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Artificial Intelligence In Wealth Management To Provide Hybrid Services

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Artificial Intelligence (AI) refers to intelligent machines that work and react like humans. AI helps to deliver insights to complex client questions in real time through its virtual conversational interface between business and clients. AI enabled applications such as natural language generation (NLG) is closing the gap between data analysis and investment decisions, providing real-time insights through automated trading strategies. For instance, according to a survey in 2018 by Forbes, 34% of wealth management companies have currently deployed AI within their firms and around 99% plan on deploying AI within the next 3 years. Companies such as Wells Fargo and Bank of America have already deployed AI services to better serve clients.


Artificial intelligence content writing ramps up publishing

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Content production helps connect brands with customers, governments with citizens and organizations with their supporters. But, while important, content production can also be a labor- and time-intensive task. Producing articles, infographics, videos and a variety of other content requires a significant amount of work from writers and editors. With organizations producing and managing content daily, many have begun to turn to various technologies to help. One of the more capable of these technologies is AI.


Evolution of Natural Language Generation

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Since the dawn of Sci-Fi cinema, society has been fascinated with Artificial Intelligence. Whenever we hear the term "AI", our first thought is typically one of a futuristic robot from movies such as Terminator, The Matrix and I, Robot. Although we might still be a few years away from robots that can think for themselves, there have been significant developments in the fields of machine learning and natural language understanding over the past few years. Applications such as Personal Assistants (Siri/Alexa), chatbots and Question-Answering bots are truly revolutionizing the way we interface with machines and go about our daily lives. Natural Language Understanding (NLU) and Natural Language Generation (NLG) are among the fastest growing applications of AI due to the increasing need to understand and derive meaning from language, with its numerous ambiguities and varied structure. According to Gartner, "By 2019, natural-language generation will be a standard feature of 90 percent of modern BI and Analytics platforms".


Ultimate Guide to Artificial Intelligence in the Enterprise

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A Google search for AI use cases turns up millions of results, an indication of the many ways in which AI is applied in the enterprise -- or at least can be applied (see section "Adoption in the enterprise"). AI use cases span industries from financial services -- an early adopter -- to healthcare, education, marketing and retail. AI has made its way into every business department, from marketing, finance and HR to IT and business operations. Additionally, the use cases incorporate a range of AI applications. Among them: natural language generation tools used in customer service, deep learning platforms used in automated driving, and biometric identifiers used by law enforcement. Here is a sampling of current AI use cases in multiple industries and business departments with links to the TechTarget articles that explain each one in depth.


Can AI Replace Writers?

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"I would say everyone has read at least once an algorithmically produced article," said Robert Weissgraeber, CTO and Managing Director of AX Semantics. In many cases, readers don't see a difference between human- and bot-authored copy, Weissgraeber told Built In. His company, AX Semantics, is one of several -- including Narrative Science and Automated Insights -- exploring natural language generation, or automated writing. The technology can be used to generate product descriptions, quarterly earnings reports, fantasy football recaps and journalism. The Washington Post, for instance, has developed an AI-enabled bot, Heliograf, that helps generate election and sports coverage.


Senior Data Scientist

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Identify root causes and develop solutions to improve robustness for the data science teams systems. Drive improvement of code quality and serve as an example to follow through code reviews. Deliver complex large-scoped features independently, including designing and implementing a solution that is running successfully in production. Develop data models to effectively gather information from disparate sources, analyze it, identify trends, extract useful information and surface the information onto our system platform. Develop end-to-end machine learning and NLP-based systems to extract structured information from unstructured data.


BI without AI is like corn flakes without the milk

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One that would quickly highlight any recent changes or key findings." SAS report summary helps to describe the report in a few sentences to replicate a speech-template. The functionality provides a dynamic description of the report: Conditional text, Dynamic values and Natural Language Generation ... a key requirement if you are going to claim this as AI-enhanced business intelligence. Further examples and details of how to utilize this feature can be found within this article written by Xavier Bizoux. Report summaries are also particularly useful if your audience includes individuals with visual impairments. The report summary can easily be read by screen readers. But wait, there's more ...


Logical Natural Language Generation from Open-Domain Tables

arXiv.org Artificial Intelligence

Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language. In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t.\ logical inference. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order. In our experiments, we comprehensively survey different generation architectures (LSTM, Transformer, Pre-Trained LM) trained with different algorithms (RL, Adversarial Training, Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained LM can significantly boost both the fluency and logical fidelity metrics, 2) RL and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine generation can help partially alleviate the fidelity issue while maintaining high language fluency. The code and data are available at \url{https://github.com/wenhuchen/LogicNLG}.


Inside the AI Trends Every Techie Should Be Watching

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Narrative Science uses a natural language generation (NLG) engine to help businesses make sense of complex enterprise data and tell clearer stories, especially in uncertain times. That said, Nate Nichols, distinguished principal of product strategy and architecture, sees the increased buy-in across the board as nothing but good news. "Their work is helping to make the idea of computers creating language or stories more mainstream than we've seen before," Nichols said. As advanced data processing techniques become more commonplace, so do the errors associated with them. Such errors include implicit bias as a result of fair prediction, or predicting outcomes for one group well and another group poorly, according to Stats Perform Director of Computer Vision Sujoy Ganguly. It's a side effect that he and Relativity Senior Data Scientist Rebecca BurWei are looking to avoid as trends like learned user trust gain steam. "Building AI that understands and responds to user trust could help us build systems that are more accurate and less biased," BurWei said.


OneConnect's Gamma Lab wins FinTech Team of the Year award at The Asset for two consecutive years

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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.