That’s the tool on the IBM cloud:
https://speech-to-text-demo.ng.bluemix.net/
For meeting or phone recordings’ transcription, its detection and speaker identification worked with ~75-80% accuracy
Understanding adversarial examples requires a theory of artefacts for deep learning | Nature Machine Intelligence
May 5, 2022Thought this was a good perspective:
https://www.nature.com/articles/s42256-020-00266-y. Liked the way it connects AlphaFold’s success in exploiting “inscrutable” features in residue-residue interactions to “artefacts” exploited by adversarial attacks
QT:{{”
Returning to debate over Ilyas et al.’s results, suppose for the sake of argument that there are scientific disciplines in which progress may depend in some crucial way on detecting or modelling predictively useful but human-inscrutable features. To ground the discussion in a speculative but plausible example, let us return to protein folding. For many years in the philosophy of science, protein folding was regarded as paradigm evidence for ‘emergent’ properties36—prop- erties that only appear at higher levels of investigation, and which humans cannot reduce to patterns in lower-level structures. The worry here is that the interactions among amino acids in a protein chain are so complex that humans would never be able to explain biochemical folding principles in terms of lower-level physics37. Instead, scientists have relied on a series of analytical ‘energy land- scape’ or ‘force field’ models that can predict the stability of final fold configurations with some degree of success. These principles are intuitive and elegant once understood, but their elements can- not be reduced to the components of a polypeptide chain in any straightforward manner, and there seem to be stark upper limits on their prediction accuracy. By contrast, AlphaFold38 on its first entry in the CASP protein-folding competition was able to beat state-of-the-art analytical models on 40 out of 43 of the test pro- teins, and achieve an unprecedented 15% jump in accuracy across the full test set.
Subsequent work39 has suggested that the ability of DNNs to so successfully predict final fold configurations may depend on the identification of ‘interaction fingerprints’, which are distributed across the full polypeptide chain. We might speculate that these interaction fingerprints are like the non-robust features that cause image-classifying networks to be susceptible to adversarial attacks, in that they are complex, spatially distributed, predictively useful, and not amenable to human understanding. Suppose this is all the case, for the sake of argument; whether protein science should rely on such fingerprints depends on whether they are artefacts, and if so whether we can understand their origins.
…
Researchers should develop a systematic taxonomy of the kinds of features learned by DNNs and tools to distinguish them from one another and gauge their suitability for various scientific projects. The first cut in this taxonomy would divide those features that are reliably predictive from those that are not; this distinction has long been a central focus of research in machine learning and is explored by standard methods like cross-validation. The next cut would distinguish predictive features that are scrutable to humans (robust) from those that humans find inscrutable (non-robust); this is the cut that Ilyas et al., and Zhou and Firestone have begun to explore. Finally, the third cut divides the predictive-but-inscrutable features into artefacts and inherent data patterns detectable only by non-human processing, with the former targeted for more suspi- cion until a theory of their origins and techniques for mitigation can be deployed; Goh’s Distill response has made some initial steps here. More research on the last two cuts is urgently needed to understand the full implications of DNNs’ susceptibility to adversarial attack
“}}
The Science of Mind Reading | The New Yorker
May 4, 2022QT:{{”
…second, they thought that they had devised a method for
communicating with such “locked-in” people by detecting their unspoken thoughts.
…
Osgood became known not for the results of his surveys but for the method he invented to analyze them. He began by arranging his data in an imaginary space with fifty dimensions—one for fair-unfair, a second for hot-cold, a third for fragrant-foul, and so on. Any given concept, like tornado, had a rating on each dimension—and, therefore, was situated in what was known as high-dimensional space. Many concepts had similar locations on multiple axes: kind-cruel and
honest-dishonest, for instance. Osgood combined these dimensions. Then he looked for new similarities, and combined dimensions again, in a process called “factor analysis.”
When you reduce a sauce, you meld and deepen the essential flavors. Osgood did something similar with factor analysis. Eventually, he was able to map all the concepts onto a space with just three dimensions. The first dimension was “evaluative”—a blend of scales like good-bad, beautiful-ugly, and kind-cruel. The second had to do with “potency”: it consolidated scales like large-small and strong-weak. The third measured how “active” or “passive” a concept was. Osgood could use these three key factors to locate any concept in an abstract space. Ideas with similar coördinates, he argued, were neighbors in meaning.
For decades, Osgood’s technique found modest use in a kind of personality test. Its true potential didn’t emerge until the nineteen-eighties, when researchers at Bell Labs were trying to solve what they called the “vocabulary problem.” People tend to employ lots of names for the same thing. This was an obstacle for computer users, who accessed programs by typing words on a command line.
…
They updated Osgood’s approach. Instead of surveying undergraduates, they used computers to analyze the words in about two thousand technical reports. The reports themselves—on topics ranging from graph theory to user-interface design—suggested the dimensions of the space; when multiple reports used similar groups of words, their dimensions could be combined. In the end, the Bell Labs researchers made a space that was more complex than Osgood’s. It had a few hundred dimensions. Many of these dimensions described abstract or “latent” qualities that the words had in common—connections that wouldn’t be apparent to most English speakers. The researchers called their technique “latent semantic analysis,” or L.S.A.
…
In the following years, scientists applied L.S.A. to ever-larger data sets. In 2013, researchers at Google unleashed a descendant of it onto the text of the whole World Wide Web. Google’s algorithm turned each word into a “vector,” or point, in high-dimensional space. The vectors generated by the researchers’ program, word2vec, are eerily accurate: if you take the vector for “king” and subtract the vector for “man,” then add the vector for “woman,” the closest nearby vector is “queen.” Word vectors became the basis of a much improved Google Translate, and enabled the auto-completion of sentences in Gmail. Other companies, including Apple and Amazon, built similar systems. Eventually, researchers realized that the “vectorization” made popular by L.S.A. and word2vec could be used to map all sorts of things.
“}}
I was also very impressed with how the article explained concepts related to LSA and word2vec. Thought it was interesting that they were derived, in a sense, from Charles Osgood’s seminal work.
https://www.newyorker.com/magazine/2021/12/06/the-science-of-mind-reading
linkstream2 microblog 