AI analog chip startup Mythic raise $70m in Series C funding
Mythic, a analog AI processor company, has announced $70 million in Series C funding. The new funding is to help accelerate plans for mass production of its M1108 AI inferencing chips and increase support for the company’s growing customer base, spanning across APAC, Europe, and the US.
The funding was led by BlackRock and Hewlett Packard Enterprise (HPE) and brings Mythic’s total funding to $165.2 million. Money raised will also be used to develop its next-generation hardware platform and build out its software portfolio.
“Customers are finding that powerful AI leads to significant differentiation of their products, and our cost-effective solution lets them deploy AI at an unprecedented scale,” said Mike Henry, CEO, Cofounder, and Chairman of Mythic. “We are thrilled to have a world-class firm like BlackRock lead this round, allowing us to massively scale up the production of our solutions, invest in our technology roadmap, and better serve the needs of our current and future customers across many different verticals.”
Analog Matrix Processor for AI applications
In November 2020 Mythic unveiled the first Analog Matrix Processor for AI applications, combining high performance with good power efficiency in a cost-effective solution. Mythic’s integrated hardware and software platform is making it easier and more affordable for companies to deploy powerful AI applications for the smart home, AR/VR, drone, video surveillance, smart city, and manufacturing markets.
According to Mythic their M1108 Mythic AMP makes this possible ‘by using analog computing and integrated flash memory to deliver significantly faster results at much lower cost and power consumption compared to typical digital processors’.
“Mythic is perfectly positioned to take advantage of the rapidly-growing demand for AI with the company’s groundbreaking analog compute technology, strong leadership team, and go-to-market strategy targeting a wide variety of industries,” said Paul Glaser, vice president and head of Hewlett Packard Pathfinder.
Mythic has said they will continue to move full steam ahead to help companies unlock the limitations of AI and cost-effectively deploy AI at an unprecedented scale.
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’.