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"The construction of computer programs that simulate aspects of social behaviour can contribute to the understanding of social processes."
– Nigel Gilbert. Computational Social Science: Agent-based social simulationCentre for Research on Social Simulation, University of Surrey. Guildford, UK. 6 November 2005; revised and updated 20 May 2007.
It is the nature of our cognitive systems that we alternate between heuristics and deliberative reasoning. Heuristics are reasoning'shortcuts' based on patterns that help speed up decision making in familiar circumstances. Deliberation takes more attention and energy, but it can go beyond immediately available information and enables complex computations, comparisons, planning, and choice. This'dual mind' theory--as brought to popular attention in books by Kahneman,3 Rugg,7 and Evans1--explains why the heuristics associated with evolution for survival in a dangerous hunter-gatherer world are also responsible for causing systematic biases in our judgments. Says Kahneman: "Jumping to conclusions is efficient if the conclusions are likely to be correct and the costs of an occasional mistake acceptable. Jumping to conclusions is risky when the situation is unfamiliar, the stakes are high and there is no time to collect more information."
This post was originally featured on the 30SecondsToFly blog in 2017. Ready Player One, Ernest Cline's first novel (soon to be adapted to film by Steven Spielberg), portrays a futuristic dystopian society that consumed all the energetic resources on earth and spends most of its time emerged in a Virtual Reality platform, the Oasis. Besides the brilliant references and tributes to the '80s presented in the book, the author also makes some propositions about the role Artificial Intelligence will play in our lives in a (not so) distant future. Instead of going to the physical stores to complain about a product or a service, the customers only have to put on their VR headsets and introduce their requests and complaints to a virtual assistant (who is operated anywhere else in the world by another human being). In one of the book's chapters, an interaction between a customer and an IT assistant takes place inside of the Oasis.
In this paper we present a computational modeling account of an active self in artificial agents. In particular we focus on how an agent can be equipped with a sense of control and how it arises in autonomous situated action and, in turn, influences action control. We argue that this requires laying out an embodied cognitive model that combines bottom-up processes (sensorimotor learning and fine-grained adaptation of control) with top-down processes (cognitive processes for strategy selection and decision-making). We present such a conceptual computational architecture based on principles of predictive processing and free energy minimization. Using this general model, we describe how a sense of control can form across the levels of a control hierarchy and how this can support action control in an unpredictable environment. We present an implementation of this model as well as first evaluations in a simulated task scenario, in which an autonomous agent has to cope with un-/predictable situations and experiences corresponding sense of control. We explore different model parameter settings that lead to different ways of combining low-level and high-level action control. The results show the importance of appropriately weighting information in situations where the need for low/high-level action control varies and they demonstrate how the sense of control can facilitate this.
Smart technology is already all around us. Whether we're fully aware of it or not, it tracks our digital footprint every step of the way. Whether it's location tracking, personalized ads, or even keyboard word suggestions, there's always an algorithm standing behind it. We experience its influence in our lives often without even realizing it's there. We feed the machines based on our knowledge, experience, and perceptions – and there's nothing wrong with that. But as humans, we're also naturally loaded with cognitive imperfections, influencing our daily lives without us even noticing it.
Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. However, the process of training these capabilities often necessitates millions of annotated examples -- far more than humans typically need in order to achieve a passing level of mastery on similar tasks. Thus, while contemporary methods in machine learning can produce agents that exhibit super-human performance, their rate of learning per opportunity in many domains is decidedly lower than human-learning. In this work we formalize a theory of Decomposed Inductive Procedure Learning (DIPL) that outlines how different forms of inductive symbolic learning can be used in combination to build agents that learn educationally relevant tasks such as mathematical, and scientific procedures, at a rate similar to human learners. We motivate the construction of this theory along Marr's concepts of the computational, algorithmic, and implementation levels of cognitive modeling, and outline at the computational-level six learning capacities that must be achieved to accurately model human learning. We demonstrate that agents built along the DIPL theory are amenable to satisfying these capacities, and demonstrate, both empirically and theoretically, that DIPL enables the creation of agents that exhibit human-like learning performance.
If you work in the ADAS/Autonomous Vehicles field, you are probably familiar with HD maps – virtual recreations of real-world roads including their 3D profile, driving rules, inter-connectivity of lanes etc. A lot of these HD maps go into the simulation domain, where car makers and suppliers leverage them to train new ADAS/AV systems or for verification/validation of features from those domains. The reason to use HD maps of real-world roads (rather than just generic, fictional routes created from scratch) is simple: In the end, you want your system to perform in the real world – so you want to optimize for real-world conditions as early as possible, starting in simulation. As we all know, the real world is nothing if not random, and you will encounter many situations you would rarely find in generic data sets. So far, so good: These HD maps can be used to properly train lane-keep assistance or lane-departure warning systems, validate speed limit sign detection and many other systems. However, a map only contains the static features of an environment – what about ADAS/AV features that are supposed to react to other traffic participants?
Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management. Over the years, AI has been able to furnish a host of solutions to many of our everyday challenges. Voice assistants like Alexa and Siri, for example, are now reasonably good at interpreting human speech correctly. They're already providing precise, targeted information in many instances. That said, implementing AI systems has become a real game-changer not only for private use but also in the corporate environment.
The human brain is capable of incredible things, but it's also extremely flawed at times. Science has shown that we tend to make all sorts of mental mistakes, called "cognitive biases", that can affect both our thinking and actions. These biases can lead to us extrapolating information from the wrong sources, seeking to confirm existing beliefs, or failing to remember events the way they actually happened! To be sure, this is all part of being human--but such cognitive biases can also have a profound effect on our endeavors, investments, and life in general. For this reason, today's infographic from DesignHacks.co is particularly handy.
Creating virtual humans with embodied, human-like perceptual and actuation constraints has the promise to provide an integrated simulation platform for many scientific and engineering applications. We present Dynamic and Autonomous Simulated Human (DASH), an embodied virtual human that, given natural language commands, performs grasp-and-stack tasks in a physically-simulated cluttered environment solely using its own visual perception, proprioception, and touch, without requiring human motion data. By factoring the DASH system into a vision module, a language module, and manipulation modules of two skill categories, we can mix and match analytical and machine learning techniques for different modules so that DASH is able to not only perform randomly arranged tasks with a high success rate, but also do so under anthropomorphic Figure 1: Our system, dynamic and autonomous simulated constraints and with fluid and diverse motions. The modular design human (DASH), is an embodied virtual human modeled off also favors analysis and extensibility to more complex manipulation of a child. DASH is able to manipulate tabletop objects with a skills.