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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The business models to support a prosperous future for news media
By Ross DawsonI 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
By Ross DawsonThe 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
By Ross DawsonJorge 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
By Ross DawsonArguably 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
By Ross DawsonSince 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.
Read more →
Applying Chain-of-Thought to AI-enhanced human thinking
By Ross DawsonAmong 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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The long quest for news discovery apps that don’t suck
By Ross DawsonArtifact, the news discovery app launched a year ago by the founders of Instagram, has just announced it is shutting down, saying “the market opportunity isn’t big enough to warrant continued investment in this way.”
As pointed out in TechCrunch, Artifact “hadn’t quite defined what it wanted to be”, iterating from its initial personalized news discovery platform to include conversations about news articles and the essence of a social network.
Given my lifelong focus on information discovery I was excited by the launch of Artifact and was an early user. I found it somewhat useful in surfacing interesting articles, but not significantly better than other similar apps.
News discovery still sucks
Having closely followed the information discovery space for over two decades, I continue to be amazed at how poor our tools are.
In my recent book Thriving on Overload I wrote:
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My 2024 Ventures Map
By Ross DawsonI am now entering a new phase of my work and ventures built over the last 18 months.
Here is the visual framework of my current ventures, followed by an explanation of each of the elements.
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Scenario planning in practice in an accelerating world
By Ross DawsonI have been helping clients implement scenario planning for a quarter century, since 1998, in a very wide variety of contexts.
Therea are situations where scenario planning is more and less applicable. Especially pertinent today, as I have noted:
There are many factors that impact the degree of uncertainty in decision-making, including timeframes, industries, information availability, technological context, and many more.
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The most dangerous idea ever is that humans will be vastly transcended by AI
By Ross DawsonThe advent of next-generation AI has brought into sharp focus one of the biggest divides of all: our perception of humanity’s place in the Universe.
I endlessly read people arguing that humans will be to AI as animals or insects are to humans. They envision a future where AI’s relentless advancement transcends every faculty we possess.
The countervailing stance is that human potential is unlimited. We have deliberately and consistently increased our capabilities and knowledge, and now we will use the tools we have created to continue to advance.
The rise of AI has intensified this debate, leading us to question: Are we, as humans, inherently limited or unlimited?
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