According to a new report by Deloitte, the economic impact of the metaverse in India could potentially range from $79 to $148 billion per annum by 2035, which is about 1.3% to 2.4% of the country’s GDP.
With over half of its population under 30, India produces the highest number of STEM graduates globally and is demographically well-positioned to impart digital labor to the metaverse.
The report also estimates that the metaverse impact on GDP in Asia could range between $0.8-1.4 trillion per year by 2035, almost 1.3 to 2.4 percent of overall GDP.
The report emphasizes digital payments and gaming/entertainment as the key sectors where the metaverse would impact India. According to the report, digital payments will be an indispensable component of the metaverse for trading digital assets. India could feature robustly in the fields, as the rates of real-time digital payments of the country are the highest in the world, the report added.
According to the report, awareness of the metaverse in Asia is high. Millions of people in the region are already using early metaverse platforms for gaming, socializing, purchasing items, creating digital twins, and attending concerts. However, a fully immersive metaverse with real-time offerings of visually rich worlds is still years away.
Waymo has disclosed that its latest vehicle sensor arrays are producing real-time weather maps in hopes of improving ride-hailing services in Phoenix and San Francisco. The Alphabet subsidiary’s robotaxis detect the intensity of conditions such as fog or rain by measuring the droplets on the windows.
When compared to radar, satellites, and airport weather stations, Waymo’s technology provides a considerably more precise picture of the environment. It can detect inland-moving coastal fog or drizzle that radar would often miss. In places like San Francisco and elsewhere where the weather can change drastically between areas, insights from this real-time weather-based data might be quite helpful.
Millions of data points were gathered by Waymo’s fleet of robotaxi autonomous vehicles as they traveled through the foggy streets of San Francisco to create the map. Waymo is able to develop a new meteorological metric in conjunction with advanced weather-detecting vehicles outfitted with visibility sensors, which it then feeds to its autonomous “Waymo Driver AI” to support its decision-making.
With the help of this map, Waymo’s fleet can monitor the buildup of coastal fogs coming off the Pacific Ocean as well as how quickly they dissipate in the morning. It can sense drizzle and light rainfall that cause slippery roadways in adverse conditions when the National Weather Service’s local Doppler weather radar is ineffective. With the use of these weather monitoring tools, Waymo can determine specific locations where the weather is starting to get worse or better.
Image Credit: Waymo
The mapping technology also enables Waymo One to offer better ride-hailing services to consumers at a certain time and location and provides Waymo Via trucking customers with more precise delivery updates.
As Waymo moves closer to introducing completely autonomous vehicles as part of its for-profit robotaxis service in California, this degree of on-the-ground accuracy will become increasingly crucial. After getting certification from the California DMV, the Alphabet company is almost ready to start delivering “rider-only” trips in San Francisco.
Daniel Rothenberg, a trained meteorologist and a member of the company’s weather team told The Verge, “Waymo will create similar weather maps for additional cities as we scale.”
The surge in the number of autonomous vehicles in recent years has brought more attention to the safety of driving autonomous vehicles. While manufacturing autonomous vehicles is easy, as it is fairly similar to making non-autonomous vehicles, the real challenge lies in enabling the vehicle to navigate through adverse weather conditions. Though autonomous vehicles are equipped with sensors like LIDAR, radar, and cameras, they fail during unexpectedly changing weather conditions like heavy snowfall, fog, or rain. Cameras’ view can be obstructed due to fog and heavy snow, thereby rendering them unable to see the roadsigns, lane dividers, bends, etc. Even LIDAR lasers become less accurate while attempting to run through snowflakes and showers.
For the advanced AI technologies powering these vehicles, insight into the precise prevailing road conditions is crucial. For this reason, manufacturers are working to develop systems that can gather all the data required to drive autonomous vehicles in extreme weather conditions.
Amazon Web Services, or AWS, introduce a novel method for evaluating facial recognition models and detecting biases. The proposed method does not utilize standard identification annotations and estimates the model’s performance based on previous demographic data.
Artificial intelligence-based models often experience algorithmic bias. Consequently, the area has become an emerging domain of study. The proposed method focuses on examining biases in facial recognition. A simple way to determine if a facial recognition algorithm is biased is to train the model on a massive dataset, including faces from several demographics. However, this requires identity annotation.
The method proposed by Amazon evaluates biases without identity annotations. While annotations are not necessary, it is necessary for the model to have some way of determining which subjects belong to each category. Where standard models generate vector representations (embeddings) in a single space, this method represents the same subject in two embeddings placed at a distance lesser than a predetermined cutoff.
The researchers then hypothesized that there exists a distribution to which these distances belong and another distribution to which the remaining distances (between two non-identical subjects) belong. The model learns both of the distributions, and the difference between them provides a measure of the model’s accuracy.
The researchers are optimistic about the method being useful for AI as the model shows appreciable results when compared to Bayesian calibration, as seen in the paper.
Google has recently announced a new approach to Reinforcement Learning algorithms called Reincarnating Reinforcement Learning (RRL). This article provides an overview of the RRL algorithms.
Reinforcement Learning is a kind of machine learning technique that focuses on training intelligent agents with related experiences in a way that they can learn to solve decision-making problems like playing video games, designing hardware chips, and flying stratospheric balloons.
Due to the generality of Reinforcement Learning, researchers focus on RL research to develop intelligent agents that can efficiently learn Tabula Rasa. Generally, the term Tabula Rasa is used to describe the chance for a fresh start. For example, when a student’s family migrates to a different location, they must begin the year at a new school in a completely blank state. This means Tabula Rasa is an opportunity to start again with no historical record.
Tabula Rasa RL systems are typically the exceptions rather than the standards for solving large-scale RL problems. Large-scale RL systems like OpenAI Five have achieved human-level performance on Dota2 after experiencing multiple algorithmic changes during the development cycle. But including the algorithmic changes to the RL systems from scratch, can be very challenging and expensive.
Therefore, the inefficient nature of Tabula Rasa Reinforcement Learning research to train agents from scratch can make it challenging for many researchers to handle computationally demanding problems. For example, the standard benchmark to train a deep RL agent on the 50+Atari 2600 games in ALE for 200M frames needs 1000 + GPU days. As the deep RL algorithms move toward complex problems, the computational barrier to entering RL research will become even higher.
Therefore, to address such inefficiencies of Tabula Rasa, Google has introduced a new algorithm called ‘Reincarnating Reinforcement Learning (RRL). It will also present the complete research about the RRL algorithm at the NeurIPS 2022 conference. In this research, Google has proposed an alternative approach to RL research where prior work like learned models, logged data, policies, and more can be reused or transferred between design interactions of the RL agent or from one agent to another. RL uses prior computation in some cases, but most RL agents are still trained from scratch. However, there has not been an inclusive effort to use prior computational work to train workflow in RL research.
Reincarnating Reinforcement Learning (RRL) is a more computational and efficient workflow based on resuing prior computational work while training new RL agents or improving existing RL agents even in the same environment. RRL can standardize RL research by allowing researchers to handle complex RL problems without requiring excessive computational resources. Moreover, RRL can enable a benchmarking example where researchers continually improve and update existing trained RL agents, specifically on problems that impact the real world, like ballon navigation and chip design. The real-world use cases of RL are likely to be used in scenarios where prior computation is available.
RRL is an alternative research workflow for RL that does not train the RL agents from scratch. Instead, it updates the existing RL agents. Suppose a researcher wants to train an agent named A1 for a particular time but now wishes to experiment with better algorithms. In this case, the Tabula Rasa workflow requires retraining another agent from scratch. In contrast, RRL workflow provides an essential option of transferring the existing agent A1 to another agent and training this agent or simply fine-tuning A1.
Reinforcement Learning assumes that agents interact with the online environment to learn from their own past experiences or records. But these algorithms are very challenging to implement in real-life applications like robotics or autonomous driving because you need to train agents in every situation. However, Google assumes RRL will be helpful when a Reinforcement Learning algorithm is costly and time-consuming, where the prior computation can be brought to practice rather than retraining the agents in RL from scratch.
Oregon Attorney General Ellen Rosenblum announced that Google would pay about $391.5 million in settlements to 40 states over its location tracking practices.
While users thought they had turned off their location tracking of Google account settings, Google continued to collect information about their movements. According to the settlement, Google has agreed to significantly improve its location tracking disclosures and user controls in the next year.
The Google settlement was led by Ellen Rosenblum and Nebraska AG Doug Peterson, who declared that Google prioritized profit over their users’ privacy. Google has been crafty and deceptive in saving users’ information secretly and using it for advertising purposes.
According to the press release from Oregan’s, the Google settlement is the largest consumer privacy settlement in U.S history. Due to Oregon’s leadership role in the settlement and investigation, Oregon will receive $14,800 563.
As per the release, the Attorney General opened the Google investigation by following the 2018 Associated Press article that disclosed Google’s strategy to record the users’ movements when they have explicitly told not to do it. The article highlighted two essential features of Google account settings: Location History and Web & Activity. The Location History is ‘off’ until a user turns on the setting. Still, the Web & Activity, a separate account setting, is automatically ‘on’ when users set up a Google account.
According to the settlement, Google has to be more transparent about its practices with users. Google must show additional information to users whenever they turn a location-related setting on or off. It should make essential information about location tracking unavoidable for users. Google should provide detailed information about the types of location data that Google collects and how it is used in the enhanced ‘Location Technologies’ webpage. Besides Oregon and Nebraska, other states involved in the settlements are Florida, Arkansas, New Jersey, North Carolina, and more.
On November 10, Cisco announced its plan to open a new design center for developing next-generation semiconductor devices in Spain. Chuck Robbins, Chair and Executive of Cisco, made the announcement in a meeting with H.E Pedro Sanchez, the Prime Minister of Spain.
Cisco’s global strategy is to enable a scalable, reliable, and sustainable global semiconductor supply chain. Therefore, it plans to set up an engineering design center for designing and prototyping next-generation semiconductor devices under the Spanish strategic project for the PERTE Microchip (Recovery and Economic Transformation of Microelectronics and Semiconductors).
With Cisco’s knowledge and experience, the new center can help grow the European chips ecosystem. Pedro Sanchez stated that Spain is on its way to becoming a significant player in achieving the EU objective of reaching 20% of the world’s chip market by 2030. Spain has approved the PERTE Microchip and has roadmaps, incentives, and reforms to attract talent and strengthen the current Spanish ecosystem.
Cisco’s long-standing committee helps strengthen Spain’s digitalization by encouraging entrepreneurship and innovation, growing digital infrastructure and skills, and enhancing cybersecurity. Cisco is also helping Spain develop critical technologies such as 5G/Wi-Fi, cloud, AI, and next-generation networks.
Cisco’s Country Digital Acceleration Program, Digitaliza, was launched in 2019 in Spain. As per Digitaliza, Cisco has planned to educate and reskill 40,000 workers, students, and unemployed people in digital technologies for the next 12 months with the non-profit Cisco Networking Academy. This can increase the number of Spanish people participating in Cisco Networking Academy courses to 300,000 by the end of 2023.
HPE, or Hewlett Packard Enterprise, announces two versions of its Cray supercomputers to make supercomputing accessible and more affordable for enterprises. The expanded Cray portfolio will include the new HPE Cray EX and HPE Cray XD supercomputers and deliver end-to-end compute technologies built on purpose.
The HPE Cray EX2500 supercomputers share their architecture with the Cray EX4000, the fastest exascale-class system in the world. The plus point is that EX2500 is roughly 24% smaller than its predecessor, making it easier to fit in an enterprise data center. Additionally, it also features 100% direct-liquid cooling for enhanced efficiency and cost-effectiveness.
The HPE Cray XD2000 and XD6500 supercomputers offer use-case-specific servers made to integrate HPE and the Cray portfolio for advanced workloads and AI modeling. The resulting supercomputers are compatible with traditional data centers with standard CPUs, accelerators, interconnect, storage, and cooling options.
Both types can support the latest GPUs and CPUs. HPE Cray EX2500 will support the 4th-generation AMD EPYC processors and the 4th-generation Xeon Scalable processors. Whereas the XD6500 will support 4th-generation Xeon Scalable processors and NVIDIA H100 Tensor Core GPUs.
HPE aims to help enterprises harness meaningful insights, solve complex problems, and innovate faster with its energy-efficient supercomputers. These supercomputers will provide artificial intelligence at scale for data-centric workloads, speed up machine learning jobs, and smoothen product delivery to the market.
Nike is launching a new web3 platform, “.Swoosh,” to offer its NFT and virtual apparel on Polygon. .Swoosh will be the epicenter of Nike’s web3 explorations and will enable future clientele to become co-creators and transact digital product royalties.
Grab your kicks 👟 + BRING 👏🏽 YOUR 👏🏽 A 👏🏽 GAME 👏🏽@Nike is building their web3 experiences exclusively #onPolygon 💪🏾
This is the first step of the journey, and we can't wait to see how Nike engages its community through #web3https://t.co/bk19RLNodX
The NFT apparel will now be minted on Polygon, an Ethereum-based sidechain network. Polygon is a layer 2 blockchain technology that helps improve transaction speed and lowers costs without duplicating Ethereum’s functionality.
After acquiring RTFKT, a digital creator platform using blockchain, augmented reality, and NFT, Nike established itself as one of the greatest digital fashion leaders in the world. Nike’s first virtual kicks, the RTFKT x Nike Dunk Genesis CryptoKicks, performed well in the metaverse. Since then, the company has been working to bring itself into the space.
Now, the .Swoosh platform will be a launch hub for Nike’s virtual apparel, including t-shirts and sneakers for metaverse avatars within web3 games. Additionally, it will make use of Web3 technology to give consumers access to tangible benefits like special physical clothing or conversations with professional athletes.
As per Ron Faris, General Manager of Nike Virtual Studios, .Swoosh will shape a marketplace of the future and will be an epitome for web3-curios people. He added, “In this new space, the .Swoosh community and Nike can create, share, and benefit together.”
GO is a considerably more difficult game to master than chess. In GO, there are 250 valid movements in any given situation as opposed to the typical 35 in chess. A GO board can be set up in more different ways than there are atoms in the universe. As a result, a blend of critical thinking, strategy, imagination, and intellect is needed to solve the GO puzzle. For this reason, AlphaGo’s victory over Lee Sedol in the game of GO in 2016 is seen as a significant turning point in the development of artificial intelligence technology.
Since 2021, KataGo has gained popularity as an open-source AI capable of defeating the best human GO players. With several upgrades and enhancements, KataGo was taught using a method similar to AlphaZero. It is capable of reaching the top levels quickly and completely from scratch with no outside data, progressing purely via self-play.
A paper outlining a strategy to defeat KataGo by using adversarial techniques that exploit KataGo’s blind spots was published last week by a group of AI researchers from MIT, UC Berkeley, and FAR AI. A far inferior hostile GO-playing program can cause KataGo to lose by making unexpected plays outside of its training set.
The primary disadvantage of deep learning-based algorithms is that they are only as good as the data they are trained on. Consequently, introducing false data might lead to the deep learning model malfunctioning. A model may be subjected to an adversarial assault by being given false or deceptive data while it is being trained or by being given data that has been purposefully created to fool a model that has already been trained. The researchers looked for and discovered a vulnerability in KataGo in their latest endeavor.
KataGo might struggle against opponents who play in unfamiliar or unusual ways since it is trained on “standard” methods to play the game of GO. The researchers suggested that attempting to stake out a small corner of the board may be one approach to playing GO in a hostile manner. By controlling the whole rest of the board, this strategy deceives KataGo into believing it has already won the game. One of the principles of GO is that if one player passes and the other follows suit, the game is over, and the winners are determined by adding up their points.
The opposition scores more points and triumphs because it receives all the points for its little corner territory, and KataGo does not receive any points for the undefended territory that has adversarial stones. In this manner, the adversary wins by fooling KataGo into prematurely stopping the game at a position beneficial to the enemy. Researchers reported that attack defeats KataGo with a win rate of >99% when no search is used and a win rate of >50% when KataGo employs sufficient search to be almost superhuman. The researchers point out that the trick only works with KataGo; attempting to use it against humans (even amateurs) would quickly fail since they will instinctively understand what is going on.
The key takeaway is that learning GO does not provide the opponent an advantage, nor is it superior than KataGo. The adversary’s primary goal is to exploit an unforeseen weakness in KataGo, which it easily does. This discovery has significantly wider ramifications because practically every deep-learning AI system may experience similar situations.
Adam Gleave, a doctoral student at UC Berkeley and one of the paper’s co-authors, explains that research demonstrates that AI systems that appear to function at a human level frequently do so in a very ‘alien’ way and as a result, might fail in ways that are unexpected to humans. Gleave claims that while this outcome in GO is amusing, failures of a similar nature in safety-critical systems might be catastrophic.
For instance, imagine a self-driving vehicle AI that runs into a very unusual situation that it didn’t anticipate, allowing a human to manipulate it into engaging in risky activities. This study underscores the necessity for improved automated testing of AI systems to uncover worst-case failure modes, not only assess average-case performance, according to Gleave.
Following the impact of FTX’s collapse, speculations began to circulate online that El Salvador was in trouble because it kept part or all of its Bitcoin assets in FTX. Changpeng Zhao, the CEO of Binance, took to Twitter last week to put these rumors to rest, sharing that he spoke with President Nayib Bukele, who denied that the country used FTX to store its Bitcoin. Most recently, President Bukele tweeted that ‘FTX is the opposite of Bitcoin,” while explaining the inner workings of the Bitcoin protocol, emphasizing how Bitcoin protocol prevents bad actors like FTX CEO Sam Bankman-Fried from financial wrongdoings.
FTX is the opposite of #Bitcoin#Bitcoin ’s protocol was created precisely to prevent Ponzi schemes, bank runs, Enron’s, WorldCom’s, Bernie Madoff’s, Sam Bankman-Fried’s…
The Central American country of El Salvador made history when it became the first nation to recognize bitcoin as a legal tender more than a year ago. In 2021, President Bukele bought about US$300 million worth of Bitcoin to help the Central American country build its infrastructure. With President Bukele’s decision, both Bitcoin and the US dollar, El Salvador’s other official money, can theoretically be accepted by all companies.
The government offered residents financial incentives to download a special cryptocurrency app called “Chivo Wallet” (chivo is Salvadoran slang for “cool”) in a bid to popularize and regularize its use. Each Chivo Wallet will include US$30 worth of bitcoin as a government gesture, and the Chivo app was stated to function in tandem with Chivo ATMs, where users would be able to swap their bitcoin for cash without paying any fees. When the bitcoin law came into effect, the app was downloaded by half of all households in the country. However, the most recent cryptocurrency crisis and the impending collapse of the FTX market have raised concerns about the project’s overall success.
The proposal had drawn criticism from major international financial organizations like the International Monetary Fund. According to numerous polls, a huge percentage of Salvadorans opposed the initiative. Trade unions and citizens also railed against the dangers posed by the volatility of bitcoin and its possible use for money laundering. In contrast, Bukele’s constant assurances that bitcoin usage will be voluntary, the established legal mandate that all businesses accept bitcoin payments was considered extremely alarming. On the other side, though surveys portrayed El Salvador as a nation that is plagued with cynicism, in reality, only a small number of individuals have demonstrated against the move amid the fears it would bring instability and inflation to the impoverished nation.
The decision to legalize Bitcoin by President Bukele was built on the belief that it would draw in international investment, create employment, and motivate cryptocurrency entrepreneurs to set up businesses in the country. In El Salvador, almost 70% of residents are without a bank account, although the majority have smartphones. Cryptocurrency enthusiasts have long touted the potential of new digital currencies to assist the unbanked and underbanked, and El Salvador is keen on employing Bitcoin’s ability to boost a population’s commercial and economic prospects.
Another benefit anticipated was that bitcoin would speed up and reduce the cost of remittances from the Salvadoran immigrant community, which annually provide US$6 billion, or one-fifth of the nation’s GDP. His aspirations went as far as creating a whole “Bitcoin city,” a tax-free sanctuary financed by the issuance of US$1 billion in government bonds. The idea was to invest half of the bond proceeds in the city and the other half in Bitcoin, with the earnings from the latter being used to pay back the bondholders.
More than a year later, there is more than enough proof to say that Bukele had no idea what he was doing. Before moving forward with his ambitious plans, Bukele, who had referred to himself as “the world’s coolest dictator” in response to criticism of his growing authoritarianism, did not conduct any research or publish even a single technical report weighing the benefits and drawbacks of making bitcoin legal tender. Pouring public funds into a scheme to replace physical currency with cryptocurrencies was a dangerous gamble in a nation where only about a third of the population has access to the internet, and some areas even lack power. In other words, Bukele’s ignorance of the relationship between Bitcoin and GDP made the idea of utilizing public funds to place significant speculative bets on bitcoin incredibly irresponsible.
In retrospect, the bungled technical and communication components of the original, seemingly hurried introduction of Bitcoin in El Salvador were the biggest disaster. Just 20% of Salvadoran families surveyed in a nationally representative study of 1,800 homes in February reported using Chivo Wallet to deal in bitcoin. Many Salvadorans said their US$30 registration incentive had been spent before they had access to the system, and there were numerous accusations of identity theft. On the comical side of things, many citizens downloaded the app solely to get their hands on the US$30. Additionally, there was a lack of clarity on the exact requirements of the Bitcoin Law, notably those that applied to merchants.
With the collapse of the cryptocurrency market, El Salvador’s ability to fulfill its upcoming bond payment and avoid defaulting on its financial obligations is becoming less and less feasible. El Salvador has two US$800 million bonds that are due in 2023 and 2025, and experts are questioning if the country will be able to repay those. To make things worse, the rollout of Bitcoin as a legal tender did not mention much about the purchases. Experts, analysts, critics, and advocates are still mostly reliant on Bukele’s tweets because his administration hasn’t disclosed the on-chain or off-chain location of their alleged bitcoin purchases. The value of the bitcoins that the Bukele government purchased for more than US$100 million is currently less than US$50 million. This would be devastating for the nation’s economy and the government’s capacity to carry out duties effectively. Things could be worse if the US$1 billion Bitcoin bonds proposal did not stall, mostly because of the Russian invasion of Ukraine. The state of emergency declared in the nation at the end of March due to gang violence also put brakes on the project.
Fortunately, there is a silver lining amid the losses. More often than most people realize, the usage of bitcoin to send remittances from overseas has been proven to be effective. Eight months after the Bitcoin Law’s passage, it was announced in May of this year that 1.9% of remittances to El Salvador, or US$96.3 million, were sent using cryptocurrencies. According to reports, Salvadorans spend US$400 million a year only on remittance costs. Assuming the addition of half-price costs to 1.9% of US$400 million, one can see that Salvadorans saved slightly under US$4 million in remittance expenses in the first eight months by switching to bitcoin. If Salvadoran acceptance of bitcoin for remittances increases by just 2% per year, the nearly US$100 million spent on Bitcoin network infrastructure may pay for itself in less than a decade.
Apart from remittance relief, President Bukele has shared that adopting bitcoin has helped in reviving its tourism industry. The World Tourism Organization reports that El Salvador has increased its tourism-related revenue by 6% in comparison to 2019 pre-pandemic levels. This analysis is in agreement with what the government has been arguing about the impact of bitcoin use in the nation ever since it became legal tender. The tourism sector has grown 30% since this incident, according to Morena Valdez, Minister of Tourism, who made this claim in February. With foreign tourism accounting for around 5% of Salvadoran GDP prior to the Bitcoin Law, a sustained increase of that scale could enhance El Salvador’s GDP by more than 3%.
Even Alphapoint, which offers software infrastructure technology to Chivo, shared its plans to ensure that Salvadorans of various digital literacy levels utilize the app securely and dependably. Alphapoint also added working on wider Chivo customer care support and improved merchant transaction processing speed.
To help El Salvador further navigate through its economic woes, China has recently offered to buy a large amount of distressed foreign debt. Though Vice President Felix Ulloa assured they would exercise caution before agreeing to the offer, this development can be assuring as the nation is working to reduce strain within its bond market and avoid defaulting on its dollar-denominated debt.
After El Salvador entered its bitcoin era, it did face many challenges ranging from project rollout to local violence and hacks. The nation is still keen on this initiative, in the thick of criticism by the likes of IMF and opposition, it is also trying its best to channel itself out of the economic fiasco.
