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Artificial Intelligence: Fusing Technology and Human Judgment?

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We usually think of the term "technology" in very modern, even futuristic contexts. Yet the word has a long history, deriving from the Greek tekhnologia, meaning "science of craft" or "systematic treatment" of actions. These traits have been with us since humans first discovered tools. In fact, the investment-analyst profession emerged from ad hoc investment approaches, using systematic processes to analyze and evaluate the health and value of companies. Increasingly, those processes are being undertaken by what we usually mean when we say "technology": computer hardware and software.


Pegasystems Builds AI Transparency Into Customer Decision Hub Pre-GDPR

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The latest version of its Pega Customer Decision Hub (CDH) includes a new technology which gives organizations direct control over the level of transparency within their AI customer engagement models. With the latest version of CDH, users can safely deploy AI algorithms based on transparency thresholds set by their business. "The T-Switch feature of our AI-powered Pega Customer Decision Hub enables organizations to set the appropriate thresholds for AI model transparency or opaqueness," said Vince Jeffs, director of Strategy and Product Marketing at Pegasystems. The transparency scores guide business users to build AI systems using the right models to get the job done while also meeting their organization's transparency requirements."


How AI Careers Fit into the Data Landscape – Insight Data

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The goal of newly-formed AI teams is to build intelligent systems, focused on quite specific tasks, that can be integrated into the scalable data transformations of Data Engineering work and the data products and business decisions of Data Science work. The differences between Artificial Intelligence, Data Science, and Data Engineering can vary considerably among companies and teams. Artificial Intelligence, or AI, focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes. These models typically require very large datasets, so while efficient manipulation and use of large amounts of data is a fundamental aspect of Data Engineering work, it is crucial for state-of-the-art AI systems.


Can artificial intelligence combat behavioural biases?

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The Capital.com app is similar to the US' Robinhood or Europe's Trading212, but has a specific AI-powered function that provides investors with tailored content based on behavioural analysis. Dubbed SmartFeed, this function helps users identify common trading biases and behavioural patterns, and provides them with relevant educational content whenever these biases are detected. Speaking to World Finance, Akula described the core functions of the app's AI features: "SmartFeed monitors the user's trading activity, providing all the necessary data, analytics and educational materials. This is not to mention cases of computer trading programs running amok, generating huge losses on the stock exchange, as has happened several times in the world's largest exchanges.


Summary of Unintuitive Properties of Neural Networks

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Neural network are powerful learning models especially deep learning networks on visual and speech recognition problems. In spite of having made a lot of efforts (e.g., a researcher created a popular toolkit called Deep Visualization Toolbox) to capture step by step how a neural network get trained, what we can see inside these layers is still very intricate. For a deep acoustic model used by Android voice search, a Google research team showed that nearly all of the improvement by training an ensemble of deep neural nets can be distilled into a single neural net of the same size which is much easier to deploy. In an experiment to answer how the pre-training work, it was empirically shown the influence of pre-training in terms of model capacity, training example number, and architecture depth.


Preparing the Network for AI and Machine Learning - insideBIGDATA

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Other organizations can leverage business data to drive data-informed project management, allowing business leaders to more accurately determine how long certain operations may take and will cost. The fundamentals of these technologies are rooted in data-driven algorithms that enable machines to develop learned responses or predictive capabilities. As a result, with AI and machine learning comes data--big data--that requires resources to be allocated, not only specialists like programmers, but additional on-premises resources such as storage, server CPUs, networking bandwidth, and cloud-hosted storage services. As businesses look to develop their digital transformation strategies and create unique competitive advantage, AI and machine learning are increasingly considered the keys to unlocking the value of an organization's accumulated data.


Preparing the Network for AI and Machine Learning - insideBIGDATA

#artificialintelligence

Other organizations can leverage business data to drive data-informed project management, allowing business leaders to more accurately determine how long certain operations may take and will cost. The fundamentals of these technologies are rooted in data-driven algorithms that enable machines to develop learned responses or predictive capabilities. As a result, with AI and machine learning comes data--big data--that requires resources to be allocated, not only specialists like programmers, but additional on-premises resources such as storage, server CPUs, networking bandwidth, and cloud-hosted storage services. As businesses look to develop their digital transformation strategies and create unique competitive advantage, AI and machine learning are increasingly considered the keys to unlocking the value of an organization's accumulated data.


What is machine learning?

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Machine learning helps create software that can modify and improve its performance without the need for humans to explain to it how to accomplish tasks. One of the most prevalent is "supervised learning," in which you train the algorithm with labeled data and map a set of inputs to a set of outputs. While machine learning is a subset of artificial intelligence, deep learning is a specialized subset of machine learning. Instead of directly mapping input to output, deep learning algorithms rely on several layers of processing units.


Why Your Next Boss Will Be A Robot – Hacker Noon

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In this model, human employees augment and assist AI software leveraging its natural language processing (NLG), analytics, image recognition or other ML functionality to run business processes and make important decisions. Companies can also leverage the power of complex image recognition software to automatically assess the performance of employees in contexts where it can not be properly measured by human supervisors. AI software is in charge of important business decisions, planning and performance assessment in many on-demand mobility and delivery services that make up the so called gig economy. Deliveroo's algorithmic system carefully monitors a courier's performance calculating his/her average "time to accept orders", "travel time", and "unassigned orders".


Machine Learning Classification Algorithms using MATLAB

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This course is designed to cover one of the most interesting areas of machine learning called classification. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Ouput Codes and Ensembles. Though it does not cover Matlab toolboxes etc, it is still a great basic introduction for the platform.