AI News, Challenges in Deep Learning
Challenges in Deep Learning
Deep Learning has become one of the primary research areas in developing intelligent machines.
Deep Learning algorithms mimic human brains using artificial neural networks and progressively learn to accurately solve a given problem.
In the words of Andrew Ng, one of the most prominent name in Deep Learning: “I believe Deep Learning is our best shot at progress towards real AI.” If you look around, you might realize the power of the above statement by Andrew.
As human brain needs a lot of experiences to learn and deduce information, the analogous artificial neural network requires copious amount of data.
In the case of deep neural networks, this number can be in the range of millions, tens of millions and in some cases even hundreds of millions.
At times, the there is a sharp difference in error occurred in training data set and the error encountered in a new unseen data set.
To ensure better efficiency and less time consumption, data scientists switch to multi-core high performing GPUs and similar processing units.
Industry level Deep Learning systems require high-end data centers while smart devices such as drones, robots other mobile devices require small but efficient processing units.
Google DeepMind’s Research Scientist Raia Hadsell summed it up: “There is no neural network in the world, and no method right now that can be trained to identify objects and images, play Space Invaders, and listen to music.” Most of our systems work on this theme, they are incredibly good at solving one problem.
Researchers from Google Brain Team and University of Toronto presented a paper on MultiModel, a neural network architecture that draws from the success of vision, language and audio networks to simultaneously solve a number of problems spanning multiple domains, including image recognition, translation and speech recognition.
Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts. Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. Deep learning is a class of machine learning algorithms that:(pp199–200) Most modern deep learning models are based on an artificial neural network, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in Deep Belief Networks and Deep Boltzmann Machines. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.
Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks. Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference. The universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions. In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik. The probabilistic interpretation derives from the field of machine learning.
More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. The probabilistic interpretation was introduced by researchers including Hopfield, Widrow and Narendra and popularized in surveys such as the one by Bishop. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons. The first general, working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Alexey Ivakhnenko and Lapa in 1965. A 1971 paper described a deep network with 8 layers trained by the group method of data handling algorithm. Other deep learning working architectures, specifically those built for computer vision, began with the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1989, Yann LeCun et al.
Each layer in the feature extraction module extracted features with growing complexity regarding the previous layer. In 1995, Brendan Frey demonstrated that it was possible to train (over two days) a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm, co-developed with Peter Dayan and Hinton. Many factors contribute to the slow speed, including the vanishing gradient problem analyzed in 1991 by Sepp Hochreiter. Simpler models that use task-specific handcrafted features such as Gabor filters and support vector machines (SVMs) were a popular choice in the 1990s and 2000s, because of ANNs' computational cost and a lack of understanding of how the brain wires its biological networks.
In 2003, LSTM started to become competitive with traditional speech recognizers on certain tasks. Later it was combined with connectionist temporal classification (CTC) in stacks of LSTM RNNs. In 2015, Google's speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which they made available through Google Voice Search. In 2006, publications by Geoff Hinton, Ruslan Salakhutdinov, Osindero and Teh  showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation. The papers referred to learning for deep belief nets.
It was believed that pre-training DNNs using generative models of deep belief nets (DBN) would overcome the main difficulties of neural nets. However, it was discovered that replacing pre-training with large amounts of training data for straightforward backpropagation when using DNNs with large, context-dependent output layers produced error rates dramatically lower than then-state-of-the-art Gaussian mixture model (GMM)/Hidden Markov Model (HMM) and also than more-advanced generative model-based systems. The nature of the recognition errors produced by the two types of systems was characteristically different, offering technical insights into how to integrate deep learning into the existing highly efficient, run-time speech decoding system deployed by all major speech recognition systems. Analysis around 2009-2010, contrasted the GMM (and other generative speech models) vs.
While there, Ng determined that GPUs could increase the speed of deep-learning systems by about 100 times. In particular, GPUs are well-suited for the matrix/vector math involved in machine learning. GPUs speed up training algorithms by orders of magnitude, reducing running times from weeks to days. Specialized hardware and algorithm optimizations can be used for efficient processing. In 2012, a team led by Dahl won the 'Merck Molecular Activity Challenge' using multi-task deep neural networks to predict the biomolecular target of one drug. In 2014, Hochreiter's group used deep learning to detect off-target and toxic effects of environmental chemicals in nutrients, household products and drugs and won the 'Tox21 Data Challenge' of NIH, FDA and NCATS. Significant additional impacts in image or object recognition were felt from 2011 to 2012.
DNNs have proven themselves capable, for example, of a) identifying the style period of a given painting, b) 'capturing' the style of a given painting and applying it in a visually pleasing manner to an arbitrary photograph, and c) generating striking imagery based on random visual input fields. Neural networks have been used for implementing language models since the early 2000s. LSTM helped to improve machine translation and language modeling. Other key techniques in this field are negative sampling and word embedding.
A compositional vector grammar can be thought of as probabilistic context free grammar (PCFG) implemented by an RNN. Recursive auto-encoders built atop word embeddings can assess sentence similarity and detect paraphrasing. Deep neural architectures provide the best results for constituency parsing, sentiment analysis, information retrieval, spoken language understanding, machine translation, contextual entity linking, writing style recognition, Text classifcation and others. Google Translate (GT) uses a large end-to-end long short-term memory network. GNMT uses an example-based machine translation method in which the system 'learns from millions of examples.' It translates 'whole sentences at a time, rather than pieces.
These failures are caused by insufficient efficacy (on-target effect), undesired interactions (off-target effects), or unanticipated toxic effects. Research has explored use of deep learning to predict biomolecular target, off-target and toxic effects of environmental chemicals in nutrients, household products and drugs. AtomNet is a deep learning system for structure-based rational drug design. AtomNet was used to predict novel candidate biomolecules for disease targets such as the Ebola virus and multiple sclerosis. Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables.
An autoencoder ANN was used in bioinformatics, to predict gene ontology annotations and gene-function relationships. In medical informatics, deep learning was used to predict sleep quality based on data from wearables and predictions of health complications from electronic health record data. Deep learning has also showed efficacy in healthcare. Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and assimilated before a target segment can be created and used in ad serving by any ad server. Deep learning has been used to interpret large, many-dimensioned advertising datasets.
On the one hand, several variants of the backpropagation algorithm have been proposed in order to increase its processing realism. Other researchers have argued that unsupervised forms of deep learning, such as those based on hierarchical generative models and deep belief networks, may be closer to biological reality. In this respect, generative neural network models have been related to neurobiological evidence about sampling-based processing in the cerebral cortex. Although a systematic comparison between the human brain organization and the neuronal encoding in deep networks has not yet been established, several analogies have been reported.
systems, like Watson (...) use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning.' As an alternative to this emphasis on the limits of deep learning, one author speculated that it might be possible to train a machine vision stack to perform the sophisticated task of discriminating between 'old master' and amateur figure drawings, and hypothesized that such a sensitivity might represent the rudiments of a non-trivial machine empathy. This same author proposed that this would be in line with anthropology, which identifies a concern with aesthetics as a key element of behavioral modernity. In further reference to the idea that artistic sensitivity might inhere within relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep (20-30 layers) neural networks attempting to discern within essentially random data the images on which they were trained demonstrate a visual appeal: the original research notice received well over 1,000 comments, and was the subject of what was for a time the most frequently accessed article on The Guardian's web site.
Some deep learning architectures display problematic behaviors, such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images and misclassifying minuscule perturbations of correctly classified images. Goertzel hypothesized that these behaviors are due to limitations in their internal representations and that these limitations would inhibit integration into heterogeneous multi-component AGI architectures. These issues may possibly be addressed by deep learning architectures that internally form states homologous to image-grammar decompositions of observed entities and events. Learning a grammar (visual or linguistic) from training data would be equivalent to restricting the system to commonsense reasoning that operates on concepts in terms of grammatical production rules and is a basic goal of both human language acquisition and AI. As deep learning moves from the lab into the world, research and experience shows that artificial neural networks are vulnerable to hacks and deception.
ANNs have been trained to defeat ANN-based anti-malware software by repeatedly attacking a defense with malware that was continually altered by a genetic algorithm until it tricked the anti-malware while retaining its ability to damage the target. Another group demonstrated that certain sounds could make the Google Now voice command system open a particular web address that would download malware. In “data poisoning”, false data is continually smuggled into a machine learning system’s training set to prevent it from achieving mastery.
Deep Vision Data Creates Synthetic Training Data For Machine Learning Systems Such As Neural Networks
Self-driving cars, personal assistants like Alexa, Siri and Google Home, language translation and facial recognition are all applications of a class of machine learning called deep neural networks.
This process works great for recognizing cats in photos because there’s millions of cat photos available to deep learning researchers.
Synthetic data is artificially created using computer graphics, real-time simulators, custom software and many other methods, and has been proven by researchers at Apple, Google, NVIDIA and many other companies to work as well as the real thing.
A fleet of twenty cars can accumulate one million miles of training data in a year, but it’s possible that one billion miles of data is required to fully train an autonomous vehicle system.
What is possible is to create real-time driving simulators (think super-good video game) and then leverage those systems with a thousand people driving 24 hours a day.
In fact, it beat the original AlphaGo (which was trained with thousands of human amateur and professional games to learn how to play Go) 100 games to zero.
A: Machine learning systems are expected to become a part of our everyday living just like the internet, mobile devices and social networking.
We have the designers, engineers, software developers, real-time simulator developers and digital artists to handle almost any synthetic data creation challenge.
- On Tuesday, June 18, 2019
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