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It’s not just Texas. The faux panic and textbook wars fit into a long history – CNN

I inwardly breathe a weary, cyclone-force sigh whenever I hear the words "critical race theory."

"This is very clearly an attack on diversity, equity (and) inclusion. It very much feels like a political overreach based on misinformation," Ana Ramn, deputy director of advocacy at the Intercultural Development Research Association, told CNN's Nicole Chavez. "Teaching critical race theory in K-12 would be like teaching quantum physics in K-12. ... There's no curriculum that has been adopted in Texas classrooms."

Maybe the most disturbing thing about the tub-thumping about CRT (which, it's worth repeating, isn't taught in grade school) is that the core impulse is hardly new -- but instead fits into a long, messy history of fights over classroom instruction. As students return to school, adults could benefit from more context about what's going on.

Here's what these ever-simmering battles reveal about the US's socio-political anxieties over, among other things, race, gender and immigration.

How did the backlash to CRT creep into schools?

Republicans trust that playing up these conflicts will be electorally useful to them, as they train their attention on the 2022 midterms and beyond.

The orchestrated attack on CRT takes a toll on teachers, staff and students.

It isn't a stretch to say that the current struggle over how schools teach not just history but the ways history moves in the present might affect students' understanding of the world around them for years to come.

Is this the first time the political right has freaked out over learning about race and racism?

No. This dispute has existed in a variety of forms since at least the 1800s.

Have there been education disputes over things other than race?

Afraid so.

For instance, World War I set off a burst of xenophobia aimed not only at German immigrants and Americans of German descent but also at the German language. Senator William H. King of Utah introduced a bill to ban teaching German in Washington's public schools.

More specifically, the legislature, made up of a near-majority of Ku Klux Klan members, passed a law that banned the use in public schools of any textbook that "speaks slightingly of the founders of the republic, or of the men who preserved the union, or which belittles or undervalues their work."

"Fights in and about the classroom -- classroom wars -- formed a crucial crucible in which the powerful political notion of 'family values' was contested and constructed," she writes.

So while the present-day backlash to CRT might feel unique, really, it's not. It's just the latest iteration of an age-old tendency to turn the classroom into a battlefield.

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It's not just Texas. The faux panic and textbook wars fit into a long history - CNN

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Large-Scale Simulations Of The Brain May Need To Wait For Quantum Computers – Forbes

Will quantum computer simulations crack open our understanding of the biological brain?

Looking back at the history of computers, its hard to overestimate the rate at which computing power has scaled in the course of just a single human lifetime. But yet, existing classical computers have fundamental limits. If quantum computers are successfully built and eventually fully come online, they will be able to tackle certain classes of problems that elude classical computers. And they may be the computational tool needed to fully understand and simulate the brain.

As of this writing, the fastest supercomputer in the world is Japans Fugaku supercomputer, developed jointly by Riken and Fujitsu. It can perform 442 peta-floating-point operations per second.

Lets break that number down in order to arrive at an intuitive (as much as possible) grasp of what it means.

A floating-point number is a way to express, or write down, a real number - real in a mathematical sense - with a fixed amount of precision. Real numbers are all the continuous numbers from the number line. 5, -23, 7/8, and numbers like pi (3.1415926 ...) that go on forever are all real numbers. The problem is a computer, which is digital, has a hard time internally representing continuous numbers. So one way around this is to specify a limited number of digits, and then specify how big or small the actual number is by some base power. For example, the number 234 can be written as 2.34 x 102, because 2.34 x 100 equals 234. Floating point numbers specify a fixed number of significant digits the computer must store in its memory. It fixes the accuracy of the number. This is important because if you do any mathematical operation (e.g. addition, subtraction, division or multiplication) with the fixed accuracy version of a real number, small errors in your results will be generated that propagate (and can grow) throughout other calculations. But as long as the errors remain small its okay.

A floating point operation then, is any arithmetic operation between two floating-point numbers (abbreviated as FLOP). Computer scientists and engineers use the number of FLOP per second - or FLOPS - as a benchmark to compare the speed and computing power of different computers.

One petaFLOP is equivalent to 1,000,000,000,000,000 - or one quadrillion - mathematical operations. A supercomputer with a computing speed of one petaFLOPS is therefore performing one quadrillion operations per second! The Fugaku supercomputer is 442 times faster than that.

For many types of important scientific and technological problems however, even the fastest supercomputer isnt fast enough. In fact, they never will be. This is because for certain classes of problems, the number of possible combinations of solutions that need to be checked grow so fast, compared to the number of things that need to be ordered, that it becomes essentially impossible to compute and check them all.

Heres a version of a classic example. Say you have a group of people with differing political views, and you want to seat them around a table in order to maximize constructive dialogue while minimizing potential conflict. The rules you decide to use dont matter here, just that some set of rules exist. For example, maybe you always want to seat a moderate between a conservative and a liberal in order to act as a bit of a buffer.

This is what scientists and engineers call an optimization problem. How many possible combinations of seating arrangements are there? Well, if you only have two people, there are only two possible arrangements. One individual on each side of a table, and then the reverse, where the two individuals change seats. But if you have five people, the number of possible combinations jumps to 120. Ten people? Well, now youre looking at 3,628,800 different combinations. And thats just for ten people, or more generally, any ten objects. If you had 100 objects, the number of combinations is so huge that its a number with 158 digits (roughly, 9 x 10157). By comparison, there are only about 1021 stars in the observable universe.

Imagine now if you were trying to do a biophysics simulation of a protein in order to develop a new drug that had millions or billions of individual molecules interacting with each other. The number of possible combinations that would need to be computed and checked far exceed the capability of any computer that exists today. Because of how theyre designed, even the fastest supercomputer is forced to check each combination sequentially - one after another. No matter how fast a classical computer is or can be, given the literally greater than astronomical sizes of the number of combinations, many of these problems would take a practical infinity to solve. It just becomes impossible.

Related, the other problem classical computers face is its impossible to build one with sufficient memory to store each of the combinations, even if all the combinations could be computed.

The details of how a quantum computer and quantum computing algorithms work is well beyond the scope or intent of this article, but we can briefly introduce one of the key ideas in order to understand how they can overcome the combinatorial limitations of classical computers.

Classical computers represent information - all information - as numbers. And all numbers can be represented as absolute binary combinations of 1s and 0s. The 1 and 0 each represent a bit of information, the fundamental unit of classical information. Or put another way, information is represented by combinations of two possible states. For example, the number 24 in binary notation is 11000. The number 13 is 1101. You can also do all arithmetic in binary as well. This is convenient, because physically, at the very heart of classical computers is the transistor, which is just an on-off electrical switch. When its on it encodes a 1, and when its off it encodes a 0. Computers do all their math by combining billions of tiny transistors that very quickly switch back and forth as needed. Yet, as fast as this can occur, it still takes finite amounts of time, and all calculations need to be done in an appropriate ordered sequence. If the number of necessary calculations become big enough, as is the case with the combinatorial problems discussed above, you run into an unfeasible computational wall.

Quantum computers are fundamentally different. They overcome the classical limitations by being able to represent information internally not just as a function of two discrete states, but as a continuous probabilistic mixing of states. This allows quantum bits, or qubits, to have many more possible states they can represent at once, and so many more possible combinations of arrangements of objects at once. Put another way, the state space and computational space that a quantum computer has access too is much larger than that of a classical computer. And because of the wave nature of quantum mechanics and superposition (concepts we will not explore here), the internal mixing and probabilistic representation of states and information eventually converge to one dominant solution that the computer outputs. You cant actually observe that internal mixing, but you can observe the final computed output. In essence, as the number of qubits in the quantum computer increase, you can exponentially do more calculations in parallel.

The key concept here is not that quantum computers will necessarily be able to solve new and exotic classes of problems that classical computers cant - although computer scientists have discovered a theoretical class of problem that only quantum computers can solve - but rather that they will be able to solve classes of problems that are - and always will be - beyond the reach of classical computers.

And this isnt to say that quantum computers will replace classical computers. That is not likely to happen anytime in the foreseeable future. For most classes of computational problems classical computers will still work just fine and probably continue being the tool of choice. But for certain classes of problems, quantum computers will far exceed anything possible today.

Well, it depends on the scale at which the dynamics of the brain is being simulated. For sure, there has been much work within the field of computational neuroscience over many decades successfully carrying out computer simulations of the brain and brain activity. But its important to understand the scale at which any given simulation is done.

The brain is exceedingly structurally and functionally hierarchical - from genes, to molecules, cells, network of cells and networks of brain regions. Any simulation of the brain needs to begin with an appropriate mathematical model, a set of equations that capture the chosen scale being modeled that then specify a set of rules to simulate on a computer. Its like a map of a city. The mapmaker needs to make a decision about the scale of the map - how much detail to include and how much to ignore. Why? Because the structural and computational complexity of the brain is so vast and huge that its impossible given existing classical computers to carry out simulations that cut across the many scales with any significant amount of detail.

Even though a wide range of mathematical models about the molecular and cell biology and physiology exist across this huge structural and computational landscape, it is impossible to simulate with any accuracy because of the sheer size of the combinatorial space this landscape presents. It is the same class of problem as that of optimizing people with different political views around a table. But on a much larger scale.

Once again, it in part depends on how you choose to look at it. There is an exquisite amount of detail and structure to the brain across many scales of organization. Heres a more in depth article on this topic.

But if you just consider the number of cells that make up the brain and the number of connections between them as a proxy for the computational complexity - the combinatorial space - of the brain, then it is staggeringly large. In fact, it defies any intuitive grasp.

The brain is a massive network of densely interconnected cells consisting of about 171 trillion brain cells - 86 billion neurons, the main class of brain cell involved in information processing, and another 85 billion non-neuronal cells. There are approximately 10 quadrillion connections between neurons that is a 1 followed by 16 zeros. And of the 85 billion other non-neuronal cells in the brain, one major type of cell called astrocyte glial cells have the ability to both listen in and modulate neuronal signaling and information processing. Astrocytes form a massive network onto themselves, while also cross-talking with the network of neurons. So the brain actually has two distinct networks of cells. Each carrying out different physiological and communication functions, but at the same time overlapping and interacting with each other.

The computational size of the human brain in numbers.

On top of all that structure, there are billions upon billions upon billions of discrete electrical impulses, called action potentials, that act as messages between connected neurons. Astrocytes, unlike neurons, dont use electrical signals. They rely on a different form of biochemical signaling to communicate with each other and with neurons. So there is an entire other molecularly-based information signaling mechanism at play in the brain.

Somehow, in ways neuroscientists still do not fully understand, the interactions of all these electrical and chemical signals carry out all the computations that produce everything the brain is capable of.

Now pause for a moment, and think about the uncountable number of dynamic and ever changing combinations that the state of the brain can take on given this incredible complexity. Yet, it is this combinatorial space, the computations produced by trillions of signals and billions of cells in a hierarchy of networks, that result in everything your brain is capable of doing, learning, experiencing, and perceiving.

So any computer simulation of the brain is ultimately going to be very limited. At least on a classical computer.

How big and complete are the biggest simulations of the brain done to date? And how much impact have they had on scientists understanding of the brain? The answer critically depends on whats being simulated. In other words, at what scale - or scales - and with how much detail given the myriad of combinatorial processes. There certainly continue to be impressive attempts from various research groups around the world, but the amount of cells and brain being simulated, the level of detail, and the amount of time being simulated remains rather limited. This is why headlines and claims that tout ground-breaking large scale simulations of the brain can be misleading, sometimes resulting in controversy and backlash.

The challenges of doing large multi-scale simulations of the brain are significant. So in the end, the answer to how big and complete are the biggest simulations of the brain done to date and how much impact have they had on scientists understanding of the brain - is not much.

First, by their very nature, given a sufficient number of qubits quantum computers will excel at solving and optimizing very large combinatorial problems. Its an inherent consequence of the physics of quantum mechanics and the design of the computers.

Second, given the sheer size and computational complexity of the human brain, any attempt at a large multi-scale simulation with sufficient detail will have to contend with the combinatorial space of the problem.

Third, how a potential quantum computer neural simulation is set up might be able to take advantage of the physics the brain is subject to. Despite its computational power, the brain is still a physical object, and so physical constraints could be used to design and guide simulation rules (quantum computing algorithms) that are inherently combinatorial and parallelizable, thereby taking advantage of what quantum computers do best.

For example, local rules, such as the computational rules of individual neurons, can be used to calculate aspects of the emergent dynamics of networks of neurons in a decentralized way. Each neuron is doing their own thing and contributing to the larger whole, in this case the functions of the whole brain itself, all acting at the same time, and without realizing what theyre contributing too.

In the end, the goal will be to understand the emergent functions of the brain that give rise to cognitive properties. For example, large scale quantum computer simulations might discover latent (hidden) properties and states that are only observable at the whole brain scale, but not computable without a sufficient level of detail and simulation from the scales below it.

If these simulations and research are successful, one can only speculate about what as of yet unknown brain algorithms remain to be discovered and understood. Its possible that such future discoveries will have a significant impact on related topics such as artificial quantum neural networks, or on specially designed hardware that some day may challenge the boundaries of existing computational systems. For example, just published yesterday, an international team of scientists and engineers announced a computational hardware device composed of a molecular-chemical network capable of energy-efficient rapid reconfigurable states, somewhat similar to the reconfigurable nature of biological neurons.

One final comment regarding quantum computers and the brain: This discussion has focused on the potential use of future quantum computers to carry out simulations of the brain that are not currently possible. While some authors and researchers have proposed that neurons themselves might be tiny quantum computers, that is completely different and unrelated to the material here.

It may be that quantum computers will usher in a new era for neuroscience and the understanding of the brain. It may even be the only real way forward. But as of now, actually building workable quantum computers with sufficient stable qubits that outperform classical computers at even modest tasks remains a work in progress. While a handful of commercial efforts exist and have claimed various degrees of success, many difficult hardware and technological challenges remain. Some experts argue that quantum computers may in the end never be built due to technical reasons. But there is much research across the world both in academic labs and in industry attempting to overcome these engineering challenges. Neuroscientists will just have to be patient a bit longer.

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Large-Scale Simulations Of The Brain May Need To Wait For Quantum Computers - Forbes

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NSA: We ‘don’t know when or even if’ a quantum computer will ever be able to break today’s public-key encryption – The Register

America's National Security Agency has published an FAQ about quantum cryptography, saying it does not know "when or even if" a quantum computer will ever exist to "exploit" public-key cryptography.

In the document, titled Quantum Computing and Post-Quantum Cryptography, the NSA said it "has to produce requirements today for systems that will be used for many decades in the future." With that in mind, the agency came up with some predictions [PDF] for the near future of quantum computing and their impact on encryption.

Is the NSA worried about the threat posed by a "cryptographically relevant quantum computer" (CRQC)? Apparently not too much.

"NSA does not know when or even if a quantum computer of sufficient size and power to exploit public key cryptography (a CRQC) will exist," it stated, which sounds fairly conclusive though in 2014 the agency splurged $80m looking for a quantum computer that could smash current encryption in a program titled Owning the Net, so the candor of the paper's statements is perhaps open to debate.

What the super-surveillance agency seems to be saying is that it's not a given that a CRQC capable of breaking today's public-key algorithms will ever emerge, though it wouldn't be a bad idea to consider coming up with and using new techniques that could defeat a future CRQC, should one be built.

It's almost like the NSA is dropping a not-so-subtle hint, though why it would is debatable. If it has a CRQC, or is on the path to one, it might want to warn allies, vendors, and citizens to think about using quantum-resistant technologies in case bad people develop a CRQC too. But why would the spies tip their hand so? It's all very curious.

Progress on quantum computers has been steadily made over the past few years, and while they may not ever replace our standard, classical computing, they are very effective at solving certain problems

Eric Trexler, VP of global governments at security shop Forcepoint, told The Register: "Progress on quantum computers has been steadily made over the past few years, and while they may not ever replace our standard, classical computing, they are very effective at solving certain problems. This includes public-key asymmetric cryptography, one of the two different types of cryptosystems in use today."

Public-key cryptography is what the world relies on for strong encryption, such as TLS and SSL that underpin the HTTPS standard used to help protect your browser data from third-party snooping.

In the NSA's summary, a CRQC should one ever exist "would be capable of undermining the widely deployed public key algorithms used for asymmetric key exchanges and digital signatures" and what a relief it is that no one has one of these machines yet. The post-quantum encryption industry has long sought to portray itself as an immediate threat to today's encryption, as El Reg detailed in 2019.

"The current widely used cryptography and hashing algorithms are based on certain mathematical calculations taking an impractical amount of time to solve," explained Martin Lee, a technical lead at Cisco's Talos infosec arm. "With the advent of quantum computers, we risk that these calculations will become easy to perform, and that our cryptographic software will no longer protect systems."

Given that nations and labs are working toward building crypto-busting quantum computers, the NSA said it was working on "quantum-resistant public key" algorithms for private suppliers to the US government to use, having had its Post-Quantum Standardization Effort running since 2016. However, the agency said there are no such algos that commercial vendors should adopt right now, "with the exception of stateful hash signatures for firmware."

Smart cookies will be glad to hear that the NSA considers AES-256 and SHA-384 "safe against attack by a large quantum computer."

Jason Soroko, CTO of Sectigo, a vendor that advertises "quantum safe cryptography" said the NSA report wasn't conclusive proof that current encryption algos were safe from innovation.

"Quantum computers alone do not crack public key cryptography," he said, adding that such a beast would need to execute an implementation of Shors algorithm. That algo was first described in 1994 by an MIT maths professor and allows for the calculation of prime factors of very large numbers; a vital step towards speeding up the decryption of the product of current encryption algorithms.

"Work on quantum resistant cryptographic algorithms is pushing forward based on the risk that Universal quantum computers will eventually have enough stable qubits to eventually implement Shors algorithm," continued Soroko. "I think its important to assume that innovation in both math and engineering will potentially surprise us."

While advances in cryptography are of more than merely academic interest to the infosec world, there is always the point that security (and data) breaches occur because of primarily human factors. Ransomware, currently the largest threat to enterprises, typically spreads because someone's forgotten to patch or decommission a machine on a corporate network or because somebody opens an attachment from a malicious email.

Or there's the old joke about rubber hose cryptanalysis, referring to beating the passwords out of a captured sysadmin.

Talos' Lee concluded: In a world where users will divulge their passwords in return for chocolate or in response to an enticing phishing email, the risk of quantum computers might not be our biggest threat.

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NSA: We 'don't know when or even if' a quantum computer will ever be able to break today's public-key encryption - The Register

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What Are Altcoins? – Motley Fool

Altcoins (alternative coins) is a term used to describe all cryptocurrencies other than Bitcoin (CRYPTO:BTC). Their name comes from the fact that they're alternatives to Bitcoin and traditional fiat money.

The first altcoins launched in 2011, and, by now, there are thousands of them. Early altcoins aimed at improving aspects of Bitcoin such as transaction speeds or energy efficiency. More recent altcoins serve a variety of purposes depending on the goals of the developers.

Since altcoins are such a big part of the market, every crypto investor should understand how they work. Keep reading to learn about what altcoins are used for, their pros and cons, and much more.

Image source: Getty Images.

There are several different types of altcoins, including stablecoins, mining-based coins, staking-based coins, and governance tokens. The type of altcoin depends on how it works and what its purpose is. Here are the main types of cryptocurrencies you'll find when researching altcoins.

Stablecoins are cryptocurrencies designed to follow the price of another asset. Most of the biggest stablecoins are pegged to the U.S. dollar and attempt to mimic its value. If the price fluctuates, the issuer of the coin will take steps to correct it.

Because stablecoins are intended to maintain the same value, they're normally not chosen as a cryptocurrency investment. Instead, people use stablecoins for savings or to send money. It's also possible to earn interest on stablecoins by lending them out or through certain savings protocols.

This type of cryptocurrency use a process called mining to verify transactions and add more coins to the supply. Miners use devices to solve mathematical equations. Typically, the first miner to solve the equation gets to verify a block of transactions. In return, miners who verify blocks receive crypto rewards.

Since Bitcoin is a mining-based cryptocurrency, mining was the first method used to process crypto transactions. One disadvantage of mining is that it requires significant energy.

These cryptocurrencies use a process called staking to verify transactions and add more coins to the supply. Holders of a staking-based cryptocurrency can choose to stake their coins, meaning they're pledging those coins to be used for transaction processing. The cryptocurrency's blockchain protocol chooses a participant to verify a block of transactions. In return, participants receive crypto rewards.

An early altcoin called Peercoin (CRYPTO:PPC) was the first to introduce the concept of staking. Although Peercoin hasn't become a household name, staking has become popular because it's more energy-efficient than mining.

Governance tokens are cryptocurrencies that give holders voting rights to help shape the future of the project. In most cases, these tokens allow you to create and vote on proposals related to the cryptocurrency. This helps make the cryptocurrency a decentralized project since all the holders have a say, and decisions aren't made by one central authority.

Here are the pros and cons of altcoins:

Pros

Cons

Improve on aspects of Bitcoin.

Don't have Bitcoin's first mover advantage or market share.

Offer high potential rewards.

Significant risk, as many altcoins are scams or end up failing.

Large selection of altcoins, all with their own unique purposes and competitive advantages.

Many altcoins are hard to buy because they're only available on certain altcoin exchanges.

There are a few things that separate altcoins and Bitcoin:

The crypto market includes thousands of altcoins. Here's an early example and a couple of the top altcoins:

You should consider investing in altcoins if you're going to make crypto part of your portfolio and you have time to spend researching them. Some altcoins are ambitious projects that offer more use cases than Bitcoin, which is primarily used as a store of value. Since altcoins aren't as well-known, they could see larger price increases if they catch on.

There are notable downsides to buying altcoins. Because of the sheer number of them, it's challenging to pick out the best altcoins to invest in. Altcoins present a greater risk, and many of the smaller altcoins are dubious investments or scams.

To sum it up, altcoins are worth checking out for hands-on cryptocurrency investors willing to do their homework. If you're looking for a lower-risk or less time-intensive investment, cryptocurrency stocks are a better way to go. Remember that taking on too much risk isn't recommended, so even if you decide to buy altcoins, they should only make up a small part of your portfolio.

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What Are Altcoins? - Motley Fool

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3 reasons why Polkadot could be the next altcoin to hit a new all-time high – Cointelegraph

Layer-1 smart contract platforms increased their market share throughout August after the Ethereum networkLondon hard fork did little to solve the major issues of high transaction fees and network congestion.

One top-10 protocol that has been gaining momentum, but has yet to experience a significant price breakout to new highs like some of its competitors is Polkadot (DOT), a multichain protocol focused on facilitating the creation of cross-chain bridges between separate blockchain networks.

Data from Cointelegraph Markets Pro and TradingView shows that after bottoming out at $10.36 on July 20, the price of DOT increased 205% to an intraday high at $31.70 on Aug. 31 as the chatter of an impending altseason begins to rise.

Three reasons for the increasingly bullish outlook for DOT are its upcoming parachain auctions, a rapidly growing ecosystem of projects interested in launching on the network and a steady increase in daily trading volume.

One of the biggest drivers of momentum for the Polkadot ecosystem is the upcoming parachain auctions where projects vie for community votes to obtain one of the limited slots available to launch on the network.

Polkadots wild cousin Kusama has been in the process of conducting its auctions, with the first batch having been chosen at the end of July and the second batch of auctions scheduled to begin on Sep. 1.

As part of the parachain crowdloan process, users vote for projects by locking up DOT tokens for a designated term as a way to bootstrap funding for projects that are chosen to fill one of the limited slots.

This has the effect of reducing the circulating supply of tokens available adding pressure on the price of DOT. The Polkadot network will undergo its own parachain auctions once all auctions are complete on the Kusama network. The process has been fully audited and to date, the Kusama-bas parachains are running smoothly.

Another reason for the recent strength of DOT is the large number of projects interested in obtaining a parachain slot and launching on the network.

As evidenced by the graphic above, the Polkadot ecosystem has seen extensive growth in terms of protocols and supporting infrastructure over the past year and this is outmatched by only a small number of competing networks in the space.

With the Polkadot parachain auctions expected, its likely that the ecosystem will continue to expand and welcome new projects and proof of this comes from the fact that the Kusama parachain process has thus far been a relatively smooth .

Related: Will Polkadot save decentralized finance from Ethereums scaling problems?

A third reason for the bullish outlook for DOT has been its surging 24-hour trading volume which is now back at levels not seen since the market-wide sell-off in late May.

According to data from CoinMarketCap, DOT's 24-hour trading volume surged more than 300% on Aug. 31 to a high of $5.41 billion as anticipation for the upcoming Kusama parachain auctions excited the Polkadot investors who view KSM's success as a proxy of what can occur with DOT price.

If the Kusama network can continue the smooth rollout of its auction process and clear the way for the process to begin on the Polkadot network, the demand for DOT could rise and this could translate to higher prices for the asset.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.com. Every investment and trading move involves risk, you should conduct your own research when making a decision.

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3 reasons why Polkadot could be the next altcoin to hit a new all-time high - Cointelegraph

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These 7 Altcoins Are About To Pop, According to Altcoin Daily – The Daily Hodl

Altcoin Daily host Aaron Arnold is listing seven cryptocurrencies that he expects to rally.

The cryptocurrency influencer tells his 947,000 YouTube subscribers that Ethereum (ETH) is one of the digital assets that is about to pop.

Aaron argues that Ethereums ecosystem is thriving and that the asset is undergoing a supply shock as a result of the fee-burning mechanism that was introduced with the network upgrade earlier this month.

Speaking of cryptocurrencies about to pop, we need to talk about Ethereum. This is Ethereums cycle to lose, my friends. There is so much good going on in the Ethereum ecosystem. How can you not be bullish?

[03:42] Ethereum looks ready to pop and is definitely going through a supply shock right now. Ethereum is trading on top of stable support, while selling pressure behind it is drastically diminishing.

Next up on the crypto influencers list is Ethereum competitor Cardano (ADA). According to Arnold, Cardanos interoperability is growing as it gears up to launch smart contracts, which could be a bullish signal.

This is big news for Cardano as theyre approaching their smart contracts September 12th upgrade. Something else has popped up, which is pretty, pretty cool. Cardano is getting an Ethereum-compatible side chain

The side chain will use wrapped ADA as the assets to pay for transaction fees This is better for Ethereum, this is better for Cardano. The point is everything is becoming interoperable, and its good that all of these networks are working with each other and talking to each other. Hugely bullish on Ethereum. Hugely bullish on Cardano.

Arnold highlights Solana (SOL) and blockchain oracle Chainlink (LINK) as additional altcoins with growing interoperability.

Solana developers can now use Chainlinks DeFi [decentralized finance] price feed. Chainlink oracle is now live on Solanas network. Builders now have access to reliable price data. So like I said, bullish on Solana and also Chainlink.

Fifth on the crypto YouTubers list is the native token of smart contract platform Avalanche (AVAX), which Arnold says is currently expanding its ecosystem.

Just like Im bullish on Cardano, just like Im bullish on Solana, also bullish on Avalanche

Why is Avalanche soaring? Well, its pretty simple. Avalanche is onboarding blue chip DeFi protocols

Avalanche has recently attracted blue chip DeFi projects like Aave [AAVE], Curve [CRV] and Sushiswap [SUSHI] Besides that, several other nascent DeFi projects in the Avalanche ecosystem are likely contributing to the growth.

Next up on Arnolds list is gaming and non-fungible token (NFT) blockchain platform Enjin (ENJ), which recently notched a partnership with a social network.

I would consider Enjin a blue chip gaming cryptocurrency project protocol. The news is that social network Blockster collaborates with Enjin and is rewarding 400,000 early users with NFTs.

The final altcoin on Arnolds list is XRP, the native digital asset on Ripples XRP Ledger. Arnold says that his bullishness for XRP is based on a reading of the parabolic stop and reverse (SAR) indicator.

XRP is on the verge of a 30% price move. XRP looks ready to spike in volatility. Could see its price move by more than 30%

the Parabolic [SAR] points have turned bullish, indicating that a breakout is underway.

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These 7 Altcoins Are About To Pop, According to Altcoin Daily - The Daily Hodl

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Cardano Price Predictions: How High Can Altcoin Season Take the ADA Crypto? – Investorplace.com

Today, crypto investors are increasingly looking toward altcoins for growth. And why not? Cryptocurrencies like Cardano(CCC:ADA-USD) have performed extremely well, doubling over the past month. Accordingly, investors may be wondering what the expertCardano price predictions suggest for upside on the horizon.

Source: Stanslavs / Shutterstock

Part of this may be due to the fact that many experts are now calling for a new altcoin season. As more investors move away from the juggernauts like Bitcoin(CCC:BTC-USD) andEthereum(CCC:ETH-USD) toward altcoins with unique and specific use cases, these altcoins could have much more potential upside. For long-term crypto investors, thats a very enticing idea.

Cardano in particular has a lot going on right now. The companys five-stage implementation of the Alonzo hard fork is set to see smart contracts introduced shortly. Like other hard fork updates, Cardano is about to become much more useful, very quickly. Currently, Cardano is finishing up stage two of its hard fork update. Indeed, investors bullish on Cardano are looking forward to what the future may hold with this cryptocurrency. Many investors see smart contracts as the wave of the future.

Thus, its no surprise to see some very bullish price predictions for ADA. Lets dive into what those are right now.

At the time of writing, ADA currently trades at $2.79 per token.

On the date of publication, Chris MacDonald did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

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XRP Price Predictions: How High Can the New Altcoin Season Take the XRP Crypto? – Investorplace.com

Given the increasing interest around altcoins, Ripple(CCC:XRP-USD) and XRP are both in the spotlight today. Interest is starting to build around what the expert XRP price predictions suggest is in store for this embattled cryptocurrency.

Source: Shutterstock

Indeed, Ripple and XRP have been engaged in a lengthy battle with regulators for some time. The U.S. Securities and Exchange Commission (SEC) vs. Ripple saga has continued today, with Judge Sarah Netburn setting a noon EST conference call to discuss the privilege dispute, which has been ongoing in recent months. Today, it appears Ripple investors seem to like what theyre hearing, as XRP has shot up nearly 3% since the conference call began.

However, other news has been spurring interest in Ripple and XRP today. Reports that we could be headed into an altcoin-dominated future has many investors excited about XRPs potential moving forward. Should investors continue to shift their focus away from Bitcoin(CCC:BTC-USD) andEthereum(CCC:ETH-USD) toward smaller cap altcoins such as Ripple, the potential upside of these smaller cap tokens could outperform the sector leaders.

Accordingly, lets dive into where the experts think XRP could be headed.

Currently, XRP trades at $1.18 per token at the time of writing.

On the date of publication, Chris MacDonald did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Article printed from InvestorPlace Media, https://investorplace.com/2021/08/xrp-price-predictions-how-high-can-the-new-altcoin-season-take-the-xrp-crypto/.

2021 InvestorPlace Media, LLC

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The Altcoin Evolution – Part IV: The Challenges – The Sales Pitch – NewsBTC

With the ever-changing landscape of tech development and crypto innovation, regulation tends to lag behind in order to have time to react to whats happening. Many altcoin projects are currently growing exponentially as they are exploring largely untouched use cases. As the tech continues to proliferate and expand, more and more problems are arising that require solutions.

This obviously provides more space for viable contenders to occupy in a crowded market. This provides a robust growth argument for many altcoins, but there is a catch. The giant growth gains are fantastic, but we also must consider that large corporate banks do not tend to react positively to any challenges to their dominance of the financial industry. The Federal Reserve and other federal government bodies certainly have an eye open to the chaos as well. Ripple (XRP) is a prime example of this, as the project has been experiencing ongoing back-and-forth dialogue with the SEC for some time now, all while still sitting in the top 10 of token market caps.

At this point, its rather difficult to predict the trajectory of these contingencies. Regulation is always a few steps behind, but it is a certainty. Despite these hurdles, which are far-reaching and constantly evolving, every project must have growth objectives. As we have with past publications of Altcoin Evolution, we will continue to look at the emergence of projects impacting creators, such as those involving NFTs, as prime examples of how difficult these challenges can be for altcoins.

In our last two Altcoin Evolution articles, we took a birds eye view on the challenges, implications, and importance of factors like use case and accessibility. Now, well take a high level look at the importance of altcoins having a sales pitch. In a world where constraints around marketing and visibility are ever-present, leveraging the aforementioned use case and accessibility assets for projects is vital in selling how respective projects stand out.

Related Reading | Cardano Founder Responds To Criticism Over New Crypto Partnership

As mentioned in the previous iteration of Altcoin Evolution, the brass at OnlyFans attempted to rebrand themselves as a non-pornographic site, in order to further align themselves with the values of banks that do business with them. At the time of writing, there has been such a huge backlash that the company has been forced to rescind the proposed changes, after receiving assurances that the banks will support all genres of creators.

This whirlwind news story is a perfect example of how unique digital currencies can instantly have a utility from where there was none. What projects can take advantage of these opportunities, and have the stickiness of a sales pitch that can resonate with crypto consumers?

2021 has been a booming year for altcoins. At the beginning of the year, bitcoin made up about 70% of the crypto market. By July, that number was down to about 48%, according to TradingView. There are over 10,000 altcoins all vying for a slice of this growing market.

Whether a project is a meme token, a DeFi utilization tool, or an NFT platform, one thing remains constant: increasing accessibility and informing consumers about ubiquitous project utilization will be paramount in selling a project to potential investors or users.

We see altcoins best sales pitches carrying typically one (or sometimes multiple) numbers of these buckets:

These are the major buckets that crypto projects can lean on to spread word with consumers. How they go about spreading that word has often boiled down to building community which is why Discord and Telegram have become so prominent for crypto users.

That wraps up Altcoin Evolution with regards to challenges for emerging altcoins. In our final installment next week, we will wrap up the series with a summarizing piece that recaps everything weve covered so far, and answers the simple question what should altcoins be doing in todays market?

Thanks for stopping by well see you next week.

Related Reading | 60K ETH Exit Exchanges, Heres Why Its Bullish For Ethereum

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This service is declaring that it’s crypto altseason again – Cointelegraph

Every week, subscribers of Cointelegraph's data analytics platform receive a detailed breakdown of each algorithmic tool's performance.

Over the weekend, Cointelegraph's Markets Pro data intelligence service, which offers institutional-grade research tools for crypto traders, shared the latest VORTECS Report with its subscriber community.

The full report, available only to subscribers, zooms in on the past weeks biggest-gaining tokens as identified by the system's artificial intelligence tools and offers interpretations of the data that it makes available to traders. Here are some of the highlights of the latest report:

The Markets Pro Altseason Indicator metric is designed to help traders figure out whether it is a good time to be stocking on altcoins or to be prioritizing BTC investments over the next 14 days. The indicator takes into account the same variables as the VORTECS Score price movement, tweet volume, trading volume and social sentiment plus additional data sources such as altcoin listings and crypto projects press coverage.

When Bitcoin struggles and the market turns bearish, many traders tend to see BTC as a safer place to park value than more volatile alternative crypto assets. Conversely, when Bitcoins position is robust and investor optimism carries over to the altcoin market, money flows to the side of alts, where massive gains can be made.

In May, following months of a blooming altseason that started in early 2021, the Altseason Indicator flipped to the BTC side amid Bitcoins troubles and the corresponding bearish trend in the overall crypto market. However, the recent bullish turn meant that it was only a matter of time before a new altseason began.

Within separate seven-day periods over several weeks now, altcoins have on average been generating larger gains than Bitcoin. Yet, the way it usually works is that Bitcoin must first gain a very solid footing, and only after the original cryptocurrency is healthy enough can altcoins finally break out.

After BTC stabilized in the $45,000$50,000 corridor, the path was clear for alts to storm to new highs. Now, the indicator is 33% on the altcoin side, meaning that historical conditions are favorable for trading alts. While this is still not a very strong Altseason Indicator score, the reversal is itself remarkable.

To get more data-powered insights like this, join hundreds of Cointelegraph Markets Pro subscribers who derive actionable insight from the platforms data tools and its vibrant Discord community daily.

Cointelegraph Markets Pro is a simple, easy-to-use dashboard powered by the same technology and data used by the leading institutional investors at a fraction of the cost. For the full report available exclusively to members, visit pro.cointelegraph.com.

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