Framework: Generative AI to Accelerate Scientific Discovery

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The history of humanity has been one of accelerating scientific and technological progress. Now, by amplifying the cognition and capabilities of scientists with AI, we can dramatically step up the pace of discovery.

By the very nature of discovery, we cannot know what we will learn. The coming advances in life and brain sciences and medicine alone will be absolutely transformative, let alone those in energy, materials science, food, and more.

This framework is a high-level distillation of a few of the cognitive tools and techniques for GenAI to assist scientists. As always it is a Beta version 1, please let me know any feedback or input to improve it!
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Framework: Levels of AI delegation in decision-making

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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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The Exploitation-Exploration dilemma’s fundamental role in intelligence and our lives

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The Exploitation-Exploration dilemma is deeply relevant, not only to business strategy, but to many aspects of our lives.

It is also a fundamental underpinning of intelligence, human and artificial.

The dilemma is essentially: should we continue with what we have, or leave that behind to find something better?
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Future job prosperity: 13 reasons to believe in a positive future of work

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Many are crying doom about the future of work. I believe that a highly prosperous future of job is possible. It is even likely, on condition we do the right things today.

In order to create that future, we must believe it is possible. In this mini-report I have distilled the major arguments that a prosperous future of jobs is possible.
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The business models to support a prosperous future for news media

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I believe the media industry has a prosperous future. Today at the fabulous humAIn conference by Unmade I made that case in the concluding “debAIt”, arguing strongly against the proposition “AI is news media’s extinction level event”.

What this debate really boils down to is the business models for news media.

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Framework: Humans + AI in institutional investment portfolio decision-making

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The primary application of my Humans + AI work over the last 18 months has been in strategic decision-making, working with boards and executive teams to augment their decision-making processes and outcomes using generative AI.

The other major Humans + AI application I’ve been working on is in institutional investment decision-making. I am currently shifting more of my attention and work into this space. 

This framework provides a very high-level slice of a few of the many human and generative AI roles in different phases of portfolio decision-making.

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Jorge Luis Borges and the impact of AI on human creativity

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Jorge Luis Borges has been one of my favorite authors since my teens. Over the last couple of years I have often thought of one of his masterpieces, Pierre Menard, Author of The Quixote, written in 1939, which turns out to be extremely relevant to the age of AI. 

The story recounts how a contemporary writer, Pierre Menard, rewrites Cervantes’ epic Don Quixote word for word. However he writes it entirely from his own context, making the text laden with layers of meaning missing from the original. The reviewer played by Borges in the piece finds the new Quixote, coinciding word for word with the original, enriched, astounding, and more significant than the original.

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Humans + AI in financial decision-making: consumer, portfolio, and organizational

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Arguably the entire finance industry can be framed as a massive inter-related array of decisions, by both clients and financial insttutions.

In this world AI wil be transformative, in most cases not by supplanting humans in the decision-making process, but in playing a role in ‘Humans + AI’ decision-making.

I was recently interviewed for the NAB Digital Next podcast for an episode titled Futurist Ross Dawson on humans and AI achieving more together. You can listen here, with some distilled reflections below.
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Humans + AI forecasting far outperforms either alone: 6 lessons learned

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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.

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Applying Chain-of-Thought to AI-enhanced human thinking

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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.

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