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Three months ago we held a vigil for the death of the cryptocurrency bull market. Last month, my portfolio went up by 50%. What a wild ride. HodlBot While this volatility doesn’t bother long-term HODLers like myself, price instability is a large barrier to mass-adoption of cryptocurrency. It’s hard to use crypto to pay for stuff, when prices can fluctuate 20% day-to-day. Stablecoins —the holy grail of cryptocurrency Stablecoins claim to achieve price stability by pegging their own prices to stable assets like the USD. The promise of stability has led many to call stablecoins the “holy grail of cryptocurrencies”. Despite the praise, history has taught us over and over that currency pegs can break, and cause wide-spread catastrophe when they do. To name a few: 1994 Mexican peso crisis 1997 Asian financial crisis 1998 Russian financial crisis Argentine great depression Pegging cryptocurrencies is dangerous. And it is a great irony that we do not recognize the stability stablecoins offer is just as elusive as . Chrétien de Troyes’s holy grail In this article, I will demonstrate the weaknesses and dangers of each of the 3 categories of stablecoins while providing poignant real life examples. Inspired by Haseeb Stablecoins backed by Real World Collateral Stablecoins backed by real world collateral are simply a digital representation of fiat currencies or valuable commodities like gold. is an example of stablecoin backed by real-world collateral. It is owned and operated by , one of the world’s largest cryptocurrency exchanges. USDT claims to keep $1 USD on reserve for every unit of USD Tether issued. They also claim that Tethers are redeemable for USD on a 1:1 basis. USD Tether (USDT) Bitfinex Major Weaknesses While this seems like a great way to achieve price stability, there are some major weaknesses to this approach. We need to trust that the issuer will act honestly and not under-collateralize the digital assets issued.We need frequent professional audits to prove that the collateral is there because real world assets are not visible on the blockchain.Because collateral is not transparent, the stablecoin is weak to speculation and false rumours about how it is not sufficiently backed.There are many regulatory hoops to jump through when converting tokens for real-world assets. Barriers to convertibility makes the token less valuable. The USDT Controversy USDT is being accused by the cryptocurrency community of minting tethers not backed by USD, and critics have demanded independent audits to verify USDT’s integrity. So far, the situation does not look great. USDT’s r after Freidman asked for additional information that USDT was not willing to provide. elations with their original auditor, Friedman LLP, deteriorated Recently, have been released implicating USDT in fraud. Currently they are seeking another auditor to validate their records which , a harsh critic of USDT, claims to be just a stalling tactic. damning audio recordings Bitfinex’ed Impact of USDT Fraud on Cryptocurrency Market Many think USDT fraud would only impact a small part of the total cryptocurrency market cap. But . Given that USDT is effectively one method of fiat-inflow, every USDT used to purchase cryptocurrency increases the overall market cap by $50. JP Morgan estimates a 1:50 ratio between fiat-inflow and market cap If fake USDT are issued, then worthless USDT is being used to artificially prop a significant portion of the entire cryptocurrency market. Once the fraud becomes clear, the market goes boom. Credit to for the inspiration u/arsonbunny Here’s how /u/arsonbunny sees the collapse: Tether-enabled exchanges will see a massive spike in Bitcoin and cryptocurrency prices as everyone leaves Tether. Noobs in these exchanges will think they are now millionaires until they realize they are rich in tethers but poor in dollars.Exchanges that have not integrated Tether will experience large drops in Bitcoin and altcoins as experienced investors flee crypto into USD.There will be a flight of Bitcoin from Tether-integrated exchanges to non-Tether exchanges with fiat off-ramps. Exchanges running small fractional reserves will be exposed, further increasing calls for greater reserves requirements.The exchanges might slam the doors shut on withdrawals.Many exchanges that own large balances of Tether, especially Bitfinex, will likely become insolvent. Scary right? USDT is an example of what can happen when investors blindly place their trust in stablecoins to hold the correct amount of collateral. Let’s not forget the purpose of cryptocurrency is to move away from this kind of centralization. Stablecoins backed by Cryptocurrency Collateral So now that we’ve seen how stablecoins backed by real world assets have trust issues, let’s turn our attention to stablecoins that use cryptocurrency as collateral. How they work This category of stablecoins claims to achieve price stability by over-collateralizing cryptocurrency against the issued tokens. For example, if we wanted to create a stablecoin that is backed by Ethereum, we’d issue 1 stablecoin worth $1 that is in turn collateralized by $2 of Ethereum (as opposed to 1 USD) . If ETH falls by 50%, our stablecoin would absorb the shock and still be valued at $1. These stablecoins don’t have the same trust issues because they can prove that the assets in reserve are locked up in smart contracts on the blockchain. However, they are much more vulnerable to price shocks than their fiat-commodity counterparts. If the backed collateral were to crash hard enough, which is very possible in a volatile cryptocurrency market, price-stability would shatter. Major Weaknesses Not capital efficient. Requires a ratio of collateral to stablecoin >1:1Only stable when cryptocurrency prices move in a neutral or positive directionFragile to major price shocks that causes sell-offs and a broken pegWhile transparent in the assets that are being held, they are opaque how they actually work and are affected by the market. Their short-comings are disguised by technical white-papers. They are wolves in sheep’s clothing. BitUSD — A Cautionary Tale BitUSD, a stablecoin created in 2013, exemplifies the weaknesses found in this model. Pegs, like promises, are meant to be broken Warning: this model is pretty complex and its shortcomings are disguised by fancy jargon. We’ll need to make an effort if we want to uncover the wolf in sheep’s clothing. Rather than making myself sick explaining how BitUSD works, I’m going to let take this one. He does a good job explaining it in plain English: Christopher Georgen Suppose that Alice wishes to create $1.00 worth of bitUSD (1 bitUSD).To do so, she must lock away $1.00 worth of Bitshares (the company’s own issued cryptocurrency) plus some excess collateral amount to ensure that she has enough Bitshares locked away even if the price relative to USD drops.Once she has locked away this total amount, she receives 1 bitUSD from the BitShares network.Alice now sells this 1 bitUSD to Bob for $1.05 worth of Bitshares. Alice insists on charging Bob more than $1.00 because her collateral is at risk should the bitUSD/Bitshares price shift.Bob can redeem the 1 bitUSD he holds by submitting this request to the BitShares network. In such an event, an open market position with the least-collateral is forced to accept Bob’s bitUSD in exchange for an amount of Bitshares determined by the current price. Do you see the glaring problem? If the price of Bitshares always rises, then everything is fine. The collateral gains in value relative to issued token so it can always cover what is owed. But when prices start moving downwards fast enough, the excess, but now devalued collateral held by the least-collateralized position is not enough to cover what is owed. What happens thereafter: The collateral evaporates faster than gasolineMaintaining the peg is impossible. It’s going to break.A massive sell-off is triggered and the entire speculative bubble around BTSX goes poof.meirl trying to explain BitUSD — It’s a Fugazi MakerDAO Dai — Not so Stable Either MakerDAO’s “DAI” stablecoin is breaking did such a good job explaining BitUSD I’ll let him explain Dai too. Christopher Georgen Suppose that Alice wishes to create $1.00 worth of Dai (1 Dai).To do so, she must lock away $1.00 worth of ether, plus a specified amount of excess collateral, determined so that the position is collateralized above the .Once she has locked away her collateral, she receives 1 Dai from the Dai system.Alice now sells this 1 Dai to Bob for $1.05 worth of ether. Alice insists on charging Bob more than $1.00 because her collateral is at risk and she must also pay a mandatory . liquidation ratio stability fee Like with BitUSD, everything is kosher when prices go up. But when ETH prices go down, MakerDAO starts liquidating ETH collateral in anticipation so the value of Ether backing Dai doesn’t go below 1:1. In mild price downturns this is fine. But black swan crashes happen fast and they don’t give you time to recover. In market meltdowns, selling ETH to liquidate Dai would actually contribute to . driving the price down faster MakerDAO has a contingency plan for this. The community of MakerDAO MKR token holders will bailout DAI as a buyer of last resort. Not to underestimate the MakerDAO community, but I really doubt any such community can stand against a torrent of sell-orders in a market crash. Trying to maintain the peg during a market sell-offStablecoins Without Collateral Now that we’ve already examined the 2 categories of stablecoins that require collateral, maybe you’re thinking: “what if we don’t need any collateral for a stablecoin?”. What if people just believe that the price should always go to $1, so they resolves discrepancies in the market through arbitrage? After all, fiat currencies are not backed by anything and they work. Well my friend, we’ve made it to the final category of stablecoins and perhaps the most misunderstood: stablecoins without collateral. We must go deeper How does it work We issue a currency that has only one mandate: it should trade at $1.We ensure the $1 trading price by controlling the monetary supply.If the price goes to $2, we increase the monetary supply by minting new tokens to reduce the price back to $1.If the price goes to $0.5, we reduce the monetary supply by buying back tokens to bring the price back to $1. It is always possible to mint new tokens and increase the monetary supply. But, there is a limit to how many tokens we can buy back because we don’t have infinite money. For this reason when prices go below $1, non-collateralized stablecoins typically issue a bond that can be purchased for 1 coin. This bond is then redeemable in the future for $1 when the price of the coin goes back up. Although it sounds like a fair solution, participants are only incentivized to purchase the bonds if they believe that the price of the coin will go up in the future. And in order to do that, there must be new entrants and net new inflow to subsidize the profit-taking of bond holders. Like we’ve seen before, stablecoins that only work when the market goes in a positive direction, tend to be not very stable at all. Weaknesses Participants are only properly incentivized when the price goes upRequires inflows > outflows and a continuous stream of new buyers to maintain its pegExtremely vulnerable to crash — no collateral to back anything upOpaque system that is weaker to speculation because it is difficult to ascertain its true stabilityShortcomings disguised by fancy whitepapers Basecoin — A Proposed Solution to Stablecoins Without Collateral Basecoin attempts to peg its price to 1 USD without the use of any collateral by controlling the money supply. In order to do so, they issue two different kinds of tokens Base BondsBase Shares Base Shares are issued to early-adopters of Basecoin. When the price of Basecoin is above $1, new Basecoins are paid to Base Share holders as dividends. Newly minted Basecoins are then sold on the open market, increasing total monetary supply and bringing the price back down to $1. When the price is below $1, Base Bonds are sold on the market for Basecoin. The bonds cost 1 Base Coin each and come with the promise to repay the bond holder $1 when the price of Basecoin goes back to the peg. The Basecoins used to buy these bonds are taken out of circulation and reduce the monetary supply, bringing the price back up to $1. While this sounds fine and dandy, both Base Share and Base Bond holders are only properly incentivized when the demand and price of Basecoin goes up. After all who would want Base Shares if new coins are never minted? Who would want Base Bonds if they are never repaid with $1 Basecoins? Token holders need to be able to take profits, or at least believe that they can. Otherwise they don’t have a reason to want Base Shares and Base Bonds. For this to happen, they they need to be able to sell their assets to new entrants. The new entrants then need to find even more noobs, and on and on it goes. No wonder that critics say Basecoin looks like a pyramid scheme. Like DAI and Bitshares, everything works when the market goes up, but going downwards is a precarious fall. When prices slip under $1 and there are too few bonds being purchased and not enough new entrants, the only way to keep the peg is to for insiders to simulate demand by making large artificial buys. But at some point, it becomes too expensive to subsidize the difference what Basecoin think the price should be vs. what the market thinks the price should be. And as a result, the peg breaks. My prediction for Basecoin — a broken peg Parting Thoughts — The Antifragility of Complex Systems Although I appreciate the sentiment towards building a price-stable cryptocurrency, I’m just dubious about forcing volatile markets to be stable through man-made efforts. Markets are complex systems that thrive on volatility. When we try to artificially repress natural movements, we are mistaking a complex system that does not respond linearly to inputs, for a simple one that does. The Cat and the Washing Machine from the book Antifragile illustrates the difference complex systems vs. simple man-made systems. In the short-term we are able to achieve some artificial stability as long as market participants play along. But stablecoins claim to be stable, forever. And that is a promise that cannot be kept. If the history of markets have taught us anything it is that: the market will always decide what the price of something should be, rather than the other way around. Maybe the answer to price-stability is one we don’t want to hear. And that is price stability can only come with time when more real life uses emerge for cryptocurrency. Rather than stablecoins, perhaps that is the holy grail we should all be searching for instead. About the Author I’m the founder of hodlbot.io We automatically diversify and rebalance your cryptocurrency portfolio into the top 20 coins by market cap. You can read more about our methodology . here We ignore all stablecoins even if they are within the top 20 coins by market cap because we believe they are not a worthwhile long-term investment . The short-term stability they offer, comes with the risks as I have aforementioned.

The Instability of Stablecoins

Table of Links Abstract and I. Introduction II. Expectations of Blockchain Accessibility III. Approach I: Maintain a Ledger Locally - Run a Full Node IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) V. Approach III: Light Node - External Query & Local Verification VI. Concluding Remark and References VI. CONCLUDING REMARKS Ultimately, any blockchain accessibility solution has to decide on a split of responsibilities: either the user does most of the computation necessary or some third party does. A spectrum of solutions may be developed based on different types and needs of users, from a full node to a full third-party service. However, we consider that any solution must still adhere to reasonable security expectations: integrity, availability, and privacy. Most blockchain research is based on the assumption that users run their local nodes. This means the fact that running a full node is still the best way to provide integrity, availability, and privacy. Indeed we surveyed third-party service providers in the field and noted the lack of a data integrity guarantee when the provider is not trusted. We also recalled events that render the providers unable to provide service. These facts mean that third-party service providers can and do fail, and thus relying on them can become risky. At the same time, considering the high cost of running a full node locally, we believe that users should have more choice that does not impact the security guarantees. We observe the emergence of so-called ultralight nodes, which provides a way for third-party services to give a verifiable statement that requires little hardware power from the client. Many open questions remain regarding the accessibility of blockchains, even regarding full-node solutions and third-party providers. Any number of light node protocols can also be built to serve different users’ needs. We reiterate that ensuring the accessibility of blockchains is paramount for the technology. If we believe that blockchains are a common good, then making them accessible to all in a secure and available manner is critical for the future development of Web3 technology. Any information is as useful as how it can be read, and such reasoning also applies to blockchains. REFERENCES [1] AL-BASSAM, M., SONNINO, A., AND BUTERIN, V. Fraud and data availability proofs: Maximising light client security and scaling blockchains with dishonest majorities. arXiv preprint arXiv:1809.09044 (2018). [2] ALCHEMY. Alchemy. https://alchemy.com/, 2022. [3] ALL THAT NODE. All That Node. https://allthatnode.com/, 2022. [4] ANKR. Ankr. https://www.ankr.com/, 2022. [5] BADDOUR, R. Setting up a Litecoin super node: A complete step-bystep recipe that anyone can follow. https://medium.com/@baddour/sett ing-up-a-litecoin-full-node-a-complete-step-by-step-recipe-that-anyon e-can-follow-fb18b82d5cfe, 2022. [6] BINANCE. Binance. https://www.binance.us/, 2022. [7] BITCOIN PROJECT. Minimum requirements. https://bitcoin.org/en/ful l-node#minimum-requirements, 2022. [8] BITCOIN PROJECT. RPC API reference. https://developer.bitcoin.org/ reference/rpc/#, 2022. [9] BLOCKDAEMON. BlockDeamon. https://blockdaemon.com/, 2022. [10] BUNZ, B., KIFFER, L., LUU, L., AND ZAMANI, M. Flyclient: Superlight clients for cryptocurrencies. In 2020 IEEE Symposium on Security and Privacy (SP) (2020), IEEE, pp. 928–946. [11] CHAINSTACK. Chainstack. https://chainstack.com/, 2022. [12] CHATZIGIANNIS, P., BALDIMTSI, F., AND CHALKIAS, K. Sok: Blockchain light clients. In International Conference on Financial Cryptography and Data Security (2022), Springer, pp. 615–641. [13] CLEPER, Y., AND BINDER, G. Lava: A decentralized RPC network for blockchains - litepaper v0.1. Tech. rep., Lava Protocol Inc., 2022. [14] COINCASHEW. How to run your own Monero node. https://www.co incashew.com/coins/overview-xmr/guide-or-how-to-run-a-full-node, 2022. [15] CONSENSYS. Privacy policy. http://web.archive.org/web/2022112109 5716/https://consensys.net/privacy-policy/, 2022. [16] CRYPTO.COM. Crypto.com. https://crypto.com/us, 2022. [17] DAIAN, P., GOLDFEDER, S., KELL, T., LI, Y., ZHAO, X., BENTOV, I., BREIDENBACH, L., AND JUELS, A. Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability. In 2020 IEEE Symposium on Security and Privacy (SP) (2020), IEEE, pp. 910–927. [18] DASH CORE GROUP. Masternode requirements. https://docs.dash.org/ en/stable/masternodes/understanding.html#masternode-requirements, 2022. [19] DATAHUB. DataHub. https://datahub.figment.io/, 2022. [20] DENNIS, R., AND DISSO, J. P. An analysis into the scalability of bitcoin and ethereum. In Third International Congress on Information and Communication Technology (2019), Springer, pp. 619–627. [21] ELECTRIC COIN COMPANY. Troubleshooting guide. https://zcash.read thedocs.io/en/latest/rtd pages/troubleshooting guide.html, 2022. [22] ETHEREUM FOUNDATION. Ethereum portal network. https://www.ethp ortal.net/, 2022. [23] ETHEREUM FOUNDATION. Light Ethereum subprotocol (LES). https: //github.com/ethereum/devp2p/blob/master/caps/les.md, 2022. [24] ETHEREUM FOUNDATION. Light node. https://ethereum.org/en/develo pers/docs/nodes-and-clients/#light-node, 2022. [25] ETHEREUM FOUNDATION. Node as a service. https://ethereum.org/en/ developers/docs/nodes-and-clients/nodes-as-a-service/, 2022. [26] ETHEREUM FOUNDATION. Requirements. https://ethereum.org/en/deve lopers/docs/nodes-and-clients/run-a-node/#requirements, 2022. [27] ETHEREUM FOUNDATION. Who should run a node? https://ethereum .org/en/run-a-node/, 2022. [28] FANTI, G., KOGAN, L., AND VISWANATH, P. Economics of proof-ofstake payment systems, 2019. [29] GETBLOCK. GetBlock. https://getblock.io/, 2022. [30] GOES, C. The interblockchain communication protocol: An overview. arXiv preprint arXiv:2006.15918 (2020). [31] GRIFFITH, E. Why the crypto collapse matters. The New York Times (Nov 2022). [32] INFSTONES. InfStones. https://infstones.com/, 2022. [33] INFURA. Infura. https://infura.io/, 2022. [34] KALEIDO. Kaleido. https://kaleido.io/, 2022. [35] LI, K., CHEN, J., LIU, X., TANG, Y. R., WANG, X., AND LUO, X. As strong as its weakest link: How to break blockchain dapps at rpc service. In NDSS (2021). [36] MALWA, S. Two Polygon, Fantom front ends hit by DNS attack. https: //www.coindesk.com/tech/2022/07/01/two-polygon-fantom-front-end s-hit-by-dns-attack/, 2022. [37] MEE, D. Bitcoin full node on RBP3 (revised). https://medium.com/@ meeDamian/bitcoin-full-node-on-rbp3-revised-88bb7c8ef1d1, 2017. [38] MEE, D. Litecoin full node on RBP3. https://medium.com/@meeDam ian/litecoin-full-node-on-rbp3-69f851f74f1b, 2017. [39] METAMASK SUPPORT. Why Infura cannot serve certain areas. https: //metamask.zendesk.com/hc/en-us/articles/360059386712-Why-MetaM ask-and-Infura-cannot-serve-certain-areas, 2022. [40] MORALIS. Moralis. https://moralis.io/, 2022. [41] NAKAMOTO, S. Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review (2008), 21260. [42] NELSON, B. J. Remote procedure call. PhD thesis, Carnegie Mellon University, 1981. [43] NOWNODES. NOWNodes. https://nownodes.io/, 2022. [44] PAAVOLAINEN, S., AND CARR, C. Security properties of light clients on the ethereum blockchain. IEEE Access 8 (2020), 124339–124358. [45] POCKET NETWORK. Pocket Network. https://www.pokt.network/, 2022. [46] QUICKNODE. QuickNode. https://www.quicknode.com/, 2022. [47] RIVET. Rivet. https://rivet.cloud/, 2022. [48] SENSEINODE. SenseiNode. https://senseinode.com/, 2022. [49] SETTLEMINT. SettleMint. https://console.settlemint.com/, 2022. [50] SOLANA FOUNDATION. Validator requirements. https://docs.solana.co m/running-validator/validator-reqs, 2022. [51] SONG, Y.-D., AND ASTE, T. The cost of bitcoin mining has never really increased. Frontiers in Blockchain (2020), 44. [52] WATCHDATA. WatchData. https://watchdata.io/, 2022. [53] XRP LEDGER. System requirements. https://xrpl.org/system-requireme nts.html, 2022. [54] YAISH, A., AND ZOHAR, A. Correct cryptocurrency asic pricing: Are miners overpaying. arXiv preprint arXiv:2002.11064 (2020). [55] ZMOK. ZMOK. https://zmok.io/, 2022. Authors:
(1) Zhongtang Luo, Purdue University (luo401@purdue.edu);
(2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com);
(3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Expectations of Blockchain Accessibility II. Expectations of Blockchain Accessibility III. Approach I: Maintain a Ledger Locally - Run a Full Node III. Approach I: Maintain a Ledger Locally - Run a Full Node IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) V. Approach III: Light Node - External Query & Local Verification V. Approach III: Light Node - External Query & Local Verification VI. Concluding Remark and References VI. Concluding Remark and References VI. CONCLUDING REMARKS Ultimately, any blockchain accessibility solution has to decide on a split of responsibilities: either the user does most of the computation necessary or some third party does. A spectrum of solutions may be developed based on different types and needs of users, from a full node to a full third-party service. However, we consider that any solution must still adhere to reasonable security expectations: integrity, availability, and privacy. Most blockchain research is based on the assumption that users run their local nodes. This means the fact that running a full node is still the best way to provide integrity, availability, and privacy. Indeed we surveyed third-party service providers in the field and noted the lack of a data integrity guarantee when the provider is not trusted. We also recalled events that render the providers unable to provide service. These facts mean that third-party service providers can and do fail, and thus relying on them can become risky. At the same time, considering the high cost of running a full node locally, we believe that users should have more choice that does not impact the security guarantees. We observe the emergence of so-called ultralight nodes, which provides a way for third-party services to give a verifiable statement that requires little hardware power from the client. Many open questions remain regarding the accessibility of blockchains, even regarding full-node solutions and third-party providers. Any number of light node protocols can also be built to serve different users’ needs. We reiterate that ensuring the accessibility of blockchains is paramount for the technology. If we believe that blockchains are a common good, then making them accessible to all in a secure and available manner is critical for the future development of Web3 technology. Any information is as useful as how it can be read, and such reasoning also applies to blockchains. REFERENCES [1] AL-BASSAM, M., SONNINO, A., AND BUTERIN, V. Fraud and data availability proofs: Maximising light client security and scaling blockchains with dishonest majorities. arXiv preprint arXiv:1809.09044 (2018). [2] ALCHEMY. Alchemy. https://alchemy.com/, 2022. [3] ALL THAT NODE. All That Node. https://allthatnode.com/, 2022. [4] ANKR. Ankr. https://www.ankr.com/, 2022. [5] BADDOUR, R. Setting up a Litecoin super node: A complete step-bystep recipe that anyone can follow. https://medium.com/@baddour/sett ing-up-a-litecoin-full-node-a-complete-step-by-step-recipe-that-anyon e-can-follow-fb18b82d5cfe, 2022. [6] BINANCE. Binance. https://www.binance.us/, 2022. [7] BITCOIN PROJECT. Minimum requirements. https://bitcoin.org/en/ful l-node#minimum-requirements, 2022. [8] BITCOIN PROJECT. RPC API reference. https://developer.bitcoin.org/ reference/rpc/#, 2022. [9] BLOCKDAEMON. BlockDeamon. https://blockdaemon.com/, 2022. [10] BUNZ, B., KIFFER, L., LUU, L., AND ZAMANI, M. Flyclient: Superlight clients for cryptocurrencies. In 2020 IEEE Symposium on Security and Privacy (SP) (2020), IEEE, pp. 928–946. [11] CHAINSTACK. Chainstack. https://chainstack.com/, 2022. [12] CHATZIGIANNIS, P., BALDIMTSI, F., AND CHALKIAS, K. Sok: Blockchain light clients. In International Conference on Financial Cryptography and Data Security (2022), Springer, pp. 615–641. [13] CLEPER, Y., AND BINDER, G. Lava: A decentralized RPC network for blockchains - litepaper v0.1. Tech. rep., Lava Protocol Inc., 2022. [14] COINCASHEW. How to run your own Monero node. https://www.co incashew.com/coins/overview-xmr/guide-or-how-to-run-a-full-node, 2022. [15] CONSENSYS. Privacy policy. http://web.archive.org/web/2022112109 5716/https://consensys.net/privacy-policy/, 2022. [16] CRYPTO.COM. Crypto.com. https://crypto.com/us, 2022. [17] DAIAN, P., GOLDFEDER, S., KELL, T., LI, Y., ZHAO, X., BENTOV, I., BREIDENBACH, L., AND JUELS, A. Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability. In 2020 IEEE Symposium on Security and Privacy (SP) (2020), IEEE, pp. 910–927. [18] DASH CORE GROUP. Masternode requirements. https://docs.dash.org/ en/stable/masternodes/understanding.html#masternode-requirements, 2022. [19] DATAHUB. DataHub. https://datahub.figment.io/, 2022. [20] DENNIS, R., AND DISSO, J. P. An analysis into the scalability of bitcoin and ethereum. In Third International Congress on Information and Communication Technology (2019), Springer, pp. 619–627. [21] ELECTRIC COIN COMPANY. Troubleshooting guide. https://zcash.read thedocs.io/en/latest/rtd pages/troubleshooting guide.html, 2022. [22] ETHEREUM FOUNDATION. Ethereum portal network. https://www.ethp ortal.net/, 2022. [23] ETHEREUM FOUNDATION. Light Ethereum subprotocol (LES). https: //github.com/ethereum/devp2p/blob/master/caps/les.md, 2022. [24] ETHEREUM FOUNDATION. Light node. https://ethereum.org/en/develo pers/docs/nodes-and-clients/#light-node, 2022. [25] ETHEREUM FOUNDATION. Node as a service. https://ethereum.org/en/ developers/docs/nodes-and-clients/nodes-as-a-service/, 2022. [26] ETHEREUM FOUNDATION. Requirements. https://ethereum.org/en/deve lopers/docs/nodes-and-clients/run-a-node/#requirements, 2022. [27] ETHEREUM FOUNDATION. Who should run a node? https://ethereum .org/en/run-a-node/, 2022. [28] FANTI, G., KOGAN, L., AND VISWANATH, P. Economics of proof-ofstake payment systems, 2019. [29] GETBLOCK. GetBlock. https://getblock.io/, 2022. [30] GOES, C. The interblockchain communication protocol: An overview. arXiv preprint arXiv:2006.15918 (2020). [31] GRIFFITH, E. Why the crypto collapse matters. The New York Times (Nov 2022). [32] INFSTONES. InfStones. https://infstones.com/, 2022. [33] INFURA. Infura. https://infura.io/, 2022. [34] KALEIDO. Kaleido. https://kaleido.io/, 2022. [35] LI, K., CHEN, J., LIU, X., TANG, Y. R., WANG, X., AND LUO, X. As strong as its weakest link: How to break blockchain dapps at rpc service. In NDSS (2021). [36] MALWA, S. Two Polygon, Fantom front ends hit by DNS attack. https: //www.coindesk.com/tech/2022/07/01/two-polygon-fantom-front-end s-hit-by-dns-attack/, 2022. [37] MEE, D. Bitcoin full node on RBP3 (revised). https://medium.com/@ meeDamian/bitcoin-full-node-on-rbp3-revised-88bb7c8ef1d1, 2017. [38] MEE, D. Litecoin full node on RBP3. https://medium.com/@meeDam ian/litecoin-full-node-on-rbp3-69f851f74f1b, 2017. [39] METAMASK SUPPORT. Why Infura cannot serve certain areas. https: //metamask.zendesk.com/hc/en-us/articles/360059386712-Why-MetaM ask-and-Infura-cannot-serve-certain-areas, 2022. [40] MORALIS. Moralis. https://moralis.io/, 2022. [41] NAKAMOTO, S. Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review (2008), 21260. [42] NELSON, B. J. Remote procedure call. PhD thesis, Carnegie Mellon University, 1981. [43] NOWNODES. NOWNodes. https://nownodes.io/, 2022. [44] PAAVOLAINEN, S., AND CARR, C. Security properties of light clients on the ethereum blockchain. IEEE Access 8 (2020), 124339–124358. [45] POCKET NETWORK. Pocket Network. https://www.pokt.network/, 2022. [46] QUICKNODE. QuickNode. https://www.quicknode.com/, 2022. [47] RIVET. Rivet. https://rivet.cloud/, 2022. [48] SENSEINODE. SenseiNode. https://senseinode.com/, 2022. [49] SETTLEMINT. SettleMint. https://console.settlemint.com/, 2022. [50] SOLANA FOUNDATION. Validator requirements. https://docs.solana.co m/running-validator/validator-reqs, 2022. [51] SONG, Y.-D., AND ASTE, T. The cost of bitcoin mining has never really increased. Frontiers in Blockchain (2020), 44. [52] WATCHDATA. WatchData. https://watchdata.io/, 2022. [53] XRP LEDGER. System requirements. https://xrpl.org/system-requireme nts.html, 2022. [54] YAISH, A., AND ZOHAR, A. Correct cryptocurrency asic pricing: Are miners overpaying. arXiv preprint arXiv:2002.11064 (2020). [55] ZMOK. ZMOK. https://zmok.io/, 2022. Authors: (1) Zhongtang Luo, Purdue University (luo401@purdue.edu); (2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com); (3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). Authors: Authors: (1) Zhongtang Luo, Purdue University (luo401@purdue.edu); (2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com); (3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Balancing Blockchain Access: Local Nodes vs. Third-Party Services

How to Avoid the Null Trap in Privilege Mode Drivers TL;DR: Using null pointers in critical code can crash your system Problems Memory access violation
Unpredictable behavior
Null Pointer Dereference
Unexpected program termination
System instability
No healing/recovery strategy
Security Risk Solutions Avoid using NULLs
Use address sanitizers
Make controlled releases to mission-critical software
Create better rollback strategies instead of BSOD
Use Smart Pointers: Manage memory automatically and avoid null pointers with smart pointers
Create self-healing software.
Apply defensive programming
Improve your QA tests before deploying to production. Context When you use nulls in a privileged driver, you risk causing serious issues. Privilege mode drivers run with high permissions, and if you use a null pointer, the system might try to access an invalid memory address. For example, trying to read from address 0x9c (156) or using 0x0 as a special value can lead to critical errors. You can't just abort the program in privileged mode, so you must handle these cases carefully. In privileged drivers, null pointer usage poses significant risks. You can mitigate these risks using modern C++ features like std::optional. This problem caused one of the worst software blackouts in 2024. Sample Code Wrong // This case is not exactly what happened with Crowdstrike
// It is here for illustration purposes
void* get_data() {
  if (data_available) {
    return data_ptr;  // This could be null!
  } else {
    // Uh oh, what if data_ptr is null here?
    return NULL;  
    // Using Null to indicate no data
    // knowing Null is schizophrenic
  }
}

int process_data(void* data) {
  if (data != NULL) { 
    // Maybe a null check, but not guaranteed!
    // Accessing data... (crash if data is Null)
    return *data;
  }
  // No check? Silent failure or unexpected behavior.
  return -1;
} Right // You should ideally replace the null with a polymorphic call
// You can see the technique in related articles

std::unique_ptr<int> get_data() { 
  if (data_available) {
    return std::make_unique<int>(data_value);
  } else {
    return nullptr;  // Explicitly return nullptr
  }
}

int process_data(const std::unique_ptr<int>& data) {
  if (data) { // Check for valid pointer
    return *data;
  } else {
    // Handle no data case (e.g., return default value)
    return 0;
  }
} Important Notes About this Code Smell Detection [x]Semi-Automatic You can detect this smell by checking for null pointer usage in critical parts of your code. Look for functions that process pointers and see if they handle null pointers safely. Human code reviews are good for checking this kind of problem. Tags Null Level [x]Advanced /AI Generation AI generators can sometimes produce this smell, especially if they generate code without context about the environment where the code will run. AI generators are fed with code with NULL usage even though his creator told us to avoid it altogether. AI Detection AI tools can detect this smell with specific instructions. AI can be trained to identify code patterns. Teaching it the nuances of privileged driver development and null safety best practices might require more advanced techniques. Use static analysis tools to flag null pointer dereferences. Conclusion Voyager 1's software has been running for more than 50 years. It was designed to be robust, reliable, and redundant which is sadly uncommon in some immature systems in 2024. Avoid using null pointers in privileged mode drivers. I have written a book on clean code and a whole chapter #15 on how to avoid NULL and all the consequences it carries. Hopefully, Crowdstrike engineers will read it! Related Code Smells Code Smell 12 - Null Code Smell 126 - Fake Null Object Code Smell 208 - Null Island Further Reading https://hackernoon.com/null-the-billion-dollar-mistake-8t5z32d6?embedable=true https://github.com/google/sanitizers/wiki/AddressSanitizer?embedable=true https://x.com/perpetualmaniac/status/1814376668095754753?embedable=true Disclaimer: Code Smells are my opinion. I couldn't resist the temptation to put in a null reference, simply because it was so easy to implement. This has led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage in the last forty years. Tony Hoare https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series on HackerNoon. https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true How to Avoid the Null Trap in Privilege Mode Drivers TL;DR: Using null pointers in critical code can crash your system TL;DR: Using null pointers in critical code can crash your system Problems Memory access violation Unpredictable behavior Null Pointer Dereference Unexpected program termination System instability No healing/recovery strategy Security Risk Memory access violation Unpredictable behavior Null Pointer Dereference Unexpected program termination System instability No healing/recovery strategy Security Risk Solutions Avoid using NULLs Use address sanitizers Make controlled releases to mission-critical software Create better rollback strategies instead of BSOD Use Smart Pointers: Manage memory automatically and avoid null pointers with smart pointers Create self-healing software. Apply defensive programming Improve your QA tests before deploying to production. Avoid using NULLs NULLs Use address sanitizers address sanitizers Make controlled releases to mission-critical software Create better rollback strategies instead of BSOD BSOD Use Smart Pointers: Manage memory automatically and avoid null pointers with smart pointers Create self-healing software. Apply defensive programming Improve your QA tests before deploying to production. Context When you use nulls in a privileged driver, you risk causing serious issues. Privilege mode drivers run with high permissions, and if you use a null pointer, the system might try to access an invalid memory address. For example, trying to read from address 0x9c (156) or using 0x0 as a special value can lead to critical errors. You can't just abort the program in privileged mode, so you must handle these cases carefully. In privileged drivers, null pointer usage poses significant risks. You can mitigate these risks using modern C++ features like std::optional . std::optional This problem caused one of the worst software blackouts in 2024. This problem Sample Code Wrong // This case is not exactly what happened with Crowdstrike
// It is here for illustration purposes
void* get_data() {
  if (data_available) {
    return data_ptr;  // This could be null!
  } else {
    // Uh oh, what if data_ptr is null here?
    return NULL;  
    // Using Null to indicate no data
    // knowing Null is schizophrenic
  }
}

int process_data(void* data) {
  if (data != NULL) { 
    // Maybe a null check, but not guaranteed!
    // Accessing data... (crash if data is Null)
    return *data;
  }
  // No check? Silent failure or unexpected behavior.
  return -1;
} // This case is not exactly what happened with Crowdstrike
// It is here for illustration purposes
void* get_data() {
  if (data_available) {
    return data_ptr;  // This could be null!
  } else {
    // Uh oh, what if data_ptr is null here?
    return NULL;  
    // Using Null to indicate no data
    // knowing Null is schizophrenic
  }
}

int process_data(void* data) {
  if (data != NULL) { 
    // Maybe a null check, but not guaranteed!
    // Accessing data... (crash if data is Null)
    return *data;
  }
  // No check? Silent failure or unexpected behavior.
  return -1;
} Right // You should ideally replace the null with a polymorphic call
// You can see the technique in related articles

std::unique_ptr<int> get_data() { 
  if (data_available) {
    return std::make_unique<int>(data_value);
  } else {
    return nullptr;  // Explicitly return nullptr
  }
}

int process_data(const std::unique_ptr<int>& data) {
  if (data) { // Check for valid pointer
    return *data;
  } else {
    // Handle no data case (e.g., return default value)
    return 0;
  }
} // You should ideally replace the null with a polymorphic call
// You can see the technique in related articles

std::unique_ptr<int> get_data() { 
  if (data_available) {
    return std::make_unique<int>(data_value);
  } else {
    return nullptr;  // Explicitly return nullptr
  }
}

int process_data(const std::unique_ptr<int>& data) {
  if (data) { // Check for valid pointer
    return *data;
  } else {
    // Handle no data case (e.g., return default value)
    return 0;
  }
} Important Notes About this Code Smell Detection [x]Semi-Automatic [x] Semi-Automatic [x] You can detect this smell by checking for null pointer usage in critical parts of your code. Look for functions that process pointers and see if they handle null pointers safely. Human code reviews are good for checking this kind of problem. Tags Null Null Level [x]Advanced [x] Advanced [x] /AI Generation AI generators can sometimes produce this smell, especially if they generate code without context about the environment where the code will run. AI generators are fed with code with NULL usage even though his creator told us to avoid it altogether. his creator told us AI Detection AI tools can detect this smell with specific instructions. AI can be trained to identify code patterns. Teaching it the nuances of privileged driver development and null safety best practices might require more advanced techniques. Use static analysis tools to flag null pointer dereferences. Conclusion Voyager 1's software has been running for more than 50 years. It was designed to be robust, reliable, and redundant which is sadly uncommon in some immature systems in 2024. Avoid using null pointers in privileged mode drivers. I have written a book on clean code and a whole chapter #15 on how to avoid NULL and all the consequences it carries. clean code #15 Hopefully, Crowdstrike engineers will read it! Related Code Smells Code Smell 12 - Null Code Smell 12 - Null Code Smell 126 - Fake Null Object Code Smell 126 - Fake Null Object Code Smell 208 - Null Island Code Smell 208 - Null Island Further Reading https://hackernoon.com/null-the-billion-dollar-mistake-8t5z32d6?embedable=true https://hackernoon.com/null-the-billion-dollar-mistake-8t5z32d6?embedable=true https://github.com/google/sanitizers/wiki/AddressSanitizer?embedable=true https://github.com/google/sanitizers/wiki/AddressSanitizer?embedable=true https://x.com/perpetualmaniac/status/1814376668095754753?embedable=true https://x.com/perpetualmaniac/status/1814376668095754753?embedable=true Disclaimer: Code Smells are my opinion. Disclaimer: Code Smells are my opinion. Disclaimer: Code Smells are my opinion . opinion I couldn't resist the temptation to put in a null reference, simply because it was so easy to implement. This has led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage in the last forty years. I couldn't resist the temptation to put in a null reference, simply because it was so easy to implement. This has led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage in the last forty years. Tony Hoare Tony Hoare https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series on HackerNoon. This article is part of the CodeSmell Series on HackerNoon. https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-i-xqz3evd?embedable=true

Code Smell 260 - Crowdstrike NULL

Artificial intelligence (AI) is rapidly changing our reality, penetrating all spheres of life and radically transforming work processes. In 2024, AI implementation will become not just a trend but a necessity for business survival and prosperity. But what does it mean for employees and companies? Let's look at the key trends and see how AI is already shaping the performance capacities of tomorrow. AI and workplace transformation AI seamlessly integrates with automation and innovation, streamlining routine tasks and unlocking new realms of creativity and development. Imagine a future office where AI assistants manage calendars, organize meetings, and analyze data. These technologies, such as Google Assistant and Microsoft Cortana, are already in active use. Robotic Process Automation (RPA) and smart assistants are essential to modern workflows. Software robots handle repetitive tasks, from data processing to managing customer inquiries, freeing human resources for more strategic and creative endeavors. AI assistants, like chatbots, are increasingly common, helping employees with routine tasks and boosting productivity. The 2023 McKinsey report "Generative AI and the Future of Work in America" explores the impact of generative AI across various economic sectors. The study reveals that up to 30% of working hours could be automated by 2030 due to generative AI, offering immense opportunities for businesses to enhance efficiency and work quality significantly. Employee expectations and new skills demand As AI evolves, so do employee expectations. To remain competitive, employees must adapt and acquire new skills. Companies are increasingly offering courses and training to enhance employees' abilities in AI and data analysis. This not only boosts productivity but also motivates employees, providing them with confidence in the future. IBM exemplifies this approach through its IBM SkillsBuild platform, which offers free AI courses and certifications. This investment in employees highlights how major companies recognize that the future is intertwined with AI. New professional roles, such as AI strategists, AI ethicists, and AI solution developers, are emerging. These professions will be in high demand in the coming years, and those who acquire the necessary skills will be at the forefront of the technological revolution. AI enhances the efficiency of remote work by analyzing productivity and optimizing working conditions. For instance, Microsoft employs AI to monitor working hours and identify the most productive times for each employee. Additionally, AI helps develop personalized work plans based on individual preferences and strengths, leading to higher employee satisfaction and retention. Ethical and social challenges The introduction of AI brings both opportunities and challenges that require careful consideration and responsible management. Ensuring that AI algorithms are fair and non-discriminatory based on gender, age, or race is crucial. A notable example of poor configuration is the incident with Microsoft's AI bot launched in February 2024, which quickly began creating and distributing racist and offensive images due to user manipulation and false learning, leading to the promotion of hatred and discrimination. Studies have shown that algorithms can reproduce biases present in the data they are trained on. Therefore, it is essential to regularly check and adjust them to maintain fairness and transparency. Additionally, the introduction of AI raises concerns among employees about potential job loss due to automation. People fear that machines may replace their jobs, leading to increased unemployment and social instability. Companies need to invest in retraining and upskilling programs to help employees adapt to the new environment and retain their jobs. According to the World Economic Forum's "The Future of Jobs Report" for 2020 and 2023, 85 million jobs are expected to be replaced by automation and technological changes. However, they also report that 97 million new jobs will be created as humans, machines, and algorithms increasingly interact. This presents incredible opportunities for those willing to learn and adapt. Cybersecurity and AI: New Challenges AI also introduces significant cybersecurity challenges. Companies utilizing AI must prioritize protecting employee and customer data from leaks and misuse. This involves implementing multi-factor authentication, data encryption, and continuous network activity monitoring with AI tools to detect and prevent cyber threats early on. Data processed by AI can be stored in either local (on-premises) libraries or cloud (online) services. On-premises solutions offer more control over data but require substantial resources for maintenance and protection. Conversely, cloud solutions provide greater flexibility and scalability, but necessitate a careful approach to security management. For instance, in January 2024, OpenAI experienced a data breach due to a bug in the open-source Redis library. This bug allowed users to access some data of other active users, including names, addresses, and partial credit card details. Although the breach was caused by an accidental vulnerability rather than intentional actions, the incident underscored the importance of robust data protection and swift responses to cyber threats. To learn more about how AI is assisting companies in managing data and related challenges, you can read my upcoming article, "The Role of AI in the Future of Business Management." Key AI Trends In the Future of the Workplace AI technologies are evolving rapidly, and several key trends are emerging that will shape the future of work. Integrating AI with other technologies, such as the Internet of Things (IoT) and blockchain, to create smart and autonomous systems is becoming increasingly common. AI, which is capable of understanding and analyzing natural language, is transforming into a powerful business tool. It aids not only in data analysis but also in strategic decision-making, enhancing overall business processes. AI is becoming an integral part of corporate strategy, driving innovation and improving efficiency. Companies that effectively integrate AI into their culture will gain a competitive edge over others. To sum up AI is not just a tool but a powerful catalyst for change, shaping the future of work and production capacity. Companies and employees must prepare for this challenge by developing new skills and adapting to evolving conditions. AI offers enormous opportunities for those ready to embrace and leverage them. Don't miss the chance to be part of this revolution. Start learning AI today, invest in training, and stay ahead to face the future with confidence. Share your thoughts and experiences in the comments, and let's discuss how AI is transforming our world and how we can harness its potential for good. Artificial intelligence (AI) is rapidly changing our reality, penetrating all spheres of life and radically transforming work processes. In 2024, AI implementation will become not just a trend but a necessity for business survival and prosperity. But what does it mean for employees and companies? Let's look at the key trends and see how AI is already shaping the performance capacities of tomorrow. Artificial intelligence (AI) is rapidly changing our reality, penetrating all spheres of life and radically transforming work processes. In 2024, AI implementation will become not just a trend but a necessity for business survival and prosperity. But what does it mean for employees and companies? Let's look at the key trends and see how AI is already shaping the performance capacities of tomorrow. AI and workplace transformation AI and workplace transformation AI seamlessly integrates with automation and innovation, streamlining routine tasks and unlocking new realms of creativity and development. Imagine a future office where AI assistants manage calendars, organize meetings, and analyze data. These technologies, such as Google Assistant and Microsoft Cortana, are already in active use. Robotic Process Automation (RPA) and smart assistants are essential to modern workflows. Software robots handle repetitive tasks, from data processing to managing customer inquiries, freeing human resources for more strategic and creative endeavors. AI assistants, like chatbots, are increasingly common, helping employees with routine tasks and boosting productivity. The 2023 McKinsey report "Generative AI and the Future of Work in America" explores the impact of generative AI across various economic sectors. The study reveals that up to 30% of working hours could be automated by 2030 due to generative AI, offering immense opportunities for businesses to enhance efficiency and work quality significantly. Employee expectations and new skills demand As AI evolves, so do employee expectations. To remain competitive, employees must adapt and acquire new skills. Companies are increasingly offering courses and training to enhance employees' abilities in AI and data analysis. This not only boosts productivity but also motivates employees, providing them with confidence in the future. IBM exemplifies this approach through its IBM SkillsBuild platform, which offers free AI courses and certifications. This investment in employees highlights how major companies recognize that the future is intertwined with AI. New professional roles, such as AI strategists, AI ethicists, and AI solution developers, are emerging. These professions will be in high demand in the coming years, and those who acquire the necessary skills will be at the forefront of the technological revolution. AI enhances the efficiency of remote work by analyzing productivity and optimizing working conditions. For instance, Microsoft employs AI to monitor working hours and identify the most productive times for each employee. Additionally, AI helps develop personalized work plans based on individual preferences and strengths, leading to higher employee satisfaction and retention. Ethical and social challenges The introduction of AI brings both opportunities and challenges that require careful consideration and responsible management. Ensuring that AI algorithms are fair and non-discriminatory based on gender, age, or race is crucial. A notable example of poor configuration is the incident with Microsoft's AI bot launched in February 2024, which quickly began creating and distributing racist and offensive images due to user manipulation and false learning, leading to the promotion of hatred and discrimination. Studies have shown that algorithms can reproduce biases present in the data they are trained on. Therefore, it is essential to regularly check and adjust them to maintain fairness and transparency. Additionally, the introduction of AI raises concerns among employees about potential job loss due to automation. People fear that machines may replace their jobs, leading to increased unemployment and social instability. Companies need to invest in retraining and upskilling programs to help employees adapt to the new environment and retain their jobs. According to the World Economic Forum's " The Future of Jobs Report " for 2020 and 2023 , 85 million jobs are expected to be replaced by automation and technological changes. However, they also report that 97 million new jobs will be created as humans, machines, and algorithms increasingly interact. This presents incredible opportunities for those willing to learn and adapt. The Future of Jobs Report The Future of Jobs Report 2023 2023 Cybersecurity and AI: New Challenges AI also introduces significant cybersecurity challenges. Companies utilizing AI must prioritize protecting employee and customer data from leaks and misuse. This involves implementing multi-factor authentication, data encryption, and continuous network activity monitoring with AI tools to detect and prevent cyber threats early on. Data processed by AI can be stored in either local (on-premises) libraries or cloud (online) services. On-premises solutions offer more control over data but require substantial resources for maintenance and protection. Conversely, cloud solutions provide greater flexibility and scalability, but necessitate a careful approach to security management. For instance, in January 2024, OpenAI experienced a data breach due to a bug in the open-source Redis library. This bug allowed users to access some data of other active users, including names, addresses, and partial credit card details. Although the breach was caused by an accidental vulnerability rather than intentional actions, the incident underscored the importance of robust data protection and swift responses to cyber threats. To learn more about how AI is assisting companies in managing data and related challenges, you can read my upcoming article, "The Role of AI in the Future of Business Management." Key AI Trends In the Future of the Workplace AI technologies are evolving rapidly, and several key trends are emerging that will shape the future of work. Integrating AI with other technologies, such as the Internet of Things (IoT) and blockchain, to create smart and autonomous systems is becoming increasingly common. AI, which is capable of understanding and analyzing natural language, is transforming into a powerful business tool. It aids not only in data analysis but also in strategic decision-making, enhancing overall business processes. AI is becoming an integral part of corporate strategy, driving innovation and improving efficiency. Companies that effectively integrate AI into their culture will gain a competitive edge over others. To sum up AI is not just a tool but a powerful catalyst for change, shaping the future of work and production capacity. Companies and employees must prepare for this challenge by developing new skills and adapting to evolving conditions. AI offers enormous opportunities for those ready to embrace and leverage them. Don't miss the chance to be part of this revolution. Start learning AI today, invest in training, and stay ahead to face the future with confidence. Share your thoughts and experiences in the comments, and let's discuss how AI is transforming our world and how we can harness its potential for good.

The AI Train is Leaving: Are You Onboard?

The Chosen One: Consistent Characters in Text-to-Image Diffusion Models: References

Apache DolphinScheduler is a distributed, decentralized big data workflow scheduling system that supports big data task scheduling. To set DolphinScheduler to start automatically upon system boot, you typically need to configure it as a system service. The following are general steps, which may vary depending on your operating system: Linux System Create a system service file: Edit a file named dolphinscheduler.service, usually placed in the /etc/systemd/system/directory. Fill in the service file according to your DolphinScheduler installation path and configuration. Example: [Unit]
Description=DolphinScheduler Service
After=network.target
[Service]
type=exec
ExecStart=/opt/module/dolphinscheduler/bin/start-all.sh
ExecStop=/opt/module/dolphinscheduler/bin/stop-all.sh
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target 2. Start the service, and set it to start on boot: Use the systemctl command to start the service, and enable it to start on boot: systemctl daemon-reload
systemctl start dolphinscheduler.service
systemctl enable dolphinscheduler.service 3. Verify the service status: Check the service status using the following command: systemctl status dolphinscheduler.service Windows System Create a batch file: Create a batch file (e.g., start-dolphinscheduler.bat) to start DolphinScheduler.


Add the command to start DolphinScheduler in the batch file. 2. Add the batch file to the Startup folder: Place the batch file in the Startup folder to run automatically when the system boots. Press Win + R to open the Run dialog. Type shell:startup to open the Startup folder. Drag and drop the batch file into the Startup folder. 3. Restart Verification: Restart the computer to check if DolphinScheduler starts with the system. Please note that the above steps may need to be adjusted based on your specific environment and the DolphinScheduler version. Ensure you have sufficient permissions and a good understanding of system services to avoid potential service conflicts or system instability. If the DolphinScheduler service briefly shows as green (indicating the service is running) after restarting and then changes to a non-green state (typically indicating the service is no longer running), but the related processes are still running, this may be because the service manager (systemd) thinks the service has stopped because it does not detect a continuously running main process. This situation usually occurs in the following cases: Service Script Issues: If the service script (start-all.sh) completes without keeping any process running in the background, systemd will think the service has stopped.


Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot, systemd might think the service has stopped as soon as the script finishes executing.


Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. To resolve this issue, you can try the following steps: Check the Service Script: Ensure the start-all.shscript keeps running after starting all necessary processes. If the script does not keep any process running in the background after execution, you might need to add some logic to keep it running. Adjust the Service File: If the script correctly starts all necessary processes and those processes run in the background, you may need to adjust the Type and other configuration options in the systemd service file. Use Type=forking: If your service script is a forking script, ensure you use Type=forking in the systemdservice file. This way, systemd will wait for the child process created by the script. Use Type=notify (if supported): If DolphinScheduler supports communication with the service manager, you can use Type=notify. This way, the service can send status notifications to systemd via the sd_notify() function. Use RemainAfterExit=yes: If the processes started by the service script run independently of the script itself, you can use RemainAfterExit=yes in the systemd service file. This way, even if the script exits, systemd will consider the service to be still running.
Reload and Restart the Service: After modifying the service file, reload systemd and restart the service: systemctl daemon-reload 
systemctl restart dolphinscheduler.service Check Service Status: Check the service status again to see if it is active (running). If the above steps do not resolve the issue, you may need to examine the DolphinScheduler log files or the output of the service script in more detail to determine why systemd believes the service has stopped. Finally, the configuration works well. Apache DolphinScheduler is a distributed, decentralized big data workflow scheduling system that supports big data task scheduling. To set DolphinScheduler to start automatically upon system boot, you typically need to configure it as a system service. The following are general steps, which may vary depending on your operating system: Linux System Create a system service file: Create a system service file: Edit a file named dolphinscheduler.service, usually placed in the /etc/systemd/system/directory. Edit a file named dolphinscheduler.service , usually placed in the /etc/systemd/system/ directory. dolphinscheduler.service /etc/systemd/system/ Fill in the service file according to your DolphinScheduler installation path and configuration. Example: Fill in the service file according to your DolphinScheduler installation path and configuration. Example: [Unit]
Description=DolphinScheduler Service
After=network.target
[Service]
type=exec
ExecStart=/opt/module/dolphinscheduler/bin/start-all.sh
ExecStop=/opt/module/dolphinscheduler/bin/stop-all.sh
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target [Unit]
Description=DolphinScheduler Service
After=network.target
[Service]
type=exec
ExecStart=/opt/module/dolphinscheduler/bin/start-all.sh
ExecStop=/opt/module/dolphinscheduler/bin/stop-all.sh
RemainAfterExit=yes
[Install]
WantedBy=multi-user.target 2. Start the service, and set it to start on boot: Use the systemctl command to start the service, and enable it to start on boot: Use the systemctl command to start the service, and enable it to start on boot: systemctl systemctl daemon-reload
systemctl start dolphinscheduler.service
systemctl enable dolphinscheduler.service systemctl daemon-reload
systemctl start dolphinscheduler.service
systemctl enable dolphinscheduler.service 3. Verify the service status: Check the service status using the following command: Check the service status using the following command: systemctl status dolphinscheduler.service systemctl status dolphinscheduler.service Windows System Create a batch file: Create a batch file: Create a batch file (e.g., start-dolphinscheduler.bat) to start DolphinScheduler. Add the command to start DolphinScheduler in the batch file. Create a batch file (e.g., start-dolphinscheduler.bat) to start DolphinScheduler. Create a batch file (e.g., start-dolphinscheduler.bat ) to start DolphinScheduler. start-dolphinscheduler.bat Add the command to start DolphinScheduler in the batch file. Add the command to start DolphinScheduler in the batch file. 2. Add the batch file to the Startup folder: Place the batch file in the Startup folder to run automatically when the system boots. Place the batch file in the Startup folder to run automatically when the system boots. Press Win + R to open the Run dialog. Press Win + R to open the Run dialog. Type shell:startup to open the Startup folder. Type shell:startup to open the Startup folder. shell:startup Drag and drop the batch file into the Startup folder. Drag and drop the batch file into the Startup folder. 3. Restart Verification: Restart the computer to check if DolphinScheduler starts with the system. Restart the computer to check if DolphinScheduler starts with the system. Please note that the above steps may need to be adjusted based on your specific environment and the DolphinScheduler version. Ensure you have sufficient permissions and a good understanding of system services to avoid potential service conflicts or system instability. If the DolphinScheduler service briefly shows as green (indicating the service is running) after restarting and then changes to a non-green state (typically indicating the service is no longer running), but the related processes are still running, this may be because the service manager ( systemd ) thinks the service has stopped because it does not detect a continuously running main process. systemd This situation usually occurs in the following cases: Service Script Issues: If the service script (start-all.sh) completes without keeping any process running in the background, systemd will think the service has stopped. Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot, systemd might think the service has stopped as soon as the script finishes executing. Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. Service Script Issues: If the service script (start-all.sh) completes without keeping any process running in the background, systemd will think the service has stopped. Service Script Issues: If the service script ( start-all.sh ) completes without keeping any process running in the background, systemd will think the service has stopped. start-all.sh Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot, systemd might think the service has stopped as soon as the script finishes executing. Incorrect Service Type Configuration: In the systemd service file, the Type configuration may need to be corrected. If the service is set to simple, it should be forking or oneshot , systemd might think the service has stopped as soon as the script finishes executing. systemd Type forking oneshot systemd Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. Process Exit Code Issues: If the service script correctly starts all necessary processes but exits quickly (e.g., because the commands in the script finish soon), systemd might determine the service status based on the script's exit code. systemd To resolve this issue, you can try the following steps: Check the Service Script: Ensure the start-all.shscript keeps running after starting all necessary processes. If the script does not keep any process running in the background after execution, you might need to add some logic to keep it running. Check the Service Script: Ensure the start-all.sh script keeps running after starting all necessary processes. If the script does not keep any process running in the background after execution, you might need to add some logic to keep it running. start-all.sh Adjust the Service File: If the script correctly starts all necessary processes and those processes run in the background, you may need to adjust the Type and other configuration options in the systemd service file. Adjust the Service File: If the script correctly starts all necessary processes and those processes run in the background, you may need to adjust the Type and other configuration options in the systemd service file. Use Type=forking: If your service script is a forking script, ensure you use Type=forking in the systemdservice file. This way, systemd will wait for the child process created by the script. Use Type=forking : If your service script is a forking script, ensure you use Type=forking in the systemd service file. This way, systemd will wait for the child process created by the script. Type=forking Type=forking systemd systemd Use Type=notify (if supported): If DolphinScheduler supports communication with the service manager, you can use Type=notify. This way, the service can send status notifications to systemd via the sd_notify() function. Use Type=notify (if supported): If DolphinScheduler supports communication with the service manager, you can use Type=notify . This way, the service can send status notifications to systemd via the sd_notify() function. Type=notify Type=notify systemd sd_notify() Use RemainAfterExit=yes: If the processes started by the service script run independently of the script itself, you can use RemainAfterExit=yes in the systemd service file. This way, even if the script exits, systemd will consider the service to be still running. Reload and Restart the Service: After modifying the service file, reload systemd and restart the service: Use RemainAfterExit=yes : If the processes started by the service script run independently of the script itself, you can use RemainAfterExit=yes in the systemd service file. This way, even if the script exits, systemd will consider the service to be still running. RemainAfterExit=yes RemainAfterExit=yes systemd systemd Reload and Restart the Service: After modifying the service file, reload systemd and restart the service: systemd systemctl daemon-reload 
systemctl restart dolphinscheduler.service systemctl daemon-reload 
systemctl restart dolphinscheduler.service Check Service Status: Check the service status again to see if it is active (running). Check Service Status: Check the service status again to see if it is active (running). If the above steps do not resolve the issue, you may need to examine the DolphinScheduler log files or the output of the service script in more detail to determine why systemd believes the service has stopped. If the above steps do not resolve the issue, you may need to examine the DolphinScheduler log files or the output of the service script in more detail to determine why systemd believes the service has stopped. systemd Finally, the configuration works well.

How to Enable Auto-Start for Apache DolphinScheduler

Why Is the AI Surge Boosting HDD Stocks? Finis Conner Says: Drives Are to AI as Batteries Are to EVs

Life in 2050 According to Jail-Broken GPT-4o 

A Voice Controlled Website With AI Embedded in Chrome

ক্রোমে এম্বেডেড এআই সহ একটি ভয়েস নিয়ন্ত্রিত ওয়েবসাইট

Eine sprachgesteuerte Website mit in Chrome eingebetteter KI

Un sitio web controlado por voz con IA integrada en Chrome

Un site Web à commande vocale avec IA intégrée dans Chrome

क्रोम में ए.आई. एम्बेडेड के साथ एक आवाज नियंत्रित वेबसाइट

Chrome に AI が埋め込まれた音声制御のウェブサイト

Chrome에 AI가 내장된 음성 제어 웹사이트

Um site controlado por voz com IA incorporada no Chrome

Веб-сайт с голосовым управлением и искусственным интеллектом, встроенным в Chrome

Chrome'a Yerleştirilmiş Yapay Zeka ile Sesle Kontrol Edilen Bir Web Sitesi

Trang web được điều khiển bằng giọng nói với AI được nhúng trong Chrome

Chrome 中嵌入 AI 的语音控制网站

Machine Learning Enhanced Characterization of Labyrinthine Structures in Bi-doped Films

Just like anything else in the tech industry, blockchain and crypto are evolving, facing growing pains, and gradually integrating into the mainstream. These past few years have basically been a roller coaster of emotions as bull and bear market cycles have swung investor sentiments wildly, creating a landscape of rapid highs and lows. In 2022, the value of popular cryptocurrencies declined precipitously with a series of dramatic events. This included the failure of multiple stablecoins, such as TerraUSD and Luna in May, and the collapse of FTX in November. Governments and regulatory bodies worldwide are also tightening their oversight of the crypto industry, seeking to address issues such as fraud, money laundering, and consumer protection. High-profile failures like FTX have led to calls for stricter regulations, adding to market instability. However, these cycles of boom and bust are not new to the crypto world; similar patterns were observed during previous market corrections in 2013 and 2018. 2023 saw the cryptocurrency industry recover from the scandals and declines of 2022. With crypto assets rebounding and market activity increasing throughout the year, many believe that the crypto winter is endingand a new growth phase is on the horizon. And Bitcoin (BTC) finally made a sweet revenge. The price again reached an all-time high in 2024, as values exceeded over 73,000 USD in March 2024. The Current State of Cryptocurrency So, where are we now? Let’s dive into this ever-evolving ecosystem to understand its present state and the path forward. Market Dynamics The year 2023 was primarily a recovery period for Bitcoin and cryptoassets, marked by significant events that influenced the outlook for the following years. Notably, BlackRock's spot Bitcoin ETF filing in June 2023 has already paved the way for greater adoption of this emerging asset class in 2024 and beyond. Ethereum's path towards increased scalability and efficiency, exemplified by the adoption of Layer 2 networks like Polygon and Arbitrum, represents a significant leap forward in enhancing transaction throughput and reducing fees. This progress is poised to boost Ethereum's adoption in DeFi and NFTs, highlighting the broader theme of technological advancements within the crypto-asset space. The DeFi sector is also on the verge of substantial growth, driven by institutional interest and the incorporation of KYC protocols. This fusion of traditional financial principles with decentralized frameworks signifies a maturing industry, ready for broader adoption and reflective of the evolving financial services landscape. In this context, One Trading highlights the importance of regulatory compliance, operating within a framework that adheres to European regulatory standards to provide a secure and transparent trading environment for all participants. Not only that, but the NFT sector is undergoing a vibrant resurgence, expanding into diverse areas such as art, gaming, and digital collectibles. The introduction of Bitcoin-based NFTs and the growth of marketplaces demonstrate the sector's versatility and rising mainstream appeal, emphasizing the broader integration of blockchain technology into digital culture and commerce. Institutional Adoption Companies like MicroStrategy made headlines by allocating a portion of their treasury reserves to Bitcoin. Investment banks such as Goldman Sachs and Morgan Stanley also have begun offering cryptocurrency investment products to their clients, which signals a shift toward mainstream acceptance. Institutional adoption of cryptocurrencies is gaining momentum, with major financial institutions, corporations, and investment funds incorporating digital assets into their portfolios. The entrance of major players like BlackRock into the Bitcoin space has set the stage for increased institutional adoption. Throughout 2023, numerous new participants emerged on public permissionless networks, providing direct on-chain access to tokenized US Treasury exposure. As a result, total assets held in US Treasury-like exposure on-chain have surged sixfold to over $786 million, as digitally native users have pursued yields unlinked to traditional crypto sources. In June 2024, the total market cap for all cryptocurrencies hit an impressive $2.3 trillion, showing just how much institutions and investors around the globe are getting into crypto. Regulatory Environment Regulatory scrutiny of cryptocurrencies is intensifying globally by reflecting authorities' efforts to balance innovation with investor protection and financial stability. While regulatory clarity remains a challenge in some jurisdictions, proactive regulatory frameworks are beginning to emerge, providing a more stable operating environment for crypto businesses. Countries like Switzerland and Singapore have established clear regulatory frameworks for cryptocurrencies– which create innovation while implementing investor protection. Social Impact Cryptocurrency's potential to drive positive social impact is increasingly recognized, particularly in the areas of financial inclusion and remittances. Initiatives leveraging blockchain technology to facilitate micropayments, cross-border transfers, and access to financial services in underserved regions are gaining traction. Initiatives like the Stellar Development Foundation's partnership with the Ministry of Digital Transformation in Ukraine aim to digitize the country's national currency and improve financial inclusion. In Africa, projects like BitPesa also facilitate cross-border remittances and access to financial services for the unbanked population. It goes without saying that the adoption of Bitcoin and other crypto assets like Ethereum is still in its early stages in most developed countries. Recent surveys on retail adoption in 2023 indicate a global adoption rate of around 18%, meaning roughly one in five individuals worldwide currently holds some form of cryptoasset. There were over 34,000 Bitcoin ATMs all over the world as of January 1, 2024. The Upcoming Challenges… Of course, there are risks for crypto in the future as well. Here are some of the significant challenges on the horizon: Tougher Regulation As the cryptocurrency market matures, it faces the prospect of stricter regulatory oversight from governments and regulatory bodies worldwide. Regulatory challenges such as recent SEC actions against major exchanges like Binance and Coinbase highlight the growing regulatory challenges within the industry. Increased regulation could impact the accessibility and operation of crypto exchanges, projects, and financial products, potentially stifling innovation and adoption. Long-term Viability Questions surrounding the long-term viability of cryptocurrencies persist, particularly regarding their ability to maintain value and relevance over time. Factors such as technological advancements, market sentiment, and competition from traditional financial systems could influence the sustainability of cryptocurrencies as viable assets and mediums of exchange. Environmental Concerns The environmental impact of cryptocurrency mining, particularly for proof-of-work-based networks like Bitcoin, remains a pressing concern. Rising energy consumption and carbon emissions associated with mining operations could exacerbate environmental degradation and contribute to climate change. This, for sure, prompts calls for greener alternatives and sustainable practices within the industry. According to an analysis by the University of Cambridge, bitcoin mining is estimated to consume 121.36 terawatt-hours annually. This surpasses the energy consumption of Argentina and exceeds the combined consumption of tech giants like Google, Apple, Facebook, and Microsoft. It Has Come a Long Way …. One thing that is consistent in the crypto industry is the inconsistency. It continues its journey of growth, expansion, and evolution. Not every protocol will withstand the challenges ahead, but with ongoing adoption by users, developers, and institutions, coupled with the emergence of innovative applications, cryptocurrency is poised to continue its maturation process. Editor’s Note: This story was written using the Bitcoin Writing Contest template. To take part in the contest, feel free to head to the following link for a chance to win thousands of dollars in prizes. Need more ideas? See submissions from other writers under the #bitcoin tag. Just like anything else in the tech industry, blockchain and crypto are evolving, facing growing pains, and gradually integrating into the mainstream. blockchain and crypto These past few years have basically been a roller coaster of emotions as bull and bear market cycles have swung investor sentiments wildly, creating a landscape of rapid highs and lows. In 2022, the value of popular cryptocurrencies declined precipitously with a series of dramatic events. This included the failure of multiple stablecoins, such as TerraUSD and Luna in May, and the collapse of FTX in November. the collapse of FTX the collapse of FTX Governments and regulatory bodies worldwide are also tightening their oversight of the crypto industry, seeking to address issues such as fraud, money laundering, and consumer protection. High-profile failures like FTX have led to calls for stricter regulations, adding to market instability. However, these cycles of boom and bust are not new to the crypto world; similar patterns were observed during previous market corrections in 2013 and 2018. cycles of boom cycles of boom 2023 saw the cryptocurrency industry recover from the scandals and declines of 2022. With crypto assets rebounding and market activity increasing throughout the year, many believe that the crypto winter is ending and a new growth phase is on the horizon. the crypto winter is ending And Bitcoin (BTC) finally made a sweet revenge. The price again reached an all-time high in 2024, as values exceeded over 73,000 USD in March 2024. The Current State of Cryptocurrency The Current State of Cryptocurrency So, where are we now? Let’s dive into this ever-evolving ecosystem to understand its present state and the path forward. Market Dynamics Market Dynamics The year 2023 was primarily a recovery period for Bitcoin and cryptoassets, marked by significant events that influenced the outlook for the following years. Notably, BlackRock's spot Bitcoin ETF filing in June 2023 has already paved the way for greater adoption of this emerging asset class in 2024 and beyond. Ethereum's path towards increased scalability and efficiency, exemplified by the adoption of Layer 2 networks like Polygon and Arbitrum, represents a significant leap forward in enhancing transaction throughput and reducing fees. This progress is poised to boost Ethereum's adoption in DeFi and NFTs, highlighting the broader theme of technological advancements within the crypto-asset space. The DeFi sector is also on the verge of substantial growth, driven by institutional interest and the incorporation of KYC protocols. This fusion of traditional financial principles with decentralized frameworks signifies a maturing industry, ready for broader adoption and reflective of the evolving financial services landscape. In this context, One Trading highlights the importance of regulatory compliance, operating within a framework that adheres to European regulatory standards to provide a secure and transparent trading environment for all participants. Not only that, but the NFT sector is undergoing a vibrant resurgence, expanding into diverse areas such as art, gaming, and digital collectibles. The introduction of Bitcoin-based NFTs and the growth of marketplaces demonstrate the sector's versatility and rising mainstream appeal, emphasizing the broader integration of blockchain technology into digital culture and commerce. Institutional Adoption Institutional Adoption Companies like MicroStrategy made headlines by allocating a portion of their treasury reserves to Bitcoin. Investment banks such as Goldman Sachs and Morgan Stanley also have begun offering cryptocurrency investment products to their clients, which signals a shift toward mainstream acceptance. Goldman Sachs and Morgan Stanley also have begun offering cryptocurrency investment Goldman Sachs and Morgan Stanley also have begun offering cryptocurrency investment Institutional adoption of cryptocurrencies is gaining momentum, with major financial institutions, corporations, and investment funds incorporating digital assets into their portfolios. The entrance of major players like BlackRock into the Bitcoin space has set the stage for increased institutional adoption. Throughout 2023, numerous new participants emerged on public permissionless networks, providing direct on-chain access to tokenized US Treasury exposure. As a result, total assets held in US Treasury-like exposure on-chain have surged sixfold to over $786 million, as digitally native users have pursued yields unlinked to traditional crypto sources. In June 2024, the total market cap for all cryptocurrencies hit an impressive $2.3 trillion, showing just how much institutions and investors around the globe are getting into crypto. Regulatory Environment Regulatory Environment Regulatory scrutiny of cryptocurrencies is intensifying globally by reflecting authorities' efforts to balance innovation with investor protection and financial stability. While regulatory clarity remains a challenge in some jurisdictions, proactive regulatory frameworks are beginning to emerge, providing a more stable operating environment for crypto businesses. Countries like Switzerland and Singapore have established clear regulatory frameworks for cryptocurrencies– which create innovation while implementing investor protection. clear regulatory frameworks clear regulatory frameworks Social Impact Social Impact Cryptocurrency's potential to drive positive social impact is increasingly recognized, particularly in the areas of financial inclusion and remittances. Initiatives leveraging blockchain technology to facilitate micropayments, cross-border transfers, and access to financial services in underserved regions are gaining traction. Initiatives like the Stellar Development Foundation's partnership with the Ministry of Digital Transformation in Ukraine aim to digitize the country's national currency and improve financial inclusion. In Africa, projects like BitPesa also facilitate cross-border remittances and access to financial services for the unbanked population. Stellar Development Foundation's partnership with the Ministry of Digital Transformation Stellar Development Foundation's partnership with the Ministry of Digital Transformation It goes without saying that the adoption of Bitcoin and other crypto assets like Ethereum is still in its early stages in most developed countries. Recent surveys on retail adoption in 2023 indicate a global adoption rate of around 18%, meaning roughly one in five individuals worldwide currently holds some form of cryptoasset. There were over 34,000 Bitcoin ATMs all over the world as of January 1, 2024. Recent surveys 34,000 Bitcoin ATMs The Upcoming Challenges… The Upcoming Challenges… Of course, there are risks for crypto in the future as well. Here are some of the significant challenges on the horizon: Tougher Regulation Tougher Regulation As the cryptocurrency market matures, it faces the prospect of stricter regulatory oversight from governments and regulatory bodies worldwide. Regulatory challenges such as recent SEC actions against major exchanges like Binance and Coinbase highlight the growing regulatory challenges within the industry. Increased regulation could impact the accessibility and operation of crypto exchanges, projects, and financial products, potentially stifling innovation and adoption. Long-term Viability Long-term Viability Questions surrounding the long-term viability of cryptocurrencies persist, particularly regarding their ability to maintain value and relevance over time. Factors such as technological advancements, market sentiment, and competition from traditional financial systems could influence the sustainability of cryptocurrencies as viable assets and mediums of exchange. Environmental Concerns Environmental Concerns The environmental impact of cryptocurrency mining, particularly for proof-of-work-based networks like Bitcoin, remains a pressing concern. Rising energy consumption and carbon emissions associated with mining operations could exacerbate environmental degradation and contribute to climate change. This, for sure, prompts calls for greener alternatives and sustainable practices within the industry. According to an analysis by the University of Cambridge , bitcoin mining is estimated to consume 121.36 terawatt-hours annually. This surpasses the energy consumption of Argentina and exceeds the combined consumption of tech giants like Google, Apple, Facebook, and Microsoft. analysis by the University of Cambridge analysis by the University of Cambridge It Has Come a Long Way …. It Has Come a Long Way …. One thing that is consistent in the crypto industry is the inconsistency. It continues its journey of growth, expansion, and evolution. Not every protocol will withstand the challenges ahead, but with ongoing adoption by users, developers, and institutions, coupled with the emergence of innovative applications, cryptocurrency is poised to continue its maturation process. Editor’s Note: This story was written using the Bitcoin Writing Contest template. To take part in the contest, feel free to head to the following link for a chance to win thousands of dollars in prizes. Editor’s Note: This story was written using the Bitcoin Writing Contest template. To take part in the contest, feel free to head to the following link for a chance to win thousands of dollars in prizes. Editor’s Note: This story was written using the Bitcoin Writing Contest template. To take part in the contest, feel free to head to the following link for a chance to win thousands of dollars in prizes. link Need more ideas? See submissions from other writers under the #bitcoin tag. Need more ideas? See submissions from other writers under the #bitcoin tag. #bitcoin

Crypto Chronicles: Riding the Digital Rollercoaster

ক্রিপ্টো ক্রনিকলস: ডিজিটাল রোলারকোস্টার রাইডিং

Crypto Chronicles: Eine Fahrt auf der digitalen Achterbahn

Crypto Chronicles: Montando la montaña rusa digital

Crypto Chronicles : sur les montagnes russes numériques

क्रिप्टो क्रॉनिकल्स: डिजिटल रोलरकोस्टर की सवारी

暗号クロニクル: デジタルジェットコースターに乗る

Crypto Chronicles: 디지털 롤러코스터를 타고

Crypto Chronicles: andando na montanha-russa digital

Криптохроники: катание на цифровых американских горках

Crypto Chronicles: Dijital Hız Trenine Binmek

Biên niên sử tiền điện tử: Đi tàu lượn siêu tốc kỹ thuật số

加密货币纪事：乘坐数字过山车

Authors:
(1) J. Quetzalcóatl Toledo-Marín, University of British Columbia, BC Children’s Hospital Research Institute Vancouver BC, Canada (Email: j.toledo.mx@gmail.com);
(2) James A. Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA (Email: jaglazier@gmail.com);
(3) Geoffrey Fox, University of Virginia, Computer Science and Biocomplexity Institute, 994 Research Park Blvd, Charlottesville, Virginia, 22911, USA (Email: gcfexchange@gmail.com). Table of Links Abstract and Introduction Methods Results Discussion Conclusions and References 2 Methods The steady state of the diffusion equation is precisely the convolution of the equation’s associated Green’s function and the initial state. Hence a naive approach would consider a deep convolutional NN (CNN) where the height and width in each layer is kept fixed and equal to the input. CNNs are good at detecting edges and as the number of convolution layers increases, the CNN can detect more sophisticated elements. So can a CNN predict the stationary solution? We argue that it would require a large number of convolution layers such that each convolution would be proportional to a timestep. Adding many convolutions would lead to a rather harder and slower training, as it would increase the chances of under or overflow in the gradient. Furthermore, the number of convolution layers would need to be variable is some form or fashion in order to mimic the timesteps required to reach the stationary solution given an initial condition. There are ways to address each of the previous setbacks but there’s also a trade-off for every decision (e.g., increasing the number of convolutional layers increases the model’s instability which can be addressed by adding more regularization layers which makes the model larger and requires more time to train). Experience tells us that deep convolutional NNs where the height and width in each layer is kept fixed and equal to the input are not good for our task [17]. We designed and built an encoder-decoder CNN (ED-CNN) as our architecture. Fig. 2 provides a sketch of our NN architecture. We denote by |x〉 and |yˆ〉 the input and output images, that is the initial condition layout of the source cells and the predicted stationary solution of the diffusion equation, respectively. The input |x〉 goes through convolutional blocks depicted in Fig. 2 and generates the prediction |yˆ〉 which we then compare with the ground truth |y〉. The core feature of an encoder-decoder is the cone-like structure whereby the information is being compressed when it reaches the bottle neck and then it’s decompressed. Consider the following: A CNN can be seen as a set of operators {Oi} acting on the input, i.e., where the i tags the convolutional layer. Notice that in the particular case where {Oi} are unitary linear operators and all the same O = 1− Ad t, Eq. (1) becomes Notice that the previous Eq. is akin to a ResNet architecture [19]. By defining d t = t/n, where t is time, and taking the limit n → ∞ leads to |yˆ〉 = exp(At)|x〉. Hence, when |yˆ〉 equals the ground truth |y〉, the CNN has learnt the operator exp(At). Notice that taking the derivative with respect to time leads to the diffusion equation: Typically, numerical methods for searching for the stationary solution of a diffusion equation operate by comparing the solution at time t with the solution at time t +∆t, and halting the process when the difference between |y(t)〉 and |y(t+∆t)〉is smaller in magnitude than some pre-determined error value. However, the time t at which the stationary solution is reached depends on the initial conditions and the inhomogeneity term in the diffusion equation. This analogy suggests that, similar to traditional numerical methods, CNNs also require a flexible number of layers to reach the stationary solution, rather than an infinite number of layers. There have been recent developments in addressing the issue of a variable number of layers. For example, in reference [20], the authors propose a method in which the dropout parameters are trained in conjunction with the model parameters. In the context of ED-CNNs, it is a widely held belief that the cone-like architecture leads to improved performance due to the compression in the bottleneck. However, we argue that this is not the sole, if any, contributing factor. It is important to note that the ED-CNN architecture involves summing over the lattice and introducing a new variable, the depth. Additionally, past the bottleneck, the ED-CNN projects back to the original space. In this sense, we say the ED-CNN learns a propagator in a reciprocal space similar to methods such as Laplace and Fourier transform. In Table 1 we specify the neural network by specifying the type of operation and output shape through the sequence of blocks in the NN. Notice that the bottleneck block is 2048∗16∗16 which is twice the size of the input. We built and trained the NN in PyTorch. We used ADAM as the optimizer [25]. Deciding on a loss function is a critical choice in the creation of the surrogate. The loss function determines the types of error the surrogate’s approximation will make compared to the direct calculation and the acceptability of these errors will depend on the specific application. The mean squared error (MSE) error is a standard choice. However, it is more sensitive to larger absolute errors and therefore tolerates large relative errors at pixels with small values. In most biological contexts we want to have a small absolute error for small values and a small relative error for large values. We explored the NN’s performance when trained using a set of different loss functions with different hyperparameters. The set of different loss functions we used were MAE, MSE, Huber loss [26] and inverse Huber loss defined as: where α is 1 or 2 for MAE or MSE, respectively and β tags the tuple in the data set (input and target). Here 〈|〉 denotes the inner product and |i〉 is a unitary vector with the same size as |yβ〉 with all components equal to zero except the element in position i which is equal to one. |1〉 is a vector with all components equal to 1 and with size equal to that of |yβ〉. Then 〈i|yβ〉 is a scalar corresponding to the pixel value at the ith position in |yβ〉, whereas 〈i|1〉 = 1 for all i. Notice that high and low pixel values will have an exponential weight ≈ 1 and ≈ exp(−1/w), respectively. This implies that the error associated to high pixels will have a larger value than that for low pixels. Another prefactor we considered was a step-wise function, viz. This paper is available on arxiv under CC 4.0 license. Authors: (1) J. Quetzalcóatl Toledo-Marín, University of British Columbia, BC Children’s Hospital Research Institute Vancouver BC, Canada (Email: j.toledo.mx@gmail.com); (2) James A. Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA (Email: jaglazier@gmail.com); (3) Geoffrey Fox, University of Virginia, Computer Science and Biocomplexity Institute, 994 Research Park Blvd, Charlottesville, Virginia, 22911, USA (Email: gcfexchange@gmail.com). Authors: Authors: (1) J. Quetzalcóatl Toledo-Marín, University of British Columbia, BC Children’s Hospital Research Institute Vancouver BC, Canada (Email: j.toledo.mx@gmail.com); (2) James A. Glazier, Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA (Email: jaglazier@gmail.com); (3) Geoffrey Fox, University of Virginia, Computer Science and Biocomplexity Institute, 994 Research Park Blvd, Charlottesville, Virginia, 22911, USA (Email: gcfexchange@gmail.com). Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Discussion Discussion Conclusions and References Conclusions and References 2 Methods The steady state of the diffusion equation is precisely the convolution of the equation’s associated Green’s function and the initial state. Hence a naive approach would consider a deep convolutional NN (CNN) where the height and width in each layer is kept fixed and equal to the input. CNNs are good at detecting edges and as the number of convolution layers increases, the CNN can detect more sophisticated elements. So can a CNN predict the stationary solution? We argue that it would require a large number of convolution layers such that each convolution would be proportional to a timestep. Adding many convolutions would lead to a rather harder and slower training, as it would increase the chances of under or overflow in the gradient. Furthermore, the number of convolution layers would need to be variable is some form or fashion in order to mimic the timesteps required to reach the stationary solution given an initial condition. There are ways to address each of the previous setbacks but there’s also a trade-off for every decision (e.g., increasing the number of convolutional layers increases the model’s instability which can be addressed by adding more regularization layers which makes the model larger and requires more time to train). Experience tells us that deep convolutional NNs where the height and width in each layer is kept fixed and equal to the input are not good for our task [17]. We designed and built an encoder-decoder CNN (ED-CNN) as our architecture. Fig. 2 provides a sketch of our NN architecture. We denote by |x〉 and |yˆ〉 the input and output images, that is the initial condition layout of the source cells and the predicted stationary solution of the diffusion equation, respectively. The input |x〉 goes through convolutional blocks depicted in Fig. 2 and generates the prediction |yˆ〉 which we then compare with the ground truth |y〉. The core feature of an encoder-decoder is the cone-like structure whereby the information is being compressed when it reaches the bottle neck and then it’s decompressed. Consider the following: A CNN can be seen as a set of operators {Oi} acting on the input, i.e., where the i tags the convolutional layer. Notice that in the particular case where {Oi} are unitary linear operators and all the same O = 1− Ad t, Eq. (1) becomes Notice that the previous Eq. is akin to a ResNet architecture [19]. By defining d t = t/n, where t is time, and taking the limit n → ∞ leads to |yˆ〉 = exp(At)|x〉. Hence, when |yˆ〉 equals the ground truth |y〉, the CNN has learnt the operator exp(At). Notice that taking the derivative with respect to time leads to the diffusion equation: Typically, numerical methods for searching for the stationary solution of a diffusion equation operate by comparing the solution at time t with the solution at time t +∆t, and halting the process when the difference between |y(t)〉 and |y(t+∆t)〉is smaller in magnitude than some pre-determined error value. However, the time t at which the stationary solution is reached depends on the initial conditions and the inhomogeneity term in the diffusion equation. This analogy suggests that, similar to traditional numerical methods, CNNs also require a flexible number of layers to reach the stationary solution, rather than an infinite number of layers. There have been recent developments in addressing the issue of a variable number of layers. For example, in reference [20], the authors propose a method in which the dropout parameters are trained in conjunction with the model parameters. In the context of ED-CNNs, it is a widely held belief that the cone-like architecture leads to improved performance due to the compression in the bottleneck. However, we argue that this is not the sole, if any, contributing factor. It is important to note that the ED-CNN architecture involves summing over the lattice and introducing a new variable, the depth. Additionally, past the bottleneck, the ED-CNN projects back to the original space. In this sense, we say the ED-CNN learns a propagator in a reciprocal space similar to methods such as Laplace and Fourier transform. In Table 1 we specify the neural network by specifying the type of operation and output shape through the sequence of blocks in the NN. Notice that the bottleneck block is 2048∗16∗16 which is twice the size of the input. We built and trained the NN in PyTorch. We used ADAM as the optimizer [25]. Deciding on a loss function is a critical choice in the creation of the surrogate. The loss function determines the types of error the surrogate’s approximation will make compared to the direct calculation and the acceptability of these errors will depend on the specific application. The mean squared error (MSE) error is a standard choice. However, it is more sensitive to larger absolute errors and therefore tolerates large relative errors at pixels with small values. In most biological contexts we want to have a small absolute error for small values and a small relative error for large values. We explored the NN’s performance when trained using a set of different loss functions with different hyperparameters. The set of different loss functions we used were MAE, MSE, Huber loss [26] and inverse Huber loss defined as: where α is 1 or 2 for MAE or MSE, respectively and β tags the tuple in the data set (input and target). Here 〈|〉 denotes the inner product and |i〉 is a unitary vector with the same size as |yβ〉 with all components equal to zero except the element in position i which is equal to one. |1〉 is a vector with all components equal to 1 and with size equal to that of |yβ〉. Then 〈i|yβ〉 is a scalar corresponding to the pixel value at the ith position in |yβ〉, whereas 〈i|1〉 = 1 for all i. Notice that high and low pixel values will have an exponential weight ≈ 1 and ≈ exp(−1/w), respectively. This implies that the error associated to high pixels will have a larger value than that for low pixels. Another prefactor we considered was a step-wise function, viz. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Architecting Neural Networks for Diffusion Prediction: A Study on Encoder-Decoder CNNs

Assessing VASP Solvency For Cryptoassets: Conclusions, and References

Authors:
(1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.);
(2) Thomas Hofmann, Department of Computer Science, ETH Zurich. Table of Links Abstract and Introduction Related Work Preliminaries Simplifying Transformer Blocks Further Experimental Analysis Discussion, Reproducibility Statement, Acknowledgements and References A Duality Between Downweighted Residual and Restricting Updates In Linear Layers B Block Layouts C Additional Experiments D Implementation Details 5 FURTHER EXPERIMENTAL ANALYSIS Having detailed all of our simplifications in Sec. 4, we now provide further experimental results of our simplified blocks in a range of settings, as well as details of the efficiency gains afforded by our simplifications. In the interest of space, additional experimental details can be found in App. D. Depth Scaling Given that signal propagation theory often focuses on large depths, where signal degeneracies usually appear, it is natural to ask whether the improved training speeds of our simplified transformer blocks also extend to larger depths. In Fig. 6, we see that scaling depth from 18 to 72 blocks leads to an increase in performance in our models as well as the Pre-LN transformer, indicating that our simplified models are able to not only train faster but also to utilise the extra capacity that more depth provides. Indeed, the per-update trajectories of our simplified blocks and Pre-LN are near-indistinguishable across depths, when using normalisation. On the other hand, we see that Value-SkipInit (He et al., 2023) actually trains slower per update at depth 72 compared to 18 despite the increase in capacity and parameter count. Moreover, the gap in performance between Value-SkipInit and the other models increases with larger depth, which implies poor scalability of the previous method. We note that 72 blocks is already reasonably deep by publically-available modern standards (Hoffmann et al., 2022; Touvron et al., 2023). BERT Next, we demonstrate our simplified blocks’ performance extends to different datasets and architectures besides autoregressive decoder-only, as well as on downstream tasks. We choose the popular setting of the bidirectional encoder-only BERT model Devlin et al. (2018) for masked language modelling, with downstream GLUE benchmark. In particular, we adopt the “Crammed” BERT setup of Geiping & Goldstein (2023), which asks how well one can train a BERT model with a modest training budget: 24 hours on a single consumer GPU. The authors provide an architecture, data pipeline and training setup that has been optimised for this low resource setting. We note that the Crammed architecture uses the Pre-LN block, and describe other setup details in App. D. We plug-in our simplified blocks, keeping the existing optimised hyperparameters, besides tuning learning rate and weight decay. In Fig. 7, we see that our simplified blocks (especially with normalisation) match the pre-training speed on the masked language modelling task compared to the (Crammed) Pre-LN baseline within the 24 hour runtime. On the other hand, the removal of skip connections without modifying the values and projections (as in He et al. (2023)) once again leads to a significant loss of training speed. In Fig. 24, we provide the equivalent plot in terms of microbatch steps. Moreover in Table 1, we find that our methods match the performance of the Crammed BERT baseline after finetuning on the GLUE benchmark. We provide a breakdown over the downstream tasks in Table 2. We use the same finetuning protocol as Geiping & Goldstein (2023) (5 epochs, constant hyperparameters across tasks, dropout regularisation) for a fair comparison. Interestingly, Value-SkipInit is largely able to recover from its poor pre-training in the fine-tuning phase. This, combined with the need for dropout when fine-tuning, suggests that factors besides pre-training speed are also important for fine-tuning. As the focus of our work primarily concerns training speed from random initialisations, we leave this to future work. Relatedly, we found removing normalisations (Sec. 4.4) to cause instabilities when fine-tuning, where a small minority of sequences in some downstream datasets had NaN values in the initial forward pass from the pre-trained checkpoint. Efficiency Gains In Table 1, we also detail the parameter count and training speeds of models using different Transformers blocks on the masked language modelling task. We compute the speed as the ratio of the number of microbatch steps taken within the 24 hours of pre-training, relative to the baseline Pre-LN Crammed BERT. We see that our models use 16% fewer parameters, and SAS-P & SAS are 16% & 9% faster per iteration, respectively, compared to the Pre-LN block in our setting. We note that in our implementation the Parallel block is only 5% faster than the Pre-LN block, whereas Chowdhery et al. (2022) observed 15% faster training speeds, suggesting that further throughout increases may be possible with a more optimised implementation. Our implementation, like Geiping & Goldstein (2023), uses automated operator fusion in PyTorch (Sarofeen et al., 2022) Longer training Finally, given the current trends of training smaller models for longer on more data (Hoffmann et al., 2022; Touvron et al., 2023), we investigate if our simplified blocks continue to match the training speeds of the Pre-LN block with longer training. To do this, we take our models from Fig. 5 on CodeParrot and train with 3× tokens. To be precise, we train for around 120K (rather than 40K) steps with batch size 128 and sequence length 128, which results in around 2B tokens. In Fig. 8, we do indeed see that our simplified SAS and SAS-P blocks continue to match or outer perform the PreLN block in training speed when trained on more tokens. This paper is available on arxiv under CC 4.0 license. Authors: (1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.); (2) Thomas Hofmann, Department of Computer Science, ETH Zurich. Authors: Authors: (1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.); (2) Thomas Hofmann, Department of Computer Science, ETH Zurich. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminaries Preliminaries Simplifying Transformer Blocks Simplifying Transformer Blocks Further Experimental Analysis Further Experimental Analysis Discussion, Reproducibility Statement, Acknowledgements and References Discussion, Reproducibility Statement, Acknowledgements and References A Duality Between Downweighted Residual and Restricting Updates In Linear Layers A Duality Between Downweighted Residual and Restricting Updates In Linear Layers B Block Layouts B Block Layouts C Additional Experiments C Additional Experiments D Implementation Details D Implementation Details 5 FURTHER EXPERIMENTAL ANALYSIS Having detailed all of our simplifications in Sec. 4, we now provide further experimental results of our simplified blocks in a range of settings, as well as details of the efficiency gains afforded by our simplifications. In the interest of space, additional experimental details can be found in App. D. Depth Scaling Given that signal propagation theory often focuses on large depths, where signal degeneracies usually appear, it is natural to ask whether the improved training speeds of our simplified transformer blocks also extend to larger depths. In Fig. 6, we see that scaling depth from 18 to 72 blocks leads to an increase in performance in our models as well as the Pre-LN transformer, indicating that our simplified models are able to not only train faster but also to utilise the extra capacity that more depth provides. Indeed, the per-update trajectories of our simplified blocks and Pre-LN are near-indistinguishable across depths, when using normalisation. Depth Scaling On the other hand, we see that Value-SkipInit (He et al., 2023) actually trains slower per update at depth 72 compared to 18 despite the increase in capacity and parameter count. Moreover, the gap in performance between Value-SkipInit and the other models increases with larger depth, which implies poor scalability of the previous method. We note that 72 blocks is already reasonably deep by publically-available modern standards (Hoffmann et al., 2022; Touvron et al., 2023). BERT Next, we demonstrate our simplified blocks’ performance extends to different datasets and architectures besides autoregressive decoder-only, as well as on downstream tasks. We choose the popular setting of the bidirectional encoder-only BERT model Devlin et al. (2018) for masked language modelling, with downstream GLUE benchmark. BERT In particular, we adopt the “Crammed” BERT setup of Geiping & Goldstein (2023), which asks how well one can train a BERT model with a modest training budget: 24 hours on a single consumer GPU. The authors provide an architecture, data pipeline and training setup that has been optimised for this low resource setting. We note that the Crammed architecture uses the Pre-LN block, and describe other setup details in App. D. We plug-in our simplified blocks, keeping the existing optimised hyperparameters, besides tuning learning rate and weight decay. In Fig. 7, we see that our simplified blocks (especially with normalisation) match the pre-training speed on the masked language modelling task compared to the (Crammed) Pre-LN baseline within the 24 hour runtime. On the other hand, the removal of skip connections without modifying the values and projections (as in He et al. (2023)) once again leads to a significant loss of training speed. In Fig. 24, we provide the equivalent plot in terms of microbatch steps. Moreover in Table 1, we find that our methods match the performance of the Crammed BERT baseline after finetuning on the GLUE benchmark. We provide a breakdown over the downstream tasks in Table 2. We use the same finetuning protocol as Geiping & Goldstein (2023) (5 epochs, constant hyperparameters across tasks, dropout regularisation) for a fair comparison. Interestingly, Value-SkipInit is largely able to recover from its poor pre-training in the fine-tuning phase. This, combined with the need for dropout when fine-tuning, suggests that factors besides pre-training speed are also important for fine-tuning. As the focus of our work primarily concerns training speed from random initialisations, we leave this to future work. Relatedly, we found removing normalisations (Sec. 4.4) to cause instabilities when fine-tuning, where a small minority of sequences in some downstream datasets had NaN values in the initial forward pass from the pre-trained checkpoint. Efficiency Gains In Table 1, we also detail the parameter count and training speeds of models using different Transformers blocks on the masked language modelling task. We compute the speed as the ratio of the number of microbatch steps taken within the 24 hours of pre-training, relative to the baseline Pre-LN Crammed BERT. We see that our models use 16% fewer parameters, and SAS-P & SAS are 16% & 9% faster per iteration, respectively, compared to the Pre-LN block in our setting. We note that in our implementation the Parallel block is only 5% faster than the Pre-LN block, whereas Chowdhery et al. (2022) observed 15% faster training speeds, suggesting that further throughout increases may be possible with a more optimised implementation. Our implementation, like Geiping & Goldstein (2023), uses automated operator fusion in PyTorch (Sarofeen et al., 2022) Efficiency Gains Longer training Finally, given the current trends of training smaller models for longer on more data (Hoffmann et al., 2022; Touvron et al., 2023), we investigate if our simplified blocks continue to match the training speeds of the Pre-LN block with longer training. To do this, we take our models from Fig. 5 on CodeParrot and train with 3× tokens. To be precise, we train for around 120K (rather than 40K) steps with batch size 128 and sequence length 128, which results in around 2B tokens. In Fig. 8, we do indeed see that our simplified SAS and SAS-P blocks continue to match or outer perform the PreLN block in training speed when trained on more tokens. Longer training This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Depth Scaling and Efficiency Gains with Simplified Transformer Blocks

Over time we have come to see that central authorities and intermediaries predominantly control financial systems, and the concept of financial sovereignty is becoming increasingly important. Now enters Bitcoin, a digital currency that operates on a decentralized network, providing a new shift to financial autonomy. Bitcoin was introduced in 2009 by the pseudonymous Satoshi Nakamoto, and offers a peer-to-peer financial system that is not governed by any single entity. This decentralization is crucial for enhancing financial sovereignty as it minimizes the risks of censorship, manipulation, and dependency on traditional financial institutions. With Bitcoin, individuals can conduct transactions directly with one another, without the need for intermediaries, leading to lower costs, faster transactions, and greater access to financial services. In this article, we will look at how Bitcoin plays a pivotal role in promoting financial sovereignty. What is Financial Sovereignty Financial sovereignty refers to the ability of individuals to have full control over their own financial assets and transactions without reliance on external intermediaries such as banks, governments, or other financial institutions. Bitcoin plays a significant role in promoting financial sovereignty due to its decentralized and peer-to-peer nature. Key Concepts of Financial Sovereignty with Bitcoin Decentralization and its Importance Bitcoin operates on a decentralized network. This means no single entity, like a central bank or government, controls Bitcoin. Instead, it is maintained by a global network of computers (nodes) that follow a consensus protocol to validate transactions and secure the network. Decentralization is crucial because it reduces the risk of censorship and interference. In traditional financial systems, governments or banks can freeze accounts, block transactions, or impose capital controls. With Bitcoin, these risks are minimized because the system is not controlled by any central authority. Elimination of Intermediaries In traditional financial systems, intermediaries like banks and payment processors play a significant role in facilitating transactions. These intermediaries charge fees, impose delays, and can block or reverse transactions. Bitcoin eliminates the need for these intermediaries by enabling direct peer-to-peer transactions. This elimination of intermediaries has several benefits: Lower Costs: Without intermediaries, transaction fees are generally lower.
Faster Transactions: Bitcoin transactions can be processed more quickly than those relying on multiple intermediary steps, especially for international transfers.
Greater Access: People without access to traditional banking services can use Bitcoin, as long as they have internet access. Empowerment of Individuals and Communities Bitcoin enhances financial autonomy by giving individuals direct control over their funds. This empowerment is particularly significant in regions with unstable or repressive financial systems. Here are some specific ways Bitcoin empowers users: Protection Against Inflation: In countries with high inflation, local currencies can rapidly lose value. Bitcoin, with its fixed supply cap of 21 million coins, provides an alternative store of value.
Financial Inclusion: Bitcoin allows anyone with internet access to participate in the global economy. This is vital for the unbanked population, which includes millions of people worldwide who do not have access to traditional banking services.
Resistance to Censorship: Because Bitcoin transactions are decentralized, they are resistant to censorship. Governments and institutions cannot easily block or reverse transactions. In 2021, El Salvador became the first country to adopt Bitcoin as legal tender. This decision aimed to promote financial inclusion and reduce remittance costs for Salvadorans living abroad. By using Bitcoin, El Salvador hopes to empower its citizens with greater financial autonomy and reduce dependency on traditional banking systems. Also in Nigeria has seen significant adoption of Bitcoin due to various factors, including inflation, currency devaluation, and restrictive banking regulations. Nigerians use Bitcoin to bypass financial restrictions, save their wealth, and make international transactions. This use case highlights Bitcoin's potential to enhance financial sovereignty in countries with unstable or restrictive financial systems. Challenges and Considerations While Bitcoin offers numerous benefits for financial sovereignty, it also faces several significant challenges. Understanding these challenges is crucial for anyone looking to navigate the Bitcoin landscape effectively. Volatility Bitcoin is notorious for its price volatility. Its value can experience dramatic swings within short periods, driven by various factors such as market speculation, regulatory news, macroeconomic trends, and technological developments. For instance, Bitcoin's price surged from around $10,000 in late 2020 to nearly $65,000 in April 2021, only to drop below $30,000 a few months later, currently now the price of Bitcoin is approximately $65,000 and it is constantly fluctuating. Such fluctuations make it a risky investment for individuals and businesses looking for stable value storage. Volatility presents a challenge for using Bitcoin as a day-to-day currency. If the value of Bitcoin can drop significantly after a transaction, it creates uncertainty and risk for both the payer and the recipient. This instability can deter businesses from accepting Bitcoin and individuals from using it for everyday purchases, limiting its adoption as a practical medium of exchange. The extreme volatility also affects investor sentiment. While some are attracted to the potential for high returns, others are wary of the risks associated with such unpredictable price movements. This divide can influence the overall market dynamics, contributing further to the volatility cycle. Regulatory Uncertainty Bitcoin operates globally, but the regulatory environment varies widely from country to country. Some nations, like Japan, have embraced Bitcoin and created clear regulatory frameworks to foster innovation and consumer protection. Others, like China, have imposed strict regulations or outright bans on Bitcoin transactions and mining. This patchwork of regulations creates uncertainty for users and businesses operating in multiple jurisdictions. Governments are still figuring out how to regulate Bitcoin effectively. Concerns about money laundering, tax evasion, fraud, and the potential use of Bitcoin for illegal activities drive much of this regulatory scrutiny. Regulatory bodies worldwide are working to balance fostering technological innovation with protecting consumers and maintaining financial stability. The regulatory landscape is continually evolving. New laws and regulations can be introduced at any time, which might significantly impact the use and value of Bitcoin. This potential for sudden regulatory changes creates uncertainty and risk for Bitcoin users and investors, who must stay informed and adaptable to navigate this complex environment. Technical Barriers Bitcoin and blockchain technology can be complex and intimidating for those not familiar with them. Concepts such as cryptographic hashing, decentralized networks, and private keys require a certain level of technical knowledge to understand fully. This learning curve can be a barrier for new users who may find it challenging to grasp how Bitcoin works and how to use it securely. Using Bitcoin securely involves understanding best practices for managing private keys and avoiding common pitfalls such as phishing attacks and malware. If a user's private key is lost or stolen, they lose access to their Bitcoin permanently. Ensuring the security of one's digital assets requires knowledge and vigilance, which can be daunting for those new to the technology. The user experience of Bitcoin wallets and other related technologies is improving, but it can still be complex compared to traditional financial services. Setting up a Bitcoin wallet, securing it, and making transactions can involve steps that are not intuitive for everyone. Simplifying these processes is crucial for broader adoption. Bitcoin’s scalability has been a topic of debate within the community. The network’s ability to process transactions quickly and efficiently is limited by its current infrastructure. While solutions like the Lightning Network and side chains are being developed to address these issues, the technology is still maturing, and widespread adoption will take time. Conclusion Bitcoin significantly enhances financial sovereignty by providing a decentralized, peer-to-peer financial system that empowers individuals and communities. It reduces dependence on traditional financial institutions, lowers transaction costs, and offers greater financial inclusion. While challenges such as volatility and regulatory uncertainty remain, the potential of Bitcoin to promote financial autonomy and privacy is undeniable. As adoption grows and technology advances, Bitcoin may play an increasingly vital role in achieving financial sovereignty in the digital age. Over time we have come to see that central authorities and intermediaries predominantly control financial systems, and the concept of financial sovereignty is becoming increasingly important. Now enters Bitcoin, a digital currency that operates on a decentralized network, providing a new shift to financial autonomy. Bitcoin was introduced in 2009 by the pseudonymous Satoshi Nakamoto, and offers a peer-to-peer financial system that is not governed by any single entity. This decentralization is crucial for enhancing financial sovereignty as it minimizes the risks of censorship, manipulation, and dependency on traditional financial institutions. With Bitcoin, individuals can conduct transactions directly with one another, without the need for intermediaries, leading to lower costs, faster transactions, and greater access to financial services. In this article, we will look at how Bitcoin plays a pivotal role in promoting financial sovereignty. What is Financial Sovereignty What is Financial Sovereignty Financial sovereignty refers to the ability of individuals to have full control over their own financial assets and transactions without reliance on external intermediaries such as banks, governments, or other financial institutions. Bitcoin plays a significant role in promoting financial sovereignty due to its decentralized and peer-to-peer nature. Key Concepts of Financial Sovereignty with Bitcoin Key Concepts of Financial Sovereignty with Bitcoin Decentralization and its Importance Decentralization and its Importance Decentralization and its Importance Bitcoin operates on a decentralized network. This means no single entity, like a central bank or government, controls Bitcoin. Instead, it is maintained by a global network of computers (nodes) that follow a consensus protocol to validate transactions and secure the network. Decentralization is crucial because it reduces the risk of censorship and interference. In traditional financial systems, governments or banks can freeze accounts, block transactions, or impose capital controls. With Bitcoin, these risks are minimized because the system is not controlled by any central authority. Elimination of Intermediaries Elimination of Intermediaries Elimination of Intermediaries In traditional financial systems, intermediaries like banks and payment processors play a significant role in facilitating transactions. These intermediaries charge fees, impose delays, and can block or reverse transactions. Bitcoin eliminates the need for these intermediaries by enabling direct peer-to-peer transactions. This elimination of intermediaries has several benefits: This elimination of intermediaries has several benefits: Lower Costs: Without intermediaries, transaction fees are generally lower. Faster Transactions: Bitcoin transactions can be processed more quickly than those relying on multiple intermediary steps, especially for international transfers. Greater Access: People without access to traditional banking services can use Bitcoin, as long as they have internet access. Lower Costs: Without intermediaries, transaction fees are generally lower. Faster Transactions: Bitcoin transactions can be processed more quickly than those relying on multiple intermediary steps, especially for international transfers. Greater Access: People without access to traditional banking services can use Bitcoin, as long as they have internet access. Empowerment of Individuals and Communities Empowerment of Individuals and Communities Empowerment of Individuals and Communities Bitcoin enhances financial autonomy by giving individuals direct control over their funds. This empowerment is particularly significant in regions with unstable or repressive financial systems. Here are some specific ways Bitcoin empowers users: Protection Against Inflation: In countries with high inflation, local currencies can rapidly lose value. Bitcoin, with its fixed supply cap of 21 million coins, provides an alternative store of value. Financial Inclusion: Bitcoin allows anyone with internet access to participate in the global economy. This is vital for the unbanked population, which includes millions of people worldwide who do not have access to traditional banking services. Resistance to Censorship: Because Bitcoin transactions are decentralized, they are resistant to censorship. Governments and institutions cannot easily block or reverse transactions. Protection Against Inflation: In countries with high inflation, local currencies can rapidly lose value. Bitcoin, with its fixed supply cap of 21 million coins, provides an alternative store of value. Financial Inclusion: Bitcoin allows anyone with internet access to participate in the global economy. This is vital for the unbanked population, which includes millions of people worldwide who do not have access to traditional banking services. Resistance to Censorship: Because Bitcoin transactions are decentralized, they are resistant to censorship. Governments and institutions cannot easily block or reverse transactions. In 2021, El Salvador became the first country to adopt Bitcoin as legal tender. This decision aimed to promote financial inclusion and reduce remittance costs for Salvadorans living abroad. By using Bitcoin, El Salvador hopes to empower its citizens with greater financial autonomy and reduce dependency on traditional banking systems. Also in Nigeria has seen significant adoption of Bitcoin due to various factors, including inflation, currency devaluation, and restrictive banking regulations. Nigerians use Bitcoin to bypass financial restrictions, save their wealth, and make international transactions. This use case highlights Bitcoin's potential to enhance financial sovereignty in countries with unstable or restrictive financial systems. Challenges and Considerations Challenges and Considerations While Bitcoin offers numerous benefits for financial sovereignty, it also faces several significant challenges. Understanding these challenges is crucial for anyone looking to navigate the Bitcoin landscape effectively. Volatility Volatility Volatility Bitcoin is notorious for its price volatility. Its value can experience dramatic swings within short periods, driven by various factors such as market speculation, regulatory news, macroeconomic trends, and technological developments. For instance, Bitcoin's price surged from around $10,000 in late 2020 to nearly $65,000 in April 2021, only to drop below $30,000 a few months later, currently now the price of Bitcoin is approximately $65,000 and it is constantly fluctuating. Such fluctuations make it a risky investment for individuals and businesses looking for stable value storage. Volatility presents a challenge for using Bitcoin as a day-to-day currency. If the value of Bitcoin can drop significantly after a transaction, it creates uncertainty and risk for both the payer and the recipient. This instability can deter businesses from accepting Bitcoin and individuals from using it for everyday purchases, limiting its adoption as a practical medium of exchange. The extreme volatility also affects investor sentiment. While some are attracted to the potential for high returns, others are wary of the risks associated with such unpredictable price movements. This divide can influence the overall market dynamics, contributing further to the volatility cycle. Regulatory Uncertainty Regulatory Uncertainty Regulatory Uncertainty Bitcoin operates globally, but the regulatory environment varies widely from country to country. Some nations, like Japan, have embraced Bitcoin and created clear regulatory frameworks to foster innovation and consumer protection. Others, like China, have imposed strict regulations or outright bans on Bitcoin transactions and mining. This patchwork of regulations creates uncertainty for users and businesses operating in multiple jurisdictions. Governments are still figuring out how to regulate Bitcoin effectively. Concerns about money laundering, tax evasion, fraud, and the potential use of Bitcoin for illegal activities drive much of this regulatory scrutiny. Regulatory bodies worldwide are working to balance fostering technological innovation with protecting consumers and maintaining financial stability. The regulatory landscape is continually evolving. New laws and regulations can be introduced at any time, which might significantly impact the use and value of Bitcoin. This potential for sudden regulatory changes creates uncertainty and risk for Bitcoin users and investors, who must stay informed and adaptable to navigate this complex environment. Technical Barriers Technical Barriers Technical Barriers Bitcoin and blockchain technology can be complex and intimidating for those not familiar with them. Concepts such as cryptographic hashing, decentralized networks, and private keys require a certain level of technical knowledge to understand fully. This learning curve can be a barrier for new users who may find it challenging to grasp how Bitcoin works and how to use it securely. Using Bitcoin securely involves understanding best practices for managing private keys and avoiding common pitfalls such as phishing attacks and malware. If a user's private key is lost or stolen, they lose access to their Bitcoin permanently. Ensuring the security of one's digital assets requires knowledge and vigilance, which can be daunting for those new to the technology. The user experience of Bitcoin wallets and other related technologies is improving, but it can still be complex compared to traditional financial services. Setting up a Bitcoin wallet, securing it, and making transactions can involve steps that are not intuitive for everyone. Simplifying these processes is crucial for broader adoption. Bitcoin’s scalability has been a topic of debate within the community. The network’s ability to process transactions quickly and efficiently is limited by its current infrastructure. While solutions like the Lightning Network and side chains are being developed to address these issues, the technology is still maturing, and widespread adoption will take time. Conclusion Bitcoin significantly enhances financial sovereignty by providing a decentralized, peer-to-peer financial system that empowers individuals and communities. It reduces dependence on traditional financial institutions, lowers transaction costs, and offers greater financial inclusion. While challenges such as volatility and regulatory uncertainty remain, the potential of Bitcoin to promote financial autonomy and privacy is undeniable. As adoption grows and technology advances, Bitcoin may play an increasingly vital role in achieving financial sovereignty in the digital age.

Bitcoin and Financial Sovereignty

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