Limitations of Deep Learning in AI Research
Artificial intelligence has achieved incredible feats thanks to deep learning, however, it still falls short of human capabilities.
February 12, 2019, by Roberto Iriondo — last updated: April 7, 2019
Deep learning a subset of machine learning, has delivered super-human accuracy in a variety of practical uses in the past decade. From revolutionizing customer experience, machine translation, language recognition, autonomous vehicles, computer vision, text generation, speech understanding, and a multitude of other AI applications .
In contrast to machine learning where an AI agent learns from data based on machine learning algorithms, deep learning is based on a neural network architecture which acts similarly to the human brain, and allows the AI agent to analyze data fed in — in a structure similar to the way humans do. Deep learning models do not require algorithms to specify what to do with the data, which is made possible thanks to the extraordinary amount of data we as humans, collect and consume — which in turn is fed to deep learning models .
The “traditional” types of deep learning incorporates a different mix of feed-forward modules (frequently convolutional neural networks) and recurrent neural networks (now and then with memory units, such as LSTM  or MemNN ). These deep learning models are restricted in their capacity to “reason”, for example to do long chains of deductions, or streamlining a method to land at an answer. The quantity of steps in a computation is restricted by the quantity of layers in feed-forward nets, and by the time-span a recurrent neural network will recollect things.
At that point there’s the murkiness problem. When a deep learning model has been trained, it is not always clear how it goes about making decisions . In numerous settings that is simply not acceptable, regardless of whether it finds the correct solution; i.e. assume a bank utilizes AI to assess your credit-value, and afterward denies you a loan, in numerous states there are laws that state that the bank needs to clarify why — if the bank is using a deep learning model for its loan decision making, their loan department (likely) will not be able to give a clear explanation as to why the loan was denied.
Most importantly there is the absence of common sense. Deep learning models might be the best at perceiving patterns. Yet they cannot comprehend what the patterns mean, and considerably less reason about them. To empower deep learning models to reason, we have to change their structure in order for them to not create a single output (i.e. the interpretability of an image, the translation of a paragraph, etc.), yet as to deliver an entire arrangement of alternative outputs (i.e. different ways a sentence can be translated). This is what energy base models are intended to do: give you a score for every conceivable configuration of the variables to be construed.
Progressively, such weaknesses are raising concerns about AI among the extensive public population, particularly as autonomous vehicles, which utilize comparable deep learning strategies to navigate the roads , get associated with setbacks and fatalities . The public has started to say, perhaps there is an issue with AI — in a world where perfection is expected; and even though deep learning on self-driving cars has proven, that it would cause incredibly less casualties than human drivers, humanity itself will not, completely have its trust in autonomous vehicles, until, no casualties are involved.
In addition, deep learning is absolutely restricted in its current form, on the grounds that practically all the fruitful uses of it              , utilize supervised machine learning with human-comment annotations which has been noted as a significant weakness — this dependence prevents deep neural networks from being applied to problems where input data is scarce. It is imperative to discover approaches to prepare extensive neural nets from “crude” non-commented data in order to catch the regularities of the real world. In which combining deep learning, with adversarial machine learning techniques   may lay the answer we are looking for.
In terms of the general population — unfortunately the public, does not have a fair understanding of deep learning. If work in deep learning was confined to only AI research labs it would be one thing. However, deep learning techniques are being used in every possible application nowadays. The level of confidence that tech executives and marketers are placing on deep learning techniques is worrisome. While deep learning is an incredible feat, it is important to not only explore its strengths, but to also focus, and be aware of its weaknesses, in order to have a plan of action.
Mrinmaya Sachan’s research on Towards Literate Artificial Intelligence  makes an interesting case in exploring how, even though we have seen notable developments on the field of artificial intelligence thanks to deep learning, today’s AI systems still lack the intrinsic nature of human intelligence. He then dives in and reflects, before humanity starts to build AI systems that posses human capabilities (reasoning, understanding, common-sense), how can we evaluate AI systems on such tasks? — in order to thoroughly understand and develop true intelligent systems. His research proposes the use of standardized tests on AI systems (similarly to the tests that students take towards progressing in the formal education system) by using two frameworks as to further develop AI systems, with notable benefits which can be applied in the form of social good and education.
On Deep Learning and Decision Making, Do we have a true theoretical understanding of a neural network?
Artificial neural networks, which try to mimic the architecture of the brain posses a multitude of connections of artificial neurons (nodes), the network itself is not an algorithm but a framework on which a variety of machine learning algorithms can function on to achieve desired tasks. The foundations of neural network engineering are almost completely based on heuristics, with a small emphasis on network architecture choices, unfortunately there is no definite theory which tell us how to decide the right number of neurons for a certain model. There are however theoretical works on the number of neurons and the overall capacity of a model   , nevertheless, those are rarely practical to apply.
Stanford Professsor, Sanjeev Arora, takes a vivid approach to the generalization theory of deep neural networks , in which he mentions the generalization mystery of deep learning as to: Why do trained deep neural networks perform well on previously unseen data? i.e. let us say that you train a deep learning model with ImageNet and train it on images with random labels, high accuracy will be the outcome. However, using normal regularization strategies which infer higher generalization do not help as much . Regardless, the trained neural net is still unable to predict the random labeling of unseen images, which in turn means that the neural network does not generalize.
Recently researchers were able to expose vulnerabilities of a deep neural network architecture by adding small nuances on a large image dataset as to alter (with high probability) the model outputs  of the neural network. The study follows several other researchers showing similar levels of brittleness defy the outputs, based on small nuances on the input. These type of results do not inspire confidence, i.e. in autonomous vehicles, the environment is prone to have nuances of all kinds (rain, snow, fog, shadows, false positives, etc.) — now imagine a visual system being thrown off by a small change on its visual input. I am sure that Tesla, Uber and several others have identified these issues and are working on a plan as to address them, however it is important for the public to be aware of them as well.
Nowadays, we are surrounded by technology. From the smart gadgets on our home, smartphones in pour pockets, computers on our desks to the routers that connect us to the internet, etc. In each one of these technologies, the base architectures function properly thanks to the solid engineering principles they were built upon, deep mathematics, physics, electrical, computer and software engineering, etc. and above all these fields — years, if not decades, of statistical testing and quality assurance.
It is important to remember, that deep learning models need a large amount of data to train an initial model (in order to have high accuracy results and not produce overfitting, keep in mind that sub-sequential tasks can learn from transfer learning), and that ultimately without a profound understanding of what is truly happening inside a “deep neural architecture,” it is not practically nor theoretically wise to build technological solutions that are sustainable on the long run.
The author would like to thank Matt Gormley, Assistant Professor at Carnegie Mellon University, and Arthur Chan, Principal Speech Architect, Curator of AIDL.io and Deep Learning Specialist, for constructive criticism in preparation of this article.
DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of Carnegie Mellon University, nor other companies (directly or indirectly) associated with the author(s). These writings are not intended to be final products, yet rather a reflection of current thinking, along being a catalyst for discussion and improvement.
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