ML

MixMasterMarshall

I no longer do ML but used to be heavy into it and spoke at an IEEE conference back in 2019. Predicting specific prices that far into the future is pure speculation, just because you ran a regression model and fed an algorithm data doesn't mean your prediction is any better than an arm chair guess. The furthest any prediction model I'll actually believe is maybe a day or two into the future. You cannot predict outside events because you don't have access to that kind of data and processing power. You yourself said the election screwed you, that's just one glaring example as to why this is WAY more complicated than plotting some linear regression. I love that you're trying but I moved away from this years ago because I realized that modeling any financial model is chasing the dragon, you'll always feel like you're close and sometimes it works but you'll never really trust it fully because it fails in an unpredictable way that ultimately undermines its very purpose. You're making an educated guess, yes it's educated but it's still a guess.

Fataaaaaal

https://coinmarketcap.com/dexscan/solana/D3ML9xvdgSpq194qtMn8QAGyywJPjLi4g7j7oFdpQaXp/ Here you can see the community votes on cmc to get it verified by them

ImmortanDrew

Of course. I max out my Roth 401k and a Roth IRA. I like the tax free wealth growth and diversity. My brother is full BTC but as we learned as children, never put ALL your eggs into one basket. I was born in 1987 and we had no idea where this Internet business would lead to. Now Im 37 and we have no real idea where this quantum computing and AI/ML obsession will land.

SnooChipmunks1441

oGPU, HEGE, ML, and HASHAI

applecraked

No I don't feel the need to watch a video from a conference dedicated to hyping up technology for external partners. On a separate note, one thing I really don't like about crypto (and other industries like AI/ML) is that people like yourself who have no understanding of how the underlying technologies work can speak so confidently about how they will change the world. Personally I have played around with and written a couple dapps using Chainlink since it's the largest oracle service, and have a decent understanding on a technical level of how blockchain, zkps, etc work. Based on your more recent responses, my guess is that you have never even attempted to read any of the ETH/BTC white papers, and have definitely never written anything in solidity related to ETH so I don't really value anything crypto related that you say

mango_sake

I built the UI using react with next js and the backend in python. I just finished my MSc in computer science (ML and stats) so i developed the algorithm first then tried to hire a bunch of devs to make the frontend as i have 0 experience whatsoever. They were all bad, took it on myself eventually. I have had a multiple personal projects before, mostly in the data science area (as is this) but all never really took off. But on this one i put the most effort by far haha

BlackWarrior322

I don’t think an LLM is the best model for this - maybe it can articulate insights for you in a better way, but you might need some other ML/DL model to get those insights

riscten

>I want to believe people have a special something AI will struggle to mimic. I think they do. I knew from the moment I looked at your piece that it wasn't AI. Double-checked for artifacts of course, and then I saw the no-AI mention in your Twitter bio. ML is only a leveler IMHO. It only works if a ton of people do the same thing, because it needs the data to derive correlations. True art is safe, at least until we have AGI.

NootropicHar

You the eth trader? lol. Google best money market rates. Probably could find something with ML or JPM.

SignInternational623

Don't sell, just use [LN](https://youtu.be/sXBwRO7ML7w?si=AAk5G-rjEzse8HEz)and check btcmap.org to find a pizza who accepts to pay on Bitcoin.

coinfeeds-bot

tldr; The article discusses the integration of zero-knowledge machine learning (ZKML) on the Mina Protocol, addressing challenges in combining AI with blockchain. ZKML uses zero-knowledge proofs to verify computations without revealing data, enhancing privacy and efficiency. This approach allows AI models to run in decentralized environments while maintaining data privacy. Mina's ZKML library, leveraging ONNX, enables developers to convert ML models into ZK circuits, facilitating the creation of privacy-preserving applications. The article highlights use cases and the potential of recursive proofs in complex ML workflows. *This summary is auto generated by a bot and not meant to replace reading the original article. As always, DYOR.

AggravatingRock8606

Because by definition: Cryptography is the art of using various methods/patterns and algorithms for encryption and decryption, as well as others such as digital signatures hashes etc. etc. you get the point Cryptocurrency is simply a digital currency that uses various cryptographic primitives (such as ECDSA) to securely facilitate verifiable digital transactions in a no -interactive fashion. These are two very different concepts. While yes, cryptocurrency uses cryptography, it’s not built upon unique mathematical concepts/constructs except for a select few shit coins/privacy coins utilizing novel constructs. Developers will need to switch from ECDSA/ECC to ML-DSA-44 (Level 1 - 128-bit security), ML-DSA-65 (Level 3 192-bit security) and ML-DSA-87 (Level 5 - 256-bit security). These are all based on the CRYSTALS-Dilithium method for digital signatures

frah90

Do you think electronic/ML engineers have a place there? Starting to think about it ;) 

elpigo

Tao will fall into obscurity after the AI hype cycle dies. The projects being built on it are a joke. Go to the DataScience or ML sub and see how many care about decentralized AI. Pretty much zero

weirdspineguy

Its hilarious how fucking low iq the majority of crypto bros are, no wonder this worthless digital poop ponzi scheme has gotten so big, truly a cult of financially illiterate low iq morons. >businesses AKA banks aren't will to take a little risk with a billion dollar client when the only punishment is getting fined. You dumb basement dwellers who never worked a day in your life coming up with armchair takes on things you know nothing about. The fed contemplated revoking HSBC's license for their lapses in AML controls, they were fined billions in relation to their lapses. Apart from monetary and reputational damage, they'll have to remediate their client files and deal with a decade long debacle with the feds. So no, no prudent bank will take risks with the view that 'only fines are involved'. Thats a braindead take from morons who never left mama's basement and have no idea how the world works. >Like this has literally happened already and continues to happen, Please tell me where a bank ignored money laundering risks for a billion dollar client because the only punishment is getting 'fined'. Go on, cite your examples. You're too dumb to realize that billionaires would be prominent public figures and carry lower ML risks, hence you came up with your cretinous vague assertion. Truly the personification of dunning-kruger effect. Go on cite your example, im waiting.

weirdspineguy

Banks launder money unwittingly, and ML transactions are a fraction of legitimate transactions. This dude laundered money wittingly, by design, with intent, and money laundered constitutes a bulk of all the transactions that run through his mixer. See the difference? Clown.

TrollKingKendu

Very bullish on bitcoin and crypto infrastructure altcoins. I first started as a Bitcoin Maxi but we need altcoins to do well. So I do hold $ML but its not to big of a bag. My big conviction investment now is $XPX! Its a new token with doxxed and well connected Dev. XPX bring insane real world usecase with your crypto. You basicly load the XPX Visa cards with Bitcoin, USDT or many other tokens. The xpxpay marketplace will onboard normies and bring liquidity to XPX in any market condition. So it really is a sustainable business model and a revolutionizing tech behind it. Sitting at 500k MC and it really is a billion dollar project once you dive in and see how XPX will change DeFi and crypto adoption! https://www.reddit.com/r/DeFi_XPX_Token/s/thVJ4sj0d3

inphenite

Bitcoin is not dumping; the entire market took a massive sh\*t yesterday. Apple down 1,68%, S&P's information tech sector down 2,28%, NVIDIA down 6,70%. Bitcoin "only" dropped 3%. But it's not just rumor, it's actually happening - it takes a second for things to take hold though. If (big if) a recession is coming, what happens is a mix of AI/ML auto-sells, and people selling their stocks to prepare - which triggers a cascade of other panic sells and auto-sells. Usually these pullbacks last a few months max, and then you're at new ATH. It's historically an amazing time to buy. Bitcoin is no exception to that pattern, but considering we haven't yet seen the halving effects (they typically begin around 100 days after and peak 400 days after), I think we're actually quite robust. Today's "dumping" was last years "wow omg ATH". Just keep that in mind.

knightysays

Comment 2/2 FUNDAMENTAL ANALYSIS: What's important to understand straight away regarding Tracelabs (parent company to subsidiary, Origintrail), is this was initially a food traceability project created while the founders were in university. It was developed as a kind of anti-trust/reassurance product that evolved into a supply chain management tool to provide consumers, and other companies, an ability to verify the origin, quality, compliance and procedural standards of a supply chain. Origintrail has evolved significantly since. Regarding revenue, their main product/service isn't actually AI - it's data, and the diverse revenue offerings that comes with it - data streams, data aggregation and processing, supply chain and logistics efficiencies, real time asset monitoring, real time asset verification/modification, consumer reassurance tools, industry standards & compliance solutions, financial models, data silo efficiencies, decentralized data marketplace (own/buy/sell datasets)... ...Basically, value from/monetization of data by any means possible. It also generates revenue from the publishing of assets on the DKG, but I'll elaborate further down below. Monetization within OT's eco system takes the form of "TRAC" token. TRAC is the economic currency token and is also, categorically, a utility token - meaning OT's entire ecosystem is dependant upon its versatile functionality, catering to payment/node incentives/network dispute mechanism/staking/governance (voting)/QA incentive and more). There is a second affiliated currency known as neuro (also developed by Tracelabs). This token however, is for AI functionality within the DKG. I'll elaborate: It's important to know, the DKG itself is inherently not an AI/LLM. The AI component comes from Neuro. Neuro itself is also not an LLM. Instead, Neuro is an AI component within OT's ecosystem that leverages various AI, ML and NLP models/techniques to provide advanced data processing, analysis and prediction, sentiment and anomaly detection, data classifications and natural language functionalities. Neuro is also capable of integrating any LLM (via APIs) only to enhance the functionality of the DKG and functionalities. The AI component utilises a method called dRAG, very similar to Meta's RAG in their AI model. The DKG itself is essentially just an advanced p2p graph database anchored to the blockchain, utilising distributed storage (each node on the network holds a portion of the data, contributing to a collective decentralized storage system). Data is secured via zero knowledge encryption, and can be further secured depending on preferred privacy settings and can even run/store data native on individual devices, nodes installed/running on. Companies/individuals either run their own nodes for full control over data security (securing sensitive/private information from the readily accessible), or can operate on publically available nodes, hosted by node runners. Data is uploaded to nodes as knowledge assets, owned by the key addresses used to upload data. Tracelabs/Origintrail offer a variety of services and SaaS solutions privately to prospective clients, to maximise their data intake/out-take/retention etc. An example of OT systems functioning is Perutnina Ptuj, a leading poultry manufacturer in Southeastern Europe. PP installed Kakaxi IoT devices to gather sensor information and images from one of their farms. Datasets included: Sensor Data, Geo Data, Traceability Data, Certificate Data, Production Data, Master Data, Transportation Data, Raw Data and more. By purchasing this data, retailers that sold Perutnina’s products under their private labels coulf reassure their consumers, stakeholders and authorities of compliance, high animal welfare standards and internally utilise the supply chain data to improve their own economical models. Users upload data, manually or autonomously, using the knowledge base for a small TRAC fee per knowledge assets published, use it to train their AI models, internal management, SOP's, sell it to consumers and the utilisation of semantic web 3 creates value from previously siloed data. Knowledge asset owners set rules pertaining to the asset, or grouping of assets (known as paranets). Rules including asset discoverability, permissions, licencing terms such as ownership, licencing/usage costs, node/validator reward payouts, usage restrictions etc. Essentially, the knowledge base has now replaced the previous data marketplaces that users bought and sold data on. Why blockchain? OT simply utilises blockchain as an immutable ledger for trustless data integrity, standardized encryption, scalability, interoperability between RWA's, web2 and web3, decentralized data marketplace and global node network (secure permissionless network, data discovery, asset connectivity/verifiability) etc, but again, primarily value is derived from countering anti-trust issues through a transparent and accessible blockchain (ledger) and mitigating data/information silos. Also, to answer an earlier concern, I believe there is feasibility also in decentralization. IMO, it eliminates the need for central/regulatory authorities that would otherwise present themselves as intermediaries or data aggregators - particularly within public sectors (health/infrastructure/education etc), minimising possible overheads and downtime. Tracelabs is able to offer custom services via private servers, but primary value proposition to clients is value from data integrity via blockchain (consumer, regulatory and public trust through blockchain transparency and immutability), value from semantic web3 capability efficiencies (SOP efficiencies), value from compliance/QA/risk mitigation efficiencies (SOP efficiencies), monetization of datasets and account settlement expedition. Closing thoughts as an investor: Personally, I see RWA use case clear as day with this one. I would like network to be more secure than what it currently is, but that also comes with adoption. Stellar base of partnerships and clients, and for years have had a functional, revenue generating product put to market. From a R:R perspective, what also makes this project speculatively valuable as an investment is you have two mutually exclusive markets within tech going parabolic at the same time, while market is trying to find confluence between them - a bull (AI) cycle within a bull (AI). The main factor driving the bubble is simply companies and investors trying to find confluence. If market adoption takes place and market finds it feasible, this will be immensely valuable. It is currently WELL underpriced, mostly due to lack of visibility. They rarely spend on any marketing, no DEX/CEX paid listings, no BS games or gimmicks in the telegrams or socials, just work.

ApeironGaming

I'm just saying: 1. Meryl Lynch owns Blackrock, 2. BoA owns ML, 3. BoA belongs to Berkshire Hathaway and own BoA. Let that sink in.

Thommasc

It's not just because hardware is good because AMD/Intel have GPUs too. It's about how Nvidia nailed the software part way earlier than AMD/Intel with CUDA and all the framework and libraries based on top of it. Suddenly 95% of AI/ML tools were based on CUDA architecture, without nvidia chips, you simply cannot run your code. You would need to tweak it and nobody wants to do that. It's going to take 2 years for AMD to recover. But platforms like huggingface are going to abstract running AI data pipelines on top of any GPU chip architecture sooner or later. The question will be how dominant nvidia position will remain in the next 5 years. Any market hates monopoly because nobody likes to pay a premium for something, you start looking for ANY alternative. So the market will balance itself soon... hopefully...

dorienh

Do you have an option for API for live data and download of historical, to facilitate training ML models?

Ecstatic-Criticism81

Starting reading ML going Infinite, say what you want but I think SBF is great person

rsa121717

Overfitting is common with ML but the idea does not only apply to ML. Put simply, if your program is overfit to the data, it means your algorithm is not robust enough.

Alive_Drummer_1222

I am not quite sure what overfitting is but if it only applies to machine learning then that is not an issue as there is no ML here.

Aurel577

I am not sure a lot here are against anti-ML but if in there mind they think you are, they can confiscate besides making your life a living hell until you prove they are wrong.

library-in-a-library

Loving how this sub is explaining why anti-ML is bad lmfao

Va3V1ctis

Yes, but highly unlikely. Debt can be managed and repaid by huge increase of productivity and then taxing that and paying the debt with it, this happen after many countries were in debt because of WW2 and then huge increase of productivity allowed many of them to pay the majority of debt. Taxing the billionaires or taxing huge companies wouldn't be enough, neither would huge decrease of government spending. Will it happen? Probably not, we see and will some increases in productivity due to LLM, ML and AI, but it probably will not be enough. And if you talk about Bitcoin being some savior, it wont be, it will be just the storage of wealth, when US Dollar plummets to the bottom, and Bitcoin value in turn skyrockets, but this is just a speculation or maybe even just wishful thinking. Plus when US Dollar some day plummets due to enormous US debt and countries and companies don't want to buy US bonds or lend USA money anymore, there will be other much worse consequences for the majority of people all around the word!

JackRipster

People really need to have a close look at the BankSocial multi chain wallet. The small additional cost for the extra security features is well worth it if you hold anything substantial. Secura Essentials is your round-the-clock wallet protection tool. Incoming, outgoing, and dApp connections are all monitored in real-time by **AI/ML** to reduce attacks on your wallet. In addition, we've partnered with companies like [Chainalysis]() to provide constant threat scanning of third party attacks on your wallet. With our patent pending Decentralized Recovery tool, you never have to worry about losing your key/passphrase again. Our evolutionary platform distributes key fragments to Credit Unions. [https://www.banksocial.io/personal/secura-web3-wallet-security](https://www.banksocial.io/personal/secura-web3-wallet-security)

DrSpeckles

As someone else said, literally everyone who works in ML has tried this. As you know the outcome is only as good as your training data. And there just isn’t the consistency you are looking for.

cool_username_91210

Of course it has been done before. I know some people who use ML for trading for fun. From what I've seen, predictions on alt coins are less successful than on BTC or ETH. But even if the chance that your algorithm will make money is close to zero, you should still go for it anyway. You will learn a lot. You might need a GPU if you plan to train a deep learning NN.

themrgq

Only an idiot dogmatically believes in the effecient market hypothesis. It's far too extreme a position. And people try and fail to use ML in both crypto and stocks. Is it possible? Maybe, we can't really say anything is impossible. Has it happened or is it likely to happen? No.

morrisdev

It may be worth trying to find seemingly unrelated trends. For example, does the fluctuations in the price or wheat match up and all? No, how about the price of salmon? No, how about the temperature of Poland? Etc. :) There really may be macro factors that affect multiple trends of unrelated things. Like how pressure changes in one country will affect the weather in another which will affect the umbrella sales in another country, which etc... ML might be able to find some entertaining stuff. At least entertaining enough to post here and watch people criticize it. :)

imjoiningreddit

There are indicators on TradingView that may not be AI or ML but do a fair job at predicting swings in price movements. Look up MarketCipherB (paid version) or VMC Cipher_B_Divergences (free version)

ExcitableSarcasm

Correct. Literally my first coding project with ML was me running Eth price for the last 4 years to see if it worked. It did not.

theproteinenby

I'm a biochemist-turned-ML researcher - I use ML for biomolecular interaction predictions, so finance is very left-field for me! My question stands. Has anyone actually tried it and published results? I found a few shitty GitHub repos that claim to do it, but beyond that, I haven't found any evidence of solid attempts. For your information, the efficient market hypothesis was mathematically disproven a little while ago for the stock market - infinitesimal price changes do not occur as a random walk; there is structure in the data. I suspect the same is true for crypto markets.

LeiaCaldarian

You’re in ML research and have never thought that someone might have tried this…? Pretty much 90% of the people that hear about the most basic form of ML *immmideately* asks wether it’s possible to predict stock prices to the point it’s a damn meme. If it was feasible to predict, it wouldn’t be profitable anymore.

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Administrative_Shake

Warren is saying crypto should have 0% ML, and if it doesn't, it should be basically choked out and shut down. Of course, that's practically impossible. No FI in the world is cracked down on for batting 99.7%.

AeroArtz

I am a developer kinda new to web3 and this comment caught my eye. I built a tool not too long ago to verify metamask transactions before clicking confirm but I want to go further and build an even better tool to help people avoid scams like these. What do you mean exactly by unverified contract and 'read code' , is that how you currently go about verifying the authenticity of a contract? metamask recently started working with blockAid and it uses ML to simulate transactions before confirming and classifies it as scam or nots scam but I believe that can be strengthened further. What if the user had the capability to read and understand the translated summary / purpose of the code in plain English and the ability to view what is being transacted BEFORE the transaction takes place ? What would you think of something like that?

hdrive1335

It would probably take an overwhelming amount of man hours to compile a list of all your transactions that would pass the 'without-a-doubt' requirement to charge you... at this time. I have no doubt in the next couple years AI/ML will easily be able to do it though. I wouldn't lean on anonymity in DEFI when used 'regularly.'

poco

I think an ML model could do much better arrive it has better training than I do.

MachaMacMorrigan

On the face of it, this is nonsense. A mine is a bunch of application specific integrated circuits with its algorithm sitting in burned-in-PROM (programmable read only memory). You know, ***hard-wired***. The bunch run in parallel to maximize the chances of any given ASIC guessing the 'soution to the next block'. They don't run in either SIMD or MIMD, with true parallel processing against multiple datasets. This bunch of boxes are ***really stupid***. They are the most dim-witted computers ever built. But for the one task they"re built for, they are dazzlingly, blindingly fast. You *cannot* use neural networks to optimize the operations of an ASIC . . . or multiple non-interworking ASICs. You can re-design the ASIC to run faster, optimize the algorithm itself, or run more ASICs. AFAIK them's your options. AI/ML don't get a look in. BTW, the word 'algorithm' gets misused a lot. It does not, *can not*, apply to neural networks. An algorithm is a set of fixed instructions ending with STOP. by definition, it is fixed and cannot learn. See Knuth, The Art of Computer Programming Vol 1.

analogOnly

Lots of bots already implement ML and some AI

Digitaljehw

I know alot of things but recently started delving into ML and AI development. By tool do you mean copy their model?

Exotic_Acadia_

Web3 diet ML

ShaidarHaran2

We've had three bitcoin halvings. Three data points is not enough for anyone to make this assumption on, and articles are a dime a dozen and just trying to get clicks, especially with the cost of written content plummeting with ML.

shahrukhatik

Just a random thought - couldn’t we do some sort of semantic analysis , I.e run an ML algo from their papers vs the white paper to see if there’s a possible match from their writing styles?

abacatesexy

Ill recomemmend my current low cap asset: BOTTO, ML, NTX, PENDLE, HYPC, CGV, MUBI and SECOND. All of them are available in MEXC

abacatesexy

Thanks for your comment. I dont own any of these, but I have MintLayer (ML). I was thinking about STX but ran outta money Ill search for TRAC and ALEX, don't actually know these.

Far_Question_8201

[https://www.amazon.ca/Bitcoin-Hashrate-BM1366-Mining-Machine/dp/B0CRHF85ML](https://www.amazon.ca/Bitcoin-Hashrate-BM1366-Mining-Machine/dp/B0CRHF85ML) any idea about this? If I but this how much BTC per day I will earn assuming working 24 hours? Apparently it does not use too much electricity.

arpcode

A lot of stake is on the Natural Language Understanding capabilities of the said Model. How good is it? Could you elaborate on 'Logic-based AI' and exactly what it entails? Why'd Logic Based AI not pop up in other language understanding applications if it is clearly superior to the contemporary ML-based NLP approaches?

Fola_Tau

1. Agoras is a token that will be fully and effectively controlled by its users. What do I mean by that? Projects in the world of blockchain claim to be decentralized but they all have a centralized development team so dev, and thus control is largely in the hands of a few rather than everyone. With Tau Net, (the platform $AGRS runs on) all development will be in control of its entire user base. Often when people say AI, they immediately think of ML based AI, there are many different types of AI. Tau Net and Agoras are based on advanced logic based AI which is able to do everything ML based can do but much more, such as giving logical proofs of correctness, enabling correct-by-construction software, collective intelligence etc. It effectively will allow users to describe software in logical sentences, and those sentences are executable in Tau's runtime so they will work as provably correct running software. Meaning that all of Tau Net's userbase will be Tau Net's developers. Users describe what they want the tokenomics should be and governance around how to change the tokenomics. Tau Net detects the agreed software and evolves the tokenomics block to block. 2. Locked token holders will be released after full Tau Net release. 3. We have not had to use any of these functions, we implement them incase our community desire us to use them and act solely based on the community's will. 4. Team are fully doxxed check the website: [Tau.net](https://Tau.net)

Fola_Tau

1. The project is in development, we're currently on the ETH blockchain and will move to our own mainnet. 2. The Tokenomics are unique as we work on decentralizing software development and give real control over the network to the users. I'll answer questions 2 & 3 simultaneously when I explain how everything works on Tau Net. We are building an advanced logical AI specification language which enables users to describe their desired software in logical sentences and the description itself is executable in Tau Net's runtime so the resulting software is provably correct according to the description. This feature enables all users to collaboratively build Tau Net collectively alongside the entire userbase. Tau Net detects where each user agrees and disagrees, while the users also define rules for the platforms governance, which are provably adhered to. Tau Net takes the agreed specification from its users, which works as software, and puts it's own next version into the next block in the blockchain. This allows users to collectively change the system from block to block. The tokenomics will be fully in control of its users. 3. This is logic based AI. When people talk about AI they usually refer to ML based AI, there are many types of AI out there. Our advanced logic based AI is able to everything machine learning can do, plus logic, so it's able to do reasoning, provide proofs of correctness and much more 4. TLDR: Tau logic based AI > All ML AI

ohad1

logical AI is never wrong, and that's exactly the main difference. however the limits for software and hardware are 1. users need to speak in a very specific language, so the machine can understand it precisely 2. it requires much more computational resources than ML

tomorrowplus

This makes me think of Kahnemans Thinking Slow Thinking Fast. Would it be completely inaccurate to say ML is like thinking fast (but fallibly), and Tau is like thinking slow (but accurately)? Could it even result Tau and ML complementing each other like the thinking modes of humans?

ohad1

that's easy: chatgpt, for example, will do so many mistakes. ML can't do logic. but a logical engine is never wrong

ohad1

adding to that: Maybe two points are worth mentioning, both of them are relevant to logical AI in general and not only for Tau. One is complexity: logic is much more computationally intensive than ML. Second would be the field of applications: logic is all about the written word. It deals with sentences. ML can also deal with sentences, however in a way much inferior to logical engines. But ML can also deal with, say, pictures.

Fola_Tau

1. Excellent questions first we need to talk about AI, there are man different types of AI. When people say AI, they're typically referring to ML based AI. We pioneer advanced logical AI. Logical AI is able to all the things machine learning AI is able to do, plus a whole heap of very important logical things, such as reasoning and being able to give a formal proof of absolute correctness etc, as you know Machine Learning gets things very incorrect sometimes and there's always a probability of incorrectness, to what degree depends on the model. Tau Net uses advanced logic based AI so that you're able to get 100% correct results according to your description. I'll explain an example. Say you describe software using Tau Net's logical AI sentences, well the description is also directly executable in Tau Net's runtime meaning that the description itself serves as software. Software of the future will look like a PDF description but will be entirely correct with proof. Further, on Tau Net, we have advantages of the software being able to refer to its own sentences, which bring me to your point of safeguarding: 2. As Tau Net has the feature of being able to refer to it's own sentences, users are able to implement rules of functionality which, for the first time in any software allows the creator to ensure undesired behaviour is rejected by the software itself. You have deep control over how updates are accepted into software in the development stage by using Tau’s complex rules and filters in what permissions are given to be able to contribute to any aspect of the software. Any update or tampering that does not comply with your embedded safety guidelines is automatically rejected by the software itself, providing an additional layer of security. Say you build the best robot in the world, with other programming languages, that software just needs to be updated for it to completely change into the worst software in the world. With Tau Language, these rules will reject even incoming updates to the system. This has never been achieved in software development in general until now. 3. We fundamentally change the concept of traditional testing. As I mentioned in my first point, all software developed using Tau Net is correct by construction according to the description, testing is no longer for bugs as each description is an executable specification, resulting in correct-by-correct by construction software. Testing is now ensuring your description is as desired. Traditional testing is obsolete. You just describe and if you want to make edits to the description, you make edits. The bugs come the description, not implementation now on Tau Net. 4. Yes, Tau Net is intended to exist across all platforms devices.

tomorrowplus

What problems does Tau have that ML doesn't? There must be some?

meeleen223

Welcome! I hope I am not late, this is really interesting topic and approach your team has taken with logic-based AI vs from my understanding ML based AIs like ChatGPT work the same as next word prediction on keyboard. > For instance, if the command "Never send private data over the network" is embedded, Tau Net will consistently honor this rule, automatically rejecting any future updates that contravene it. When using AI in dynamic systems, there are situations when system learns what parameter to take which produces errors later because it sticks to that initial parameter. Can similar happen in Tau Net? What if initial command needs changing? >On Tau Net, users are granted unprecedented control over the network. Tau Net provides an effective solution to the AI alignment problem by enabling users to define rules or embed regulations directly within its software. So my question can your AI encounter a *trolley problem* or it will just see it as tampering with the software and reject it? If not how would it deal with it? With simplicity being important for success eg ChatGPT got to 100m users in 2 months with simple to use interface. Do you see any challenges in this department?

HSuke

Depends on the purpose of mining. If you are building a neural network, you definitely don't want your trained machines to leave. That would defeat the entire purpose of training them unless it's altruistic and purposely done at an economic loss. It's like professors at a university paying to train students, only for the students to leave after 4 years. The only way this would work economically is if this isn't for AI or ML and that the subnet is just having their miners complete trapdoor functions (like in Proof of Work).

That_Handle4899

How does logic based AI have a better understanding of things than ML AI, and how is logic based AI supposed to answer all of the things on the internet and more? What are the limits of the software and hardware. What if the logic based AI show wrong information?

Azera21

I dont get ur hate for bittensor, why would miners want to leave when there are incentives for them? U say miners can just leave the network if they want, but isnt the whole point of decentralisation to prevent any one point of attack? If 1 company that have 300 miners leaves bittensor, another 300 miners from different places will gladly replace them. Bittensor is built to be for survival of the fittest, any useless ML model for a quick cash grab is quickly faded out if no one uses it. Also, to be a participant in bittensor, you need ML knowledge to begin with, so not everyone can randomly join in, leaving those who know the true value of it to participate.

Aerocryptic

If you have a good GPU maybe one of those Depin plays. If you have some coding / ML skills tao

SevereCalendar7606

Actually it does...I can run 8000 million keys per second on my old mining rig with vanity search (c++/CUDA). I still would be lucky to get a private key in my lifetime odds wise. If you had the money and time you could easily build an ASIC which would be far more efficient, making the odds super low but not as impossible. The only hurdle would be the custom SHA-256 hashing cores optimized for brute-forcing operations. However thanks to ML there are tons of options for programmable cores.

VXVWhale

Find a crypto that specializes in language modeling. Only one does so and is applying to space biology, the likes of Open AI or any other large language model do not have the capabilities of doing so. Any other crypto proclaiming AI are not constructing language models which is the tip of the spear in AI/ML today.

Lyuseefur

I’m trying to use examples to make it a little bit bit more clear But consider the problem, right You’re asking for a Solution to a really big problem set We’re not talking about trying to find one out of several trillion trillion So you have to have a way to derive a solution from a pretty complicated mathematical formula Or to potentially find a flaw in that mathematical formula that allows such problem-solving Q computing doesn’t even have a language yet that can properly load compile and resolve this kind of a large problem It’s basically an 8086 with 1 kB ram right now Now it can solve some pretty interesting problems even with that, but we’re able to do AI/ML faster. Then we can do quantum resolution at the moment. In short, we need more everything for quantum computing to start competing with what we already have with ML By that point engineers on every block chain would fork, and prevent such a move. The advantage quantum computing provides is a multi state storage and retrieval solution Consider the problem of understanding folding by proteins Traditional logic says that protein should fold the same way every time And yet it doesn’t and truthfully, we still don’t know why And the funny thing is within the quantum world. The protein can go to anyone of those states at any time. So why does it pick one state one time and a different state a different time? A quantum computer could probably tell us the answer to something like this, but that answer is still at least seven years away. So to go back to the original question how does the math work? Well, it’s complicated. And it’s complicated because we have not yet written that story. The compiler at least a good one has not been built and at the moment I’m not sure that we even know how to build one because there is so much that we still don’t know and that we are still discovering. Take a modern computer system and go back to 1976 Now try to explain every single component to engineer at that time And while you’re at it, explain to him what an ML model is To that engineer, it would sound like science fiction That’s literally where we at today with quantum computers

AdamFitri2005

That and others like ORDI, ORDS, ML and CKB

riscten

You just haven't read enough posts and comments. People talk about this all the time. The general consensus here is that it's totally OK to sell a small amount to cover emergency expenses, a down payment on a house, medical bills or even the full mortgage. I've actually never heard of anyone who sold it all here. What usually frowned upon is to try to time the market, aka "sell at the top" to "buy back at the bottom", because most of the time that usually ends up in losing more money than if you just kept DCAing or hodling. It is incredibly difficult to perform due to the fact that you are working against highly trained professionals and ML algorithms running on millions of dollars worth of hardware. Plus, as number goes up, you never know if it'll ever come back down to this number, so you're just selling now to buy later at a higher price and end up with less coin. The saying goes: "Time in the market beats timing the market". Noobs typically don't listen to this advice, try to time it a few time, lose money, then wizen up.

RunAndHeal

C'mon guys, their days are gone now! We are 2024, the age od IoT , ML, AI and Bitcoin. Stop bringing up faces from the Industrial revolution.

ZombieSlayer83

I definitely have a nice bag of STX I've been holding for a year now. It has performed great. My absolute best performing BTC L2 has been ML. Made some huge gains on that one. I searched for LBIT on gecko and CMC and couldn't find it. Want to redpill me on that?

cryptorisktaker

SQT, LCX, ML (MiltLayer)

Ilovekittens345

>ML needs very fast memory on very fast computers with very many cores and a lot of data (fast!). Blockchain doesn't help at all. The actual machine learning is not done on the blockchain. It's just a way to rent out GPU power and use the blockchain to do your administration and rent out the power. The GPU's have to do a bit of mining so they can't cheat but this is seperate from them actually doing the machine learning. So you got it completely wrong.

bhfanatik

How do I find coins that haven't gone up like 20X already? It seems like it's too late at this point, right? ML would have been an amazing buy 3 months ago.... I wish I could find a similar coin that hasn't exploded yet.

kirillborisov

OK, it's application-specific. I bought into the first page of google search. However, the miners can just sell the ASICs and lend the available electricity and buildings to whoever does the ML.

Anonymouse-C0ward

This is the point where, as a reader, I have had my laugh and now it’s just painful to watch. The A and S in ASIC stand for ***Application Specific***. Bitcoin mining ASICs are specifically designed for one single application: calculate SHA-256 hashes. ML applications do not have a need to calculate SHA-256 hashes on the computational level available in the Bitcoin mining system. You can’t reprogram an ASIC to do something else. If you want an ASIC to do something else, you design it, you don’t (and can’t) repurpose a ASIC designed for something else. Thus the hardware currently used for cryptocurrency mining will never be able to be used for AI/ML/DL computation unless there is some very odd application in that space that suddenly needs a massive number of SHA-256 hashes calculated (hint: there isn’t).

lardarz

It runs decentralised AI and ML for logistics and finance applications. RNDR has better use case as AI workloads are less intensive than graphics, and render does both and already has major partnerships and customers. Got both, and INJ also worth looking at in this segment.

TissueUnflawed

> My research shows that ASICs can be used to solve ML tasks Liar

elpigo

Yeah. Just speak with any seasoned AI/ML Engineer. So many now say that the suits are suddenly LLM/GenAI experts and ask using it for the dumbest things. So e of my friends in this space want to go back to their previous research that they were working on prior to the LLM boom. AI as a tool is great but it’s known for very long AI winters and this summer will end soon. When guys in suits are suddenly experts I’d be worried.

KimCheeHoo

Chiefs ML

kirklennon

> They're just NOW starting to touch the genre What a strange thing to say about a company that started including a “Neural Engine” in their processors in 2017 (meaning work on that started at least a decade ago). It’s highly-specialized and expensive hardware meant entirely for accelerating ML/“AI” workloads. As for OP’s question, there’s no chance Apple ever holds any bitcoin assets or adds any sort of support within Apple Pay. It’s fiscally irresponsible and counter to their environmental goals.