are they important?
A chatbot is an artificial intelligence (AI) programme that is designed to simulate communications with customers. Customers can engage directly with chatbots through chat windows, messaging, or voice applications.
They can be extremely beneficial for companies as they offer a fast and engaging customer experience by providing troubleshooting services, an on-demand help desk, and a personal assistant all at the same time. It is more convenient and takes less effort and time for customers to converse with a chatbot. A bot uses artificial intelligence to instantly search through sizable quantities of information and accurately select the most relevant answer for a consumer.
What does the future look like for chatbots?
In 2020, the chatbot market was valued at $17.17 billion and is projected to reach $102.29 billion by 2026, registering the compound annual growth rate (CAGR) of 34.75% in the forecast period 2021 - 2026, according to Landbot.
As technology develops and the chatbots get more complex and start being more lifelike, the one-size-fits-all approach starts not being viable. When choosing a chatbot vendor, or implementing your own chatbot, it is important to make sure the chatbot you use will learn from its past experiences. Modern chatbots make use of the advancements in AI and ML in order to learn what your customers ask, and how they can better answer that.
Hyro, a leader in conversational artificial-intelligence (AI) solutions, recently announced that it has closed a $10.5 million Series A funding round to replace chatbots and IVR systems with Adaptive Communications.
“Hyro is pushing the reset button on chatbots and IVR systems. We’re reinventing the way companies and their customers interact by offering adaptive communications, as opposed to intent-based solutions that often break. Chatbots have become a mainstay within customer engagement, but because many are intent-based, they constantly need to be retrained and have limited ability to scale, which forces the enterprise to slow down while also alienating its customers,” said Israel Krush, Co-Founder and CEO of Hyro.
The knowledge and the full potential of chatbots are still being discovered, and companies are starting to understand and leverage their potential more effectively. Keeping up with this age of personalisation and the need for satisfaction will certainly be a testing time for companies, but chatbots could provide a hand.
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’.