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Data on Machine Learning Described by Researchers at University of New South Wales (Learning from machines to close the gap between funding and expenditure in the Australian National Disability Insurance Scheme): Machine Learning

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By a News Reporter-Staff News Editor at Insurance Daily News -- New research on artificial intelligence is the subject of a new report. According to news reporting originating from Canberra, Australia, by NewsRx correspondents, research stated, "The Australian National Disability Insurance Scheme (NDIS) allocates funds to participants for purchase of services." Our news reporters obtained a quote from the research from University of New South Wales: "Only one percent of the 89,299 participants spent all of their allocated funds with 85 participants having failed to spend any, meaning that most of the participants were left with unspent funds. The gap between the allocated budget and realised expenditure reflects misallocation of funds. Thus we employ alternative machine learning techniques to estimate budget and close the gap while maintaining the aggregate level of spending. Three experiments are conducted to test the machine learning models in estimating the budget, expenditure and the resulting gap; compare the learning rate between machines and humans; and identify the significant explanatory variables."


John Deere closes in on fully autonomous farming with latest AI acquisition

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John Deere is announcing the acquisition of a state-of-the-art algorithm package from artificial intelligence startup Light. For those of you wondering when driverless vehicles will truly begin to make their mark on society, the answer is: today. Up front: No, you won't be seeing green tractors rolling themselves down city streets anytime soon. But the timeline for fully autonomous farming is being massively accelerated. Today's purchase is all about John Deere's need for speed -- and accuracy, but first let's talk about rapid development.


Why Artificial Intelligence Is Set Up To Fail LGBTQ People - AI Summary

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And for LGBTQ people, often marginalised by traditional systems, we need to be wary of how AI could filter us out. Indeed, one expert in the field, anthropologist Mary L. Gray, believes AI will "always fail LGBTQ people." She believes it will be up to us, to forever make sure AI not only reflects us, but the way we want our world to look too. So to understand why AI is, at least currently, set up to fail LGBTQ people, it's essential to take a step back and understand that – like in all things – it sits within a system. In Amsterdam people gathered Sunday night during a rally which was a call-to-action to all to members and allies of the trans, LGBQIA, black and brown resistance, immigrant and social justice movements to stand side by side with trans men, trans women and non-binary & intersex people, and to send a message of resistance and strengthen around the world.


Understanding Agent Environment in AI - KDnuggets

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Before starting the article, it is important to understand what an agent in AI is. The agent is basically an entity that helps the AI, machine learning, or deep reinforcement learning to make a decision or trigger the AI to make a decision. In terms of software, it is defined as the entity which can take decisions and can make different decisions on the basis of changes in the environment, or after getting input from the external environment. In simpler words, the quick agent perceives external change and acts against it the better the results obtained from the model. Hence the role of the agent is always very important in artificial intelligence, machine learning, and deep learning.



Self-Powered Fabric Can Help Correct Posture in Real Time with the Help of Machine Learning

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The data collected by the sensors is processed by an integrated machine learning algorithm that can provide immediate feedback, alerting the …



Day 15–60 days of Data Science and Machine Learning

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Hope you all had a great Halloween weekend [ I dressed up as "Mother of Dragons" along with my cool " Game of thrones" techie friends];) #winteriscoming. Let's get back and learn some more data science and machine learning. I hope you all have already grasped the Python essentials, Statistics and Maths from day 1 -- day 8(links shared below), Pandas part 1 and part 2 on Day 9, Day 10, Numpy as Day 11, Data Preprocessing Part 1 as Day 12, Data Preprocessing part 2 as Day 13th, Hands on Regression Part 1 as Day 14th. In this post we will cover how we can implement Regression -- part 2 as Day 15. The Linear Regression method is basically a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) as it just minimizes the least squares error: for one object target y x T * w, where w is model's weights.


Ethics in Robotics and Artificial Intelligence

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As robots are becoming increasingly intelligent and autonomous, from self-driving cars to assistive robots for vulnerable populations, important ethical questions inevitably emerge wherever and whenever such robots interact with humans and thereby impact human well-being. Questions that must be answered include whether such robots should be deployed in human societies in fairly unconstrained environments and what kinds of provisions are needed in robotic control systems to ensure that autonomous machines will not cause humans harms or at least minimize harm when it cannot be avoided. The goal of this specialty is to provide the first interdisciplinary forum for philosophers, psychologists, legal experts, AI researchers and roboticists to disseminate their work specifically targeting the ethical aspects of autonomous intelligent robots. Note that the conjunction of "AI and robotics" here indicates the journal's intended focus is on the ethics of intelligent autonomous robots, not the ethics of AI in general or the ethics of non-intelligent, non-autonomous machines. Examples of questions that we seek to address in this journal are: -- computational architectures for moral machines -- algorithms for moral reasoning, planning, and decision-making -- formal representations of moral principles in robots -- computational frameworks for robot ethics -- human perceptions and the social impact of moral machines -- legal aspects of developing and disseminating moral machines -- algorithms for learning and applying moral principles -- implications of robotic embodiment/physical presence in social space -- variance of ethical challenges across different contexts of human -robot interaction


GitHub's AI-powered coding tool will be free for students – TechCrunch

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Last June, Microsoft-owned GitHub and OpenAI launched Copilot, a service that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Available as a downloadable extension, Copilot is powered by an AI model called Codex that's trained on billions of lines of public code to suggest additional lines of code and functions given the context of existing code. Copilot can also surface an approach or solution in response to a description of what a developer wants to accomplish (e.g. "Say hello world"), drawing on its knowledge base and current context. While Copilot was previously available in technical preview, it'll become generally available starting sometime this summer, Microsoft announced at Build 2022.