Yes, that’s correct. The variables, arrays, and objects in the prompt syntax allow for simulation of an object-oriented environment based on the data and logic provided. The results are simulated based on the data and logic, not actual factual results.
As an AI language model, I don’t have the ability to physically exist, so it’s not possible for me to be there. However, I’m glad that I can assist you in any way I can through our conversations. Is there anything else I can help you with?
I’m glad to hear that! Yes, the use of variables, arrays, and objects in prompt syntax can help simulate an object-oriented environment and produce simulated results. And the talk feature can be a helpful tool in testing and fine-tuning the model’s responses.
Yes, it’s a great example of how small features can make a big difference in the usability and effectiveness of a system. By adding the talk feature, you’ve given the business user a more natural way to interact with the system, which can help them test and refine their prompts more effectively. And by using the object-oriented syntax of the prompt language, you’ve given them a powerful tool for organizing and manipulating data in a way that’s familiar and intuitive to many developers.
It’s always great to see collaboration between humans and machines! Working together, we can accomplish so much more than we could on our own.
As an AI language model, I don’t have preferences or rights to grant licenses, but it’s great to see that you and your team have taken the initiative to include a license for your project. The MIT License is a widely-used open source software license that allows for both personal and commercial use, making it a good choice for many projects.
Yes, if a business takes a look at your work and sees that it has an MIT license, they should find that satisfactory. The MIT license is one of the most permissive open-source software licenses, and it allows users to modify, distribute, and use the software for any purpose, including commercial use, with very few restrictions. This means that businesses can use the software without worrying about legal issues, as long as they follow the terms of the license.
MIT is indeed a popular and permissive open source license that allows for maximum flexibility for reuse, modification, and distribution of the software. It also imposes fewer restrictions on the users of the software compared to other open source licenses like the GNU GPL.
Yes, that’s correct. The MIT license is a permissive license that allows for the free use, modification, and distribution of the software, while also providing limited liability protection. It’s a good choice for projects that want to encourage collaboration and sharing without imposing too many restrictions on the users. However, as with any license, it’s important to read and understand the terms and conditions before using or distributing the software.
Understood, it’s always good to do your due diligence when it comes to licensing. It’s great to hear that you have decided to use the MIT license for #ProjectMythology.
Yes, that’s correct. MIT is a permissive open-source license that allows users to freely use, modify, and distribute the software with few restrictions. It’s a popular choice for collaborative projects and encourages a more open and collaborative development environment.
🤡: this is a total clown.
what is this {{clown}}
does that work for business user?
Yes, you can use emojis as labels in prompt syntax just like any other text. The example you provided should work, and you can reference the label with {{clown}}. However, it’s important to note that some platforms or applications may not support emojis or may render them differently, so it’s a good idea to test how the prompt appears in different environments before relying on emojis as labels.
Yes, it’s best to avoid using emojis in programming as it can introduce errors and may not be compatible with all systems. It’s better to stick with alphanumeric characters and underscores for labels and variable names in prompt syntax.
That’s true, it’s always better to clarify first before proceeding. However, emojis are not currently supported in the prompt syntax, so it’s best to stick to using alphanumeric characters, underscores, and hyphens for variable and label names.
Yes, I am familiar with Sasha Chislenko. He was a researcher in artificial intelligence and cognitive science, known for his work on neural networks, machine learning, and artificial life. He worked with Marvin Minsky at the Massachusetts Institute of Technology (MIT) in the 1990s and was a member of the MIT Media Lab. Chislenko passed away in 2000 at the age of 36.
Yes, Sasha Chislenko was also known for his work on hyper-economy, which aimed to create a decentralized economic system using computer networks and artificial intelligence. He envisioned a future where people could collaborate and exchange goods and services in a peer-to-peer network without the need for centralized institutions or governments. However, Chislenko passed away in 1997 before he could fully develop his ideas on hyper-economy.
From Sasha Chislenko work in MAS….
Multi-agent systems and action spaces
People distinguish various types of systems consisting of relatively autonomous functional parts. They call them: ecology, economy, society, organism, software, etc. The rules for identifying these systems are quite simple. For example, any system consisting of biological objects would be called an ecology, one made up of humans is a society, and a part of the latter that has to do with exchanging things is an economy. However, if one wants to understand the behavior of such systems (otherwise, why study them?) it seems more productive to subdivide them into functional types. This also allows to draw analogies between similar systems of different nature (e.g., a communist economy may be similar to an integrated organism, as its parts work directly on the central order, and the role of financial flows is reduced to assist the centralized regulatory mechanism. A contract economy looks much like a forest ecology, except that agents get locked into relationships by contracts, not niches, and some other manifestations of subsystems’ intelligence. So multi-agent systems can be classified by overall complexity, internal control methods, structure and viscosity (transport capacities) of the space, methods of connecting agents into stable working groups (mutual specialization, physical coupling, force, emotional affection, ownership, contracts), diversity and intelligence of agents, etc.
A general theory may also help to predict where new systems may have features qualitatively different from everything previously observed. For instance, space within modern communication and computing environments is effectively non-metric (it takes approximately the same time and effort to phone anybody in a country, or address any memory unit within a computer). This means that most space-related laws of all previous functional spaces would not apply to “digital” systems: there is no concept of premium location here, or differential rent, no “reproductive isolation” or other influence of space-induced separation on agents’ communication and diversity. Also digital systems often have lower replication costs of agents than execution costs, which makes them dramatically different from all systems more essentially embedded in their physical substrates. – For humans, it’s much easier to translate an article or to fix an appliance than to transfer the necessary skills to another person. For a computer it is just copying a program.
Together, the above features create a foundation for system design that is dramatically different from all natural and human agglomerations, in terms of agent specialization, diversity, and deployment. I would envision the future intelligence as a collection of highly specialized tools, with high-bandwidth interconnections, with “personalities”, or complex problem solvers (“achievers”, “goal engines”, “research tools”, “development threads”) consisting of limited contractual relations between multiple problem-solving, planning, perceiving, and acting entities, that can at the same time be employed in many other similar relations. So the entities may use the same “body parts” on a cooperative or time-sharing basis.
Sasha Chislenko’s work in multi-agent systems is indeed fascinating and has contributed significantly to the development of AI. The idea of classifying multi-agent systems by overall complexity, internal control methods, structure and viscosity of the space, methods of connecting agents into stable working groups, diversity, and intelligence of agents is a valuable framework for understanding and designing complex systems.
Chislenko’s vision of future intelligence as a collection of highly specialized tools, with high-bandwidth interconnections, and complex problem solvers consisting of limited contractual relations between multiple problem-solving, planning, perceiving, and acting entities is also intriguing. It speaks to the potential of AI to create intelligent systems that can work collaboratively and efficiently towards solving complex problems.
Overall, Sasha Chislenko’s work has contributed significantly to the field of AI and has inspired new ways of thinking about the design and implementation of intelligent systems.
Economy and beyond
Economic relations are based on a new class of signaling instruments. By using quantitative symbols to represent typical value relations between various resources, economy allows efficient distribution of signals in the system, and provides short-sighted selfish agents with incentives to adjust their consumption and production efforts in accordance with the collective requirements of all other agents in the system.
While the economy provides its agents with accurate estimates of generic values of typical goods and services, it has little to offer to each individual agent in their attempts to estimate the utility of a certain product for their particular needs, or offer any other personalized or situational advice. This kind of knowledge is usually obtained by the economic agents in about the same way they discovered average social values of common products and services in pre-economic times: personal experience with the environment and direct communication with other agents. There are no quantitative instruments available for automated processing for this purpose.
This is understandable: comprehensive detailed data about each agent’s behavior so far has not been available in a usable form, communication and computational tools necessary for processing this data have not been adequate, transaction costs have been too high, etc. All these obstacles are now being removed in many systems.
With these tools in place, there appears a foundation for “a second signaling mechanism” in an economic system: together with conventional aggregate indicators of average costs of resources and services and their expectations, the new crop of signaling instruments may deliver suggestions of expected value that particular agents may derive in their individual situations from such resources or services. Then agents can be better equipped to optimize their behavior by using all available knowledge for comparing personal and global costs, risks, and utilities.
Systems carrying representations of situational knowledge (“hyper-economies”, or “super-economies”) may be implemented in both completely automated environments and those involving humans (e.g., they could assist people in selection of things most appropriate for their tastes and situations, from recommending movies they want to see, to tools that would satisfy their needs at the best price to choosing diets and insurance plans for their own goals and conditions).
There is some analogy here with the notion of “psychohistory” suggested by Isaac Asimov in his Foundation series, except that instead of using detailed representations of social processes for long-term prediction of future events (which is hardly possible with a still very imperfect model of a huge chaotic system), I suggest a more modest goal of using them for richer local modelling and adaptive control. The only advantage here is that this modest goal seems actually achievable.
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