NEWS DIGEST 31.10.2021: AI and data science weekend digest

Good morning readers and Happy Hallowe’en! Today’s news comes from Twitter.

Among many fields and branches of mathematics, Probabilities plays a significantly important in both Artificial Intelligence and Data Science. Conditional Probability can be explained as the probability of an event’s occurrence concerning one or multiple other events. The conditional probability would be, the probability of both Event A and B happening ie. In a discrete probability distribution, every possible value of the discrete random variable has a non-zero probability. We know, Conditional Probability can be explained as the probability of an event’s occurrence concerning one or multiple other events. The mathematics, we’ve learned Statistics and Probabilities are widely used in Data Science – Importance Of Probability In Machine Learning And Data Science (C# Corner)

The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. It is aimed at people that are not professional coders, people who manage data scientists, BI experts, MBA professionals, and people from other fields, with an interest in understanding the mechanics of some state-of-the-art machine learning techniques, without having to spend months or years learning mathematics, programming, and computer science. The Excel version has the advantage of being interactive, and you can share it with people who are not data scientists. So even if we have two nodes, one for the keyword data, and one for the keyword data science, in version 1,0, they are not overlapping: text buckets contain either data and not data science, or data science. The accuracy is much higher in the test data set, in a cross-validation framework where HDT is performed on a control data set, and performance measured on a different data set called test data set – Advanced Machine Learning with Basic Excel (TechTarget Data Science Central)

The announcement in the opening session of the Group of 20 summit marked the world’s most aggressive attempt yet to stop opportunistic companies like Apple and Bristol Myers Squibb from sheltering profits in so-called tax havens, where tax rates are low and corporations often maintain little physical presence beyond an official headquarters. “Today, every G20 head of state endorsed an historic agreement on new international tax rules, including a global minimum tax that will end the damaging race to the bottom on corporate taxation,” Treasury Secretary Janet L. Yellen, who joined Mr. Biden in Rome, said in a statement. The global minimum tax that Mr. Biden endorsed would be enacted separately by every country, in an attempt to eliminate havens with rock-bottom tax rates. Mr. Biden’s proposed domestic minimum tax would exclude a few deductions, like for clean energy, but otherwise try to raise money from companies that have reduced their tax bills through a variety of incentives in the code like deductions for investment. The Biden administration estimates these measures, along with other changes to the international side of the tax code, will raise $350 billion in tax revenue over a decade. The National Association of Manufacturers said in a statement that the domestic minimum tax would punish investment and “harm our industry’s ability to drive our economic recovery.” Infighting among Democrats also jeopardized the Biden administration’s strategy to raise $700 billion in tax revenue without increasing tax rates at all. Briefing reporters on Friday evening, a senior administration official, speaking on the condition of anonymity in order to preview the first day of the summit, said Biden aides were confident that world leaders were sophisticated and understood the nuances of American politics, including the challenges in passing Mr. Biden’s tax plans in Congress – Biden Finds Raising Corporate Tax Rates Easier Abroad Than at Home (The New York Times)

Natural Language Processing: This technology enables machines to read, understand, and interpret human language as well as humans. The purpose of robotics is to produce machines that can help people. Machine Learning: Machine Learning, a branch of AI, uses a variety of coding methods and patterns to solve problems. Deep Learning: Deep Learning can be pointed out as an in-depth part of Machine Learning. Healthcare: The health field is one of the most widely used fields of AI. Here are some ways in which AI can be used in the field of health. Currently, machine learning is used to identify risks, identify trends, determine how to save manpower, and determine future plans – Basics Of Artificial Intelligence (Lakshan Bandara/Medium)

From AI bias and data quality issues to considerable market failures, the progress and efficacy of AI in healthcare continues to face extreme scrutiny. Real World Evidence and Clinical Effectiveness: An “exciting Time” for Healthcare AI. “Personally I think it’s a very exciting time for AI in healthcare,” said Suchi Saria, Ph.D, CEO and CSO at Bayesian Health, an AI-based clinical decision support platform for health systems using electronic health record systems. To develop a responsible AI model – and help to fix AI’s credibility problem – President of the Mayo Clinic Platform, John Halamka notes that there are a number of data “must-haves”: a longitudinal data record, including structured and unstructured data, telemetry and images, omics, and even digital pathology. Dataset shifts occur when the data used to train machine learning models differs from the data the model uses to provide diagnostic, prognostic, or treatment advice. Born out of Stanford University’s Artificial Intelligence in Medicine and Imaging Center, BunkerHill does not develop AI algorithms itself, but instead is building a platform and network of health systems to allow them to test algorithms against different data sets – Artificial Intelligence Myth Vs Reality: Where Do Healthcare Experts Think We Stand? (Forbes)

Summarised with SMMRY.

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