Jeff Hawkins Is Finally Ready to Explain His Brain Research (Published 2018)

Jeff Bezos Is Funding a Wild Hunt for the Brain’s ‘Core Algorithm’

With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.

The Grug Brained Developer

this collection of thoughts on software development gathered by grug brain developer

Graham Fleming | Software & Hardware Engineer

The Human Co-Pilot for Early-Stage Founders. Expert software & hardware engineer specializing in Next.js, AI integration, scaling infrastructure, and fixing tech debt.

Care at the Edge of Automation – Topos Institute

Technologies don’t just solve problems, they change us. We invent technologies, and they invent us in turn, shaping our lives and worlds. This is the phenomenon that Terry Winograd and Fernando Flores, in Understanding Computers and Cognition (1986), called “ontological design.” It matters now more than ever—along with a second lesson they saw clearly. Technological research is always guided (and sometimes misguided) by deep ontological assumptions about, e.g., the nature of cognition, agency, and communication. If we are to create technologies that truly serve human flourishing and care, we must bring these hidden assumptions into the open and question them at their roots.

The Second Brain That Grows Smarter and Lives on your Computer

How a spark from Andrej Karpathy, app called Obsidian, and “The Curator”, the app I built are changing the way I think.

The Mind-Expanding Ideas of Andy Clark

The tools we use to help us think—from language to smartphones—may be part of thought itself.

Brainrot: Deskilling and Addiction are Overlooked AI Risks | Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.

AGIHound on Twitter / X

"Demis Hassabis says our brains are likely to be approximate Turing machines."Dear Lord. This Alan Turing worship never ends. It's a cult. 🤦‍♂️I research the visual system of the brain. I've come to understand that the most important principle of intelligence is the precise… https://t.co/isLnLqjFkw— AGIHound (@TrueAIHound) May 5, 2026

Just back from a truly impactful few days at Asilomar for the Brain and Mind, hosted by the brilliant team at BrainMind. The spirit of the 1975 Recombinant DNA Conference felt palpable as we gathered… | Sean White

Just back from a truly impactful few days at Asilomar for the Brain and Mind, hosted by the brilliant team at BrainMind. The spirit of the 1975 Recombinant DNA Conference felt palpable as we gathered to chart ethical standards for the accelerating field of Neurotech. While our focus was on neuroethics, from investor diligence questions and board-level risk matrices to user/stakeholder engagement and toolkits for neuroentrepreneurs, the key themes shared a powerful parallel with AI Ethics: maintain and enhance human agency, combat misuse and mistrust, and acknowledge that innovation is moving faster than its guardrails. Both fields present novel challenges that demand we adapt our existing frameworks and principles to meet new challenges. At the heart of it all is human agency. Whether we're discussing neurodevices or large language models, our core responsibility as builders, executives, investors, and policymakers is to ensure people are informed, in control, and never burdened to fight for their own empowerment or autonomy. We have the fundamental responsibility to care about the people we serve. This commitment to responsible, human-centric development is fundamental to our work at Inflection AI. We're building with emotional intelligence at the core. Every product decision we make starts with a simple question: does this expand what people can do, or does it quietly shift the burden onto them? AI should work for the humans using it, amplifying judgment, not replacing it. Earning trust through transparency rather than demanding it through dependency. A special thank you to Diana Saville for once again outdoing herself at every event and to Michael McCullough, MD for his endless support of the ecosystem. And to all the Braingels who showed up and rolled up their sleeves! Let's translate the urgency from Asilomar into action and continue to drive positive change.

Why the Human Brain Project Went Wrong--and How to Fix It

Two years in, a $1-billion-plus effort to simulate the human brain is in disarray. Was it poor management, or is something fundamentally wrong with Big Science?

Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli

Understanding how the human brain processes the world around us is one of the greatest open challenges in neuroscience. Breakthroughs here could transform how we understand and treat neurological conditions affecting hundreds of millions of people — and improve AI systems by directly guiding their development from neuroscientific principles.

Partner with Neuroscience

Partner with J&J Neuroscience to collaborate on licensing, academia, venture deals and shared innovation across Alzheimer’s, Parkinson’s and more.

The brain in the machine: How AI could help explain how we think | IBM

Scientists are using large AI models to predict patterns of brain activity at scale, a development that researchers say is pushing neuroscience toward a new kind of digital imaging.

BrainMind

At this important juncture in neuroscience, the BrainMind ecosystem is combining the most talented of these groups in novel ways to collectively shepherd, support, and fund ideas that can be translated from labs into interventions that will benefit humanity.

Cognitive engineering

Cognitive engineering is an interdisciplinary field that applies principles from cognitive psychology, cognitive neuroscience, and human factors to design and develop engineering systems that effectively support or enhance human cognitive processes.[1][2] The field emerged in the 1980s when Donald Norman and others recognized the need to better understand how humans interact with complex technological systems.[3]