NaaS Technology one of the largest and fastest growing electric vehicle charging service providers in China, recently announced the launch of its first self-developed automatic charging robot with features including active vehicle locating, smart charging, and automatic payment settlement to meet the rising demand for mobile charging of electric vehicles ("EV"). With the future popularization of self-driving vehicles, compatible automatic charging robots will become indispensable infrastructure. During an automatic charging session, robots with mechanical arms will automatically dock with the ports of EVs and complete the charging and settlement processes in one go. Empowered by deep learning, 5G, V2X, simultaneous localization and mapping, and other underlying technologies, NaaS' waterproof and shock-proof charging robot brings science fiction to life with one-click ordering, active vehicle locating, precise self-parking, automatic docking, charging and undocking via mechanical arms, and automatic return and recharging functions. It is available in various charging power and battery capacity configurations and can connect with major OEMs seamlessly through an open API, enabling EV owners to enjoy unmanned service anywhere, around the clock, saving much time and effort.

You can't scan the headlines lately without seeing buzz around generative artificial intelligence (AI). The product innovations are only beginning. But even with the best technology out there, you'll still be faced with a key question: How can you implement AI at scale in a way that maximizes the return on your investment? Let's look at one model company you can learn from. Schneider Electric, a global energy management and industrial automation company, has formalized an AI program under a new Chief AI Officer and scaled it to every corner of the company.

On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...

Alif Semiconductor, supplier of low-power, secure, AI/ML-enhanced fusion processors and microcontrollers (MCUs), announced collaboration with Telit, a global leader in the Internet of Things (IoT), to deliver developer kits that provide cloud-connected hardware and software reference designs for a wide variety of distributed and IoT edge applications. The kits focus on connected AI/ML-enhanced vision, voice, vibration, and sensor applications such as AI cameras, smart home, city infrastructure, biometric access control, and wearables. Ensemble devices utilize innovative aiPM power management technology that feature a High Efficiency, always-on region that senses the environment using initial AI/ML processing, while a separate High Performance region wakes as needed to rapidly execute additional heavy AI/ML workloads and returns to sleep. In addition to smart power management, the Ensemble family provides multiple layers of security based on a secure identity and strong root-of-trust for complete lifecycle management handling keys, certificates, secure boot, remote updates and more. The developer kits include Telit's wireless connectivity modules from Wi-Fi and Bluetooth to LTE and 5G cellular, including low-power wide-area (LPWA) offerings in Cat-M and NB-IoT for IoT applications that require lower power consumption and longer battery life.

The onshoring and buildout of dozens of fabs, many costing tens of billions of dollars, is beginning to spill over into other areas that are critical for chip manufacturing. Materials, in particular, which often gets little attention outside of chip manufacturing, witnessed a big spike in September 2022. In fact, seven materials companies covered in this report made up more than a third of the month's total reported investments, with three of the companies garnering more than $200 million. Other investment targets were sputtering equipment and evaporation materials for deposition, high-purity polycrystalline silicon, fluorine-containing electronic gases, and silicon carbide. In the AI hardware arena, numerous startups are focusing on in-memory and near-memory compute, reducing the volume of data that needs to be moved back and forth between memory and processing elements. Novel architectures also are appearing, such as one that uses sparse mathematics.

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Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.

IBM announced Monday that LG Electronics is joining the Quantum Network. The two companies will work to explore how quantum computing can be used for a variety of applications, ranging from IoT and data to AI and robotics. The three-year deal will give LG Electronics access to IBM's quantum computing systems, experts, and their "Qiskit" open-source quantum information software development kit. It would be the harbinger of an entirely new medium of calculation, harnessing the powers of subatomic particles to obliterate the barriers of time in solving incalculable problems. "Based on our open innovation strategy, we plan to use IBM Quantum to develop our competency in quantum computing," said Byoung-Hoon Kim, CTO and executive vice president of LG Electronics.

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. Decisions about how to respond involve coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations. Currently, playbooks are used to automate portions of a response process, but often leave complex decision-making to a human analyst. In this work, we present a deep reinforcement learning approach to autonomous response and recovery in large industrial control networks. We propose an attention-based neural architecture that is flexible to the size of the network under protection. To train and evaluate the autonomous defender agent, we present an industrial control network simulation environment suitable for reinforcement learning. Experiments show that the learned agent can effectively mitigate advanced attacks that progress with few observable signals over several months before execution. The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network. The learned policy is also more robust to changes in attacker behavior than playbook approaches.