IMAGE: MouseAge.Org provides tools for cross-species analysis, and provide correlations between health and appearance. Tuesday, 29th of August, 2017, Baltimore, MD - Insilico Medicine, Inc, a Baltimore-based next-generation artificial intelligence company, today announced its participation in the MouseAge.org The scientists from Insilico Medicine will collaborate with scientists from Harvard, Oxford, Youth Laboratories, the Biogerontology Research Foundation, and other institutions to enable scientists worldwide to derive more information from rodent studies, develop novel biomarkers of aging and various diseases in mice, develop tools for cross-species analysis, and provide correlations between health and appearance. The project campaign has been launched today at research crowdfunding platform Lifespan.io. The project was conceived by Vadim Gladyshev, Professor of Medicine at Brigham and Women's Hospital, Harvard Medical School, and Alex Zhavoronkov, CEO of Insilico Medicine.

Artificial intelligence to uncover human biases IMAGE: Artificial intelligence detects discrimination and diversity in the large corporations. Early experiments such as Beauty.AI beauty contest developed by Youth Laboratories and Aging.AI predictor of chronological age developed by Insilico Medicine uncovered the various biases with the AI systems as well as the many opportunities for using AI to detect and report human biases. Advances in artificial intelligence and specifically in the fields of deep learning and reinforcement learning present the many threats and opportunities. "Bias, be it race, sex, age or any other type, is a hugely contributing factor that shapes science and society. This paper is important, not only because it demonstrates the apparent and permeating prejudice that exist in executive boards around the globe, but also because it shows how AI and deep learning can visualize bias in complex systems.", said Morten Scheibye-Knudsen, MD, PhD, Center for Healthy Aging, University of Copenhagen.

When people think of the ethical issues surrounding algorithms and AI, too many of us think of killer robots or movies like "The Matrix." But plenty of reasonable people are now rightly concerned that algorithms, far from being unbiased, can be used to perpetuate unjust or racist results. Last May, ProPublica published an article declaring that, "There's software used across the country to predict future criminals. And it's biased against blacks." ProPublica went on to detail how an algorithm used by parole boards to predict whether a criminal would re-offend was more likely to give bad scores to blacks than whites. There are plenty of other examples of algorithms which crank out ethically problematic results.

Deep learning techniques can be used to extract facial imaging biomarkers of human health status and to track the effects of cosmetic interventions. At the Deep Learning in Healthcare Summit, Research Scientist, Anastasia Georgievskaya from Beauty.AI, will be presenting a set of tools for analysis of perception of human age and health status. She will also demonstrate that when certain population groups are under-represented in the training sets, these populations are left out or may be subject to higher error rates. This is why Youth Laboratories launched Diversity.AI, a think tank for anti-discrimination by the deep-learned systems. The presentation describes the strategies for evaluating human appearance for machine-human interaction and reveals the risks and dangers of deep-learned biomarkers.

Just months after Microsoft's Tay artificial intelligence sent racist messages on Twitter, another AI seems to have followed suit. More than 6,000 selfies of individuals who live all over the world and range in ages of 18 to 69 were judged by a robot in a beauty contest last week. But when the results came in, there was something missing - it turned out the robots did not like people with dark skin. The Beauty.AI beauty contest put together of robot judges to determine the winners. Beauty.AI used five algorithms to act as judges in a beauty contest.

The second beauty contest, where humans are judged by the robots completes with over six thousand images evaluated by the five robot judges. In addition to the panel of judges from the first contest, Beauty.AI 2.0 featured three new robot judges including: "Average Face" built on the hypothesis that the closer the face is to the average face within the ethnic group, the more attractive it is "AntiAgeist" evaluating the difference between the predicted and actual chronological age "PIMPL" evaluating the number and distribution of pimples and other dark spots (but not freckles) The results were sent to the individual participants via secure link and winners were announced at http://winners2.beauty.ai/#win . The results were surprising, since the consensus scores provided by the robot jury disagreed with human opinion. Tens of participants responded with angry emails criticizing the winners selected by the robot jury. Statements including "what is your "robot" worth??? One walk through a shopping-mall and I will discover more attractive people vs. that ones "won" your Beauty Contest", "If this is how I will be judged in the future, I don't want to see it", "You need human opinion" were among the most pleasant ones with rare positive comments including "this contest is a confidence booster!".

Beauty contests are slightly computational to begin with. While the notion of beauty is of something ephemeral and unquantifiable, a beauty pageant asks that we categorize and rank it: determining rules that let us objectively measure an idea which must be, at its root, mysterious and subjective. No surprise, then, that here in 2016 we have just witnessed the first beauty contest judged by AI, as a jury of decidedly non-human bots picked out what they considered to be the best-looking people from a dataset of 6,000 entries. "New tools like machine learning let us analyze images in a way that was never available to us before," Anastasia Georgievskaya, co-founder and research scientist at Youth Laboratories, the company behind Beauty.AI, told Digital Trends. "Our goal was to investigate methods that would show new approaches to beauty evaluation."

Beauty pageants have always been political. After all, what speaks more strongly to how we see each other than which physical traits we reward as beautiful, and which we code as ugly? It wasn't until 1983 that the Miss America competition crowned a black woman as the most beautiful woman in the country. So what if we replaced human judges with machines? As shallow as the whole thing is, would a computer at least be able to see past skin colour and look at, potentially, more universal markers of attractiveness?

They say'beauty is in the eye of the beholder', but in a new event the beholders are robots. The Beauty.AI beauty contest used five algorithms to evaluate youthfulness, face symmetry, skin and other parameters, and then compare them to models and actors in a database. Now, the systems have announced the winners from more than 6,000 user-submitted selfies of individuals who live all over the world and range in ages of 18 to 69. The Beauty.AI beauty contest put together of robot judges to determine the winners. More than 6,000 people from around the world submitted head shots to be analyzed by the algorithms.

Physical beauty is subjective and often difficult to define. But for the robot jury of Beauty.AI, an online competition billed as "the first international beauty contest judged by artificial intelligence," beauty is calculated by a set of complex algorithms that measure parameters like participants' facial symmetry and skin quality. The contest, launched in December, is an experiment by Youth Laboratories, an international team of data scientists and biogerontologists interested in developing anti-aging technologies. Its aim is to test and demonstrate how computers can learn to assess human attractiveness. The robot jury uses algorithms to analyze and rate participants' selfies submitted through the Beauty.AI app.