Generative AI is growing quickly as individuals, corporations, and investors all try to stay ahead. The rapid change is creating optimistic visions of the future while also creating significant anxiety as people justifiably worry about their role in a future of abundant intelligent machines
AI will force us to broaden our view of intelligence. The real success in AI development will be in discovering forms of intelligence that go beyond anything we've known, transforming how we understand and interact with the world around us.
Recent research demonstrates through empirical evidence that GPT-4 can autonomously develop pricing strategies that edge towards collusion without explicit human direction or inter-firm communication.
Generative AI is growing quickly as individuals, corporations, and investors all try to stay ahead. The rapid change is creating optimistic visions of the future while also creating significant anxiety as people justifiably worry about their role in a future of abundant intelligent machines
AI will force us to broaden our view of intelligence. The real success in AI development will be in discovering forms of intelligence that go beyond anything we've known, transforming how we understand and interact with the world around us.
Recent research demonstrates through empirical evidence that GPT-4 can autonomously develop pricing strategies that edge towards collusion without explicit human direction or inter-firm communication.
On April 9th, we presented our research update on the State of AI & Complex Change which focused on confusion about AI, status of AI adoption and deployments, anxiety about AI, and AI in the Wild.
There is a lot of chatter about whether companies are realizing value from generative AI. Realizing value from generative AI will take time to prepare, capture, and create the data that allows generative AI to understand the context of our complex human lives.
The Artificiality Weekend Briefing: About AI, Not Written by AI
Slides and videos from our February 2024 research update for Artificiality Pro
In our Artificiality Pro update for January, we covered our 10 research obsessions for 2024.
In our Artificiality Pro update for December, we covered several key industry updates in AI and introduced mechanistic interpretability and memory vs. margins.
The future of the extended mind is not a fixed destination but an ongoing journey that requires our active participation and reflection as we redefine what it means to be human in an age of artificial intelligence.
AI could help humans appreciate the diverse perceptual worlds of animals by simulating their senses and thought processes.
Sensemaking is going to change. AI will allow us to find story-less, a-narrative yet meaningful correlations. Our minds will have to be open to a new kind of awe: that which a machine can make sense of that we cannot.
By understanding the principles behind the evolving field of prompt engineering, we can craft better queries and engage more effectively with AI. They're insights we can all use to sharpen our own interactions with AI, even if we're not writing the code ourselves.
Enterprises face a critical choice in their generative AI adoption strategy: fine-tuning or Retrieval-Augmented Generation (RAG)? While fine-tuning has been the go-to approach for early adopters, a new study suggests that RAG may be the more powerful and sustainable path forward.
By enabling different AI models to 'speak' to each other and combine their strengths, CALM opens up new possibilities for solving complex problems across various domains and tackling tasks with expertise and precision, in a data and compute efficient way.
By emphasizing critical engagement, transparency, bias mitigation, deliberate decision-making, user autonomy, and continuous education, Microsoft's research offers valuable guidelines for designing AI systems that promote appropriate reliance and user empowerment.
A review of research by Phil Tetlock and other experts on crafting better prompts by investigating if human forecasting can be improved through the use of a large language model.
A guide for organizational transformation for generative AI. By taking these critical steps, organizations can lay the foundation for effective use, setting themselves up for future success in an increasingly AI-driven world.
Enterprises are failing to see enough value add to justify purchasing new generative AI systems like Microsoft CoPilot. Why? Adapting to AI is a complex change that requires different methods for evaluation and change management.
Complex change recognizes that change is non-linear, emergent, and deeply interconnected with the system in which it occurs. This is even more important as we adapt to the complexity of generative AI.
AI's potential is vast but many applications remain incremental, simply grafting AI onto outdated frameworks. This is why we embrace complexity. We think about designing incentives, understanding self-organization and distributed control, and planning for emergence and adaptation.
We speculate on how the GPT store might act a market mechanism for discovering the most valuable use cases for AGI
What is Happening When Models Learn to Generalize.
Adaptability and flexibility of learned algorithms.
IBM's principles for generative AI applications underscore the necessity of a thoughtful, user-centered approach to designing GenAI applications.
In 2016, AI experts predicted radiologists would be obsolete within years as machines outperform humans. This did not transpire.
ChatGPT and similar tools can significantly alter workflows by changing how we match tasks with skills. Think of a two-by-two matrix: on one axis, you have the skill needed for a task; on the other, the worker's proficiency level.
It appears that there is one effect many researchers are finding across multiple fields: generative AI has a significant impact on lower skilled and less experienced people. However, if we automate difficult tasks we cut ourselves off from the essential components for achieving mastery like flow.
We extend our Mind for our Minds series with an exploration of the relationship between creativity and generative AI. We wonder how AI can enhance human creativity and dig into different types of creativity.
A conversation with Jonathan Coulton about generative AI, songwriting, and creativity.
An interview with Harpreet Sareen about Cyborg Botany and the intersection of the digital and material worlds.
An interview with Juan Noguera about generative AI in industrial design.
I agree with the government that antitrust is important. That said, I would hate to see data privacy and security diminished just as we are trying to take advantage of AI’s power to be a personalized digital partner.
An interview with Boldstart Founder, Ed Sim, about venture investing and AI.
The Biden Administration's Executive Order has stirred up discussion about the regulatory capture in AI. For good reason.
OpenAI made a splash on ChatGPT's first birthday with something for everyone to be excited and wary.
Does consciousness attribution hold the key to funding AI futures? The shift towards AI intimacy might reclaim our attention but at the potential cost of human-to-human connections.
Of all the interesting parts of Google’s Gemini announcement, one is keeping me up at night wondering about the possibilities for the future: dynamic coding.
Intimacy with technology has been the territory of science fiction. What happens if we are able to live those stories ourselves?
In this episode, we speak with Julia Rhodes Davis, a Senior Advisor at Data & Society, about her recent report "Advancing Racial Equity Through Technology Policy" published by the AI Now Institute.
An interview with University of British Columbia professor, Wendy Wong, about her book We, the Data: Human Rights in the Digital Age.
WFH surveillance is growing but employers need to move on.
The math of COVID-19 may mean some level of opt-in tracking is vital to stop repeated outbreaks
AI's potential is vast but many applications remain incremental, simply grafting AI onto outdated frameworks. This is why we embrace complexity. We think about designing incentives, understanding self-organization and distributed control, and planning for emergence and adaptation.
An interview with Steven Sloman, professor of cognitive, linguistic, and psychological sciences at Brown University, about LLMs and deliberative reasoning.
Discover how to prioritize exploration and exploitation in your analysis for optimal results.
A guide for improving decision making that will lead towards a happier life using nudges from our book Make Better Decisions.
Modularize tasks with generative AI to make them more achievable.
Access all digitized human knowledge by exploring with generative AI.
Developing the skill to craft effective prompts is a critical aspect of working with generative AI. It's about understanding what you want, knowing how to articulate that desire in a way the machine understands, and strategically using the AI's strengths to your advantage.
How to go beyond prompting and develop your conversational AI skills. Now, as more AI collaboration is designed inside of applications, do you still need to learn how to prompt? If you want to get the most out of AI, we would say, yes.
Artificiality Co-founders, Helen and Dave Edwards, will be speaking on Generative AI & Data Culture at the Starbucks Innovation Expo on May 14 & 15 in Seattle.
Artificiality Co-founders, Helen and Dave Edwards, will be speaking on Rethinking Value in the Age of AI at the EY Innovation Realized conference at the Palace of Fine Arts in San Francisco on April 16 & 17.
Artificiality Co-founders, Helen and Dave Edwards, will be attending the ASU+GSV Summit in San Diego on April 14 and 15.
The Artificiality Weekend Briefing: About AI, Not Written by AI
