We are on the verge of another revolution in health care: deeply personalized medicine. It's the next computerized step in tailoring medical treatments and medical drugs to your specific body, your very unique anatomy, the specific ways your body works and doesn't, and your path to live your life and keep healthy. But we may soon run into problems of ethics and personal privacy that could make the recent furor over Facebook and data mining look small by comparison. Personalized health and wellness comes from the intersection of improved body-worn sensors, data science, computational physiology, individually customized health assistance and -- if necessary -- highly targeted medical treatment, all coming together at once. This dramatic health revolution is enabled by the vastly reduced cost of reading and analyzing our genomes, and of huge, cheap quantities of computer power that allow us to make more precise predictions about our future health from our genetic setup.

A new algorithm in artificial intelligence enables a 3D model of a person to be created in just a few seconds after videoing their features. Artificial intelligence is used during video games and virtual reality to create 3D objects of people and objects. But typically it requires special equipment when filming in order to transfer the video of someone into a 3D figure. New video software is able to take the footage and transfer it into the model in seconds from just one angle. A minute-and-a-half long video shows how the algorithm is able to transform the images of men and women into a 3D character after they turn around themselves, Science Magazine reported.

Transporting yourself into a video game, body and all, just got easier. Artificial intelligence has been used to create 3D models of people's bodies for virtual reality avatars, surveillance, visualizing fashion, or movies. But it typically requires special camera equipment to detect depth or to view someone from multiple angles. A new algorithm creates 3D models using standard video footage from one angle. The system has three stages.

Machines don't actually have bias. AI doesn't'want' something to be true or false for reasons that can't be explained through logic. Unfortunately human bias exists in machine learning from the creation of an algorithm to the interpretation of data – and until now hardly anyone has tried to solve this huge problem. A team of scientists from Czech Republic and Germany recently conducted research to determine the effect human cognitive bias has on interpreting the output used to create machine learning rules. The team's white paper explains how 20 different cognitive biases could potentially alter the development of machine learning rules and proposes methods for "debiasing" them. Biases such as "confirmation bias" (when a person accepts a result because it confirms a previous belief) or "availability bias" (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity) can render the interpretation of machine learning data pointless.

This paper investigates to what extent do cognitive biases affect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, effects) are covered, as are possibly effective debiasing techniques that can be adopted by designers of machine learning algorithms and software. While there seems no universal approach for eliminating all the identified cognitive biases, it follows from our analysis that the effect of most biases can be ameliorated by making rule-based models more concise. Due to lack of previous research, our review transfers general results obtained in cognitive psychology to the domain of machine learning. It needs to be succeeded by empirical studies specifically aimed at the machine learning domain.

This paper focuses on care support knowledge (especially focuses on the sleep related knowledge) and tackles its cognitive bias and humanity aspects from machine learning perspective through discussion of whether machine learning can correct commonly accepted knowledge and provide understandable knowledge in care support domain. For this purpose, this paper starts by introducing our data mining method (based on association rule learning) that can provide only necessary number of understandable knowledge without probabilities even if its accuracy slightly becomes worse, and shows its effectiveness in care plans support systems for aged persons as one of healthcare systems. The experimental result indicates that (1) our method can extract a few simple knowledge as understandable knowledge that clarifies what kinds of activities (e.g., rehabilitation, bathing) in care house contribute to having a deep sleep, but (2) the apriori algorithm as one of major association rule learning methods is hard to provide such knowledge because it needs calculate all combinations of activities executed by aged persons.

In this AAAI Spring symposium 2018, we discuss cognitive bias and humanity in the context of well-being AI. We define “well-being AI” as an AI research paradigm for promoting psychological well-being and maximizing human potential. The goals of well-being AI are (1) to understand how our digital experience affects our health and our quality of life and (2) to design well-being systems that put humans at the center. The important challenges of this research are how to quantify subjective things such as happiness, personal impressions, and personal values, and how to transform them into scientific representations with corresponding computational methods. One of the important keywords in understanding machine intelligence in human health and wellness is cognitive bias. Advances in big data and machine learning should not overlook some new threats to enlightened thought, such as the recent trend of social media platforms and commercial recommendation systems being used to manipulate people's inherent cognitive bias. The second important keyword is humanity. Rational thinking, on which early AI researchers had been focused their efforts, is recently and rapidly replacing human thinking by machines. Many people might have begun to believe that irrational thinking is the root of humanity. Empirical and philosophical discussions on AI and humanity would be welcome. This paper describes the detailed motivation, technical, and philosophical challenges of this symposium proposal.

The present approach targets to assist decision-making by identifying and by-passing Cognitive Bias of speakers-participants and evaluators in regard to spoken texts.

Four months ago I attempted to synthesize Wikipedia's crazy list of cognitive biases, and after banging my head against the wall for weeks, came up with this Cognitive Bias Cheat Sheet which John Manoogian III,beautifully organized into the above poster. Since then, I've started working on a book proposal (get on the email list!) around these topics, and wanted to start by creating an actual cheat sheet that doesn't take so long to read. There are four qualities of the universe that limit our own intelligence and the intelligence of every other person, collective, organism, machine, alien, or imaginable god. All 200-ish of our known biases are attempts to work around these conundrums! The first conundrum is that there's too much information in the universe for any individual within the universe to process.

Stay tuned for additional content in this series. So you want to build a cognitive application, but you want it to be great. You want it to be useful, exciting, and inspiring -- in essence, to create a truly cognitive experience. You might be wondering what is a cognitive experience? Should the application I'm designing be cognitive?