A joint project between Google scientists and researchers from Janella Research Campus in Virginia has created a high resolution 3D map of the fruit fly brain, the most detailed 3D model of a brain yet created. While fruit flies have tiny brains, roughly the size of a poppy seed, they behave in ways that indicate substantial intelligence. These behaviors include complex courtship dances and a tendency to investigate for hazards like toxic chemicals before choosing to move to new locations. In total, the fruit fly brain as around 100,000 neurons, about the same amount as a lobster, but less than a cockroach, which has around a million. Researchers had previously created a computer model of the fruit fly brain by analyzing a series of microscopic images, but this is the first time a map has been built from real 3D data.

Artificial intelligence (AI) has already had a profound impact on business and society. Applied AI and machine learning (ML) are creating safer workplaces, more accurate health diagnoses and better access to information for global citizens. The Fourth Industrial Revolution will represent a new era of partnership between humans and AI, with potentially positive global impact. AI advancements can help society solve problems of income inequality and food insecurity to create a more "inclusive, human-centred future" according to the World Economic Forum (WEF). There is nearly limitless potential to AI innovation, which is both positive and frightening.

Artificial intelligence is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to old but persistent problems. From a technological point of view, the amount of daily data produced in the digital universe now allows for state-of-the-art approaches, which may lead to innovative solutions in these underserved areas. AI for social good turned into a reality for us at Tryolabs after we collaborated with an NGO to improve upon how African lions are tracked, which helps with species preservation. We will go into more detail on that timely case, especially as wildlife conservation faces the immense challenges posed by devastating megafires threatening the lives of millions of animals in historic ways.

A remarkable combination of artificial intelligence (AI) and biology has produced the world's first "living robots". This week, a research team of roboticists and scientists published their recipe for making a new lifeform called xenobots from stem cells. The term "xeno" comes from the frog cells (Xenopus laevis) used to make them. One of the researchers described the creation as "neither a traditional robot nor a known species of animal", but a "new class of artifact: a living, programmable organism". Xenobots are less than 1mm long and made of 500-1000 living cells.

The U.S. is locked in a race when it comes to advanced technology like artificial intelligence and machine learning – chiefly against China. Speaking about technological progress from the World Economic Forum in Davos, Switzerland, MIT Sloan's Erik Brynjolfsson and Andrew McAfee pointed out how inextricably linked the AI race is with immigration policies and government investment – and particularly the red tape that hinders smart people from coming to the U.S. AI and machine learning are thought of technologies that will fundamentally reshape productivity and growth. Companies from around the world from Amazon to Google as well as more mundane companies in manufacturing are employing these technologies. According to Accenture, within five years AI will drive "significant innovation" and "unleash new levels of human productivity and creativity." Brynjolfsson said the U.S. is still the leader in developing these modern technologies -- and commercializing them.

The most well-funded US artificial intelligence startup is Nuro, with just over $1B in disclosed equity funding, including a $940M Series B from SoftBank in February 2019. The California-based startup is developing autonomous vehicles, with a focus on last-mile delivery. Nuro is followed by New York's UiPath ($1B in disclosed equity funding) and Illinois' Avant ($655M). There are 9 unicorn startups on our map: robotic process automation vendor UiPath ($7.1B valuation), autonomous vehicles software provider Argo AI ($7B), agtech startup Indigo Agriculture ($3.5B), Nuro ($2.7B), alternative lending startup Avant ($1.9B), AI-powered predictive sales platform InsideSales.com The startup with the least funding on the list is Rhode Island's The Innovation Scout, a SaaS platform that connects enterprises with startups, accelerators, and more.

Many companies have turned to artificial intelligence to lead job candidacy searches and cherry pick job applicants… Welcome to the'Wild West of Hiring.' Pixabay The new job search question to ask yourself: What if AI doesn't like me? This is the obstacle now faced by college graduates hoping to land their first dream job. Yes, the human resources departments at companies are steering further away from humans and embracing artificial intelligence in their job candidacy searches. SEE ALSO: Can Artificial Intelligence Determine If You Have a Toxic Workplace? According to CNN, career counselors at bigwig schools, such as Duke University, Purdue University and the University of North Carolina at Charlotte, are priming students on what companies use AI--and how to outfox the algorithms. Gone are such job interview preparations as mentioned in this old-timey video from the archaic days of 2010.

Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints. This paper extends upper confidence reinforcement learning for settings in which the reward function and the constraints, described by cost functions, are unknown a priori but the transition kernel is known. Such a setting is well-motivated by a number of applications including exploration of unknown, potentially unsafe, environments. We present an algorithm C-UCRL and show that it achieves sub-linear regret ($ O(T^{\frac{3}{4}}\sqrt{\log(T/\delta)})$) with respect to the reward while satisfying the constraints even while learning with probability $1-\delta$. Illustrative examples are provided.

A variety of computationally challenging constrained optimization problems in several engineering disciplines are solved repeatedly under different scenarios. In many cases, they would benefit from fast and accurate approximations, either to support real-time operations or large-scale simulation studies. This paper aims at exploring how to leverage the substantial data being accumulated by repeatedly solving instances of these applications over time. It introduces a deep learning model that exploits Lagrangian duality to encourage the satisfaction of hard constraints. The proposed method is evaluated on a collection of realistic energy networks, by enforcing non-discriminatory decisions on a variety of datasets, and on a transprecision computing application. The results illustrate the effectiveness of the proposed method that dramatically decreases constraint violations by the predictors and, in some applications, increases the prediction accuracy.

How can societies learn to enforce and comply with social norms? Here we investigate the learning dynamics and emergence of compliance and enforcement of social norms in a foraging game, implemented in a multi-agent reinforcement learning setting. In this spatiotemporally extended game, individuals are incentivized to implement complex berry-foraging policies and punish transgressions against social taboos covering specific berry types. We show that agents benefit when eating poisonous berries is taboo, meaning the behavior is punished by other agents, as this helps overcome a credit-assignment problem in discovering delayed health effects. Critically, however, we also show that introducing an additional taboo, which results in punishment for eating a harmless berry, improves the rate and stability with which agents learn to punish taboo violations and comply with taboos. Counterintuitively, our results show that an arbitrary taboo (a "silly rule") can enhance social learning dynamics and achieve better outcomes in the middle stages of learning. We discuss the results in the context of studying normativity as a group-level emergent phenomenon.