"As for why I tell a lot of stories, there's a joke about that. There was once a man who had a computer, and he asked it, 'Do you compute that you will ever be able to think like a human being?' And after assorted grindings and beepings, a slip of paper came out of the computer that said, 'That reminds me of a story . . . "
– from ANGELS FEAR: TOWARDS AN EPISTEMOLOGY OF THE SACRED. Gregory Bateson & Mary Catherine Bateson. (Part III 'Metalogue').
With advances in technology and easy access to paid and free tools to create spin-offs or rewrite content, people can now use Artificial Intelligence (AI) to make fake news, fabricated reviews, and fictitious social media accounts and thereby spreading misinformation. However, an AI can now also detect fake written material! This AI tool named Giant Language model Test Room or GLTR was developed by a collaborative effort of Harvard University and MIT-IBM Watson Lab researchers and developers whose goal was to teach information and raise awareness about generated text. The GLTR tool can determine whether a text is written by a human or is an AI spin generating tool by detecting statistical patterns. The AI text generating programs use an algorithm and specific code to detect data spinning patterns.
Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) are dazzling in and of themselves, but believe it or not, leveraged in isolation, they are limited in their potential. These technologies do not interpret data by themselves: they are tied either to deterministic, hard coded software programs created by humans or they are linked to a form of artificial intelligence that can interpret human language into a form ML and DL algorithms can understand. The umbrella term for this gateway AI technology is natural language processing (NLP). Other terms associated with NLP include natural language understanding, natural language generation, voice recognition, and speech recognition. Tractica considers all of these terms and technologies as part of the family of NLP technologies, and each of them are addressed in some fashion in this report.
BERT is a state-of-the-art natural language processing (NLP) model that allows pretraining on unlabelled text data and later transfer training to a variety of NLP tasks. Due to its promising novel ideas and impressive performance we chose it as a core component for a new natural language generation product. Reading a paper, maybe following a tutorial with example code and putting a working piece of software into production are, however, two totally different things. In this session, we will tell you how we trained a custom version of the BERT network and included it into a natural language generation (NLG) application. You will hear how we arrived at the decision to use BERT and what other approaches we tried.
The term "fake news" barely made a blip on most people's radars a few years ago, yet many observers are saying that this pernicious form of disinformation -- now weaponized to spread virally thanks to social media -- could potentially destabilize democracies around the world. But so-called "fake news" is nothing new: after all, the practice of spreading of false information to influence public opinion has been a relatively common one since at least ancient times. However, what's alarming today is that fake news will likely no longer be only generated by humans. While there are automated methods to detect fake news created by humans, with recent AI advancements, especially in the field of natural language generation (NLG), it will now be possible for machines to produce convincing disinformation, written in the language and tone of established news sources -- on a much larger scale and to potentially much more devastating effect -- than ever before. So how to catch this kind of machine-generated propaganda?
The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.1 1.The online survey was in the field from February 6 to February 16, 2018, and garnered responses from 2,135 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP. AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular,2 2.The nine capabilities are natural-language text understanding, natural-language speech understanding, natural-language generation, virtual agents or conversational interfaces, computer vision, machine learning, physical robotics, autonomous vehicles, and robotic process automation (RPA). Some would argue that RPA should not be classified as AI in and of itself, but in our experience, RPA systems are increasingly incorporating AI capabilities.
The best approach towards NLP that is a blend of Machine Learning and Fundamental Meaning for maximizing the outcomes. Machine Learning only is at the core of many NLP platforms, however, the amalgamation of fundamental meaning and Machine Learning helps to make efficient NLP based chatbots. Machine Language is used to train the bots which leads it to continuous learning for natural language processing (NLP) and natural language generation (NLG). Both ML and FM has its own benefits and shortcomings as well. Best features of both the approaches are ideal for resolving the real-world business problems.
The exposure bias problem refers to the training-inference discrepancy caused by teacher forcing in maximum likelihood estimation (MLE) training for recurrent neural network language models (RNNLM). It has been regarded as a central problem for natural language generation (NLG) model training. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore to remove exposure bias, there is little work showing how serious the exposure bias problem is. In this work, starting from the definition of exposure bias, we propose two simple and intuitive approaches to quantify exposure bias for MLE-trained language models. Experiments are conducted on both synthetic and real data-sets. Surprisingly, our results indicate that either exposure bias is trivial (i.e. indistinguishable from the mismatch between model and data distribution), or is not as significant as it is presumed to be (with a measured performance gap of 3%). With this work, we suggest re-evaluating the viewpoint that teacher forcing or exposure bias is a major drawback of MLE training.
The launch comes after years of research and improvements using artificial intelligence (AI) and natural language generation (NLG). According to Statista 2.14 billion people worldwide will buy goods and services online by 2021. In Canada, PayPal reports that eCommerce businesses are growing 28 times more than those who are not selling online. As more consumers turn to the web to research and purchase items, product content becomes increasingly important in order to drive this level of growth. According to Salsify 51% of the time, a product listing with more bullets will convert at a higher rate and outrank its top competitor.
How can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity but not quality, as models that occasionally emit low quality samples would be insufficiently penalized. In this paper, we propose a unified framework which evaluates both diversity and quality, based on the optimal error rate of predicting whether a sentence is human- or machine-generated. We demonstrate that this error rate can be efficiently estimated by combining human and statistical evaluation, using an evaluation metric which we call HUSE. On summarization and chit-chat dialogue, we show that (i) HUSE detects diversity defects which fool pure human evaluation and that (ii) techniques such as annealing for improving quality actually decrease HUSE due to decreased diversity.