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Demystifying Black-Box Models with SHAP Value Analysis - DataScienceCentral.com

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As an Applied Data Scientist at Civis, I implemented the latest data science research to solve real-world problems. We recently worked with a global tool manufacturing company to reduce churn among their most loyal customers. A newly proposed tool, called SHAP (SHapley Additive exPlanation) values, allowed us to build a complex time-series XGBoost model capable of making highly accurate predictions for which customers were at risk, while still allowing for an individual-level interpretation of the factors that made each of these customers more or less likely to churn. To understand why this is important, we need to take a closer look at the concepts of model accuracy and interpretability. Until recently, we always had to choose between an accurate model that was hard to interpret, or a simple model that was easy to explain but sacrificed some accuracy.


The Collaboration Muscle: LinkedIn's Ya Xu

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Over the course of her nine-year tenure at LinkedIn, Ya Xu has held technology roles with increasing responsibility. Today, she heads the data function for the online professional networking platform. Ya Xu has been a driving force in transforming LinkedIn into a data-first company since she first joined the organization in 2013. As head of data, she leads a global team of about 1,000 data scientists and AI engineers whose work is at the core of delivering economic opportunities to LinkedIn's members and customers. Xu's emphasis on responsible AI and data science ensures that LinkedIn's AI systems put people first and enables the company to empower its members, better serve its customers, and benefit society. In addition to her work at LinkedIn, Xu has coauthored the book Trustworthy Online Controlled Experiments (Cambridge University Press, 2020), has been named to Fortune's 40 under 40 in tech, and was nominated for VentureBeat's Women in AI Awards. She has delivered countless speeches, including a commencement speech to Stanford's class of 2019 in mathematics, statistics, and mathematical and computational science. Previously, Xu worked at Microsoft and earned a Ph.D. in statistics from Stanford University. Ya joins hosts Sam Ransbotham and Shervin Khodabandeh in this episode of the Me, Myself, and AI podcast, where she discusses AI's essential role in helping LinkedIn create the best "matches" -- content creators with content consumers, job seekers with employers, and buyers with sellers -- within its three key marketplaces. Ya also describes how the company has fostered a data-first culture from the top down, and how its vast amount of economic activity data is helping governments and policy makers worldwide.


Resonance as a Design Strategy for AI and Social Robots

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Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.


First FDA Approved AI Software Can Now Read Dental Xrays

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The Food and Drug Administration has approved the first artificial intelligence software to be used to interpret dental x-rays, allowing dentists to better screen for oral pathologies. Pearl's Second Opinion is the first and only FDA-cleared AI radiologic detection aid for dentists at the chairside, and it can assist dentists to discover a variety of common dental diseases such as tooth decay, calculus, and root abscesses. Pearl gathered over 100 million dental x-rays from dental practices and academic institutes to create Second Opinion. The AI platform highlights anomalies in x-rays and also acts as a patient communication tool, allowing dentists to exhibit alternative models of a patient's teeth and highlight trouble regions. Pearl's announcement is a significant step forward in the field of technology-assisted dentistry.


Things You Need To Know About Data Science

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The area of data science is large and fast expanding. It's no surprise that so many people want to learn more about it! But what is data science, and what do you need to know if you want to work in this field? One of the most important things to understand about data science is that it is a very hands-on and ever-changing discipline. It's critical to keep learning new things in order to stay current with the latest trends and practices in the field.


How AI could Unlock Effects Psychedelic Drugs on Our Brains?

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Psychedelics, also known as hallucinogenic drugs have been widely stigmatized as dangerous illegal drugs. These drugs are psychoactive drugs that are used to alter sensory perceptions, energy levels, and thought processes. But very little is really known about what these substances actually do to our brains. AI is crucial to unlocking the potential of psychedelic drugs. To better understand how these subjective effects manifest in the brain, some scientists are using AI methods to figure it out and drug companies are now employing artificial intelligence in their research.


AI in Medicine -- Prospective versus Retrospective

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Just like Sedol Lee was defeated by AlphaGo three or four years ago, there was an atmosphere that artificial intelligence would replace experts in medicine and replace everything in the world. The achievements of AI in the medical field were recorded one by one in an IEEE Spectrum ("AI versus Doctor"; https://ieeexplore.ieee.org/document/8048826). However, since a year or two ago, the main focus has moved to the role of artificial intelligence as an assistance tool for experts, and recently, it is not uncommon to hear that artificial intelligence is not making a profit in business. Even IBM's Watson was sold with some criticism. There may be a problem in some way, so why are we hearing these news?


Researchers use new AI tech to improve polyp detection - eMedNews

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When diagnosed at its earliest stage more than 9 in 10 people with bowel cancer will survive their disease for over five years compared with 1 in 10 when it's diagnosed late. The study is hoping to recruit over 2000 participants before September 2022. Colorectal cancer affects 1 in 15 men and 1 in 18 women in the UK with 16,600 deaths every year; it is the UK's second most deadly cancer. Bowel cancer starts when a polyp (or'adenoma') progresses to cancer, but it can be prevented if detected early enough. Colonoscopy is the'gold standard' assessment for bowel cancer and Adenoma Detection Rate (ADR) (which measures how many polyps the doctor removes) has a notable impact on bowel cancer outcomes.


Here We Go Again

Communications of the ACM

Did you mean print ("Hello")? The SyntaxError message makes the assumption that this error is due to a misconception (print as a statement) instilled from experience with Python 2. We can repurpose this idea generally to language transitions and help programmers more efficiently resolve error messages. Implication III--Be intentional about programming language syntax, semantics, and pragmatics. Certain programming languages anticipate that new adopters arrive through common pathways. We expect most new Rust users to come from systems programming languages such as C, and we expect most new TypeScript users to come directly from JavaScript.


Dr. Stephanie Seneff: Covid-19 Vaccines and Neurodegenerative Disease

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Dr. Seneff is a Senior Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory in Cambridge, Massachusetts, USA. She has a BS from MIT in biology and MS, EE, and PhD degrees from MIT in electrical engineering and computer science. Her recent interests have focused on the role of toxic chemicals and micronutrient deficiencies in health and disease, with a special emphasis on the pervasive herbicide, glyphosate, and the mineral, sulfur. This is an edited segment from the weekly live General Assembly meeting on January 3, 2022. The full meeting can be viewed here. This clip is also available on Rumble and Odysee. "Thank you so much Dr. Seneff!!! Genius presentation, so many important information brought to us easy to understand." -Dr. "Thank you for your important work. The mitigating treatments are hopeful for those who have been coerced into accepting these injections." "Dr Seneff, I hope you will come back and tell us more about your work and the mechanism for the other types of harms that your work has predicted." "Thank you so much, Dr. Seneff." -Helena K "Thank you Dr Seneff, amazing presentation." "Thank you Dr Seneff, that was amazing!" -Dr Tess Lawrie "Beautiful and substantial presentation – thank you, Dr. Seneff!" – Susan I just want to read this quote at the end of this book "The Real Anthony Fauci" and it's because it's Martin Luther King Jr.