If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial Intelligence ( AI) is a vast branch of computer science that deals with the development of smart machines capable of executing tasks that usually require human intelligence. AI is an interdisciplinary science with different approaches, but in nearly every field of the education field, software industry, developments in machine learning and deep learning are causing a paradigm change. How is artificial intelligence operation? Are robots able to think? Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: "Can machines think?"
Can artificial intelligence learn the moral values of human societies? Can an AI system make decisions in situations where it must weigh and balance between damage and benefits to different people or groups of people? Can AI develop a sense of right and wrong? In short, will artificial intelligence have a conscience? This question might sound irrelevant when considering today's AI systems, which are only capable of accomplishing very narrow tasks.
We propose an approach for meeting real-time constraints in AI systems that views (1) time as a resource that should be considered when making control decisions, (2) plans as ways of expressing control decisions, and (3) approximate processing as a way of satisfying time constraints that cannot be achieved through normal processing. In this approach, a real-time problem solver estimates the time required to generate solutions and their quality. This estimate permits the system to anticipate whether the current objectives will be met in time. The system can then take corrective actions and form lower-quality solutions within the time constraints. These actions can involve modifying existing plans or forming radically different plans that utilize only rough data characteristics and approximate knowledge to achieve a desired speedup.
In this article, we present Spar (simultaneous planner for assembly robots), an implemented system that reasons about high-level operational goals, geometric goals, and uncertainty-reduction goals to create task plans for an assembly robot. These plans contain manipulations to achieve the assembly goals and sensory operations to cope with uncertainties in the robot's environment. High-level goals (which we refer to as operational goals) are satisfied by adding operations to the plan using a nonlinear, constraint-posting method. Geometric goals are satisfied by placing constraints on the execution of these operations. If the geometric configuration of the world prevents this, Spar adds new operations to the plan along with the necessary set of constraints on the execution of these operations.
The challenging timeline for DARPA's Orbital Express mission demanded a flexible, responsive, and (above all) safe approach to mission planning. Mission planning for space is challenging because of the mixture of goals and constraints. Every space mission tries to squeeze all of the capacity possible out of the spacecraft. For Orbital Express, this means performing as many experiments as possible, while still keeping the spacecraft safe. Keeping the spacecraft safe can be very challenging because we need to maintain the correct thermal environment (or batteries might freeze), we need to avoid pointing cameras and sensitive sensors at the sun, we need to keep the spacecraft batteries charged, and we need to keep the two spacecraft from colliding... made more difficult as only one of the spacecraft had thrusters.
The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates. In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore the topic of decision-making in the shipping and commodities markets. Before we proceed, it is important to note four characteristics of the freight shipping industry that were highlighted by Roar Adland, a professor of shipping economics at the Norwegian School of Economics. In an August 2017 blog post on LinkedIn: 4 things shipping had long before Uber, he noted the following: First, shipping inherently utilizes dynamic pricing because of the volatile nature of rates, and this has been the case for a few centuries. Second, the industry already matches demand and supply in a highly efficient manner.
This article is part of "the philosophy of artificial intelligence," a series of posts that explore the ethical, moral, and social implications of AI today and in the future Can artificial intelligence learn the moral values of human societies? Can an AI system make decisions in situations where it must weigh and balance between damage and benefits to different people or groups of people? Can AI develop a sense of right and wrong? In short, will artificial intelligence have a conscience? This question might sound irrelevant when considering today's AI systems, which are only capable of accomplishing very narrow tasks.
The objective of this post is three-fold. The first part discusses the motivation behind sparsemax and its relation to softmax, summary of the original research paper in which this activation function was first introduced, and an overview of advantages from using sparsemax. Part two and three are dedicated to the mathematical derivations, concretely finding a closed-form solution as well as an appropriate loss function. In the paper "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification", Martins et al. propose a new alternative to the widely known softmax activation function by introducing Sparsemax. While softmax is an appropriate choice for multi-class classification that outputs a normalized probability distribution over K probabilities, in many tasks, we want to obtain an output that is more sparse.
The problem of 3D object detection is of particular importance in robotic applications that require decision making or interactions with objects in the real world. While recently developed 2D detection algorithms are capable of handling large variations in viewpoint and clutter, accurate 3D object detection largely remains an open problem despite some promising recent work. They first regress relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box. Given estimated orientation and dimensions and the constraint that the projection of the 3D bounding box fits tightly into the 2D detection window, they recover the translation and the object's 3D bounding box. In order to study this article mathematically, we need a coordinate system.
"History is called the mother of all subjects", said Marc Bloch. So, let's talk about how the famous Sudoku even came into existence. The story dates back to the late 19th Century and it originated from France. Le Siecle, a French daily published a 9x9 puzzle that required arithmetic calculations to solve rather than logic and had double-digit numbers instead of 1-to-9 with similar game properties like Sudoku where the digits across rows, columns, and diagonals if added, will result in the same number. In 1979 a retired architect and puzzler named Howard Garns is believed to be the creator behind the modern Sudoku which was first published by Dell Magazines in the name of Number Place.