In May 2016, more than 8,000 varieties of crops -- including sheep's food and chili peppers -- from Germany, Thailand, New Zealand, and the World Vegetable Center were deposited into the Svalbard Global Seed Vault. "Ideally, we want to have a copy of each unique plant variety around the globe in Svalbard so if anything were to happen in any other seed bank around the globe, we could rest assured that we can still find the variety that we're looking for and be able to use it for natural breeding for the plant that we need," Haga said. It's an act of human intervention that is becoming more urgently needed as time goes on. "It is just so much more important to have access to this material now than it was in the past because human intervention is challenging biological stability," says Haga, adding people need to understand that "we have so severely impacted our natural environment that we have got to safeguard what we have."

The Evolving Trading Desk: from Humans to Machines to AI-Assisted Humans Finance Magnates


This article was written By Henri Waelbroeck, Head of Research at Portware. Execution management has matured from laying the foundation for electronification by automating repetitive workflows to extracting progressively more value from the infrastructure as it evolves. Each generation in execution management technology has pushed automation one level higher in the decision hierarchy. The FM London Summit is almost here. Today, we are seeing the dawning of the next generation of execution management systems: one where AI works with the trader to combine the best of quantitative optimization (at speed and at scale) and the trader's domain knowledge.

Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning Artificial Intelligence

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally in order to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.

Sauvik Banerjjee: Artificial intelligence & human intervention


One trending article on how bots incorrectly ranked news on Facebook and another similar headline for Microsoft and suddenly everyone was taking a stab at how artificial intelligence (AI) is still not in ready state. Let's clear some ambiguity on this matter. What are the areas of artificial intelligence, which are impacting our lives? Artificial intelligence is broken down into a few strong areas that impact our way of life. Chat bots are much in the news but they play a very minuscule role.

AI With An Ethic: European Experts Release Draft Guidelines


In this Oct. 31, 2018, photo, Watrix employees demonstrate their firm's gait recognition software at their company's offices in Beijing. A Chinese technology startup hopes to begin selling software that recognizes people by their body shape and how they walk, enabling identification when faces are hidden from cameras. By hook or by crook, Europe needs to differentiate itself, in its approach to artificial intelligence, from mighty competitors such as the U.S. and China. To be fair, competitors might not be the right word given that, for the time being at least, there's no real competition. According to some of the latest data available, AI investment in Europe totaled $3 to $4 billion in 2016, compared with $8 to $12 billion in Asia and $15 to $23 billion in North America.