Saffron is naturally poisonous, but even if it was only 50% toxic on its own, the heat would destroy the plant, which is why it is grown in underground containers at temperatures far below the heat source (often below 5C). In fact, for some reason I suspect saffron is considered more toxic than other plants, as it does smell rather strongly.
In a few thousand years, we will have a huge network of artificial neural networks (ANNs), which could in theory handle tasks that today’s computers do poorly. A new paper by a group of Carnegie Mellon University researchers in 2015 highlights the benefits of using them to make more efficient web searches and image recognition — tasks that are currently dominated by big companies and their cloud computing providers.
“Our research aims to develop more efficient models for machine learning, which should significantly improve the performance of modern machine learning algorithms,” the researchers said in their paper, released earlier this month through the machine learning journal arXiv, published by the University of Chicago.
“A key goal of our research is to apply this power to the search and image recognition arenas where machines in the cloud are already dominant – in this case Google – but it is not clear whether they would scale to serve many more domains.”
The paper’s co-author, Andrew Good, a computer science PhD student who is now at Carnegie Mellon, says that the team is not necessarily interested in building artificial intelligence systems for specific products – they are just trying to understand the strengths, weaknesses and trade-offs associated with using artificial neural networks and deep neural networks.
“It’s not a goal in and of itself — it’s the learning process. You don’t build them to find solutions to specific problems,” said Good (PDF). “It’s to learn how these networks can solve specific problems — and this is how we think we might do it.”
The basic idea for making a machine learning system do better than a human is that the human learns from experience, and it is hard to learn the same thing a thousand times. The researchers say that a few years ago, for example, Google introduced a feature of its search service called the Knowledge Graph that allowed users to sort by a wide set of search terms. In the years since, users have found ways to improve the feature by adding new results. But that’s still limited in scope.
A new paper in 2015 by Carnegie Mellon University researchers in 2015 highlighted the benefits of using ANNs to make more efficient web searches.
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