Greetings from Amsterdam where I’ve been invited to speak about the impending explosion in different kinds of augmented intelligence. I have been exploring this particular intersection of combining, rather than replacing humans with AI since around 2016, and have spoken & written about this approach as openly as possible ever since. While the topic never felt compelling enough to warrant a book or long-form writing, I also feel it is becoming more important than ever to consider this middle ground as the most fertile space for deploying your personal or collective AI strategy.
Unless you work in data science, natural language processing, machine learning or are directly involved with programming these algorithms, you are likely best off strenghtening your AI skills as a complement to your existing job, or value-creating role in society. There is plenty of space for those wanting to go all-in with their ML skills and maybe even change career, but most of us will be left incorporating many kinds of adjacent intelligences into our routines and workflows regardless of industry, sector or field.
In fact, the unimitable Ethan Mollick together with BCG last week concluded that consultants complete more tasks more quickly and with higher quality when using AI augmentation. Centaurs act like a “skill leveler” with the lowest performing consultants improving the most with Al, but even top consultants improve using such tools. Researchers at DeepMind and Oxford had previously observed a nearly 3x increase in accuracy of historians restoring damaged texts when using a Centaur approach, moreso than using AI alone.
I have a lot more to say on this and will be fleshing out new ideas and experiments in public through this very newsletter. As some of you have found out, we at Envisioning are building a tool to act in this very intersection, and are learning a ton in deploying AI-augmented creative sessions with workshop participants every week.
As always, reach out if you have questions or ideas to share.
MZ
PS. I’m in Lisbon next week Tuesday 26 - DM if you’re around and up for coffee in the afternoon.
We must shape the AI tools that will in turn shape us 🔧
Reid Hoffman argues that AI has the transformative power to become humanity's cognitive GPS. He believes we should not slow down its development but rather accelerate it to solve societal challenges.
-
Cognitive GPS: Hoffman's idea of AI as a 'steam engine of the mind' is compelling, emphasizing its pervasive influence.
-
Accelerate, not Slow Down: The focus is on leveraging technology to address current and future challenges, rather than fearing it.
-
Human-AI Symbiosis: The concept that humans shape tools and are, in turn, shaped by them adds a philosophical dimension to the AI debate.
Silicon Valley’s vision for AI is religion, repackaged 🤲🏼
Explore the surprising similarities between the rhetoric surrounding AI in Silicon Valley and religious narratives, particularly Christian eschatology. The article highlights how futurist Ray Kurzweil's ideas have contributed to this confluence, creating an unexpected blend of technology and theology.
-
AI as Religion: Points out the startling parallels between the futuristic visions of AI and religious eschatology, emphasizing how technological and spiritual ideas can converge.
-
End Times Narrative: Discusses how the AI conversation incorporates ideas of doom or salvation, much like religious narratives about the end of the world.
-
Ray Kurzweil's Influence: Highlights how one individual's futurist ideas have had a significant impact on shaping this curious blend of technological and religious thought.
Bill Gurley on Regulatory Capture 🎚️
Venture capitalist Bill Gurley dissects the pitfalls of regulatory capture in various sectors including telecom and healthcare. He also discusses the potential adverse effects of regulation on democracy, capitalism, and innovation. The talk aims to raise awareness about these challenges and encourage informed discussions.
-
Regulatory Capture: Gurley dives deep into how regulations can favor industries over public interest, resonating with George Stigler's theories.
-
Tech & Regulation: Highlights the paradox of large tech companies advocating for regulations that may actually benefit them financially.
-
Transparency & Accountability: Advocates for drastic transparency measures to hold governments and corporations accountable, illuminating the path to potential solutions.
Sam Altman interview on Bloomberg 💬
Sam Altman delves into the state of AI, sharing insights from his global tour and discussing OpenAI's evolving language models. He talks about the excitement and concerns over AI worldwide, its impact on jobs, and calls for equitable AI benefits.
-
Global Perspective: Altman's world tour gives a unique international lens to AI's impact and reception.
-
Scientific Acceleration: Highlights the untapped potential of language models in speeding up scientific discoveries.
-
Equitable Sharing: Advocates for the fair distribution of AI benefits, contributing to ongoing discussions on AI ethics.
Citizen’s Digital Rights for AI in Europe 🇪🇺
Ursula von der Leyen, President of the European Commission, outlines the EU's ambition to lead in creating a global framework for AI. She highlights Europe's achievements in digital rights and tech regulation, mentioning the upcoming AI Act as a blueprint for global standards. Von der Leyen calls for a global panel of experts for governance and suggests more collaboration between AI developers and the EU.
-
Global AI Leadership: Indicates the EU's ambition to set global standards for AI, extending its influence beyond Europe.
-
Stringent Regulation: Reinforces the EU's reputation as a formidable tech regulator, signaling more rules for major tech companies.
-
Dialogue & Collaboration: Emphasizes the need for cooperation between the private sector and regulators, hinting at voluntary commitments from AI companies.
Emerging Vocabulary
Hyperparameter
A parameter whose value is set before the learning process begins. Hyperparameters differentiate from the parameters of a model because the latter are the values that the model learns from the data during the training process, such as weights in a neural network. Hyperparameters are crucial because they can directly influence the behavior of the training algorithm and have a significant impact on the performance of the model.
View all emerging vocabulary entries →
From Substack
[
Centaurs and Cyborgs on the Jagged Frontier
A lot of people have been asking if AI is really a big deal for the future of work. We have a new paper that strongly suggests the answer is YES. For the last several months, I been part of a team of social scientists working with Boston Consulting Group, turning their offices into the largest pre-registered experiment on the future of professional work …
Read more
2 years ago · 76 likes · 4 comments · Ethan Mollick
- Forwarding this issue to colleagues and friends.
You are receiving this newsletter because you signed up on envisioning.io.