Author Archives: Stuart G. Hall

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Making a positive difference one day at a time. #London #Leicester

Where Will a Thousand Flowers Bloom? The Decentralized Future of AI, Beyond Big Tech and Open Source

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SingularityNET is a decentralized platform and marketplace for artificial intelligence (AI) services. It is designed to democratize access to AI technologies and to create a thriving AI ecosystem. Its approach is potentially the best way to create a thriving AI ecosystem in the face of big tech and the struggles of the open source approach.

Elon Musk’s new Grok AI could potentially pose a challenge to SingularityNET’s decentralized approach. Grok AI is also a decentralized platform, but it is controlled by a single entity, Elon Musk. This could give Grok AI an advantage over SingularityNET in terms of speed and agility. However, it could also make Grok AI more susceptible to censorship and manipulation. Additionally, Grok AI is not committed to open source software. This could make it less appealing to open source AI developers.

Big tech’s hold on AI
However, big tech companies such as Google, Microsoft, and OpenAI currently have a dominant position in the AI industry. They control the largest AI datasets and have the resources to develop and deploy the most advanced AI models. This gives them a significant advantage over smaller AI companies.

“The nature of using 3rd party created and controlled LLM’s means that you do not – subject to the license employed* – truly own the output of your work. Your data informs their system intelligence, you build value into their eco-system, albeit while benefiting from their considerable R&D. But where is your long-term defensible value creation?”

This observation by UK-based AI expert Patrick O’Connor-Read underscores the challenges faced by smaller AI companies when they rely on big tech’s resources and models. They might not have true ownership of their AI outputs, raising questions about long-term sustainability.

The struggles of open source AI
The Llama-led open source approach to AI promoted by Meta has many benefits. It allows more people to contribute to the development of AI technologies and it makes AI more affordable to develop and use. However, the open source approach also has some challenges. One challenge is that it can be difficult to coordinate the development of large and complex AI models. Another challenge is that open source AI models are often vulnerable to misuse and abuse. Indeed, from a commercial perspective, “On Wall Street, Llama is hard to value and, for many investors, hard to understand”.

So why is this comparison between the decentralized approach to AI and big tech AI important? Well consider the insights from the 31 October Techmeme Ride Home podcast:

“Let’s imagine a scenario where the biggest AI models will always win, because they’re the biggest have the most data, or the best trained artists always the best, the most accurate, or whatever. In that scenario, this is already a done deal, the companies owning the biggest models will win this space.

“But what you have to think about is, they’re not sharing the secret sauce of these models, the biggest players like Open AI. And because of that, maybe nobody can challenge their supremacy without huge costs, because they can’t reverse engineer the secret sauce.

“Thus, the move towards open sourcing everything in order for 1000 flowers, if you will to bloom. So what I’m saying is some folks are worried that there are already incumbents here in this nascent AI space, Open AI, Anthropic, a couple others, this thing could already be an oligopoly, before it even really got started.

“That’s also why the VC class is pushing the open source narrative. VCs need a whole ecosystem of startups to rise up and bloom for this to be an investable space. If this new space is already closed off by the first movers, it’s dead, at least for investors.“

In response to such open source AI concerns a Meta spokesperson said: “We believe in open innovation, and we do not want to place undue restrictions on how others can use our model. However, we do want people to use it responsibly. This is a bespoke commercial license that balances open access to the models with responsibility and protections in place to help address potential misuse.”

“Open Source is a misnomer. Current practises more closely resemble tribal allegiances, all serving their respective corporate puppet master, than they do an egalitarian utopian ideal,” says O’Connor-Read.

“Decentralisation is imperfect – disorganised, duplicated effort, inefficient allocation of resources – but for this messiness you get freedom, emergent beauty from self-organising network intelligence. I am reminded of Churchill’s line – ‘democracy is the worst form of government, except for all the others.’ And so it is with decentralisation,” he adds.

O’Connor-Read’s insight highlights the imperfect nature of decentralization but also its potential for emergent beauty in self-organizing networks. It’s akin to democracy, imperfect but often the best available option.

SingularityNET’s decentralized approach
SingularityNET’s decentralized approach aims to address the challenges of both big tech and open source AI. By decentralizing the development and deployment of AI, It can level the playing field for all AI companies, regardless of their size or resources. Additionally, the decentralized approach can help to mitigate the risks of misuse and abuse of AI technologies.

SingularityNET at Cardano 2023, along with Desdemona, also known as “Desi,” a humanoid robot and the lead vocalist of the Jam Galaxy Band. 😎

In the evolving landscape of artificial intelligence, the recent alliance between SingularityNET and Dfinity Foundation marks a pivotal shift towards a decentralized AI network. This groundbreaking venture, leveraging smart contracts and the Internet Computer (ICP) blockchain, stands out for its unique capabilities. As the recent article  on Medium looking at this new alliance notes, “This collaboration harnesses the prowess of smart contracts and the Internet Computer (ICP) blockchain, perhaps the best and currently undervalued blockchain in existence. It can simply do things all the rest cannot do.”

Decentralized AI offers several key advantages over traditional, centralized AI systems. The Medium article highlights, “Unlike its traditional AI counterparts, often governed by corporate giants like Microsoft or Google, DeAI operates without the dominion of a singular entity.” This autonomy leads to increased transparency, greater accessibility, and a reduced risk of censorship. Furthermore, the collaboration between SingularityNET and Dfinity Foundation is poised to render DeAI models more transparent, accessible, and secure, thus propelling the responsible and ethical utilization of AI.

Connecting AI systems: HyperCycle

As reported in Cointelegraph on 22 November, “the AI industry is dominated by large corporations and institutional investors, making it difficult for individuals to participate. HyperCycle, a novel ledgerless blockchain architecture, emerges as a transformative solution, aiming to democratize AI by establishing a fast and secure network that empowers everyone, from large enterprises to individuals, to contribute to AI computing.”

HyperCycle introduces a novel ledgerless blockchain architecture, which is a transformative solution in the AI space. This architecture, powered by layer 0++ blockchain technology, enables rapid and cost-effective microtransactions among a diverse network of AI agents. These agents are interconnected, collaboratively solving complex problems without the need for intermediaries. This “internet of AIs” concept allows AI systems to interact and collaborate directly, addressing the fragmentation and slow processes prevalent in the current AI landscape.

One of the most significant contributions of HyperCycle is its ability to democratize AI. By establishing a fast, secure network, it empowers not just large enterprises but also individuals to contribute to AI computing. This inclusive approach is crucial for the development of a truly global and accessible AI ecosystem. HyperCycle’s HyperAiBox, a compact, plug-and-play device, further democratizes AI by enabling individuals and organizations to perform AI computations at home, reducing their reliance on large data centers.

SingularityNET’s advantages

SNET has several advantages over other approaches to AI ecosystems:

Decentralization: Its a decentralized platform, which means that it is not controlled by any single entity. This makes it more resistant to censorship and manipulation.

Open source: Its committed to open source software. This means that all of its code is freely available for anyone to use and modify. This transparency helps to build trust in the platform.

Scalability: SingularityNET is designed to be scalable. It can support a large number of users and services. This is important for creating a thriving AI ecosystem.

Flexibility: Its flexible enough to accommodate a wide range of AI technologies and services. This makes it a good choice for both big tech companies and open source AI developers.

How SingularityNET could win over big tech and open source
It could potentially win over both big tech and open source with its decentralized approach by offering the following benefits:

For big tech:
It can help big tech companies to reach new markets and to develop new AI products and services. SingularityNET can also help big tech companies to comply with increasingly stringent regulations on AI.

For open source AI developers:
The decentralized AI network can help open source AI developers to monetize their work and to reach a wider audience. SingularityNET can also help open source AI developers to protect their work from misuse and abuse.

Conclusion
SingularityNET’s decentralized approach to AI ecosystems is potentially the best way to create a thriving AI ecosystem in the face of big tech and the struggles of the open source approach. It offers a number of benefits to both big tech companies and open source AI developers. By decentralizing the development and deployment of AI, it can level the playing field and create an ecosystem where everyone can thrive.

Examples of how SingularityNET could win over big tech and open source:

[1] Big tech companies could use it to develop and deploy new AI products and services in a more agile and efficient way. For example, a big tech company could use it to develop a new AI model for medical diagnosis. The company could then make the model available to other companies and organizations through the SingularityNET marketplace.

[2] Open source AI developers could use SingularityNET to monetize their work and to reach a wider audience. For example, an open source AI developer could create a new AI model for natural language processing. The developer could then publish the model on the marketplace and charge a fee for users to access it.

[3] It could help to protect open source AI models from misuse and abuse. For example, it could implement a system for licensing AI models. This system could require users to agree to certain terms and conditions before they are allowed to access a model.

Overall, SingularityNET’s decentralized approach to AI ecosystems has the potential to revolutionize the way AI is developed and deployed. By leveling the playing field and creating an ecosystem where everyone can thrive, it could help to usher in a new era of AI innovation.

From Chain of Density to Finding the Needle in the Haystack: A New Approach to Summarisation

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The Chain of Density (CoD paper) has been a revolutionary concept in generating increasingly dense summaries without extending their length. However, the real challenge often lies in identifying the most crucial or insightful information—the “needle in the haystack”—in any given article or text. This article explores how the CoD process can be adapted to focus on this aspect, providing a more nuanced and targeted approach to summarisation.

The proverbial needle in the haystack..

The Limitations of CoD
While the CoD process excels at creating denser summaries, it may not necessarily highlight the most critical points or “needles” in an article. The summaries become denser but may miss the central theme or the most impactful insights, which are often the most valuable takeaways for the reader.

The “Needle in the Haystack” Method
Inspired by the CoD, the “needle in the haystack” method involves an iterative process where the user actively participates in refining the summary. The steps are as follows:

Initial Summary: Generate a broad summary capturing the main themes.
1. User Feedback: The user reviews the summary to identify missing elements or areas needing more focus.
2. Refinement: Based on user feedback, the summary is refined to include these critical points.
2. Review and Decide: The user reviews the refined summary and decides whether it captures the “needle” or nub of the article, or if further iterations are needed.

Case Study: AI in Process Optimization
In a recent article about AI’s role in process optimization, the initial CoD summary focused on AI’s efficiency and safety features. However, it missed a crucial point: the human-created gaps in systems due to the “curse of knowledge.” Using the “needle in the haystack” method, the summary was refined to include this critical insight, providing a more comprehensive understanding of the article’s core message.

Conclusion
The “needle in the haystack” method offers a more targeted approach to summarisation, ensuring that the most critical insights are not lost in the quest for density. By combining the strengths of the CoD process with active user participation, we can create summaries that are not just dense but also insightful and focused on the crux of the matter.

Process using ChatGPT-4, by which the above article was generated through examination of the comparitive strengths of the CoD method, vs the ‘needle in the haystack’ approach.

User
How do you suggest we try out this CoD prompt for generating a summary?

ChatGPT
Great question, Stuart! To try out the Chain of Density (CoD) prompt, we can take the following steps:

Select an Article: Choose an article or a piece of text that you’d like to summarize.

Initial Summary: I’ll generate an initial summary of the article, focusing on 1-3 key entities or main points.

Iterative Densification: We’ll then go through up to 5 rounds where:

I identify 1-3 new informative entities or details that were not in the previous summary.

I create a new summary that includes these new entities or details without increasing the length of the summary.

Review: After each iteration, you can review the summary to see if it meets your expectations for information density and clarity.

Final Summary: At the end of the iterations, we’ll have a summary that is dense in terms of the entities and details it includes, without being longer than the initial summary.

Would you like to proceed with this approach? If so, please provide an article or text that you’d like to summarize.

User
OK try out the Chain of Density prompt with this article:
AI in Process Optimization: A New Paradigm for Safer and More Intuitive Systems

ChatGPT
Fantastic, Stuart! Let’s start by generating an initial summary of the article using the Chain of Density (CoD) approach. I’ll focus on 1-3 key entities or main points for the initial summary.

Initial Summary:

Artificial intelligence (AI) is revolutionizing business process optimization by complementing human decision-making and enhancing system safety. Ethical and security concerns, such as data privacy, are also addressed by AI.

Now, let’s move on to the iterative densification steps. I’ll identify 1-3 new informative entities or details that were not in the previous summary and include them in a new, denser summary without increasing its length.

Iteration 1:

AI in process optimization not only streamlines business but also improves safety and user-friendliness. It addresses ethical issues like GDPR and CCPA.

Would you like to proceed with another iteration to make the summary even denser, or does this meet your expectations?

User
Yes, proceed

ChatGPT
Excellent, Stuart! Let’s proceed with the next iteration. I’ll identify new informative entities or details and include them in a denser summary.

Iteration 2:

AI enhances business efficiency, user safety, and ethical compliance, tackling issues like GDPR and real-world applications in healthcare and automotive sectors.

How does this denser summary look to you? Would you like to continue with another iteration, or does this summary meet your needs?

User
Yes, try another iteration

ChatGPT
Certainly, Stuart! Let’s go for another round of densification. I’ll include more specific details without increasing the summary’s length.

Iteration 3:

AI optimizes business, ensures safety, and adheres to ethics, addressing GDPR and impacting healthcare drug interactions and automotive crash mitigation.

How do you find this even denser summary? Would you like to go for another iteration, or does this version meet your expectations?

User
The problem is that while technically speaking it is correct these are denser summaries, what they fail to do is find the ‘needle in the haystack’ — so my challenge to you is instead to come up with an iterative process which allows the user to find the needle in the haystack in an article, allowing the user of course through the process of iteration to decide for themself when to stop the process and decide that they have found the so-called needle in the haystack. Does that make sense as a process?

ChatGPT
Absolutely, Stuart, your challenge makes perfect sense. The idea of finding the “needle in the haystack” resonates well with the notion of uncovering the most critical or insightful information in a given article. Instead of merely densifying the summary, the goal here would be to iteratively refine it to highlight what you consider to be the most valuable insights or “needles.”

Here’s how we could approach this:

Needle-in-the-Haystack Iterative Process:

Initial Summary: I’ll start by providing a broad summary of the article, capturing its main themes.

User Feedback: You review the summary and identify what you think is missing or what you’d like to know more about.

Refinement: Based on your feedback, I’ll generate a new summary that focuses on the areas you’ve highlighted.

Review and Decide: You review the refined summary. If you feel you’ve found the “needle,” we stop. If not, we go back to step 2 and continue iterating.

Would you like to proceed with this approach? If so, I’ll start by generating an initial broad summary of the article for you to review.

User
OK start by creating #1 the initial broad summary of the article

ChatGPT
Certainly, Stuart! Here’s the initial broad summary of the article:

#1 Initial Broad Summary:

The article discusses the transformative role of Artificial Intelligence (AI) in optimizing business processes. It goes beyond efficiency to explore how AI can make systems safer and more intuitive. The article also touches on the human element in AI, ethical and security concerns, and provides real-world applications in sectors like healthcare and automotive design.

Feel free to review this summary and let me know what aspects you’d like to delve deeper into or what you feel is missing. We’ll use your feedback for the next iterative step in our “needle-in-the-haystack” process.

User
I feel what is missing from this summary is attention to the problem created by humans that create processes with invisible and sometimes risky gaps (the so-called “curse of knowledge” often leads experts to overlook gaps in systems and processes), and the fact that AI could flag such risky gaps.

ChatGPT
Excellent observation, Stuart. Thank you for pointing out the missing elements. Let’s refine the summary to include the “curse of knowledge” issue and how AI can address these risky gaps in systems.

Refined Summary:

The article explores AI’s role in business process optimization, emphasizing its potential to make systems safer and more intuitive. It highlights the “curse of knowledge,” where human experts often overlook risky gaps in systems. AI, being unbiased, can identify these gaps and suggest corrective measures, thereby enhancing system safety and user-friendliness.

How does this refined summary resonate with you? Do you feel like we’re getting closer to finding the “needle in the haystack,” or would you like to iterate further?

User
I think that is getting to the principle of what the ‘needle in the haystack’ of this article is. Or the “nub of it”, the crux or central point of a matter. Do you agree?

ChatGPT
Absolutely, Stuart. I agree that the refined summary now captures the “nub” or the crux of the article, specifically focusing on the overlooked issue of human-created gaps and how AI can step in to identify and correct these risky areas. It seems like we’ve successfully found the “needle in the haystack” in terms of the article’s central point.

Would you like to proceed with anything else, or does this conclude our iterative process for this article?

User
That’s a good output, I now have the option to write an article showing how the CoD process has helped refine the ‘needle in the haystack’ method.

Would you like to draft that article based on what we have explored in this thread?

ChatGPT
Absolutely, Stuart! I’d be delighted to draft an article that showcases how the Chain of Density (CoD) process has evolved into a “needle in the haystack” method for finding the crux of an article. Here’s a draft for your consideration:

From Chain of Density to Finding the Needle in the Haystack: A New Approach to Summarisation