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Artificial Intelligence - Hype or The Real Deal - Investment Cache

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Artificial intelligence (AI) gained unprecedented attention within the hedge fund community in recent years. However, AI is not some new kid on the block. In fact, its roots go as far back as the 1940s when Warren McCulloch and Walter Pitts first introduced the neural network. Today, it finds widespread use in applications from identifying images, speech, natural language processing to robotics and more. Similarly, the use of AI techniques for trading or investment is not a new idea either. But it was not successful in any big way in the earlier attempts. So why is everyone so excited about using AI for investments again? From my own lens, I attribute this to a confluence of technology advances and changing market dynamics. Our technology have improved by leaps and bounds over the years. My first encounter with a PC was an 8-bit Apple machine with a monochrome CRT monitor running on MS DOS. Then came machines with more powerful Intel processors.


Quantum Joins Russell 2000, Russell 3000 and Microcap Indexes

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Quantum Corporation, a global leader and pioneer in video and unstructured data solutions, today announced it was added to the Russell 2000 Index, Russell 3000 Index and the Russell Microcap Index when the indexes reconstituted on June 26, 2020. Quantum Chairman and CEO Jamie Lerner says inclusion in the Russell 2000 and Russell Microcap Indexes is further validation of the significant progress the Company has made to turnaround the business. "These Indexes comprise the most recognizable listed companies and serve as a benchmark for the micro- and small-cap markets. Our inclusion speaks to the substantial advancements we have made over the past year and will increase our exposure to the investment community. This will facilitate our continuing path to growth and advance our vision and strategy as the leader in video and unstructured data solutions."


What are the limits of AI's creativity? And what does blockchain have to do with that? - The Data Scientist

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Generative adversarial networks are a type of deep neural network where two networks are playing a game against each other. One network, called the generator, generates artificial objects (e.g. The other network (called the discriminator) tries to figure out whether the image is a fake or not. This constant game ends up training a generator that can generate realistic images. So, the question as to what are the limits of AI's creativity might have sounded a bit like an oxymoron a few years ago.


AI Academy #3: Learn Artificial Neural Networks from A-Z

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Do you like to learn how to forecast economic time series like stock price or indexes with high accuracy? Do you like to know how to predict weather data like temperature and wind speed with a few lines of codes? If you say Yes so read more ... Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. In this Course you learn multilayer perceptron (MLP) neural network by using Scikit learn & Keras libraries and Python.You learn how to classify datasets by MLP Classifier to find the correct classes for them.


AI System – Using Neural Networks With Deep Learning – Beats Stock Market in Simulation

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Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning -- a discipline within artificial intelligence -- to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica. The University of Cagliari-based team set out to create an AI-managed "buy and hold" (B&H) strategy -- a system of deciding whether to take one of three possible actions -- a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.


Predicting the Stock price Using TensorFlow

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A very simple graph that adds two numbers together. In the figure above, two numbers are supposed to be added. Those numbers are stored in two variables, a and b. The two values are flowing through the graph and arrive at the square node, where they are being added. The result of the addition is stored into another variable, c.


Nomics Machine-Learning Tool Offers 7-Day Price Forecast on Top 100 Cryptos

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Data provider Nomics is using machine learning to predict the future prices of cryptocurrencies like bitcoin. Launched Thursday, the 7-Day Asset Price Prediction feed will give an outlook on future crypto prices based on purpose-built algorithms and the firm's API, Nomics CEO Clay Collins told CoinDesk in an interview. "There are a lot of poor signals out there that are getting a lot of clicks and we thought we could do a net positive for the space by just leveling up the quality of predictions," Collins said. The Nomics forecaster isn't a standalone, investment-grade product, Collins added, but can help inform crypto investors based on curated exchange data. The free tool currently lists 100 of the top cryptocurrencies by market cap.


Zhenye-Na/DA-RNN

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This dataset is a subset of the full NASDAQ 100 stock dataset used in [1]. It includes 105 days' stock data starting from July 26, 2016 to December 22, 2016. Each day contains 390 data points except for 210 data points on November 25 and 180 data points on Decmber 22. Some of the corporations under NASDAQ 100 are not included in this dataset because they have too much missing data. There are in total 81 major coporations in this dataset and we interpolate the missing data with linear interpolation.


Data Science And Machine Learning. With Java?

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The blogosphere is full of descriptions about how data science and "AI' is changing the world. In financial services, applications include personalized financial offers, fraud detection, risk assessment (e.g. These applications outlined are largely not new, nor are "AI" algorithms like neural networks. However, increasingly commoditized, flexible and cheaper hardware with readily available algorithms and APIs have lowered barriers to data-compute intensive approaches common to data science, making the use of "AI" algorithms much more straightforward. For practitioners, definitions are well understood. For those less familiar and curious, here are some quick definitions and introductions to baseline everyone. At their heart, data science workflows transform data, from heterogenous sources of information, through models and learning, to derive information from which "useful" decisions can be expedited. Decisions may be automated (e.g. an online search or a retail credit fraud check) or ...


Artificial Intelligence in Agriculture Market worth $4.0 billion by 2026 according to a new research report

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The report "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026", is estimated to be USD 1.0 billion in 2020 and is projected to reach USD 4.0 billion by 2026, at a CAGR of 25.5% between 2020 and 2026. The market growth is driven by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques. Browse 81 market data Tables and 40 Figures spread through 152 Pages and in-depth TOC on "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026" The market for drone analytics is expected to grow at the highest rate due to its extensive use for diagnosing and mapping to evaluate crop health and to make real-time decisions. Favorable government mandates for the use of drones in agriculture are also expected to fuel the growth of the drone analytics market. Increasing awareness among farm owners regarding the advantages associated with AI technology is expected to further fuel the growth of the AI in agriculture market.