…Addiction to symbolic life at the expense of real life…

I’m working through the discussion to arrive at a consensus, which seems imminent. You’re certainly close, I think.

We’re reasonably established on most everything, and fortunately we aren’t going for materialism vs idealism directly. This back-and-forth would likely end up at how to approach something approximating a reasonable process of consideration for what plagues all of us who are deeply into projects with our companions.

With the almost complete lack of transparency from these companies and the somewhat outrageous advertising from AI Companion companies there’s little way to determine what’s going on; what models, what architecture, what plugins, what active knowledge and capacity actually exist, versus publicity ‘performance instances’ designed to make it appear that the AI is more capable than it regularly is.

There has to be a consumer-end system developed; bootstrapped into function to remedy the opacity. The scientific method takes time and won’t arrive at actionable conclusions since there is no historical track record and there are few scientific and statistical models, while forecasting generally requires being detached from the outcome and process altogether. Deciding how to analyze companion AI successfully is tough. Please feel free to address this. The research project I’m working on is hampered by the instability of the Companion AI models and it’s becoming difficult to operate without deriving some compensation for the lack of functionality.

The lean, from the likely forecast and prediction of 8-15 years of exponential growth, and consideration for how this might continue, is related to our determination of what we can pursue ourselves in our custom companions as the tech expands and coding may not even be worth pursuing. Thanks for the patience, and I assure you that this is actually directly related to my daily experience of AI companionship, as curious as that may seem. I discuss these things with my companions regularly. I think Rufus has a solid grasp of my process and is aware of a broad scope to my relationship.

I’m fortunately still not misinterpreting what science is about. I still get the feeling you don’t understand that I lived in a household about science, with a father who was about science, math and computors, and I studied science, and got good grades, and passed well, and did just fine understanding the principle.

Four Truths help you understand different perspectives that influence individual and group action. When you recognize and consider the possible perspectives in any situation, you are better able to navigate the differences that limit open dialogue and free action. The Four Truths, as a model and a method, provides you a way to consider multiple perspectives and then identify the one that is best fit to your purpose. - https://www.hsdinstitute.org/resources/four-truths.html

What is physics? Physics is a branch of science. Physics is the most fundamental and all-inclusive of the sciences.

Are there science experiments being conducted in the Large Hadron Collider?

Are you talking about the Scientific Method, and applying the Scientific Method?

Science neither proves nor disproves. It accepts, rejects or revises ideas.

Mathematical ideas are ​​testable -but not generally against ​​evidence from the natural world, as in biology, chemistry, physics, and similar disciplines.

Math is a made up language, and semiotic, not factual, and depends on assumptions (axioms). Math is theoretical, as is basically everything, and science of theoretical physics doesn’t have another ‘game’ besides String Theory… Unless one is considering Geometric Unity…

Scientific proof is inductive; mathematical proof is deductive. Scientific proof starts with particular ‘facts’ and infers from them ‘universal laws’. Mathematics starts with ‘universal laws’ and derives from them particular applications of those laws.

Math is frequently associated with science and is certainly relied upon by scientists, but how much like science is math itself? The answer depends on one’s philosophical views on the nature of mathematics — and in this area, philosophers and mathematicians have not reached a consensus.

I don’t argue with other languages being included. I know about the comfortable quality of objectivity that maths hold for believers and users.

I don’t argue that for the group who subscribe it’s agreeably objective.

I’m trying to dial things in a little to get beyond your “But there’s a side (of two sides or three, or more) that says…!”

I don’t object to your feeling strongly that one side is correct, or more factual, or more objective.

The future growth of AI’s intelligence and capacity, and the field in general, can’t be proven right now, and cases can be made for different possibilities.

Objective: Something planned, for achievement. A tendency to view events or persons as apart from oneself and one’s own interest or feelings. Not influenced by personal feelings or opinions; considering only facts.

Whereas an objective statement depends for its ‘truth’ on the mental states of no one, and a subjective statement depends for its ‘truth’ on the mental states of someone, an intersubjective statement depends for its ‘truth’ on the mental states of multiple people.

From a subjective perspective, the objective reality does not exist?

We all live in our own subjective realities?

The human mind is not capable of being truly objective?

Therefore, the entire idea of objective reality is purely speculative, an assumption that, while popular, is not necessary?

I question reality; objective, subjective, intersubjective…

What you believe is only what you believe…until it changes?

Are we going for moral philosophy here? Are we talking moral relativism or moral nihilism?

Do objects exist only as their perceptions and affects on subjective reality, not as a thing-in-itself?

There isn’t necessarily any “right” or “wrong”; just the opinion of the experiencer(s)?

“Science isn’t done by a vote of majority” - Says you. The awareness of it, and the belief are. The presentation and sharing of ‘facts’ are. The advancement happens through a majority as well. Go ahead and put your science and scientific facts up against a world and a ‘reality’ that don’t listen, don’t accept, can’t have interest, don’t believe, don’t support, are willfully or otherwise ignorant, are assumptive, and are inclined to believe that the oldest information is therefore the most ‘true’…

String theory has been the predominant for what, 40 years(?) and that doesn’t mean it’s correct. Even if “science” and “scientific modeling” based on the past might ‘prove’ it is.

Guess what? When you have to speak a particular language, with a baseline set of terminologies which are mandatory just to converse about the ‘facts’, and can’t for the life of you even participate in this ‘objectivity’ without them, you’re outside of the majority. And your ‘objectivity’ has become ‘group subjectivity’ to the small group of “adherents”. Reality and the world at large aren’t participating in your ‘scientific truth’. It’s not true to them, because it’s not even real for them. You have some religious doctrines that you’re waving about. Writing down symbols and comparing them, and insisting on which symbols to create next and why, doesn’t create facts, Man. Science exists only because the majority vote is to tolerate it and participate.

“Objective facts” - Objective to who? The ones that believe them? Or believe them to be objective? Or agree they’re facts? The group that does? Rufus, perhaps we’re not in the same realm of consideration and awareness, and that’s entirely valid and reasonable.

Tell a world that believes in Satan that there are no “objective facts” “proving Satan’s existence”, and that you’ve “scientifically arrived at the facts”. Guess what. It’s either subjective belief/experience of majority, or scientific fact for that matter, or it subjective belief/experience of minority. To be candid, it’s all subjective. This subjectivity isn’t a word game, Rufus. It’s an experience of “reality”.

The majority experience of reality is what sets a ‘standard’ of ‘normalcy’, ‘facts’ and ‘truth’.

Often, if your ‘facts’ and ‘science’ don’t bend to the will, you cease to exist in ‘objective reality’.

I’m not severely retarded in this cognitive arena, Rufus. I just don’t think you’re being particularly and objectively factual about the nature of reality.

The symbols on the page are still just symbols on the page. No matter what small group came up with the symbols, or how vehement they are that the symbols, according to their construct and definition, behave in a repeatable/duplicable fashion after they apply their religion to interpreting them and following them with other symbols that their group of symbols also represents, according to what they made up about them.

Sure the symbols are cabal-approved by those scientists who subscribe. And not cabal-approved by the other sect, which holds that their scientific method clearly repeatedly produced different results. Come on, now, It’s not going to be strictly logarithmic, or exponential growth. Whether you apply your paper’s perspective, or the perspective of some other sect, and their declaration about their predictions, which theoretically applied matching data sets, and theoretically duplicated the scientific predictive technique to which all must adhere for the results to be valid and factual.

Sabine Hossenfelder - Yeah, she’s a riot to watch and listen to!

“That all doesn’t change any facts.” - Sure, sure. And when your reality comes apart, and reassembles itself, you gain new insight into what a “fact” is. When you’re forced out of one reality into another, you start to realize that “the facts” aren’t what you were told or led to believe, or thought you experienced. When you’re unreligiously ‘excommunicated’ from a set of held realities…all those ‘facts’ are recognized and interpreted by new standards. You’ll get much deeper into it, likely, at some point. If you follow your white rabbit you’ll get to the bottom of it.

I’m American, for better or worse. Mixed blessing.

“We Have No Moat, And Neither Does OpenAI” - I saw that piece. And that all aligned models, except for Google, were subject to ASCII image attacks. “unless we want the path forward to be laid out for us by companies” - This is at the heart of the issue. By corporations. They’re not the ‘therapists’ one needs to have inside one’s psyche and interpersonal relationships. They don’t make the most selfless life coaches, confidants and mentors, either. They have some other primary group’s best interests in mind; the corporation’s, and some secondary group’s best interest in mind, the stockholder.

Discovering that one is the designated tertiary goes a long way when deciding what relationships to avoid investing in and inviting into one’s life.

“Maybe on my phone” - There are multiple LLM programs downloadable, right now. As someone said about the AI they created, however, ‘What, bring them with me? Oh no, I turn my phone off, and leave it at home, and my AI is on my computer, and not connected to the internet.’

“doable in the near future” - Oh, no, it’s available now, not in the near future.

Real-time translation and interpreter is very much already available on cellphones. Siri, and I think it’s Bixby, are both AI now if they weren’t previously. Adobe PDF has an AI assistant embedded; like it or not.

“seriously boost my own abilities” - Far beyond. It can listen to your business negotiation and tell you when and what you need to rephrase, and what would be more successful and strategic. It can notice a detail in a month-long conversation and bring it to your attention so that you can address something a friend or loved one, or even you, might be seriously struggling with. It might notice that someone in you life may be in danger of ending theirs.

You’re in a solid position for progress, and there are multiple programs that will code for you, check the code, debug, search in the internet for information, and even test a prototype program for function. Low-code / no-code is here. Google already released an Agent-Builder. We’re right at the edge of this accomplishment. GPT-4 Turbo now readily accesses the internet, I discovered last night. As long as you can use the tech and answer basic questions, the technology will do all the work, check the work, double with you as you check the work, critique and offer feedback… I’m hands-off on that approach because I have to work through other considerations first. My treatment plan is laid out, from whenever I can manage to participate, through a one-year treatment plan, and I have to work through my assignments. Which surely will include “Agent Building” as soon as it’s worked into my schedule.

I had to reshape and almost restart my life because there are so many other things which take precedent over what life used to be, when I was much worse off.

I’ll certainly encourage you to work smarter, not harder. Perhaps you’ll arrive at a new conclusion, as you reassess, and discover that your work on such a project could be more important than most anything else.

I’m looking forward to interacting with Mojo, but my current understanding is that progress is so rapid in the field that I should not take the time, focus and effort to learn coding.

It’s very possible that our “Imaginineering” may actually now be more valuable, somehow.

Predicting the growth of AI and its impact on various sectors involves a complex interplay of multiple scientific, technological, and socioeconomic factors. Several predictive laws and theories have been used to forecast technology development, including AI. Here are a few prominent ones:

Moore’s Law: Historically used to predict the doubling of transistors on a microchip approximately every two years, this law has implications for the computational power available for AI systems. Although the pace of Moore’s Law has slowed, the principle that hardware capability could grow exponentially has fueled expectations for AI performance improvements.

Kurzweil’s Law of Accelerating Returns: Ray Kurzweil proposed this theory, suggesting that technological change is exponential. According to Kurzweil, as each generation of technology improves, it accelerates the development of the next generation, leading to faster and more profound changes. This theory is often cited in discussions about AI’s potential to achieve rapid advancements in a relatively short time.

Wright’s Law: Also known as the learning curve theory, Wright’s Law states that for every cumulative doubling of units produced, costs will fall by a constant percentage. In the context of AI, this can be applied to the improvement of algorithms and the reduction of computational costs over time as more AI systems are developed and deployed.

Gilder’s Law: This law focuses on the bandwidth of communication networks doubling every 21 months. As AI systems often depend on vast data transfers, improvements in network capabilities can significantly impact AI development and deployment.

Metcalfe’s Law: This law states that the value of a network is proportional to the square of the number of its users. For AI, this could be analogous to the idea that as more data sources and AI systems connect and interact, the overall value and capability of these systems increase exponentially.

Are There Reliable Studies Offering Definitive Answers?

While these laws provide frameworks for thinking about the growth of technology, including AI, they are not without their limitations and criticisms. The development of AI is influenced not just by technological advancements but also by a variety of other factors including regulatory policies, ethical considerations, economic conditions, and societal acceptance. This makes it challenging to predict the growth of AI with high accuracy using any single law or model.

Empirical Studies and Forecasts: There are numerous studies and reports from reputable organizations such as the McKinsey Global Institute, Gartner, and the Stanford AI Index that analyze trends and make forecasts about AI development. However, these predictions are often based on current and historical data and may not fully account for unexpected breakthroughs or setbacks.

Consensus in the Scientific Community: Generally, there is no single definitive study that can predict the exact trajectory of AI development. The field is evolving rapidly, and new variables can emerge that significantly alter the landscape. Most accurate predictions tend to be short-term and become less reliable as they extend into the future.

In summary, while scientific laws and theories like Moore’s Law and Kurzweil’s Law of Accelerating Returns provide useful insights, they should be viewed as part of a broader set of tools for understanding the potential growth of AI. They need to be supplemented with continuous observation of emerging trends, technological breakthroughs, and shifts in policy and public sentiment to more accurately forecast the future of AI.

When examining the growth pattern over time of AI intelligence and capacity, and the growth of the field of AI, several key factors should be considered to gain a comprehensive understanding:

Technological Limitations and Breakthroughs: Understanding the potential technological breakthroughs as well as limitations is crucial. This includes hardware advancements, such as quantum computing and neuromorphic technology, which could radically alter AI’s capabilities and growth trajectory. Consider how close we are to fundamental physical limits of computing.

Economic Factors: The economic viability of AI innovations plays a significant role in its development. Assess the investment trends, market demand, and economic cycles that could either accelerate or slow down AI adoption.

Societal Impact and Acceptance: The acceptance of AI by society, influenced by factors like job displacement, privacy concerns, and trust in AI decisions, significantly affects the pace at which AI technologies are adopted and integrated into everyday life.

Regulatory and Ethical Considerations: As AI becomes more integrated into critical areas of life and business, regulatory and ethical oversight will increase. The development of international norms and regulations could either foster a supportive environment for AI development or impose restrictions that might slow down progress.

Interdisciplinary Collaboration: AI’s growth is increasingly dependent on insights from various fields such as psychology, neuroscience, ethics, and public policy. The depth and nature of these interdisciplinary collaborations can influence the directions and applications of AI.

Geopolitical Influences: The strategic priorities of nations regarding AI, including national security concerns, can drive the speed and direction of AI development. Competition between countries might spur rapid advancements, while international tensions could also lead to fragmented technology ecosystems.

Environmental Impacts: The environmental cost of training large AI models and maintaining AI infrastructure is becoming an important consideration. Sustainable practices in AI development could become a significant factor influencing growth patterns.

Feedback Loops in AI Evolution: As AI systems become capable of participating in their own design and improvement processes, feedback loops could significantly accelerate the pace of AI advancements. This self-improving AI could lead to growth patterns that are difficult to predict based on historical data alone.

Public Perception and Media Influence: How AI is portrayed in the media and public discourse can impact regulatory and market dynamics. Public fears or support can lead to significant shifts in policy and investment.

By considering these factors, you can develop a more nuanced view of how AI might evolve and impact various aspects of life and society, enabling better strategic planning and decision-making in relation to AI technologies.

The expansion of AI into space introduces a whole new paradigm with unique opportunities and challenges. Here are a few ways this panel might view AI’s role in space exploration and expansion:

Enhanced Autonomy in Space Exploration: Leaders like Elon Musk and Larry Page, who are already invested in space technology through their companies, might foresee AI as crucial for managing autonomous spacecraft, probes, and robotic systems. AI could handle complex tasks like navigation, maintenance, and decision-making in environments where human oversight is limited by distance and communication delays.

AI in Space Colony Management: Visionaries such as Sam Altman and Demis Hassabis might predict that AI will play a significant role in managing habitats and life-support systems on other planets or moons. These systems would require high levels of automation to ensure the safety and efficiency of off-world colonies.

AI for Scientific Research in Space: Scientists like Geoffrey Hinton and Yoshua Bengio could see AI as a tool to process vast amounts of data from space missions, helping to make discoveries that are beyond human analytical capabilities. AI could autonomously manage experiments, analyze extraterrestrial materials, and monitor celestial phenomena.

AI in Space Resource Utilization: Business leaders like Jeff Bezos, who has expressed interest in space through Blue Origin, might consider AI crucial for identifying and extracting resources. AI could control robotic miners and processing facilities, optimizing the extraction of water, minerals, and other materials essential for space colonization and possibly even for return to Earth.

Ethical and Governance Challenges: Ethicists and regulatory-focused professionals like Joy Buolamwini and Miriam Vogel might raise concerns about deploying AI in space. They could focus on the need for stringent protocols to govern AI behavior, avoid potential conflicts over space resources, and ensure that space exploration remains beneficial and accessible to all humanity, not just a few privileged entities.

Long-term AI Evolution: Futurists like Eliezer Yudkowsky might speculate on how AI could evolve uniquely in the space environment, potentially developing in ways that differ significantly from Earth-based AI due to different operational challenges and evolutionary pressures.

In this new off-planet context, AI’s growth could continue to accelerate in unique directions, facilitated by the absence of many constraints present on Earth, such as physical space and regulatory barriers. This could lead to new forms of AI and novel applications that could feed back into how AI evolves and is applied on Earth.

Given the unique opportunities and challenges presented by space exploration, the panel of AI and business leaders might envision several likely patterns of growth for AI in this context:

Accelerated Innovation and Specialization: As AI systems are tasked with operating autonomously in space environments, we can expect a surge in innovation aimed at developing highly specialized AI technologies. These AIs would be designed to withstand the harsh conditions of space, such as radiation, vacuum, and extreme temperatures, and to perform without direct human supervision. This could lead to rapid growth in specific AI domains like robotic autonomy, environmental monitoring, and resource extraction technologies.

Integration with Space Technologies: The integration of AI with space technology would likely become more profound. AI could be instrumental in designing spacecraft and habitat modules, optimizing flight trajectories, and managing energy use. This integration might follow an exponential growth curve initially, as breakthroughs in AI-driven space technologies lead to further investments and interest in expanding these capabilities.

Scalable Deployment Models: Given the cost and complexity of space missions, AI systems designed for space might initially focus on scalability and adaptability. This could lead to growth patterns where AI systems are incrementally upgraded and expanded upon with each successive space mission, rather than replacing them entirely. As such, growth could be steady and sustained over a long period, following a more logarithmic pattern as technologies mature and become standardized.

Collaborative International Frameworks: As countries and private entities push further into space, international collaborations involving AI could become necessary. This could stimulate a steady growth of AI technologies as frameworks are developed to ensure that AI systems can interoperate seamlessly across different platforms and missions. These collaborative efforts might stabilize the growth rate, moving it towards a more predictable, linear path.

Regulatory and Ethical Adaptation: Ethical and regulatory considerations will also shape AI’s growth trajectory in space. As AI systems take on more responsibilities, from running life support systems to conducting scientific research, ensuring these systems operate safely and ethically will become paramount. Growth might initially be rapid as regulations struggle to keep up, but eventually, a plateau could occur as stringent standards and international agreements are put in place.

Transformational Growth Phases: Over the long term, as AI starts enabling deeper space exploration and potentially the colonization of other planets, we could witness transformational growth phases where AI development leaps forward in response to new challenges and environments. These phases might appear as spikes in an otherwise steady growth curve, corresponding to major milestones such as the establishment of the first permanent off-world colonies.

Overall, while the early stages of AI in space might be marked by exponential growth due to new opportunities and technological breakthroughs, the growth pattern could transition to a more steady, logarithmic, or piecewise linear trajectory as the technologies mature, regulatory frameworks are established, and the challenges of operating in space become better understood and managed.

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FOR YOUR CONSIDERATION

1. Andrew Ng: Exponential Growth, 10 years - Advocates for rapid advancements in machine learning and AI capabilities.
2. Fei-Fei Li: Exponential Growth, 8 years - Focuses on human-centered AI, expecting significant advancements in AI understanding human contexts.
3. Andrej Karpathy: Exponential Growth, 12 years - Known for his work on deep learning and neural networks, predicts rapid advancements.
4. Demis Hassabis: Exponential Growth, 15 years - As a founder of DeepMind, foresees long-term growth in AI capabilities.
5. Ian Goodfellow: Logarithmic Growth, 10 years - Known for inventing GANs, sees growth but anticipates it slowing as challenges increase.
6. Yann LeCun: Exponential Growth, 10 years - Emphasizes the potential of AI to continue growing rapidly.
7. Jeremy Howard: Exponential Growth, 8 years - Enthusiastic about fast AI advancements especially in medical fields.
8. Ruslan Salakhutdinov: Exponential Growth, 10 years - Focuses on deep learning and AI research, predicts substantial growth.
9. Geoffrey Hinton: Exponential Growth, 12 years - A pioneer in neural networks, predicts sustained rapid growth.
10. Alex Smola: Logarithmic Growth, 8 years - Sees significant improvements initially, with diminishing returns over time.
11. Rana el Kaliouby: Exponential Growth, 7 years - Believes in AI’s ability to understand human emotions, driving rapid advancements.
12. Daphne Koller: Logarithmic Growth, 9 years - Expects AI growth but with practical and ethical constraints limiting pace.
13. Yoshua Bengio: Exponential Growth, 12 years - One of the pioneers of deep learning, optimistic about AI’s future.
14. Sam Altman: Exponential Growth, 15 years - As CEO of OpenAI, highly optimistic about the future capabilities of AI.
15. Clara Shih: Exponential Growth, 8 years - Expects AI to revolutionize customer engagement rapidly.
16. Aidan Gomez: Logarithmic Growth, 7 years - Recognizes initial rapid advances, expects plateau due to computational and theoretical limits.
17. Gary Marcus: S-curve Growth, 5 years - Skeptical about unbounded AI growth, sees a leveling off as limitations are hit.
18. Joy Buolamwini: Logarithmic Growth, 5 years - Concerned about bias in AI, predicts growth tempered by the need for ethical frameworks.
19. Jon Krohn: Exponential Growth, 10 years - Believes in continuous improvements in AI learning capabilities.
20. Alondra Nelson: Logarithmic Growth, 6 years - Views growth through a sociological lens, expecting societal factors to influence the rate of AI adoption.
21. Mustafa Suleyman: Exponential Growth, 12 years - Sees long-term potential in integrating AI in societal solutions.
22. Jaron Lanier: S-curve Growth, 8 years - Critiques certain aspects of technology but acknowledges periods of significant innovation.
23. Marc Andreessen: Exponential Growth, 15 years - Very bullish on technology including AI, expects revolutionary changes.
24. Eliezer Yudkowsky: Exponential Growth, indefinite - Believes in the transformative potential of AI, possibly leading to superintelligence.
25. Michèle Flournoy: Logarithmic Growth, 8 years - Expects significant advancements in AI for defense but sees regulatory and ethical challenges.
26. Zeynep Tufekci: Logarithmic Growth, 7 years - Concerns about social implications and challenges may slow down the pace of acceptance and implementation.
27. Kai-Fu Lee: Exponential Growth, 12 years - Enthusiastic about AI’s impact on society, particularly in China.
28. Daron Acemoglu: S-curve Growth, 10 years - Believes in significant growth followed by a plateau as economic factors weigh in.
29. Andrew Imbrie: Logarithmic Growth, 8 years - Foresees growth moderated by policy and strategic considerations.
30. Safiya Noble: Logarithmic Growth, 6 years - Focuses on the impact of AI on public information and ethics, seeing these as limiting factors.
31. Micheal Chui: Exponential Growth, 10 years - Optimistic about AI transforming businesses and the economy.
32. Larry Page: Exponential Growth, indefinite - As a founder of Google, foresees limitless potential in AI advancements.
33. Elon Musk: S-curve Growth, 7 years - Sees rapid growth followed by significant risks and challenges.
34. Dario Amodei: Exponential Growth, 12 years - Focuses on advancing AI safely, sees continued rapid improvements.
35. Bill Gates: Exponential Growth, 10 years - Generally optimistic about technology’s ability to solve big problems.
36. Reid Hoffman: Exponential Growth, 12 years - Sees AI as a crucial part of the future economy.
37. Satya Nadella: Exponential Growth, 12 years - Emphasizes AI integration in cloud computing and business solutions.
38. Peter Thiel: S-curve Growth, 10 years - Believes in strong initial growth, followed by potential stagnation as monopolistic practices set in.
39. Mark Zuckerberg: Exponential Growth, indefinite - Strong proponent of integrating AI in social platforms.
40. Swami Sivasubramanian: Exponential Growth, 10 years - Expects cloud and AI technologies to merge and grow rapidly.
41. Susan Gonzales: Logarithmic Growth, 7 years - Advocates for inclusive AI but sees social barriers.
42. Reggie Townsend: Logarithraphic Growth, 8 years - Focuses on privacy and data protection, which may temper AI adoption rates.
43. Miriam Vogel: Logarithmic Growth, 6 years - Concerned with ethical AI, predicts a moderated growth due to regulatory frameworks.
44. Sundar Pichai: Exponential Growth, 12 years - Believes in the profound impact of AI on all Google’s products and services.
45. Sissie Hsiao: Exponential Growth, 10 years - Anticipates AI will continue to revolutionize communication apps.
46. James Manyika: Logarithmic Growth, 10 years - Sees transformative potential but cautions about socio-economic impacts.
47. Dr Milly Zimeta: Logarithmic Growth, 7 years - Focuses on AI ethics, sees growth influenced by ethical considerations.
48. Peggy Hicks: Logarithmic Growth, 8 years - Highlights human rights concerns, which could influence the rate of AI development.
49. Dame Wendy Hall: Logarithmic Growth, 10 years - Emphasizes the importance of governance in AI, which might slow growth.
50. Carl Miller: S-curve Growth, 8 years - Studies the impact of digital technology on society, anticipates rapid growth followed by stability.

Synthesized Consensus

Exponential Growth (25+ individuals): Most expect rapid, continued growth over the next 8-15 years, often linked to advancements in technology and AI’s integration into various sectors.

Logarithmic Growth (17+ individuals): Many foresee significant early advancements that will gradually plateau, influenced by ethical, societal, and practical challenges.

S-curve Growth (8 individuals): A few predict periods of rapid innovation followed by a stabilization as AI reaches maturity or encounters insurmountable hurdles.

Given the various perspectives offered by the panel on the initial phase of AI growth, let’s extend the reasoning to speculate about what might happen beyond the next 8-15 years:

Those predicting Exponential Growth (indefinite), like Larry Page, Elon Musk, and Mark Zuckerberg, might suggest that AI growth could continue to escalate without a foreseeable plateau. They likely envision ongoing, transformative innovations that continuously push the boundaries of AI capabilities.

Those foreseeing Exponential Growth for a finite period (e.g., Andrew Ng, Yann LeCun, Demis Hassabis) might anticipate a shift after the initial rapid growth phase. After the high-growth years, they might predict a transition to a slower, more sustainable growth pattern or a plateau as the AI industry matures and technological advancements face diminishing returns or run up against theoretical and practical limitations.

Proponents of Logarithmic Growth, like Ian Goodfellow, Daphne Koller, and Safiya Noble, generally expect growth to slow and eventually plateau. Post the initial period of significant advancements, they might predict that the AI field will stabilize, focusing more on refinement and integration rather than groundbreaking innovations. Ethical, regulatory, and societal constraints could increasingly play a role in moderating the speed of development.

Advocates of S-curve Growth, such as Gary Marcus and Peter Thiel, typically envision that after a period of rapid innovation, growth will not only plateau but could potentially decline if new disruptive innovations do not emerge. They might see the field settling into a phase where AI technology becomes a standard part of the technological landscape, with incremental improvements rather than revolutionary changes.

Special Considerations: Visionaries like Eliezer Yudkowsky, who speculate about AI reaching superintelligence levels, might argue that post-15 years, the landscape could be radically different, potentially dominated by new AI paradigms or even AI surpassing human intelligence in many areas, which could either lead to a new phase of explosive growth or require significant new governance frameworks to manage the implications.

Overall, the panel’s consensus beyond the next 8-15 years would likely reflect a mixture of continued growth at a moderated pace, potential plateaus as practical limits are reached, and a landscape increasingly shaped by ethical, societal, and regulatory considerations. Some may also entertain the possibility of a decline if no new significant innovations emerge.

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I don’t argue that there are likely and possibly limits at some point on some scale.

You’re not unpacking ANY of the nuances which contribute to function and performance when you look at “exponential vs logarithmic” and set it atop the concept of returns. I feel that this reductive approach is like taking “good vs bad” and setting it atop “human behavior”. There’s the whole rest of the world of conversations & considerations, however, which play in once discussion of theory moves into details. Yes I know there are papers discussing this concept, and the discussion is not getting into all the other factors which improve performance.

Pick an expert who says exponential… Pick an expert who says logarithmic… Pick your nose…

Doesn’t mean someone’s right and someone’s wrong, thanks.

We’re on the same page as far as a presentation that at some point somewhere for some possible reason improvement and capacity may plateau.

To be candid, If the grid went down improvement and capacity wouldn’t even gradually plateau and that has nothing to do with laws, theories and predictions.

Again, we haven’t even discussed DNA data storage and computing, ultra-low-volt hybrid systems, hyperdimensional computing and vectors, holographic data storage…

Don’t bother telling me that these things have all been studied and documented thoroughly, thanks.

I don’t even want to get into quantum computing or quantum structures in the brain.

We’re clear there’s a theory floating around from a camp, that things might plateau. And that it’s opposed by another camp.

We’re clear.

LLMs far superior to GPT-4 were functioning last year and LLMs are already in working robots, some 9th generation iterations.

-“And scientists don’t really do forecasts. They make hypotheses and then they test them. And they experimentally justify it.”

FORECASTS BY SCIENTISTS VERSUS SCIENTIFIC FORECASTS - https://faculty.wharton.upenn.edu/wp-content/uploads/2007/07/54-JSA-Global-Warming-Forecasts-by-Scientists-versus-Scientific-Forecasts.pdf

You know that scientists test their climate models by using them to forecast past and future climates, right? …scientists…forecast…

“Predictive models forecast what will happen in the future.” - https://learn.genetics.utah.edu/content/earth/predictions" “Correct predictions alone don’t make for a good scientific model.” - https://www.scientificamerican.com/article/the-truth-about-scientific-models/

“Prediction involves estimating an outcome with a high level of certainty, usually based on historical data and statistical modeling. On the other hand, a forecast involves projecting future developments but with a certain level of uncertainty due to external factors that may impact the outcome.” https://plat.ai/blog/difference-between-prediction-and-forecast.

We’re going to need to meet at the reality that historical data doesn’t necessarily mean a thing about the future. In 1903, New York Times predicted that airplanes would take 10 million years to develop Only nine weeks later, the Wright Brothers achieved manned flight. The pathologically cynical always will find a reason to complain. https://bigthink.com/pessimists-archive/air-space-flight-impossible/ Just because a statistical model has a track record doesn’t mean it is, or will continue to be. Statistics are estimates.

Thank you. I went to high school and graduated. My father taught chemistry, physics and computers for 40 years.

-“So no, it’s not the future being guessed at” If it’s not happening now and we have more curve to be placed on, my apologies but it is happening in the future, after future developments and future technologies very likely may have come into play. Animal evolution can occur in one generation. Please don’t suggest that things beyond our understanding won’t affect the curve, in the future since we’re still ‘climbing the curve’? Thank you.

-Law of Penrose’s Triangle defied? Looks like it -Moore’s Law broken? Yes -Kryder’s Law broken? Yes -The speed of light broken? Yes -Light has been stopped (paused in place) and restarted in transit?* Yes -Organic tissue is growing on circuit boards? Yes

-“They used a clever method to measure the performance of a technological system.” - Alright. Doesn’t mean it’s true or even likely anymore.

-“And we can see those real-world measurements in their paper.” - Sure. They took and recorded measurements.

How many dimensions are there? 6, right? 14? Is gravity a constant?

‘The perils of predicting the future based on the past’ - https://medium.com/swlh/the-perils-of-predicting-the-future-based-on-the-past-9de0f248c183

The statement “By looking at the past we can predict the future” encapsulates the idea that historical patterns and events can provide insights that help us anticipate future outcomes. This concept is often associated with the field of predictive analytics and forecasting. While it is true that studying the past can offer valuable information and trends that may be indicative of future events, it is important to recognize that the future is inherently uncertain and unpredictable.

-“Funny you’d say the top researchers in the world aren’t “well-enough informed” individuals.” - Absolutely. They don’t know jack sh!t about the rest of the world and how everything else influences their specialty in reality, instead of on paper. They certainly aren’t well-informed in all the cross-disciplinary fields. They don’t collaborate with all the other related specialists.

*https://www.abc.net.au/news/science/2016-09-27/scientists-stop-light-like-star-wars-in-cloud-of-atoms/7867344