Imagine an autonomous vehicle traffic sign detector whose accuracy plummets when dealing with rain or unexpected inputs. With machine learning (ML) an increasingly integral part of our daily lives, it is crucial that developers identify such potentially dangerous scenarios before real-world deployment. The rigorous performance evaluation and testing of models has thus become a high priority in the ML community, where an understanding of how and why ML system failures might occur can help with reliability, model refinement, and identifying appropriate human oversight and engagement actions. The process of identifying and characterizing ML failures and shortcomings is however extremely complex, and there is currently no effective universal approach for doing so. To address this, a Microsoft research team recently introduced Error Analysis, a responsible AI toolkit for describing and explaining system failures. Error Analysis starts with error identification illustrated using error heatmaps or decision trees guided by errors.

Covid-19 has had a negative impact on machine learning model performance at over a third of banks in the UK, the Bank of England has found.

In 2007, some of the leading thinkers behind deep neural networks organized an unofficial "satellite" meeting at the margins of a prestigious annual conference on artificial intelligence. The conference had rejected their request for an official workshop; deep neural nets were still a few years away from taking over AI. The bootleg meeting's final speaker was Geoffrey Hinton of the University of Toronto, the cognitive psychologist and computer scientist responsible for some of the biggest breakthroughs in deep nets. He started with a quip: "So, about a year ago, I came home to dinner, and I said, 'I think I finally figured out how the brain works,' and my 15-year-old daughter said, 'Oh, Daddy, not again.'" Hinton continued, "So, here's how it works."

Some healthcare provider organizations are using machine learning and other forms of artificial intelligence to provide clinicians with the best evidence-based care pathways. A group's aim could be to improve a patient's care plan based on personalized analytics. Another goal could be the further merging of evidence-based care paths with historical utilization and outcomes in order to offer optimal patient care. Provider organizations might be using social determinants of health combined with machine learning to offer clinically meaningful services. Healthcare IT News talked over these ideas with Niall O'Connor, chief technology officer at Cohere Health, a vendor of artificial intelligence technology and services designed to improve the provider, patient and payer experiences.

ARC Advisory Group engaged in an informative discussion with Derek Gittoes, VP Supply Chain Management Product Strategy at Oracle, as part of ARC's Digital Supply Chain Forum. Derek recently authored an article on Logistics Viewpoints describing recent advancements in logistics predictability through the application of machine learning. And we thought this to be a great opportunity to get further details on this hot topic from a practitioner on the front line of logistics application development. We asked Derek to provide greater detail on a few key points. Namely, how does machine learning help with predicting shipping transit times? Secondly, why should shippers and logistics service providers consider using machine learning in their transportation management systems, and why now?

When I was in graduate school in the 1990s, one of my favorite classes was neural networks. Back then, we didn't have access to TensorFlow, PyTorch, or Keras; we programmed neurons, neural networks, and learning algorithms by hand with the formulas from textbooks. We didn't have access to cloud computing, and we coded sequential experiments that often ran overnight. There weren't platforms like Alteryx, Dataiku, SageMaker, or SAS to enable a machine learning proof of concept or manage the end-to-end MLops lifecycles. I was most interested in reinforcement learning algorithms, and I recall writing hundreds of reward functions to stabilize an inverted pendulum.

CTO & MD at AX Semantics, the SaaS-based, Natural Language Generation Platform that creates any content, in any language, at any scale. The pandemic brought on economic, logistical and technological challenges on a massive global scale, leaving businesses scrambling to adapt. Amidst the upheaval, businesses turned to video conferencing platforms like Zoom and Google Meet to stay connected. Technologies like artificial intelligence (AI) and machine learning (ML) helped augment human efforts to take on everything fromhealth tocybersecurity. Equally, businesses looked toward strategic execution and technology to remain agile among industry shifts and provide a greater return on investments.

When I heard the 60th annual Grammy Awards show was going to feature artificial intelligence, I immediately thought "this is a marketing ploy." But then I found out IBM's Watson was the AI in question. Watson, you see, doesn't have a problem rolling up its non-existent sleeves and doing some good old fashioned hard work. Don't expect a silly robot rolling around doing a human impersonation on the red carpet, IBM's machines show up to solve problems and optimize workflows. And while that isn't very sexy – hard work seldom is – it's incredibly important.

In our day-to-day life we come across many problems in which we have certain problems that revolves around choosing a category such as pass/fail, win/lose, alive/dead,healthy/sick,Yes/No, etc. Decision making plays an important role in our life and selecting any of the choice has its own consequences. By reading the above stuff's, you may dwell with the question whether to proceed with this blog or skip it? Come on lets dive in assuming that you have chosen the YES Category. It was a good choice. It was an easy task for you but what if I have asked you that whether a random person with your age is likely to read my blog or not?

Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recently, machine learning techniques have emerged as a powerful and cost-effective strategy to learn from existing datasets and perform predictions on unseen molecules. Accordingly, the explosive rise of data-driven techniques raises an important question: What confidence can be assigned to molecular property predictions and what techniques can be used for that purpose? Areas covered: In this work, we discuss popular strategies for predicting molecular properties relevant to drug design, their corresponding uncertainty sources and methods to quantify uncertainty and confidence. First, our considerations for assessing confidence begin with dataset bias and size, data-driven property prediction and feature design.