Simulations verify the theoretical results.

'It's going to be really bad': Fears over AI bubble bursting grow in Silicon Valley At OpenAI's DevDay this week, OpenAI boss Sam Altman did what American tech bosses rarely do these days: he actually answered questions from reporters. I know it's tempting to write the bubble story, Mr Altman told me as he sat flanked by his top lieutenants. In fact, there are many parts of AI that I think are kind of bubbly right now. In Silicon Valley, the debate over whether AI companies are overvalued has taken on a new urgency. Sceptics are privately - and some now publicly - asking whether the rapid rise in the value of AI tech companies may be, at least in part, the result of what they call financial engineering.

Notwithstanding astonishing advances in computer vision technologies, detecting ships and floating matters in these images is challenging due to factors such as object distance.

Both challenges involve modeling long-term dependencies.

Figure 1: Image of a Brown Thrasher and how the ProtoPool (left) and ProtoPool-Concepts (ours, right) explain their decisions. Prototype classifications are made by finding patches in the image similar to learned prototypical parts. Single-visualization methods such as ProtoPool can make visually ambiguous decisions when the semantic features underlying a prototype are unclear.