Introducing Tenzin 1.0:
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Tarun Mittal PRO
Tar9897
AI & ML interests
I believe that LLMs can never get us to AGI. Sure there may be neat tricks here and there but to create true consciousness requires something else. It requires a cross between Mathematics, Philosophy, Biology, and Computer Science. Only then will we be able to get somewhere. I personally think through adapting and emulating emotional learning from the very start and then improving our model based on synthetic and meta-data would lead us to better places than would LLMs which would pretty much run into a wall very soon.
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3 months ago
DW their only fault is they like strawberries too much. Forget the earth bears other fruits too ;)
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6 months ago
This week most likely the entire model on our website with a 2 week-free trial. We are but a small team and will post some papers as well as evaluation metrics along with our Data Card for everyone to review. All of that will be this week most likely :)
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6 months ago
Members are showcased once they launch to a market. For more info on Octave-X's membership status you can contact [email protected] for more info mentioning our startup's name.
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6 months ago
Let's start with that calling our website full of "AI slop" is same as saying that Mona Lisa is just a bunch of paint splatters. If you squint hard enough, you may see some resemblance, but it doesn't mean you are right. Now, I could explain how our AI architecture works, filled with all kinds of code snippets and mathematical proof, which would leave you half-witted. You'd barely understand half of it, and I'd be wasting my breath.
So tell me, do you know what it's like trying to explain a joke to somebody who doesn't get it? It's like trying to teach a fish to ride a bicycle. It's pointless and frustrating all the way around. Rather than spend all my time explaining our approach through jargon, though, I will put it to you in layperson's terms: This AI of ours is so intelligent, that it makes your average cult look like a kindergarten playdate. The truth is that when you think the adjectives and descriptions are vague, believe us, we're trying to damn well dumb them down enough for folks like you.
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I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language.
In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences.
The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback.
I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences.
The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback.
I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
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As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
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6 months ago
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I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language.
In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences.
The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback.
I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences.
The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback.
I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
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- 20 replies
posted
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update
6 months ago
Post
3444
I believe in order to make models reach Human-Level Learning, serious students can start by developing an intelligent neuromorphic agent. We develop an intelligent agent and make it learn about grammar patterns as well as about different word categories through symbolic representations, following which we dwell into making the agent learn about other rules of the Language.
In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences.
The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback.
I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
In parallel with grammar learning, the agent would also use language grounding techniques to link words to their sensory representations and abstract concepts which would mean the agent learns about the word meanings, synonyms, antonyms, and semantic relationships from both textual data as well as perceptual experiences.
The result would be the agent developing a rich lexicon and conceptual knowledge base that underlies its language understanding as well as generation. With this basic knowledge of grammar and word meanings, the agent can then learn to synthesize words and phrases so as to express specific ideas or concepts. Building on this, the agent would then learn how to generate complete sentences which the agent would continuously refine and improve. Eventually the agent would learn how to generate sequence of sentences in the form of dialogues or narratives, taking into account context, goals, as well as user-feedback.
I believe that by gradually learning how to improve their responses, the agent would gradually also acquire the ability to generate coherent, meaningful, and contextually appropriate language. This would allow them to reason without hallucinating which LLMs struggle at.
Developing such agents would not require a lot of compute and the code would be simple & easy to understand. It will definitely introduce everyone to symbolic AI and making agents which are good at reasoning tasks. Thus solving a crucial problem with LLMs. We have used a similar architecture to make our model learn constantly. Do sign up as we start opening access next week at https://octave-x.com/
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- 20 replies
reacted to
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6 months ago
Post
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As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
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- 9 replies
posted
an
update
6 months ago
Post
3277
As we advance on the path towards true Artificial General Intelligence (AGI), it's crucial to recognize and address the limitations inherent in current technologies, particularly in large language models (LLMs) like those developed by OpenAI. While LLMs excel in processing and generating text, their capabilities are largely constrained to the domains of natural language understanding and generation. This poses significant limitations when dealing with more complex, abstract mathematical concepts such as topological analysis, 3D geometry, and homotopy type theory.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
Topological Analysis and 3D Geometry: LLMs currently do not possess the inherent ability to understand or interpret the spatial and geometric data that is critical in fields like robotics, architecture, and advanced physics. These models lack the capacity to visualize or manipulate three-dimensional objects or comprehend the underlying properties that govern these forms.
Homotopy Type Theory is a branch of mathematics that combines homotopy theory and type theory. Homotopy type theory provides tools for a more robust handling of equivalences and transformations, something that LLMs are not designed to handle directly.
For the development of AGI, it is not sufficient to merely enhance existing models' capacities within their linguistic domains. Instead, a synthesis of symbolic AI with an understanding of homotopy type theory could pave the way. Symbolic AI, which manipulates symbols and performs logical operations, when combined with the abstract mathematical reasoning of homotopy type theory, could lead to breakthroughs in how machines understand and interact with the world.
To address these limitations we have developed Tenzin, which is a one-of-a-kind model with a planned release date within the next 1-2 weeks . To learn more join the waitlist at https://octave-x.com/.
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- 9 replies