Artificial Intelligence Technology Solutions, Inc., ( OTCPK:AITX), a global leader in AI-driven security and productivity solutions for enterprise clients today increased its revenue guidance for its fiscal Q3 ending November 30, 2021. This press release features multimedia. AITX CEO Steve Reinharz introduces RAD's yet unnamed robotic dog, part of the RAD 3.0 product offering, at AITX Investors Open House held October 13, 2021, at the company's REX (RAD Excellence Center) in Detroit, Michigan (Photo: Business Wire) As previously disclosed, AITX through its wholly owned subsidiary Robotic Assistance Devices Inc. (RAD), is expected to close October 2021 with Recurring Monthly Revenue (RMR) of over $80,000. "Sales activity has now accelerated to the pace where we can anticipate topping the $100,000 RMR milestone by the end of November 2021," commented Mark Folmer, RAD COO and President. "This is such exciting progress for AITX. We expect significant revenue events before Q3 closes," said Steve Reinharz, CEO and President of AITX.

She has been described as "a vision of the future" who is every bit as good as other abstract artists today, but Ai-Da – the world's first ultra-realistic robot artist – hit a temporary snag before her latest exhibition when Egyptian security forces detained her at customs. Ai-Da is due to open and present her work at the Great Pyramid of Giza on Thursday, the first time contemporary art has been allowed next to the pyramid in thousands of years. But because of "security issues" that may include concerns that she is part of a wider espionage plot, both Ai-Da and her sculpture were held in Egyptian customs for 10 days before being released on Wednesday, sparking a diplomatic fracas. "The British ambassador has been working through the night to get Ai-Da released, but we're right up to the wire now," said Aidan Meller, the human force behind Ai-Da, shortly before her release. According to Meller, border guards detained Ai-Da at first because she had a modem, and then because she had cameras in her eyes (which she uses to draw and paint).

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.S. troops in Syria were targeted Thursday with a "deliberate and coordinated" drone attack, military officials told Fox News, saying that the U.S. has the "inherent right of self defense" and will "respond at a time and place of our choosing." Bill Urban confirmed that the al-Tanf Garrison area "was subjected to a deliberate and coordinated attack." "Based on initial reports, the attack utilized both unmanned aerial systems and indirect fire," Urban said.

NASA scientists are using artificial intelligence to calibrate photographs of the Sun to improve data for solar studies. NASA's Solar Dynamics Observatory (SDO) has been providing high-definition photos of the Sun for nearly a decade since its launch on February 11, 2010. The photos have offered an in-depth examination of a variety of solar phenomena. SDO's Atmospheric Imaging Assembly (AIA) observes the Sun continuously and generates a lot of data about our Sun that has never been possible before. AIA degrades over time as a result of continual looking, and the data must be calibrated frequently.

Riverside Research is seeking a Research Scientist with a general background in machine learning and artificial intelligence and a focus on signal processing to join a dynamic, growth-focused Artificial Intelligence and Machine Learning Lab. The Lab performs research and development focused on providing solutions to the Defense and Intelligence Communities. As a key member of our Open Innovation Center, the research scientist will execute as well as assist in growing opportunities with government research organizations (e.g. DARPA, IARPA, service labs, etc.), perform on our corporate-wide Independent Research & Development (IR&D) efforts in artificial intelligence and machine learning, manage existing R&D contracts, and transition technology into our other business units. The Research Scientist will work with team members located in the Dayton OH, Washington DC, New York City, and Boston office locations while reporting to the Director of the Artificial Intelligence and Machine Learning Lab of the Open Innovation Center Business Unit.

We consider the classical problem of scheduling task graphs corresponding to complex applications on distributed computing systems. A number of heuristics have been previously proposed to optimize task scheduling with respect to metrics such as makespan and throughput. However, they tend to be slow to run, particularly for larger problem instances, limiting their applicability in more dynamic systems. Motivated by the goal of solving these problems more rapidly, we propose, for the first time, a graph convolutional network-based scheduler (GCNScheduler). By carefully integrating an inter-task data dependency structure with network settings into an input graph and feeding it to an appropriate GCN, the GCNScheduler can efficiently schedule tasks of complex applications for a given objective. We evaluate our scheme with baselines through simulations. We show that not only can our scheme quickly and efficiently learn from existing scheduling schemes, but also it can easily be applied to large-scale settings where current scheduling schemes fail to handle. We show that it achieves better makespan than the classic HEFT algorithm, and almost the same throughput as throughput-oriented HEFT (TP-HEFT), while providing several orders of magnitude faster scheduling times in both cases. For example, for makespan minimization, GCNScheduler schedules 50-node task graphs in about 4 milliseconds while HEFT takes more than 1500 seconds; and for throughput maximization, GCNScheduler schedules 100-node task graphs in about 3.3 milliseconds, compared to about 6.9 seconds for TP-HEFT.

While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such models to correct inaccurate outputs while leaving the model otherwise intact is desirable. However, the distributed, black-box nature of the representations learned by large neural networks makes producing such targeted edits difficult. If presented with only a single problematic input and new desired output, fine-tuning approaches tend to overfit; other editing algorithms are either computationally infeasible or simply ineffective when applied to very large models. To enable easy post-hoc editing at scale, we propose Model Editor Networks with Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model. MEND learns to transform the gradient obtained by standard fine-tuning, using a low-rank decomposition of the gradient to make the parameterization of this transformation tractable. MEND can be trained on a single GPU in less than a day even for 10 billion parameter models; once trained MEND enables rapid application of new edits to the pre-trained model. Our experiments with T5, GPT, BERT, and BART models show that MEND is the only approach to model editing that produces effective edits for models with tens of millions to over 10 billion parameters. Increasingly large neural networks have become a fundamental tool in solving data-driven problems in computer vision (Huang et al., 2017) and natural language processing (Vaswani et al., 2017) in particular. However, a key challenge in deploying and maintaining such models is issuing patches to adjust model behavior after deployment (Sinitsin et al., 2020).

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

Cybercriminals are no longer a threat to be taken lightly in today's world. Each year, data theft affects more than a hundred thousand people. Unfortunately, this number is rising despite the availability of effective cybersecurity measures. In this particular context, how can AI play a role in improving cybersecurity? Many businesses and individuals are rushing to refresh their systems and protect their data. Due to the dramatic increase in threats, the need for security checks has increased.

The robotic artist known as Ai-Da was scheduled to display her artwork alongside the great pyramids of Egypt on Thursday, though the show was nearly called off after both the robot and her human sculptor, Aidan Meller, were detained by Egyptian authorities for a week and a half until they could confirm that the artist was actually a spy. The incident began when border guards objected over Ai-da's camera eyes, which it uses in its creative process, and its on-board modem. "I can ditch the modems, but I can't really gouge her eyes out," Meller told The Guardian. The robot artist, which was built in 2019, typically travels via specialized cargo case and was held at the border until clearing customs on Wednesday evening, hours before the exhibit was scheduled to begin. "The British ambassador has been working through the night to get Ai-Da released, but we're right up to the wire now," Meller said, just before Ai-Da was sprung from robo-jail.