banking & finance

C# Machine Learning Projects [PDF] - Programmer Books


Machine Learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising, finance and scientific research. This book help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects. You will get an overview of the machine learning systems and how C#, Net users can apply your existing knowledge to the wide gamut of intelligent applications through a project-based approach. You will start by setting up your C# environment for machine learning with required packages, Accord.NET, LiveCharts, Deedle. We will then take you right from classification models for spam email filtering, NLP techniques for Twitter sentiment analysis, time-series data for forecasting foreign exchange rates to drawing insights from Customer segmentation in E-commerce.

Software micromanages call center employees by monitoring their vocal cues

Daily Mail - Science & tech

Artificial intelligence could soon replace the need for office managers - in call centers, at least. According to a recent report from the New York Times, new software by AI firm Cogito can micro-manage workers by monitoring when they talk too fast, lack enthusiasm, or even when their voices aren't conveying enough empathy. Workers are then notified of their performance in real-time via symbolized prompts like a coffee cup or cartoon heart depending on which metrics the program deems are lacking. And the tech is gaining traction: the Times reports that MetLife now uses Cogito - which claims its has 20,000 users, including the health insurance company, Humana - for 1,500 of its call center workers and claims the AI has helped boost customer satisfaction by 13 percent. Call center employees for the insurance giant MetLife are managed by an artificially intelligent boss that can offer tips based on their vocal cues.

Munich Re partners with GIC consortium on catastrophe damage analytics - Reinsurance News


Munich Re has announced a new partnership with re/insurance industry consortium the Geospatial Intelligence Center (GIC) to provide its members with access to automated damage classification analytics following major catastrophe events. The GIC provides its members with access to imagery and data that enhance underwriting assessments, expedite claims, and improve fraud detection following hurricanes and other disaster events. By collaborating with Munich Re, the consortium will now be able to provide its members with a damage assessment heat map layer to complement its imagery and improve situational awareness post-disaster. Munich Re's analytics solutions can process the GIC's aerial imagery with machine learning models to detect building shapes and damage to individual properties, as well as to impacted geographic areas at large. In the lead up to a catastrophe event, the reinsurer's model can also predict estimated losses using data on property characteristics, weather forecasts, and weather stations.

Empowering the Manufacturing Industry Through Decentralised AI


As AI algorithms--and the computing power that drives them--improve year-on-year, their ability to positively transform the world in which we live is unquestionable. In fact, PwC predicts that AI could contribute up to $15.7 trillion to the global economy by 2030. Indeed, as many as one-in-five (20 percent) of the 1,000 US organisations recently surveyed by PwC had plans to implement AI enterprise-wide in 2019. The PwC research also reveals how companies are increasingly initiating AI models at the very core of their production processes, in a bid to enhance operational decision-making and provide forward-looking intelligence to people in every function throughout the business. To many, this move to AI is no surprise.

Can The AI Economy Really Be Worth $150 Trillion By 2025?


Artificial intelligence is set to transform global productivity, working patterns and lifestyles and create enormous wealth. Research firm Gartner expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. By the same year, PricewaterhouseCoopers reckons on $15.7 trillion - more than the current combined output of China and India. Tech investor Tej Kohli, however, believes the impact will be much faster and exponentially larger, however, potentially worth $150 trillion by 2025. That's nearly double the IMF's forecast of $88 trillion for global gross domestic product this year but Kohli is undaunted.

Artificial Intelligence Helsinki Smart Region


Helsingin Sanomat responded to the bafflement by stating that the election engine's algorithm was built to recommend parties, not single candidates, that best correspond to a citizen's political views. This arrangement left perfectly suited candidates to the sidelines of the engine's suggestions. As a result of public discussion about the engine's function the newspaper published its algorithm for everyone to see. It also modified the algorithm based on the suggested improvements it received. Meeri Haataja was thrilled about the conversation.

Samsung looking at 6G, Blockchain and Artificial Intelligence Technologies


Samsung is looking at investments in sixth-generation mobile networks, system semiconductors, blockchain and artificial intelligence technologies, Bloomberg recently reported. In the report, Samsung's Vice Chairman Jay Y. Lee noted that the company is facing challenges due to the global environment causing pressure on the company's profits. Executive Opinion Bloomberg quoted Jay Y. Lee of VC of Samsung Electronics, saying that, "We should challenge ourselves with a resolution to make new foundations, moving beyond the scope of protecting our past achievements." Samsung's Plans Bloomberg quoted an emailed statement by Samsung, where it was noted discussions among company leaders on collaboration with platform companies on 6G mobile networks, blockchain technologies and artificial intelligence. Maybe the rivals of the company (mainly Apple Inc. and Huawei Technologies Co.) led Lee in making this first ever public statement for 6G technology as these tech giants race to commercialize 5G network services, which launched in South Korea during the month of April.

Machine Learning: Lessons Learned from the Enterprise


This article summarizes the lessons learned after two years of our team engaging with dozens of enterprise clients from different industries including manufacturing, financial services, retail, entertainment, and healthcare, among others. What are the most common ML problems faced by the enterprise? What is beyond training an ML model? How to address data preparation? How to scale to large datasets?

Enabling end-to-end machine learning pipelines in real-world applications


Ben Lorica is the Chief Data Scientist at O'Reilly Media, Inc. and is the Program Director of both the Strata Data Conference and the Artificial Intelligence Conference. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.

How Artificial Intelligence Can Aid Post-Merger Integration - Newport Credentialing


More than just a buzzword, artificial intelligence is already proving to be a game changer for organizations across a variety of industries--despite being in its infancy stage. Building off digital transformation, AI empowers organizations to leverage vast of amounts of data that is collected and generated. Using machine learning, AI enables mountains of data to be analyzed for trends and insights at rates much faster than what any human can deliver. For example, wealth management companies utilize AI and algorithms to scan data in the markets to predict the best stock or portfolio based on preferences. Advertisers use AI to target consumers by seeking out specific audience characteristics.