Last Week in AI #138: Singapore trials surveillance robots, Deepfake voice used to rob a bank, Google studies the limits of large-scale pretraining
Singapore's patrol robots send live video feeds to the police, Google researchers study nuances of scaling large neural network models, Deepfake voice over the phone used to steal money, and more!
A new robot named Xavier has been developed to patrol the streets of Singapore. It is outfitted with sensors for autonomous navigation, and has a 360 degree camera feed to record what’s happening near it. Officers can receive the video feed and are able to monitor and control multiple robots simultaneously. Its aim is to detect undesirable behavior, such as smoking in prohibited areas, illegal hawking, improperly parked bicycles, and more. In response to such situations it can trigger real-time alerts and display appropriate messages deter such behaviours.
With advances from models like GPT-3 and the even more recent 530B-parameter Megatron model from Microsoft/NVIDIA, it's natural to wonder just how much more performance we can get by scaling up model and training data size. Researchers have already studied scaling laws for natural language models, but a recent paper from Google Research provides further detail. The researchers systematically conducted more than 4,800 experiments on Vision Transformer, MLP-Mixer and ResNet architectures with 10 million to 10 billion parameters and evaluated them on over 20 downstream image recognition tasks. Among other things, they found that with increasing performance on upstream tasks, downstream tasks reached different saturation points. The researchers concluded that scaling does not lead to a "one size fits all" model and that researchers should make architectural choices to improve performance on a range of downstream tasks, rather than focusing on just one. Furthermore, upstream and downstream performance might sometimes be in tension. For researchers and engineers, the answer to developing capable models seems not to be as simple as recent advances might suggest.
A recent investigation has found that deepfake audio technology was used to mislead a bank manager from the United Arab Emirates to distribute funds to a theif's account. The bank manager reported receiving a phone call from someone who sounded liked the director of a company whom the bank manager personally knows, and he transferred the money as requested. Deepfake audios use neural networks, often trained on a dataset of the voice of a particular person, to generate new voice that imitates that person. It is notable that in this case, running deepfake audio during a phone call requires real-time neural network speech generation, something that has only been realized recently.
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