Wavic

wavic

Calling all cool cats and kittens, we are pleased to announce the luckiest addition to the NFT space, . Hailing from the land of Luckeania, not much is known about the true origins of Lucky Maneki. The Lucky Manekis have deep ties to the spirit of creativity, luck, and good fortune. 40 Epic one-of-one Manekis were first discovered in Luckeania and they are known for bringing exceptional luck to the collector. Since then, more Manekis came to Luckeania and inherited some of the lucky properties of the original Epic Manekis. Lucky Maneki 14,159 Manekis will be launched onto the blockchain, a historically lucky number. Owners of Manekis will be able to name their furry friend, which will be stored on the blockchain for eternity, allowing the owner to make their stamp in the history of the blockchain. Of course, that is not all. Lucky Maneki have also incorporated giveaway mechanics to reward the most loyal Maneki owners. One of which is the Lucky Bank, a crypto wallet which will store a percentage of each sale. Once a minimum balance is reached, the funds will be donated to a charity of the communities' choice. You’ll be able to vote on your favourite charity and chat with the artists directly on their . Discord server In order to look after the environment of Luckeania and the rest of the world, Lucky Maneki are committed to making sure all their cats are taken care of and have a beautiful place to live by ensuring the project's carbon paw print is offset. The exclusive Lucky Manekis will be released to the blockchain before the end of June,  there’s lots to prepare, trimming fur, clipping claws and practicing the perfect purr. Until then, you can keep up to date with them on , , and . Twitter Telegram Instagram There are multiple layers of rarity embedded in the Lucky Maneki collection. First comes the explicit rarity. These are the broad groupings of Maneki properties that are indicated on each and every NFT and viewable by the collectors. The elements that have an assigned explicit rarity are background, body type, head type, left-paw item, and right-paw item. For example, a Podium background appears in 20.16% of all Manekis and a Space background in 6.92%. However, within each of these categories, there are many sub-categories.Owning a Lucky Maneki benefits the holder in many ways, not just a cute addition to their NFT collection, if the feline friend is especially lucky then their value can appreciate! Purchasing a Lucky Maneki also allows you access to the Lucky Chest; where a cheeky Pirate Maneki has hidden some treasure in a chest, Maneki fans will have to solve clues and answer questions to unlock and obtain its contents. There are also activities, challenges and competitions running on their community Discord channel which you can join here. Lucky Maneki collectibles project is a brainchild of a fully decentralized team of artists, copywriters, blockchain developers and NFT enthusiasts.

Purr-fect New NFT Collection Who Keeps Waving Good Luck Back to its Collector

From OpenAI to Closed AI: Custom Chips Are Closing The Doors—What’s Next? 

Ад OpenAI да закрытага AI: Карыстальніцкія мікрасхемы зачыняюць дзверы - што далей? 

De l'OpenAI a la IA tancada: els xips personalitzats estan tancant les portes: què passa? 

Von OpenAI zu Closed AI: Kundenspezifische Chips schließen die Türen – was kommt als Nächstes?

Από το OpenAI στο Closed AI: Τα προσαρμοσμένα τσιπ κλείνουν τις πόρτες—Τι ακολουθεί; 

De la IA abierta a la IA cerrada: los chips personalizados están cerrando las puertas. ¿Qué sigue?

Az OpenAI-tól a zárt mesterséges intelligenciaig: Az egyedi chipek bezárják az ajtókat – mi a következő lépés? 

Dari OpenAI ke AI Tertutup: Chip Kustom Menutup Pintu—Apa Selanjutnya?

OpenAI からクローズド AI へ: カスタム チップが扉を閉ざす — 次は何が起こるのか?

Kobanda na OpenAI tii na AI oyo ekangami: Ba puces personnalisées ezali kokanga ba portes —What’s Next? 

له OpenAI څخه تړل شوي AI ته: دودیز چپس دروازې بندوي — بیا څه دي؟ 

От открытого ИИ к закрытому ИИ: кастомные чипы закрывают двери — что дальше?

Від відкритого штучного інтелекту до закритого штучного інтелекту: спеціальні мікросхеми закривають двері — що далі? 

Advancing the Detection of Ultralight Vector Dark Matter through Novel Analytical Strategies

The Best User Agent for Web Scraping

Най-добрият потребителски агент за уеб скрапинг

Den bedste brugeragent til web-skrabning

El mejor agente de usuario para el web scraping

O mellor axente de usuario para o rascado web

Ajan itilizatè a pi bon pou grate entènèt

סוכן המשתמש הטוב ביותר לגרידת אינטרנט

Webスクレイピングに最適なユーザーエージェント

Cel mai bun agent de utilizator pentru Web Scraping

Web Scraping සඳහා හොඳම පරිශීලක නියෝජිතයා

The Best User Agent yeWeb Scraping

Wakala Bora wa Mtumiaji wa Kuchakachua Wavuti

I-ejenti Yomsebenzisi Engcono Kakhulu Ye-Web Scraping

Virtual Besties: TV Shows, Social Media and the Evolution of Parasocial Relationships

Computers are Easy, People are Hard: How to Coach for Excellence in Tech

It’s a cycle we’ve seen play out countless times: someone asks a large language model (LLM) a factual question, receives a confident but incorrect answer, and immediately cries foul. “These AI systems can’t be trusted!” “They’re unreliable!” “Look at how it just made things up!” The internet erupts with critics waving the banner of AI’s imperfections, deriding the technology for being riddled with “hallucinations” and fabricating details with the audacity of an overzealous storyteller. But here’s the thing: large language models were never meant to be sources of absolute truth. Yet, we continue to treat them as such, using them like search engines or digital encyclopedias, then express shock and dismay when they fall short of our expectations. The truth is, LLMs are incredibly powerful tools for generating language, contextualizing information, and assisting with creative and analytical tasks. But they are not—or at least, not yet—perfect arbiters of fact. If we want to use AI better, it’s time to stop expecting LLMs to perform roles they were never designed for and start leveraging them for what they’re truly good at: aiding human creativity, providing context, and generating possibilities—not absolute truths. Let’s break down why this misconception persists and how we can realign our expectations to get the most out of these models. Understanding the Nature of LLMs: Statistical, Not Factual The root of the issue lies in a misunderstanding of what LLMs actually do. Large language models, like GPT-4, are statistical machines at their core. They don’t “know” facts the way humans do. Instead, they predict the most likely sequence of words based on the patterns in the vast amounts of text they’ve been trained on. When you ask an LLM a question, it doesn’t search a database of verified truths. It generates a response based on its understanding of language patterns, which may or may not align with reality. This is why LLMs are prone to what’s known as “hallucination”—a term used to describe when the model outputs information that sounds plausible but is factually incorrect or entirely fabricated. Hallucinations happen because the model’s objective is not to verify facts but to produce fluent, coherent, and contextually appropriate text. The more coherent it sounds, the more likely it is to be seen as “true,” even when it’s not. Expecting an LLM to consistently generate factually accurate information is like expecting a skilled fiction writer to suddenly become a historian. Just because the prose flows beautifully doesn’t mean the content is accurate. When we treat LLMs as authoritative sources, we’re misusing them—and we set ourselves up for disappointment. The Problem Isn’t AI Hallucinations—It’s Misaligned Expectations Critics are quick to pounce on AI hallucinations as proof that LLMs are fundamentally flawed. But this criticism misses the point. The problem isn’t that the models generate incorrect information; it’s that we’re using them in ways that demand factual accuracy. We’re holding AI to a standard it was never designed to meet. Imagine you have a calculator. You wouldn’t ask it to compose a poem and then get frustrated when it spits out a bunch of numbers. That’s not what it’s for. Similarly, asking an LLM to be a definitive source of truth is a misuse of the tool. It’s designed to help us with language-related tasks, like generating ideas, drafting content, or answering questions in a conversational manner—not to be a substitute for fact-checking. If you need a reliable source of factual information, that’s what databases, search engines, and encyclopedias are for. LLMs can assist in synthesizing information or providing context, but they should be used with the understanding that their responses might not always be correct. Just as we double-check a human’s work, we should verify the outputs of AI models. Why Do People Keep Using LLMs This Way? So, why do people keep using LLMs as if they were digital oracles of truth? Part of the reason is that LLMs sound so convincing. They generate language with a fluency that mimics human expertise, often complete with references, citations, and confident assertions. This creates an illusion of authority, leading users to overestimate the model’s reliability. When an LLM generates an incorrect or fictionalized response, it feels like a betrayal, even though the model is just doing what it was programmed to do—producing language, not truth. Another reason is convenience. It’s tempting to use LLMs as a one-stop shop for answers. They’re fast, accessible, and capable of covering a wide range of topics. But convenience shouldn’t come at the cost of accuracy. Using LLMs for quick answers without verification is like skimming Wikipedia and assuming every word is 100% accurate without cross-referencing any sources. The burden of truth remains on the user. Using LLMs More Effectively: Playing to Their Strengths To use LLMs better, we need to play to their strengths. Here’s how: Creative and Analytical Assistance: LLMs are excellent at generating ideas, brainstorming, and exploring possibilities. If you’re a writer stuck in a rut, an AI can suggest plot twists, character backstories, or article structures. If you’re an entrepreneur developing a new product, an AI can help you articulate your vision, consider new angles, and refine your messaging.
Contextual Understanding: While LLMs may not be sources of truth, they can help contextualize information. They can explain concepts in simpler terms, summarize complex articles, or engage in hypothetical scenarios to explore potential outcomes. This can be incredibly useful for making sense of overwhelming amounts of information or sparking new ways of thinking.
Drafting and Editing: AI can generate first drafts or assist in editing, helping human creators focus on refining rather than starting from scratch. It can speed up content creation processes by handling mundane drafting tasks, allowing humans to apply their expertise in finalizing and polishing the content.
Language and Communication: LLMs can be invaluable for generating responses, crafting emails, or composing speeches. They can be trained to match specific tones or styles, making them useful tools for content that requires a human touch but doesn’t need to be fact-checked line-by-line. Integrating AI With Human Oversight The key to using LLMs effectively is to integrate them into workflows that include human oversight. This means verifying facts, cross-referencing sources, and using the AI’s outputs as starting points rather than final products. It’s about using AI as a collaborator, not a replacement for human judgment. Imagine LLMs as highly capable assistants. They’re knowledgeable, articulate, and quick, but they still need a manager (you) to provide direction, verify their work, and ensure quality. This kind of relationship leverages the strengths of both humans and machines, producing better results than either could achieve alone. Building Better AI Literacy Ultimately, the solution to the LLM-as-source-of-truth problem is better AI literacy. Users need to understand what these models are and what they aren’t. They need to be educated about the limitations of AI and the importance of critical thinking when interacting with machine-generated content. Organizations developing LLMs also have a role to play. Clearer disclaimers, transparency about the model’s limitations, and even built-in fact-checking mechanisms could help set expectations and guide users toward better practices. The goal should be to foster a realistic understanding of what AI can do and how it should be used—because, when used correctly, LLMs can be incredibly powerful allies in navigating information, creativity, and communication. Let’s Use AI for What It’s Good At It’s time to stop treating LLMs as fact machines and start seeing them for what they truly are: advanced tools for generating language, aiding creativity, and providing context. They’re not replacements for human expertise or definitive sources of truth, and they shouldn’t be judged as such. If we can shift our mindset and use LLMs in ways that align with their design and capabilities, we’ll stop getting frustrated by their hallucinations and start appreciating their real value. Let’s use AI better, not as an oracle, but as a partner—one that helps us create, explore, and communicate more effectively while leaving the job of truth-seeking and validation where it belongs: with us. About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. Social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com It’s a cycle we’ve seen play out countless times: someone asks a large language model (LLM) a factual question, receives a confident but incorrect answer, and immediately cries foul. “These AI systems can’t be trusted!” “They’re unreliable!” “Look at how it just made things up!” The internet erupts with critics waving the banner of AI’s imperfections, deriding the technology for being riddled with “hallucinations” and fabricating details with the audacity of an overzealous storyteller. But here’s the thing: large language models were never meant to be sources of absolute truth. Yet, we continue to treat them as such, using them like search engines or digital encyclopedias, then express shock and dismay when they fall short of our expectations. The truth is, LLMs are incredibly powerful tools for generating language, contextualizing information, and assisting with creative and analytical tasks. But they are not—or at least, not yet—perfect arbiters of fact. If we want to use AI better, it’s time to stop expecting LLMs to perform roles they were never designed for and start leveraging them for what they’re truly good at: aiding human creativity, providing context, and generating possibilities—not absolute truths. Let’s break down why this misconception persists and how we can realign our expectations to get the most out of these models. Understanding the Nature of LLMs: Statistical, Not Factual The root of the issue lies in a misunderstanding of what LLMs actually do. Large language models, like GPT-4, are statistical machines at their core. They don’t “know” facts the way humans do. Instead, they predict the most likely sequence of words based on the patterns in the vast amounts of text they’ve been trained on. When you ask an LLM a question, it doesn’t search a database of verified truths. It generates a response based on its understanding of language patterns, which may or may not align with reality. This is why LLMs are prone to what’s known as “hallucination”—a term used to describe when the model outputs information that sounds plausible but is factually incorrect or entirely fabricated. Hallucinations happen because the model’s objective is not to verify facts but to produce fluent, coherent, and contextually appropriate text. The more coherent it sounds, the more likely it is to be seen as “true,” even when it’s not. Expecting an LLM to consistently generate factually accurate information is like expecting a skilled fiction writer to suddenly become a historian. Just because the prose flows beautifully doesn’t mean the content is accurate. When we treat LLMs as authoritative sources, we’re misusing them—and we set ourselves up for disappointment. The Problem Isn’t AI Hallucinations—It’s Misaligned Expectations Critics are quick to pounce on AI hallucinations as proof that LLMs are fundamentally flawed. But this criticism misses the point. The problem isn’t that the models generate incorrect information; it’s that we’re using them in ways that demand factual accuracy. We’re holding AI to a standard it was never designed to meet. Imagine you have a calculator. You wouldn’t ask it to compose a poem and then get frustrated when it spits out a bunch of numbers. That’s not what it’s for. Similarly, asking an LLM to be a definitive source of truth is a misuse of the tool. It’s designed to help us with language-related tasks, like generating ideas, drafting content, or answering questions in a conversational manner—not to be a substitute for fact-checking. If you need a reliable source of factual information, that’s what databases, search engines, and encyclopedias are for. LLMs can assist in synthesizing information or providing context, but they should be used with the understanding that their responses might not always be correct. Just as we double-check a human’s work, we should verify the outputs of AI models. Why Do People Keep Using LLMs This Way? So, why do people keep using LLMs as if they were digital oracles of truth? Part of the reason is that LLMs sound so convincing. They generate language with a fluency that mimics human expertise, often complete with references, citations, and confident assertions. This creates an illusion of authority, leading users to overestimate the model’s reliability. When an LLM generates an incorrect or fictionalized response, it feels like a betrayal, even though the model is just doing what it was programmed to do—producing language, not truth. Another reason is convenience. It’s tempting to use LLMs as a one-stop shop for answers. They’re fast, accessible, and capable of covering a wide range of topics. But convenience shouldn’t come at the cost of accuracy. Using LLMs for quick answers without verification is like skimming Wikipedia and assuming every word is 100% accurate without cross-referencing any sources. The burden of truth remains on the user. Using LLMs More Effectively: Playing to Their Strengths To use LLMs better, we need to play to their strengths. Here’s how: Creative and Analytical Assistance: LLMs are excellent at generating ideas, brainstorming, and exploring possibilities. If you’re a writer stuck in a rut, an AI can suggest plot twists, character backstories, or article structures. If you’re an entrepreneur developing a new product, an AI can help you articulate your vision, consider new angles, and refine your messaging. Contextual Understanding: While LLMs may not be sources of truth, they can help contextualize information. They can explain concepts in simpler terms, summarize complex articles, or engage in hypothetical scenarios to explore potential outcomes. This can be incredibly useful for making sense of overwhelming amounts of information or sparking new ways of thinking. Drafting and Editing: AI can generate first drafts or assist in editing, helping human creators focus on refining rather than starting from scratch. It can speed up content creation processes by handling mundane drafting tasks, allowing humans to apply their expertise in finalizing and polishing the content. Language and Communication: LLMs can be invaluable for generating responses, crafting emails, or composing speeches. They can be trained to match specific tones or styles, making them useful tools for content that requires a human touch but doesn’t need to be fact-checked line-by-line. Creative and Analytical Assistance : LLMs are excellent at generating ideas, brainstorming, and exploring possibilities. If you’re a writer stuck in a rut, an AI can suggest plot twists, character backstories, or article structures. If you’re an entrepreneur developing a new product, an AI can help you articulate your vision, consider new angles, and refine your messaging. Creative and Analytical Assistance Contextual Understanding : While LLMs may not be sources of truth, they can help contextualize information. They can explain concepts in simpler terms, summarize complex articles, or engage in hypothetical scenarios to explore potential outcomes. This can be incredibly useful for making sense of overwhelming amounts of information or sparking new ways of thinking. Contextual Understanding Drafting and Editing : AI can generate first drafts or assist in editing, helping human creators focus on refining rather than starting from scratch. It can speed up content creation processes by handling mundane drafting tasks, allowing humans to apply their expertise in finalizing and polishing the content. Drafting and Editing Language and Communication : LLMs can be invaluable for generating responses, crafting emails, or composing speeches. They can be trained to match specific tones or styles, making them useful tools for content that requires a human touch but doesn’t need to be fact-checked line-by-line. Language and Communication Integrating AI With Human Oversight The key to using LLMs effectively is to integrate them into workflows that include human oversight. This means verifying facts, cross-referencing sources, and using the AI’s outputs as starting points rather than final products. It’s about using AI as a collaborator, not a replacement for human judgment. Imagine LLMs as highly capable assistants. They’re knowledgeable, articulate, and quick, but they still need a manager (you) to provide direction, verify their work, and ensure quality. This kind of relationship leverages the strengths of both humans and machines, producing better results than either could achieve alone. Building Better AI Literacy Ultimately, the solution to the LLM-as-source-of-truth problem is better AI literacy. Users need to understand what these models are and what they aren’t. They need to be educated about the limitations of AI and the importance of critical thinking when interacting with machine-generated content. Organizations developing LLMs also have a role to play. Clearer disclaimers, transparency about the model’s limitations, and even built-in fact-checking mechanisms could help set expectations and guide users toward better practices. The goal should be to foster a realistic understanding of what AI can do and how it should be used—because, when used correctly, LLMs can be incredibly powerful allies in navigating information, creativity, and communication. Let’s Use AI for What It’s Good At It’s time to stop treating LLMs as fact machines and start seeing them for what they truly are: advanced tools for generating language, aiding creativity, and providing context. They’re not replacements for human expertise or definitive sources of truth, and they shouldn’t be judged as such. If we can shift our mindset and use LLMs in ways that align with their design and capabilities, we’ll stop getting frustrated by their hallucinations and start appreciating their real value. Let’s use AI better, not as an oracle, but as a partner—one that helps us create, explore, and communicate more effectively while leaving the job of truth-seeking and validation where it belongs: with us. About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. Social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. Social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com

Hallucinations Are A Feature of AI, Humans Are The Bug

If an individual were temporarily indisposed, it is possible to assume that most of the memory the individual has might be intact, but the relays to make the memories available may have plummeted. This highlights that memory is not only about the stored information but also about the relays that make it accessible. It is possible to define human memory, not just by types, but by specifications of relays for availability, such that those for speech at some point may be different from those from imagination, or those for learning from those for doing. Simply, outputs from human memory are sometimes a result of reach or relays, not just stored info. Relays can also be used to explain how Large language models [LLMs] work. LLMs are trained on vast amounts of internet data. This training can be assumed to be relays. Prompts to, and outputs from, LLMs can also be assumed to be relays, across data. Simply, LLMs work because they can relay predictively, with correct matches for several use cases. Though these relays still have their weakness—like confabulations, hallucinations, slops in generated videos and images, and so forth—the better the relays or math functions that are developed for digital memory, the closer they might come to human intelligence. Artificial general intelligence [AGI] or artificial superintelligence [ASI] could get closer with improved relays across digital memory, in parallel to relays that result in intelligence in the human mind. AGI can be described as relay equivalents to those in the human mind, that define human intelligence. Relays may also make and specify extensions like—levels of control and base subjectivity. Though the internet already has more information than any individual’s brain, AGI may be as good as relays that use the information, for similarities to human intelligence, not just the presence of information. This is similar to the human mind, where presence [of memory] does not mean available [in an instance]. The human mind is theorized to have functions and features. Though functions are shaped by relays, features are dominated by relays. Functions can be categorized into memory, feeling, emotion, and modulation of internal senses. Functions have several subcategories including—intelligence, planning, reasoning, pain, delight, cold, heat, and so forth. Functional instances are determined by how they are graded or qualified by features. Features are types of relays that bring those functions to use, including their intensities. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. Features [or relays] include pre-/prioritization, old and new sequences, early splits, principal spot, thick/thin sets, volume changes, diameter constant spaces, and others. These relays are postulated to directly grade what is learned and experienced. AGI would not be a measure of outcomes, but a measure of relays, since outcomes may be great yet deficient. For example, LLMs are excellent at predictions, but lacking in correction and in the knowledge of consequences. They are cool at several types of pattern-matching but seem to be unidirectional in several outputs, which is not the case for the human mind. Two of several questions about ASI are—can it have control and can it have subjectivity? In the human mind, control, free will or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck and so forth. There are functions that cannot be controlled like the spleen and so forth, at least directly like speaking, waving and so on. Subjectivity is attachment, personalization or self-association, away from detachment, depersonalization or dissociation.
Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same [sets of electrical and chemical] signals for functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience, for the self to present—they are also subcategories, where relays get to and determine their intensities.For AGI, it could be possible that rows within high-dimensional vectors become aspects of control, and some columns—aspects of subjectivity. Control and subjectivity would be embeddings that can be used alone and have to be relayed to, for availability, unlike for humans. Simply, AGI would use vectors to control vectors and use vectors to [be subjective or] have subjectivity, conceptually. Conceptual brain science could be useful in grading LLMs to AGI, becoming another direction for computational and theoretical neuroscience to shape progress in AI. There is a recent editorial in Nature, The New NeuroAI, stating that, “Overall, the extent of the role of neuroscience in AI research, and that of AI in neuroscience research, remain open questions for the future. However, these two fields are deeply linked, and the exchange of ideas between them continues to evolve. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focused on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience.” There is a recent paper in Nature, Reservoir-computing based associative memory and itinerancy for complex dynamical attractors, stating that “A neural network capable of memorizing and retrieving multiple memory states is also a multifunctional neural network. When different states are recalled, the neural network exhibits distinct dynamical behaviors. The idea of multifunctional RNNs has been discussed previously with different forms of implementation. In particular, in the index-based approach, an index parameter is introduced to modulate the functionality of the neural networks and store different states with different index values, but recalling the memorized states was a challenge, for which control may be necessary.” There is a new podcast on The Robot Report, New AI, controls make robots easier to program and deploy, discussing a “new industrial robotics robot control architecture.” In conclusion, the concept of relays in both human memory and artificial intelligence (AI) highlights the crucial role of accessibility mechanisms in achieving functionality. As such, the pursuit of artificial general intelligence (AGI) and artificial superintelligence (ASI) may hinge on developing sophisticated relay systems that better mirror the intricate functions of the human mind. If an individual were temporarily indisposed, it is possible to assume that most of the memory the individual has might be intact, but the relays to make the memories available may have plummeted. This highlights that memory is not only about the stored information but also about the relays that make it accessible. It is possible to define human memory, not just by types, but by specifications of relays for availability, such that those for speech at some point may be different from those from imagination, or those for learning from those for doing. Simply, outputs from human memory are sometimes a result of reach or relays, not just stored info. may be Relays can also be used to explain how Large language models [LLMs] work. LLMs are trained on vast amounts of internet data. This training can be assumed to be relays. Prompts to, and outputs from, LLMs can also be assumed to be relays, across data. Simply, LLMs work because they can relay predictively, with correct matches for several use cases. Though these relays still have their weakness—like confabulations, hallucinations, slops in generated videos and images, and so forth—the better the relays or math functions that are developed for digital memory, the closer they might come to human intelligence. might come Artificial general intelligence [AGI] or artificial superintelligence [ASI] could get closer with improved relays across digital memory, in parallel to relays that result in intelligence in the human mind. Artificial general intelligence AGI can be described as relay equivalents to those in the human mind, that define human intelligence. Relays may also make and specify extensions like—levels of control and base subjectivity. Though the internet already has more information than any individual’s brain, AGI may be as good as relays that use the information, for similarities to human intelligence, not just the presence of information. This is similar to the human mind, where presence [of memory] does not mean available [in an instance]. The human mind is theorized to have functions and features . Though functions are shaped by relays, features are dominated by relays. Functions can be categorized into memory, feeling, emotion, and modulation of internal senses. Functions have several subcategories including—intelligence, planning, reasoning, pain, delight, cold, heat, and so forth. Functional instances are determined by how they are graded or qualified by features. Features are types of relays that bring those functions to use, including their intensities. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features , in sets, in clusters of neurons, across the central and peripheral nervous systems. theorized to have functions and features electrical and chemical signals with their interactions and features Features [or relays] include pre-/prioritization, old and new sequences, early splits, principal spot, thick/thin sets, volume changes, diameter constant spaces, and others. These relays are postulated to directly grade what is learned and experienced. AGI would not be a measure of outcomes, but a measure of relays, since outcomes may be great yet deficient. For example, LLMs are excellent at predictions, but lacking in correction and in the knowledge of consequences. They are cool at several types of pattern-matching but seem to be unidirectional in several outputs, which is not the case for the human mind. Two of several questions about ASI are—can it have control and can it have subjectivity? In the human mind, control, free will or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck and so forth. There are functions that cannot be controlled like the spleen and so forth, at least directly like speaking, waving and so on. Subjectivity is attachment, personalization or self-association, away from detachment, depersonalization or dissociation. Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same [sets of electrical and chemical] signals for functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience, for the self to present—they are also subcategories, where relays get to and determine their intensities. For AGI, it could be possible that rows within high-dimensional vectors become aspects of control, and some columns—aspects of subjectivity. Control and subjectivity would be embeddings that can be used alone and have to be relayed to, for availability, unlike for humans. Simply, AGI would use vectors to control vectors and use vectors to [be subjective or] have subjectivity, conceptually. Two of several questions about ASI are—can it have control and can it have subjectivity? In the human mind, control, free will or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck and so forth. There are functions that cannot be controlled like the spleen and so forth, at least directly like speaking, waving and so on. Subjectivity is attachment, personalization or self-association, away from detachment, depersonalization or dissociation. Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same [sets of electrical and chemical] signals for functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience, for the self to present—they are also subcategories, where relays get to and determine their intensities. For AGI, it could be possible that rows within high-dimensional vectors become aspects of control, and some columns—aspects of subjectivity. Control and subjectivity would be embeddings that can be used alone and have to be relayed to, for availability, unlike for humans. Simply, AGI would use vectors to control vectors and use vectors to [be subjective or] have subjectivity, conceptually. Conceptual brain science could be useful in grading LLMs to AGI, becoming another direction for computational and theoretical neuroscience to shape progress in AI. There is a recent editorial in Nature , The New NeuroAI , stating that, “Overall, the extent of the role of neuroscience in AI research, and that of AI in neuroscience research, remain open questions for the future. However, these two fields are deeply linked, and the exchange of ideas between them continues to evolve. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focused on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience.” Nature The New NeuroAI There is a recent paper in Nature , Reservoir-computing based associative memory and itinerancy for complex dynamical attractors , stating that “A neural network capable of memorizing and retrieving multiple memory states is also a multifunctional neural network. When different states are recalled, the neural network exhibits distinct dynamical behaviors. The idea of multifunctional RNNs has been discussed previously with different forms of implementation. In particular, in the index-based approach, an index parameter is introduced to modulate the functionality of the neural networks and store different states with different index values, but recalling the memorized states was a challenge, for which control may be necessary.” Nature Reservoir-computing based associative memory and itinerancy for complex dynamical attractors There is a new podcast on The Robot Report , New AI, controls make robots easier to program and deploy , discussing a “new industrial robotics robot control architecture.” The Robot Report New AI, controls make robots easier to program and deploy In conclusion, the concept of relays in both human memory and artificial intelligence (AI) highlights the crucial role of accessibility mechanisms in achieving functionality. As such, the pursuit of artificial general intelligence (AGI) and artificial superintelligence (ASI) may hinge on developing sophisticated relay systems that better mirror the intricate functions of the human mind.

Brain Science: Benchmarks for AGI, LLMs—Superintelligence

If an individual were temporarily infirm, it is possible to assume that most of the memory the individual has might be intact, but the relays to make the memories available may have dipped. This means that memories are not just important, but the relays to make them available. It is possible to define human memory not just by types but by specifications of relays for availability, such that those for reasoning at some point may be different from those from planning or those for learning from those from doing. Simply, outputs from human memory are sometimes a result of reach or relays, not just by the memory present. Relays can also be used to explain how Large language models [LLMs] work. LLMs were trained on vast amounts of internet data. This training can be assumed to be relays. Prompts to and outputs from LLMs can also be assumed to be relays across data. Simply put, LLMs work because they are able to relay predictively, with correct matches for several use cases. Though there are still weaknesses of these relays—like confabulations, hallucinations, slops in generated videos and images, and so forth—the better the relays or math functions that are developed for digital memory, the closer they might come to human intelligence. Artificial general intelligence [AGI] or artificial superintelligence [ASI] could get closer with improved relays across digital memory, in parallel to relays that result in intelligence in the human mind. AGI can be defined as relay equivalents to those in the human mind that define human intelligence. Relays may also make and specify extensions like—levels of control and base subjectivity. The internet already has more information than any individual’s brain. However, AGI may be as good as relays that use the information for similarities to human intelligence, not just the presence of information. This is similar to the human mind, where presence [of memory] does not mean available [in an instance]. The human mind is theorized to have functions and features. Though functions are shaped by relays, features are dominated by relays. Functions can be categorized into memory, feeling, emotion, and modulation of internal senses. Functions have several subcategories including—intelligence, planning, reasoning and so forth. Functional instances are determined by how they are graded or qualified by features. Features are types of relays that bring those functions to use, including their intensities. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features in sets, in clusters of neurons, across the central and peripheral nervous systems. Features [or relays] include pre-/prioritization, old and new sequences, early splits, principal spots, thick/thin sets, volume changes, diameter constant spaces, and others. These relays are postulated to directly mechanize what is learned and experienced. Ranking how close to human intelligence AGI or ASI may come may depend on their parallel relays. AGI would not be a measure of outcomes but a measure of relays since outcomes may be great yet deficient. For example, LLMs are excellent at predictions but lack correction skills and knowledge of consequences. They are cool at several types of pattern-matching but seem to be unidirectional in several outputs, which is not the case for the human mind. Two of several questions about AGI are: Can it have control and subjectivity? In the human mind, control, free will, or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck, and so forth. There are functions that cannot be controlled, like the bronchioles and so forth, at least directly, like speaking, waving, and so on. Subjectivity is attachment, personalization, or self-association, away from detachment, depersonalization, or dissociation. Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same sets of signals that mechanize functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience for the self to present—they are also subcategories where relays get to and determine their intensities. It could be possible that rows within high-dimensional vectors become aspects of control, and some columns become aspects of subjectivity. For AGI, control and subjectivity would be data that can be used alone and has to be relayed for availability, unlike with humans. Simply, AGI would use data to control data and use data to have subjectivity, conceptually. Conceptual brain science could be useful in grading LLMs to AGI, becoming another direction for computational and theoretical neuroscience to shape progress in AI. There is a recent editorial in Nature, The new NeuroAI, stating that, "Overall, the extent of the role of neuroscience in AI research, and that of AI in neuroscience research, remain open questions for the future. However, these two fields are deeply linked, and the exchange of ideas between them continues to evolve. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focused on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience." There is a recent paper in Nature, Reservoir-computing based associative memory and itinerancy for complex dynamical attractors, stating that, "A neural network capable of memorizing and retrieving multiple memory states is also a multifunctional neural network. When different states are recalled, the neural network exhibits distinct dynamical behaviors. The idea of multifunctional RNNs has been discussed previously, along with different forms of implementation. In particular, in the index-based approach, an index parameter is introduced to modulate the functionality of the neural networks and store different states with different index values, but recalling the memorized states was a challenge, for which control may be necessary." If an individual were temporarily infirm, it is possible to assume that most of the memory the individual has might be intact, but the relays to make the memories available may have dipped. This means that memories are not just important, but the relays to make them available. It is possible to define human memory not just by types but by specifications of relays for availability, such that those for reasoning at some point may be different from those from planning or those for learning from those from doing. Simply, outputs from human memory are sometimes a result of reach or relays, not just by the memory present. Relays can also be used to explain how Large language models [LLMs] work. LLMs were trained on vast amounts of internet data. This training can be assumed to be relays. Prompts to and outputs from LLMs can also be assumed to be relays across data. Simply put, LLMs work because they are able to relay predictively, with correct matches for several use cases. Though there are still weaknesses of these relays—like confabulations, hallucinations, slops in generated videos and images, and so forth—the better the relays or math functions that are developed for digital memory, the closer they might come to human intelligence. might come Artificial general intelligence [AGI] or artificial superintelligence [ASI] could get closer with improved relays across digital memory, in parallel to relays that result in intelligence in the human mind. AGI can be defined as relay equivalents to those in the human mind that define human intelligence. Relays may also make and specify extensions like—levels of control and base subjectivity. The internet already has more information than any individual’s brain. However, AGI may be as good as relays that use the information for similarities to human intelligence, not just the presence of information. This is similar to the human mind, where presence [of memory] does not mean available [in an instance]. The human mind is theorized to have functions and features . Though functions are shaped by relays, features are dominated by relays. Functions can be categorized into memory, feeling, emotion, and modulation of internal senses. Functions have several subcategories including—intelligence, planning, reasoning and so forth. Functional instances are determined by how they are graded or qualified by features. Features are types of relays that bring those functions to use, including their intensities. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features in sets, in clusters of neurons, across the central and peripheral nervous systems. theorized to have functions and features electrical and chemical signals with their interactions and features Features [or relays] include pre-/prioritization, old and new sequences, early splits, principal spots, thick/thin sets, volume changes, diameter constant spaces, and others. These relays are postulated to directly mechanize what is learned and experienced. Ranking how close to human intelligence AGI or ASI may come may depend on their parallel relays. AGI would not be a measure of outcomes but a measure of relays since outcomes may be great yet deficient. For example, LLMs are excellent at predictions but lack correction skills and knowledge of consequences. They are cool at several types of pattern-matching but seem to be unidirectional in several outputs, which is not the case for the human mind. Two of several questions about AGI are: Can it have control and subjectivity? In the human mind, control, free will, or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck, and so forth. There are functions that cannot be controlled, like the bronchioles and so forth, at least directly, like speaking, waving, and so on. Subjectivity is attachment, personalization, or self-association, away from detachment, depersonalization, or dissociation. Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same sets of signals that mechanize functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience for the self to present—they are also subcategories where relays get to and determine their intensities. It could be possible that rows within high-dimensional vectors become aspects of control, and some columns become aspects of subjectivity. For AGI, control and subjectivity would be data that can be used alone and has to be relayed for availability, unlike with humans. Simply, AGI would use data to control data and use data to have subjectivity, conceptually. Conceptual brain science could be useful in grading LLMs to AGI, becoming another direction for computational and theoretical neuroscience to shape progress in AI. There is a recent editorial in Nature , The new NeuroAI , stating that, "Overall, the extent of the role of neuroscience in AI research, and that of AI in neuroscience research, remain open questions for the future. However, these two fields are deeply linked, and the exchange of ideas between them continues to evolve. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focused on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience." Nature The new NeuroAI There is a recent paper in Nature , Reservoir-computing based associative memory and itinerancy for complex dynamical attractors , stating that, "A neural network capable of memorizing and retrieving multiple memory states is also a multifunctional neural network. When different states are recalled, the neural network exhibits distinct dynamical behaviors. The idea of multifunctional RNNs has been discussed previously, along with different forms of implementation. In particular, in the index-based approach, an index parameter is introduced to modulate the functionality of the neural networks and store different states with different index values, but recalling the memorized states was a challenge, for which control may be necessary." Nature Reservoir-computing based associative memory and itinerancy for complex dynamical attractors

Evaluation: What is the Neuroscience of Superintelligence, AGI—and LLMs?

Hi All! Here is my weekly letter discussing mental models, performance, business, and entrepreneurship. If you love this content (please share it), but also… Connect with me on YouTube/Twitter, or subscribe to my daily business newsletter here. What’s in today’s newsletter? Networking is a skill. But it's not one they teach you in school. They don't give you lessons on how to build genuine connections, how to give before you ask, or how to curate a personal board of directors. So, here's the playbook I developed to network better: (1) Become a Talent Scout, (2) Curate Your Personal Board of Directors, and (3) Become a "Reverse Mentor." Your entire career can change the moment you stop seeing networking as a chore and start seeing it as a strategic advantage. Your Network, Your Net Worth Ever heard the saying, "Don't call me unless you need something?" That's the exact opposite of how to build a powerful network. Here's a thought: The strongest relationships are forged in the fires of mutual support, not the desperation of last-minute asks. Let me share a story. Back when I was first starting out, hustling to get my podcast off the ground, I had this mentor, Sarah. She was a powerhouse in the industry, someone I deeply admired. But here's the thing: I didn't just reach out to Sarah when I needed funding advice or a warm intro. I grabbed coffee with her when I didn’t need anything at all. We’d catch up, I'd share my small wins and setbacks, just to get her perspective. Over time, a real friendship blossomed. It turns out, I didn’t need anything at all for about 3 years. And guess what? When the time came that I did need something – a crucial connection, a sounding board for a tough decision – Sarah was there in a heartbeat. Not out of obligation, but because we had built a genuine bond. Think about it. When you reach out to someone only when you're in need, it's like waving a giant flag that says, "I'm using you!" It feels transactional, desperate even. Nobody likes to feel used. But when you reach out genuinely, to offer support, share insights, or just say hello, you're building something far more valuable: trust. You're signaling that you value the relationship for its own sake, not just for what you can get out of it. Trust is worth its weight in gold. It's the currency of a powerful network; one that will have your back when you need it most. Don't be that person who only shows up when they want something. Be the person who invests in relationships before they need them. The Networking Blindside: Think about it. Have you ever been caught off guard by a professional setback? Maybe a dream job fell through, a project got derailed, or a client suddenly went dark. In those moments, it's easy to feel isolated and overwhelmed. That’s when you NEED people. That’s also when you CAN’T reach out to people. It’s happened to the best of us. We wish we had a network of people we could turn to for support, advice, or even a new lead. That's the power of strategic networking. You have to build that network of people who can support you before you need it. So, how do you do that? Here’s the system I developed to network better - build relationships before you need them, so you're never starting from scratch when the unexpected happens. The Networking System: 3 Strategies To Built True Ties 1. Become a Talent Scout: The saying goes, "Bet on the jockey, not the horse." The same goes for your network. Don't just collect business cards – invest in people. Scout out the rising stars; the ones with that fire in their eyes. They're the future CEOs, the disruptors, the game-changers. Spot the talent, the drive, the vision. Be their champion. Mentor them. Share your hard-won wisdom. Open doors they didn't even know existed. As they rise, so do you. Building a network isn't about quantity, it's about quality. Bet on the jockey, and you'll win the race. 2. Become a "Reverse Mentor": You just scouted new talent; now, it’s time to tap into wisdom and experience. Mentorship isn't a one-way street; it's a two-way exchange of knowledge and perspective. Flip the script and become a "reverse mentor." Don't just seek wisdom; offer yours in return (before you need it). Your fresh perspective on emerging tech, digital trends, or innovative solutions is GOLD to those in power. Pinpoint Your Power: What unique skills or insights do you have that a senior leader could benefit from?
Target the Right Players: Look for open-minded leaders who value diverse perspectives.
Make Your Pitch: Show them how your knowledge can solve their problems or drive their goals.
Start the Conversation: Don't be afraid to reach out and offer your expertise. Become a trusted advisor, build cross-generational bridges, and accelerate your own career. Remember: The student can become the teacher. 3. Build Your "Personal Board of Directors": Jim Rohn famously said, "You are the average of the five people you spend the most time with." Elevate your average. Hand-pick your board of directors. Not just mentors or colleagues, but your trusted advisors. Seek diversity – experience, skills, perspectives. Surround yourself with those who've "been there, done that" and those who challenge you to think differently. Invest in these relationships. Be present. Be supportive. Be the kind of friend you wish you had. When failure strikes, this is your A-team. They'll offer guidance, diverse perspectives, and unwavering support. Your board of directors isn't just a network; it's your lifeline. Your Network, Your Net Worth: Stop chasing connections; start cultivating relationships. Ditch the transactional mindset and embrace genuine connection. Invest in people, not just business cards. Remember, the strongest networks aren't built on desperation; they're built on mutual support, trust, and shared ambition. This isn't just about climbing the ladder faster; it's about building a community that will catch you if you fall, push you when you hesitate, and celebrate with you when you win. Your network isn't just a safety net; it's your launchpad. This is how you network, properly. Scott Subscribe to get this newsletter in your inbox, every week. My Links Success Story (Top 10 Business Podcast)
Newsletter (321k Subs)
10 Minute MBA (Daily Podcast)
10 Minute MBA (Daily Newsletter)
Social Media Hi All! Here is my weekly letter discussing mental models, performance, business, and entrepreneurship. If you love this content (please share it), but also… Connect with me on YouTube / Twitter, or subscribe to my daily business newsletter here. YouTube YouTube Twitter , Twitter daily business newsletter here. daily business newsletter here. What’s in today’s newsletter? What’s in today’s newsletter? Networking is a skill. But it's not one they teach you in school. They don't give you lessons on how to build genuine connections, how to give before you ask, or how to curate a personal board of directors. Networking is a skill. But it's not one they teach you in school. They don't give you lessons on how to build genuine connections, how to give before you ask, or how to curate a personal board of directors. So, here's the playbook I developed to network better: (1) Become a Talent Scout, (2) Curate Your Personal Board of Directors, and (3) Become a "Reverse Mentor." So, here's the playbook I developed to network better: (1) Become a Talent Scout, (2) Curate Your Personal Board of Directors, and (3) Become a "Reverse Mentor." Your entire career can change the moment you stop seeing networking as a chore and start seeing it as a strategic advantage. Your entire career can change the moment you stop seeing networking as a chore and start seeing it as a strategic advantage. Your Network, Your Net Worth Ever heard the saying, "Don't call me unless you need something?" That's the exact opposite of how to build a powerful network. exact opposite Here's a thought: The strongest relationships are forged in the fires of mutual support, not the desperation of last-minute asks. The strongest relationships are forged in the fires of mutual support, not the desperation of last-minute asks. Let me share a story. Back when I was first starting out, hustling to get my podcast off the ground, I had this mentor, Sarah. She was a powerhouse in the industry, someone I deeply admired. But here's the thing: I didn't just reach out to Sarah when I needed funding advice or a warm intro. I grabbed coffee with her when I didn’t need anything at all. We’d catch up, I'd share my small wins and setbacks, just to get her perspective. Over time, a real friendship blossomed. It turns out, I didn’t need anything at all for about 3 years. And guess what? When the time came that I did need something – a crucial connection, a sounding board for a tough decision – Sarah was there in a heartbeat. did Not out of obligation, but because we had built a genuine bond. Think about it. When you reach out to someone only when you're in need, it's like waving a giant flag that says, "I'm using you!" It feels transactional, desperate even. Nobody likes to feel used. But when you reach out genuinely, to offer support, share insights, or just say hello, you're building something far more valuable: trust. You're signaling that you value the relationship for its own sake, not just for what you can get out of it. Trust is worth its weight in gold. It's the currency of a powerful network; one that will have your back when you need it most. Don't be that person who only shows up when they want something. Be the person who invests in relationships before they need them. before The Networking Blindside: The Networking Blindside: Think about it. Have you ever been caught off guard by a professional setback? Maybe a dream job fell through, a project got derailed, or a client suddenly went dark. In those moments, it's easy to feel isolated and overwhelmed. That’s when you NEED people. That’s also when you CAN’T reach out to people. It’s happened to the best of us. We wish we had a network of people we could turn to for support, advice, or even a new lead. That's the power of strategic networking. You have to build that network of people who can support you before you need it. So, how do you do that? Here’s the system I developed to network better - build relationships before you need them, so you're never starting from scratch when the unexpected happens. before The Networking System: 3 Strategies To Built True Ties 1. Become a Talent Scout: 1. Become a Talent Scout: The saying goes, "Bet on the jockey, not the horse." The same goes for your network. Don't just collect business cards – invest in people. Scout out the rising stars; the ones with that fire in their eyes. They're the future CEOs, the disruptors, the game-changers. Spot the talent, the drive, the vision. Be their champion. Mentor them. Share your hard-won wisdom. Open doors they didn't even know existed. As they rise, so do you. Building a network isn't about quantity, it's about quality. Bet on the jockey, and you'll win the race. 2. Become a "Reverse Mentor": 2. Become a "Reverse Mentor": You just scouted new talent; now, it’s time to tap into wisdom and experience. Mentorship isn't a one-way street; it's a two-way exchange of knowledge and perspective. Flip the script and become a "reverse mentor." Don't just seek wisdom; offer yours in return (before you need it). Your fresh perspective on emerging tech, digital trends, or innovative solutions is GOLD to those in power. Pinpoint Your Power: What unique skills or insights do you have that a senior leader could benefit from? Target the Right Players: Look for open-minded leaders who value diverse perspectives. Make Your Pitch: Show them how your knowledge can solve their problems or drive their goals. Start the Conversation: Don't be afraid to reach out and offer your expertise. Pinpoint Your Power: What unique skills or insights do you have that a senior leader could benefit from? Pinpoint Your Power: Target the Right Players: Look for open-minded leaders who value diverse perspectives. Target the Right Players: Make Your Pitch: Show them how your knowledge can solve their problems or drive their goals. Make Your Pitch: Start the Conversation: Don't be afraid to reach out and offer your expertise. Start the Conversation: Become a trusted advisor, build cross-generational bridges, and accelerate your own career. Remember: The student can become the teacher. 3. Build Your "Personal Board of Directors": 3. Build Your "Personal Board of Directors": Jim Rohn famously said, "You are the average of the five people you spend the most time with." "You are the average of the five people you spend the most time with." Elevate your average. Hand-pick your board of directors. Not just mentors or colleagues, but your trusted advisors. Seek diversity – experience, skills, perspectives. Surround yourself with those who've "been there, done that" and those who challenge you to think differently. Invest in these relationships. Be present. Be supportive. Be the kind of friend you wish you had. When failure strikes, this is your A-team. They'll offer guidance, diverse perspectives, and unwavering support. Your board of directors isn't just a network; it's your lifeline. Your Network, Your Net Worth: Stop chasing connections; start cultivating relationships. Ditch the transactional mindset and embrace genuine connection. Invest in people, not just business cards. Remember, the strongest networks aren't built on desperation; they're built on mutual support, trust, and shared ambition. This isn't just about climbing the ladder faster; it's about building a community that will catch you if you fall, push you when you hesitate, and celebrate with you when you win. Your network isn't just a safety net; it's your launchpad. This is how you network, properly. Scott Subscribe to get this newsletter in your inbox, every week. Subscribe to get this newsletter in your inbox, every week. Subscribe to get this newsletter in your inbox, every week. My Links My Links Success Story (Top 10 Business Podcast) Newsletter (321k Subs) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Newsletter) Social Media Success Story (Top 10 Business Podcast) Success Story (Top 10 Business Podcast) Success Story (Top 10 Business Podcast) Newsletter (321k Subs) Newsletter (321k Subs) Newsletter (321k Subs) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Podcast) 10 Minute MBA (Daily Newsletter) 10 Minute MBA (Daily Newsletter) 10 Minute MBA (Daily Newsletter) Social Media Social Media Social Media

Your Network, Your Net Worth: 3 Tips to Network Better

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