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At CAGR 36.2%, Artificial Intelligence Market 2020: Future Challenges And Industry Growth Outlook 2025

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Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.


New Report of Global Machine Learning as a Service (MlaaS) Market Overview, Manufacturing Cost Structure Analysis, Growth Opportunities – Crypto Daily

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Absolute Reports is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in depth market research. We are one of the top report resellers in the market, dedicated towards bringing you an ingenious concoction of data parameters.


Is it Possible to Make Machine Learning Algorithms without Coding?

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Thousands of data sources exist nowadays from which we can extract, transform and load data ranging stock prices, medical records, surveys, population census, and logged behaviors, among others. Also, there's a huge variety of fields in which we can apply these techniques and a wide range of useful applications inside each field, such as fraud detection, credit scoring and asset allocation in relation to the finance domain. But how much can I contribute with this knowledge to a company? A LOT! Just put yourself in the situation of a credit risk analyst at a bank. "Should I lend money to this client or should I reject his application? How much information should I request him or her without risking to lose the interest rate associated with the lending? Are his periodical payslips enough? Or should I also ask him credit records from other financial institutions to guarantee the repayment?".


Jack Dorsey Details Twitter's Blockchain Strategy at Oslo Freedom Forum - CoinDesk

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As for Bitcoin Twitter, as it exists today, Dorsey broadly spoke to the importance of safeguarding users' identities, which may be the key to healthy discourse. Plus, Twitter's staff are amping up reliance and machine-learning tools to help identify non-authentic user behavior, aka propaganda.


Introduction to Machine Learning in R

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This course material is aimed at people who are already familiar with ... What you'll learn This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very good guess about stock prices movement in the market.


Explaining Deep Learning Forecasts

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We already covered in a previous post, how important it is to deal with uncertainty in financial Deep Learning forecasts. In this post, we'll attempt a first introduction on how we deal with explainability. Neural networks have been applied to various tasks including stock price prediction. Although highly successfully, these models are frequently treated as black boxes. In most cases we know that the performance on the test data is satisfying, but we do not know why the model came up with a specific output.


CIA's latest initiative promises Blockchain, DLT, AI, and Machine Learning research - Morning Tick

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The US Central Intelligence Agency has launched a research and development wing, dubbed'CIA Labs'. In a press statement, the Agency stated that this initiative is an effort to bring together private sector academia and CIA operatives to develop and produce tech solutions along various streams. Specifically, the research would take place across several spheres, including Blockchain, DLT (Distributed Ledger Technology), Artificial Intelligence, Machine Learning, and Data Analytics. The CIA Labs project aims to conduct research, development, and testing in multiple disciplines to address new challenges It will also adapt or improve existing solutions to technological problems. This multifaceted research will focus on several technological ideas that have not been fully developed yet.


TRON Plagued By Infestation Of dApp Bots: AnChain Report

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AnChain is a new analytics startup, and it's on a mission: to uncover dApp bots wherever they hide. During Q1 of 2019, the firm surveyed TRON's top ten gambling dApps and found a large number of bots. Roughly 31% of surveyed accounts and 19% of transactions were bot-driven, accounting for a whopping $270 million of dApp volume. For TRON's critics, this is an enticing follow-up to reports about TRON's bot-driven Twitter traffic. It previously found that on EOS, bots accounted for 51% of surveyed accounts and 75% of transactions.


6 Uses of AI, Machine Learning and NLP in Finance and Insurance

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There are swathes of blogs covering the impact of AI on both the financial and insurance industries, however, many look at farfetched AI and ML concepts, not yet tested or applied in either. The below list of'uses' documents application methods or techniques which are currently being implemented, albeit quietly, slowly and behind the scenes. The below are six ways in which we think AI is best being utilised in both the finance and insurance industries. Considered one of the more sought after applications of AI in Finance, it is suggested that the use of AI for fraud detection could detect billions of dollars worth of fraudulent transactions. Whilst AI is already somewhat prevalent in the financial industry, it is expected that by the end of 2021, the amount spent on applying AI in finance with specific focus on fraud detection is set to triple.


Top 5 RegTech companies trending in the European market

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RegTech is the management of regulatory processes within the financial industry through technology. The main functions of RegTech include regulatory monitoring, reporting, and compliance. Europe is home to about 140 regtech startups- 30% of those startups specialize in compliance management, 27% focus on know your customer (KYC) and anti-money laundering (AML) automation, and 26% leverage technology and data to provide risk management tools, according to a data provided by XAnge, a Franco-German venture capital firm. London headquartered ComplyAdvantage is a RegTech company that leverages uses AI and ML to help firms manage compliance obligations. The company provides data intelligence to help firms understand the risk of who they're doing business with, while automating compliance and risk processes.