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Top 3 Machine Learning Certification and Training Programs for Career Growth

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Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091 openings with an averMachine Learning Engineer age nationwide salary of $131,276 USD. The San Francisco Bay Area is the high-end of the salary range at $193,485 with Eden Prairie, Minnesota at $106,780. ZipRecruiter calculates the average US Machine Learning Engineer salary at $130,530. Our first pick is the Machine Learning Engineer -- learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker, Deep Learning Topics within Computer Vision and NLP, Developing Your First ML Workflow, Operationalizing Machine Learning Projects, and a Capstone Project -- Inventory Monitoring at Distribution Centers, Second, the Machine Learning with PyTorch Open Source Torch Library -- machine learning, and for deep learning specifically, are presented with an eye toward their comparison to PyTorch, scikit-learn library, similarity between PyTorch tensors and the arrays in NumPy or other vectorized numeric libraries,clustering with PyTorch, image classifiers, And third, AWS Certified Machine Learning -- AWS Machine Learning-Specialty (ML-S) Certification exam, AWS Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services, Machine Learning Modeling.


Study Could Help Reduce Agricultural Greenhouse Gas Emissions - Eurasia Review

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A team of researchers led by the University of Minnesota has significantly improved the performance of numerical predictions for agricultural nitrous oxide emissions. The first-of-its-kind knowledge-guided machine learning model is 1,000 times faster than current systems and could significantly reduce greenhouse gas emissions from agriculture. The research was recently published in Geoscientific Model Development, a not-for-profit international scientific journal focused on numerical models of the Earth. Researchers involved were from the University of Minnesota, the University of Illinois at Urbana-Champaign, Lawrence Berkeley National Laboratory, and the University of Pittsburgh. Compared to greenhouse gases such as carbon dioxide and methane, nitrous oxide is not as well-known.


12 Black Women in AI paving the way for a better world

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At The Good AI, we strongly believe Artificial Intelligence (AI) should be inclusive and celebrate diversity. However, AI is also the reflector of its creators and this translates into the reproduction of certain biases into AI products related to race, gender or sexual orientation among others. The following article from the MIT Technology Review explains how. In the light of this, the tech industry has an important responsibility towards society, and the death of George Floyd at the hands of a city police officer in Minneapolis, USA on 25 May 2020, -one in a long series of racists attacks against African Americans -, should urge us to take action. We need to make sure we are not perpetuating and letting racism or any other kind of discrimination take roots in our AI systems.


Global Big Data Conference

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When hiring, many organizations use artificial intelligence tools to scan resumes and predict job-relevant skills. Colleges and universities use AI to automatically score essays, process transcripts and review extracurricular activities to predetermine who is likely to be a "good student." With so many unique use-cases, it is important to ask: can AI tools ever be truly unbiased decision-makers? In response to claims of unfairness and bias in tools used in hiring, college admissions, predictive policing, health interventions, and more, the University of Minnesota recently developed a new set of auditing guidelines for AI tools. The auditing guidelines, published in the American Psychologist, were developed by Richard Landers, associate professor of psychology at the University of Minnesota, and Tara Behrend from Purdue University.


Meaningful Standards for Auditing High-Stakes Artificial Intelligence

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When hiring, many organizations use artificial intelligence tools to scan resumes and predict job-relevant skills. Colleges and universities use AI to automatically score essays, process transcripts and review extracurricular activities to predetermine who is likely to be a "good student." With so many unique use-cases, it is important to ask: can AI tools ever be truly unbiased decision-makers? In response to claims of unfairness and bias in tools used in hiring, college admissions, predictive policing, health interventions, and more, the University of Minnesota recently developed a new set of auditing guidelines for AI tools. The auditing guidelines, published in the American Psychologist, were developed by Richard Landers, associate professor of psychology at the University of Minnesota, and Tara Behrend from Purdue University.


What Happens When Police Use AI to Predict and Prevent Crime? - JSTOR Daily

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Bias in law enforcement has long been a problem in America. The killing of George Floyd, an unarmed Black man, by Minneapolis police officers in May 2020 most recently brought attention to this fact--sparking waves of protest across the country, and highlighting the ways in which those who are meant to "serve and protect" us do not serve all members of society equally. With the dawn of artificial intelligence (AI), a slew of new machine learning tools promise to help protect us--quickly and precisely tracking those who may commit a crime before it happens--through data. Past information about crime can be used as material for machine learning algorithms to make predictions about future crimes, and police departments are allocating resources towards prevention based on these predictions. The tools themselves, however, present a problem: The data being used to "teach" the software systems is embedded with bias, and only serves to reinforce inequality.


Predictive Oncology set to market its flagship artificial intelligence drug discovery platform

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Predictive Oncology Inc (NASDAQ: POAI) is set to be a first mover in the artificial intelligence (AI) powered drug discovery market that the company estimates will grow to $20 billion in the next three years. The Minneapolis, Minnesota-based company outlined its strategy for 2022 and revealed that it plans to leverage its existing pharma relationships to market PeDAL in a move that will take the proprietary platform out of the research lab into the pipelines of oncology drug discovery companies. Predictive Oncology recently completed its Discovery 21 evaluation, which is the proof-of-concept for PeDAL. CoRE, the company's AI program, together with tumor profile data, human tumor samples and active machine learning, power PeDAL to determine the most effective drug treatment for a specific cancer type. Investors responded well, sending shares of Predictive Oncology nearly 5% higher to $0.84 in the pre-market trading session.


How AI can identify people even in anonymized datasets

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How you interact with a crowd may help you stick out from it, at least to artificial intelligence. When fed information about a target individual's mobile phone interactions, as well as their contacts' interactions, AI can correctly pick the target out of more than 40,000 anonymous mobile phone service subscribers more than half the time, researchers report January 25 in Nature Communications. The findings suggest humans socialize in ways that could be used to pick them out of datasets that are supposedly anonymized. It's no surprise that people tend to remain within established social circles and that these regular interactions form a stable pattern over time, says Jaideep Srivastava, a computer scientist from the University of Minnesota in Minneapolis who was not involved in the study. "But the fact that you can use that pattern to identify the individual, that part is surprising."


Maria Gini wins the 2022 ACM/SIGAI Autonomous Agents Research Award

AIHub

Maria Gini is Professor of Computer Science and Engineering at the University of Minnesota, and has been at the forefront of the field of robotics and multi-agent systems for many years, consistently bringing AI into robotics. Her work has spanned both the design of novel algorithms and practical applications. These applications have been utilized in settings as varied as warehouses and hospitals, with uses such as surveillance, exploration, and search and rescue. Maria has been an active member and leader of the agents community since its inception. She has been a consistent mentor and role model, deeply committed to bringing diversity to the fields of AI, robotics, and computing.


What Happens When an AI Knows How You Feel?

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In May 2021, Twitter, a platform notorious for abuse and hot-headedness, rolled out a "prompts" feature that suggests users think twice before sending a tweet. The following month, Facebook announced AI "conflict alerts" for groups, so that admins can take action where there may be "contentious or unhealthy conversations taking place." Amazon's Halo, launched in 2020, is a fitness band that monitors the tone of your voice. Wellness is no longer just the tracking of a heartbeat or the counting of steps, but the way we come across to those around us. Algorithmic therapeutic tools are being developed to predict and prevent negative behavior.