Dystopian Kurzweil: As Big Tech continues frantically pushing AI development and funding, many users have become concerned about the outcome and dangers of the latest AI advancements. However, one man is more than sold on AI's ability to bring humanity to its next evolutionary level.

Raymond Kurzweil is a well-known computer scientist, author, and artificial intelligence enthusiast. Over the years, he has promoted radical concepts such as transhumanism and technological singularity, where humanity and advanced technology merge to create an evolved hybrid species. Kurzweil's latest predictions on AI and the future of tech essentially double down on twenty-year-old predictions.

In a recent interview with the Guardian, Kurzweil introduced his latest book, "The Singularity Is Nearer," a sequel to his bestselling 2005 book, "The Singularity Is Near: When Humans Transcend Biology." Kurzweil predicted that AI would reach human-level intelligence by 2029, with the merging between computers and humans (the singularity) happening in 2045. Now that AI has become the most talked-about topic, he believes his predictions still hold.

Kurzweil believes that in five years, machine learning will possess the same abilities as the most skilled humans in almost every field. A few "top humans" capable of writing Oscar-level screenplays or conceptualizing deep new philosophical insights will still be able to beat AI, but everything will change when artificial general intelligence (AGI) finally surpasses humans at everything.

Bringing large language models (LLM) to the next level simply requires more computing power. Kurzweil noted that the computing paradigm we have today is "basically perfect," and it will just get better and better over time. The author doesn't believe that quantum computing will turn the world upside down. He says there are too many ways to continue improving modern chips, such as 3D and vertically stacked designs.

Kurzweil predicts that machine-learning engineers will eventually solve the issues caused by hallucinations, uncanny AI-generated images, and other AI anomalies with more advanced algorithms trained on more data. The singularity is still happening and will arrive once people start merging their brains with the cloud. Advancements in brain-computer interfaces (BCIs) are already occurring. These BCIs, eventually comprised of nanobots "noninvasively" entering the brain through capillaries, will enable humans to possess a combination of natural and cybernetic intelligence.

Kurzweil's imaginative nature as a book author and enthusiastic transhumanist is plain to see. Science still hasn't discovered an effective way to deliver drugs directly into the brain because human physiology doesn't work the way the futurist thinks. However, he remains confident that nanobots will make humans "a millionfold" more intelligent within the next twenty years.

Kurzweil concedes that AI will radically change society and create a global automated economy. People will lose jobs but will also adapt to new employment roles and opportunities advanced tech brings. A universal basic income will also ease the pain. He expects the first tangible transformative plans will emerge in the 2030s. The inevitable Singularity will enable humans to live forever or extend our living prospects indefinitely. Technology could even resurrect the dead through AI avatars and virtual reality.

Kurzweil says people are misdirecting their worries regarding AI.

"It is not going to be us versus AI: AI is going inside ourselves," he said. "It will allow us to create new things that weren't feasible before. It'll be a pretty fantastic future."

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Transhumanist author predicts artificial super-intelligence, immortality, and the Singularity by 2045 - TechSpot

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Researchers from Cleveland Clinic and IBM have recently published findings in the Journal of Chemical Theory and Computation that could lay the groundwork for applying quantum computing methods to protein structure prediction.

For decades, researchers have leveraged computational approaches to predict protein structures. A protein folds itself into a structure that determines how it functions and binds to other molecules in the body. These structures determine many aspects of human health and disease.

By accurately predicting the structure of a protein, researchers can better understand how diseases spread and thus how to develop effective therapies. Cleveland Clinic postdoctoral fellow Bryan Raubenolt, Ph.D. and IBM researcher Hakan Doga, Ph.D. spearheaded a team to discover how quantum computing can improve current methods.

In recent years, machine learning techniques have made significant progress in protein structure prediction. These methods are reliant on training data (a database of experimentally determined protein structures) to make predictions. This means that they are constrained by how many proteins they have been taught to recognize. This can lead to lower levels of accuracy when the programs/algorithms encounter a protein that is mutated or very different from those on which they were trained, which is common with genetic disorders.

The alternative method is to simulate the physics of protein folding. Simulations allow researchers to look at a given protein's various possible shapes and find the most stable one. The most stable shape is critical for drug design.

The challenge is that these simulations are nearly impossible on a classical computer, beyond a certain protein size. In a way, increasing the size of the target protein is comparable to increasing the dimensions of a Rubik's cube. For a small protein with 100 amino acids, a classical computer would need the time equal to the age of the universe to exhaustively search all the possible outcomes, says Dr. Raubenolt.

To help overcome these limitations, the research team applied a mix of quantum and classical computing methods. This framework could allow quantum algorithms to address the areas that are challenging for state-of-the-art classical computing, including protein size, intrinsic disorder, mutations and the physics involved in protein folding. The framework was validated by accurately predicting the folding of a small fragment of a Zika virus protein on a quantum computer, compared to state-of-the-art classical methods.

The quantum-classical hybrid framework's initial results outperformed both a classical physics-based method and AlphaFold2. Although the latter is designed to work best with larger proteins, it nonetheless demonstrates this framework's ability to create accurate models without directly relying on substantial training data.

The researchers used a quantum algorithm to first model the lowest energy conformation for the fragment's backbone, which is typically the most computationally demanding step of the calculation. Classical approaches were then used to convert the results obtained from the quantum computer, reconstruct the protein with its sidechains, and perform final refinement of the structure with classical molecular mechanics force fields.

The project shows one of the ways that problems can be deconstructed into parts, with quantum computing methods addressing some parts and classical computing others, for increased accuracy.

"One of the most unique things about this project is the number of disciplines involved," says Dr. Raubenolt. "Our team's expertise ranges from computational biology and chemistry, structural biology, software and automation engineering, to experimental atomic and nuclear physics, mathematics, and of course, quantum computing and algorithm design. It took the knowledge from each of these areas to create a computational framework that can mimic one of the most important processes for human life."

The team's combination of classical and quantum computing methods is an essential step for advancing our understanding of protein structures, and how they impact our ability to treat and prevent disease. The team plans to continue developing and optimizing quantum algorithms that can predict the structure of larger and more sophisticated proteins.

"This work is an important step forward in exploring where quantum computing capabilities could show strengths in protein structure prediction," says Dr. Doga. "Our goal is to design quantum algorithms that can find how to predict protein structures as realistically as possible."

More information: Hakan Doga et al, A Perspective on Protein Structure Prediction Using Quantum Computers, Journal of Chemical Theory and Computation (2024). DOI: 10.1021/acs.jctc.4c00067

Continue reading here:

Researchers apply quantum computing methods to protein structure prediction - Phys.org

Researchers from Cleveland Clinic and IBM recently published findings in the Journal of Chemical Theory and Computation that could lay the groundwork for applying quantum computing methods to protein structure prediction. This publication is the first peer-reviewed quantum computing paper from the Cleveland Clinic-IBM Discovery Accelerator partnership.

For decades, researchers have leveraged computational approaches to predict protein structures. A protein folds itself into a structure that determines how it functions and binds to other molecules in the body. These structures determine many aspects of human health and disease.

By accurately predicting the structure of a protein, researchers can better understand how diseases spread and thus how to develop effective therapies. Cleveland Clinic postdoctoral fellow Bryan Raubenolt, Ph.D., and IBM researcher Hakan Doga, Ph.D., spearheaded a team to discover how quantum computing can improve current methods.

In recent years, machine learning techniques have made significant progress in protein structure prediction. These methods are reliant on training data (a database of experimentally determined protein structures) to make predictions. This means that they are constrained by how many proteins they have been taught to recognize. This can lead to lower levels of accuracy when the programs/algorithms encounter a protein that is mutated or very different from those on which they were trained, which is common with genetic disorders.

The alternative method is to simulate the physics of protein folding. Simulations allow researchers to look at a given proteins various possible shapes and find the most stable one. The most stable shape is critical for drug design.

The challenge is that these simulations are nearly impossible on a classical computer, beyond a certain protein size. In a way, increasing the size of the target protein is comparable to increasing the dimensions of a Rubik's cube. For a small protein with 100 amino acids, a classical computer would need the time equal to the age of the universe to exhaustively search all the possible outcomes, says Dr. Raubenolt.

To help overcome these limitations, the research team applied a mix of quantum and classical computing methods. This framework could allow quantum algorithms to address the areas that are challenging for state-of-the-art classical computing, including protein size, intrinsic disorder, mutations and the physics involved in proteins folding. The framework was validated by accurately predicting the folding of a small fragment of a Zika virus protein on a quantum computer, compared to state-of-the-art classical methods.

The quantum-classical hybrid framework's initial results outperformed both a classical physics-based method and AlphaFold2. Although the latter is designed to work best with larger proteins, it nonetheless demonstrates this framework's ability to create accurate models without directly relying on substantial training data.

The researchers used a quantum algorithm to first model the lowest energy conformation for the fragments backbone, which is typically the most computationally demanding step of the calculation. Classical approaches were then used to convert the results obtained from the quantum computer, reconstruct the protein with its sidechains, and perform final refinement of the structure with classical molecular mechanics force fields. The project shows one of the ways that problems can be deconstructed into parts, with quantum computing methods addressing some parts and classical computing others, for increased accuracy.

Multidisciplinary collaboration was essential to achieve this framework.

One of the most unique things about this project is the number of disciplines involved, says Dr. Raubenolt. Our teams expertise ranges from computational biology and chemistry, structural biology, software and automation engineering, to experimental atomic and nuclear physics, mathematics, and of course quantum computing and algorithm design. It took the knowledge from each of these areas to create a computational framework that can mimic one of the most important processes for human life.

The teams combination of classical and quantum computing methods is an essential step for advancing our understanding of protein structures, and how they impact our ability to treat and prevent disease. The team plans to continue developing and optimizing quantum algorithms that can predict the structure of larger and more sophisticated proteins.

This work is an important step forward in exploring where quantum computing capabilities could show strengths in protein structure prediction, says Dr. Doga. Our goal is to design quantum algorithms that can find how to predict protein structures as realistically as possible.

The rest is here:

Clinic, IBM apply quantum computing to protein research - Cleveland Clinic Newsroom

May 31 2024Reviewed by Lexie Corner

Recent findings from IBM and Cleveland Clinic researchersmay pave the way for applying quantum computing techniques to protein structure prediction. These findings are publishedin the Journal of Chemical Theory and Computation.This publication represents the Cleveland Clinic-IBM Discovery Accelerator collaboration's first peer-reviewed paper on quantum computing.

For many years, researchers have used computational methods to predict protein structures. A protein folds into a structure that controls its molecular interactions and mode of action. These structures determine numerous facets of human health and illness.

Researchers can create more effective treatments by better understanding how diseases spread through precise protein structure predictions. Bryan Raubenolt, Ph.D., a Postdoctoral Fellow at the Cleveland Clinic, and Hakan Doga, Ph.D., a researcher at IBM, led a team to discover how quantum computing can enhance existing techniques.

Machine learning techniques have significantly advanced the prediction of protein structure in recent years. To make predictions, these techniques rely on training data, a database of protein structuresdetermined through experimentation. This indicates that the number of proteins they have been trained to identify is a limitation. When programs or algorithms come across a protein that is mutated or significantly different from the ones they were trained on, as is frequently the case with genetic disorders, this can result in decreased accuracy levels.

A different approach is to model the physics involved in protein folding. Through simulations, scientists can examine multiple protein configurations and determine the most stable form, whichis essential for drug design.

The challenge is that these simulations are nearly impossible on a classical computer beyond a certain protein size. In a way, increasing the size of the target protein is comparable to increasing the dimensions of a Rubik's cube. For a small protein with 100 amino acids, a classical computer would need the time equal to the age of the universe to exhaustively search all the possible outcomes.

Dr. Bryan Raubenolt, Postdoctoral Fellow, Cleveland Clinic

The research team combined quantum and classical computing techniques to get around these restrictions. Within this framework, quantum algorithms can tackle problems that current state-of-the-art classical computing finds difficult, such as the physics of protein folding, intrinsic disorder, mutations, and protein size.

The accuracy with which the framework predicted, on a quantum computer, the folding of a small fragment of the Zika virus protein, compared to the most advanced classical methods, served as validation.

The initial results of the quantum-classical hybrid framework outperformed both AlphaFold2 and a method based on classical physics. The latter shows that this framework can produce accurate models without directly relying on large amounts of training data, even though it is optimized for larger proteins.

The most computationally intensive part of the calculation usually involves modeling the lowest energy conformation for the fragment's backbone, which the researchers accomplish using a quantum algorithm. After that, classical methods were employed to translate the quantum computer's output, rebuild the protein along with its sidechains, and refine the structure one last time using force fields from classical molecular mechanics.

The project illustrates how problems can be broken down into smaller components for better accuracy. Some components can be addressed by quantum computing techniques, while classical computing methods can handle others.

Working across disciplines was crucial to creating this framework.

One of the most unique things about this project is the number of disciplines involved. Our teams expertise ranges from computational biology and chemistry, structural biology, software, and automation engineering to experimental atomic and nuclear physics, mathematics, and, of course,quantum computing and algorithm design. It took the knowledge from each of these areas to create a computational framework that can mimic one of the most important processes for human life.

Dr. Bryan Raubenolt, Postdoctoral Fellow, Cleveland Clinic

The teams combination of classical and quantum computing methods is essential for advancing our understanding of protein structures and how they impact our ability to treat and prevent disease. The team plans to continue developing and optimizing quantum algorithms that can predict the structure of larger and more sophisticated proteins.

This work is an important step forward in exploring where quantum computing capabilities could show strengths in protein structure prediction. Our goal is to design quantum algorithms that can find how to predict protein structures as realistically as possible.

Dr. Hakan Doga, Researcher, IBM

Doga, H., et al. (2024) A Perspective on Protein Structure Prediction Using Quantum Computers. Chemical Theory and Computation. doi.org/10.1021/acs.jctc.4c00067

Read the rest here:

Unveiling Protein Structures with Quantum Computing - AZoQuantum

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The popular self-custodial hot wallet is looking to add Bitcoin support, reports CoinDesk citing people familiar with the matter.

The wallet provider hopes to roll out Bitcoin support within the next month, but those plans are subject to change. Bitcoin features could start small and grow over time, CoinDesk adds. Though MetaMask already expanded beyond the Ethereum ecosystem with the inclusion of Snaps, the move would add one of the most popular blockchains onto the most popular digital wallet platforms.

MetaMask primarily supports Ethereum, Ethereum Layer 2s and networks compatible with the Ethereum Virtual Machine (EVM), such as Avalanche, Polygon, Optimism and Arbitrum. However, MetaMask expanded beyond the Ethereum ecosystem when it included Snaps, a type of JavaScript application, in September 2023.

MetaMask was the most popular wallet by downloads, hitting 22 million downloads in August 2023. The wallet's developers recently added other features to bolster user experience, such as incorporating Blockaid-based security alerts for numerous blockchains, Ethereum validator staking and a feature letting users check their eligibility for airdrops and NFT claims.

Consensys, MetaMask's core developer, raised $450 million in Series D funding led by ParaFi capital, giving Consensys a $7 billion valuation in March 2022. The firm sued the Securities and Exchange Commission in late April regarding the regulator's contradictory stance over whether ether is a security and if the regulator has jurisdiction over the asset's regulation. The SEC had issued a Wells notice, which notes an intent to pursue legal action against the recipient, earlier that month.

Bitcoin traded at $70,240 at 12:45 p.m. ET (16:45 UTC) on May 22.The Block's Data Dashboard shows that the Bitcoin network saw 14.48 million transactions in April.

Disclaimer: The Block is an independent media outlet that delivers news, research, and data. As of November 2023, Foresight Ventures is a majority investor of The Block. Foresight Ventures invests in other companies in the crypto space. Crypto exchange Bitget is an anchor LP for Foresight Ventures. The Block continues to operate independently to deliver objective, impactful, and timely information about the crypto industry. Here are our current financial disclosures.

2023 The Block. All Rights Reserved. This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

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MetaMask to add Bitcoin support: report - The Block

Fitiquity Gym has just celebrated their 4 year anniversary in Burbank and is kicking off the summer with a Health and Fitness Parking Lot Event on June 1st.

The gym which was recently named Best Boxing Gym in the 2024 myBurbanks Best Contest, has a full size boxing ring and bag area. Fitiquity is also a full service gym that includes updated cardio equipment, weight-training equipment and free weights, a spacious aerobics room, and a spin room with state-of-the-art bikes.

The family-owned gym is celebrating their 4 years in Burbank with a Health & Fitness Parking Lot Event on June 1st from 10:00am to 2:00pm and will have everything from demo classes, food trucks, fit tests, health and wellness vendors, samples, gym tours, fitness competitions, giveaways and prizes, and much more.

Class demos will be happening every half hour inside the gym so guests can take a peek at their bootcamp, spin, boxing and trampoline classes. Inside the ring, boxing coach JP will be working on the mitts, and entering people in a chance to win a set of boxing gloves.

At 12:00PM, try your luck in their fitness competition for a chance to win a month membership or free personal training sessions. Competitions include the longest plank, the most pull ups, and the most push ups.

Fitiquity will have a DJ getting the crowd pumped, a fit area to workout at within the parking lot, and will be doing gym merch and membership giveaways all throughout the day.

Other things youll find at the Fitiquity Health & Fitness Parking Lot Event.

Fitiquity Gym is located at 2010 N Hollywood Way. If you are interested in trying to gym prior to the event, go to their website http://www.fitiquity.com to claim a free three day guest pass.

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Fitiquity Gym to Hold Health & Fitness Parking Lot Event on June 1st - MyBurbank.com

Valeria Blokhina

U.Todays news digest helps you stay tuned on the most recent events in the crypto world

Check out the top three news stories over the past day presented to you by U.Today.

A recent X post from Ripple CTO David Schwartzhas sparked heated discussions on the topic of the real identity of pseudonymous Bitcoin creator Satoshi Nakamoto. It all started with a claim from a prominent Bitcoin community member known as "Ryuushi," according to whom Craig Wright remains "the most likely person to be Satoshi," despite recent legal setbacks. The claim immediately caught Schwartz's attention;the CTO replied that Wright had a perfect opportunity to prove his identity as Nakamoto during the trial, but he failed to do so. "I'm more likely to be Satoshi than Craig is," concluded Schwartz in his X post. Such a reaction has once again ignited rumors of Schwartz's potential connection to Nakamoto. Some theorists consider the Ripple CTO to be a plausible candidate for Nakamoto's identity, given his extensive background in cryptography.

As tokenization is gaining traction in the United Kingdom, UK Finance, the British trade association, is expanding its pilot project aimed attesting tokenized deposits. According to a recentreport by Bloomberg, among the banking giants that participated in the pilot are Barclays, Lloyds Banking Group Plc and Citigroup Inc., with Mastercard and Visa, the worlds biggest credit card networks, also being involved. The trial is anticipated to last up to three years, before the commercial implementation of the technology. The first results of the experiment will be revealed in August of this year. Meanwhile, per recent reports, the future of the digital pound, or Britcoin, is surrounded with uncertainty. Although the Bank of England first started exploring it back in 2021, the project has not seen much progress.

Jan3 CEO and renowned Bitcoin enthusiast Samson Mow has recently taken to X platformto share his take on when he expects the Omega time for the Bitcoin price to arrive and to make some remarks on the upcoming Bitcoin halving event.In his X thread, Mow wrote that fear of certain negative developments in the Middle East led to Bitcoin's price plunge over the weekend, with TradeFi markets having their share of panic. However, Mow believes, this is all an overreaction and will wash over soon. After that, the "Omega time" for Bitcoin will follow. Then, the Jan3 CEO touched upon the BTC halving, referring to it as the spark of a massive supply shock. Mow underscored that the Bitcoin demand shock is happening right now, as spot Bitcoin ETFs have been absorbing immense amounts of BTC since mid-January, when the SEC approved ETF trading.

About the author

Valeria Blokhina

Valeria is the community manager at U.Today. She is a crypto enthusiast and believes that cryptocurrency is the future of finance. Currently, Valeria covers the latest news in the world of crypto and blockchain.

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Ripple CTO Shares Unexpected Satoshi Nakamoto Statement, Major British Banks Testing Tokenized Deposits: Crypto News Digest by U.Today - U.Today

Paul Morphy, a name synonymous with chess brilliance, stands as one of the most enigmatic and celebrated figures in the history of the game. Born on June 22, 1837, in New Orleans, Louisiana, Morphy's meteoric rise to prominence and his unparalleled mastery of chess continue to fascinate enthusiasts and scholars alike. His legacy, shrouded in mystery and admiration, transcends mere chess mastery; it embodies the essence of genius and the timeless allure of intellectual pursuit.At his game.

Early Years: A Prodigy in the Making

From an early age, Morphy exhibited an extraordinary aptitude for chess. Legend has it that at the tender age of nine, he defeated his father, Alonzo Morphy, a distinguished lawyer, in a game. Recognizing his son's remarkable talent, Alonzo enlisted the guidance of renowned chess masters to hone Paul's skills further. Under their tutelage, Morphy's understanding of the game flourished, and his tactical prowess became increasingly apparent.A wonder even at a young age.

The Triumph of Genius: Morphy's Chess Ascendancy

Morphy's breakthrough came in 1857 when he participated in the First American Chess Congress in New York. Despite his young age and limited experience in formal competition, Morphy's brilliance shone through as he swept aside seasoned opponents with ease. His remarkable performance earned him widespread acclaim and established him as a force to be reckoned with in the world of chess.

The pinnacle of Morphy's career came during his tour of Europe in 1858-1859. In a series of dazzling displays, he faced and conquered the foremost chess players of the era, including Adolf Anderssen and Johann Lwenthal. Morphy's style was marked by bold, sacrificial play and flawless execution, earning him the admiration of both peers and spectators. His dominance was unparalleled, and he was widely hailed as the unofficial world champion, despite the absence of an official title at the time.Would this person be able to win Paul Morphy at chess?

Legacy and Influence: Morphy's Enduring Impact

Morphy's retirement from competitive chess at the height of his powers remains one of the most enigmatic aspects of his life. After returning from Europe, he withdrew from the public eye and never again participated in formal tournaments. Despite numerous entreaties from admirers and the chess community, Morphy chose to pursue a career in law rather than continue his chess exploits.

Nevertheless, Morphy's legacy endures as a testament to his unparalleled genius and his profound impact on the development of chess theory. His games continue to be studied and analyzed by generations of players, serving as a source of inspiration and instruction. Morphy's contributions to the game laid the groundwork for future champions, shaping the evolution of chess strategy and tactics.

The Enigma of Morphy: Myth and Reality

Throughout history, Morphy has been the subject of speculation and myth, with numerous anecdotes and legends surrounding his life and legacy. Tales of his eccentricities and reclusive nature abound, further adding to the mystique surrounding his persona. Despite the efforts of historians and biographers, much of Morphy's private life remains shrouded in mystery, leaving ample room for conjecture and interpretation.

Yet, amidst the myth and speculation, one undeniable truth remains: Paul Morphy's impact on the world of chess is immeasurable. His legacy transcends the confines of the chessboard, serving as a testament to the boundless potential of the human mind and the enduring allure of intellectual pursuit. In Morphy, we find not merely a chess prodigy, but a symbol of genius and inspiration for generations to come.

Thank you for for reading,as always comments are much appreciated!

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Unraveling the Legend of Paul Morphy - Chess.com

Shiba Inu is now the top most traded cryptocurrency in India in 2024, reported the countrys leading trading platform WazirX. SHIB outpaced Bitcoin to become the most traded token in March last month. The dog-themed token topped the list of buyers as it spiked nearly 280% in 30 days.

Also Read: Shiba Inu (SHIB) Investor Misses Out on Earning $10 Million

SHIB skyrocketed in price from a low of $0.000009 to a high of $0.00004 in just two weeks last month. The token delivered stellar returns to investors who took an entry position early this year. Below is the top traded cryptocurrency in India in March 2024, according to data provided by WazirX.

Also Read:Cryptocurrency: 3 Coins Can Double in Price in April 2024

Surprisingly, four out of the five most traded cryptocurrencies in India are meme coins. Only Bitcoin stands apart from the rest being the second most traded cryptocurrency in the country. The rise in Bitcoin investments comes after it rallied and hit a new all-time high of $73,737.

Also Read: Shiba Inu: $4,400 Investment Turns Into $172 Million in April 2024

Investors in India made the most out of Bitcoin and Shiba Inus run despite the government levying a 30% tax. Data shows that around 100 million people in India have invested in cryptocurrencies between 2021 to 2024. When WazirX listed SHIB on its platform in May 2021, the exchange crashed due to high volumes of trade.

Even after three years, SHIB still commands the highest buyers be it in the bear or bull markets. The interest in accumulating Shiba Inu tokens has barely dipped in three years making it the only cryptocurrency with a buying pressure throughout.

Also Read: Shiba Inu: AI Predicts SHIB Price For April 5, 2024

The official SHIB page on X tweeted out the achievement thanking the Indian people for buying the cryptocurrency. SHIB was one of the hottest coins on WazirX India in March! The SHIB Army is growing strong in India. Namaste!

Read more from the original source:

Shiba Inu Becomes the Most Traded Cryptocurrency in 2024 - Watcher Guru

Our website is a comprehensive guide to digital currency investing & trading.Here are a few suggestions to get started, and get the most from us:

Begin with Bitcoin for Beginners.

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Check out our Crypto YouTube channel, that includes beginners and advanced tutorial videos and market updates. Dont forget to subscribe!

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Heres What Vitalik Buterin Proposes in Case of a Quantum Emergency - CryptoPotato