Inflated-beam soft robots, such as tip-everting vine robots, can control curvature by contracting one beam side via pneumatic actuation. This work develops a general finite element modeling approach to characterize their bending. The model is validated across four pneumatic actuator types (series, compression, embedded, and fabric pneumatic artificial muscles), and can be extended to other designs. These actuators employ two bending mechanisms: geometry-based contraction and material-based contraction. The model accounts for intricate nonlinear effects of buckling and anisotropy. Experimental validation includes three working pressures (10, 20, and 30 kPa) for each actuator type. Geometry-based contraction yields significant deformation (92.1% accuracy) once the buckling pattern forms, reducing slightly to 80.7% accuracy at lower pressures due to stress singularities during buckling. Material-based contraction achieves smaller bending angles but remains at least 96.7% accurate. The open source models available at http://www.vinerobots.org support designing inflated-beam robots like tip-everting vine robots, contributing to waste reduction by optimizing designs based on material properties and stress distribution for effective bending and stress management.

Artificial intelligence (AI) has enabled a new paradigm of smart applications -- changing our way of living entirely. Many of these AI-enabled applications have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and vehicles). Hence, smaller and quantized deep neural network (DNN) models are developed for mobile devices, which provide faster and more energy-efficient computation for mobile AI applications. However, how AI models consume energy in a mobile device is still unexplored. Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources. In this paper, we introduce a comprehensive study of mobile AI applications considering different DNN models and processing sources, focusing on computational resource utilization, delay, and energy consumption. We measure the latency, energy consumption, and memory usage of all the models using four processing sources through extensive experiments. We explain the challenges in such investigations and how we propose to overcome them. Our study highlights important insights, such as how mobile AI behaves in different applications (vision and non-vision) using CPU, GPU, and NNAPI. Finally, we propose a novel Gaussian process regression-based general predictive energy model based on DNN structures, computation resources, and processors, which can predict the energy for each complete application cycle irrespective of device configuration and application. This study provides crucial facts and an energy prediction mechanism to the AI research community to help bring energy efficiency to mobile AI applications.

Who joins Alphabet (GOOGL), Meta Platforms (FB) (formerly known as Facebook), and Alibaba (BABA) on this list of Warren Buffett stocks based on the investing strategy of the Berkshire Hathaway CEO? In addition to GOOGL, FB and BABA, Sprouts Farmers Market (SFM), Evercore (EVR), Teradyne (TER), and Williams-Sonoma (WSM) are among 21 names featured on this stock screen.

KNIME, a unified software platform for creating and productionizing data science, announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials. Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers.

EPAM Systems, Inc. (NYSE: EPAM), a leading global provider of digital platform engineering and software development services, today announced that it has been named a Diamond Global Business Partner of UiPath, an enterprise Robotic Process Automation (RPA) software company. EPAM and UiPath's partnership will enable its joint customers to increase efficiencies and improve customer experience by leveraging intelligent automation (IA) solutions and UiPath's RPA platform. Forrester recently predicted that, in 2019, automation would become the tip of the digital transformation spear. While early automation implementations focused on cost optimization, this new wave will achieve multiple goals, including driving both customer and employee experience, changing the nature of work, and even empowering the next generation of startup companies. With more than 10 years of business process management, robotics and cognitive expertise, EPAM has over 100 certified UiPath advanced developers as part of its team of more than 700 machine learning and RPA engineers.

The purpose of this report is to describe in detail an information Processing model of elementary human symbolic learning processes. This model is realized by a computer program called the Elementary Perceiver and Memorizer (EPAM). The critical evaluation of EPAM must ultimately depend not upon the interest which it may have as a learning machine, but upon its ability to explain and Predict the phenomena of verbal learning. I should like to preface my discussion of the simulation of verbal learning with some brief remarks about the class of information processing models of which EPAM is a member. These are models of mental processes, not brain hardware. No physiological or neuro assumptions are made, nor is any attempt made to explain information processes in terms of more elementary neural processes. The central processing mechanism is assumed to be serial; i.e., capable of doing only one (or a very few) things at a time. These models use as a basic unit the information symbol; i.e., a pattern of bits which is assumed to be the brain's internal representation of environmental data.

The purpose of this report is to describe in detail an informationProcessing model of elementary human symbolic learning processes. Thismodel is realized by a computer program called the Elementary Perceiverand Memorizer (EPAM).The EPAM program is the precise statement of an information processingtheory of verbal learning that provides an alternative to other verballearning theories which have been proposed.1 It is the result of an attemptto state quite precisely a parsimonious and plausible mechanism sufficientto account for the rote learning of nonsense syllables. The criticalevaluation of EPAM must ultimately depend not upon the interest whichit may have as a learning machine, but upon its ability to explain andPredict the phenomena of verbal learning. Proceedings of the Western Joint Computer Conference, 1961, 19:121-132. Reprinted in Feigenbaum & Feldman, Computers and Thought (1963).