ANI and AGI
ANI and AGI
ANI and AGI
ANI and AGI
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
OpenAI’s charter refers to AGI as “...highly autonomous systems that outperform humans at most economically valuable work.” In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks.
In the AGI spectrum what is the relation with DeepMoneys AI system and the six levels?
The first level is zero (0) like a calculator. Then there are five ascending levels:
1. Emerging, 2. Competent, 3. Expert, 4. Virtuoso, and 5. Superhuman.
The following OpenAI matrix distinguishes between narrow (ANI) and general (AGI). For instance, there already exists level 5. Superhuman ANI systems like AlphaFold. In contrast AGI is at level 1. emerging.
Literature Ref. https://arxiv.org/abs/2311.02462
DeepMoney matrix
DeepMoney checks one AGI box:
The above matrix demonstrates DeepMoney core engines versus the five levels of narrow and general AI. “Level 1. Emerging AGI” is inherent to the architecture of the DeepMoney that manages multi-assets and multi-markets.
DeepMoney checks three ANI boxes:
Level 3. Expert | CFA/CTA advisor our machine is as competent at the top 90% experts
Level 4. Virtuoso | Our patent pending price action memory AI maybe matched by few unique extraordinary humans (we have yet to encounter)
Level 5. Superhuman | Our AI quant engine can read live market data and calculate super pivots and set-up at a speed no human can match, 24/7 millions of calculations.
The Spectrum of markets and financial products
There are an immense number of financial products across a multitude of markets. This environment is forever expanding and moving and therefore an AI system that can learn new asset classes and new markets will provide many benefits.
DeepMoney building blocks for AI engines are structured in a generalised manner.
By way of example if an AI system claims to support two assets for example BTC and ETH only on one exchange, Binance. The trade style may be Long only, swing trades.It likely would not fall into the category of generalised AI because it is hindered in learning, maintaining a narrow focus.
On the other hand an AI system like DeepMoney that supports 30 crypto assets, 20 US stocks, 15 FOREX pairs, 4 commodities and across multiple market exchanges. Trading long/short on multi time frame, and multi strategy already demonstrates a broad general skillset and with the ability to scale and evolve, endows it with the ability of AGI.
The convergence
Directionally we will next see a convergence of layers and verticals as diverse AI systems like healthcare, robotics, personal assistants and financial products develop interoperability.
The opportunities for best in class financial AI systems such as ours are to excel in ‘niche’ domains with open communication bridges to the array of other transformer models that dominate their own niche.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
OpenAI’s charter refers to AGI as “...highly autonomous systems that outperform humans at most economically valuable work.” In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks.
In the AGI spectrum what is the relation with DeepMoneys AI system and the six levels?
The first level is zero (0) like a calculator. Then there are five ascending levels:
1. Emerging, 2. Competent, 3. Expert, 4. Virtuoso, and 5. Superhuman.
The following OpenAI matrix distinguishes between narrow (ANI) and general (AGI). For instance, there already exists level 5. Superhuman ANI systems like AlphaFold. In contrast AGI is at level 1. emerging.
Literature Ref. https://arxiv.org/abs/2311.02462
DeepMoney matrix
DeepMoney checks one AGI box:
The above matrix demonstrates DeepMoney core engines versus the five levels of narrow and general AI. “Level 1. Emerging AGI” is inherent to the architecture of the DeepMoney that manages multi-assets and multi-markets.
DeepMoney checks three ANI boxes:
Level 3. Expert | CFA/CTA advisor our machine is as competent at the top 90% experts
Level 4. Virtuoso | Our patent pending price action memory AI maybe matched by few unique extraordinary humans (we have yet to encounter)
Level 5. Superhuman | Our AI quant engine can read live market data and calculate super pivots and set-up at a speed no human can match, 24/7 millions of calculations.
The Spectrum of markets and financial products
There are an immense number of financial products across a multitude of markets. This environment is forever expanding and moving and therefore an AI system that can learn new asset classes and new markets will provide many benefits.
DeepMoney building blocks for AI engines are structured in a generalised manner.
By way of example if an AI system claims to support two assets for example BTC and ETH only on one exchange, Binance. The trade style may be Long only, swing trades.It likely would not fall into the category of generalised AI because it is hindered in learning, maintaining a narrow focus.
On the other hand an AI system like DeepMoney that supports 30 crypto assets, 20 US stocks, 15 FOREX pairs, 4 commodities and across multiple market exchanges. Trading long/short on multi time frame, and multi strategy already demonstrates a broad general skillset and with the ability to scale and evolve, endows it with the ability of AGI.
The convergence
Directionally we will next see a convergence of layers and verticals as diverse AI systems like healthcare, robotics, personal assistants and financial products develop interoperability.
The opportunities for best in class financial AI systems such as ours are to excel in ‘niche’ domains with open communication bridges to the array of other transformer models that dominate their own niche.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
OpenAI’s charter refers to AGI as “...highly autonomous systems that outperform humans at most economically valuable work.” In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks.
In the AGI spectrum what is the relation with DeepMoneys AI system and the six levels?
The first level is zero (0) like a calculator. Then there are five ascending levels:
1. Emerging, 2. Competent, 3. Expert, 4. Virtuoso, and 5. Superhuman.
The following OpenAI matrix distinguishes between narrow (ANI) and general (AGI). For instance, there already exists level 5. Superhuman ANI systems like AlphaFold. In contrast AGI is at level 1. emerging.
Literature Ref. https://arxiv.org/abs/2311.02462
DeepMoney matrix
DeepMoney checks one AGI box:
The above matrix demonstrates DeepMoney core engines versus the five levels of narrow and general AI. “Level 1. Emerging AGI” is inherent to the architecture of the DeepMoney that manages multi-assets and multi-markets.
DeepMoney checks three ANI boxes:
Level 3. Expert | CFA/CTA advisor our machine is as competent at the top 90% experts
Level 4. Virtuoso | Our patent pending price action memory AI maybe matched by few unique extraordinary humans (we have yet to encounter)
Level 5. Superhuman | Our AI quant engine can read live market data and calculate super pivots and set-up at a speed no human can match, 24/7 millions of calculations.
The Spectrum of markets and financial products
There are an immense number of financial products across a multitude of markets. This environment is forever expanding and moving and therefore an AI system that can learn new asset classes and new markets will provide many benefits.
DeepMoney building blocks for AI engines are structured in a generalised manner.
By way of example if an AI system claims to support two assets for example BTC and ETH only on one exchange, Binance. The trade style may be Long only, swing trades.It likely would not fall into the category of generalised AI because it is hindered in learning, maintaining a narrow focus.
On the other hand an AI system like DeepMoney that supports 30 crypto assets, 20 US stocks, 15 FOREX pairs, 4 commodities and across multiple market exchanges. Trading long/short on multi time frame, and multi strategy already demonstrates a broad general skillset and with the ability to scale and evolve, endows it with the ability of AGI.
The convergence
Directionally we will next see a convergence of layers and verticals as diverse AI systems like healthcare, robotics, personal assistants and financial products develop interoperability.
The opportunities for best in class financial AI systems such as ours are to excel in ‘niche’ domains with open communication bridges to the array of other transformer models that dominate their own niche.
Google DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. This is the fundamental structure of DeepMoney and our first order design principles.
OpenAI’s charter refers to AGI as “...highly autonomous systems that outperform humans at most economically valuable work.” In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks.
In the AGI spectrum what is the relation with DeepMoneys AI system and the six levels?
The first level is zero (0) like a calculator. Then there are five ascending levels:
1. Emerging, 2. Competent, 3. Expert, 4. Virtuoso, and 5. Superhuman.
The following OpenAI matrix distinguishes between narrow (ANI) and general (AGI). For instance, there already exists level 5. Superhuman ANI systems like AlphaFold. In contrast AGI is at level 1. emerging.
Literature Ref. https://arxiv.org/abs/2311.02462
DeepMoney matrix
DeepMoney checks one AGI box:
The above matrix demonstrates DeepMoney core engines versus the five levels of narrow and general AI. “Level 1. Emerging AGI” is inherent to the architecture of the DeepMoney that manages multi-assets and multi-markets.
DeepMoney checks three ANI boxes:
Level 3. Expert | CFA/CTA advisor our machine is as competent at the top 90% experts
Level 4. Virtuoso | Our patent pending price action memory AI maybe matched by few unique extraordinary humans (we have yet to encounter)
Level 5. Superhuman | Our AI quant engine can read live market data and calculate super pivots and set-up at a speed no human can match, 24/7 millions of calculations.
The Spectrum of markets and financial products
There are an immense number of financial products across a multitude of markets. This environment is forever expanding and moving and therefore an AI system that can learn new asset classes and new markets will provide many benefits.
DeepMoney building blocks for AI engines are structured in a generalised manner.
By way of example if an AI system claims to support two assets for example BTC and ETH only on one exchange, Binance. The trade style may be Long only, swing trades.It likely would not fall into the category of generalised AI because it is hindered in learning, maintaining a narrow focus.
On the other hand an AI system like DeepMoney that supports 30 crypto assets, 20 US stocks, 15 FOREX pairs, 4 commodities and across multiple market exchanges. Trading long/short on multi time frame, and multi strategy already demonstrates a broad general skillset and with the ability to scale and evolve, endows it with the ability of AGI.
The convergence
Directionally we will next see a convergence of layers and verticals as diverse AI systems like healthcare, robotics, personal assistants and financial products develop interoperability.
The opportunities for best in class financial AI systems such as ours are to excel in ‘niche’ domains with open communication bridges to the array of other transformer models that dominate their own niche.
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