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Artificial intelligence success is tied to ability to augment, not just automate

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Artificial intelligence is only a tool, but what a tool it is. It may be elevating our world into an era of enlightenment and productivity, or plunging us into a dark pit. To help achieve the former, and not the latter, it must be handled with a great deal of care and forethought. This is where technology leaders and practitioners need to step up and help pave the way, encouraging the use of AI to augment and amplify human capabilities. Those are some of the observations drawn from Stanford University's recently released report, the next installment out of its One-Hundred-Year Study on Artificial Intelligence, an extremely long-term effort to track and monitor AI as it progresses over the coming century.


Mathematics Hidden Behind Linear Regression

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This is about the mathematics that is used in the linear regression (with gradient descent) algorithm. This was a part of my IB HL Mathematics Exploration. Linear Regression is a statistical tool that produces a line of best fit for a given dataset analytically. To produce the regression line manually, one needs to perform operations such as mean-squared error and optimizing the cost function; both are explained in detail later in the document. The main problem arises when the size of the dataset is so large that it becomes computationally inefficient to be done by hand. Therefore, when a dataset size becomes large the computer can perform the task much quicker just with a few simple lines of code in any language. Linear regression algorithm uses a dataset (pairs of input and output values) to generate a line of best fit for that dataset. To start, the algorithm generates a hypothesis in the form??


AI Can Help Address Inequity -- If Companies Earn Users' Trust

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Bullish predictions suggest that artificial intelligence (AI) could contribute up to $15.7 trillion to the global economy by 2030. From autonomous cars to faster mortgage approvals and automated advertising decisions, AI algorithms promise numerous benefits for businesses and their customers. Unfortunately, these benefits may not be enjoyed equally. Algorithmic bias -- when algorithms produce discriminatory outcomes against certain categories of individuals, typically minorities and women -- may also worsen existing social inequalities, particularly when it comes to race and gender. From the recidivism prediction algorithm used in courts to the medical care prediction algorithm used by hospitals, studies have found evidence of algorithmic biases that make racial disparities worse for those impacted, not better. Many firms have put considerable effort into combating algorithmic bias in their management and services.


Some Tesla owners are losing trust in Elon Musk's promises of 'full self-driving'

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Washington, DC(CNN) Frustrated Tesla owners continue to wait for "full self-driving," an expensive and long-delayed software feature that isn't even guaranteed to help their cars' resale values. Some of the company's earliest backers of the "full self-driving" option are even beginning to lose faith in the promise of ever enjoying a truly autonomous Tesla. Years-long delays, buggy beta software, and the risk of no return on their investment in the option package have left some Tesla owners disappointed. Tesla CEO Elon Musk's prognostications, and Tesla's actual reality have diverged so much that some owners describe to CNN Business that they've lost confidence in his predictions. Some otherwise satisfied Tesla owners describe feeling duped into buying "full self-driving" ahead of its polished release, because Musk warned that the price would increase.


Various Machine learning methods in predicting rainfall - Tutors India Blog

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The term machine learning (ML) stands for "making it easier for machines," i.e., reviewing data without having to programme them explicitly. The major aspect of the machine learning process is performance evaluation. Four commonly used machine learning algorithms (BK1) are Supervised, semi-supervised, unsupervised and reinforcement learning methods. The variation between supervised and unsupervised learning is that supervised learning already has the expert knowledge to developed the input/output [2]. On the other hand, unsupervised learning takes only the input and uses it for data distribution or learn the hidden structure to produce the output as a cluster or feature [3].


Problems in the deployment of machine-learned models in health care

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Herein we use the term "machine-learned model" to refer to a model that has been created by running a supervised machine learning algorithm on a labelled data set. Machine-learned models are trained on specific data sets, known as their training distribution. Training data are typically drawn from specific ranges of demographics, country, hospital, device, protocol and so on. Machine-learned models are not dynamic unless they are explicitly designed to be, meaning that they do not change as they are used. Typically, a machine-learned model is deterministic, having learned a fixed set of weights (i.e., coefficients or parameters) that do not change as the model is run; that is, for any specific input, it will return the same prediction every time.


Machine learning is moving beyond the hype

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Machine learning has been around for decades, but for much of that time, businesses were only deploying a few models and those required tedious, painstaking work done by PhDs and machine learning experts. Over the past couple of years, machine learning has grown significantly thanks to the advent of widely available, standardized, cloud-based machine learning platforms. Today, companies across every industry are deploying millions of machine learning models across multiple lines of business. Tax and financial software giant Intuit started with a machine learning model to help customers maximize tax deductions; today, machine learning touches nearly every part of their business. In the last year alone, Intuit has increased the number of models deployed across their platform by over 50 percent.


Top 5 Statistical Data Analysis Techniques a Data Scientist Should Know

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Statistical data analysis is a procedure of performing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data, and typically, applies some form of statistical analysis. Quantitative data involves descriptive data, such as survey data and observational data. Statistical data analysis generally involves some form of statistical tools, which a layman cannot perform without having any statistical knowledge. Linear Regression, is the technique that is used to predict a target variable by providing the best linear relationship among the dependent and independent variables where best fit indicates the sum of all the distances amidst the shape and actual observations at each data point is as minimum as achievable.


Cybersecurity can be made agile with zero-shot AI

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Modern security information and event management and intrusion detection systems leverage ML to correlate network features, identify patterns in data and highlight anomalies corresponding to attacks. Security researchers spend many hours understanding these attacks and trying to classify them into known kinds like port sweep, password guess, teardrop, etc. However, due to the constantly changing attack landscape and the emergence of advanced persistent threats (APTs), hackers are continuously finding new ways to attack systems. A static list of classification of attacks will not be able to adapt to new and novel tactics adopted by adversaries. Also, due to the constant flow of alarms generated by multiple sources in the network, it becomes difficult to distinguish and prioritize particular types of attacks--the classic alarm flooding problem.


Fake News Detection Using Machine Learning Ensemble Methods

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The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners. The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. Besides other use cases, news outlets benefitted from the widespread use of social media platforms by providing updated news in near real time to its subscribers. The news media evolved from newspapers, tabloids, and magazines to a digital form such as online news platforms, blogs, social media feeds, and other digital media formats [1]. It became easier for consumers to acquire the latest news at their fingertips.