The Reserve Bank of India (RBI) has planned to start the first pilot launch of the Digital Rupee – Wholesale version (e₹-W) on Tuesday, November 1st, 2022. In this launch, nine banks will be participating, including the State Bank of India, Union Bank of India, ICICI Bank, HDFC Bank, Kotak Mahindra Bank, Yes Bank, IDFC First Bank, Bank of Baroda, and HSBC.
This pilot launch’s objective is to settle secondary market transactions in government securities. With the use of e₹-W, the interbank market is expected to be more efficient. The central bank says, “Settlement in the central bank will result in reducing transaction costs by preventing the need for settlement guarantee infrastructure or for collateral to overcome settlement risks.”
The central bank also stated that other wholesale transactions and cross-border payments would be the focus of future pilots based on this pilot’s learning. RBI says, “the first pilot in the digital rupee-retail segment is said to be launched within a month in select locations in closed user groups, including customers and merchants. The details regarding the working of the e₹-R pilot will be communicated in due course.”
The wholesale version of Central Bank Digital Currency (CBDC) or the digital rupee does not impact the common person. It is designed for restricted access to select financial institutions. At the same time, the retail version of CBDC can be used by the private sector, non-financial consumers, and businesses.
RBI released a concept note on October 7th, 2022, on Central Bank Digital Currency for India, where there would be two broad categories of digital currency-retail and wholesale, that enable businesses and customers to use digital currency seamlessly.
NITI Aayog’s initiative Atal Innovation Mission (AIM), which promotes innovative thinking among students, will be integrated into the CBSE school curriculum. Artificial Intelligence (AI) and Internet of Things (IoT), which were independent topics earlier, will now be synchronized in the specific chapters of each subject.
Under the project, almost 59 schools having Atal Tinkering Lab (ATL) at their campuses are selected. Manuals are provided in the school to help teachers develop lesson plans by blending concepts of AI and IoT throughout subjects. The students will also be able to get hands-on experience at labs.
At least four chapters for each subject and projects that students will carry out in the tinkering lab are mapped with AI and IoT activities. It will provide practical knowledge which will be gained through multiple disciplines taught to the students.
The heads and the principals will work as ‘innovation leaders’ in their own schools. Apart from monitoring the effective implementation of the curriculum, the leaders will have to ensure that the optimum use of available resources at ATLs for promoting the use of AI-IOT is recorded, leading to increased participation of students at tinkering labs.
Integrating AI in subjects such as Maths, Hindi, Science, and Social Science will be a game changer. Starting from foundation classes, teachers are successfully using AR and VR tools/apps in their lesson plans for smooth learning integration among our students. Also, with an integrated approach, numerous online AI activities, including face, object, and color detection, are conducted for students in ATLs.
AI and IoT technologies will smoothly convert verbal speech to sign language and vice versa. This will boost learning outcomes for students with disabilities as teachers can now use sensor-connected gloves to provide educational assistance and help students to carry out lab activities.
Elon Musk has dissolved Twitter’s board of directors, cementing his control over the social media platform as the ‘sole director.’ After buying the company last week, the multi-billionaire will be its chief executive, ending months of back and forth over the $44bn (£38.3bn) deal.
The nine ousted directors include Bret Taylor, Parag Agrawal, Mimi Alemayehou, Omid Kordestani, Patrick Pichette, Egon Durban, Fei-Fei Li, David Rosenblatt, Martha Lane Fox, according to the SEC filing.
Musk has moved quickly to put his mark on the firm. The reforms he is contemplating include changes to how Twitter verifies accounts and job cuts. The Washington Post reported that the first round of cuts is still under discussion that could affect 25% of the company’s staff.
Top executives have already been removed, as Musk brings in high-profile allies to the company. Twitter co-founder Jack Dorsey has rolled his entire stake of 18m shares, worth almost $978m at the buyout price of $54.20, into the new private company.
Musk’s takeover has faced widespread scrutiny as he executes plans to overhaul how Twitter has moderated the information spread on its platform, including from sources such as state politicians, celebrities, and media. Musk said the company would create a new council to make those decisions and that no changes will occur just yet
With 28% of US businesses adopting AI over six years ago, compared to 25% in India and 20% in the UK, India has a more mature market in terms of leveraging AI. India scored 64 (out of 100) on Peak’s DI maturity scale, whereas the US charted 52 and the UK just 44.
Internal communication and education on artificial intelligence to ensure broad support sets Indian businesses apart. Almost18% of US workers were unsure whether their business used AI, compared to only 2% of Indian workers. Moreover, 78% of Indian junior staff expect AI to positively impact workers’ well-being over the next five years, compared to 47% of those in the US.
The report also found that how businesses structure data teams is vital to successful AI adoption, with most Indian businesses having data practitioners in commercial teams to support analysis. By contrast, most US businesses have a central data team.
Based on a survey of 3,000 decision-makers from businesses with a minimum of 100 employees across the US, UK, and India, Peak’s Decision Intelligence Maturity Index defines several critical indicators for commercial AI readiness across five pillars: decision-making, strategy, data, and technology, people and process, and value.
Each pillar within the framework is weighted and contributes to an overall index score of 0 (least) to 100 (most mature), an indicator of a business’s ability to adopt, deploy and leverage DI effectively.
The Japanese city of Toda, Saitama, has implemented a metaverse-schooling service to motivate pupils to attend school, particularly those who live far away.
The metaverse schooling service that the city of Toda has implemented allows kids to study in virtual classrooms and tour the campus. However, pupils must get permission from their respective school principals to opt for metaverse schooling, as per local media NHK.
According to Japanese government data, 244,940 elementary and junior high school students were absent for more than 30 days in the academic year 2021. According to NHK, a fifth-grader prefers to speak online rather than attend school in person. Even though the youngsters have not physically attended school in over two years, they expressed a desire to meet up virtually.
While efforts to raise school attendance continue to be difficult, Japanese officials are betting on metaverse education to help kids engage with those around them. Toda’s education center director, Sugimori Masayuki, desires to see metaverse students grow up and live freely in society.
Soichiro Takashima, the mayor of Fukuoka, another city in Japan, confirmed the city’s ambitions to lead the Web3 effort with Astar Japan Labs. “We have to achieve in the framework of Web3 what huge enterprises did for the world when Japan was powerful,” they said.
Sota Watanabe, the founder of Astar Network, stated his ambition to operate closely with Fukuoka City to recruit additional developers and entrepreneurs.
The researchers at IIIT Allahabad have proposed T2CI-GAN, a novel deep learning model that generates compressed images from input text. It was developed by researchers from the Computer Vision and Biometrics Laboratory at the institute and will form a robust groundwork for future image-storing and content-sharing technologies.
Existing methods of generating images from input texts utilize GANs (generative adversarial networks) for image generation and then compress the generated images in the following step.
This novel method expands the existing ones by directly generating compressed images reducing the workload and processing time.
The researchers created two GAN-based models to generate compressed images. The first was trained using a dataset of compressed JPEG DCT (discrete cosine transform) images, and the second used a set of RGB photos. The second model was developed to enhance the production of JPEG-compressed DCT representations.
T2CI-GAN will be essential as machines need data to be read or understood in compressed forms. The model currently only produces JPEG-compressed images. Therefore, the long-term objective of the researchers is to expand it to produce photos in any compressed form without any limitations on the compression algorithm.
Researchers at Georgia Tech propose a new sampling algorithm called ‘LABOR.’ This new technique combines the traditional neighborhood and node sampling techniques while addressing dependency issues in the existing neighborhood sampling method alone.
Traditional sampling techniques use Neighborhood Explosion Phenomenon (NEP) to sample. However, this technique poses a high dependency of node embeddings (in graphical neural networks) on their neighbor’s embeddings. The NEP has the most significant effect on node-based sampling techniques.
However, it was found that node-based approaches sample subgraphs with insufficient depth. Layer-based sampling, in which sampling is done collectively for each layer, was therefore suggested.
Unlike node- and layer-based sampling methods typically sample recursive layers, subgraph sampling methods use a single subgraph for all levels. Researchers, therefore, explored sampling a subgraph of the batch’s nodes to solve the dependency issue.
But sampling subgraphs resulted in higher biases than their node and layer-based counterparts. Thus, the research ultimately focused on using a combination of these techniques in developing LABOR.
LABOR’s main contribution was using Poisson’s Sampling technique. A significant decrease in computation, memory, and communication is realized for the sampled points due to correlating the methods of layers and nodes.
Also, LABOR and neighbor sampling employs the same hyperparameters; they can be used interchangeably.
A new class of materials has been developed by engineers at UCLA in California that can adjust in real-time to dynamic external forces by learning behaviors through time and creating its own “muscle memory.” Known as mechanical neural networks (MNNs), the materials consist of a structural system made up of adjustable beams that may change the shape and behaviors of the material in response to shifting stimuli.
A mechanical neural network built from beams of variable stiffness Lee et al., Sci. Robot.
The study’s findings, which have potential in building construction, aviation, and imaging technology, were published in Science Robotics on Wednesday. According to the authors, the experimental study establishes the groundwork for AI-architected materials that can be used in the construction of buildings, aircraft, and imaging technologies.
It was in 1944 when Warren McCullough and Walter Pitts, two University of Chicago scholars who later transferred to MIT in 1952, made the first theoretical proposal for neural networks. A neural network is made up of hundreds or even millions of intricately intertwined simple processing nodes that are vaguely modeled after the human brain. The majority of neural networks used today are composed of node layers that include an input layer, one or more hidden layers, and an output layer.
The authors explained that mechanical neural networks are lattices of linked, tunable beams that unite at nodes and are propelled by input and output forces or displacements. To train the lattice so that it can learn desired mechanical behaviors (such as shape morphing, acoustic wave propagation, and mechanical computation) and bulk properties (such as Poisson’s ratio, shear, and Young’s modulus, and density), the stiffness values of the interconnected beams are optimized as network weights. This is how the new class of architected materials—also known as mechanical metamaterials—got introduced in this research. These materials learn after being exposed to unexpected ambient stress conditions over time.
Prior to this research, acoustic metamaterials, such as the acoustic analog computing (AAC) system, have been proposed by others, but since they are not neural networks, they cannot learn. In 2019, Tyler Hughes et al. suggested an acoustic metamaterial that mimics the behavior of a trained neural network. However, a fabricated version of the proposed design was unable to learn new behaviors since training is done during the design process by simulating the adjustment of the mass within a vibrating plate. While alternative mechanical concepts have also been suggested and tested over the last two years using just simulation, this is the first time the mechanical neural network concept mentioned in this UCLA study has been physically and experimentally verified.
The concept can also be expanded to complicated three-dimensional (3D) lattices that can fill arbitrary-shaped volumes and meet required fixturing requirements for practical material applications. Further, mechanical neural networks function as deep neural networks that may learn several complicated behaviors at once. This is because they generally have several layers of nodes comparable to the neurons in artificial neural networks. One major advantage of the mechanical neural network is that it has the ability to relearn previously mastered behaviors and learn new behaviors as needed with exposure to changing environmental scenarios, in case it is broken, chopped to occupy an alternative volume, or fixtured differently. This feature is not present in other neural networks.
Lee et al., Sci. Robot.
The researchers present an illustration of how the metamaterial might be applied to airplane wings. To increase efficiency and maneuverability, the mechanical neural networks may learn to change the form of the wings in response to changing wind patterns while the aircraft is in flight or incurs internal damage. A mechanical neural network-based wing might stiffen and relax its connections in response to each of these situations to maintain desirable properties like directional strength. The wing gradually adopts and maintains new qualities by iterative algorithmic changes, adding each new behavior to the rest of its repertoire in a manner akin to muscle memory. In applications involving infrastructure, where earthquakes or other natural or man-made disasters pose a concern, study authors pointed out that it is also possible that the material might aid increase stiffness and general stability.
Ryan Lee of the University of California, Los Angeles, and his colleagues created a network of 21 beams, each 15 cm long, and placed them in a triangular lattice. Each beam is equipped with a voice coil, flexures, and strain gauges. According to the researchers, these characteristics allow the beam to modify its length, adapt to its changing surroundings in real-time, and interact with other beams in the system. When forces are applied to the beam, the voice coil is employed to start a precisely tuned compression or expansion. The algorithm controls the learning behavior using information gathered by the strain gauge from the beam’s velocity. The system’s movable beams are connected to it via the flexures.
Every beam has sensors that determine how far each “neuron,” or beam joint, is out of alignment, as well as a tiny linear motor that can change the stiffness of the beam. By adjusting the beam stiffness, a computer can train the network as a result. After this is completed, the structure no longer has to be calculated externally, and the different beam stiffnesses are fixed.
The network’s response to applied forces is controlled by an optimization algorithm, which uses data from all of the strain gauges to create a combination of rigidity values. In order to verify the actions done by the strain gauge-monitored system, cameras were trained on the system’s output nodes. Although early versions had problems with the delay between input and reaction, the team worked for five years to work out the issues until the mechanical neural network material could learn and react in real-time.
Even though the system is around the size of a microwave oven, the researchers want to make the mechanical neural network design simpler so that thousands of networks could be produced on the micro-scale within 3D lattices for useful material applications.
The researchers propose that mechanical neural networks could be included in armor to deflect shockwaves or in acoustic imaging technologies to harness soundwaves in addition to being used in cars and building materials.
An autonomous vehicle startup, Argo AI, which emerged in 2017 with a $1 billion investment, is now shutting down. According to people familiar with the matter, its parts are being absorbed into its two principal backers: Ford and Volkswagen (VW).
During an all-hands meeting on Wednesday, Argo AI employees were informed that some people will receive offers from the two automakers. It is unclear how many would be hired into VW or Ford and which companies would get Argo’s technology.
Employees were told they would receive a severance package including insurance and two different bonuses — an annual award plus a transaction bonus upon the deal close with Ford and VW. All Argo employees will receive these. Those who Ford or VW does not retain will receive termination and severance pay, including health insurance.
Ford said in its third-quarter earnings report released Wednesday that it decided to shift its resources to developing advanced driver assistance systems, and not autonomous vehicle technology that can be applied to robotaxis. The company said it recorded a $2.7 billion non-cash, pretax impairment on its Argo AI investment, which resulted in an $827 million net loss for the third quarter.
That decision appears to have been fueled by Argo’s inability to attract new investors. Ford CEO Jim Farley acknowledged that the company anticipated being able to bring autonomous vehicle technology broadly to market by 2021. Ford had recently announced that it would launch self-driving cars by the end of this year in association with Agro AI.
Argo’s other primary backer, VW, has indicated plans to shift resources and will no longer invest in Argo AI. The company said it would use its software unit Cariad to drive forward the development of highly automated and autonomous driving with Bosch and, in the future, in China with Horizon Robotics.
Nubank, a Brazillian fintech company, plans to use Polygon technology to create their own crypto assets as loyalty tokens in a new rewards program. Nubank will launch the rewards program in the first half of 2023. 70 million customers will access the plan and receive NuCoins to acknowledge their engagement with the bank.
2000 patrons will be invited to discuss the development, features, and essential web3 components required for the NuCoin loyalty program. Nubank officials believe this will help refine the product ahead of the public launch and ensure people’s needs are met.
Fernando Czapski, NuCoin’s GM, said that the project was a way to develop blockchain technology and democratize the sale-purchase of cryptocurrencies via the Nu app. Sandeep Nailwal, the founder of Polygon, also acknowledged the utility of blockchain and said that the collaboration is a “strong testament” to the technology.
Nubank is one of many banks that make its debut in the crypto and web3 markets by launching NuCoin. JPM Coin is another cryptocurrency launched by a leading investment banking company JP Morgan Chase. It is a stablecoin that seeks to keep the dollar as its primary benchmark for value.
Since last year, several other investment banking companies, including Goldman Sachs, HSBC, and the Swedish Central Bank, have used web3 and crypto to address problems in traditional finance.
