Fireflies raise $14M for its AI video conferencing assistant
Fireflies, an AI voice assistant that joins video conferencing meetings to take notes, has announced that it has raised $14m in a Series A funding round led by Khosla Ventures, with participation from their seed investor, Canaan Partners. The company plans to use the money to expand its platform and acquire new customers, as well as to grow the size of Fireflies’ workforce.
Last year COVID-19 had a huge impact on businesses and forced a shift to remote working. This meant organisations had to change the way they worked and communicated with colleagues, leading to a rise in video calls / conferencing. Global Market Insights predicts that the video conferencing market will grow 19% between 2020 and 2026, reaching $50 billion in value by 2026. Over the past year, Fireflies has been in meetings, taking notes for over 2 million people across 200,000 organisations.
How does it work?
Fireflies Voice Assistant was rolled out in January 2020, just before the pandemic, for all the major video conferencing platforms including Zoom, Google Meet, MSFT Teams, Webex, GotoMeeting, and Skype.
Fireflies provide note-taking tools and deep learning technologies like AI-powered transcriptions. Once invited to a meeting, the company’s virtual agent begins transcribing in real-time, with a search feature that lets users filter for action items and other key moments.
The Fireflies vision is expanding beyond automatic meeting notes and now they want to automate the work that happens post meetings in apps. After the meeting, the platform will orchestrate actions in your systems of records where you already work. For example, Fireflies can auto-log calls and notes in your CRM, create tasks in Asana, or save meeting recaps to a Dropbox folder.
“When we first designed Fireflies, we wanted it to work with the ecosystem of tools that we use every single day,” said Krish Ramineni, co-founder and CEO of Fireflies. “Deep work is about streamlining repetitive tasks, so that people don’t lose context while switching between meetings, calendars, emails, and collaboration apps. To be able to orchestrate and automate complex business workflows with just the sound of our voice is something we hope to make possible for every person in the workplace. It starts with democratising voice-powered AI for everyday use cases like meeting notes.”
In the past couple of months, the company has expanded to supporting several dialers and telephony systems like Aircall and Ringcentral. In addition to the voice integrations, users can kick off workflows to dozens of CRMs & collaboration apps. As companies return to the office and as more hybrid work structures form, Fireflies will have a larger role to play in keeping remote and in-person employees in the loop.
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’.