Chief Minister of Tamil Nadu, MK Stalin, announces the launch of new artificial intelligence (AI)-powered panic buttons in buses of the capital city Chennai as a step to improve the safety of women.
போக்குவரத்துத் துறை சார்பில் பொது மக்களின் பாதுகாப்பான பயணத்திற்காக நிர்பயா பாதுகாப்பான நகரத் திட்டத்தின் கீழ், 2500 மாநகர போக்குவரத்துக் கழக பேருந்துகளில் சிசிடிவி கேமராக்கள் மற்றும் அவசர பொத்தான்கள் பொருத்தும் பணியின் முதற்கட்டமாக 500 பேருந்துகளில்
The AI-enabled panic button will also be integrated with the CCTV surveillance system and will be initially deployed in 500 buses across the capital city.
PTI reported that this new development is part of the state transportation department’s Nirbhaya safe city project and will be integrated into nearly 2500 buses on a phase-wise basis.
The newly announced system will have four panic buttons, AI-enabled Mobile Network Video Recorder (MNVR), and three cameras each. The MNVR is linked to a cloud-based control center via a 4G GSM SIM service.
Apart from providing safety to women, this AI-powered system can also be used by law enforcement agencies to track missing people, identify criminals, and perform other related tasks to maintain public order.
After its deployment, female travelers will be able to push the panic button to record the entire episode if they are inconvenienced, uncomfortable, or threatened by other passengers. Once activated, the system will automatically send an alert to the transportation and police authorities with a video recording of the bus.
Along with CM Stalin, Tamil Nadu Transport Minister SS Sivasankar was also present when the state secretariat launched the project. The Metropolitan Transport Corporation (MTC) has brought 31 bus depots and 35 bus terminuses under surveillance, an official press release from the Tamil Nadu government mentioned.
The Indian Institute of Technology (IIT) Gandhinagar partners with leading engineering research and development services company L&T Technology to work on areas like artificial intelligence (AI) and mechatronics.
According to the latest development, L&T Technology has started talks with IIT Gandhinagar to incubate and offer cutting-edge solutions in AI and Mechatronics. L&T Technology and IIT Gandhinagar leadership teams discussed plans to open new labs, develop collaborative projects, and engage students through internships.
Moreover, they also plan to conduct several workshops and seminars in Smart Manufacturing, Robotics, Biomechatronics, and ‘intelligent’ Products and Systems as a part of this partnership.
CEO and Managing Director of L&T Technology, Amit Chadha, said, “The interdisciplinary nature of AI in Engineering, Mechatronics and Robotics presents great opportunities for LTTS and our global clients in this fast-changing world. Our in-depth domain experience and multi-vertical expertise across the engineering value chain holds the key to building a scalable future.”
He also mentioned that he feels that the proposed industry-academic engagement with a best-in-class scientific institution like IIT-Gandhinagar will foster cutting-edge research and help to develop a digitally proficient workforce.
Aside from this collaboration, IIT Gandhinagar researchers have also developed a comprehensive framework for reducing damage to power transmission infrastructure in coastal areas during storms.
Officiating Director at IIT Gandhinagar, Prof Amit Prashant, said, “Considering the increasing scope and relevance of integration of AI, Mechatronics, and Robotics, we at IITGN have been advancing research in the field and contributing towards the development of an AI ecosystem.”
He further added that the initiation of talks with an organization like LTTS would be a step toward developing qualified individuals with knowledge and practical training, along with benefiting academia by providing opportunities to work on relevant technologies.
Less than a month ago, autonomous vehicle company Pony.ai had become the first autonomous driving company in the world to receive a commercial taxi license in China. This allowed it to operate and charge for autonomous ride-hail in Guangzhou, which requires a safety operator to be in the driver’s seat. The Toyota-backed robotaxi service Pony.ai started running 100 driverless vehicles in Guangzhou’s Nansha area this month, with plans to extend to other parts of the city later. In November, the Chinese startup was granted permission to operate 67 vehicles in Beijing, but not a license.
Following this breakthrough, Pony.ai and the Chinese tech behemoth Baidu were granted licenses to operate autonomous ride-hailing services to the public on Beijing’s open roads. The Beijing High-Level Automated Driving allowed Baidu to deploy ten driverless vehicles in Beijing, adding to the company’s current Apollo Go fleet of around 100 cars in the capital city.
Apollo Go users could book a ride using the app from 10 a.m. to 4 p.m., while Pony customers can book a ride using the PonyPilot+ app from 9 a.m. to 5 p.m.
Though many cities in China have authorized autonomous vehicle manufacturers to test self-driving vehicles without a human safety operator in the driver’s seat, this is the first time a fully driverless service has been approved. But unlike California’s driverless permits, which require no human in the car aside from the passenger, Beijing’s permit requires the companies to have a safety operator in the front passenger seat. This is still a huge chapter for the automobile industry in China, as the nation has come a long way since 2016, when the nascent yet ambitious startups started venturing into the autonomous vehicles sector.
China has the potential to become the top market for self-driving cars in the world. According to several studies, driverless vehicles might eventually take over the majority of the Chinese automobile sector. In general, these vehicles are projected to transfer a significant portion of the mobility market value away from products (vehicle purchases) and towards auto-vehicular services. The Mandarin nation is slowly inching towards mass deployment of SAE (Society of Automotive Engineers) Level 4 in the future while connecting up sectors such as automotive, transportation, software, hardware, and data services.
China overtook the United States as the world’s largest and most significant automobile market in 2009, accounting for roughly 70% more than the US. The United States manufactured 11,314,705 automobiles in 2018. China made 27,809,196 units. The world’s third-biggest automaker, Japan, produced 9,728,528 automobiles, while India came in fourth with 5,174,645, implying that China manufactured more vehicles than Japan, India, and the United States combined. This put undue strain on the country’s transportation system, increasing traffic congestion and pollution. As a result, autonomous cars are being offered as a possible solution to some of these infrastructural constraints.
So far, the autonomous vehicle industry in China is moving ahead strongly, as a new generation of challengers and established manufacturers are continuing to entice substantial investor interest without being much affected by the pandemic jolt.
The self-driving vehicle industry in China is filled with hope; a new generation of upstarts is attracting substantial investor interest, and major technology corporations and conventional automakers alike are coming on board. At the same time, the Chinese government is pushing this sector with favorable policies.
According to Capgemini statistics, China’s market preference for autonomous driving is 60%, far greater than the remainder of the world, suggesting China’s adoption of new technology like autonomous driving. This also implies that while Americans are becoming warier about autonomous vehicles, Chinese citizens are becoming more open-minded. Apart from readiness and customer demand, there are many driving factors that are pushing for this change.
The Chinese government is proactively encouraging the sector through regulatory incentives and development efforts to ensure that China and Chinese enterprises will be leading the helm if autonomous driving takes off. For instance, the State Council announced the national strategy plan Made in China 2025 in 2015 to transform and upgrade China’s manufacturing industry. One of the plan’s objectives has been the development of intelligent equipment and goods, including the study and commercialization of self-driving automobiles. In 2017, China issued a number of major legislation and regulations on intelligent cars as part of the Made in China 2025 strategy, including the National Road Testing Guideline.
In addition, new regulations help to legitimize the business and standardize its products, establishing a more defined legal framework within which both operators and investors may operate. Last September, the State Administration for Market Regulation and the Standardization Administration joined hands to release the first national guidelines for grading autonomous driving, which would take effect in March this year and serve as a benchmark for automakers developing the future technology. The Chinese categorization is more precise and explicit than the SAE definition, which is a little vague because it defines L2 as “partial automated driving” and L4 as “high-level automated driving.”
The L0, L1, and L2 levels in China require the driver and the autonomous driving system to collaborate on recognizing and responding to objects and events, compared to the SAE version that requires drivers to do these functions. Since it could constantly handle all dynamic driving activities within its planned operating circumstances, L3 is called Conditionally Automated Driving. At L4, which stands for highly automated driving, the vehicle can take steps to lessen the danger of an accident if the driving automation system fails to conduct the required tasks. Finally, L5 is a completely autonomous vehicle. Here, the system has no operational design range and can handle all dynamic driving functions continuously under any driving situation. In situations, when the driving automation system can no longer execute dynamic driving duties, it automatically takes steps to lower the vehicle’s accident risk to a safe level.
Earlier, the Standing Committee of the Shenzhen Municipal People’s Congress published the Draft for Comments of Shenzhen Special Economic Zone Regulations on the Administration of Intelligent and Connected Vehicles (the “Shenzhen Draft Regulations”) on its website for the public comment on March 23, 2021. The move highlights that Shenzhen is eyeing to bag the title of first Chinese city to commercialize self-driving vehicles. These regulations cover the whole spectrum of autonomous vehicle development, including road testing, access registration, usage management, road transport, traffic accidents, accidents, violation handling, and legal liability. Shenzhen is definitely competing to be the first Chinese city to commercialize driverless vehicles.
On the following day, the Ministry of Public Security of China announced the Draft Proposed Amendments to the Road Traffic Safety Law also called the MPS Proposed Amendments. The proposed amendments to the MPS emphasize the requirements for road testing and access by cars with autonomous driving capabilities, as well as the allocation of accountability for traffic offenses and accidents. It’s the first time that China has suggested autonomous vehicle regulation.
On the investors’ front, the pandemic accelerated the spending and funding on autonomous vehicles as they proved resourceful in delivering medical supplies and food to healthcare professionals and the general public, amid following social distancing protocols. It also helped in disinfecting hospitals and public surfaces to reduce the spread of coronavirus. Furthermore, during the COVID-19 pandemic, a considerable number of individuals were considering giving up their cars for environmental concerns and adopting alternate modes of transportation. For example, Baidu’s autonomous vehicle platform, Apollo, collaborated with Neolix, a local self-driving company, to transport meals and supplies to Beijing Haidian Hospital. Together they offered daily meal delivery to over 100 frontline staff members who were nursing a rising patient population. Apollo and Neolix also used autonomous vehicles to sanitize all roadways on Shanghai Zhangjiang Artificial Intelligence Island on a regular basis. Apollo had also made its low-speed driverless micro-car kits and autonomous driving cloud services accessible to enterprises focused on combating Covid-19 at free of cost.
Analysts consider Baidu’s recent ventures in self-driving vehicles as a future growth driver for the firm as well as the industry as a whole. Consider Baidu’s announcement last year that it will team up with state-owned carmaker BAIC Group to manufacture 1,000 autonomous cars over the next three years and eventually launch a robotaxi service across China.
The Apollo Moon vehicles will be built under BAIC’s ARCFOX electric vehicle brand, with Baidu providing autonomous driving technologies and software.
The above instances also highlight several other factors that are playing an instrumental role in the expansion and growth of the autonomous vehicle industry in China. Industry analysts estimate that mainstream commercialization of autonomous driving systems without a safety driver would take at least a decade. Though the rollout of commercial autonomous vehicles will most likely be at a modest pace, it is already happening region-by-region (Shenzhen, Beijing, etc.) in specialized modes of transportation (Robo-taxis, driverless trucks, etc.) with huge variances in availability across China. Furthermore, COVID-19 has bolstered funding in continuing the R&D of these vehicles and fostered greater collaboration in this area for a variety of applications.
At the same time, Chinese self-driving car businesses are expanding beyond their own country. For example, Guangzhou-based WeRide and Shenzhen-based AutoX have the testing permits for both driven and autonomous testing in California as of November 2021.
While government regulations, consumer attitudes, increased investment, and other factors are all contributing to China being a worldwide leader in autonomous vehicles, there are some caveats.
China’s dependence on foreign chip manufacturers, in the midst of the semiconductor industry crisis can be a fatal price to pay. As a result, given the emergence of self-driving vehicles that demand increasingly complex AI processors, several Chinese manufacturers are turning to the domestic market in semiconductor development and production to protect themselves from future shortages like the current one. Last year, Beijing-based chipmaker Horizon Robotics revealed the Journey 5, an auto-grade processor with up to 128 trillion operations per second of AI computational capacity for L4, or level 4, autonomous driving.
The necessity for truly autonomous technology to demonstrate that it can outperform people, also demands adjusting to local driving styles, changing traffic and terrain conditions, and legislation. Regulations on autonomous cars might vary significantly between provinces, even within the same city. If a startup receives permission to operate a fully autonomous trial in one place isn’t necessarily more technically sound than its competitors. It could indicate the local regulator has relaxed rules to support self-driving trials and market.
Compliance, security & productivity solutions provider AGAT launches its new AI sentiment analysis tool for chats and meetings in Microsoft Teams and Webex.
AGAT’s patented sentiment analysis AI engine, specializing in transcript and chat, has been trained to recognize certain emotions such as pleasure, happiness, surprise, anger, disgust, and sadness.
Sentiment analysis tools can be very effective for organizations as the reports generated by them can be used to compare performance and encourage everyone to improve.
Moreover, sentiment analysis can also help companies identify negative employee-client interactions, allowing them to improve.
According to the company, its sentiment analysis tool comes with a robust artificial intelligence engine that analyses customer feedback and uncovers underlying thoughts about the company. It can be used to analyze text or voice to understand interactions and determine required future actions.
CEO of AGAT Software, Yoav Crombie, said, “AGAT’s Sentiment analysis engine is built specifically to enhance customer satisfaction and employee experience. Sentiment analysis leverages Natural Language Processing (NLP) and Machine Learning to extract sentimental insights from your company data, whether in text or voice format.”
The company mentioned that some sample reports that the sentiment analysis system generates are identifying –
Most negative and positive meetings.
Most negative and positive chats.
Most negative and positive employees.
Employee relationship with all others.
Trend changes in communication in the above categories.
Interested users can visit the official website of AGAT Software to get a free trial of its new AI sentiment analysis tool.
The US Department of Defense (DoD) selects object data infrastructure provider OStream’s Percept for Vision AI solution to secure ports across the country.
According to DoD, the solution will be deployed to overcome multiple challenges such as workflow problems and others.
The DoD can use Percept’s AI Hub to connect any camera to more than 300 artificial intelligence services, resulting in real-time insights that are stored in a consolidated and private data lake.
Real-time analytics for the movement and orchestration of people, vehicles, and cargo are among the use cases for US ports.
“Vision AI is a strategic initiative at many sites, including the Port of Hueneme. OStream has proven that their data infrastructure products facilitate smooth integration of cameras to hundreds of different leading AI providers,” said Alan Jaeger from the Department of the Navy.
As backend providers for object detection, tracking, and correlation, several Percept AI service partners were chosen. A funded deployment at the Port of Hueneme includes OStream’s Percept object data technology along with its wire-free 1KM range object cameras.
Los Angeles-based self-funded firm OStream is a software and device company founded with the aim of solving deployment issues that prevent computer vision and AI from being widely used. The founders of OStream have 20 years of experience in IoT, video, streaming media, and artificial intelligence.
“Percept can integrate any camera with an entire ecosystem of AI leaders, allowing groups such as the DoD to choose the very best providers without forklifting their existing cameras,” said CEO of OStream, Kerry Shih.
Kerry also mentioned that Vision AI is difficult to integrate from a system and infrastructure perspective, and they are pleased that Percept can fill this role for the DoD.
Communication platform providing company Zoom to acquire conversational AI and automation startup Solvvy.
Zoom plans to use Solvvy’s expertise to offer the best possible customer service to a worldwide company base and move rapidly to seize new contact centers and customer support possibilities.
However, neither company revealed any information regarding the valuation of this recently signed acquisition deal.
The acquisition is a step for the company to further strengthen its newly launched Zoom Contact Center, which aims to redefine the industry using a unique combination of unified communications and customer experience, and Zoom believes that Solvvy’s team can help them achieve its goal.
President of Product and Engineering at Zoom, Velchamy Sankarlingam, said, “The nature of customer experience is transforming fundamentally, as enterprises increasingly need to deliver exceptional, personalized, and effortless customer experiences. Solvvy understands this shift and is the ideal platform to enhance our Zoom Contact Center offering.”
He further added that Solvvy’s differentiated AI and machine learning technology, deep talent team, and a simple-to-deploy solution would aid in accelerating their roadmap to providing concierge-level service to consumers around the world.
Moreover, Zoom recently launched its artificial intelligence-powered solution called ZoomIQ, which focused on assisting organizations’ sales teams. The AI-enabled tool can be used to analyze sales meanings and leads to generate actionable insights, helping sales teams to make better data-driven decisions.
United States-based machine learning startup Solvvy was founded by Justin Betteridge, Mahesh Ram, and Mehdi Samadi in 2013. It is best known for its Solvvy for Support solution that provides a superior self-service platform that boosts customer happiness while lowering expenses. To date, the startup has raised more than $16 million from investors like Scale Venture Partners, True Ventures, and others over two funding rounds.
“Zoom’s Contact Center brings the same level of scalability, simplicity, and respect for the end-user, making Zoom the premier communications platform for businesses worldwide. When combined with our modern tech stack, talented team, and AI expertise, we believe we can fundamentally transform the customer experience,” said CEO and Co-founder of Solvvy, Mahesh Ram.
He also mentioned that Zoom’s substantial technical knowledge, industry-leading platform, and worldwide reach would allow them to expand their influence on existing customers while also serving new ones.
Technology giant Google launched its new language model named LaMDA 2 and an app called AI Test Kitchen to showcase the capabilities of LaMDA 2.
Meet LaMDA. Advances in natural language understanding are making it possible for us to build even more conversational AI. #GoogleIOpic.twitter.com/HJ8FgHtbFp
This announcement was recently made by the company’s CEO Sundar Pichai during the Google I/O 2022 event.
LaMDA 2 (Language Model for Dialog Applications) is the second generation of LaMDA, built on a neural network architecture that Google Research invented and open-sourced in 2017.
Google claims that LaMDA 2 is an artificial intelligence system designed for dialogue applications, which can understand millions of topics and generate natural conversations that never repeat generated texts.
Pichai said, “We are continuing to advance our conversational capabilities. Conversation and natural language processing (NLP) are powerful ways to make computers more accessible to everyone. Large language models are key to this.”
Moreover, the AI Test Kitchen application can effectively showcase what Google’s new LaMDA 2 can do. The app allows users to interact with and provide feedback on some of Google’s most recent AI technologies. Interested candidates can visit the official website of Google to check out the AI Test Kitchen.
Though LaMDA is yet not perfect, Google has made substantial improvements in safety and accuracy in the latest version of LaMDA. The AI Test Kitchen offers three demos that demonstrate the capabilities of LaMDA. The demos involve a tool that asks the app to help users imagine various scenarios, a demonstration of the model capable of staying on a topic, and a demo of LaMDA having complex conversations.
“These experiences show the potential of language models to one day help us with things like planning, learning about the world, and more,” added Pichai.
Global data-centric software company NetApp partners with NVIDIA to accelerate high-performance computing (HPC) and artificial intelligence (AI) with Turnkey supercomputing infrastructure.
NetApp announced that its NetApp EF600 all-flash NVMe storage combined with the BeeGFS parallel file system is now certified for NVIDIA DGX SuperPOD. This development further simplifies AI and HPC to enable faster implementation of use cases.
Organizations can now create AI Centers of Excellence by combining the capability of DGX SuperPOD on-premises with NetApp storage.
The qualification of NetApp EF600 and BeeGFS file systems for DGX SuperPOD is the latest addition to the company’s growing portfolio of AI solutions.
Vice President of Solutions and Alliances at NetApp, Phil Brotherton, said, “The NetApp and NVIDIA alliance has delivered industry-leading innovation for years, and this new qualification for NVIDIA DGX SuperPOD builds on that momentum.”
He further added that as performance and data demands grow, NVIDIA DGX SuperPOD, in conjunction with the ONTAP AI platform and the DGX Foundry AI service, ensures that the customers receive the best-in-class model training infrastructure.
According to NetApp, its range of NVIDIA-powered solutions includes ONTAP AI, which uses a field-proven reference architecture and a preconfigured, integrated solution that is easy to acquire and deploy.
“Organizations modernizing their business with AI and HPC need performance, flexibility, and choice as they architect their infrastructure,” said Vice President of DGX systems at NVIDIA, Charlie Boyle.
He also mentioned that customers building their own AI Centers of Excellence may now choose NetApp storage for their NVIDIA DGX SuperPOD and use the same leading platform offered as hosted infrastructure through NVIDIA DGX Foundry, thanks to their partnership with NetApp.
Smartphone manufacturing giant Apple sues chip manufacturing startup Rivos for allegedly stealing the company’s trade secrets.
The lawsuit has been filed in the United States against Rivos and two other individuals for theft and contract breach. There has been controversy around the corner of Rivos hiring multiple Apple’s top engineers and Apple claims that Rivero also stole the company chipset trading secrets along with employees.
According to Apple, during its final days at Apple, some staff removed gigabytes of crucial SoC specifications and design data after accepting jobs at Rivos. Ex-Apple employees are accused of stealing information by using USB drives and AirDrop to transfer confidential Apple data to their personal devices.
Moreover, Apple says that the employees also stole presentations of unreleased SoCs and saved them in their cloud accounts.
The Lawsuit against Rivos mentioned, “Apple’s cutting-edge, advanced system-on-chip (“SoC”) designs, including its M1 Laptop SoC and A15 mobile phone SoC, have revolutionized the personal and mobile computing worlds.”
The case also highlighted that Apple has spent billions of dollars and more than a decade developing the exclusive technology and skills required to engineer these ground-breaking SoC architectures and establish itself as a leader in the semiconductor design sector.
Apple claims that the former Apple employees who were reportedly involved in data theft attempted to erase the evidence by wiping their Apple devices.
”Between July 26, 2021 and July 29, 2021, Mr. Wen transferred approximately 390 gigabytes from his Apple-issued computer to a personal external hard drive. Among the data transferred are confidential Apple documents describing Apple trade secrets, including aspects of the microarchitecture for Apple’s past, current, and unreleased SoCs,” says Apple.
Global technology giant Google unveils its new 10-shade skin tone scale to better identify bias in artificial intelligence (AI) algorithms.
According to Google, this new development will help build gadgets and applications that offer unbiased services to everyone.
Google’s new Monk Skin Tone Scale replaces the Fitzpatrick Skin Type, flawed six-color criteria that had gained popularity in the tech industry. The Fitzpatrick Skin Type has been in use to determine multiple offerings of gadgets for color bias.
Google partnered with sociologist Ellis Monk of Harvard University, who studies colorism, to develop and launch the Monk Skin Tone Scale. Monk has spent more than a decade researching how skin tone and colorism affect people’s lives.
Head of Product for Responsible AI and Product Inclusion in Search at Google, Tulsee Doshi, said, “The culmination of Dr. Monk’s research is the Monk Skin Tone (MST) Scale, a 10-shade scale that will be incorporated into various Google products over the coming months. We’re openly releasing the scale so anyone can use it for research and product development.”
She further added that they want the scale to promote inclusive goods and research across the industry, and they see this as an opportunity to collaborate, learn, and improve their work with others’ help.
Google claims that its research found that the new Monk Skin Tone Scale better represents the skin color of the participants in the United States when compared to other available skin tone scales in the market.
“In our research, we found that a lot of the time, people feel they’re lumped into racial categories, but there’s all this heterogeneity with ethnic and racial categories,” said Dr. Monk. He also mentioned that many classification systems, including previous skin tone scales, ignore this variability, which leads to a lack of representation. Developers and researchers can share their feedback about Monk Skin Tone Scale to Google from its official website.
