New Perspectives.  (Updated) 

Machine learning Security,   By Ben Dickson  in bdtechtalks

 At this year’s International Conference on Learning Representations (ICLR), a team of researchers from the University of Maryland presented an attack technique meant to slow down deep learning models that have been optimized for fast and sensitive operations. The attack, aptly named DeepSloth, targets “adaptive deep neural networks,” a range of deep learning architectures that cut down computations to speed up processing.

Recent years have seen growing interest in the security of machine learning and deep learning, and there are numerous papers and techniques on hacking and defending neural networks. But one thing made DeepSloth particularly interesting: The researchers at the University of Maryland were presenting a vulnerability in a technique they themselves had developed two years earlier.

In some ways, the story of DeepSloth illustrates the challenges that the machine learning community faces. On the one hand, many researchers and developers are racing to make deep learning available to different applications. On the other hand, their innovations cause new challenges of their own. And they need to actively seek out and address those challenges before they cause irreparable damage.

Shallow deep networks

One of the biggest hurdles of deep learning the computational costs of training and running deep neural networks. Many deep learning models require huge amounts of memory and processing power, and therefore they can only run on servers that have abundant resources. This makes them unusable for applications that require all computations and data to remain on edge devices or need real-time inference and can’t afford the delay caused by sending their data to a cloud server.

In the past few years, machine learning researchers have developed several techniques to make neural networks less costly. One range of optimization techniques called “multi-exit architecture” stops computations when a neural network reaches acceptable accuracy. Experiments show that for many inputs, you don’t need to go through every layer of the neural network to reach a conclusive decision. Multi-exit neural networks save computation resources and bypass the calculations of the remaining layers when they become confident about their results.  ... ' 

Machine Learning Security

 Long look at the topic covering many aspects of the idea. 

Machine learning security needs new perspectives and incentives  By Ben Dickson  in BDTechTalks

At this year’s International Conference on Learning Representations (ICLR), a team of researchers from the University of Maryland presented an attack technique meant to slow down deep learning models that have been optimized for fast and sensitive operations. The attack, aptly named DeepSloth, targets “adaptive deep neural networks,” a range of deep learning architectures that cut down computations to speed up processing.

Recent years have seen growing interest in the security of machine learning and deep learning, and there are numerous papers and techniques on hacking and defending neural networks. But one thing made DeepSloth particularly interesting: The researchers at the University of Maryland were presenting a vulnerability in a technique they themselves had developed two years earlier.

In some ways, the story of DeepSloth illustrates the challenges that the machine learning community faces. On the one hand, many researchers and developers are racing to make deep learning available to different applications. On the other hand, their innovations cause new challenges of their own. And they need to actively seek out and address those challenges before they cause irreparable damage.  ...  '

Adding Concept Learning to Neural Networks

Linking logic and deep learning,  a great idea.   Did it in the 80s to add analytics and learning to expert systems.   Still not enough support to make it work well.

Deep learning is about to get easier — and more widespread in Venturebeat   By Ben Dickson, Techtalks n @BEBDEE983

(excerpt)

" ... In a paper presented at the ICLR conference in May, researchers from MIT and IBM introduced the “Neuro-Symbolic Concept Learner ", an AI model that brings together rule-based AI and neural networks.

NSCL uses neural networks to extract features from images and compose a structured table of information (called “symbols” in AI jargon). It then uses a classic, rule-based program to answer questions and solve problems based on those symbols.

By combining the learning capabilities of neural nets and the reasoning power of rule-based AI, NSCL can adapt to new settings and problems with much less data. The researchers tested the AI model on CLEVR, a dataset for solving visual question answering (VQA). In VQA, an AI must answer questions about the objects and elements contained in a given picture.

AI models based purely on neural networks usually need a lot of training examples to solve VQA problems with decent accuracy. However, NSCL was able to master CLEVR with a fraction of the data.  ... "