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2020.07.16

IBMとMITの円卓会議:AIの大きな課題を解決するにはハイブリッドアプローチが必要

こんにちは、丸山満彦です。

IBMのリサーチラボとMITのAIに関するバーチャル円卓会議が公開されていますね。

IBM 

・2020.07.15 (blog) IBM & MIT Roundtable: Solving AI’s Big Challenges Requires a Hybrid Approach

・(news room) IBM Research & MIT Roundtable: Solving AI’s Big Challenges Requires a Hybrid Approach by Larry Greenemeier

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AI and automation are largely synonymous when you talk about industrial uses, said panelist David Cox, IBM Director of the MIT-IBM Watson AI Lab. “A lot of what people mean when they talk about AI today is automation,” he added. “But automation is incredibly labor-intensive today, in a way that really just doesn’t work for the problems we want to solve.”

To leverage tools like machine learning and deep learning, “you need to have huge amounts of carefully curated and bias-balanced data to be able to use them well,” Cox said. “And for the vast majority of the problems we face, actually, we don’t have those giant rivers of data. Most of the hard problems we have in the world that we’d love to solve with automation, with AI, we don’t really have the right tools for that.”

Machine learning is good at problems that require the interpretation of signals—such as image recognition—but the training process requires a lot of data and computing power, agreed panelist Leslie Kaelbling, an MIT Professor of Computer Science and Engineering.

“For years people tried to directly solve problems such as finding faces in images, and directly engineering those solutions didn’t work at all,” Kaelbling said. “Instead, it turns out we’re much better at engineering algorithms that can take that data, and from the data derive a solution. For some problems, however, we don’t have the formulations yet that would let us learn from the amount of data we have available. So we really have to focus on learning from smaller amounts of data.”

Neuro-Symbolic and Other Hybrid Approaches

One way to find value in smaller data sets is to leverage a combination of AI approaches, the panelists agreed. Neuro-symbolic AI is one such hybrid method. Symbols were the original approach to AI, where programmers would codify knowledge, said panelist Josh Tenenbaum, an MIT Professor of Computational Cognitive Science. But that approach did not scale, he said, nor are end-to-end neural networks the answer, given the amount of data and computing power that would involve.

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真の問題の解決には、労働集約的な仕事の解決ではない。。。

機械学習のためにはバイアスが無いような学習に適した大量のデータセットと計算量が必要。。。

でも実際にはそんなデータセットは無いから、少量のデータセットでも適切に学習できるような手段が必要。。。

より小さいデータセットで価値を見つける方法は、AIアプローチの組み合わせを活用する方法。。。

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と興味深い議論が続きます。

最後は良いですね。

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The COVID-19 Factor

The panelists were asked how the ongoing COVID-19 pandemic has impacted AI research. In general, the pandemic introduced a lot of unforeseen challenges that have “broken a lot of models,” Cox said.

An AI system that, for example, might have been designed prior to the pandemic to better understand whether people who eat at fancy restaurants also shop at fancy grocery stores would have been upended. For a while, very few people were going to restaurants of any type. The same would be true of an algorithm designed last year to predict demand for N95 face masks in 2020. The pandemic’s unexpected and often unpredictable impact on society highlights the need for resilience in AI systems.

The pandemic shows a need for a more robust approach to understanding the world when it comes to creating AI, Tenenbaum said. That requires model building, not just large amounts of data that may or may not be available.

The pandemic has also taught the AI research community the value of virtual conferences, something that was rarely considered before the current travel restrictions. Even if conferences go back to being large physical gatherings, the researchers agreed that virtual conferences will not go away, having made it much easier for more people around the world to access and contribute to important discussions, which will have a lasting positive impact on the field moving forward.

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溜めていたデータがあったとしてもCOVID-19で環境がガラッと変わり使いづらくなったかもですね。。。

 

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