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STRONG ARTIFICIAL INTELLIGENCE, WEAK ARTIFICIAL INTELLIGENCE, WHATS THE DIFFERENCE?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered, mostly correct!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was sent by Sahir, from the Caribbean, and he wants to know what’s the difference between weak and strong artificial intelligence?

Sahir, you came to the right place.  At your age that’s a brilliant question!  Let’s start by explaining weak artificial intelligence (Weak AI).   Weak AI is also known as narrow AI.  Weak AI refers to AI systems that are designed and trained for a specific task or narrow sets of tasks. Weak AI systems excel in performing predefined tasks but are not remotely capable of true understanding or consciousness.  Weak AI is highly specialized and does not possess “human-like” cognitive abilities or general problem-solving capabilities beyond its narrow vision.  Think of Weak AI as a horse with blinders capable of only seeing what is directly nearby and in front and nothing else.  I’ll offer you examples. Weak AI includes virtual assistants, e.g., Siri or Alexa, recommendation algorithms used by streaming services such as Spotify or YouTube, and chatbots designed for specific customer service tasks, e.g., your financial institution customer help line.  Chatbots are mostly annoying and useless, do you agree? Time for Strong AI.

Strong AI, sometimes called Artificial General Intelligence (AGI), refers to AI systems with “human-level” intelligence and understanding.  Which human, I don’t know and afraid to ask!  Lol.  Strong AI systems can perform several intellectual tasks.  Strong AI systems can adapt to different domains and possess a “primitive” form of “consciousness or self-awareness” based on computations. Strong AI is a long-term goal of AI research and would require the development of AI systems that can reason, learn, understand, and adapt across a wide range of tasks and contexts.  Strong AI is currently a theoretical concept, and no AI system comes close to general intelligence.  Meaning you can relax–for now!  Lol. The best example of Strong AI, or perhaps somewhat Strong AI, is ChatGPT4 by Open AI. 

Let’s discuss a central feature of both Weak and Strong AI–prompts.  What is a prompt?  The term prompt is derived from Latin “promptus”, meaning ready for use.  In AI lexicon, a prompt is an instruction to the AI system.  A prompt could be simple, ‘Siri play disco music’, for instance, or complex, such as asking ChatGPT4 to explain Einstein’s Theory of Relativity. Holy prompt, Batman!  Sahir, can you identify which is the Weak AI and the Strong AI in these examples? 

Prompts are critical for two reasons.  First, prompts are how we communicate with AI systems.  AI systems can’t read our minds!  Only through, and thanks to, prompts, can humans interact with Weak and/or Strong AI systems.  In other words, prompts are portals into AI systems.  Second, the quality of AI interactions depends on precise prompts.  Unlike many human interactions, AI systems cannot “know what you mean”.  Therefore, AI systems depend on precise prompts.  My own experience with ChatGPT4 cements this understanding of AI systems.  Try it and give me your conclusion.

We stop here but not before acknowledging two sources for today’s post:  Microsoft’s Introduction to AI course and AI: The Future is Now by Nicholas Marty.  Today’s proverb is from Haiti, where they wisely say, “A leaking roof fools the sun but cannot fool the rain. 

Until Next Time,

Yogi Nelson

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WHAT MONETARY SYSTEM DOES THE CARDANO BLOCKCHAIN USE?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question is from Bhante Chandra from Kandy, Sri Lanka, and he wants to know what monetary system does the Cardano Blockchain use?

Bhante, you came to the right place. Let’s start first with explaining Cardano. Cardano is a decentralized, open source blockchain network that burst onto the scene in 2017. Charles Hoskinson, Cardano’s founder, created Cardano as a third generation blockchain. Bitcoin and Ethereum were generations one and two, respectively. Of course, Bitcoin was released by an anonymous developer (Satoshi Nakamura) in 2008, and Ethereum by a team, including Hoskinson, in 2015.

Cardano is recognized for its research-based approach. Cardano technology is based on nearly 200 peer reviewed academic research reports. Critics say Cardano is slow to the market; Cardano proponents acknowledge the critique. However, Cardano advocates say they are oven cooking not microwaving. Fair point! First and second generation blockchains were energy gluttons because they were constructed with proof of work consensus systems; Cardano was built using a proof of stake consensus system making it extremely energy efficient. Love a green blockchain!

Cardano is focused on solving the blockchain trilemma problem of balancing speed, scalability, and security. Essentially, the trilemma problem in blockchain says the more one or two issues are solved the worse off are the remaining. Holy no way out, Batman! Cardano also is recognized for its smart contract capabilities. Smart contracts facilitate the transfer of value from peer to peer without intermediaries. Now about the monetary system.

The Cardano network uses crypto currency to pay for transactions. Cardano currency is known as ADA, named after the famous British female mathematician and computer programmer, Ada Lovelace. Smaller denominations of ADAs are called Lovelace and they go out six decimal places, .000006. Bust out the calculator!

Cardano began with a total supply of 45B ADA. However, not all were release upon inception; that would be irresponsible. Instead, Cardano has phased in ADA distribution. Cardano is de-inflationary by design, meaning the supply of ADA decreases with time.

Let’s talk about the Cardano treasury and your personal Cardano treasury. Cardano’s treasury has three revenue sources: minting; donations; and taxation from block rewards. Great! Now, how does an individual grow their personal Cardano treasury? Here too there are multiple avenues. You could, for example, purchase ADA on an exchange, such as Coinbase. Suppose you purchased ADA, what next? Well, you could earn additional ADA by “staking it”. Staking is blockchain/crypto jargon for earning rewards. Think of it as interest payments for locking up and “lending” your ADA. Or you can operate your own stake pool and earn ADA directly. Operating a skating pool requires technical computer skills; learn and earn!

Once you have a bag of ADA, where to keep the stash is the question. Here again you have options. You can store your ADA on a centralized exchange, e.g., Coinbase. There are pros and cons to centralized exchanges, that go beyond this post. Or you can self-custody your stack; that too comes with advantages and disadvantages. Several self-custody wallets are available, including Typhon Wallet, Flint Wallet, or Yoroi Wallet.

A special thanks to Cardano and Emurgo Academy for supplying the information for today’s post. Time to go, but not before sharing an Egyptian proverb: “You can tell whether a man is clever by his answers. You can tell a man is wise by his questions.

Until next time,

Yogi Nelson

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WHAT IS THE TURING TEST?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question, comes from Tiago, in Sao Paulo, and he wants to know what is the Turing test?

Tiago, you came to the right place. Before describing and explaining the test, let’s understand who was Alan Turing? Turing was a British mathematician and computer scientist. The Big Papi of computer science! In 1936 Mr. Turing published “On Computable Numbers”. His research paper laid down the core concepts of a computer 10 years before the computer was invented! The machine became known as the Turing Machine. Turing went further and published “Computing Machinery and Intelligence”, in 1950. Here is the link: https://academic.oup.com/mind/article/LIX/236/433/986238 Turing’s research was focused on machine intelligence–can machines think. No easy task and begs the questions, how to determine and measure whether a machine is intelligent. Turing’s solution, known today as the Turing Test, was to modify a popular game at the time known as the “imitation game”.

The game works as follows: a computer and a human receive typed questions from a human judge called the interrogator. If the interrogator cannot accurately identify the answers of the computer, the computer wins. According to Turing, if the computer wins, we cannot say it’s unintelligent thus it must have some degree of intelligence. Given Turing defined thinking as the ability to solve problems, a computer could therefore develop intelligence. Turing anticipated his critics and refuted their nine most probable and powerful arguments, which I summarize below.

Turing starts with and destroys theological objections, citing bible passages where the earth revolves around the Sun and resulting Galileo situation. He moves to objection two labeled “head in the sand”. According to Turing, this position stems from humans unwilling to consider losing intellectual supremacy. The third objection is mathematical, and in full disclosure I did not completely understand. However, I did understand Turing said man can be smarter than a machine but cannot be smarter than every machine. Let’s turn to objection 4, consciousness.

Turing readily admits the consciousness is mysterious. Nevertheless, he says “I do not think these mysteries necessarily need to be solved before we can answer the question with which we are concerned in this paper.” In other words, we don’t need to know it all to know some of it. Objection five relates to “various disabilities”. By disabilities Turing means machines are limited to narrow tasks. I’ll quote Turing’s rebuttal, “… the criticism that a machine cannot have much diversity of behavior, is just a way of saying that it does not have much storage capacity.” Fair point in 1950.

Critics say computer are incapable of surprises and thus don’t think. I’ll paraphrase Turing’s answer: “… this assumes that as soon as a fact is presented the mind fully comprehends all the consequences. While that may be a useful assumption, and sometimes correct, it is false. A natural consequence of doing so is one assumes there is no virtue in the working out consequences from data and general principles. In other words, a machine can surprise you if you give it a chance.

Continuity in the nervous system is objection seven. Turing acknowledged differences in the nervous systems between a discrete state machine (computer) and continuous machines (humans). However, for the Turing test the differences do not affect the interrogator in either direction, says Turing; hence, irrelevant.

The informality of behavior is objection eight. Critics point out humans don’t need rules for every conceivable situation whereas computers do. Turing counters by claiming computer could reach similar status given enough time and computing power. He gives a small example of what had been achieved using what was available at the time.

We conclude with objection nine, extra sensory perception, which Turing defined as telepathy, clairvoyance, pre-cognition, and psychokinesis. Turing had a somewhat dismissive rebuttal–put all Turing test participants into telepathy-proof rooms. Lol!

We reached the end with a proverb from Brazil, “Never poke a jaguar with a short stick”.

Until next time,

Yogi Nelson

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WHAT IS THE FUTURE OF BLOCKCHAIN?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question comes from Ashley in Texas, who wants to know, what is the future of blockchain?

Ashley, you came to the right place. Blockchain technology has enormous potential; however, it must conquer immediate challenges. Let’s begin with five challenges facing blockchain technology today: 1) scalability; 2) initial set-up costs; 3) smooth transition; 4) consensus mechanisms; and 5) privacy and security. I’ll focus on a few solutions, commencing with “sharding”

Sharding is a process for handling large data sets. Sharding accelerates processing by dividing the computational workload and storage space issues. It ensures no single mode (computer) is responsible for processing the network’s transactional load. Sharding increases security through transparent processing on a decentralized network. Sharding is not alone; it has a friend known as sidechains; not to be confused with 2Chainz the rapper! Lol.

Sidechains are separate blockchains attached to the mainchain. Often, sidechains are deployed to test new software before joining the mainchain. Sidechains offer more security when moving digital assets from one blockchain to another and reduce the mainchain’s workload; hence, making the blockchain faster and more reliable. What else is under construction? Answer—state channels.

Despite their Orwellian name, state channels are not spooky. State channels basically “lock” the blockchain while participants agree amongst themselves off-chain. Once participants reach consensus the transaction is uploaded onto the mainchain for processing. Let’s keep rolling with “Roll-Ups”.

Rollups are scaling solutions. Roll-ups move computation off-chain while keeping transaction data on-chain. Keeping the data on-chain allows anyone to locally process all computation in the roll-up and detect fraud. There are two types of roll-ups: Optimistic and ZK. Optimistic roll-ups assume the data/transactions are correct. However, to ensure accuracy, transactions are not final for one week. During the data verification week anyone may submit fraud proof. If no honest individual spots an error, transactions are final. If errors are spotted the transactions can be reversed. What about ZK-Rollups? ZK-Rollups stands for zero knowledge. No, it does not mean the protocol is a know nothing, lol! It means every rollup batch contains a cryptographic hash, thus making transaction more secure. In other words, possession of the private key opens the file and nothing else is required. However, with the increase in security also comes computationally intensive processes. Time, money, and energy consumption.

Charles Hoskinson, Cardano founder, is fond of saying blockchains need their “wi-fi” moment. Said differently, blockchains must achieve interoperability. Imagine having to switch providers every moment to access wi-fi. What a mess! Blockchain interoperability equals users having a seamless integration of capabilities, communication with and between intermediaries, and greater decentralization. Get it done!

Let’s wrap it up with Ricardian contracts. A Ricardian contract is a mechanism to record a document as law and link it to other sectors, i.e., accounting. A Ricardian contract is responsible for executing contracts between two parties and recording the details in forms that are readable by humans and machines. Moreover, the dual abilities of Ricardian contracts equal superior user experiences when compared to smart contracts. Holy dual use!

Today’s “proverb” is curtesy of Yogi Berra to old time baseball star who once said, “it’s tough to make predictions, especially about the future”! lol. Nevertheless, I predict blockchain will evolve and eventually become mainstream technology with widespread applications. Thanks to the Emurgo Academy for providing the source material of today’s post. The art work is by Gerardo Cloquell and on display in Dorado, PR.

Until next time,

Yogi Nelson

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WHAT ARE THE 10 TERMS IN ARTIFICIAL INTELLIGENCE YOU SHOULD KNOW?

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered, mostly correct! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question was submitted by Alberto from Montebello, CA, and he wants to know the 10 terms in artificial intelligence (AI) that he should know.

Alberto, you came to the right place. Great question. Learning the vocabulary is an intelligent way to master the topic. Let’s roll it in alphabetical order.

Algorithm: I’m sure you have heard the term algorithm tossed about like a basketball. Let’s settle what it means; it’s not complicated. Algorithms are the step-by-step set of instructions followed by computers to perform a specific task. That’s it. Nothing more.

Artificial Intelligence (AI): is essentially teaching machines to learn from data and derive a variety of useful insights. AI is commonly defined as a simulation of human intelligence in machines that are programmed to think and learn like humans. Basically, AI enables computers to mimic human intelligence (hopefully a smart human) using logic.

Data Mining is all about discovery. In data mining patterns of knowledge from large data sets are discovered. Data discoveries are designed to assist with decision making, predictions, etc. Put on your computer hardhat and start digging!

Deep Learning (not to be confused with the Deep State, ha ha) is a specialized form of machine learning (as if machine learning is not sufficiently specialized!). Deep learning involves many layers of neural networks; hence, the label “deep”. (See below for neural networks.).

Machine Learning (ML): is a subset of AI that provide systems the ability to automatically learn and improve from the experience without being explicitly programmed. The key is not explicitly programmed.

Natural Language Processing (NLP): is the ability of a computer program to understand spoken and written human language. Holy linguist! However, human language is more than just sounds and we often have misunderstandings. Hence, it might take more time for software program to truly understand human language.

Neural Networks: a neural network is a computer system based on the human brain and nervous system. Which human I don’t know and afraid to ask! Ha, ha.

Reinforced learning is focused on finding a balance. A balance between what? A balance between exploration of new ground and exploitation of what is already known. Think of it as the yin/yang of machine learning.

Supervised Learning is a form of machine learning (see above). In supervised learning the system uses algorithm (see above) to identify patterns in the data and then “learn” from the patterns. Computer swamis call it supervised because the program is being directed by the instructions coded in the software. A good example of supervised learning is your email spam detector. Thank the universe for the spam detector, otherwise I may have fallen for a love letter from a 20-year-old Russian beauty queen! Ha, ha.

Unsupervised Learning is where the AI tells the boss to get loss! Ha, ha. Just kidding. In unsupervised learning the machine uses algorithms (see above) to analyze and cluster unlabeled data. In other words, in unsupervised learning the machine learns to uncover patterns without direct human direction. Transcribing audio is a good example.

A special acknowledgement to the Cardano Foundation Emurgo Academy, and these two books: AI Made Simple and Artificial Intelligence 2023.

Alberto, please remember those terms and the proverb from Suriname, where they say: You cannot fly with wings made of butter.

Until next time.

Yogi Nelson

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