What is ‘liquid’ machine learning?
Machine learning has taken another step forward after researchers at MIT cracked what has been dubbed ‘liquid’ machine learning networks.
What is liquid machine learning?
Most AI works on the basis of a training phase, during which the algorithm is exposed to massive amounts of training data. Once sufficiently trained, the ML is implemented in a real-world situation, applying what it has learned to new data. With liquid machine learning, the algorithms are flexible and continue learning once they are operational. That means they can adapt to data streams that change over a period of time.
How is liquid machine learning useful?
The researchers say liquid machine learning could help with medical diagnosis and autonomous driving. The study’s lead author, Ramin Hasani, says, “This is a way forward for the future of robot control, natural language processing, video processing – any form of time series data processing. The potential is really significant.”
Is liquid machine learning ready to implement now?
Not yet. The research is being presented at the AAAI Conference on Artificial Intelligence in February 2021 by CSAIL researchers and others who have worked on the project. But assuming it gets a positive reception, it would then be moved forward. Hasani is excited about the possibilities: “The real world is all about sequences. Even our perception – you’re not perceiving images, you’re perceiving sequences of images,” he says. “So, time series data actually create our reality.”
So liquid machine learning hasn’t been tested yet?
It hasn’t been out to the wider research community but in tests predicting future values in datasets from atmospheric chemistry to traffic patterns, it bested other top-level time series algorithms. “In many applications we see the performance is really high,” Hasani says, “Everyone talks about scaling up their network. We want to scale down, to have fewer but richer nodes.”
What are nodes in liquid machine learning?
Liquid machine learning is heavily influenced by a microscopic nematode called C. Elegans. “It only has 302 neurons in its nervous system,” Hasani says, “yet it can generate unexpectedly complex dynamics… Just changing the representation of a neuron, you can really explore some degrees of complexity you couldn’t explore otherwise.”
Where next for liquid machine learning?
Hasani continues to improve the system in the hope of implementing it in industrial applications. “We have a provably more expressive neural network that is inspired by nature,” he says, “but this is just the beginning of the process. The obvious question is how do you extend this? We think this kind of network could be a key element of future intelligence systems.
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’.