Talk to Transformer

Artificial intelligence is spreading into more and more application areas. American scientists have now developed a system that can supplement texts: “Talk to Transformer”. The user enters a few sentences – and the AI system adds further passages. “The system is based on a method called DeepQA, which is based on the observation of patterns in the data. This method has its limitations, however, and the system is only effective for data on the order of 2 million words, according to a recent news article. For instance, researchers say that the system cannot cope with the large amounts of data from an academic paper. Researchers have also been unable to use this method to augment texts from academic sources. As a result, DeepQA will have limited application, according to the researchers. The scientists also note that there are more applications available in the field of text augmentation, such as automatic transcription, the ability to translate text from one language to another and to translate text into other languages.” The sentences in quotation marks are not from the author of this blog. They were written by the AI system itself. You can try it via

Honey, I shrunk the AI

Some months ago, researchers at the University of Massachusetts showed the climate toll of machine learning, especially deep learning. Training Google’s BERT, with its 340 million data parameters, emitted nearly as much carbon as a round-trip flight between the East and West coasts. According to Technology Review, the trend could also accelerate the concentration of AI research into the hands of a few big tech companies. “Under-resourced labs in academia or countries with fewer resources simply don’t have the means to use or develop such computationally expensive models.” (Technology Review, 4 October 2019) In response, some researchers are focused on shrinking the size of existing models without losing their capabilities. The magazine wrote enthusiastically: “Honey, I shrunk the AI” (Technology Review, 4 October 2019) There are advantages not only with regard to the environment and to the access to state-of-the-art AI. According to Technology Review, tiny models will help bring the latest AI advancements to consumer devices. “They avoid the need to send consumer data to the cloud, which improves both speed and privacy. For natural-language models specifically, more powerful text prediction and language generation could improve myriad applications like autocomplete on your phone and voice assistants like Alexa and Google Assistant.” (Technology Review, 4 October 2019)

Towards Full Body Fakes

“Within the field of deepfakes, or ‘synthetic media’ as researchers call it, much of the attention has been focused on fake faces potentially wreaking havoc on political reality, as well as other deep learning algorithms that can, for instance, mimic a person’s writing style and voice. But yet another branch of synthetic media technology is fast evolving: full body deepfakes.” (Fast Company, 21 September 2019) Last year, researchers from the University of California Berkeley demonstrated in an impressive way how deep learning can be used to transfer dance moves from a professional onto the bodies of amateurs. Also in 2018, a team from the University of Heidelberg published a paper on teaching machines to realistically render human movements. And in spring of this year, a Japanese company developed an AI that can generate whole body models of nonexistent persons. “While it’s clear that full body deepfakes have interesting commercial applications, like deepfake dancing apps or in fields like athletics and biomedical research, malicious use cases are an increasing concern amid today’s polarized political climate riven by disinformation and fake news.” (Fast Company, 21 September 2019) Was anyone really in this area, did he or she really take part in a demonstration and throw stones? In the future you won’t know for sure.