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Build 3 Operations Management Skills for AI Success

#artificialintelligence

Data and analytics leaders know that data and platform capabilities and the correct application of data and AI skills deliver successful AI applications. However, the majority of organizations miss the critical collaboration required across data management and AI disciplines when organizing these roles. Only 1 in 10 organizations are able to get 75% or more of their AI model prototypes into production, according to the Gartner AI in Organizations Survey. The survey also revealed that several barriers prevent organizations from successfully moving AI applications beyond prototypes. The survey revealed data dependency as a high barrier for operational AI. To mitigate this key dependency, data and analytics leaders must establish interdisciplinary practices across data management and AI.


How Teachers Can Use Chatbots to Analyze a Student's Learning Skills

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Artificial intelligence is getting a little smarter every day. Digital learning continues its expansion at all levels of education. Bots are designed to make our lives easier, more informative, and more interesting. Their machine learning capabilities make them a promising technology in education. The knowledge base of chatbots will only grow, and the bots themselves will be able to learn along with students.


COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

arXiv.org Artificial Intelligence

Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown promising results on several commonsense benchmark datasets. However, the reliability and comprehensiveness of these benchmarks towards assessing model's commonsense reasoning ability remains unclear. To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs. We propose a pairwise accuracy metric to reliably measure an agent's ability to perform commonsense reasoning over a given situation. The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples. To facilitate a systematic analysis of commonsense capabilities, we design our dataset along the dimensions of knowledge domains, reasoning scenarios and numeracy. Experimental results demonstrate that our strongest baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and ~51% pairwise accuracy, well below human performance (~95% for both metrics). The dataset is available at https://github.com/PlusLabNLP/Com2Sense.


NC State preparing students for artificial intelligence as tech companies come to Triangle

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It's something most people use without realizing it. From phones to search engines, social media, and smart devices in homes -- each uses artificial intelligence technology. "When we have our conversational assistance in our homes and we're talking with one of these and we're asking what's the weather going to be like or what's the capital of Tanzania. Those are kind of questions that are easy to answer," said North Carolina State University Distinguished Professor James Lester. Lester is also the Director of the Center for Educational Informatics where they conduct research on AI technologies for education.


Build 3 Operations Management Skills for AI Success

#artificialintelligence

Data and analytics leaders know that data and platform capabilities and the correct application of data and AI skills deliver successful AI applications. However, the majority of organizations miss the critical collaboration required across data management and AI disciplines when organizing these roles. Only 1 in 10 organizations are able to get 75% or more of their AI model prototypes into production, according to the Gartner AI in Organizations Survey. The survey also revealed that several barriers prevent organizations from successfully moving AI applications beyond prototypes. The survey revealed data dependency as a high barrier for operational AI. To mitigate this key dependency, data and analytics leaders must establish interdisciplinary practices across data management and AI.


Skillearn: Machine Learning Inspired by Humans' Learning Skills

arXiv.org Artificial Intelligence

Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active recalling, to name a few. These learning skills and methodologies enable humans to learn new topics more effectively and efficiently. We are interested in investigating whether humans' learning skills can be borrowed to help machines to learn better. Specifically, we aim to formalize these skills and leverage them to train better machine learning (ML) models. To achieve this goal, we develop a general framework -- Skillearn, which provides a principled way to represent humans' learning skills mathematically and use the formally-represented skills to improve the training of ML models. In two case studies, we apply Skillearn to formalize two learning skills of humans: learning by passing tests and interleaving learning, and use the formalized skills to improve neural architecture search. Experiments on various datasets show that trained using the skills formalized by Skillearn, ML models achieve significantly better performance.


Blue Prism Machine Learning Skills

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Highest Rated What you'll learn What does this mean for you? What will this course do for you? After this we train our very own machine learning model without writing a single line of code! This is a truely revolutionary offering. This means you can tailor Machine Learning in Blue Prism to your unique problems and challenges in business processes.


5 machine learning skills you need in the cloud

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Machine learning and AI continue to reach further into IT services and complement applications developed by software engineers. IT teams need to sharpen their machine learning skills if they want to keep up. Cloud computing services support an array of functionality needed to build and deploy AI and machine learning applications. In many ways, AI systems are managed much like other software that IT pros are familiar with in the cloud. But just because someone can deploy an application, that does not necessarily mean they can successfully deploy a machine learning model.


Artificial Intelligence Vs Machine Learning - SKILL MONKS

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Artificial Intelligence and Machine Learning are trending technologies today and an inherent part of the computer sciences that are also correlated. These are two hot buzzwords today. Essentially used for creating intelligent systems, we can say that AI is a bigger concept, involving the creation of intelligent machines, which have the capacity to simulate human thinking and behavior. On the other hand, machine learning is a subset of AI, allowing the machines, to learn from data without any sort of explicit programming. Today there are broad waves of technological change sweeping through the world and in it both these concepts have caught the imagination of the people. This is an exercise to understand both the terms so that the basic differences are understood through this understanding and analysis.


You Can Now Practice Machine Learning Skills On All-New MachineHack Platform

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MachineHack has launched a'Practice' section on their all-new website for beginners to advance their machine learning skills. This news followed the recent announcement of MachineHack's revamped website, which is currently offering an entirely new interface with improved features and robust user experience to the participants. And, with this new'Practice' section on the website, beginners will now be able to put their skills in practice before participating in the highly intense MachineHack hackathons. Conceptualised in 2018, MachineHack is an engaging online platform that offers best-in-class hackathons for data scientists and ML practitioners to participate and elevate their skills to newer heights. Currently, the platform accommodates 20,000 data scientists and machine learning enthusiasts, who are participating daily to solve some of the most challenging real-world problems.