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) …
Deep learning computer vision startup allegro.ai is set to showcase its latest product offering, hosted at the Intel partner booth (booth #307), during the Embedded Vision Summit which will take place in Santa Clara, California on May 20-May 23, 2019. The company's platform and product suite simplify the process of developing and managing deep learning-powered perception solutions - such as for autonomous vehicles, medical imaging, drones, security, logistics and other use cases. The platform enables engineering and product managers to get the visibility and control they need, while research scientists focus their time on research and creative output. The result is meaningfully higher quality products, faster time-to-market, increased returns to scale, and materially lower costs. The company's investors include Robert Bosch Venture Capital GmbH, Samsung Catalyst Fund, Hyundai Motor Company, and other venture funds.
A recent report emerging from the center of U.S. auto manufacturing rains on the AI parade with research results claiming autonomous vehicle algorithms fare poorly in bad weather. The study by researchers at Michigan State University found that even light rain or drizzle can interfere with algorithms used in self-driving car cameras. That could mean future fleets might initially be restricted to sunny states like Arizona, California and Florida. The Michigan State study determined that the core problem stems not from cameras used as primary sensors for detecting obstacles but the algorithms used to sort through computer vision data. "When we run these algorithms, we see very noticeable, tangible degradation in detection," Hayder Radha, a Michigan State University professor of electrical and computer engineering, told Automotive Newsin late November.
Hyundai has announced a strategic investment in Allegro.ai, a start-up focused on developing deep learning technologies for our future cars. On Monday, the Seoul, South Korean-based firm said its interest in Allegro.ai is based in the firm's deep learning technologies used for computer vision in self-driving vehicles. Founded in 2016, Allegro.ai is the creator of a platform which supports the development of deep learning and artificial intelligence solutions, including -- but not limited to -- autonomous vehicles, drones, and security applications. The automaker has invested into Allegro.ai However, investment figures have not been disclosed.
Allegro enables you to spawn model subsets per edge-device and continuously train each one with newly acquired data from the edge-device where it operates. Creating increasingly accurate personalized models which are built to run within the compute constraints of the respective edge device. Essentially, your edge-devices become smarter, each tailored to its own unique environment and resources.
Artificial intelligence and the application of it across nearly every aspect of our lives is shaping up to be one of the major step changes of our modern society. Today, a startup that wants to help other companies capitalise on AI's advances is announcing funding and emerging from stealth mode. Allegro.AI, which has built a deep learning platform that companies can use to build and train computer-vision-based technologies -- from self-driving car systems through to security, medical and any other services that require a system to read and parse visual data -- is today announcing that it has raised $11 million in funding, as it prepares for a full-scale launch of its commercial services later this year after running pilots and working with early users in a closed beta. The round may not be huge by today's startup standards, but the presence of strategic investors speaks to the interest that the startup has sparked and the gap in the market for what it is offering. It includes MizMaa Ventures -- a Chinese fund that is focused on investing in Israeli startups, along with participation from Robert Bosch Venture Capital GmbH (RBVC), Samsung Catalyst Fund and Israeli fund Dynamic Loop Capital.
Automated planning is a major topic of research in artificial intelligence, and enjoys a long and distinguished history. The classical paradigm assumes a distinguished initial state, comprised of a set of facts, and is defined over a set of actions which change that state in one way or another. Planning in many real-world settings, however, is much more involved: an agent's knowledge is almost never simply a set of facts that are true, and actions that the agent intends to execute never operate the way they are supposed to. Thus, probabilistic planning attempts to incorporate stochastic models directly into the planning process. In this article, we briefly report on probabilistic planning through the lens of probabilistic programming: a programming paradigm that aims to ease the specification of structured probability distributions. In particular, we provide an overview of the features of two systems, HYPE and ALLEGRO, which emphasise different strengths of probabilistic programming that are particularly useful for complex modelling issues raised in probabilistic planning. Among other things, with these systems, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting.
The aim of this paper is to propose a new approach interweaving preference elicitation and search to solve multiobjective optimization problems. We present an interactive search procedure directed by an aggregation function, possibly non-linear (e.g. an additive disutility function, a Choquet integral), defining the overall cost of solutions. This function is parameterized by weights that are initially unknown. Hence, we insert comparison queries in the search process to obtain useful preference information that will progressively reduce the uncertainty attached to weights. The process terminates by recommending a near-optimal solution ensuring that the gap to optimality is below the desired threshold. Our approach is tested on multiobjective state space search problems and appears to be quite efficient both in terms of number of queries and solution times.
High-level programming languages are an influential control paradigm for building agents that are purposeful in an incompletely known world. GOLOG, for example, allows us to write programs, with loops, whose constructs refer to an explicit world model axiomatized in the expressive language of the situation calculus. Over the years, GOLOG has been extended to deal with many other features, the claim being that these would be useful in robotic applications. Unfortunately, when robots are actually deployed, effectors and sensors are noisy, typically characterized over continuous probability distributions, none of which is supported in GOLOG, its dialects or its cousins. This paper presents ALLEGRO, a belief-based programming language for stochastic domains, that refashions GOLOG to allow for discrete and continuous initial uncertainty and noise. It is fully implemented and experiments demonstrate that ALLEGRO could be the basis for bridging high-level programming and probabilistic robotics technologies in a general way.