Soon virtually every decision will involve AI. For every decision we make, the critical first step is to determine the level of AI delegation. And then select the specific Humans + AI architecture most appropriate to the decision.
This framework is the latest iteration of the AI decision-making delegation frameworks I’ve been developing and using with clients, made concise for communication and easy digestion.
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Since well before the advent of Generative AI, machine learning models exceeded human forecasting performance across a whole range of specific domains. Within a bounded domain with sufficient data, machine learning is often extremely good at predicting outcomes.
However, machine learning can only work within defined domains where there is sufficient data. In most real world decision-making situations their forecasts need to be taken with a high degree of caution.
One of the critical differences between most traditional analytic AI approaches and Large Language Models (LLMs) is that the former almost always applies to bounded domains, while the nature of LLMs is that their scope is unbounded. As such, it has the potential to help make better forecasts in conjunction with humans across various domains including business, economics, politics, science, and more.
Among the most important recent innovations for improving the value and reliability of Large Language Models are Chain-of-Thought and its derivatives including Tree-of-Thought and Graph-of-Thought.
These structures are also extremely valuable in designing effective Humans + AI workflows for better thinking.
In this article I’ll provide a high-level view of Chain-of-Thought and then look at applications to AI-augmented human intelligence.
To tap the power of AI in organizations it is critical to understand the distinction between Generative AI and more traditional AI, which is perhaps best termed ‘Analytic AI’.
Recently I have frequently seen these domains confused. Generative AI is not all AI, as many imply. It is a relatively new domain with characteristics distinct from much of mroe traditional AI.
The following chart lays out some of the most important distinctions. Click on the image below for the full size image, and scroll below for more discussion.
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We seem to be in a three decade cycle of belief in open-ended potential for positive change.
I was born too late to experience it properly, but when I was younger I always felt the 1960s had been the most magical time in human history.
For the first time ever the established hierarchies had been seriously questioned and challenged,. Still today some of the most extraordinary music ever came from that period, transcending existing genres time and again. The civil rights movement shifted nations. Psychedelics became mainstream, shaping the worldview of many who became social activists or some later became CEOs.
This was a time when the young – for a moment- believed that they could change the world, throwing away the past and transforming society in sometimes unimaginable ways.
Powerful forces of decentralization over the last decades, underpinned by the Internet, flowing through to societal shifts, and expanded by distributed technologies such as blockchain, have reshaped business and society.
However governments and regulators have largely tried to block these forces, often successfully.
The global economy is still founded almost completely on joint-stock companies, with legislated centralized structures of ownership and governance.
So what is the best path to decentralized organizations, how can we best innovate in structures for participative value creation?
“Future lift” is a concept developed by Ross Dawson that refers to the ability of a technology, business, or idea to improve the future. He visualizes it as an upward lift or force that elevates the trajectory of the future.
Some examples of things that provide future lift include:
• Technologies like artificial intelligence and biotechnology that could fundamentally improve health, longevity, and human capabilities.
• Social innovations and movements that help people reach their potential and improve well-being, such as advances in education and skills development.
• New business models and ideas, such as the sharing economy, that provide people and societies more value and prosperity.
• Scientific breakthroughs that could help solve major problems, such as new energy technologies or drought-resistant crops.
The concept of future lift is a useful way to evaluate and explore how different trends, technologies, and ideas could positively shape the future and make the world better. It helps us focus on the uplifting forces of progress.
This is a hallucination by the Anthropic AI chatbot. I have never said or written the words “future lift” that I can remember or find.
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What if technology could help you discover what you found most attractive, in people, art, or your environment?
In Alfred Bester’s SF novel The Deceivers, Demi Jeroux evolves her appearance to match what her lover finds most attractive.
Now existing in real life, a recent paper Brain-computer interface for generating personally attractive images describes the process of identifying what people find the most attractive.
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When I was a teenager my father encouraged me to learn to touch type, in those days this being on electric typewriters. His rationale was that if I was preparing my resume I wouldn’t be able to give it to the typing pool to do. Needless to say I have benefited from his encouragement greatly over many years, in more ways than preparing my resume.
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Machines becoming conscious, self-aware, and having feelings would be an extraordinary threshold. We would have created not just life, but conscious beings.
There has already been massive debate about whether that will ever happen. While the discussion is largely about supra-human intelligence, that is not the same thing as consciousness.
Now the massive leaps in quality of AI conversational bots is leading some to believe that we have passed that threshold and the AI we have created is already sentient.
An article in Washington Post The Google engineer who thinks the company’s AI has come to life tells the story of a member of Google’s Responsible AI team, Blake Lemoine, who has become convinced that the Google’s (Language Model for Dialogue Applications) LaMDA chatbot platform has become sentient, and after being placed on administrative leave by Google, ‘blew the whistle’ to media.
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