Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra' inherent complexity and variability make the manual analysis challenging, even for domain experts. For the same reason, the use of traditional Statistical and Machine Learning (ML) techniques could not guarantee for accurate and reliable results. ML models, combined with robust optimization techniques, offer the possibility to improve the classification accuracy and enhance the resilience of predictive models. In this paper, we investigate the performance of a novel robust formulation for Support Vector Machine (SVM) in classifying COVID-19 samples obtained from Raman Spectroscopy. Given the noisy and perturbed nature of biological samples, we protect the classification process against uncertainty through the application of robust optimization techniques. Specifically, we derive robust counterpart models of deterministic formulations using bounded-by-norm uncertainty sets around each observation. We explore the cases of both linear and kernel-induced classifiers to address binary and multiclass classification tasks. The effectiveness of our approach is validated on real-world COVID-19 datasets provided by Italian hospitals by comparing the results of our simulations with a state-of-the-art classifier.

Artificial intelligence is increasingly becoming a part of our daily lives, both in the workplace and at home. Some AI experts are stressing the need to focus on making AI ethical and keeping it human friendly. Bias in programming, security concerns, and a lack of public knowledge about how AI works are all issues that need to be addressed to develop and maintain a healthy relationship between humans and the technology we use. "This is the year AI ethics become absolutely mandatory functions in most businesses, not just talk," Alex Spinelli, chief technology officer at LivePerson and former global head of Alexa OS for Amazon, told Hypergrid Business. Companies are just starting to consider responsible use of AI as a part of their business model.

Our mission to make business better is fueled by readers like you. To enjoy unlimited access to our journalism, subscribe today. Alex Spinelli, chief technologist for business software maker LivePerson, says the recent U.S. Capitol riot shows the potential dangers of a technology not usually associated with pro-Trump mobs: artificial intelligence. The same machine-learning tech that helps companies target people with online ads on Facebook and Twitter also helps bad actors distribute propaganda and misinformation. In 2016, for instance, people shared fake news articles on Facebook, whose A.I. systems then funneled them to users.

Online dating is rapidly changing as technology progresses in our society. It has become a more popular and more accessible way to meet people and express attraction. While dating apps open up new opportunities, especially during this time of social distancing, the majority of online daters are still struggling with the process of online dating and the reality of harassment. Romantic attraction is difficult to predict. While data on personality traits like the Big Five and attachment types can effectively predict how much individuals want to be in partnerships and how desirable they may be as partners, romantic and sexual compatibility and relationship longevity are difficult to trace and anticipate.

It's clear many enterprises are turning to artificial intelligence to increase productivity and meet business goals. What's less clear is where and how exactly it is being used in the workplace. One recent global study took a look at how IT departments are evolving in the current health crisis to maintain business continuity and meet the needs of customers. The Evolution of IT report from Logic Monitor, a Santa Barbara, Calif.-based IT performance monitoring company, analyzed survey responses from 500 IT decision-makers from North America, the United Kingdom, Australia and New Zealand during the COVID-19 pandemic. The results showed many organizations are turning to AI and automation to address the problems created by the closure of physical offices and the rise of remote work.

It may not yet be clear how societies will guard against the potential downside of artificial intelligence -- including algorithmic bias, invasions of privacy and unjustified profiling -- but it's already abundantly clear that safeguards are needed. That's the bottom line from Wednesday night's panel discussion on AI bias, presented in Seattle by EqualAI and LivePerson. Both of the panel's presenters have a stake in figuring out how to address AI's downsides: LivePerson is interested in how chatbots and other AI-enabled tools can smooth interactions between companies and the customers they serve, while EqualAI is an initiative supported by the likes of Arianna Huffington, Wikipedia's Jimmy Wales and LivePerson CEO Robert Locascio to reduce AI bias. "Companies are creating AI to change the world," said EqualAI executive director Miriam Vogel, who focused on equal-pay issues and bias training for law enforcement during her time at the Obama White House and the Justice Department. "They're trying to do good, they're trying to reach people who have not been reached, start conversations that haven't happened otherwise -- knowing that [implicit bias] is not necessarily coming from a malicious act. It's coming from human actions," she said.

Our program in AI is part of the larger departmental focal area of cybernetics which integrates both AI and brain theory (BT). Our research also draws upon a new and expanding interdepartmental program in cognitive science that brings together researchers in cybernetics, linguistics, philosophy, and psychology. This interdisciplinary approach to AI has already led to a number of fruitful collaborations in the areas of cooperative computation, learning, natural language parsing, and vision.