Artificial Intelligence (AI) is transforming the workplace in significant ways that are being used by recruiters to expand and improve their workforce. These AI applications are not about replacing human beings as much as efficiently finding the best human beings as candidates for open job positions. AI is extremely efficient in data mining to find the keywords that will be the optimal choices for advertising copy about open job positions. AI is also effective in screening potential candidates to find a match. To have job listings rank high on the search engine results page, recruiters use AI-driven keyword optimization techniques to have the best results.

A fleet of 30 Starship autonomous delivery robots has been deployed at the University of Houston, home to over 53,000 students, faculty and staff. In partnership with Chartwells Higher Education, UH is the first institution of higher education in the state of Texas to offer robotic food deliveries on campus. The recipient can even track the delivery -- made to a building's nearest outdoor entrance -- in real time. "This revolutionary delivery method will make it more convenient for the campus community to take advantage of our diverse dining program from anywhere on campus while expanding the hours of operation," said Emily Messa, UH associate vice president for administration. "By opening our campus to this innovative service, which is paid for by the customers, the university didn't have to spend any money purchasing the technology, yet we're enhancing our food delivery capabilities."

Hyderabad, India: Millets have sometimes been hailed as the next quinoa but researchers collating a global database of millet products have found this ancient grain to be orchestrating a silent food revolution that could see quinoa outstripped. The "Millet Finder", launched today, discovered a surge in the use of millets, with over a thousand modern convenient products in a very wide range, across all the inhabited continents. Launched today at FoodTec Expo by the International Crops Research Institute of the Semi-Arid Tropics (ICRISAT) and the ICAR-Indian Institute of Millets Research (IIMR), the "Millet Finder" will help users find over 500 products across 30 countries. Another 500 products are identified and set to be included and mapped by end of the year by the Smart Food team at ICRISAT, who created the database and will continue growing it. "Unless there is a consumer driven demand and movement to diversify diets, farms cannot diversify and agriculture cannot be sustainable. By diversifying staples, we can have a major impact on diets, farms and the environment. ICRISAT strongly believes in creating awareness and helping consumers make informed choices while keeping their health and the environment in view. In that respect, millets check every box," said Dr Jacqueline d'Arros Hughes, Director General, ICRISAT, and Chair, Smart Food Executive Council.

A Recurrent Neural Network or RNN is a popular multi-layer neural network that has been utilised by researchers for various purposes including classification and prediction. The applications of this network include speech recognition, language modelling, machine translation, handwriting recognition, among others. The recurrent neural network is an interesting topic and what's more about this article is that all the courses mentioned here are free to learn. Below here, we listed down the top 10 free resources, in no particular order, to learn recurrent neural networks (RNNs). About: Here, you will understand how to implement recurrent neural networks (RNNs).

In recent years the empirical success of transfer learning with neural networks has stimulated an increasing interest in obtaining a theoretical understanding of its core properties. Knowledge distillation where a smaller neural network is trained using the outputs of a larger neural network is a particularly interesting case of transfer learning. In the present work, we introduce a statistical physics framework that allows an analytic characterization of the properties of knowledge distillation (KD) in shallow neural networks. Focusing the analysis on a solvable model that exhibits a non-trivial generalization gap, we investigate the effectiveness of KD. We are able to show that, through KD, the regularization properties of the larger teacher model can be inherited by the smaller student and that the yielded generalization performance is closely linked to and limited by the optimality of the teacher. Finally, we analyze the double descent phenomenology that can arise in the considered KD setting.

By studying the underlying policies of decision-making agents, we can learn about their shortcomings and potentially improve them. Traditionally, this has been done either by examining the agent's implementation, its behaviour while it is being executed, its performance with a reward/fitness function or by visualizing the density of states the agent visits. However, these methods fail to describe the policy's behaviour in complex, high-dimensional environments or do not scale to thousands of policies, which is required when studying training algorithms. We propose policy supervectors for characterizing agents by the distribution of states they visit, adopting successful techniques from the area of speech technology. Policy supervectors can characterize policies regardless of their design philosophy (e.g. rule-based vs. neural networks) and scale to thousands of policies on a single workstation machine. We demonstrate method's applicability by studying the evolution of policies during reinforcement learning, evolutionary training and imitation learning, providing insight on e.g. how the search space of evolutionary algorithms is also reflected in agent's behaviour, not just in the parameters.

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data. In real-world problems where data cannot be stored indefinitely due to limited storage or privacy issues, the query selection and the model update should be performed as soon as a new data sample is observed. Various online active learning methods have been studied to deal with these challenges; however, there are difficulties in selecting representative query samples and updating the model efficiently. In this study, we propose Message Passing Adaptive Resonance Theory (MPART) for online active semi-supervised learning. The proposed model learns the distribution and topology of the input data online. It then infers the class of unlabeled data and selects informative and representative samples through message passing between nodes on the topological graph. MPART queries the beneficial samples on-the-fly in stream-based selective sampling scenarios, and continuously improve the classification model using both labeled and unlabeled data. We evaluate our model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets with comparable query selection strategies and frequencies, showing that MPART significantly outperforms the competitive models in online active learning environments.

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In the past year, lockdowns and other COVID-19 safety measures have made online shopping more popular than ever, but the skyrocketing demand is leaving many retailers struggling to fulfill orders while ensuring the safety of their warehouse employees. Researchers at the University of California, Berkeley, have created new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments. The technology is described in a paper published online today (Wednesday, Nov. 18) in the journal Science Robotics. Automating warehouse tasks can be challenging because many actions that come naturally to humans--like deciding where and how to pick up different types of objects and then coordinating the shoulder, arm and wrist movements needed to move each object from one location to another--are actually quite difficult for robots. Robotic motion also tends to be jerky, which can increase the risk of damaging both the products and the robots. "Warehouses are still operated primarily by humans, because it's still very hard for robots to reliably grasp many different objects," said Ken Goldberg, William S. Floyd Jr. Distinguished Chair in Engineering at UC Berkeley and senior author of the study.