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Neural Networks are designed to learn, there’s no pre-determined way or code, although there’s an algorithm that assists in finding the desired answer.
By Jesus Rodriguez, Intotheblock. I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Please give it a try by subscribing below: Machine reading comprehension(MRC) is an emergent discipline in the field of deep learning. From a conceptual standpoint, MRC focuses on deep learning models that can answer intelligent questions about specific text documents. For humans, reading comprehension is a native cognitive skill developed since the early days of school or even before. At we are reading a text, we are instinctively extracting the key ideas that will allow us to answer future questions about that subject. In the case of artificial intelligence(AI) models, that skill is still largely underdeveloped. The first widely adopted generation of natural language understanding(NLU) techniques has focused mostly on detecting the intentions and concepts associated with a specific sentence. We can think about these models as a first tier of knowledge to enable reading comprehension. However, full machine reading comprehension needs additional building blocks that can extrapolate and correlate questions to specific sections of a text and build knowledge from specific sections of a document. One of the biggest challenges in the MRC domain is that most models are based on supervised training with datasets that contain not only the documents but potential questions and answers. As you can imagine, this approach is not only very difficult to scale but practically impossible to implement in some domains in which the data is simply not available. Recently, researchers from Microsoft proposed an interesting approach to deal with this challenge in MRC algorithms. In a paper titled “Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension”, Microsoft’s Research introduced a technique called two stage synthesis networks or SynNet that applies transfer learning to reduce the effort to train a MRC model. SynNet can be seen as a two phase approach to build knowledge related to a specific text. In the first phase, SynNet learns a general pattern of identifying potential “interestingness” in a text document. These are key knowledge points, named entities, or semantic concepts that are usually answers that people may ask for. Then, in the second stage, the model learns to form natural language questions around these potential answers, within the context of the article. The fascinating thing about SynNet is that, once trained, a model can be applied to a new domain, read the documents in the new domain and then generate pseudo questions and answers against these documents. Then, it forms the necessary training data to train an MRC system for that new domain, which could be a new disease, an employee handbook of a new company, or a new product manual. Many people erroneously associate MRC technique with the more developed field of machine translation. In the case of MRC models such as SynNet, the challenge is that they need to synthesize both questions and answers for a document. While the question is a syntactically ﬂuent natural language sentence, the answer is mostly a salient semantic concept in the paragraph, such as a named entity, an action, or a number. Since the answer has a different linguistic structure than the question, it may be more appropriate to view answers and questions as two different types of data. SynNet materializes in that theory by decomposing the process of generating question-answer pairs into two fundamental steps: The answer generation conditioned on the paragraph and the question generation conditioned on the paragraph and the answer. Image Credit: Microsoft Research You can think about SynNet as a teacher that is very good at generating questions from documents based on its experience. As it learn about the relevant questions in one domain, it can apply the same patterns to documents in a new domain. Microsoft researchers have applied the principles of SynNet to different MRC models including the recently published ReasoNet which have shown a lot of promise towards making machine reading comprehension a reality in the near future. Original. Reposted with permission. Related:
News: O'Reilly Radar - Insight, analysis, and research about emerging technologies
Leaving Amazon (Tim Bray) — May 1st was my last day as a VP and Distinguished Engineer at Amazon Web Services, after five years and five months of rewarding fun. I quit in dismay at Amazon firing whistleblowers who were making noise about warehouse employees frightened of Covid-19. Observability is a Many-Splendoured Thing (Charity Majors) — if you can’t predict all the questions you’ll need to ask in advance, or if you don’t know what you’re looking for, then you’re in o11y territory. Using Neural Networks to Find Answers (Google) — deep learning to figure out how to turn natural language questions into queries over tables of data. Redesigning Trust: Blockchain Deployment Toolkit — World Economic Forum report on distributed ledger deployments, with advice. This toolkit provides tools, resources, and know-how to organizations undertaking blockchain projects. It was developed through lessons from and analysis of real projects, to help organizations embed best practices and avoid possible obstacles in deployment of distributed ledger technology
The goal is to help build trust and transparency in business outcomes, the company said. IBM has unveiled a slew of announcements designed to help businesses scale their use of AI. The company also announced the rollout of new capabilities for its Watson platform. IBM researchers have built a hybrid question-answering system called Neuro-Symbolic-QA (NSQA) that for the first time uses neurosymbolic AI to allow an AI system to offer "and"/ "or" to its recommendations. This will ultimately position the system to perform better in real-world situations, IBM said. "This enhanced reasoning capability comes as a result of an entirely new foundational AI method created by IBM researchers called Logical Neural Networks (LNN), IBM said. LNNs are a modification of today's neural networks so that they become equivalent to a set of logic statements, but they also retain the original learning capability of a neural network, the company explained in a blog post. SEE: Artificial intelligence: Cheat sheet (TechRepublic) QA is designed to meet the significant challenges in language-based AI, in particular the fact that the training of NLP models still requires massive amounts of data, it's expensive, and these models can't demonstrate true intelligence—they only share what they're already trained to know and ultimately fall apart in spontaneous, real-life situations, IBM said. "To date, the QA system has achieved state-of-the-art results on two major industry NLP benchmarks, QALD and LC-QUAD, which is significant because it's the first time a non-deep learning based NLP method has achieved top performances on these tests," the company said. Further, IBM said the system operates off significantly less data: A dataset of 400 questions versus the industry standard of around 10,000. IBM said its hope is that the system will help it advance AI, including NLP models, beyond the narrow restraints of pattern-based deep learning, which offers only solutions they are trained to know. Instead, the goal is the system can demonstrate flexibility and offer solutions not included in training data as well as efficiency by using far less data, all while maintaining accuracy. The new capabilities for Watson are designed to improve the automation of AI, provide a higher degree of precision in natural language processing, and foster greater trust in outcomes derived from AI predictions, the company said. They include: Reading Comprehension is based on an innovative question-answering (QA) system from IBM Research. Currently in beta in IBM Watson Discovery, it is planned as a new feature that can help identify more precise answers in response to natural language queries from vast troves of complex enterprise documents. It also provides scores that indicate how confident the system is in each answer. FAQ Extraction uses a novel NLP technique from IBM Research to automate the extraction of Q&A pairs from FAQ documents. Currently in beta in IBM Watson Assistant's search skill, it is planned as a new feature to help businesses keep virtual assistants up-to date with the latest answers and reduce the time-consuming process of manual updates. A new intent classification model is now available in Watson Assistant. It is designed to improve a user's interactions with a virtual assistant and enables faster training times and more accurate results from less data. This can help businesses go live with virtual assistants in a few days with high accuracy. Watson Discovery now includes support for 10 new languages including Bosnian, Croatian, Danish, Finnish, Hebrew, Hindi, Norwegian (Bokmål), Norwegian (Nynorsk), Serbian, and Swedish. IBM also announced plans to commercialize IBM Research-developed "AI Factsheets" in Watson Studio in Cloud Pak for Data throughout 2021. "Like nutrition labels for foods or information sheets for appliances, AI Factsheets are designed to provide information about a product's important characteristics,'' the company said. "Standardizing and publicizing this information will help build trust in AI services across the industry." To complement this, IBM Services for AI at Scale, a new consulting offering, provides businesses with a framework, methodology, and underlying technology to guide organizations on their journey to trustworthy and ethical AI. IBM Cloud Pak for Data also has new capabilities to provide a complete foundation for AI that can run on any cloud. Data, Analytics and AI Newsletter Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays Sign up today Also see
And how to get started with it with no prior experience in Machine Learning. By Pradeep Sharma, Developer Relations at Jina AI TL;DR: Neural Search is a new approach to retrieving information using neural networks. Traditional techniques to search typically meant writing rules to “understand” the data being searched and return the best results. But with neural search, developers don’t need to wrack their brains for these rules; The system learns the rules by itself and gets better as it goes along. Even developers who don’t know machine learning can quickly build a search engine using open-source frameworks such as Jina. What is Neural Search? There is a massive amount of data on the web; how can we effectively search through it for relevant information? And it’s not just the web where we need it: Our computers store terabytes of company and personal data that we need to work with; we need effective search to get our day-to-day job done. And what do I mean by effective search Can we go beyond just matching keywords? Can we search using natural language, just like we would write or speak? Can we make the search smart enough to forgive our minor mistakes? Can we search for things that aren’t an exact match but are “close enough”? We can answer all those questions with one word: Yes. To understand how, we need to enter the world of Natural Language Processing. NLP is a field of computer science that deals with analyzing natural language data, like the conversations people have every day. NLP is the foundation of intelligent search, and we have seen three different approaches in this field as follows. Evolution of search methods Rules (1950–1990s) Complex handwritten rules that emulate Natural Language Understanding. Drawbacks: Handwritten rules can only be made more accurate by increasing their complexity, which is a much more difficult task that becomes unmanageable over time. Statistics (1990s — 2010s) Probabilistic decisions based on weights, machine learning and feature engineering. Creating and managing rules was solved with machine learning, where the system automatically learns rules by analysing large real-world texts. Drawbacks: These statistical methods require elaborate feature engineering. Neural Networks (Present) Advanced machine learning methods such as deep neural networks and representation learning. Since 2015, statistical methods have been largely abandoned, and there has been a shift to neural networks in machine learning. Popular techniques using this method make it a more accurate and a scalable alternative. It involves Use of word embeddings to capture semantic properties of words Focus on end-to-end learning of higher-level tasks (e.g., question answering) When you use Neural Networks to make your search smarter, we call this a Neural Search System. And as you will see, it addresses some of the critical shortcomings of other methods. Note that the applications of Neural Search are not just limited to text. It goes well beyond what NLP covers. With neural search, we get additional capabilities to search images, audio, video, etc. Let’s look at a comparison of the extreme ends of search methods — “Rules” vs “Neural Networks”: Rules (Symbolic Search) vs Neural Networks (Neural Search) Comparison of Symbolic Search vs Neural Search While the Neural Search method has become more widespread since 2015, and should be the primary focus area of any new search system, we shouldn’t completely rule out Symbolic (rule-based) Search methods. In fact, using a combination of Neural Search and Symbolic Search may result in optimized results. Let’s look at some of the powerful applications of Neural Search Applications Of Neural Search Semantic search 🔍 addidsa trosers (misspelled brand and category, still returns relevant results similar to query “adidas trousers”) Search between data types With Neural Search, you can use one kind of data to search another kind of data, for example using text to search for images, or audio to search for video. Example of cross-modal search Search with multiple data types With Neural Search, you can build queries with multiple query data types e.g. search images with text+image Example of multi-modal search Get started with Neural Search For rule-based searches, Apache Solr, Elasticsearch, and Lucene are the de-facto solutions. On the other hand, Neural Search is relatively new on the scene, there aren’t so many off-the-shelf packages. Not to mention, training the neural network for such a system requires quite a bit of data. These challenges can be solved using Jina, an open-source neural search framework. To get started with building your own Neural Search system using Jina: References Bio: Pradeep Sharma writes code & articles on productivity, software engineering, team building, remote work, etc. Original. Reposted with permission. Related:
Are you looking to continue your learning of natural language processing? This small collection of 3 free top notch courses will allow you to do just that. By Matthew Mayo, KDnuggets. Natural language processing (NLP) is an in-demand set of skills among employers and one of the most sought-after and pursued topics among learners. Previously, we presented 10 Free Top Notch Natural Language Processing Courses, a collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics. But with spring now upon us, what better time to have a fresh look at a topic like NLP, and do so with some new learning resources. Here is a small collection of 3 curated NLP courses ready to help you get your spring learning on, and help increase your understanding and expertise of the vast field of natural language processing. 1. Algorithms for NLP, Carnegie Mellon University This CMU course is taught by Emma Strubell, Yulia Tsvetkov, and Robert Frederking. Available resources include slides, readings, projects, assignments. This course will explore foundational statistical techniques for the automatic analysis of natural (human) language text. Towards this end the course will introduce pragmatic formalisms for representing structure in natural language, and algorithms for annotating raw text with those structures. The dominant modeling paradigm is corpus-driven statistical learning, covering both supervised and unsupervised methods. Algorithms for NLP is a lab-based course. This means that instead of homeworks and exams, you will mainly be graded based on four hands-on coding projects. Slides, materials, and projects for this iteration of Algorithms for NLP are borrowed from Jacob Eisenstein’s course at Georgia Tech, Dan Jurafsky at Stanford, Dan Klein and David Bamman at UC Berkeley, and Nathan Schneider at Georgetown University. 2. Neural Networks for NLP, Carnegie Mellon University This CMU course is tuaght by Graham Neubig, with co-instructor Pengfei Liu. Available resources includes videos, slides, readings, projects, assignments, code. You can find a direct link to the course lecture videos here. Neural networks provide powerful new tools for modeling language, and have been used both to improve the state-of-the-art in a number of tasks and to tackle new problems that were not easy in the past. This class will start with a brief overview of neural networks, then spend the majority of the class demonstrating how to apply neural networks to natural language problems. Each section will introduce a particular problem or phenomenon in natural language, describe why it is difficult to model, and demonstrate several models that were designed to tackle this problem. In the process of doing so, the class will cover different techniques that are useful in creating neural network models, including handling variably sized and structured sentences, efficient handling of large data, semi-supervised and unsupervised learning, structured prediction, and multilingual modeling. 3. Natural Language Processing Specialization, DeepLearning.AI This Coursera-hosted DeepLearning.AI specialization (4 courses) is taught by Younes Bensouda Mourri, Łukasz Kaiser, and Eddy Shyu. Available resources includes videos, slides, readings, projects, assignments, code (see note below). Note that you can pay for a certificate, which also gets you access to tutors and assignment grading, but other materials are freely-accessible for those auditing. Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper. These are 3 free NLP courses you can take in your spare time to ramp up your skills. Looking for more? Be sure to check out 10 Free Top Notch Natural Language Processing Courses! Related:
This freely available text on deep learning is fully interactive and incredibly thorough. Check out "Dive Into Deep Learning" now and increase your neural networks theoretical understanding and practical implementation skills. By Matthew Mayo, KDnuggets. Thanks to the current realities associated with COVID-19, lots of us around the world are spending more time at home than we normally do, and some of us may have additional idle time on ours hands. For those of us looking to spend some of this idle time learning something new or reviewing something previously learned, we have been (and hope to continue) spotlighting a few select standout textbooks of interest in data science and related fields. This is the next entry in the series. Once you have acquired the requisite mathematical foundations for machine learning, perhaps you are interested in turning your attention to neural networks and deep learning. There are many fine books available for someone looking to go this route, though few offerings tick the boxes of being freely available, up to date, and incredibly thorough. One such exemplar is Dive Into Deep Learning, by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, a book which rightly bills itself as "[a]n interactive deep learning book with code, math, and discussions, based on the NumPy interface." This book is great for a number of reasons. First off, and perhaps most importantly, it delivers on the promise of being interactive. The book is written in Jupyter notebooks, and so the code in chapters can be executed to see immediate results, as well as fine-tuned for inquisitive comparison. There is flexibility in how to execute these notebooks: Download the entirety of the book in notebook form to read and execute locally; Execute them on AWS using Amazon SageMaker Launch Google Colab notebooks directly from corresponding chapters by clicking the "Colab" link in the online version of the book, as shown below) Of course, if you just want to download a PDF to read like it's 2015, you can do that, too. Another attribute of the book is that this second iteration has adopted a Numpy interface approach for its code examples. The benefit of this is an immediate sense of familiarity for those who have been dabbling in the Python ecosystem for any length of time. In a world where numerous deep learning frameworks have implemented their own API styles, its nice to see this text adopts the use of tools such as PyTorch and MXNet's Gluon and their Numpy-like interface approach. This makes the transition more seamless for those coming from, and already understanding, the Python stack built on top of Numpy. The book is also up to date, with a major revision having taken place within the past 2 weeks of this article's writing — a revamp of the NLP chapters, including the addition of sections on BERT and language inference. This means you aren't learning the best practices of 3 years ago (which is a very long time in the world of neural networks, at least in some respects), and claims of demonstrated cutting edge and SOTA really are what they purport to be here. The full table of contents is as follows: Introduction Preliminaries Linear Neural Networks Multilayer Perceptrons Deep Learning Computation Convolutional Neural Networks Modern Convolutional Neural Networks Recurrent Neural Networks Modern Recurrent Neural Networks Attention Mechanisms Optimization Algorithms Computational Performance Computer Vision Natural Language Processing: Pretraining Natural Language Processing: Applications Recommender Systems Generative Adversarial Networks Appendix: Mathematics for Deep Learning Appendix: Tools for Deep Learning Given the book was written by academics who seem to have written it with the focus of being used in an academic setting, it should not be a surprise that there would be a course developed by (at least) one of the authors which is built from accessible and complementary materials such as slides, videos, and the like. For a sense of the elegant and effective prose you will find in the book, here's an excerpt taken from 14.8.1. From Context-Independent to Context-Sensitive: For example, by taking the entire sequence as the input, ELMo is a function that assigns a representation to each word from the input sequence. Specifically, ELMo combines all the intermediate layer representations from pretrained bidirectional LSTM as the output representation. Then the ELMo representation will be added to a downstream task’s existing supervised model as additional features, such as by concatenating ELMo representation and the original representation (e.g., GloVe) of tokens in the existing model. On one hand, all the weights in the pretrained bidirectional LSTM model are frozen after ELMo representations are added. On the other hand, the existing supervised model is specifically customized for a given task. Leveraging different best models for different tasks at that time, adding ELMo improved the state of the art across six natural language processing tasks: sentiment analysis, natural language inference, semantic role labeling, coreference resolution, named entity recognition, and question answering. Are you interested, but don't know if you should take my word for it? Here's what others have said about the book. "In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time." — Jensen Huang, Founder and CEO, NVIDIA "This is a timely, fascinating book, providing with not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing. Dive into this book if you want to dive into deep learning!" — Jiawei Han, Michael Aiken Chair Professor, University of Illinois at Urbana-Champaign "This is a highly welcome addition to the machine learning literature, with a focus on hands-on experience implemented via the integration of Jupyter notebooks. Students of deep learning should find this invaluable to become proficient in this field." — Bernhard Schölkopf, Director, Max Planck Institute for Intelligent Systems Dive Into Deep Learning is less a book on deep learning than it is a fully interactive experience on the topic. Whether you are starting out your neural networks journey or are looking to refine your understanding, Dive Into Deep Learning and its presentation format will undoubtedly be helpful. Related:
Machine Learning, Computer Vision, Artificial Intelligence, Natural Language Processing are all buzz words in the tech world right now. At the heart of all these buzz words lies one concept that is the base for many solutions- Neural Networks. But what exactly is a neural network and why is it called so? Let’s start with a bit of Biology. The diagram given below is of a neuron that we have all have seen in our 10th grade biology textbook. In simpler words, it is the basic unit of the nervous system. Neurons make nerves, nerves along with the brain and the spinal cord make the nervous system. The nervous system is responsible for all our decisions, actions and thoughts. But what is the function of a neuron at its core? It is to receive and send information. Just like it is shown in the diagram, the neuron receives and further sends an electrical or chemical signal to other neurons. Thus, acting like an f(x) function. It has been a long time since we humans started our attempts to make computers think, perceive and react like humans do. Whether it is the automation of various jobs in a factory or your search engine recommending you the best document according to your information need. One of the attempts which has worked brilliantly is the concept of Neural Networks. What is so special about it? It is the fact that neural networks imitate (or at least try to) the human nervous system. The basic unit of a neural network is also called a neuron. But it does actual math. It takes an input value and gives an output value to the next “neuron” just like in humans. This diagram beautifully presents the analogy between the parts of a human neuron and the neural network neuron. Another interesting thing I want to point out is how there is similarity in their interconnections too. This figure clearly brings out the similarity in this aspect too- one is connected to many which is further connected to many and so on. The “math” here is the multiplication of the inputs (x_i) with some weights (w_i) and adding a bias (b). “Why is it done?” is another question to which the answer is a bit long and since I aim to answer only the basic question of “What is a neural network at its core?” I shall not be diving into the answer for the former question. How are these weights decided? The answer is simple. Let me take an example, let’s say the expected output for a specific set of inputs is y_e and I start with the weights of w_i=0. Let’s say that I got an output of y_a using these weights. Now, I know that there is an error of y_a- y_e. If the absolute error is high, I know that my weight values are deviating from the right one by a huge margin. If the absolute error is small, I know that I am very close to the right values! The core principle being that I use the information of “How far is my output value from the desired output?” and modify my weight values accordingly. This technique is called “Back-propagation.” For electrical engineers like me it would be more appropriate to understand this technique with the help of this control systems diagram which also makes use of the error to give the desired output. The function of the feedback mechanism is to guide the value of the output in the right direction and so is the function of weights- to push the output value in the right direction! Note that this is just an analogy and not the exact way that neural networks function. One question I want to put across is “How do humans learn?” We experience something, learn from that experience and then try to apply it in similar situations later. This is another aspect that is common between neural networks and humans. Neural networks learn from experience or what is called the “training set.” It finds the suitable values for the weights by “experiencing” the training set and trying to achieve the desired output. This way it has “learnt” from its experiences and then applies it on a “testing set” to understand if the solution is right and robust. This resemblance of neural networks to the human brain is not coincidental. It was a deliberate attempt to mimic how the brain works by Warren McCulloch (neurophysiologist and cybernetician) and Walter Pitts (a computational neuroscientist). The similarities between human nervous system and neural networks are huge. Yet, computers haven’t been able to exactly mimic humans. There is more to be explored and done in order to achieve the human-like-computers dream. But there is one idea to still ponder upon: just like a neuron cannot be replaced if one dies, the success of neural networks is irreplaceable at least in the near future.
Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding. By Kevin Vu, Exxact Corp. Humans have a lot of senses, and yet our sensory experiences are typically dominated by vision. With that in mind, perhaps it is unsurprising that the vanguard of modern machine learning has been led by computer vision tasks. Likewise, when humans want to communicate or receive information, the most ubiquitous and natural avenue they use is language. Language can be conveyed by spoken and written words, gestures, or some combination of modalities, but for the purposes of this article, we’ll focus on the written word (although many of the lessons here overlap with verbal speech as well). Over the years we’ve seen the field of natural language processing (aka NLP, not to be confused with that NLP) with deep neural networks follow closely on the heels of progress in deep learning for computer vision. With the advent of pre-trained generalized language models, we now have methods for transfer learning to new tasks with massive pre-trained models like GPT-2, BERT, and ELMO. These and similar models are doing real work in the world, both as a matter of everyday course (translation, transcription, etc.), and discovery at the frontiers of scientific knowledge (e.g. predicting advances in material science from publication text [pdf]). Mastery of language both foreign and native has long been considered an indicator of learned individuals; an exceptional writer or a person that understands multiple languages with good fluency is held in high esteem, and is expected to be intelligent in other areas as well. Mastering any language to native-level fluency is difficult, imparting an elegant style and/or exceptional clarity even more so. But even typical human proficiency demonstrates an impressive ability to parse complex messages while deciphering substantial coding variations across context, slang, dialects, and the unshakeable confounders of language understanding: sarcasm and satire. Understanding language remains a hard problem, and despite widespread use in many areas, the challenge of language understanding with machines still presents plenty of unsolved problems. Consider the following ambiguous and strange word or phrase pairs. Ostensibly the members of each pair have the same meaning but undoubtedly convey distinct nuance. For many of us the only nuance may be a disregard for the precision of grammar and language, but refusing to acknowledge common use meanings mostly makes a language model look foolish. Couldn’t care less = (?) Could care less Irregardless = (?) Regardless Literally = (?) Figuratively Dynamical = (?) Dynamic Primer: Generalization and Transfer Learning Much of the modern success of deep learning has been due to the utility of transfer learning. Transfer learning allows practitioners to leverage a model’s previous training experience to more quickly learn a novel task. With the raw parameter counts and computational requirements of training state of the art deep networks, transfer learning is essential for the accessibility and efficiency of deep learning in practice. If you are already familiar with the concept of transfer learning, skip ahead to the next section to have a look at the succession of deep NLP models over time. Transfer learning is a process of fine-tuning: rather than training an entire model from scratch, re-training only those parts of the model which are task-specific can save time and energy of both computational and engineering resources. This is the “don’t be a hero” mentality espoused by Andrej Karpathy, Jeremy Howard, and many others in the deep learning community. Fundamentally, transfer learning involves retaining the low-level, generic components of a model while only re-training those parts of the model that are specialized. It’s also sometimes advantageous to train the entire pre-trained model after only re-initializing a few task-specific layers. A deep neural network can typically be separated into two sections: an encoder, or feature extractor, that learns to recognize low-level features and a decoder which transforms those features to a desired output. This cartoon example is based on a simplified network for processing images, with the encoder made up of convolutional layers and the decoder consisting of a few fully connected layers, but the same concept can easily be applied to natural language processing as well. In deep learning models, there is often a distinction between the encoder, a stack of layers that mainly learns to extract low-level features, and the decoder, the portion of the model that transforms the feature output from the encoder into classifications, pixel segmentations, next-time-step predictions, and so on. Taking a pre-trained model and initializing and re-training a new decoder can achieve state-of-the-art performance in far less training time. This is because lower-level layers tend to learn the most generic features, characteristics like edges, points, and ripples in images (i.e., Gabor filters in image models). In practice, choosing the cutoff between encoder and decoder is more art than science, but see Yosinki et al. 2014 where researchers quantified the transferability of features at different layers. The same phenomenon can be applied to NLP. A well-trained NLP model trained on a general language modeling task (predicting the next word or character given preceding text) can be fine-tuned to a number of more specific tasks. This saves on the substantial energy and economic costs of training one of these models from scratch, and it’s the reason we have such masterpieces as “AI-generated recipes” by Janelle Shane (top recipes include “chocolate chicken chicken cake”) or a generative text-based dungeon game. Both of those examples are built on top of OpenAI’s GPT-2, and these and most other generative NLP projects fall squarely into the realm of comedy more than anywhere else. But transfer learning with general-purpose NLP transformers like GPT-2 is quickly sliding down the slope of silliness to the uncanny valley. After that happens, we’ll be on the verge of believability where text generated by machine-learning models can serve as a stand-in for human-written copy. It’s anyone’s guess how close we are to making those leaps, but it’s likely that doesn’t matter as much as one might think. NLP models don’t have to be Shakespeare to generate text that is good enough, some of the time, for some applications. A human operator can cherry-pick or edit the output to achieve the desired quality of output. Natural Language Processing (NLP) progress over the last decade has been substantial. Along the way, there have been a number of different approaches to improving performance on tasks like sentiment analysis and the BLEU machine translation benchmark. A number of different architectures have been tried, some of which may be more appropriate for a given task or hardware constraint. In the next few segments, we’ll take a look at the family tree of deep learning NLP models used for language modeling. Recurrent Neural Networks One or more hidden layers in a recurrent neural network has connections to previous hidden layer activations. The key to the graphics in this and other diagrams in this article is below: Language is a type of sequence data. Unlike images, it’s parsed one chunk at a time in a predetermined direction. Text at the beginning of a sentence may have an important relationship to elements later on, and concepts from much earlier in a piece of writing may need to be remembered to make sense of information later on. It makes sense that machine learning models for language should have some sort of memory, and Recurrent Neural Networks (RNNs) implement memory with connections to previous states. The activations in a hidden layer at a given time state depend on the activations from one step earlier, which in turn depend on their preceding values and so on until the beginning of a language sequence. As the dependency between input/output data can reach far back to the beginning of a sequence, the network is effectively very deep. This can be visualized by “unrolling” the network out to its sequence depth, revealing the chain of operations leading to a given output. This makes for a very pronounced version of the vanishing gradient problem. Because the gradients used to assign credit for mistakes are multiplied by numbers less than 1.0 over each preceding time step, the training signal is continuously attenuated, and the training signal for early weights becomes very small. One workaround to the difficulty of training long-term time dependencies in RNNs is to just not. Reservoir Computing and Echo State Networks An echo state network is like an RNN but with recurrent connections that use fixed, untrained weights. This fixed part of the network is generally termed a reservoir. Echo state networks are a sub-class of RNNs that have fixed recurrent connections. Using static recurrent connections avoids the difficulty of training them with vanishing gradients, and in many early applications of RNNs echo state networks outperformed RNNs trained with back-propagation. A simple learning layer, often a fully-connected linear one, parses the dynamic output from the reservoir. This makes training the network easier, and it is essential to initialize the reservoir to have complex and sustained, but bounded output. Echo state networks have chaotic characteristics in that an early input can have long-lasting effects on the state of the reservoir later on. Therefore the efficacy of echo state networks is due to the “kernel trick” (inputs are transformed non-linearly to a high-dimensional feature space where they can be linearly separated) and chaos. Practically this can be achieved by defining a sparse recurrent connection layer with random weights. Echo state networks and reservoir computing have largely been superseded by other methods, but their avoidance of the vanishing gradient problem proved useful in several language modeling tasks such as learning grammar or speech recognition. Reservoir computing never made much of an impact in the generalized language modeling that has made NLP transfer learning possible, however. LSTMs and Gated RNNs Long short term memory introduced gates to selectively persist activations in so-called cell states. LSTMs were invented in 1997 by Sepp Hochreiter and Jürgen Schmidhuber [pdf] to address the vanishing gradient problem using a “constant error carousel,” or CEC. The CEC is a persistent gated cell state surrounded by non-linear neural layers that open and close “gates” (values squashed between 0 and 1 using something like a sigmoid activation function). These nonlinear layers choose what information should be incorporated into the cell state activations and determine what to pass to output layers. The cell state layer itself has no activation function, so when its values are passed from time-step to time-step with a gate value of nearly 1.0, gradients can flow backwards intact across very long distances in the input sequence. There have been many developments, and new versions of LSTMs adapted to improve training, simplify parameter count, and for application to new domains. One of the most useful of these improvements was the forget gate developed by Gers et al. in 2000 (shown in the figure), so much so that the LSTM with forget gates is typically considered the “standard” LSTM. A gated or multiplicative RNN uses an element-wise multiply operation on the output from the last hidden state to determine what will be incorporated into the new hidden state at the current time step. A gated or multiplicative RNN (MRNN) is a very similar construct to an LSTM, albeit less complicated. Like the LSTM, the MRNN uses a multiplicative operation to gate the last hidden states of the network, and the gate values are determined by a neural layer receiving data from the input. MRNNs were introduced for character-level language modeling in 2011 by Sutskever et al. [pdf] and expanded to gating across depth in deeper MRNNs (gated feedback RNNs) by Chung et al. in 2015. Perhaps because they are a bit simpler, MRNNs and gated feedback RNNs can outperform LSTMs on some language modeling scenarios, depending on who is handling them. LSTMs with forget gates have been the basis for a wide variety of high-profile natural language processing models, including OpenAI’s “Unsupervised Sentiment Neuron” (paper) and a big jump in performance in Google’s Neural Machine Translation model in 2016. Following the demonstration of transfer learning from the Unsupervised Sentiment Neuron model, Sebastian Ruder and Jeremy Howard developed Unsupervised Language Model Fine-tuning for Text Classification (ULM-FiT), which leveraged pre-training to attain state-of-the-art performance on six specific text classification datasets. Although absent from ULM-FiT and Unsupervised Sentiment Neuron, a key component of the improvements in Google’s LSTM-based translation network was the liberal application of attention, and not just engineering attention but the specific machine learning concept of learning to attend to specific parts of input data. Attention applied to NLP models was such a powerful idea that it led to the next generation of language models, and it is arguably responsible for the current efficacy of transfer learning in NLP. Enter the Transformer Graphic description of the attention mechanism concept used in the transformer model from “Attention is all you Need.” At a given point in a sequence and for each data vector, a weight matrix generates key, query, and value tensors. The attention mechanism uses the key and query vectors to weight the value vector, which will be subjected to a softmax activation along with all the other key, query, value sets and summed to produce the input to the next layer. The attention mechanism used in language models like Google’s 2016 NMT network worked well enough, and at a time when machine learning hardware accelerators had become powerful enough, to lead developers to the question “What if we just use attention on its own?” As we now know, the answer is that attention is all you need to achieve state-of-the-art NLP models (which is the name of the paper introducing the attention only model architecture). These models are known as transformers, and unlike LSTMs and other RNNs, transformers consider an entire sequence at the same time. They learn to use attention to weight the influence of each point in the input text sequence. A simple explanation of the attention mechanism used by the original Transformer model accompanies the figure above, but a more in-depth explanation can be had from the paper or this blog post by Jay Alammar. Considering the entire sequence at the same time might seem like it limits the model to parsing sequences of the same fixed length that it was trained on, unlike models with recurrent connections. However, transformers make use of a positional encoding (in the original Transformer, it is based on a sinusoidal embedding vector) that can facilitate forward passes with variable input sequence lengths. The all-at-once approach of transformer architectures does incur a stiff memory requirement, but it’s efficient to train on high-end modern hardware and streamlining the memory, and computational requirements of transformers are at the forefront of current and recent developments in the space. Conclusions and Caveats to Deep Neural Networks in NLP Deep NLP has certainly come into its own in the last two to three years, and it’s starting to spread effectively into applications beyond the highly-visible niches of machine translation and silly text generation. NLP development continues to follow in the figurative footsteps of computer vision, and unfortunately, that includes many of the same missteps, trips, and stumbles as we’ve seen before. One of the most pressing challenges is the “Clever Hans Effect,” named after a famous performing horse of the early 20th century. In short, Hans was a German horse that was exhibited to the public as an arithmetically gifted equine, able to answer questions involving dates and counting. In fact, he was instead an expert in interpreting subconscious cues given by his trainer, Wilhelm von Osten. In machine learning, the Clever Hans effect refers to models achieving impressive, but ultimately useless, performance by learning spurious correlations in the training dataset. Examples include classifying pneumonia in x-rays based on recognizing the type of machine used at hospitals with sicker patients, answering questions about people described in a text by just repeating the last-mentioned name, and modern phrenology. While most NLP projects produce only a comedy of errors when they don’t work properly (e.g. the recipe and dungeon generators mentioned above), a lack of understanding of how NLP and other machine learning models break down paves the way for justifications of modern pseudoscience and correspondingly bad policy. It’s also bad for business. Imagine spending thousands or millions of dollars on developing NLP-enabled search for a clothing store that returns search queries for stripeless shirts results like those in the Shirt without Stripes Github repo. It’s clear that, while recent advances have made deep NLP more effective and accessible, the field has a long way to go before demonstrating anything close to human understanding or synthesis. Despite its shortcomings (no Cortana, nobody wants you to route every utterance into an internet search on Edge browser), NLP is the basis of many products and tools in wide use today. Directly in line with the shortcomings of NLP, the need for systematic rigor in evaluating language models has never been clearer. There’s clearly important work to be done not just in improving models and datasets, but also in breaking those models in informative ways. Original. Reposted with permission. Related:
Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use. By Mani Sarkar, Java champion, polyglot, software craftsperson. What I share here is a glimpse of what’s out there and each one of you might have discovered many more aspects of Artificial Intelligence, Machine Learning, and Deep Learning as part of your daily professional and personal pursuits. One of my motivations for putting this post and the links below together comes from the discussion we had during the LJC Unconference in November 2018, where Jeremie, Michael Bateman and I along with a number of LJC JUG members gathered at a session discussing a similar topic. And the questions raised by some were in the lines of where does Java stand in the world of AI-ML-DL. How do I do any of these things in Java? Which libraries and frameworks to use? AI-ML-DL and Java and their outreach Another confession, I didn’t spend too much time trying to gather and categorise these topics, thanks to Twitter and the Internet for helping me find them and use them. I hope whatever content has been put together here quantifies to more than the answer to the above questions. And in case you feel further improvements can be made to the content, categorisation, layout, please feel free to contribute, you can start by visiting the git repo and creating a pull request. Please watch, fork, start the repo to get updates of the changes to come. Language Processing (aka NLP) – An introduction to natural language processing and a demo using opensource libraries (Tweet) – Implementing NLP Attention Mechanisms with DeepLearning4J (Tweet) – How Stanford CoreNLP, a popular Java natural language tool can help you perform Natural Language Processing tasks(Tweet) – FREE AI talk on Natural LanguageOnce again, pull requests are very welcome. From my several weeks to few months of intense experience, I suggest if you want to get your hands dirty with Artificial Intelligence and it’s off-springs , don’t shy away from it, just because it is not Java / JVM based. It’s best to start high-level with whatever you have, and when you have understood the subject enough to try to apply them in the languages you are at home with, be that Java or any other JVM language you may know. I’m not claiming I know them, but merely sharing my mileage. One of the things we came up during our discussions was that AI, ML, and DL have strong contributions from academia and they use tools and languages best known to them and sometimes most appropriate for the task in hand. Follow the community and the tools that drive the innovation and inspiration to become better at the subject of choice. In this case, it applies to Artificial Intelligence and its variants . Quick Shoutouts First, to @java for sharing many AI, ML, DL related resources with the wider community. And also to organisations like @skymindio (https://skymind.ai) who are doing an awesome job in bridging the gap between the Java/JVM and AI/ML/DL worlds. Also, I would like to thank the good folks (Helen and team) behind the ML Study group in London — supported by @RWmeetamentor, who have been working hard to bring everyone together to learn ML and related topics. They may have even very indirectly influenced me to write this post. wink, wink Summary So, to sum up, our discussion at the LJC Unconference 2018, we mentioned other languages like Python, R, Julia, Matlab, and the likes, contribute more to AI, ML, and DL than another programming language. I know it is not going to make me popular by saying this, but my humble request to all developers would be that not to think or expect everything possible from a single programming language. Any language and in the context of this post, Java and other JVM languages are meant and written for a purpose, and no doubt, we can replicate efforts made in other languages in Java/JVM languages. But at the end of the day, they should all be treated as tools and be used where appropriate. Original. Reposted with permission. Bio: Mani Sarkar is a Java champion, polyglot, software craftsperson through @adoptopenjdk, @graalvm, and @truffleruby. Mani is also involved in the developer communities, #containers, #DevOps, #AI #ML #DL, as well as a speaker and blogger. Related:
- Startup specialized in AI natural language processing 'Brain Ventures' won a technology diagnosis project for commercialization of overseas source technology from Korean Ministry of SMEs and Startups SEONGNAM, South Korea, Dec. 1, 2020 /PRNewswire/ -- Brain Ventures (CEO Kim Won-hoe), a tenant company of the ICT-Culture Convergence Center run by Korean Ministry of Science and ICT and the National IT Industry Promotion Agency, announced on Thurs., November 26 that it won a technology diagnosis project for commercialization of overseas source technology by Korean Ministry of SMEs and Startups. This project is carried out as a joint research work with researchers of 'Ashmanov Neural Networks', a world-class AI research institute in Russia. It aims to commercialize polarity evaluation technology that extracts the correct meanings of texts and identifies positive and negative meanings. During the project, Brain Ventures plans to have joint publication, technology transfer, and commercialization of SCI research papers, and the research period is from November 23, 2020 to May 22, 2021. If this technology is commercialized through this research, it will become possible to evaluate writing or essay on a specific topic and apply the technology to evaluate short-answer questions of the state-run scholastic ability test for university admission as well as essay writing. CEO Won-hoe Kim said, "If we develop technology further in the future, we will be able to evaluate high-level articles related to research topics such as papers. Our ultimate goal is to make AI write according to an individual topic. On the other hand, Brain Ventures also set a foothold for corporate growth by attracting qualified angel investment this year, acknowledged for its technological capability and development potential in the ICT field. In the future, Brain Ventures plans to create an additional angel investment matching fund for Korean venture investment and obtain a venture investment company certification. SOURCE Brain Ventures
In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019. By Elvis Saravia, Affective Computing & NLP Researcher 2019 was an impressive year for the field of natural language processing (NLP). In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019. I will mostly focus on NLP but I will also highlight a few interesting stories related to AI in general. The headlines are in no particular order. Stories may include publications, engineering efforts, yearly reports, the release of educational resources, etc. Warning! This is a very long article so before you get started I would suggest bookmarking the article if you wish to read it in parts. I have also published the PDF version of this article which you can find at the end of the post. Table of Content Publications ML/NLP Creativity and Society ML/NLP Tools and Datasets Articles and Blog Posts Ethics in AI ML/NLP Education Publications 📙 Google AI introduces ALBERT which a lite version of BERT for self-supervised learning of contextualized language representations. The main improvements are reducing redundancy and allocating the model’s capacity more efficiently. The method advances state-of-the-art performance on 12 NLP tasks. Earlier this year, researchers at NVIDIA published a popular paper (coined StyleGAN) which proposed an alternative generator architecture for GANs, adopted from style transfer. Here is a follow-up work where that focuses on improvements such as redesigning the generator normalization process. One of my favorite papers this year was code2seq which is a method for generating natural language sequences from the structured representation of code. Such research can give way to applications such as automated code summarization and documentation. Ever wondered if it’s possible to train a biomedical language model for biomedical text mining? The answer is BioBERT which is a contextualized approach for extracting important information from biomedical literature. After the release of BERT, Facebook researchers published RoBERTa which introduced new methods for optimization to improve upon BERT and produced state-of-the-art results on a wide variety of NLP benchmarks. Researchers from Facebook AI also recently published a method based on an all-attention layer for improving the efficiency of a Transformer language model. More work from this research group includes a method to teach AI systems on how to plan using natural language. Explainability continues to be an important topic in machine learning and NLP. This paper provides a comprehensive overview of works addressing explainability, taxonomies, and opportunities for future research. Sebastian Ruder published his thesis on Neural Transfer Learning for Natural Language Processing. A group of researchers developed a method to perform emotion recognition in the context of conversation which could pave the way to affective dialogue generation. Another related work involves a GNN approach called DialogueGCN to detect emotions in conversations. This research paper also provides code implementation. The Google AI Quantum team published a paper in Nature where they claim to have developed a quantum computer that is faster than the world’s largest supercomputer. Read more about their experiments here. As mentioned earlier, one of the areas of neural network architectures that require a lot of improvement is explainability. This paper discusses the limitations of attention as a reliable approach for explainability in the context of language modeling. Neural Logic Machine is a neural-symbolic network architecture that is able to do well at both inductive learning and logic reasoning. The model does significantly well on tasks such as sorting arrays and finding shortest paths. And here is a paper that applies Transformer language models to Extractive and Abstractive Neural document summarization. Researchers developed a method that focuses on using comparisons to build and train ML models. Instead of requiring large amounts of feature-label pairs, this technique compares images with previously seen images to decide whether the image should be of a certain label. Nelson Liu and others presented a paper discussing the type of linguistic knowledge being captured by pretrained contextualizers such as BERT and ELMo. XLNet is a pretraining method for NLP that showed improvements upon BERT on 20 tasks. I wrote a summary of this great work here. This work from DeepMind reports the results from an extensive empirical investigation that aims to evaluate language understanding models applied to a variety of tasks. Such extensive analysis is important to better understand what language models capture so as to improve their efficiency. VisualBERT is a simple and robust framework for modeling vision-and-language tasks including VQA and Flickr30K, among others. This approach leverages a stack of Transformer layers coupled with self-attention to align elements in a piece of text and the regions of an image. This work provides a detailed analysis comparing NLP transfer learning methods along with guidelines for NLP practitioners. Alex Wang and Kyunghyun propose an implementation of BERT that is able to produce high-quality, fluent generations. Here is a Colab notebook to try it. Facebook researchers published code (PyTorch implementation) for XLM which is a model for cross-lingual model pretraining. This works provides a comprehensive analysis of the application of reinforcement learning algorithms for neural machine translation. This survey paper published in JAIR provides a comprehensive overview of the training, evaluation, and use of cross-lingual word embedding models. The Gradient published an excellent article detailing the current limitations of reinforcement learning and also providing a potential path forward with hierarchical reinforcement learning. And in a timely manner, a couple of folks published an excellent set of tutorials to get started with reinforcement learning. This paper provides a light introduction to contextual word representations. ML/NLP Creativity and Society 🎨 Machine learning has been applied to solve real-world problems but it has also been applied in interesting and creative ways. ML creativity is as important as any other research area in AI because at the end of the day we wish to build AI systems that will help shape our culture and society. Towards the end of this year, Gary Marcus and Yoshua Bengio debated on the topics of deep learning, symbolic AI and the idea of hybrid AI systems. The 2019 AI Index Report was finally released and provides a comprehensive analysis of the state of AI which can be used to better understand the progress of AI in general. Commonsense reasoning continues to be an important area of research as we aim to build artificial intelligence systems that not are only able to make a prediction on the data provided but also understand and can reason about those decisions. This type of technology can be used in conversational AI where the goal is to enable an intelligent agent to have more natural conversations with people. Check out this interview with Nasrin Mostafazadeh having a discussion on commonsense reasoning and applications such as storytelling and language understanding. You can also check out this recent paper on how to leverage language models for commonsense reasoning. Activation Atlases is a technique developed by researchers at Google and Open AI to better understand and visualize the interactions happening between neurons of a neural network. Check out the Turing Lecture delivered by Geoffrey Hinton and Yann LeCun who were awarded, together with Yoshua Bengio, the Turing Award this year. Tackling climate change with machine learning is discussed in this paper. OpenAI published an extensive report discussing the social impacts of language models covering topics like beneficial use and potential misuse of the technology. Emotion analysis continues to be used in a diverse range of applications. The Mojifier is a cool project that looks at an image, detects the emotion, and replaces the face with the emojis matching the emotion detected. Work on radiology with the use of AI techniques has also been trending this year. Here is a nice summary of trends and perspectives in this area of study. Researchers from NYU also released a Pytorch implementation of a deep neural network that improves radiologists’ performance on breast cancer screening. And here is a major dataset release called MIMIC-CXR which consists of a database of chest Xrays and text radiology reports. The New York Times wrote a piece on Karen Spark Jones remembering the seminal contributions she made to NLP and Information Retrieval. OpenAI Five became the first AI system to beat a world champion at an esports game. The Global AI Talent Report provides a detailed report of the worldwide AI talent pool and the demand for AI talent globally. If you haven’t subscribed already, the DeepMind team has an excellent podcast where participants discuss the most pressing topics involving AI. Talking about AI potential, Demis Hassabis did an interview with The Economist where he spoke about futuristic ideas such as using AI as an extension to the human mind to potentially find solutions to important scientific problems. This year also witnessed incredible advancement in ML for health applications. For instance, researchers at Massachusetts developed an AI system capable of spotting brain hemorrhages as accurate as humans. Janelle Shane summarizes a set of “weird” experiments showing how machine learning can be used in creative ways to conduct fun experimentation. Sometimes this is the type of experiment that’s needed to really understand what an AI system is actually doing and not doing. Some experiments include neural networks generating fake snakes and telling jokes. Learn to find planets with machine learning models build on top of TensorFlow. OpenAI discusses the implication of releasing (including the potential of malicious use cases) large-scale unsupervised language models. This Colab notebook provides a great introduction on how to use Nucleus and TensorFlow for “DNA Sequencing Error Correction”. And here is a great detailed post on the use of deep learning architectures for exploring DNA. Alexander Rush is a Harvard NLP researcher who wrote an important article about the issues with tensors and how some current libraries expose them. He also went on to talk about a proposal for tensors with named indices. ML/NLP Tools and Datasets ⚙️ Here I highlight stories related to software and datasets that have assisted in enabling NLP and machine learning research and engineering. Hugging Face released a popular Transformer library based on Pytorch names pytorch-transformers. It allows NLP practitioners and researchers to easily use state-of-the-art general-purpose architectures such as BERT, GPT-2, and XLM, among others. If you are interested in how to use pytorch-transformers there are a few places to start but I really liked this detailed tutorial by Roberto Silveira showing how to use the library for machine comprehension. TensorFlow 2.0 was released with a bunch of new features. Read more about best practices here. François Chollet also wrote an extensive overview of the new features in this Colab notebook. PyTorch 1.3 was released with a ton of new features including named tensors and other front-end improvements. The Allen Institute for AI released Iconary which is an AI system that can play Pictionary-style games with a human. This work incorporates visual/language learning systems and commonsense reasoning. They also published a new commonsense reasoning benchmark called Abductive-NLI. spaCy releases a new library to incorporate Transformer language models into their own library so as to be able to extract features and used them in spaCy NLP pipelines. This effort is built on top of the popular Transformers library developed by Hugging Face. Maximilien Roberti also wrote a nice article on how to combine fast.ai code with pytorch-transformers. The Facebook AI team released PHYRE which is a benchmark for physical reasoning aiming to test the physical reasoning of AI systems through solving various physics puzzles. StanfordNLP released StanfordNLP 0.2.0 which is a Python library for natural language analysis. You can perform different types of linguistic analysis such as lemmatization and part of speech recognition on over 70 different languages. GQA is a visual question answering dataset for enabling research related to visual reasoning. exBERT is a visual interactive tool to explore the embeddings and attention of Transformer language models. You can find the paper here and the demo here. Distill published an article on how to visualize memorization in Recurrent Neural Networks (RNNs). Mathpix is a tool that lets you take a picture of an equation and then it provides you with the latex version. Parl.ai is a platform that hosts many popular datasets for all works involving dialog and conversational AI. Uber researchers released Ludwig, an open-source tool that allows users to easily train and test deep learning models with just a few lines of codes. The whole idea is to avoid any coding while training and testing models. Google AI researchers release “Natural Questions” which is a large-scale corpus for training and evaluating open-domain question answering systems. Articles and Blog posts ✍️ This year witnessed an explosion of data science writers and enthusiasts. This is great for our field and encourages healthy discussion and learning. Here I list a few interesting and must-see articles and blog posts I came across: Christian Perone provides an excellent introduction to maximum likelihood estimation (MLE) and maximum a posteriori (MAP) which are important principles to understand how parameters of a model are estimated. Reiichiro Nakano published a blog post discussing neural style transfer with adversarially robust classifiers. A Colab notebook was also provided. Saif M. Mohammad started a great series discussing a diachronic analysis of ACL anthology. The question is: can a language model learn syntax? Using structural probes, this work aims to show that it is possible to do so using contextualized representations and a method for finding tree structures. Andrej Karpathy wrote a blog post summarizing best practices and a recipe on how to effectively train neural networks. Google AI researchers and other researchers collaborated to improve the understanding of search using BERT models. Contextualized approaches like BERT are adequate to understand the intent behind search queries. Rectified Adam (RAdam) is a new optimization technique based on Adam optimizer that helps to improve AI architectures. There are several efforts in coming up with better and more stable optimizers but the authors claim to focus on other aspects of optimizations that are just as important for delivering improved convergence. With a lot of development of machine learning tools recently, there are also many discussions on how to implement ML systems that enable solutions to practical problems. Chip Huyen wrote an interesting chapter discussing machine learning system design emphasizing on topics such as hyperparameter tuning and data pipeline. NVIDIA breaks the record for creating the biggest language model trained on billions of parameters. Abigail See wrote this excellent blog post about what makes a good conversation in the context of systems developed to perform natural language generation task. Google AI published two natural language dialog datasets with the idea to use more complex and natural dialog datasets to improve personalization in conversational applications like digital assistants. Deep reinforcement learning continues to be one of the most widely discussed topics in the field of AI and it has even attracted interest in the space of psychology and neuroscience. Read more about some highlights in this paper published in Trends in Cognitive Sciences. Samira Abner wrote this excellent blog post summarizing the main building blocks behind Transformers and capsule networks and their connections. Adam Kosiorek also wrote this magnificent piece on stacked capsule-based autoencoders (an unsupervised version of capsule networks) which was used for object detection. Researchers published an interactive article on Distill that aims to show a visual exploration of Gaussian Processes. Through this Distill publication, Augustus Odena makes a call to researchers to address several important open questions about GANs. Here is a PyTorch implementation of graph convolutional networks (GCNs) used for classifying spammers vs. non-spammers. At the beginning of the year, VentureBeat released a list of predictions for 2019 made by experts such as Rumman Chowdury, Hilary Mason, Andrew Ng, and Yan LeCun. Check it out to see if their predictions were right. Learn how to finetune BERT to perform multi-label text classification. Due to the popularity of BERT, in the past few months, many researchers developed methods to “compress” BERT with the idea to build faster, smaller and memory-efficient versions of the original. Mitchell A. Gordon wrote a summary of the types of compressions and methods developed around this objective. Superintelligence continued to be a topic of debate among experts. It’s an important topic that needs a proper understanding of frameworks, policies, and careful observations. I found this interesting series of comprehensive essays (in the form of a technical report by K. Eric Drexler) to be useful to understand some issues and considerations around the topic of superintelligence. Eric Jang wrote a nice blog post introducing the concept of meta-learning which aims to build and train machine learning models that not only predict well but also learn well. A summary of AAAI 2019 highlights by Sebastian Ruder. Graph neural networks were heavily discussed this year. David Mack wrote a nice visual article about how they used this technique together with attention to perform shortest path calculations. Bayesian approaches remain an interesting subject, in particular how they can be applied to neural networks to avoid common issues like over-fitting. Here is a list of suggested reads by Kumar Shridhar on the topic. Ethics in AI 🚨 Perhaps one of the most highly discussed aspects of AI systems this year was ethics which include discussions around bias, fairness, and transparency, among others. In this section, I provide a list of interesting stories and papers around this topic: The paper titled “Does mitigating ML’s impact disparity require treatment disparity?” discusses the consequences of applying disparate learning processes through experiments conducted on real-world datasets. HuggingFace published an article discussing ethics in the context of open-sourcing NLP technology for conversational AI. Being able to quantify the role of ethics in AI research is an important endeavor going forward as we continue to introduce AI-based technologies to society. This paper provides a broad analysis of the measures and “use of ethics-related research in leading AI, machine learning and robotics venues.” This work presented at NAACL 2019 discusses how debiasing methods can cover up gender bias in word embeddings. Listen to Zachary Lipton presenting his paper “Troubling Trends in ML Scholarship”. I also wrote a summary of this interesting paper which you can find here. Gary Marcus and Ernest Davis published their book on “Rebooting AI: Building Artificial Intelligence We Can Trust”. The main theme of the book is to talk about the steps we must take to achieve robust artificial intelligence. On the topic of AI progression, François Chollet also wrote an impressive paper making a case for better ways to measure intelligence. Check out this Udacity course created by Andrew Trask on topics such as differential privacy, federated learning, and encrypted AI. On the topic of privacy, Emma Bluemke wrote this great post discussing how one may go about training machine learning models while preserving patient privacy. At the beginning of this year, Mariya Yao posted a comprehensive list of research paper summaries involving AI ethics. Although the list of paper reference was from 2018, I believe they are still relevant today. ML/NLP Education 🎓 Here I will feature a list of educational resources, writers and people doing some amazing work educating others about difficult ML/NLP concepts/topics: CMU released materials and syllabus for their “Neural Networks for NLP” course. Elvis Saravia and Soujanya Poria released a project called NLP-Overview that is intended to help students and practitioners to get a condensed overview of modern deep learning techniques applied to NLP, including theory, algorithms, applications, and state of the art results — Link Microsoft Research Lab published a free ebook on the foundation of data science with topics ranging from Markov Chain Monte Carlo to Random Graphs. “Mathematics for Machine Learning” is a free ebook introducing the most important mathematical concepts used in machine learning. It also includes a few Jupyter notebook tutorials describing the machine learning parts. Jean Gallier and Jocelyn Quaintance wrote an extensive free ebook covering mathematical concepts used in machine learning. Stanford releases a playlist of videos for its course on “Natural Language Understanding”. On the topic of learning, OpenAI put together this great list of suggestions on how to keep learning and improving your machine learning skills. Apparently, their employees use these methods on a daily basis to keep learning and expanding their knowledge. Adrian Rosebrock published an 81-page guide on how to do computer vision with Python and OpenCV. Emily M. Bender and Alex Lascarides published a book titled “Linguistic Fundamentals for NLP”. The main idea behind the book is to discuss what “meaning” is in the field of NLP by providing a proper foundation on semantics and pragmatics. Elad Hazan published his lecture notes on “Optimization for Machine Learning” which aims to present machine training as an optimization problem with beautiful math and notations. Deeplearning.ai also published a great article that discusses parameter optimization in neural networks using a more visual and interactive approach. Andreas Mueller published a playlist of videos for a new course in “Applied Machine Learning”. Fast.ai releases its new MOOC titled “Deep Learning from the Foundations”. MIT published all videos and syllabus for their course on “Introduction to Deep Learning”. Chip Huyen tweeted an impressive list of free online courses to get started with machine learning. Andrew Trask published his book titled “Grokking Deep Learning”. The book serves as a great starter for understanding the fundamental building blocks of neural network architectures. Sebastian Raschka uploaded 80 notebooks about how to implement different deep learning models such as RNNs and CNNs. The great thing is that the models are all implemented in both PyTorch and TensorFlow. Here is a great tutorial that goes deep into understanding how TensorFlow works. And here is one by Christian Perone for PyTorch. Fast.ai also published a course titled “Intro to NLP” accompanied by a playlist. Topics range from sentiment analysis to topic modeling to the Transformer. Learn about Graph Convolutional Neural Networks for Molecular Generation in this talk by Xavier Bresson. Slides can be found here. And here is a paper discussing how to pre-train GNNs. On the topic of graph networks, some engineers use them to predict the properties of molecules and crystal. The Google AI team also published an excellent blog post explaining how they use GNNs for odor prediction. If you are interested in getting started with Graph Neural Networks, here is a comprehensive overview of the different GNNs and their applications. Here is a playlist of videos on unsupervised learning methods such as PCA by Rene Vidal from Johns Hopkins University. If you are ever interested in converting a pretrained TensorFlow model to PyTorch, Thomas Wolf has you covered in this blog post. Want to learn about generative deep learning? David Foster wrote a great book that teaches data scientists how to apply GANs and encoder-decoder models for performing tasks such as painting, writing, and composing music. Here is the official repository accompanying the book, it includes TensorFlow code. There is also an effort to convert the code to PyTorch as well. A Colab notebook containing code blocks to practice and learn about causal inference concepts such as interventions, counterfactuals, etc. Here are the materials for the NAACL 2019 tutorial on “Transfer Learning in Natural Language Processing” delivered by Sebastian Ruder, Matthew Peters, Swabha Swayamdipta and Thomas Wolf. They also provided an accompanying Google Colab notebook to get started. Another great blog post from Jay Alammar on the topic of data representation. He also wrote many other interesting illustrated guides that include GPT-2 and BERT. Peter Bloem also published a very detailed blog post explaining all the bits that make up a Transformer. Here is a nice overview of trends in NLP at ACL 2019, written by Mihail Eric. Some topics include infusing knowledge into NLP architectures, interpretability, and reducing bias among others. Here are a couple more overviews if you are interested: link 2 and link 3. The full syllabus for CS231n 2019 edition was released by Stanford. David Abel posted a set of notes for ICLR 2019. He was also nice to provide an impressive summary of NeurIPS 2019. This is an excellent book that provides learners with a proper introduction to deep learning with notebooks provided as well. An illustrated guide to BERT, ELMo, and co. for transfer learning NLP. Fast.ai releases its 2019 edition of the “Practical Deep Learning for Coders” course. Learn about deep unsupervised learning in this fantastic course taught by Pieter Abbeel and others. Gilbert Strang released a new book related to Linear Algebra and neural networks. Caltech provided the entire syllabus, lecture slides, and video playlist for their course on “Foundation of Machine Learning”. The “Scipy Lecture Notes” is a series of tutorials that teach you how to master tools such as matplotlib, NumPy, and SciPy. Here is an excellent tutorial on understanding Gaussian processes. (Notebooks provided). This is a must-read article in which Lilian Weng provides a deep dive into generalized language models such as ULMFit, OpenAI GPT-2, and BERT. Papers with Code is a website that shows a curated list of machine learning papers with code and state-of-the-art results. Christoph Molnar released the first edition of “Interpretable Machine Learning” which is a book that touches on important techniques used to better interpret machine learning algorithms. David Bamman releases the full syllabus and slides to the NLP courses offered at UC Berkley. Berkley releases all materials for their “Applied NLP” class. Aerin Kim is a senior research engineer at Microsoft and writes about topics related to applied Math and deep learning. Some topics include intuition to conditional independence, gamma distribution, perplexity, etc. Tai-Danae Bradley wrote this blog post discussing ways on how to think about matrices and tensors. The article is written with some incredible visuals which help to better understand certain transformations and operations performed on matrices. I hope you found the links useful. I wish you a successful and healthy 2020! Due to the holidays, I didn’t get much chance to proofread the article so any feedback or corrections are welcomed! >> PDF version << Bio: Elvis Saravia is a researcher and science communicator in Affective Computing and NLP. Original. Reposted with permission. Related:
Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers’ daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories. Amazon enables people to buy pretty-much every item imaginable and get it delivered to their home within 24 hours. The internet giants have created handy, fun and convenient digital services that consumers want to come back to time and again. And to make this possible each has developed outstanding capabilities to (a) capture huge quantities of data, (b) transform that data, (c) identify patterns for insight, (d) suggest actions and (e) monetise at scale. However, these capabilities are no longer the preserve of multi-billion dollar corporations with outsized investment budgets. Advances in cloud computing and open source software in the last decade mean that the building blocks exist for companies of all sizes to develop similar capabilities either in-house or by utilising 3rd-party providers. Of the five capabilities outlined above, ML is used mostly in (c), to identify patterns for insight. And it’s that richness of insight which will enable businesses of all sizes to advance on three fronts: Improve customer experience Accelerate revenue growth Enhance operational efficiency Now let’s delve deeper into each of these areas and share some examples to see how ML can make a difference. Improve customer experience with customer service chatbots Companies of all sizes will benefit from developing more joined-up experiences for customers. Retail consumers and business customers all expect to receive the same outstanding high levels of support regardless of whether they are contacting a company from online, by phone or in-store. And those same high levels of support should continue post-purchase if the customer needs to report a fault, return a product or simply wants a question answered. Repeat customers (and increasingly their social media comments) are the lifeblood of a sustainable business. One of the easiest and most cost-effective ways for businesses to improve customer experience online is to deploy a chatbot or virtual assistant to answer questions from customers. Chatbots are one of the most obvious examples of AI in action today. Built with Natural Language Processing and Machine Learning technologies, chatbots have proliferated across the web in the last 5 years because consumers love interacting with them. It’s often easier to ask a quick question of the chatbot than to scroll through pages of small print seeking detailed product information. Equally for the company, a well-trained chatbot can deflect multiple calls that would otherwise be made to their contact centre. Similar Natural Language Understanding technology can be used to trawl service management systems to support contact centre or field service staff in resolving customer product issues or infrastructure faults much faster than ever before, providing a virtuous feedback loop that can positively impact customer satisfaction scores. Chatbots usually need to be trained on domain-specific terminology and some effort needs to be made to create a suitable set of question and answer workflows. Question and answer flows are used to uncover the intent of the customer’s question and to find the most relevant responses. Depending on the complexity of the domain and variety of services it will be required to support, a chatbot development project could be completed from initial workshop to deployment in as little as 12 weeks. Phil Westcott, Co-Founder of Filament.AI believes: “AI is a priority item on every CEO’s agenda. In recent years, many organisations have started their AI roadmap through the adoption of chatbots or digital assistants. For organisations as varied as HSBC to the NSPCC, they have proved cost-effective to deploy and — when done well — have delivered superior customer satisfaction. The challenge becomes the cost-effective maintenance and evolution of those conversational interfaces. Like the website 2.0 revolution, the role of chatbot content management systems (including Filament’s Enterprise Bot Manager) is to empower the organisation to retrain and optimise the performance of complex chatbots over time, much like your best employees.” Accelerate revenue growth with personalised recommendations Amazon is by almost every measure the most successful online retailer of all time and anyone that has bought a book, a movie or a Pooping Dogs calendar from it will know that it has perfected the art of shopping cart management. How many times when you have gone to Amazon with the intent to buy just a single item and their recommendations of “Frequently Bought Together” or “Recommended for You” have just been too tempting and you’ve come away after adding a second or a third item to your cart and spending twice as much as you originally intended? That’s AI in action and it’s not just Amazon that’s mastered this. Google and its movie recommendations on Youtube or its app recommendations in the Google Play store for Android smartphones are using many very similar Machine Learning technologies either to increase the size of your shopping basket or to increase your viewing time on their entertainment platform in order to make you ever more popular to its advertisers (who are Google’s real customers). The secret to a good set of recommendations comes from combining several routes; understanding your customer’s past viewing or purchasing habits; understanding how the movies or products in your catalogue were watched or rated by similar customers; and understanding the customer’s current context (eg. time of day, location, weather) which could also have bearing on what they might like to buy or watch next. If you can do this effectively then you can achieve outsized revenue growth. At some point in the lifecycle of every business there comes a time when it needs to shift from chasing new customers to fostering the relationship it has built up with its existing customers; because long-term that’s where the greatest profits lie. And to achieve this goal, it becomes critical to start collecting data about your customers and your products; if you’re not doing so already! Today, however, there is no need to master advanced ML techniques like Collaborative Filtering, Matrix Factorisation or Wide and Deep Neural Networks (tools developed and used by Amazon, Google and other pioneers). Suppose, for example, you are hosting your online store with Shopify. Shopify can provide you with a whole series of app plugins for Product Recommendations on their app marketplace; Personalised Recommendations by LoopKit is one such app that uses ML for improving cross-sell, up-sell and conversion rates. Of course all the other major online commerce and cloud platforms offer very similar capabilities. Tom Adeyoola, Founder of Metail.com and Co-Founder of Extend Ventures sees: ”Matching discrete products to the needs and desires of consumers is a first order data and technology challenge. Flipping that to achieve mass customisation by tailoring clothing items to match the size and shape of individual consumers at scale is a next order challenge with additional dimensions of complexity. That’s what we set out to achieve with Metail and all sorts of opportunities still exist for mass customisation in many other consumer-focused industries. There’s still plenty of opportunity out there for people to start and build successful businesses that fulfil fundamental unmet needs.” Enhance operational efficiency with carbon footprint analytics For industrial product companies including discrete, batch and continuous manufacturers, collecting data from sensors and measuring performance during production and in-life usage has been standard practice for decades. Furthermore, in the last 10 years, relentless improvements in computing and networking technologies have meant that manufacturers can now take advantage of ever smaller IOT sensors with more powerful chips, lower energy requirements and wireless connectivity to gather and share huge quantities of data in real-time. Machine Learning can now supercharge the capabilities of manufacturers to conduct Predictive Maintenance on equipment and machinery to improve ever-important efficiency measures such as OEE and OOE². Based on quantities of time series data from sensors or image data streamed from a camera it’s now possible to use a range of quite different ML models to provide a probability of failure within a precise time window that’s very specific to an individual machine on a production line. This makes it easier to schedule maintenance activities and avoid unscheduled downtime, supply chain disruption and remediation costs required by current contracts. Competence built in one advanced area can be deployed elsewhere in a very profitable manner. Take Prognostic.io. It’s a startup that’s been active in the Predictive Maintenance space for a while. Prognostic has recently transitioned to focus exclusively on a related area; carbon footprint analytics. There’s huge foreseeable demand for its services; the UK government, for example, has recently announced plans to achieve Net Zero greenhouse gas emissions by 2050 by targeting a 78% reduction by 2035 (compared to 1990 levels) and enshrining that goal in law. In addition, the Financial Conduct Authority (FCA, the UK’s financial markets regulator) has begun since the start of this year to enforce regulations for Climate-related Financial Disclosures. Large corporations listed in the UK are now legally obliged to act. No more greenwashing; mineral extraction, petrochemical and industrial manufacturing companies (to name just a few) will now be required by law to show what measures they are taking to reduce their harmful emissions. Shravane Balabasqer Founder of Prognostic and CarbonAnalytics.com thinks: ”Before companies can commit to reductions in their carbon footprint across their value chain, they need to know what their emissions are today. And that starts with data capture. Whether you are an industrial manufacturer, a utilities company or a building and facilities operator, using sensors to collect data and analytics to understand your emissions is vital. Only then can you effectively develop strategies to reduce these emissions over time.” In our next article “Preparing for AI and dipping a toe in the water” we aim to give you as a Venture Leader some pointers on: How to prepare for using AI in the future How to get experience in managing AI projects How to recruit the right people In later articles in the series we plan to cover: AI for Venture Leaders Demystifying AI Harnessing the benefits of AI Preparing for AI and dipping a toe in the water Practicalities of AI ethics, privacy and regulation Typical use cases and platforms for AI Venture exit prices and AI I hope this has kept your interest and you’ll join us for future articles too. Please let us know in the Comments section below any thoughts or questions you may have.
Introduction to Applications of NLP Among the millions of species in this world, only homo sapiens are capable of spoken language. From cave drawings to web communication, we have come a long way! As we are progressing towards Artificial Intelligence, it only seems logical to impart the bots the skill of language and communication that is natural to humans. This is where NLP plays its part as a subset of AI to build systems that can understand language. Throw in Machine Learning(another awesome AI subset) and voila, we can build systems that can understand language, learn and improve over time without being programmed explicitly. Different Applications of NLP Given below are the different applications of NLP: 1. Text Classification Texts are a form of unstructured data that possess very rich information within them. Text Classifiers categorize and organize pretty much any form of text that we use currently. Since texts are unstructured, analyzing, sorting and classifying them can be very hard and time-consuming and sometimes even tedious work for humans, not to mention all the errors that humans are prone to make in the process. This is where Text Classification comes in to picture to serve its purpose of performing the mentioned tasks with more scalability and accuracy. Text Classification is more efficient when machine learning classifiers are trained with a few ground rules. With the methodologies of Deep Learning such as CNN and RNN the results only getting better with the increased text data that we generate. It can also be made visually appealing using “Word Clouds”. Text classification can be applied for a range task from e-mail spam filtering to brand monitoring. A very essential key and feedback for business would be how their products are touching their intended consumers and Text Classification gives answers to business questions by classifying people’s opinion on the said brand, price, and features 2. Machine Translation Achieving multilingualism can often be a tough task to accomplish, so to make our life easier at least in the aspect of communication, Machine Translation comes to the rescue. In the early 50’s IBM presented a machine translation system that had only 250 words and translated 49 carefully selected Russian sentences in the field of chemistry into English. Over the recent years with the resources to implement Neural networks, machine translation has significantly improved in its quality such that translating between languages is as simple as pressing a button on the available smartphones or tablets. Google Translate supports more than 100 languages and can even translate language images from up to 37 languages. Google translate translating English to Spanish. This type of Machine Translation is achieved with the use of a Recurrent Neural Network (RNN). RNN can be used as “Fixed to Sequence” where the input is given in the form of an image(fixed size) and with adequate training, the machine outputs a suitable caption for the same. “Sequence to Sequence” where the input is given in the form of a sequence(say language1) and the output in the form of another set of sequences(say language2). “Sequence to Fixed” where the input is given as sequence and the output is of fixed size. This is generally applied for Sentiment Analysis which is explored next. 3. Sentiment Analysis Feedback is one of the essential elements of good communication. Be it a brand-new movie or a cutting-edge tech that’s recently launched, the response of the intended audience is what makes or breaks them. Hence analyzing people’s sentiment towards a product is important now more than ever. The Bag of words(BOW) approach where the original order of word is lost, but the sentence under watch is reduced to the words that actually contribute in determining the sentiment is quite popular for sentiment analysis. This method uses statistical methods to group the words and the language takes a backseat. The BOW can be thought of as a massive dictionary that where each word holds its own unique value which contributes to conclude the sentiment. 4. Chatbots Almost every other website nowadays is being supported by a bot that is designed to make our experience better and simpler. Chatbots are the bots designed for a specific use of interaction with humans or other fellow machines using the techniques of AI. Chatbots are designed keeping in mind the human interaction. The use of Chatbots goes way back to 1966 when the first chatterbot named “ELIZA” was designed at MIT. Eliza could keep the conversation flowing with the human it interacted with, this led to the development of chatbots that could have a positive influence on people suffering from psychological issues. After Eliza came “ALICE” in 1995 that used heuristical pattern matching rules to remain engaged in conversation with the user. Alice became immensely popular for the rest of the 20th century and was also the inspiration for Apple’s SIRI and movies. Recently, a chatbot named “JOY” was developed with an intent to track and improve mental health. Joy checks with you at least once every day, asks you how you are doing and intercepts your emotions if you are feeling happy, sad or anxious based on your responses. In recent years, designing a simple chatbot has become easier than ever with API’s such as IBM’s Watson and Google’s dialog flow. 5.Virtual Assistants From setting an alarm to making the grocery list to entertain you while you are feeling bored, virtual assistants play a huge part in our daily routines. They are engineered to accept the user’s voice commands and perform the task entrusted with them. Virtual assistants are designed to interact with humans in a very human way, most of their responses would feel like the responses you would receive from a friend or colleague. In addition to NLP virtual assistants also focuses on Natural Language Understanding so as to keep up with the ever-growing slangs, sentiments, and intent behind the user’s input. Virtual Assistants learn from Artificial Neural Networks and can hold any conversation for a longer duration than chatbots. They even serve as classic examples of speech to text conversion and text to speech conversion. Virtual Assistants can also be given more complicated tasks such as decision making, and they mature with each interaction and can provide a more personalized experience Conclusion — Applications of NLP The growth of applications using NLP has only progressed over the years and continues to so. Language has always played a pivotal role in our history and with state-of-the-art machine translations we are slowing breaking the language barrier that once restricted us to be able to interact with other people and culture. Within or outside our home, we are relying on this magnificent technology for daily tasks. Now the time is better than ever to explore all the possibilities that NLP can offer us. SOURCE:
Is ALBERT short for BERT? Getting to know the differences between two of the most revolutionary state of the art algorithmic flows in Natural Language Processing. Photo by Kelly Sikkema on Unsplash BERT Algorithm is considered as a revolution in word semantic representation which has outperformed all the previously known word2vec models in various NLP tasks such as Text Classification, Entity Recognition, and Question-Answering. The original BERT (BERT-base) model is made of 12 transformer encoder layers along with a Multi-head Attention. The pretrained model has been trained on a large corpus of unlabeled text data with self-supervising by using the following tasks: 1. Mask Language Model (NLM) loss — The task is “the filling banks,” where a model uses the context words surrounding a MASKED token to try to predict what the MASKED word should be. 2. Next Sentence Prediction (NSP) loss — For an input of sentences (A, B), it estimates how likely sentence B is the second sentence in the original text. This mechanism can be a beneficial evaluation metric in conversational systems’ performance. RoBerta and XLNet are new versions of Bert that outperform original BERT on many benchmarks using more data and new NLM-loss, respectively. BERT is an expensive model in terms of memory and time consumed on computations, even with GPU. The original BERT contains 110M parameters to be fine-tuned, which takes a considerable amount of time to train the model and excellent memory to save the model’s parameters. Therefore, we prefer lighter algorithms with excellent performance as BERT. So we shall talk about a recent article that introduces a new version of BERT named ALBERT. The authors of ALBERT claim that their model brings an 89% parameter reduction compared to BERT with almost the same performance on the benchmark. We will compare ALBERT with BERT to see whether it can be a good replacement for BERT. The pretrained ALBERT model comes in two versions: “Albert-base-v1” (Not-recommended) and “Albert-base-v2” (Recommended) that can be downloaded from Hugging Face website containing all models in the Bertology domain. You can also load the model directly in your code by using the transformers module as follows: from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained(“albert-base-v2”) model = AlbertModel.from_pretrained(“albert-base-v2”) And by using this link, you can find the model and the codes for performing different tasks on benchmark data in the paper. First, we look at the innovations in ALBERT, which are the reasons that named this algorithm as “A Lite BERT.” We then discuss the question: Is ALBERT solving memory and time consumption issues of BERT? Innovations in ALBERT 1. Cross-layer parameter sharing is the most significant change in BERT architecture that created ALBERT. ALBERT architecture still has 12 transformer encoder blocks stacked on top of each other like the original BERT. Still, it initializes a set of weights for the first encoder that is repeated for the other 11 encoders. This mechanism reduces the number of “unique” parameters, while the original BERT contains a set of unique parameters for every encoder (see Figure 1). Figure 1 People who are familiar with fundamentals of Deep Learning know that every layer of a Neural Networks model is responsible for catching certain features or patterns of data and the deeper layers learn more complicated patterns and concepts, and to make this happen, each layer should contain its specific parameters different independent from other layers’. Therefore, one can conclude that this architecture can not outperform BERT architecture, and as you see in the following table, the shared parameters do not leverage the accuracy significantly, but interestingly, the results are almost the same as BERT. Table 1 2. Embedding Factorization The embedding size in BERT is equal to the size of the hidden layer (768 in original BERT). ALBERT adds a smaller size layer between vocabulary and hidden layer to decompose the embedding matrix of size E
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