A new field of science claims to be able to predict aesthetic traits, intelligence, and even moral character in embryos. Is this the next step in human evolution or something more dangerous? Consider, if you will, the translucent blob in the eye of a microscope: a human blastocyst, the biological specimen that emerges just five days or so after a fateful encounter between egg and sperm. This bundle of cells, about the size of a grain of sand pulled from a powdery white Caribbean beach, contains the coiled potential of a future life: 46 chromosomes, thousands of genes, and roughly six billion base pairs of DNA--an instruction manual to assemble a one-of-a-kind human. Now imagine a laser pulse snipping a hole in the blastocyst's outermost shell so a handful of cells can be suctioned up by a microscopic pipette. This is the moment, thanks to advances in genetic sequencing technology, when it becomes possible to read virtually that entire instruction manual. An emerging field of science seeks to use the analysis pulled from that procedure to predict what kind of a person that embryo might become. Some parents turn to these tests to avoid passing on devastating genetic disorders that run in their families. A much smaller group, driven by dreams of Ivy League diplomas or attractive, well-behaved offspring, are willing to pay tens of thousands of dollars to optimize for intelligence, appearance, and personality. Some of the most eager early boosters of this technology are members of the Silicon Valley elite, including tech billionaires like Elon Musk, Peter Thiel, and Coinbase CEO Brian Armstrong. Embryo selection is less like a build-a-baby workshop and more akin to a store where parents can shop for their future children from several available models--complete with stat cards. But customers of the companies emerging to provide it to the public may not be getting what they're paying for. Genetics experts have been highlighting the potential deficiencies of this testing for years.

Regression analysis is likely the first predictive modeling method you learned as a practitioner during your academic studies or the most common modeling method for your analytics group. Regression concepts were first published in the early 1800s by Adrien‐Marie Legrendre and Carl Gauss. Legrendre was born into a wealthy French family and contributed to a number of advances in the fi elds of mathematics and statistics. Gauss, in contrast, was born to a poor family in Germany. Gauss was a child math prodigy but throughout his life he was reluctant to publish any work that he felt was not above criticism.

The concepts in statistics and mathematics are the building blocks of the techniques and tools we use to gain deeper insights into structured and unstructured data. Statistical concepts lie at the heart of data science. In this informative session at SkillUp 2021, a two-day event organised by Analytics India Magazine, Rajeeva Karandikar of Chennai Mathematical Institute, presented a few examples (from history) to explain how to make the most of the available data and enormous computing power by combining statistical ideas with modern AI/ML tools. Rajeeva Karandikar is the Director at Chennai Mathematical Institute. He is a Fellow of the Indian Academy of Sciences and Indian National Science Academy.

In early 2018, officials at University College London were shocked to learn that meetings organized by "race scientists" and neo-Nazis, called the London Conference on Intelligence, had been held at the college the previous four years. The existence of the conference was surprising, but the choice of location was not. UCL was an epicenter of the early 20th-century eugenics movement--a precursor to Nazi "racial hygiene" programs--due to its ties to Francis Galton, the father of eugenics, and his intellectual descendants and fellow eugenicists Karl Pearson and Ronald Fisher. In response to protests over the conference, UCL announced this June that it had stripped Galton's and Pearson's names from its buildings and classrooms. After similar outcries about eugenics, the Committee of Presidents of Statistical Societies renamed its annual Fisher Lecture, and the Society for the Study of Evolution did the same for its Fisher Prize. In science, these are the equivalents of toppling a Confederate statue and hurling it into the sea. Unlike tearing down monuments to white supremacy in the American South, purging statistics of the ghosts of its eugenicist past is not a straightforward proposition. In this version, it's as if Stonewall Jackson developed quantum physics. What we now understand as statistics comes largely from the work of Galton, Pearson, and Fisher, whose names appear in bread-and-butter terms like "Pearson correlation coefficient" and "Fisher information." In particular, the beleaguered concept of "statistical significance," for decades the measure of whether empirical research is publication-worthy, can be traced directly to the trio. Ideally, statisticians would like to divorce these tools from the lives and times of the people who created them. It would be convenient if statistics existed outside of history, but that's not the case.

This article started out as an addendum to a chapter in our book, Data Visualization: A History of Visual Thinking and Graphic Communication (Friendly & Wainer, 2020). In this we claimed that much of the history of data visualization could be seen as combination of three forces: (1) important scientific problems of the day, (2) a developing abundance of data, and (3) the cognitive ability of some heroes in this history to conceive solutions to problems by visual imagination. In the book and what follows we make frequent reference to cognitive aspects of the visual understanding of phenomena and their expression in graphic displays: "inner vision", "graphic communication", "visual insight" are some of the terms we use. An early metaphor for this and an early title for our book was "A gleam in the mind's eye." We give some additional explanations and examples here. We also want to place this topic in a wider framework.

However, I would also posit that all intelligent systems, including humans, are biased, for our own cognition is predicated on our personal experience and knowledge (aka "Training Data" in parlance). With the hyperbole surrounding today, bias is being cast as an evil crippling flaw unique to that will limit its value and widespread adoption. As Jonathan Vanian notes in an article for Fortune, is only as good as the data that humans provide. Vanian goes on to write that, as practitioners, we know: "the data used to train deep- systems isn't neutral. It can easily reflect biases, conscious and unconscious, of the people who assemble it. Data can be slanted by history, with trends and patterns that reflect centuries-old discrimination."

Abstract: Although Linear Regression is arguably one of the most popular analytical techniques, I believe it isn't understood well. Several fundamental assumptions are violated during application. The objective of this note is to provide an overview of the assumptions and possible fixes. Linear regression is arguably one of the most widely used techniques in the data science world. But, a comprehensive understanding of this technique is not universal and it is at a level that is less than desired.

Linear regression is arguably one of the most widely used techniques in the data science world. But, a comprehensive understanding of this technique is not universal and it is at a level that is less than desired. First, a little history, the term regression was first used by Sir Francis Galton, a 19th century polymath. Galton was a pioneer in application of statistical methods in many branches of science, he studied the relative sizes of parents and their offsprings in various species of plants and animals. During this study he observed that a larger than average parent tends to produce a larger than average child, but the child is likely to be less large than the parent in terms of its relative position in its own generation.

Occlusions are a central phenomenon in multi-object computer vision. However, formal analyses (LOS14, ROC20) proposed in the spatial reasoning literature ignore many distinctions crucial to computer vision, as a result of which these algebras have been largely ignored in vision applications. Two distinctions of relevance to visual computation are (a) whether the occluder is a moving object or part of the static background, and (b) whether the visible part of an object is a connected blob or fragmented. In this work, we develop a formal model of occlusion states that combines these criteria with overlap distinctions modeled in spatial reasoning to come up with a comprehensive set of fourteen occlusion states, which we define as OCS14. Transitions between these occlusion states are an important source of information on visual activity (e.g. splits and merges). We show that the resulting formalism is representationally complete in the sense that these states constitute a partition of all possible occlusion situations based on these criteria. Finally, we show results from implementations of this approach in a test application involving static camera based scene analysis, where occlusion state analysis and multiple object tracking can be used for two tasks -- (a) identifying static occluders, and (b) modeling a class of interactions represented as transitions of occlusion states. Thus, the formalism is shown to have direct relevance to actual vision applications.