Despite the rapid technological progress, autonomous vehicles still face a wide range of complex driving situations that require human intervention. Teleoperation technology offers a versatile and effective way to address these challenges. The following work puts existing ideas into a modern context and introduces a novel technical implementation of the trajectory guidance teleoperation concept. The presented system was developed within a high-fidelity simulation environment and experimentally validated, demonstrating a realistic ride-hailing mission with prototype autonomous vehicles and onboard passengers. The results indicate that the proposed concept can be a viable alternative to the existing remote driving options, offering a promising way to enhance teleoperation technology and improve overall operation safety.

Large Language Models can lead researchers in the design of robots. Large Language Models (LLMs) [1], are revolutionizing the field of robotics, providing robots with the ability to understand and process natural language at a level previously thought impossible. These powerful AI tools have the potential to improve a wide range of tasks in robotics, including natural language understanding, decision making, and human-robot interaction. One of the key advantages of large language models is their ability to process large amounts of text data, such as instructions, technical manuals, and maintenance logs, and internalize an implicit knowledge containing rich information about the world from which factual answers can be extracted. In fact, the text you have just read was generated by the LLM ChatGPT-3 [2] when prompted "Can you write an introduction in a newsy style to the potential of large language models in robotics?".

In the AWS Deep Learning Challenge held from January 5, 2022, to March 1, 2022, participants from academia, startups, and enterprise organizations joined to test their skills and train a deep learning model of their choice using Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances and Habana's SynapseAI SDK. The EC2 DL1 instances powered by Gaudi accelerators from Habana Labs, an Intel company, are designed specifically for training deep learning models. Participants were able to realize the significant price/performance benefits that DL1 offers over GPU-based instances. We are excited to announce the winners and showcase some of the machine learning (ML) models that were trained in this hackathon. You will learn about some of the deep learning use cases that are supported by EC2 DL1 instances, including computer vision, natural language processing, and acoustic modeling.

Collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases. Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to misaligned initiatives and impractical use cases. Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders -- business users, data scientists, security professionals, data engineers and analysts, and those in IT operations -- and developing processes and products based on the inputs. Select cloud, on-premises or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools.

Artificial Intelligence (AI) is able to impact almost everything in much the same way electricity did in the early 1900s by replacing steam powered machines. For example, AI can transform FinTech, healthcare, logistics, search engines, etc. The obvious advantages of AI are that errors are reduced, repetitive one second human thought tasks are replaceable (e.g. is that a dog or cat in the photo), scalability and continuous operation. AI is also able to surpass human level capability such as quickly deriving insights from large volumes of data. The benefits to the user include more personalised service (e.g. more targeted advertising to increase sales) and feedback on user behaviour for R&D teams to develop new products/services or improve existing products/services.

ABSTRACT A novel network type is introduced which uses unit-length 2-vectors for local variables. As an example of its applications, associative memory nets are defined and their performance analyzed. Real systems corresponding to such'phasor' models can be e.g. INTRODUCTION Most neural network models use either binary local variables or scalars combined with sigmoidal nonlinearities. Rather awkward coding schemes have to be invoked if one wants to maintain linear relations between the local signals being processed in e.g.

ABSTRACT A novel network type is introduced which uses unit-length 2-vectors for local variables. As an example of its applications, associative memory nets are defined and their performance analyzed. Real systems corresponding to such'phasor' models can be e.g. INTRODUCTION Most neural network models use either binary local variables or scalars combined with sigmoidal nonlinearities. Rather awkward coding schemes have to be invoked if one wants to maintain linear relations between the local signals being processed in e.g.

ABSTRACT A novel network type is introduced which uses unit-length 2-vectors for local variables. As an example of its applications, associative memory nets are defined and their performance analyzed. Real systems corresponding to such'phasor' models can be e.g. INTRODUCTION Most neural network models use either binary local variables or scalars combined with sigmoidal nonlinearities. Rather awkward coding schemes have to be invoked if one wants to maintain linear relations between the local signals being processed in e.g.