SAN FRANCISCO – Of all the challenges I face as a CEO, nothing is more critical than attracting and retaining talented people. Declining birth rates are starting to deplete the global labor pool. This problem is particularly acute in Japan, China, South Korea, and most of Western Europe, which have "sub-replacement" birth rates--that is, the number of children born is below the level needed to sustain population and ultimately employment levels. It's also becoming a major concern in the United States, especially as the country's declining high-school graduation rate and soaring college costs narrow the supply of highly skilled, highly technical people. As companies worldwide compete for ever-scarcer human resources, we're going to have to get much better at identifying, attracting, evaluating, and retaining the best people.

Artificial Intelligence (AI) is changing the way law is being practiced. One of the areas where AI, and more specifically Machine Learning (ML) has been making great strides recently is contract review. The progress is not even limited to reviewing contracts: automated contract generation, negotiation, e-signing and management are fast becoming a reality. Using AI for contracts is the result of an ongoing evolution. Ever since lawyers started using word processors, they have tried to automate the process of creating contracts.

As the industry moves towards no-code artificial intelligence model training platforms, AI is being positioned as a tool for the masses. It, therefore, comes as no surprise to find that the industry bellwethers are releasing no-code or low-code platforms to build custom machine learning models that can be used with ease and security. Joining the big tech giants is China's internet major Baidu that launched an AI platform designed to make building custom ML models easier and it rules out the need for algorithmic programming. Known as EZDL, the service platform enables developers to build custom ML models with a drag-and-drop interface, Yongkang Xie, tech lead of Baidu EZDL, said in a company statement. He further emphasised developers can build deep learning models which are specific to their business needs only in four steps.

A typical warm day on South pole is 20 degrees below zero and the irony is that the data centers run by ICE CUBE Neutrino Observatory can still get overheated. A normal day at any data center involves troubleshooting, racking and stacking and, with such enormous data in-flows, that the task becomes tedious for the employees and they are prone to sometimes failing to deliver in real-time. The technicians aren't to be blamed either because a typical UPS is reactive -- it either functions flawlessly or burns out altogether. Machine learning models, on the other hand, are proactive and they work stupendously to forecast failures. Data centers are modern-day engineering marvels.

These terms are now among the most used in tech think tanks around the world. All devices and systems are already integrated with this technology to keep up with the demands of the human race, drastically improving the way we live. We've reached that age of time wherein men have created machines capable of thinking independently. These machines can learn and mimic human-like responses which can be applied in a variety of fields like medicine, transportation and manufacturing, among others. All of us have benefited from them for sure.

The idea of an artificial intelligence (AI) arms race between China and the United States is ubiquitous. Before 2016, there were fewer than 300 Google results for "AI arms race" and only a handful of articles that mentioned the phrase. Today, an article on the subject gets added to LexisNexis virtually every week, and Googling the term yields more than 50,000 hits. Some even warn of an AI Cold War. One question that looms large in these discussions is if China has, or will soon have, an edge over the United States in AI technology.

There are several companies claiming to offer AI solutions to consumer packaged goods (CPG) companies. AI solutions for business problems in the CPG industry appear to be less legitimate than we first thought. All of the companies discussed in this report employ relatively credentialed people in their C-suites, but their AI experience is generally lacking compared to other sectors we've covered (in terms of AI-related talent density, and experience actually using AI). The companies we examine in this report are older firms, who, unlike some of their startup competition, have no founding team members or C-level leadership with a strong background in AI. Many of the firms featured in this article, however, have hired experts in AI to run their AI practices and build AI-related products and services, but others have not hired any such experts to back up their claims of AI use. Fractal Analytics employs a Head of Artificial Intelligence with a PhD in Computational Neuroscience and Machine Learning that he earned from Caltech in 2007.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. A trio of researchers at Osaka University has now found a method for identifying and quantitatively evaluating facial movements on their android robot child head. Named Affetto, the android's first-generation model was reported in a 2011 publication.

Considering the creation of persistence landscape on a parametrized curve and structure of sampling, there exists a random process for which a finite mixture model of persistence landscape (FMMPL) can provide a better description for a given dataset. In this paper, a nonparametric approach for computing integrated mean of square error (IMSE) in persistence landscape has been presented. As a result, FMMPL is more accurate than the another way. Also, the sampling importance resampling (SIR) has been presented a better description of important landmark from parametrized curve. The result, provides more accuracy and less space complexity than the landmarks selected with simple sampling.

Recovering the support of sparse vectors in underdetermined linear regression models, \textit{aka}, compressive sensing is important in many signal processing applications. High SNR consistency (HSC), i.e., the ability of a support recovery technique to correctly identify the support with increasing signal to noise ratio (SNR) is an increasingly popular criterion to qualify the high SNR optimality of support recovery techniques. The HSC results available in literature for support recovery techniques applicable to underdetermined linear regression models like least absolute shrinkage and selection operator (LASSO), orthogonal matching pursuit (OMP) etc. assume \textit{a priori} knowledge of noise variance or signal sparsity. However, both these parameters are unavailable in most practical applications. Further, it is extremely difficult to estimate noise variance or signal sparsity in underdetermined regression models. This limits the utility of existing HSC results. In this article, we propose two techniques, \textit{viz.}, residual ratio minimization (RRM) and residual ratio thresholding with adaptation (RRTA) to operate OMP algorithm without the \textit{a priroi} knowledge of noise variance and signal sparsity and establish their HSC analytically and numerically. To the best of our knowledge, these are the first and only noise statistics oblivious algorithms to report HSC in underdetermined regression models.