The Internet of Things (IoT) – and what it will enable – has been a discussion point for well over a decade, but the speed, low latency and reliability of 5G promise to bring the concept to life. Network slicing will allow a wide range of product types, with distinct reliability and throughput requirements, to be run out of the same architecture, and edge computing will allow nodes to communicate directly with one another, bypassing the network's core and enhancing speed and reliability. These characteristics underpin some the most interesting projects currently making use of 5G, and have made a plethora of 5G use cases possible. Here are 18 of the best. Robots are already widely used in factories, particularly in the automotive industry.

Company acquires assets from Twenty Billion Neurons GmbH to bolster its AI Team. Qualcomm Technologies (QTI) is running a series of webinars titled "The Future of...", and the most recent edition is on AI. In this lively session, I hosted a conversation with Ziad Ashgar, QTI VP of Product Management, Alex Katouzian, QTI SVP and GM Mobile Compute and Infrastructure, and Clément Delangue, Co-Founder and CEO of the open source AI model company, Hugging Face, Inc. I've also penned a short Research Note on the company's AI Strategy, which can be found here on Cambrian-AI, where we outline some impressive AI use cases. Qualcomm believes AI is evolving exponentially thanks to billions of smart mobile devices, connected by 5G to the cloud, fueled by a vibrant ecosystem of application developers armed with open-source AI models.

Faced with profitability challenges, a global consumer electronics firm decided to restructure its business with a focus on optimizing costs, sales productivity, and customer satisfaction. To achieve these goals, the company automated customer and product master data, disputes and claims resolution, stock replenishment, and discount management. The firm also deployed automated web-crawler tools to improve competitive intelligence-gathering. With buy-in from leadership, the company was able to reduce the cost of select processes by 15% in only three years. The challenges faced by the consumer electronics firm are common among today's sales organizations.

Machine learning (ML) techniques are being increasingly used in mobile networks for network planning, operation, management, optimisation and much more. These techniques are realised using a set of logical nodes known as ML pipeline. A single network operator might have thousands of such ML pipelines distributed across its network. These pipelines need to be managed and orchestrated across network domains. Thus it is essential to have autonomic multi-domain orchestration of ML pipelines in mobile networks. International Telecommunications Union (ITU) has provided an architectural framework for management and orchestration of ML pipelines in future networks. We extend this framework to enable autonomic orchestration of ML pipelines across multiple network domains. We present our system architecture and describe its application using a smart factory use case. Our work allows autonomic orchestration of multi-domain ML pipelines in a standardised, technology agnostic, privacy preserving fashion.

As per objective we need to create an agent which can classify the incoming text(message) as ham or spam only thus based on the agent type[1] this project agent falls under simple reflex agent as it takes action based on alone the current environmental situation i.e., it maps the current percept into proper action ignoring the history of percepts. The mapping process is simply a rule-based matching algorithm which is here Naïve Bayes. A function that specifies the agent's action in response to every possible percept sequence i.e., agent function maps perceptions into action and agent program, combined with a machine architecture, implements an agent function. An environment is everything that surrounds the agent that is not part of the agent itself. This is where the agent'lives' or operates, and provides the agent with something to sense and somewhere for it to move around.

The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combined process can successfully learn adaptation knowledge applicable to nonsymbolic differences in cases. The CBR system achieves slightly lower performance overall than a baseline deep network regressor, but better performance than the baseline on novel queries.

Fifth-generation (5G) New Radio (NR) cellular networks support a wide range of new services, many of which require an application-specific quality of service (QoS), e.g. in terms of a guaranteed minimum bit-rate or a maximum tolerable delay. Therefore, scheduling multiple parallel data flows, each serving a unique application instance, is bound to become an even more challenging task compared to the previous generations. Leveraging recent advances in deep reinforcement learning, in this paper, we propose a QoS-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In contrast to state-of-the-art scheduling heuristics, the QADRA scheduler explicitly optimizes for the QoS satisfaction rate while simultaneously maximizing the network performance. Moreover, we train our algorithm end-to-end on these objectives. We evaluate QADRA in a full scale, near-product, system level NR simulator and demonstrate a significant boost in network performance. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the QoS satisfaction rate of VoIP users served by the network, compared to state-of-the-art baselines.

TOKYO--Having a robot read scripture to mourners seemed like a cost-effective idea to the people at Nissei Eco Co., a plastics manufacturer with a sideline in the funeral business. The company hired child-sized robot Pepper, clothed it in the vestments of Buddhist clergy and programmed it to chant several sutras, or Buddhist scriptures, depending on the sect of the deceased. Alas, the robot, made by SoftBank Group Corp., kept breaking down during practice runs. "What if it refused to operate in the middle of a ceremony?" said funeral-business manager Osamu Funaki. "It would be such a disaster."

Entity-Relationship (E-R) Modeling is one approach to visualize what story your data is trying to tell. This goal of this predecessor to object modeling (e.g. UML or CRC cards) is to give you a high-level, graphical view of the core components of an enterprise--the E-R diagram. An E-R diagram (sometimes called a Chen diagram, after its creator, Peter Chen) is a conceptual graph that captures meaning rather than implementation [1]. Once you have the diagram, you can convert it to a set of tables.

In this hands-on project, we will train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers. Machine learning help companies analyze customer churn rate based on several factors such as services subscribed by customers, tenure rate, and payment method. Predicting churn rate is crucial for these companies because the cost of retaining an existing customer is far less than acquiring a new one. Note: This course works best for learners who are based in the North America region.