The matrix normal model, i.e., the family of Gaussian matrix-variate distributions whose covariance matrices are the Kronecker product of two lower dimensional factors, is frequently used to model matrix-variate data. The tensor normal model generalizes this family to Kronecker products of three or more factors. We study the estimation of the Kronecker factors of the covariance matrix in the matrix and tensor normal models. For the above models, we show that the maximum likelihood estimator (MLE) achieves nearly optimal nonasymptotic sample complexity and nearly tight error rates in the Fisher-Rao and Thompson metrics. In contrast to prior work, our results do not rely on the factors being well-conditioned or sparse, nor do we need to assume an accurate enough initial guess. For the matrix normal model, all our bounds are minimax optimal up to logarithmic factors, and for the tensor normal model our bounds for the largest factor and for overall covariance matrix are minimax optimal up to constant factors provided there are enough samples for any estimator to obtain constant Frobenius error. In the same regimes as our sample complexity bounds, we show that the flip-flop algorithm, a practical and widely used iterative procedure to compute the MLE, converges linearly with high probability. Our main technical insight is that, given enough samples, the negative log-likelihood function is strongly geodesically convex in the geometry on positive-definite matrices induced by the Fisher information metric. This strong convexity is determined by the expansion of certain random quantum channels.

NASA's Marshall Space Flight Center and computing giant IBM plan to use artificial intelligence (AI) tech to improve climate change research, according to an announcement IBM posted on Feb. 1. Under the new partnership, NASA and IBM will create AI foundation models to analyze petabytes of text and remote-sensing data to make it easier to build AI applications tailored to specific climate change questions and tasks. "We hope these models will make information and knowledge more accessible to everyone and encourage people to build applications that make it easier to use our datasets to make discoveries and decisions based on the latest science," said Rahul Ramachandran, a senior research scientist at NASA's Marshall Space Flight Center. Foundational AI models can ingest massive amounts of raw data and find their underlying structure without explicit instruction. NASA is currently sitting on 70 petabytes of earth science data – a number expected to quadruple this year and into 2024 with future mission launches.

This book is intended to support data scientists and developers so they can quickly enter the area of text analytics and natural language processing. Thus, we put the focus on developing practical solutions that can serve as blueprints in your daily business. A blueprint, in our definition, is a best-practice solution for a common problem. It is a template that you can easily copy and adapt for reuse. For these blueprints we use production-ready Python frameworks for data analysis, natural language processing, and machine learning.

Our research aims to build adaptive social robots for one-on-one tutoring interactions for children. We outline a research study aimed at understanding help-seeking differences in children and discuss approaches to building a social robot capable of providing adaptive support specific to an individual student.

"A model is as good as the underlying data," said Jayachandran Ramachandran, SVP of Artificial Intelligence Labs at Course5 Intelligence during his MLDS talk "Will evolving regulations stymie AI innovations? He discussed how industries and governments recognise this problem and develop regulations and recommendations. He also touched on the recommendations and implications crelated to European Union's AI regulations draft. Today, most countries have an AI policy and strategies in place. The EU is at the forefront of AI regulations and drafts. "The EU draft in 2021 is acting as a benchmark for other countries," Ramachandran noted. The draft seeks to ensure the AI policy is human-centric, sustainable, secure, inclusive and trustworthy. Additionally, the draft focuses on a seamless transition of AI from the lab to the market. Any system deployed for the users based in the EU will be under the scope of this AI regulation. If the consumers are based outside the EU, they will not be held ...

In the era of MOOCs, online exams are taken by millions of candidates, where scoring short answers is an integral part. It becomes intractable to evaluate them by human graders. Thus, a generic automated system capable of grading these responses should be designed and deployed. In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS). We propose and explain the design and development of a system for SAS, namely AutoSAS. Given a question along with its graded samples, AutoSAS can learn to grade that prompt successfully. This paper further lays down the features such as lexical diversity, Word2Vec, prompt, and content overlap that plays a pivotal role in building our proposed model. We also present a methodology for indicating the factors responsible for scoring an answer. The trained model is evaluated on an extensively used public dataset, namely Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS shows state-of-the-art performance and achieves better results by over 8% in some of the question prompts as measured by Quadratic Weighted Kappa (QWK), showing performance comparable to humans.

Although machine learning and artificial intelligence (AI) are terms that are often used interchangeably, they are quite different. That difference becomes more important as applications for these technologies become more prevalent. Tech Briefs posed questions to machine learning/AI industry executives to get their views on issues such as machine learning platform selection, interpreting data created by these platforms, and pros and cons of implementing machine learning. Our participants are Dr. Florian Baumann, Chief Technology Officer - Automotive & AI, at Dell Technologies; Mario Bergeron, Technical Marketing Engineer at Averna Technologies; Zach Mayer, Vice President of Data Science at Data Robot; George Rendell, Senior Director of NX Design at Siemens Digital Industries Software; and Rajesh Ramachandran, Chief Digital Officer - Industrial Automation, at ABB Inc. Tech Briefs: Machine learning is a term that has confused many people, partly because its definition has taken on multiple forms. How do you define machine learning and how do you see it being used in manufacturing, medical, transportation, or other industrial applications?

HSBC is making plenty of noise about having spent $2.3 billion on improving its artificial intelligence (AI) and digital capabilities around the globe. WeChat is an "important part of our digital strategy" In an interview with South China Morning Post (SCMP), Vivek Ramachandran, head of growth and innovation for HSBC global commercial banking, said that between 2015 and 2017 the bank created new platforms and partnered with technology companies such as Tencent's WeChat. "We have found that an increasing number of clients like to use new technology to conduct bank transactions in a secure and transparent way," says Ramachandran, aka Captain Obvious. In the interview, Ramachandran says HSBC has allocated $200 million globally for investment in fintech and enterprise start-ups. He called WeChat an "important part of our digital strategy".

Last week, worldwide leaders gathered at the Artificial Intelligence in Bioscience Symposium in London to examine the growing role AI plays in healthcare, highlighting neuroscience and particularly Alzheimer's as one of the most promising applications of this new technology. Within the last few years, the field of bioscience witnessed an exponential expansion, especially with the development of the omics -- including genomics, epigenomics, metagenomics and metabolomics. Now, the use of artificial intelligence could take our understanding of biology one step further, integrating all the gathered knowledge to generate valuable predictions for therapeutic applications. "We've learned that you cannot make a definite statement about a particular gene," Winston Hide, Professor in computational biology at The University of Sheffield, explains. "An important example is the recent failure of a BACE1 inhibitor for the treatment of Alzheimer's disease."

Artificial intelligence is completely reshaping the $445 billion beauty industry by creating AI-powered shopping experiences despite the widespread brick-and-mortar retail crisis. "Cracking e-commerce for beauty has been notoriously difficult compared to other verticals," Headliner Labs co-founder Caroline Klatt told International Business Times. "Getting a recommendation from a stylist is the number one driver of sales in stores." New York-based Headliner is just one of many companies creating custom AI chatbots for beauty brands. To understand just how dramatic this high-tech shift really is, let's recall how people got beauty products just 15 years ago.