"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').
OneConnect, a leading technology-as-a-service platform serving financial institutions in China, is pleased to announce that its artificial intelligence research institute, Gamma Lab, won the FinTech Team of the Year award for its strong technical prowess, wide range of deployment scenarios across the financial sector and high-speed growth at The Asset Triple A Digital Awards 2020 held by international authoritative media The Asset. The Gamma O platform was awarded the Best Digital Financial Project for its success since launch in providing one-stop solutions that empowered financial institutions and technology service providers in connecting with each other. The Asset was founded in 1999, with its Triple A awards gaining a high level of influence and authority in Asian and international financial markets. For two consecutive years, Gamma Lab won the FinTech Team of the Year award, demonstrating OneConnect's industry leading position in both AI technology R&D and deployment. OneConnect's information extraction technology led at the international AI competition SemEval 2020, representing another world first for Gamma Lab in new AI technologies beyond the successes that the institute had achieved in terms of performance in the areas of microexpression recognition, facial action unit recognition, machine reading comprehension, natural language generation, emotion recognition and deep learning model inference.
Arria' natural language generation offers scalability, flexibility and precision Arria NLG, named a world leader in Natural Language Generation technology by Gartner, welcomed Maistering to the ever-growing list of partners using its award-winning platform. After after vetting numerous NLG providers, Maistering, a global provider of artificial intelligence applications, selected Arria NLG Studio to augment its artificial intelligence portfolio, Master Collections. Arria NLG turns data into voice and/or written narratives that enable better, more informed decision-making across an enterprise. Arria NLG Studio 3.0, the latest iteration of its natural language platform, combines advanced Language Analytics with computational linguistics to narrate and add context to any data. Arria NLG Studio can convey actionable insights and tell your data's whole story," said Sharon Daniels, CEO, Arria NLG. "Our platform brings together language analytics and advanced mathematical functions for the ...
In recent years, there has been an increasing interest in open-ended language generation thanks to the rise of large transformer-based language models trained on millions of webpages, such as OpenAI's famous GPT2 model. The results on conditioned open-ended language generation are impressive, e.g. Besides the improved transformer architecture and massive unsupervised training data, better decoding methods have also played an important role. This blog post gives a brief overview of different decoding strategies and more importantly shows how you can implement them with very little effort using the popular transformers library! All of the following functionalities can be used for auto-regressive language generation (here a refresher).
Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news. Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary's point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover.
Until 2019, it has been the case that if you come across several paragraphs of text on a consistent topic with consistent subjects, you can assume that text was written or structured by a human being. That is no longer true. Over the past year, AI researchers designed computer programs with the ability to generate multi-paragraph stories that remain fairly coherent throughout. As we explain in the video above, these programs create passages of text that seem like they were written by someone who is fluent in the language but possibly faking their knowledge. I'm not sure we needed an automated version of that person, but here it is: Depending on how you look at it, this technology is a powerful bullshit machine or a promising tool for artists.
"We are transforming the business analysis process through automation, aimed at making the life of every analyst focused on providing quality output rather than substantial manual efforts on data quality check, " Ramesh Lakshminarayanan, CIO, CRISIL told ETCIO. To improve its research reports and analytics, Crisil has been adopting automated data extraction including extraction of unstructured paragraphs, tables, etc which are automated to the data mapping (financial taxonomy mapping) and automatic text summarisation (Natural Language Generation, or NLG) of analyst opinion. Almost 90 percent of Crisil's key processes are data-driven. The company has initiated a large streamlining effort in 2019, in multiple phases. The first phase was to automate a lot of mundane web crawling/ extraction work, as the ratings agency collected a chunk of data from various sources on the web.
Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator.
The latest trend in AI is that larger natural language models provide better accuracy; however, larger models are difficult to train because of cost, time, and ease of code integration. Microsoft is releasing an open-source library called DeepSpeed, which vastly advances large model training by improving scale, speed, cost, and usability, unlocking the ability to train 100-billion-parameter models. One piece of that library, called ZeRO, is a new parallelized optimizer that greatly reduces the resources needed for model and data parallelism while massively increasing the number of parameters that can be trained. Researchers have used these breakthroughs to create Turing Natural Language Generation (Turing-NLG), the largest publicly known language model at 17 billion parameters, which you can learn more about in this accompanying blog post. The Zero Redundancy Optimizer (abbreviated ZeRO) is a novel memory optimization technology for large-scale distributed deep learning.
Massive deep learning language models (LM), such as BERT and GPT-2, with billions of parameters learned from essentially all the text published on the internet, have improved the state of the art on nearly every downstream natural language processing (NLP) task, including question answering, conversational agents, and document understanding among others. Better natural language generation can be transformational for a variety of applications, such as assisting authors with composing their content, saving one time by summarizing a long piece of text, or improving customer experience with digital assistants. Following the trend that larger natural language models lead to better results, Microsoft is introducing Turing Natural Language Generation (T-NLG), the largest model ever published at 17 billion parameters, which outperforms the state of the art on a variety of language modeling benchmarks and also excels when applied to numerous practical tasks, including summarization and question answering. This work would not be possible without breakthroughs produced by the DeepSpeed library (compatible with PyTorch) and ZeRO optimizer, which can be explored more in this accompanying blog post. We are releasing a private demo of T-NLG, including its freeform generation, question answering, and summarization capabilities, to a small set of users within the academic community for initial testing and feedback.
It has never been easier to measure and monitor business operations -- the amount of data available to organizations is staggering. Access to insight provides businesses with a clear competitive advantage, but many enterprises struggle to make sense of the seemingly endless reams of data at their disposal. To overcome hurdles with data literacy, smart businesses have embraced various business intelligence (BI) solutions to collect, aggregate, translate and present business information. An invaluable asset for enterprises worldwide, BI dashboards are data visualization tools that display the status of business analytics metrics, key performance indicators (KPIs) and other important data points on a single screen. To underscore the widespread adoption of BI, note that the global business intelligence market is projected to reach USD $147.19 billion by 2025.