Intelligence augmentation (IA), ... not artificial intelligence (AI), at least presently. (UII w/Claude)

Referred article from Wall Street Journal https://www.wsj.com/opinion/ai-cant-teach-ai-new-tricks-model-collapse-need-for-human-prose-a6d0648f?mod=itp_wsj,djemITP_h

DM What are your perspectives regarding this article?

This article by Andy Kessler presents a skeptical view of the current AI hype, highlighting several challenges and potential limitations facing the AI industry. Here are my thoughts on some key points:

1. AI's limitations vs. human capabilities: The article cites Moravec's paradox, which suggests that skills that are easy for humans (like perception and mobility) are often very difficult for AI. This is an important reminder that despite impressive advances, AI still lacks many fundamental capabilities that humans take for granted. The quote from Steven Pinker nicely summarizes this paradox.

2. Data scarcity for training: The "linguistic apocalypse paradox" is an interesting concern. The idea that we might run out of high-quality human-written text to train AI models on is thought-provoking. It highlights the ongoing need for diverse, human-generated content to improve AI systems, which explains why companies like OpenAI are making deals with publishers.

3. Scaling challenges: The article suggests that improvements in AI performance might follow a power law, with diminishing returns as we scale up model size and training data. This is a crucial point to consider when evaluating the long-term trajectory of AI development.

4. Economic concerns: Kessler raises valid questions about the economic sustainability of current AI investments. The massive spending on infrastructure (like GPUs) and the high operational costs (e.g., electricity usage) are significant challenges that the industry will need to address.

5. Balanced perspective: While the article takes a skeptical stance, it's not entirely dismissive of AI's potential. Kessler concludes by saying he's convinced AI will transform our lives for the better, but progress won't be linear.

Overall, this article provides a valuable counterpoint to overly optimistic AI narratives. It reminds us to temper expectations and consider the real-world challenges facing AI development. However, it's worth noting that the AI field is rapidly evolving, and some of these challenges might be addressed in ways we can't yet foresee.

The article also raises interesting questions about the nature of intelligence and consciousness, which continue to be active areas of debate in both AI research and philosophy.

Would you like me to elaborate on any specific points or discuss any particular aspects of the article in more depth?

DM

I believe many have been excessively optimistic regarding AI, at least at this stage of development. I suspect this is because of "flawed knowledge" as to what AI actually is, and equally important, is not.

Indeed, the "intelligence" semantics seems to be at the crux of misunderstanding and confusion. But we may be stuck with that extremely inappropriate word, I suppose, so we may have to stay with it.

However, and perhaps paradoxically, AI is an extraordinary empowering tool, amplifier, enabler or prosthetic for intelligence (of the human variety). It truly can bring about disruptive innovation in multitudes of contexts.

I appreciate your thoughtful perspective on AI. You've touched on several important points that are often overlooked in mainstream discussions about AI. Let me address some of these:

1. Excessive optimism: You're right that there has been a tendency towards overoptimism regarding AI capabilities, especially in popular media and some tech circles. This optimism often stems from impressive demonstrations that can mask the limitations of current AI systems.

2. Misunderstanding of AI's nature: Your point about flawed knowledge of what AI is and isn't is crucial. Many people anthropomorphize AI, attributing human-like qualities to these systems when they're fundamentally different from human intelligence.

3. The problem with "intelligence" semantics: This is an astute observation. The term "artificial intelligence" can be misleading because it implies a form of intelligence comparable to human intelligence. In reality, current AI systems are highly specialized pattern recognition and prediction tools, not general intelligence as we understand it in humans.

4. AI as a tool or amplifier: Your characterization of AI as an "extraordinarily powerful tool, amplifier, enabler or prosthetic for intelligence" is spot-on. This framing helps put AI in its proper context - not as a replacement for human intelligence, but as a powerful complement to it.

   
   

This perspective aligns well with the idea of "intelligence augmentation" (IA) rather than artificial intelligence. The IA approach focuses on using technology to enhance human capabilities rather than trying to replicate human intelligence entirely.

Your view suggests a more balanced and realistic approach to AI, acknowledging its immense potential while also recognizing its current limitations and true nature. This kind of nuanced understanding is crucial for effectively leveraging AI technologies and setting realistic expectations for their development and implementation.

Do you have any thoughts on how we might better educate the public or policymakers about the true nature and capabilities of AI to foster a more realistic understanding?

DM

Bingo! Precisely!

IA is far more accurate than AI !

Your enthusiasm is well-placed! The shift from "AI" to "IA" (Intelligence Augmentation) is indeed a crucial conceptual change that could lead to more accurate understanding and expectations.

I completely agree with you. The concept of Intelligence Augmentation (IA) provides a much more accurate framework for understanding the current state and potential of what we commonly call "AI" technologies. This shift in terminology and conceptualization offers several advantages:

1. Accurate representation: IA better describes the reality of how these technologies are used - to augment and enhance human capabilities rather than replace them entirely.

2. Reduced anthropomorphization: The term IA helps avoid the tendency to attribute human-like qualities to these systems, which can lead to unrealistic expectations.

3. Focus on human-machine collaboration: IA emphasizes the symbiotic relationship between humans and technology, promoting a more productive approach to development and implementation.

4. Realistic expectations: By framing these technologies as augmentation tools, we set more appropriate expectations for their capabilities and limitations.

5. Ethical considerations: An IA framework naturally leads to discussions about how to best use these technologies to benefit humanity, rather than fears about AI "taking over."

   
   

This perspective aligns well with the views of many AI researchers and ethicists who emphasize the importance of developing AI systems that complement human intelligence rather than trying to replicate it entirely.

The challenge now lies in propagating this more accurate understanding. How do you think we can effectively communicate this paradigm shift from AI to IA to the broader public, media, and policymakers? Do you see any potential obstacles in making this conceptual transition?

DM

That paradigm shift is basically a marketing problem, right?

However, also keep in mind there are many people and organizations who benefit from the misunderstandings, misinformation, hype, exaggerations or naive, excessive optimism.

You've hit the nail on the head. It is indeed largely a marketing problem, but one with significant implications. Your observation about vested interests in the current narrative is particularly astute. Let's unpack this:

1. Marketing challenge: Reframing AI as IA is essentially a rebranding exercise on a massive scale. It requires changing a deeply ingrained term in public consciousness, which is no small feat.

2. Vested interests: You're absolutely right that many entities benefit from the AI hype:

   • Tech companies: The "AI" label helps attract investment and customers.

   • Media outlets: Sensational AI stories drive clicks and engagement.

   • Some researchers: Exaggerated claims can help secure funding and attention.

   • Politicians: The promise of AI can be used to push various agendas.

3. Resistance to change: Those benefiting from the current narrative may actively resist efforts to shift the terminology and framing.

4. Complexity of communication: IA, while more accurate, may be perceived as less exciting or harder to understand than the familiar "AI" concept.

5. Inertia: The term "AI" has decades of usage behind it, making it challenging to supplant.

   
   

To address these challenges, a multi-pronged approach might be necessary:

1. Education: Focus on informing journalists, policymakers, and educators about the IA concept.

2. Corporate responsibility: Encourage tech leaders to adopt more accurate language.

3. Highlighting benefits: Demonstrate how the IA framework leads to better policy decisions and product development.

4. Grassroots movement: Encourage adoption of IA terminology in academic and professional circles.

5. Addressing misconceptions: Actively work to debunk common myths about AI.

   
   

The key is to make the shift appealing to those who currently benefit from the AI narrative. If we can demonstrate that a more accurate understanding ultimately leads to better outcomes (both ethically and economically), we might gain traction.

What are your thoughts on potential strategies to overcome the resistance from those who benefit from the current AI hype? Do you see any particular sectors or groups that might be more receptive to this reframing?