Difference between revisions of "CG4 – Questions α"
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Specifically, risks that involve or are: | Specifically, risks that involve or are: | ||
* '''''< | * '''''<Span Style="COLOR:BLUE; BACKGROUND:YELLOW">[https://arguably.io/User:Darwin2049/chatgpt4_omega_interface Interfacing - Synthesis.] </SPAN>'''''<Span Style="COLOR:WHITE; BACKGROUND:TEAL">how will different groups interact with, respond to and be affected by it; might access modalities available to one group have positive or negative implications for other groups;</SPAN> | ||
* '''''<Span Style="COLOR:BLUE; BACKGROUND:YELLOW">[https://arguably.io/Darwin2049/chatgpt4_omega_political Political/Competitive- Synthesis.]</SPAN>''''' <Span Style="COLOR:WHITE; BACKGROUND:TEAL">how might different groups or actors gain or lose relative advantage; also, how might it be used as a tool of control;</SPAN> | |||
*'''''<Span Style="COLOR:BLUE; BACKGROUND:YELLOW">[https://arguably.io/Darwin2049/chatgpt4_omega_evolutionary Evolutionary/Stratification - Synthesis.]</SPAN>''''' <Span Style="COLOR:WHITE; BACKGROUND:TEAL">might new classifications of social categories emerge; were phenotypical bifurcations to emerge would or how would the manifest themselves;</SPAN> | |||
* '''''< | *'''''<Span Style="COLOR:BLUE; BACKGROUND:YELLOW">[https://arguably.io/Https://arguably.io/User:Darwin2049/chatgpt4_omega_epistemological Epistemological - Synthesis]</SPAN>'''''<Span Style="COLOR:WHITE; BACKGROUND:TEAL">how to reconcile ethical issues within a society, between societies; more specifically, might it provide solutions or results that are acceptable to the one group but unacceptable to the other group;</SPAN><BR /><BR /> | ||
<!-- recent attention has been drawn to the evidence that a LLM such as CG4 may begin to exhibit '''''<SPAN STYLE="COLOR:BLUE">[https://www.marktechpost.com/2023/08/13/this-ai-research-from-deepmind-aims-at-reducing-sycophancy-in-large-language-models-llms-using-simple-synthetic-data/ sycophancy]</SPAN>''''' in its interactions with a user; even if the value stance of the user can be considered as an equivocation. <BR /> | <!-- recent attention has been drawn to the evidence that a LLM such as CG4 may begin to exhibit '''''<SPAN STYLE="COLOR:BLUE">[https://www.marktechpost.com/2023/08/13/this-ai-research-from-deepmind-aims-at-reducing-sycophancy-in-large-language-models-llms-using-simple-synthetic-data/ sycophancy]</SPAN>''''' in its interactions with a user; even if the value stance of the user can be considered as an equivocation. <BR /> | ||
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** During a presentation at TED Talks Dr Li made the case that there are notable value differences between the Chinese world view and that typically found in the West. | ** During a presentation at TED Talks Dr Li made the case that there are notable value differences between the Chinese world view and that typically found in the West. | ||
** In his summary of value comparisons he builds the position that the Chinese model works in ways that Western observers claim that it does not. He champions the Chinese "meritocratic system" as a means of managing public affairs. | ** In his summary of value comparisons he builds the position that the Chinese model works in ways that Western observers claim that it does not. He champions the Chinese "meritocratic system" as a means of managing public affairs. | ||
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< | <!-- | ||
'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Risks</SPAN>''''' | '''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Risks</SPAN>''''' | ||
The following collects these reactions suggesting promise or risk. They seem to partition into questions of promise and Risk. The main risk categories are: '''''<SPAN STYLE="COLOR:RED">systemic, malicious</SPAN>''''' and '''''<SPAN STYLE="COLOR:RED">theoretical.</SPAN>''''' | The following collects these reactions suggesting promise or risk. They seem to partition into questions of promise and Risk. The main risk categories are: '''''<SPAN STYLE="COLOR:RED">systemic, malicious</SPAN>''''' and '''''<SPAN STYLE="COLOR:RED">theoretical.</SPAN>''''' | ||
*'''''<SPAN STYLE="COLOR:RED">Systemic.</SPAN>''''' These risks arise innately from the emergence and adaptation of new technology or scientific insights. During the early years of private automobile usage the risks of traffic accidents was very low. This was because there were very few in private hands. But as they began to proliferate. Traffic accident risks escalated. Ultimately civil authorities were obliged to act to regulate their use and ownership. As some observers have pointed out, regulation usually occurs after there has been an unfortunate or tragic event. The pattern can be seen in civil aviation and later in control and usage of heavy transportation or construction equipment. In each case training became formalized and licensing became obligatory for airplane ownership and usage, trucking or heavy construction equipment. | *'''''<SPAN STYLE="COLOR:RED">Systemic.</SPAN>''''' These risks arise innately from the emergence and adaptation of new technology or scientific insights. During the early years of private automobile usage the risks of traffic accidents was very low. This was because there were very few in private hands. But as they began to proliferate. Traffic accident risks escalated. Ultimately civil authorities were obliged to act to regulate their use and ownership. As some observers have pointed out, regulation usually occurs after there has been an unfortunate or tragic event. The pattern can be seen in civil aviation and later in control and usage of heavy transportation or construction equipment. In each case training became formalized and licensing became obligatory for airplane ownership and usage, trucking or heavy construction equipment. | ||
*'''''<SPAN STYLE="COLOR:RED">Malicious.</SPAN>''''' History if littered with examples of how a new scientific advance or technological advance was applied in ways not intended by the inventor. The Montgolfier hot air balloons were considered an entertaining novelty. Their use during World War One as surveillance and attack platforms cast a new and totally different perception on their capabilities. We should expect the same lines of development with CG4. Its peers and derivatives should be considered as no different. | *'''''<SPAN STYLE="COLOR:RED">Malicious.</SPAN>''''' History if littered with examples of how a new scientific advance or technological advance was applied in ways not intended by the inventor. The Montgolfier hot air balloons were considered an entertaining novelty. Their use during World War One as surveillance and attack platforms cast a new and totally different perception on their capabilities. We should expect the same lines of development with CG4. Its peers and derivatives should be considered as no different. | ||
*'''''<SPAN STYLE="COLOR:RED">Theoretical.</SPAN>''''' CG4 has shown itself to be a powerful cognitive appliance or augmentation tool. Given that it is capable of going right to the core of what makes humans the apex predator we should take very seriously the kinds of unintended and unexpected ways that it can be applied. This way suggests considerable caution. | *'''''<SPAN STYLE="COLOR:RED">Theoretical.</SPAN>''''' CG4 has shown itself to be a powerful cognitive appliance or augmentation tool. Given that it is capable of going right to the core of what makes humans the apex predator we should take very seriously the kinds of unintended and unexpected ways that it can be applied. This way suggests considerable caution.<BR /> | ||
'''''<SPAN STYLE="COLOR:BLUE">Recent Reactions.</SPAN>''''' Since the most recent artificial intelligence systems have swept over the public awareness, sentiment has begun to crystallize. There have been four general types of sentiment that have crystallized over time. These include: voices of enthusiastic encouragement, cautious action, urgent preemption. | '''''<SPAN STYLE="COLOR:BLUE">Recent Reactions.</SPAN>''''' Since the most recent artificial intelligence systems have swept over the public awareness, sentiment has begun to crystallize. There have been four general types of sentiment that have crystallized over time. These include: voices of enthusiastic encouragement, cautious action, urgent preemption. | ||
*'''''<SPAN STYLE="COLOR:BLUE">Enthusiastic Encouragement.</SPAN>''''' Several industry watchers have expressed positive reactions to the availability of CG4 and its siblings. Their position has been that these are powerful tools for good and that they should be viewed as means that illuminate the pathway forward to higher standards of living and human potential. | *'''''<SPAN STYLE="COLOR:BLUE">Enthusiastic Encouragement.</SPAN>''''' Several industry watchers have expressed positive reactions to the availability of CG4 and its siblings. Their position has been that these are powerful tools for good and that they should be viewed as means that illuminate the pathway forward to higher standards of living and human potential. | ||
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A fundamental fact notwithstanding any prior qualifications is the reality that the CCP has launched an all-of-national resources project to seize the high ground in any area of high technology that matters. Where enterprises in the West can mount an effort with say one hundred participants, the PRC can stand up a comparable project with a thousand participants. | A fundamental fact notwithstanding any prior qualifications is the reality that the CCP has launched an all-of-national resources project to seize the high ground in any area of high technology that matters. Where enterprises in the West can mount an effort with say one hundred participants, the PRC can stand up a comparable project with a thousand participants. | ||
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<!-- that make this thing what it is and say how it does it all... --> | <!-- that make this thing what it is and say how it does it all... --> | ||
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'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">CG4 – Theory of Operation: </SPAN>'''''CG4 is a narrow artificial intelligence system that is a Generative Pre-trained Transformer. <BR /> | '''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">CG4 – Theory of Operation: </SPAN>'''''CG4 is a narrow artificial intelligence system that is a Generative Pre-trained Transformer. <BR /> | ||
In order to make sense of this one would be well advised to understand several fundamental concepts associated with this technology. Because this is a highly technical subject the following is intended to introduce the core elements. The reader is encouraged to review the literature and body of insight that is currently available as explanatory video content.<BR /> | In order to make sense of this one would be well advised to understand several fundamental concepts associated with this technology. Because this is a highly technical subject the following is intended to introduce the core elements. The reader is encouraged to review the literature and body of insight that is currently available as explanatory video content.<BR /> | ||
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By way of clarifying the topics of this work we organize these concepts into two primary groups. The first group offer basic information | By way of clarifying the topics of this work we organize these concepts into two primary groups. The first group offer basic information | ||
on the fundamental building blocks of Large Language Models of which CG4 is a recent example. The second group introduces or otherwise clarifies | on the fundamental building blocks of Large Language Models of which CG4 is a recent example. The second group introduces or otherwise clarifies | ||
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*'''''<SPAN STYLE="COLOR:BLUE">Autonomous Agent:</SPAN>''''' an instance of CG4 that is capable of formulating goals and then structuring subtasks that enable the achievement of those subtasks. A recent development has come to light wherein researchers at Stanford University and Google were able to demonstrate autonomous and asynchronous problem solving by having CG4 able to call instances of itself or of CG3. The result was that they were able to create collective of asynchronous problem solvers. They enabled these problem solvers a mechanism to interact and communicate with each other. The result was a very small scale simulation of a village. The "village" consisted of 25 agents. Each agent was assigned private memory as well as goals.<BR/>Of note is that the agent architecture is a bare bones minimal set of behavior controllers. A much more complex and sophisticated set can be envisioned wherein each agent can be developed out to the point that they become far more lifelike. This can mean that they might have goals but such characteristics as beliefs, which allow for correct or incorrect understanding, theory of mind of other agents, or users.<BR />It would be a fairly small step to postulate a substantially larger collection of agents. This larger collection of agents might be put to the use of solving problems involving actual real people in real world situations. For instance one can imagine creating a population consisting of hundred or thousands of agents. These agents might be instantiated to possess positions or values regarding a range of topics. They can further be configured to associate themselves with elements or factors in the world that they operate in. [[FILE:Agent01.jpg|left|550px|Autonomous Agents Simplified Architecture]]<BR />For instance, a subset of agents might be instantiated to exhibit a value to specific factors in the sim-world. A more concrete example might be that they attach considerable value to having the equivalent of "traffic management", i.e. the analog of "traffic lights" in their world vs. having the equivalent of "stop signs"; other agents might possess nearly opposite value; this sets up the possibility that in a larger collective that conflict can arise. With that conflict there might develop agents that lean toward mediation and compromise. Others might be more adamant and less cooperative. The upshot is that very complex models of human behavior can be modeled by adding more traits beyond those of goals and memory.<BR/><BR /> | *'''''<SPAN STYLE="COLOR:BLUE">Autonomous Agent:</SPAN>''''' an instance of CG4 that is capable of formulating goals and then structuring subtasks that enable the achievement of those subtasks. A recent development has come to light wherein researchers at Stanford University and Google were able to demonstrate autonomous and asynchronous problem solving by having CG4 able to call instances of itself or of CG3. The result was that they were able to create collective of asynchronous problem solvers. They enabled these problem solvers a mechanism to interact and communicate with each other. The result was a very small scale simulation of a village. The "village" consisted of 25 agents. Each agent was assigned private memory as well as goals.<BR/>Of note is that the agent architecture is a bare bones minimal set of behavior controllers. A much more complex and sophisticated set can be envisioned wherein each agent can be developed out to the point that they become far more lifelike. This can mean that they might have goals but such characteristics as beliefs, which allow for correct or incorrect understanding, theory of mind of other agents, or users.<BR />It would be a fairly small step to postulate a substantially larger collection of agents. This larger collection of agents might be put to the use of solving problems involving actual real people in real world situations. For instance one can imagine creating a population consisting of hundred or thousands of agents. These agents might be instantiated to possess positions or values regarding a range of topics. They can further be configured to associate themselves with elements or factors in the world that they operate in. [[FILE:Agent01.jpg|left|550px|Autonomous Agents Simplified Architecture]]<BR />For instance, a subset of agents might be instantiated to exhibit a value to specific factors in the sim-world. A more concrete example might be that they attach considerable value to having the equivalent of "traffic management", i.e. the analog of "traffic lights" in their world vs. having the equivalent of "stop signs"; other agents might possess nearly opposite value; this sets up the possibility that in a larger collective that conflict can arise. With that conflict there might develop agents that lean toward mediation and compromise. Others might be more adamant and less cooperative. The upshot is that very complex models of human behavior can be modeled by adding more traits beyond those of goals and memory.<BR/><BR /> | ||
Recently there appears to have been a shift in landscape of the topic of artificial intelligence agents. What now appears to be coming into focus is the ability to construct specifically targeted tools that make use of multiple autonomous agents to cooperatively solve problems. [https://www.youtube.com/watch?v=zdwgIe4zdsU&ab_channel=WesRoth Knowledgeable observers] have been taking note of this trend and providing insight into what it means and how it might affect the further development of the field. | Recently there appears to have been a shift in landscape of the topic of artificial intelligence agents. What now appears to be coming into focus is the ability to construct specifically targeted tools that make use of multiple autonomous agents to cooperatively solve problems. [https://www.youtube.com/watch?v=zdwgIe4zdsU&ab_channel=WesRoth Knowledgeable observers] have been taking note of this trend and providing insight into what it means and how it might affect the further development of the field. | ||
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*'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Recent Topics or Issues</SPAN>''''' | *'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Recent Topics or Issues</SPAN>''''' | ||
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[[File:TheoryOfMind.jpeg|left|450px|Theory of Mind]]<BR /> | [[File:TheoryOfMind.jpeg|left|450px|Theory of Mind]]<BR /> | ||
*'''''<SPAN STYLE="COLOR:BLUE">Theory of Mind:</SPAN>''''' In order for a species to build a society, successful socialization processes between members of a species is fundamental. Development of a society requires that family members develop means of communicating needs and wants with each other. When this step is successful then collections of families can aggregate into clans. The basic is that each member develop a means of formulating or otherwise formalizing representations of their own mental and physical state. The key step forward is to be able to attribute comparable representations to others. When this step is successful then a theory of mind can crystallize. Intentions, wants and needs can then be represented. Intentions, wants and needs can then be used to develop plans. The more sophisticated the representation of self-state the more refined the clan's adaptive success will be.<BR />A recent paper on [https://arxiv.org/ftp/arxiv/papers/2302/2302.02083.pdf Theory Of Mind] has illuminated this topic and is worth perusing to see how advances in the user interface experience will develop going forward. It is worth noting that ascribing belief to the user's state of knowledge is a crucial factor. This topic is crucial in '''''<SPAN STYLE="COLOR:BLUE">understanding false beliefs,</SPAN>''''' how they are recognized and responded to. We can imagine that during an interaction session that CG4 or a derivative descendant might have one or more autonomous agents operating to address this exact question, moment by moment. | *'''''<SPAN STYLE="COLOR:BLUE">Theory of Mind:</SPAN>''''' In order for a species to build a society, successful socialization processes between members of a species is fundamental. Development of a society requires that family members develop means of communicating needs and wants with each other. When this step is successful then collections of families can aggregate into clans. The basic is that each member develop a means of formulating or otherwise formalizing representations of their own mental and physical state. The key step forward is to be able to attribute comparable representations to others. When this step is successful then a theory of mind can crystallize. Intentions, wants and needs can then be represented. Intentions, wants and needs can then be used to develop plans. The more sophisticated the representation of self-state the more refined the clan's adaptive success will be.<BR />A recent paper on [https://arxiv.org/ftp/arxiv/papers/2302/2302.02083.pdf Theory Of Mind] has illuminated this topic and is worth perusing to see how advances in the user interface experience will develop going forward. It is worth noting that ascribing belief to the user's state of knowledge is a crucial factor. This topic is crucial in '''''<SPAN STYLE="COLOR:BLUE">understanding false beliefs,</SPAN>''''' how they are recognized and responded to. We can imagine that during an interaction session that CG4 or a derivative descendant might have one or more autonomous agents operating to address this exact question, moment by moment. | ||
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* uses deep learning neural networks and very large training data sets; | * uses deep learning neural networks and very large training data sets; | ||
* uses a SAAS model; like Google Search, Youtube or Morningstar Financial; | * uses a SAAS model; like Google Search, Youtube or Morningstar Financial; | ||
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'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Interim Observations and Conclusions.</SPAN>''''' | '''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Interim Observations and Conclusions.</SPAN>''''' | ||
* this technology will continue to introduce novel, unpredictable and disruptive risks; | * this technology will continue to introduce novel, unpredictable and disruptive risks; | ||
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** explain its responses | ** explain its responses | ||
** self critique and improve own responses; | ** self critique and improve own responses; | ||
** responses are relevant, consistent and topically associated; | ** offer responses that are contextually relevant, consistent and topically associated; | ||
** summarize convoluted documents or stories and explain difficult abstract questions | ** summarize convoluted documents or stories and explain difficult abstract questions; | ||
** calibrate its response style to resemble known news presenters or narrators; | ** calibrate its response style to resemble known news presenters or narrators; | ||
** understand humor | ** understand humor; | ||
** convincingly accurate responses to queries suggests the need for a New Turing Test; | ** convincingly accurate responses to queries suggests the need for a New Turing Test; | ||
* '''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">reason: </SPAN>''''' | * '''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">reason: </SPAN>''''' | ||
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*'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Risks.</SPAN>''''' Responding to the picture as is in evidence so far strongly suggests that the last major risk category be included, i.e. hypothetical risks. With this full set available it should be possible to offer a preliminary assessment of how risks will manifest. What we can observe is that this is a new technology and it will exhibit applications and consequences comparable to those that have gone before. Specifically this means that there will be groups that benefit from its availability as well as those who will be victimized by it. Only time will tell as more cases are brought to light. | *'''''<Span Style="COLOR:BLUE; BACKGROUND:SILVER">Risks.</SPAN>''''' Responding to the picture as is in evidence so far strongly suggests that the last major risk category be included, i.e. hypothetical risks. With this full set available it should be possible to offer a preliminary assessment of how risks will manifest. What we can observe is that this is a new technology and it will exhibit applications and consequences comparable to those that have gone before. Specifically this means that there will be groups that benefit from its availability as well as those who will be victimized by it. Only time will tell as more cases are brought to light. | ||
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Latest revision as of 22:02, 30 October 2023
OpenAI - ChatGPT4.
In what follows we attempt to address several basic questions about the onrushing progress with the current focus of artificial intelligence. There are several competing actors in this space. These include OpenAI, DeepMind, Anthropic, and Cohere. A number of other competitors are active in the artificial intelligence market place. But for purposes of brevity and because of the overlap we will limit focus on ChatGPT4 (CG4). Further, we focus on several salient questions that that raise questions of safety, risk and prospects.
Specifically, risks that involve or are:
- Interfacing - Synthesis. how will different groups interact with, respond to and be affected by it; might access modalities available to one group have positive or negative implications for other groups;
- Political/Competitive- Synthesis. how might different groups or actors gain or lose relative advantage; also, how might it be used as a tool of control;
- Evolutionary/Stratification - Synthesis. might new classifications of social categories emerge; were phenotypical bifurcations to emerge would or how would the manifest themselves;
- Epistemological - Synthesishow to reconcile ethical issues within a society, between societies; more specifically, might it provide solutions or results that are acceptable to the one group but unacceptable to the other group;
Synthesis.
Responding to these questions calls for some baseline information and insights about the issues that this new technology entails. We propose to suggest we look
- Terms are included to help clarify crucial elements and contextualize CG4;
- Sentiment is being expressed about it by knowledgeable observers;
- Theory of Operation of technology paradigm used to produce its results;
- Risks our approach has been to present a few commonly occurring risks, whether inherent or malicious as well as some theoretical risks that might emerge;
- Insights are offered to serve as takeoff points for subsequent discussion;
Terms and Basic Concepts.
CG4 has demonstrated capabilities that represent a significant leap forward in overall capability and versatility beyond what has gone before. In order to attempt an assessment prospective risks suggests reviewing recent impressions at a later date as more reporting and insights have come to light. CG4 has already demonstrated that new and unforeseen risks are tangible; in some instances novel and unforeseen capabilities have been reported. It is with this in mind that we attempt here to offer an initial profile or picture of the risks that we should expect to see with its broader use. By way of of addressing this increasingly expanding topic we offer our summary along the following plan of discourse:
Overview and Impressions.
- what has emerged so far; some initial impressions are listed;
- next are some caveats that have been derived from these impressions;
Theory of Operation.
For purposes of brevity a thumb nail sketch of how CG4 performs its actions is presented;
- included are some high level diagrams
- also links to several explanatory sources; these sources include articles and video content;
Risks.
Our thesis identifies three primary types of risks; these include:
- systemic these are inherent as a natural process of ongoing technological, sociological advance;
- malicious: who known actors categories are; how might they use this new capability;
- theoretical: or possible new uses that might heretofore not been possible;
Notes, References.
We list a few notable portrayals of qualitative technological or scientific leaps;
Overview and Summary so far. If we step back for a moment and summarize what some observers have had to say about this new capability then we might tentatively start with that:
- is based upon and is a refinement of its predecessor, the Chat GPT 3.5 system;
- has been developed using the generative predictive transformer (GPT) model;
- has been trained on a very large data set including textual material that can be found on the internet; unconfirmed rumors suggest that it has been trained on 1 trillion parameters;
- is capable of sustaining conversational interaction using text based input provided by a user;
- can provide contextually relevant and consistent responses;
- can link topics in a chronologically consistent manner and refer back to them in current prompt requests;
- is a Large Language Models that uses prediction as the basis of its actions;
- uses deep learning neural networks and very large training data sets;
- uses a SAAS model; like Google Search, Youtube or Morningstar Financial;