#knowledge-representation

#knowledge-representation

Neo4j Aura Agent is an end-to-end platform for creating agents, connecting them to knowledge graphs, and deploying to production in minutes. In this post, we'll explore the features of Neo4j Aura Agent that make this all possible, along with links to coded examples to get hands-on with the platform.

Generative AI is now incorporated into the workflow for many scholars across many disciplines, but the broader scientific community would benefit from taking stock of how this technology could truly benefit our work and how it might distract. We hope the symposium can provide clarity.

Frontier AI systems are simply not reliable enough to operate without human oversight in high-stakes physical environments. The Pentagon's demand was, in structural terms, a demand to eliminate the human's ability to redirect, halt, or override the system. Amodei's refusal was an insistence on maintaining State-Space Reversibility - the architectural commitment to keeping the human in the loop precisely because the system lacks the functional grounding to be trusted outside it.

Autonomous agents take the first part of their names very seriously and don't necessarily do what their humans tell them to do - or not to do. But the situation is more complicated than that. Generative (genAI) and agentic systems operate quite differently than other systems - including older AI systems - and humans. That means that how tech users and decision-makers phrase instructions, and where those instructions are placed, can make a major difference in outcomes.

The team, which is being led by Jülich neurophysics professor Markus Diesmann, will leverage the Joint Undertaking Pioneer for Innovative and Transformative Exascale Research (JUPITER) supercomputer for their simulation. JUPITER is currently the fourth most powerful supercomputer in the world according to the TOP500 list, and features thousands of graphical processing units. The team demonstrated last month that a " spiking neural network " could be scaled up and run on JUPITER, effectively matching the cerebral cortex's 20 billion neurons and 100 trillion connections.

In fact, I didn't even think to ask ChatGPT what might work in my favor if I just stayed the course.I was a "LLeMming": a term Lila Shroff uses to describe compulsive AI users in The Atlantic. Lila Shroff shares that just as the adoption of writing reduced our memory and calculators devalued basic arithmetic skills, AI could be atrophying our critical thinking skills.

Every year, poor communication and siloed data bleed companies of productivity and profit. Research shows U.S. businesses lose up to $1.2 trillion annually to ineffective communication, that's about $12,506 per employee per year. This stems from breakdowns that waste an average of 7.47 hours per employee each week on miscommunications. The damage isn't only interpersonal; it's structural. Disconnected and fragmented data systems mean that employees spend around 12 hours per week just searching for information trapped in those silos.

Consistent with the general trend of incorporating artificial intelligence into nearly every field, researchers and politicians are increasingly using AI models trained on scientific data to infer answers to scientific questions. But can AI ultimately replace scientists? The Trump administration signed an executive order on Nov. 24, 2025, that announced the Genesis Mission, an initiative to build and train a series of AI agents on federal scientific datasets "to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs."

By comparing how AI models and humans map these words to numerical percentages, we uncovered significant gaps between humans and large language models. While the models do tend to agree with humans on extremes like 'impossible,' they diverge sharply on hedge words like 'maybe.' For example, a model might use the word 'likely' to represent an 80% probability, while a human reader assumes it means closer to 65%.

Anthropic has released a new version of its mid-size Sonnet model, keeping pace with the company's four-month update cycle. In a post announcing the new model, Anthropic emphasized improvements in coding, instruction-following, and computer use. Sonnet 4.6 will be the default model for Free and Pro plan users. The beta release of Sonnet 4.6 will include a context window of 1 million tokens, twice the size of the largest window previously available for Sonnet.

Each of these achievements would have been a remarkable breakthrough on its own. Solving them all with a single technique is like discovering a master key that unlocks every door at once. Why now? Three pieces converged: algorithms, computing power, and massive amounts of data. We can even put faces to them, because behind each element is a person who took a gamble.

This process, becoming aware of something not working and then changing what you're doing, is the essence of metacognition, or thinking about thinking. It's your brain monitoring its own thinking, recognizing a problem, and controlling or adjusting your approach. In fact, metacognition is fundamental to human intelligence and, until recently, has been understudied in artificial intelligence systems. My colleagues Charles Courchaine, Hefei Qiu, Joshua Iacoboni, and I are working to change that.

For the past three years, the conversation around artificial intelligence has been dominated by a single, anxious question: What will be left for us to do? As large language models began writing code, drafting legal briefs, and composing poetry, the prevailing assumption was that human cognitive labor was being commoditized. We braced for a world where thinking was outsourced to the cloud, rendering our hard-won mental skills, writing, logic, and structural reasoning relics of a pre-automated past.

When a scientist feeds a data set into a bot and says "give me hypotheses to test", they are asking the bot to be the creator, not a creative partner. Humans tend to defer to ideas produced by bots, assuming that the bot's knowledge exceeds their own. And, when they do, they end up exploring fewer avenues for possible solutions to their problem.

Since AlexNet5, deep learning has replaced heuristic hand-crafted features by unifying feature learning with deep neural networks. Later, Transformers6 and GPT-3 (ref. 1) further advanced sequence learning at scale, unifying structured tasks such as natural language processing. However, multimodal learning, spanning modalities such as images, video and text, has remained fragmented, relying on separate diffusion-based generation or compositional vision-language pipelines with many hand-crafted designs.

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.

I've written about all of these before at greater length, but this is a short post because it's not about the technology or making a broader point, it's about me. These are rules for engaging with me, personally, on this topic. Others are welcome to adopt these rules if they so wish but I am not encouraging anyone to do so. Thus, I've made this post as short as I can so everyone interested in engaging can read the whole thing.