Zero-Knowledge Proofs for Beginners

Published here originally.

Introduction

I Spy—did you play as a kid? One person chose a room object, and the other had to guess it by answering yes or no questions. I Spy was entertaining, but did you know it could teach you cryptography?

Zero Knowledge Proofs let you show your pal you know what they picked without exposing how. Math replaces electronics in this secret spy mission. Zero-knowledge proofs (ZKPs) are sophisticated cryptographic tools that allow one party to prove they have particular knowledge without revealing it. This proves identification and ownership, secures financial transactions, and more. This article explains zero-knowledge proofs and provides examples to help you comprehend this powerful technology.

What is a Proof of Zero Knowledge?

Zero-knowledge proofs prove a proposition is true without revealing any other information. This lets the prover show the verifier that they know a fact without revealing it. So, a zero-knowledge proof is like a magician's trick: the prover proves they know something without revealing how or what. Complex mathematical procedures create a proof the verifier can verify.

Want to find an easy way to test it out? Try out with tis awesome example! ZK Crush

Describe it as if I'm 5

Alex and Jack found a cave with a center entrance that only opens when someone knows the secret. Alex knows how to open the cave door and wants to show Jack without telling him.

Alex and Jack name both pathways (let’s call them paths A and B).

1. In the first phase, Alex is already inside the cave and is free to select either path, in this case A or B.

2. As Alex made his decision, Jack entered the cave and asked him to exit from the B path.

3. Jack can confirm that Alex really does know the key to open the door because he came out for the B path and used it.

To conclude, Alex and Jack repeat:

1. Alex walks into the cave.

2. Alex follows a random route.

3. Jack walks into the cave.

4. Alex is asked to follow a random route by Jack.

5. Alex follows Jack's advice and heads back that way.

What is a Zero Knowledge Proof?

At a high level, the aim is to construct a secure and confidential conversation between the prover and the verifier, where the prover convinces the verifier that they have the requisite information without disclosing it. The prover and verifier exchange messages and calculate in each round of the dialogue.

The prover uses their knowledge to prove they have the information the verifier wants during these rounds. The verifier can verify the prover's truthfulness without learning more by checking the proof's mathematical statement or computation.

Zero knowledge proofs use advanced mathematical procedures and cryptography methods to secure communication. These methods ensure the evidence is authentic while preventing the prover from creating a phony proof or the verifier from extracting unnecessary information.

ZK proofs require examples to grasp. Before the examples, there are some preconditions.

Criteria for Proofs of Zero Knowledge

1. Completeness: If the proposition being proved is true, then an honest prover will persuade an honest verifier that it is true.

2. Soundness: If the proposition being proved is untrue, no dishonest prover can persuade a sincere verifier that it is true.

3. Zero-knowledge: The verifier only realizes that the proposition being proved is true. In other words, the proof only establishes the veracity of the proposition being supported and nothing more.

The zero-knowledge condition is crucial. Zero-knowledge proofs show only the secret's veracity. The verifier shouldn't know the secret's value or other details.

Example after example after example

To illustrate, take a zero-knowledge proof with several examples:

Initial Password Verification Example

You want to confirm you know a password or secret phrase without revealing it.

Use a zero-knowledge proof:

1. You and the verifier settle on a mathematical conundrum or issue, such as figuring out a big number's components.

2. The puzzle or problem is then solved using the hidden knowledge that you have learned. You may, for instance, utilize your understanding of the password to determine the components of a particular number.

3. You provide your answer to the verifier, who can assess its accuracy without knowing anything about your private data.

4. You go through this process several times with various riddles or issues to persuade the verifier that you actually are aware of the secret knowledge.

You solved the mathematical puzzles or problems, proving to the verifier that you know the hidden information. The proof is zero-knowledge since the verifier only sees puzzle solutions, not the secret information.

In this scenario, the mathematical challenge or problem represents the secret, and solving it proves you know it. The evidence does not expose the secret, and the verifier just learns that you know it.

My simple example meets the zero-knowledge proof conditions:

1. Completeness: If you actually know the hidden information, you will be able to solve the mathematical puzzles or problems, hence the proof is conclusive.

2. Soundness: The proof is sound because the verifier can use a publicly known algorithm to confirm that your answer to the mathematical conundrum or difficulty is accurate.

3. Zero-knowledge: The proof is zero-knowledge because all the verifier learns is that you are aware of the confidential information. Beyond the fact that you are aware of it, the verifier does not learn anything about the secret information itself, such as the password or the factors of the number. As a result, the proof does not provide any new insights into the secret.

Explanation #2: Toss a coin.

One coin is biased to come up heads more often than tails, while the other is fair (i.e., comes up heads and tails with equal probability). You know which coin is which, but you want to show a friend you can tell them apart without telling them.

Use a zero-knowledge proof:

1. One of the two coins is chosen at random, and you secretly flip it more than once.

2. You show your pal the following series of coin flips without revealing which coin you actually flipped.

3. Next, as one of the two coins is flipped in front of you, your friend asks you to tell which one it is.

4. Then, without revealing which coin is which, you can use your understanding of the secret order of coin flips to determine which coin your friend flipped.

5. To persuade your friend that you can actually differentiate between the coins, you repeat this process multiple times using various secret coin-flipping sequences.

In this example, the series of coin flips represents the knowledge of biased and fair coins. You can prove you know which coin is which without revealing which is biased or fair by employing a different secret sequence of coin flips for each round.

The evidence is zero-knowledge since your friend does not learn anything about which coin is biased and which is fair other than that you can tell them differently. The proof does not indicate which coin you flipped or how many times you flipped it.

The coin-flipping example meets zero-knowledge proof requirements:

1. Completeness: If you actually know which coin is biased and which is fair, you should be able to distinguish between them based on the order of coin flips, and your friend should be persuaded that you can.

2. Soundness: Your friend may confirm that you are correctly recognizing the coins by flipping one of them in front of you and validating your answer, thus the proof is sound in that regard. Because of this, your acquaintance can be sure that you are not just speculating or picking a coin at random.

3. Zero-knowledge: The argument is that your friend has no idea which coin is biased and which is fair beyond your ability to distinguish between them. Your friend is not made aware of the coin you used to make your decision or the order in which you flipped the coins. Consequently, except from letting you know which coin is biased and which is fair, the proof does not give any additional information about the coins themselves.

Figure out the prime number in Example #3.

You want to prove to a friend that you know their product n=pq without revealing p and q. Zero-knowledge proof?

Use a variant of the RSA algorithm. Method:

1. You determine a new number s = r2 mod n by computing a random number r.

2. You email your friend s and a declaration that you are aware of the values of p and q necessary for n to equal pq.

3. A random number (either 0 or 1) is selected by your friend and sent to you.

4. You send your friend r as evidence that you are aware of the values of p and q if e=0. You calculate and communicate your friend's s/r if e=1.

5. Without knowing the values of p and q, your friend can confirm that you know p and q (in the case where e=0) or that s/r is a legitimate square root of s mod n (in the situation where e=1).

This is a zero-knowledge proof since your friend learns nothing about p and q other than their product is n and your ability to verify it without exposing any other information. You can prove that you know p and q by sending r or by computing s/r and sending that instead (if e=1), and your friend can verify that you know p and q or that s/r is a valid square root of s mod n without learning anything else about their values. This meets the conditions of completeness, soundness, and zero-knowledge.

Zero-knowledge proofs satisfy the following:

1. Completeness: The prover can demonstrate this to the verifier by computing q = n/p and sending both p and q to the verifier. The prover also knows a prime number p and a factorization of n as p*q.

2. Soundness: Since it is impossible to identify any pair of numbers that correctly factorize n without being aware of its prime factors, the prover is unable to demonstrate knowledge of any p and q that do not do so.

3. Zero knowledge: The prover only admits that they are aware of a prime number p and its associated factor q, which is already known to the verifier. This is the extent of their knowledge of the prime factors of n. As a result, the prover does not provide any new details regarding n's prime factors.

Types of Proofs of Zero Knowledge

Each zero-knowledge proof has pros and cons. Most zero-knowledge proofs are:

1. Interactive Zero Knowledge Proofs: The prover and the verifier work together to establish the proof in this sort of zero-knowledge proof. The verifier disputes the prover's assertions after receiving a sequence of messages from the prover. When the evidence has been established, the prover will employ these new problems to generate additional responses.

2. Non-Interactive Zero Knowledge Proofs: For this kind of zero-knowledge proof, the prover and verifier just need to exchange a single message. Without further interaction between the two parties, the proof is established.

3. A statistical zero-knowledge proof is one in which the conclusion is reached with a high degree of probability but not with certainty. This indicates that there is a remote possibility that the proof is false, but that this possibility is so remote as to be unimportant.

4. Succinct Non-Interactive Argument of Knowledge (SNARKs): SNARKs are an extremely effective and scalable form of zero-knowledge proof. They are utilized in many different applications, such as machine learning, blockchain technology, and more. Similar to other zero-knowledge proof techniques, SNARKs enable one party—the prover—to demonstrate to another—the verifier—that they are aware of a specific piece of information without disclosing any more information about that information.

5. The main characteristic of SNARKs is their succinctness, which refers to the fact that the size of the proof is substantially smaller than the amount of the original data being proved. Because to its high efficiency and scalability, SNARKs can be used in a wide range of applications, such as machine learning, blockchain technology, and more.

Uses for Zero Knowledge Proofs

ZKP applications include:

1. Verifying Identity ZKPs can be used to verify your identity without disclosing any personal information. This has uses in access control, digital signatures, and online authentication.

2. Proof of Ownership ZKPs can be used to demonstrate ownership of a certain asset without divulging any details about the asset itself. This has uses for protecting intellectual property, managing supply chains, and owning digital assets.

3. Financial Exchanges Without disclosing any details about the transaction itself, ZKPs can be used to validate financial transactions. Cryptocurrency, internet payments, and other digital financial transactions can all use this.

4. By enabling parties to make calculations on the data without disclosing the data itself, Data Privacy ZKPs can be used to preserve the privacy of sensitive data. Applications for this can be found in the financial, healthcare, and other sectors that handle sensitive data.

5. By enabling voters to confirm that their vote was counted without disclosing how they voted, elections ZKPs can be used to ensure the integrity of elections. This is applicable to electronic voting, including internet voting.

6. Cryptography Modern cryptography's ZKPs are a potent instrument that enable secure communication and authentication. This can be used for encrypted messaging and other purposes in the business sector as well as for military and intelligence operations.

Proofs of Zero Knowledge and Compliance

Kubernetes and regulatory compliance use ZKPs in many ways. Examples:

1. Security for Kubernetes ZKPs offer a mechanism to authenticate nodes without disclosing any sensitive information, enhancing the security of Kubernetes clusters. ZKPs, for instance, can be used to verify, without disclosing the specifics of the program, that the nodes in a Kubernetes cluster are running permitted software.

2. Compliance Inspection Without disclosing any sensitive information, ZKPs can be used to demonstrate compliance with rules like the GDPR, HIPAA, and PCI DSS. ZKPs, for instance, can be used to demonstrate that data has been encrypted and stored securely without divulging the specifics of the mechanism employed for either encryption or storage.

3. Access Management Without disclosing any private data, ZKPs can be used to offer safe access control to Kubernetes resources. ZKPs can be used, for instance, to demonstrate that a user has the necessary permissions to access a particular Kubernetes resource without disclosing the details of those permissions.

4. Safe Data Exchange Without disclosing any sensitive information, ZKPs can be used to securely transmit data between Kubernetes clusters or between several businesses. ZKPs, for instance, can be used to demonstrate the sharing of a specific piece of data between two parties without disclosing the details of the data itself.

5. Kubernetes deployments audited Without disclosing the specifics of the deployment or the data being processed, ZKPs can be used to demonstrate that Kubernetes deployments are working as planned. This can be helpful for auditing purposes and for ensuring that Kubernetes deployments are operating as planned.

ZKPs preserve data and maintain regulatory compliance by letting parties prove things without revealing sensitive information. ZKPs will be used more in Kubernetes as it grows.

Sam Bankman Fried (SDBF) is a legendary con man. But the NYT might not tell you that...

Since SBF's empire was revealed to be a lie, mainstream news organizations and commentators have failed to give readers a straightforward assessment. The New York Times and Wall Street Journal have uncovered many key facts about the scandal, but they have also soft-peddled Bankman-Fried's intent and culpability.

It's clear that the FTX crypto exchange and Alameda Research committed fraud to steal money from users and investors. That’s why a recent New York Times interview was widely derided for seeming to frame FTX’s collapse as the result of mismanagement rather than malfeasance. A Wall Street Journal article lamented FTX's loss of charitable donations, bolstering Bankman's philanthropic pose. Matthew Yglesias, court chronicler of the neoliberal status quo, seemed to whitewash his own entanglements by crediting SBF's money with helping Democrats in 2020 – sidestepping the likelihood that the money was embezzled.

Many outlets have called what happened to FTX a "bank run" or a "run on deposits," but Bankman-Fried insists the company was overleveraged and disorganized. Both attempts to frame the fallout obscure the core issue: customer funds misused.

Because banks lend customer funds to generate returns, they can experience "bank runs." If everyone withdraws at once, they can experience a short-term cash crunch but there won't be a long-term problem.

Crypto exchanges like FTX aren't banks. They don't do bank-style lending, so a withdrawal surge shouldn't strain liquidity. FTX promised customers it wouldn't lend or use their crypto.

Alameda's balance sheet blurs SBF's crypto empire.

The funds were sent to Alameda Research, where they were apparently gambled away. This is massive theft. According to a bankruptcy document, up to 1 million customers could be affected.

In less than a month, reporting and the bankruptcy process have uncovered a laundry list of decisions and practices that would constitute financial fraud if FTX had been a U.S.-regulated entity, even without crypto-specific rules. These ploys may be litigated in U.S. courts if they enabled the theft of American property.

The list is very, very long.

The many crimes of Sam Bankman-Fried and FTX

At the heart of SBF's fraud are the deep and (literally) intimate ties between FTX and Alameda Research, a hedge fund he co-founded. An exchange makes money from transaction fees on user assets, but Alameda trades and invests its own funds.

Bankman-Fried called FTX and Alameda "wholly separate" and resigned as Alameda's CEO in 2019. The two operations were closely linked. Bankman-Fried and Alameda CEO Caroline Ellison were romantically linked.

These circumstances enabled SBF's sin.  Within days of FTX's first signs of weakness, it was clear the exchange was funneling customer assets to Alameda for trading, lending, and investing. Reuters reported on Nov. 12 that FTX sent $10 billion to Alameda. As much as $2 billion was believed to have disappeared after being sent to Alameda. Now the losses look worse.

It's unclear why those funds were sent to Alameda or when Bankman-Fried betrayed his depositors. On-chain analysis shows most FTX to Alameda transfers occurred in late 2021, and bankruptcy filings show both lost $3.7 billion in 2021.

SBF's companies lost millions before the 2022 crypto bear market. They may have stolen funds before Terra and Three Arrows Capital, which killed many leveraged crypto players.

FTT loans and prints

CoinDesk's report on Alameda's FTT holdings ignited FTX and Alameda Research. FTX created this instrument, but only a small portion was traded publicly; FTX and Alameda held the rest. These holdings were illiquid, meaning they couldn't be sold at market price. Bankman-Fried valued its stock at the fictitious price.

FTT tokens were reportedly used as collateral for loans, including FTX loans to Alameda. Close ties between FTX and Alameda made the FTT token harder or more expensive to use as collateral, reducing the risk to customer funds.

This use of an internal asset as collateral for loans between clandestinely related entities is similar to Enron's 1990s accounting fraud. These executives served 12 years in prison.

Alameda's margin liquidation exemption

Alameda Research had a "secret exemption" from FTX's liquidation and margin trading rules, according to legal filings by FTX's new CEO.

FTX, like other crypto platforms and some equity or commodity services, offered "margin" or loans for trades. These loans are usually collateralized, meaning borrowers put up other funds or assets. If a margin trade loses enough money, the exchange will sell the user's collateral to pay off the initial loan.

Keeping asset markets solvent requires liquidating bad margin positions. Exempting Alameda would give it huge advantages while exposing other FTX users to hidden risks. Alameda could have kept losing positions open while closing out competitors. Alameda could lose more on FTX than it could pay back, leaving a hole in customer funds.

The exemption is criminal in multiple ways. FTX was fraudulently marketed overall. Instead of a level playing field, there were many customers.

Above them all, with shotgun poised, was Alameda Research.

Alameda front-running FTX listings

Argus says there's circumstantial evidence that Alameda Research had insider knowledge of FTX's token listing plans. Alameda was able to buy large amounts of tokens before the listing and sell them after the price bump.

If true, these claims would be the most brazenly illegal of Alameda and FTX's alleged shenanigans. Even if the tokens aren't formally classified as securities, insider trading laws may apply.

In a similar case this year, an OpenSea employee was charged with wire fraud for allegedly insider trading. This employee faces 20 years in prison for front-running monkey JPEGs.

Huge loans to executives

Alameda Research reportedly lent FTX executives $4.1 billion, including massive personal loans. Bankman-Fried received $1 billion in personal loans and $2.3 billion for an entity he controlled, Paper Bird. Nishad Singh, director of engineering, was given $543 million, and FTX Digital Markets co-CEO Ryan Salame received $55 million.

FTX has more smoking guns than a Texas shooting range, but this one is the smoking bazooka – a sign of criminal intent. It's unclear how most of the personal loans were used, but liquidators will have to recoup the money.

The loans to Paper Bird were even more worrisome because they created another related third party to shuffle assets. Forbes speculates that some Paper Bird funds went to buy Binance's FTX stake, and Paper Bird committed hundreds of millions to outside investments.

FTX Inner Circle: Who's Who

That included many FTX-backed VC funds. Time will tell if this financial incest was criminal fraud. It fits Bankman-pattern Fried's of using secret flows, leverage, and funny money to inflate asset prices.

FTT or loan 'bailouts'

Also. As the crypto bear market continued in 2022, Bankman-Fried proposed bailouts for bankrupt crypto lenders BlockFi and Voyager Digital. CoinDesk was among those deceived, welcoming SBF as a J.P. Morgan-style sector backstop.

In a now-infamous interview with CNBC's "Squawk Box," Bankman-Fried referred to these decisions as bets that may or may not pay off.

But maybe not. Bloomberg's Matt Levine speculated that FTX backed BlockFi with FTT money. This Monopoly bailout may have been intended to hide FTX and Alameda liabilities that would have been exposed if BlockFi went bankrupt sooner. This ploy has no name, but it echoes other corporate frauds.

Secret bank purchase

Alameda Research invested $11.5 million in the tiny Farmington State Bank, doubling its net worth. As a non-U.S. entity and an investment firm, Alameda should have cleared regulatory hurdles before acquiring a U.S. bank.

In the context of FTX, the bank's stake becomes "ominous." Alameda and FTX could have done more shenanigans with bank control. Compare this to the Bank for Credit and Commerce International's failed attempts to buy U.S. banks. BCCI was even nefarious than FTX and wanted to buy U.S. banks to expand its money-laundering empire.

The mainstream's mistakes

These are complex and nuanced forms of fraud that echo traditional finance models. This obscurity helped Bankman-Fried masquerade as an honest player and likely kept coverage soft after the collapse.

Bankman-Fried had a scruffy, nerdy image, like Mark Zuckerberg and Adam Neumann. In interviews, he spoke nonsense about an industry full of jargon and complicated tech. Strategic donations and insincere ideological statements helped him gain political and social influence.

SBF' s'Effective' Altruism Blew Up FTX

Bankman-Fried has continued to muddy the waters with disingenuous letters, statements, interviews, and tweets since his con collapsed. He's tried to portray himself as a well-intentioned but naive kid who made some mistakes. This is a softer, more pernicious version of what Trump learned from mob lawyer Roy Cohn. Bankman-Fried doesn't "deny, deny, deny" but "confuse, evade, distort."

It's mostly worked. Kevin O'Leary, who plays an investor on "Shark Tank," repeats Bankman-SBF's counterfactuals.  O'Leary called Bankman-Fried a "savant" and "probably one of the most accomplished crypto traders in the world" in a Nov. 27 interview with Business Insider, despite recent data indicating immense trading losses even when times were good.

O'Leary's status as an FTX investor and former paid spokesperson explains his continued affection for Bankman-Fried despite contradictory evidence. He's not the only one promoting Bankman-Fried. The disgraced son of two Stanford law professors will defend himself at Wednesday's DealBook Summit.

SBF's fraud and theft rival those of Bernie Madoff and Jho Low. Whether intentionally or through malign ineptitude, the fraud echoes Worldcom and Enron.

The Perverse Impacts of Anti-Money-Laundering

The principals in all of those scandals wound up either sentenced to prison or on the run from the law. Sam Bankman-Fried clearly deserves to share their fate.

Read the full article here.

In this article, I'll describe a weekend project I started to make something. It was Product Hunt's #1 of the Day, #2 Weekly, and #4 Monthly product.

How did I make Landing Page Checklist so simple? Building and launching took 3 weeks. I worked 3 hours a day max. Weekends were busy.

It's sort of a long story, so scroll to the bottom of the page to see what tools I utilized to create Landing Page Checklist :x ‍

As a matter of fact, it all started with the startups-investments blog; Startup Bulletin, that I started writing in 2018. No, don’t worry, I won’t be going that far behind. The twitter account where I shared the blog posts of this newsletter was inactive for a looong time. I was holding this Twitter account since 2009, I couldn’t bear to destroy it. At the same time, I was thinking how to evaluate this account.

So I looked for a weekend assignment.

Weekend undertaking: Generate business names

Barash and I established a weekend effort to stay current. Building things helped us learn faster.

Simple. Startup Name Generator The utility generated random startup names. After market research for SEO purposes, we dubbed it Business Name Generator.

Backend developer Barash dislikes frontend work. He told me to write frontend code. Chakra UI and Tailwind CSS were recommended.

It was the first time I have heard about Tailwind CSS.

Before this project, I made mobile-web app designs in Sketch and shared them via Zeplin. I can read HTML-CSS or React code, but not write it. I didn't believe myself but followed Barash's advice.

My home page wasn't responsive when I started. Here it was:)

And then... Product Hunt had something I needed. Me-only! A website builder that gives you clean Tailwind CSS code and pre-made web components (like Elementor). Incredible.

I bought it right away because it was so easy to use. Best part: It's not just index.html. It includes all needed files. Like

• postcss.config.js

• README.md

• package.json

• among other things, tailwind.config.js

This is for non-techies.

Tailwind.build; which is Shuffle now, allows you to create and export projects for free (with limited features). You can try it by visiting their website.

After downloading the project, you can edit the text and graphics in Visual Studio (or another text editor). This HTML file can be hosted whenever.

Github is an easy way to host a landing page.

1. your project via Shuffle for export

2. your website's content, edit

3. Create a Gitlab, Github, or Bitbucket account.

4. to Github, upload your project folder.

5. Integrate Vercel with your Github account (or another platform below)

6. Allow them to guide you in steps.

Finally. If you push your code to Github using Github Desktop, you'll do it quickly and easily.

Speaking of; here are some hosting and serverless backend services for web applications and static websites for you host your landing pages for FREE!

I host landingpage.fyi on Vercel but all is fine. You can choose any platform below with peace in mind.

• Vercel

• Render

• Netlify

After connecting your project/repo to Vercel, you don’t have to do anything on Vercel. Vercel updates your live website when you update Github Desktop. Wow!

Tails came out while I was using tailwind.build. Although it's prettier, tailwind.build is more mobile-friendly. I couldn't resist their lovely parts. Tails :)

Tails have several well-designed parts. Some components looked awful on mobile, but this bug helped me understand Tailwind CSS.

Unlike Shuffle, Tails does not include files when you export such as config.js, main.js, README.md. It just gives you the HTML code. Suffle.dev is a bit ahead in this regard and with mobile-friendly blocks if you ask me. Of course, I took advantage of both.

creativebusinessnames.co is inactive, but I'll leave a deployment link :)

Adam Wathan's YouTube videos and Tailwind's official literature helped me, but I couldn't have done it without Tails and Shuffle. These tools helped me make landing pages. I shouldn't have started over.

So began my Tailwind CSS adventure. I didn't build landingpage. I didn't plan it to be this long; sorry.

I learnt a lot while I was playing around with Shuffle and Tails Builders.

Long story short I built landingpage.fyi with the help of these tools;

Learning, building, and distribution

• tailwindcss.com/docs

• Shuffle (Started with a Shuffle Template)

• Tails (Used components from here)

• Sketch (to handle icons, logos, and .svg’s)

• metatags.io (Auto Generator Meta Tags)

• Vercel (Hosting)

• Github Desktop (Pushing code to Github -super easy-)

• Visual Studio Code (Edit my code)

• Mailerlite (Capture Emails)

• Google Analytics

• Jarvis / Conversion.ai (%90 of the text on website written by AI 😇 )

• CookieHub (Consent Management)

That's all. A few things:

The Outcome

.fyi Domain: Why?

I'm often asked this.

I don't know, but I wanted to include the landing page term. Popular TLDs are gone. I saw my alternatives. brief and catchy.

CSS Tailwind Resources

I'll share project resources like Tails and Shuffle.

• Wicked Templates

• TailwindCSS Buttons

• Blocks | Wicked Templates

• nine4

• TUK — Tailwind UI KIT

• HyperUI

• Lofi UI

• Beginner Tailwind (I lately enrolled in this course but haven’t completed it yet.)

Thanks for reading my blog's first post. Please share if you like it.

“I think if I had time to do something else, I would be so excited to go after this company right now.”

Sam Altman, CEO of Open AI, recently discussed AI's present and future.

Open AI is important. They're creating the cyberpunk and sci-fi worlds.

They use the most advanced algorithms and data sets.

GPT-3...sound familiar? Open AI built most copyrighting software. Peppertype, Jasper AI, Rytr. If you've used any, you'll be shocked by the quality.

Open AI isn't only GPT-3. They created DallE-2 and Whisper (a speech recognition software released last week).

What will they do next? What's the next great chance?

Sam Altman, CEO of Open AI, recently gave a lecture about the next trillion-dollar AI opportunity.

Who is the organization behind Open AI?

Open AI first. If you know, skip it.

Open AI is one of the earliest private AI startups. Elon Musk, Greg Brockman, and Rebekah Mercer established OpenAI in December 2015.

OpenAI has helped its citizens and AI since its birth.

They have scary-good algorithms.

Their GPT-3 natural language processing program is excellent.

The algorithm's exponential growth is astounding. GPT-2 came out in November 2019. May 2020 brought GPT-3.

Massive computation and datasets improved the technique in just a year. New York Times said GPT-3 could write like a human.

Same for Dall-E. Dall-E 2 was announced in April 2022. Dall-E 2 won a Colorado art contest.

Open AI's algorithms challenge jobs we thought required human innovation.

So what does Sam Altman think?

The Present Situation and AI's Limitations

During the interview, Sam states that we are still at the tip of the iceberg.

So I think so far, we’ve been in the realm where you can do an incredible copywriting business or you can do an education service or whatever. But I don’t think we’ve yet seen the people go after the trillion dollar take on Google.

He's right that AI can't generate net new human knowledge. It can train and synthesize vast amounts of knowledge, but it simply reproduces human work.

“It’s not going to cure cancer. It’s not going to add to the sum total of human scientific knowledge.”

But the key word is yet.

And that is what I think will turn out to be wrong that most surprises the current experts in the field.

Reinforcing his point that massive innovations are yet to come.

But where?

The Next $1 Trillion AI Company

Sam predicts a bio or genomic breakthrough.

There’s been some promising work in genomics, but stuff on a bench top hasn’t really impacted it. I think that’s going to change. And I think this is one of these areas where there will be these new $100 billion to $1 trillion companies started, and those areas are rare.

Avoid human trials since they take time. Bio-materials or simulators are suitable beginning points.

AI may have a breakthrough. DeepMind, an OpenAI competitor, has developed AlphaFold to predict protein 3D structures.

It could change how we see proteins and their function. AlphaFold could provide fresh understanding into how proteins work and diseases originate by revealing their structure. This could lead to Alzheimer's and cancer treatments. AlphaFold could speed up medication development by revealing how proteins interact with medicines.

Deep Mind offered 200 million protein structures for scientists to download (including sustainability, food insecurity, and neglected diseases).

Being in AI for 4+ years, I'm amazed at the progress. We're past the hype cycle, as evidenced by the collapse of AI startups like C3 AI, and have entered a productive phase.

We'll see innovative enterprises that could replace Google and other trillion-dollar companies.

What happens after AI adoption is scary and unpredictable. How will AGI (Artificial General Intelligence) affect us? Highly autonomous systems that exceed humans at valuable work (Open AI)

My guess is that the things that we’ll have to figure out are how we think about fairly distributing wealth, access to AGI systems, which will be the commodity of the realm, and governance, how we collectively decide what they can do, what they don’t do, things like that. And I think figuring out the answer to those questions is going to just be huge. — Sam Altman CEO