Subscribe to the O'Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come: Stitcher, TuneIn, iTunes, SoundCloud, RSS In this week's episode, David Beyer, principal at Amplify Partners, co-founder of Chart.io, and part of the founding team at Patients Know Best, chats with Risto Miikkulainen, professor of computer science and neuroscience at the University of Texas at Austin. They chat about evolutionary computation, its applications in deep learning, and how it's inspired by biology. Also note, David Beyer's new free report "The Future of Machine Intelligence" is now available for download. We talk about evolutionary computation as a way of solving problems, discovering solutions that are optimal or as good as possible. In these complex domains like, maybe, simulated multi-legged robots that are walking in challenging conditions--a slippery slope or a field with obstacles--there are probably many different solutions that will work.

Scientists usually think of natural selection as a slow process, unfolding over generations of incremental change. But, as a study published today in Nature suggests, sometimes this system can take a more rapid approach, especially after a sudden event like a hurricane. As these disasters become more frequent thanks to anthropogenic climate change, understanding how hurricanes affect the species who live in the places they make landfall is vital. This study, which was mainly the result of good timing, offers evidence that, for one family of lizards at least, hurricanes may initiate a rapid natural selection process for certain traits. Just four days before Hurricane Irma reached the Turks and Caicos in 2017, ecologist Colin Donihue completed a survey of the local anole species (Anolis scriptus, a family of lizards) on two remote islands.

Germany Market Analysis Table 35: German Recent Past, Current & Future Analysis for Artificial Intelligence Analyzed with Annual Revenue Figures in US$ Million for Years 2015 through 2024 (includes corresponding Graph/Chart) 9.4.3 Italy Market Analysis Table 36: Italian Recent Past, Current & Future Analysis for Artificial Intelligence Analyzed with Annual Revenue Figures in US$ Million for Years 2015 through 2024 (includes corresponding Graph/Chart) 9.4.4

In this tutorial, we'll be using a GA to find a solution to the traveling salesman problem (TSP). Let's start with a few definitions, rephrased in the context of the TSP: Now, let's see this in action. While each part of our GA is built from scratch, we'll use a few standard packages to make things easier: We first create a City class that will allow us to create and handle our cities. These are simply our (x, y) coordinates. Within the City class, we add a distance calculation (making use of the Pythagorean theorem) in line 6 and a cleaner way to output the cities as coordinates with __repr__ in line 12. We'll also create a Fitness class.

Industrial Revolution (4IR) (Xing and Marwala, 2017), many countries (Shah et al., 2015; Ding and Li, 2015) are setting out an overarching goal of building/securing an "innovation-driven" economy. As innovation emphasizes the implementation of ideas, creativity is typically regarded as the first stage of innovation in which generating ideas becomes the dominant focus (Tang and Werner, 2017; Amabile, 1996; Mumford and Gustafson, 1988; Rank et al., 2004; West, 2002). In other words, if creativity is absent, innovation could be just luck. Though creativity can be generally understood as the capability of producing original and novel work or knowledge, the universal definition of creativity remains rather controversial, mainly due to its complex nature (Tang and Werner, 2017; Hernández-Romero, 2017). But putting it informally, by famous innovator Steve Jobs in 1995, we can think creativity like this way (Sanchez-Burks et al., 2015): "Creative people [are] able to connect experiences they've had and synthesize new things."

I talked to Dirk Erlacher, the CEO of Agilox, on this topic. Austrian headquartered Agilox designs and manufactures mobile logistics robots that use "swarm intelligence" to intelligently navigate through warehouses and factories, delivering pallets and totes where they are needed. A mobile logistics robot (MLR) is a more advanced form of an automatic guided vehicle (AGV); AGVs are used to reduce labor by taking over tasks that were traditionally performed with fork lifts. More complex AGVs have fleet management software. This software makes sure that not too many AGVs are in the same aisles, decides which AGV has the right of way at crossings, and in more complex scenarios, decides which unit will be used to complete a particular task and how it will navigate through the facility.

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.

Wagner's modularity inducing problem domain is a key contribution to the study of the evolution of modularity, including both evolutionary theory and evolutionary computation. We study its behavior under classical genetic algorithms. Unlike what we seem to observe in nature, the emergence of modularity is highly conditional and dependent, for example, on the eagerness of search. In nature, modular solutions generally dominate populations, whereas in this domain, modularity, when it emerges, is a relatively rare variant. Emergence of modularity depends heavily on random fluctuations in the fitness function; with a randomly varied but unchanging fitness function, modularity evolved far more rarely. Interestingly, high-fitness non-modular solutions could frequently be converted into even-higher-fitness modular solutions by manually removing all inter-module edges. Despite careful exploration, we do not yet have a full explanation of why the genetic algorithm was unable to find these better solutions.

Have you put a bet on the FIFA World Cup? If yes, the chances are you've made a pretty educated guess, right? You know which team has the strongest players or most favourable odds. Or maybe you've put some cash on your country's team, (which normally I'd avoid England, but given their recent performance, I could be wrong to!) Either way, you might be best casting your bets in line with San Francisco based Unanimous AI. They use a technology called Swarm AI – algorithms modelled on swarms in nature that amplifies human intelligence.