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very good direction!. we have to put science in software asap, it is interesting to see the push back but there is no way we can proceed with the curent approach that ignores that we have computers to help..

You ignore agents and programming with AI. There is huge progress in this topica, it is not that boring...

Seek meaning by helping others, or strive to conquer and shape the world through your will.

But perhaps there is room for optimism: AI holds the potential to manage the complexity that has historically condemned societies to collapse.

Betteridge's Law of Headlines states: "Any headline that ends in a question mark can be answered by 'no'."

I believe there are indications that suggest the era of big LLMs will come to an end because they will hit a price and performance wall. There is a serious possibility that they will remain an NLP tool, and real thinking will be formalized as various types of software in different niches. Multi-agent systems will resemble the early web, with thousands or millions of variations and different types of expert agents, not a single "god-like" software capable of doing everything.

It’s natural for major players to try to create a "digital god"; this is their monopolistic path. If this becomes possible, they will need to be privatized, and LLMs turned into infrastructure services for all of humanity, or else we risk ending up in a dystopia. However, there is a serious chance they won’t achieve a "digital god" and will instead, unintentionally, create a world with decentralized intelligence.

It’s better to be optimistic—we have nothing to lose, even if it’s not realistic.

Yes, details matter. The whole idea of creating AGI that is simultaneously a generalist seems more and more like wishful thinking. The reality is that to solve real problems, a large number of correct reasoning steps are required, along with the ability to make choices about which type of inference is useful at each step to avoid the explosion of complexity inherent in any brute-force approach. This suggests that we will have AI experts in different domains, perhaps superior to humans, but we will have thousands or even millions of narrow areas of expertise. To create something akin to an all-knowing superintelligent deity, we would need to combine thousands of experts, which would also consume unsustainable amounts of energy. I wouldn't bet on AGI in the coming years; it's just hype and distracts the discussion until big money finds a way to establish monopolies. However, if both UX and reasoning expertise require deep customization and specialization, we have a real chance to use AI to solve deep social problems rather than transforming society into a dystopia where humans are morally and intellectually surpassed, and those remaining are controlled by corporations that could at any moment be taken over by sociopaths.

Interesting, but we have to consider this information with skepticism since it comes from Meta. Additionally, merely open-sourcing models is insufficient; the training data must also be accessible to verify the outcomes. Furthermore, tools and applications must be freely deployable and capable of storing and sharing data under our personal control. Self-promotion: We have initiated experiments for an AI-based operating system, check AssistOS.org. We recently received a European research grant to support the improvement of AssistOS components. Contact us if you find our work interesting, wish to contribute, conduct research with us, or want to build an application for AssistOS.

It requires new best practices in governance, and any progress here is difficult. Maybe AI could help.

Let's fix the 'vulnerable distribution channels' issue. Otherwise, reglementing open source AI you just select who can abuse them. There are issues with channels that are too big, focused on making money, and ignoring social effects, we have too many systems build around extractive and mindless capitalism values. Let's look in the mirror and do the right things. AI and Open Source AIs are good mirrors that show the ugliness of various societal constructs.

The myth and intuition of a perfect machine being complete is a dominant cultural theme. I recommend Erik J. Larson's book on the myths of artificial intelligence. I see that the fundamental problem with peer review is somewhat broad. We understand inferences through induction and deduction, but we fail to understand and appreciate abduction, which is actually the basis of science and what makes us human. I believe, abstractly, this is one of the problems. I think abduction is linked to meta-rationality, another cultural difficulty.

Hehe ;)

This video discusses the evolving concept of decentralized brands, highlighting their growing relevance in an AI and blockchain-driven future. Moving away from traditional, centralized brand governance, this model involves a wider network of consumers and collaborators, using blockchain for transparency and community engagement. Decentralized brands leverage collective creativity, advocating for inclusive brand building that aligns closely with audience values, enhancing engagement and loyalty. The presentation explores the impact of decentralized brands across sectors, reimagining human organizations as decentralized entities. It posits decentralized branding as a social technology, crucial for future governance and organization, especially as AI transforms human roles. The concept builds on ideas like Open Source and Agile, aiming for more participatory and inclusive organizational structures.

Science inherently concerns itself with understanding and defining limits. However, when it comes to intelligence, there appears to be a significant gap in our grasp of these boundaries. Reflecting on my own thoughts from 35 years ago, I recall envisioning the singularity at the age of 12 or 13. Looking back, I realize that many of my conceptions from that age were either incorrect or grossly exaggerated. This realization leads me to speculate that the notion of the singularity might fall into a similar category, although concrete evidence to support this is currently lacking. While I find myself partially aligning with your points regarding magical thinking in this context, I am curious about how one might construct a robust, scientifically grounded argument in this domain.

This article into reimagining online content moderation, tackling the issue of gatekeeping that often stifles diverse voices on platforms like Wikipedia and social networks. We propose a future where intelligent AI agents, controlled by users, navigate and filter content, enabling a genuinely pluralistic exchange of ideas. This system would allow for the free flow of contributions while AI agents facilitate meaningful collaborations and sift through the clutter of noise and marketing. It's a vision that seeks to democratize digital discourse, ensuring that every idea gets a fair chance to be heard and evaluated on its merit.

I see what you mean and I see the risk, but I think we could imagine something beyond this, like having your personal AI judge various contributions and not one from the platforms...

What about implementing a trust-based system, where content aligns with different brands, schools of thought, and value systems? It can revolutionize how we view information. This approach could negate the need for censorship, allowing users to filter content based on their trust in specific brands or ideologies. It's a way to personalize content while maintaining a broad spectrum of views and reducing the impact of bias, spam or of stupid contributions.

We are entering an era where AI can potentially enhance moderation. How can we ensure it aids rather than hinders? Imagine AI not as a lazy censor, but as a tool capable of discerning the value of diverse contributions. Unlike a Wikipedia editor who might be overwhelmed by thousands of articles across numerous domains, AI could objectively evaluate scientific results in peer-reviewed journals. It could also connect current discussions with past contributions and provide gentle, rule-based corrections. Could this be the future of fair and efficient online moderation?

I completely agree. As the leader of OpenDSU technology, which is used for creating supply chain software and blockchain for businesses, we've seen some success. But a big problem is that many people from large companies don't fully understand how to use blockchain correctly. They come from different backgrounds and often mix up ideas in confusing ways. It's hard for them to quickly grasp that blockchain is for securing data, not for storing it, and the advanced cryptography and off-chain storage concepts are often beyond their immediate understanding.

Indeed, large-scale enterprise solutions like Hyperledger Fabric have made strides in bridging the gap between traditional databases and blockchain technology, finding some success. However, these solutions tend to be more effective when tailored for a few large companies. They often fall short in achieving true decentralization, especially when the network expands to include multiple partners or smaller players. A key limitation lies in their approach to data sharing. Many such solutions rely heavily on APIs, where blockchain is used more as a backend database or coupled with off-chain storage without adequate control from the on-chain component. This can lead to solutions that are questionable or even flawed in their implementation and application of blockchain security value proposal...

Summary: Future AI technologies necessitate adapted open-source licenses to ensure alignment with ethical values, enforce transparency, limit autonomy, mandate collaborative development, prepare for potential government regulations, and balance innovation with societal safety.

The decentralised brand model promotes collaborative identity and operation, leveraging blockchain and AI for transparent governance. Proposed decentralised brand licensing bridges commercial and open-source models, emphasizing brand integrity, community engagement, and ethical constraints.

We're excited to announce the launch of our new research and collaboration project aimed at leveraging blockchain and artificial intelligence technologies to drive social change. Our vision is to create a more equitable and just world through cutting-edge research and technology. This project will focus on creating a decentralised social network to research and help the growth of many decentralised brands. It aims to use blockchain technologies and Large Language Models. These fields hold immense potential for societal transformation, and we're seeking like-minded individuals and organisations to join us in this endeavour. We're Looking For: Open-Source Contributors: If you have expertise in software engineering, AI, blockchain, or related fields and are passionate about open-source development, we’d love to collaborate. Research Partners: Academic or industry researchers interested in exploring new frontiers in AI and blockchain technologies. Innovators and Thinkers: Anyone who shares our vision for a better world and is eager to contribute ideas and innovations. Why Join Us? Impactful Work: Contribute to projects that have the potential to make significant societal impacts. Collaboration and Networking: Connect with a network of experts and enthusiasts. Skill Development: Enhance your skills and knowledge in cutting-edge technologies. For more details about our vision, the project's specifics and contact info, please refer to our initial announcement post: Outfinity Social Network - Blue & Green Currencies. Interested? We'd love to hear from you! Please share your thoughts and feedback, or express your interest in collaborating with us in the comments below or via [contact details in the linkhttps://outfinity.medium.com/outfinity-social-network-blue-g...]. Let's innovate for a better world together!

Math and most scientific output is equivalent to some sort of code (automated formal proofs, descriptions of classes of simulators or statistical models,etc) We could envision the tools and "programmming" languages for various domains to replace text articles with "code"

I hope that academic publishing will transform into a truly collaborative workflow through contributions to wiki-like systems for niche subjects. These wikis could evolve into courses, educational films, and books under the guidance of LLMs trained to understand various scientific branches. The current approach is outdated and has long ceased to make sense. An interesting challenge is motivating individuals, potentially through the creation of decentralized brands of minds working together, validating and supporting each other's personal and professional growth. While this may not align with mainstream thinking, scientific research could be a pioneering field that breaks old paradigms and rapidly adopts modern technologies and imprivment oportunities...

While current Large Language Models (LLMs) offer impressive capabilities, they exhibit significant limitations, particularly in decision-making and planning. Establishing a robust orchestration mechanism to guide LLMs' 'intelligent but error-prone intuition' remains a considerable challenge. Realizing such a system might take decades, if it is even feasible. The author contends that utilizing fear as a marketing tactic is a facile means to generate hype and solidify market dominance, often with state support.

Success in human endeavors is a confluence of intelligence, timing, and fortuitous events. Extrapolating this to hypothetical future AI entities, it is unlikely that they would be exempt from similar complexities. An AI capable of negating the factor of 'luck' would likely require vast computational resources in a short period, rendering it vulnerable and fragile

How it works?

I find this approach of mixing prompts, code, dependecies loading as slowing down the development. Product people should iterate and test various prompts and developers should focus on code. Am I wrong to expect this?

Is it true that pharmaceutical companies do not prioritize antibiotic research due to low profitability? Could new types of pharmaceutical companies, perhaps organized as a network of NGOs, take on this essential research?

The main issue is that when you start a new project almoust any approach is "simple enough". The problem is that an approach that is good long term will look like an overkill complexity on the first stages of a project. I have my creations like SwarmESB and swarm communication idea as a good example... could make the microservice systems simpler but only after a level of complexity and then it is too late to invest in a new approach. Therefore such technologies will not be addopted instead other complex workarounds become succesfull..

AI centered video conference application could create new revolutions in the next years. We were promised to get "AI talking heads" in SF movies 20-30 years ago. It is an obvious idea but we are not there yet. We still need a harware "revolution" that will enable systems with powerfull AI capabilities (generative AIs, capacity to train models on your data and only based on vocal indications) Imagine such environments running on desktops or even on mobile phones. This could force a migration of all software as plugins in "video conferencing" software that allow us to talk with AIs (of your employer or entertenment AIs or with other people that have AI as first filters, AI for dead people, celebrities, etc) in a safe but totally integrated approach. I am a researcher and like to imagine possible futures but defenetly there are so many opportunities for revolutions in these areas for the next 10-50 years...

My "observation" about observability and "reactive type" of code is that old school proxies, events and ways of defining expressions that get computed when the dependecies change is a much simpler way of programm reactive UIs (for example MVVM style of architectures) compared with a functional programming approach. Of course the naive approch does not work properly because any change in dependent values will trigger undesired side effects, reentrace, infinite loops, etc. However in a programming language like JavaScript if the observability is based on a Sound PubSub System these kind of problems reduce to becoming surpinsingly irelevant. If you want to undestand more about this, take a look on https://github.com/OpenDSU/soundpubsub/ We used this approach to implement two ways binding and reactive MVVM web frameworks but this comment is to present this insight that it is possible to have a "procedural" and "spreadsheet like" method of implementing intuitive and sound rectivity without bending your mind with streams or anything very abstract. In a low code environment this could be essential.