If you're one of those who are looking for a new and exciting job, we have great news for you. Since artificial intelligence is high on demand at present, many artificial intelligence companies are looking to hire artificial intelligence professionals in their teams. Here are the best artificial intelligence companies hiring in India right now. Google is an American multinational technology company that is an expert in Internet-related services and products that include online advertising technologies, cloud computing, a search engine, software, and hardware company. The company was founded in 1998 in California, United States.

We present GTGraffiti, a graffiti painting system from Georgia Tech that tackles challenges in art, hardware, and human-robot collaboration. The problem of painting graffiti in a human style is particularly challenging and requires a system-level approach because the robotics and art must be designed around each other. The robot must be highly dynamic over a large workspace while the artist must work within the robot's limitations. Our approach consists of three stages: artwork capture, robot hardware, and planning & control. We use motion capture to capture collaborator painting motions which are then composed and processed into a time-varying linear feedback controller for a cable-driven parallel robot (CDPR) to execute. In this work, we will describe the capturing process, the design and construction of a purpose-built CDPR, and the software for turning an artist's vision into control commands. Our work represents an important step towards faithfully recreating human graffiti artwork by demonstrating that we can reproduce artist motions up to 2m/s and 20m/s$^2$ within 9.3mm RMSE to paint artworks. Changes to the submitted manuscript are colored in blue.

Abstract--Artificial agents are promising for realtime power system operations, particularly, to compute remedial actions for congestion management. Currently, these agents are limited to only autonomously run by themselves. However, autonomous agents will not be deployed any time soon. Operators will still be in charge of taking action in the future. Aiming at designing an assistant for operators, we here consider humans in the loop and propose an original formulation for this problem. We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence. We further model the operator's available attention as a budget that decreases when alarms are sent. We present the design and results of our competition "Learning to run a power network with trust" in which we benchmark the ability of submitted agents to send relevant alarms while operating the network to their best.

An intrinsic time-scale decomposition (ITD) based method for power transformer fault diagnosis is proposed. Dissolved gas analysis (DGA) parameters are ranked according to their skewness, and then ITD based features extraction is performed. An optimal set of PRC features are determined by an XGBoost classifier. For classification purpose, an XGBoost classifier is used to the optimal PRC features set. The proposed method's performance in classification is studied using publicly available DGA data of 376 power transformers and employing an XGBoost classifier. The Proposed method achieves more than 95% accuracy and high sensitivity and F1-score, better than conventional methods and some recent machine learning-based fault diagnosis approaches. Moreover, it gives better Cohen Kappa and F1-score as compared to the recently introduced EMD-based hierarchical technique for fault diagnosis in power transformers.

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium

For construction companies, the usage of data science techniques provides a huge opportunity to stand out from the competition and reinvent their business. There is a vast amount of continuously changing construction data which creates a necessity for engaging machine learning and artificial intelligent tools into different aspects of the business. Architecture is still a key place for technology and innovation to shake things up, especially with the increase of urbanization and the influx of more concentrated human populations around metropolitan areas. Realizing the difficulties within the domain of residential construction, Octett decided to deploy this initiative with the intention to solve simple problems that hold complex issues if not managed appropriately. These major inconsistencies within the sectors, left most construction specialists with little to no solutions.

Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyse quantum data better than classical computers can. While a fundamental solvability problem known as "barren plateaus" has limited the application of these neural networks for large data sets, new research overcomes that Achilles heel with a rigorous proof that guarantees scalability. "The way you construct a quantum neural network can lead to a barren plateau--or not," said Marco Cerezo, co-author of the paper titled "Absence of Barren Plateaus in Quantum Convolutional Neural Networks," published today by a Los Alamos National Laboratory team in Physical Review X. Cerezo is a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos. "We proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters."

The manufacturing process of both large-scale sectors such as the automobile industry to small-scale sectors such as device or router manufacturing is vulnerable to defects. These defects could be very minute or microscopic flaws but are capable of determining the final product quality. If not properly identified this can result in a mass recall of products that could significantly alter the brand reputation and cause millions of dollars in losses. Not only for the product manufacturers, but the defective product can also affect the buyers as well, resulting in a catastrophic accident. For instance, If a nuclear industry purchases these undetected defective items, it can lead to fatal and devastating outcomes for employees as well as the environment.

This paper explores the performance of Google's Edge TPU on feed forward neural networks. We consider Edge TPU as a hardware platform and explore different architectures of deep neural network classifiers, which traditionally has been a challenge to run on resource constrained edge devices. Based on the use of a joint-time-frequency data representation, also known as spectrogram, we explore the trade-off between classification performance and the energy consumed for inference. The energy efficiency of Edge TPU is compared with that of widely-used embedded CPU ARM Cortex-A53. Our results quantify the impact of neural network architectural specifications on the Edge TPU's performance, guiding decisions on the TPU's optimal operating point, where it can provide high classification accuracy with minimal energy consumption. Also, our evaluations highlight the crossover in performance between the Edge TPU and Cortex-A53, depending on the neural network specifications. Based on our analysis, we provide a decision chart to guide decisions on platform selection based on the model parameters and context.

All the pieces are coming together: big data, advances in hardware, emerging powerful AI algorithms, and an open source community for tools that reduces barriers to entry for industry and start-ups alike. The result: AI is being propelled out of research labs and into our everyday lives, from navigating cities, ride shares, our energy networks, to the online world. In 2018 everyone is starting to see the business value of AI. It is being added to more and more things every year, and it is getting smarter and smarter – accelerating human innovation. But as AI becomes more powerful, more autonomous and broader in its use and impact, the unsolved issue of AI safety is paramount. Risks include: bias, poor decision making, low transparency, job losses and malevolent use of AI, such as autonomous weaponry.