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Banks and insurers expect 86% rise in AI tech investment by 2025

#artificialintelligence

Banks and insurance firms are planning to increase their artificial intelligence-related investment into technology by 2025, according to research from The Economist Intelligence Unit. The report, commissioned by AI-analytics and search firm ThoughtSpot, surveyed 200 business executives and c-suite leaders at investment banks, retail banks and insurance companies in North America, Europe and Asia Pacific. It found that while a large majority (86 per cent) of respondents had a strong degree of confidence in the benefits of AI to shape the future of financial institutions, more than half of respondents said the technology was not yet in use in the business' processes and offerings, with just 15 per cent saying the technology is used extensively across the organisation. However, despite relatively low levels of implementation, the research found that many institutions are beginning to invest in AI over the next five years, with 27 per cent saying it will spur new products and services, a quarter believing it will open up new markets or industries and the same amount saying it is paving the way for innovation in their industry. Looking to the future, 29 per cent of respondents expect between 51 per cent and 75 per cent of their workloads to be supported by AI technologies in five years' time, as processes become increasingly automated.


Natural language processing for word sense disambiguation and information extraction

arXiv.org Artificial Intelligence

This research work deals with Natural Language Processing (NLP) and extraction of essential information in an explicit form. The most common among the information management strategies is Document Retrieval (DR) and Information Filtering. DR systems may work as combine harvesters, which bring back useful material from the vast fields of raw material. With large amount of potentially useful information in hand, an Information Extraction (IE) system can then transform the raw material by refining and reducing it to a germ of original text. A Document Retrieval system collects the relevant documents carrying the required information, from the repository of texts. An IE system then transforms them into information that is more readily digested and analyzed. It isolates relevant text fragments, extracts relevant information from the fragments, and then arranges together the targeted information in a coherent framework. The thesis presents a new approach for Word Sense Disambiguation using thesaurus. The illustrative examples supports the effectiveness of this approach for speedy and effective disambiguation. A Document Retrieval method, based on Fuzzy Logic has been described and its application is illustrated. A question-answering system describes the operation of information extraction from the retrieved text documents. The process of information extraction for answering a query is considerably simplified by using a Structured Description Language (SDL) which is based on cardinals of queries in the form of who, what, when, where and why. The thesis concludes with the presentation of a novel strategy based on Dempster-Shafer theory of evidential reasoning, for document retrieval and information extraction. This strategy permits relaxation of many limitations, which are inherent in Bayesian probabilistic approach.


Deep Learning for Financial Applications : A Survey

arXiv.org Machine Learning

Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.


Advances and Open Problems in Federated Learning

arXiv.org Machine Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.


Tokio Marine Asia and Ephesoft Announce Partnership to Advance Thai Language Recognition Ephesoft

#artificialintelligence

IRVINE, Calif. and Singapore – Nov. 5, 2019 – Ephesoft, Inc., an industry leader in enterprise content capture and data discovery solutions, today announced its collaboration with Tokio Marine Asia, the regional headquarters of the global insurance group that offers an extensive selection of General and Life insurance products and solutions worldwide. Ephesoft and Tokio Marine Asia, along with Tokio Marine Life Insurance (Thailand), will work together to solve language, data and document challenges that are prevalent throughout Thailand. The collaboration marks the insurance company's focus on expanding its footprint of automation in the fast-burgeoning Thai economy. Improving its approach by automating various manual heavy documentation processes across the insurance space will benefit both their customers and employees. "Ephesoft has a strong commitment to meeting our customers where they are and addressing their unique challenges, regardless of geographic location," said Ike Kavas, founder and CEO at Ephesoft.


The 2018 Survey: AI and the Future of Humans

#artificialintelligence

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.


The Future of AI Part 3

#artificialintelligence

This article will focus on the impact of AI, 5G, Edge Computing on the healthcare sector in the 2020s as well as a section on Quantum Computing's potential impact on AI, healthcare and financial services. The next in the series will deal with how we can use AI in the fight against climate change including the protection of the Amazon, smart cities and AGI. For those who are new to AI, Machine Learning and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed such as the need for improved sharing of data ...


The Future of AI Part 3

#artificialintelligence

This article will focus on the impact of AI, 5G, Edge Computing on the healthcare sector in the 2020s as well as a section on Quantum Computing's potential impact on AI, healthcare and financial services. The next in the series will deal with how we can use AI in the fight against climate change including the protection of the Amazon, smart cities and AGI. For those who are new to AI, Machine Learning and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed such as the need for improved sharing of data ...


U.S. startups look to Japan's graying population

The Japan Times

NEW YORK – U.S. startups focusing on care products and services for the elderly are tapping into the graying Japanese market, where more than 35 million people are over the age of 65. Seismic, a California-based apparel company, hopes to expand in Japan with its Powered Clothing, a body suit using robotics and sensor technology inside the garment to mimic human movements and increase strength. The body suit is meant for all ages, but Seismic has found particular success with elderly people who enjoy sports and travel in the United States, where the population is also graying. The number of people age 65 and older in the United States is projected to grow from 52 million in 2018 to 95 million by 2060, according to the Population Reference Bureau. In November, Seismic partnered with Obayashi Corp. to provide its construction workers with the suits.


Interpret Federated Learning with Shapley Values

arXiv.org Machine Learning

Federated Learning is introduced to protect privacy by distributing training data into multiple parties. Each party trains its own model and a meta-model is constructed from the sub models. In this way the details of the data are not disclosed in between each party. In this paper we investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest. For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party. We propose a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features. Our experiments indicate robust and informative results for interpreting Federated Learning models.