Steph van Schalkwyk

The concept of neuromodulating agentic memory is helpful for defining "steering behaviours" in generative AI pipelines. Vince Kellen PhD proposed a #neuromodulation approach to agentic AI memory. This idea gives many opportunities to bundle explainable memory operations in rather complex spatial memory architectures. In this post, we discuss an emerging neuromodulation formulation created by recursive generative agents that reason about the memory space using neuroscience knowledge present in the context. The resulting "steering bundles" help add more explainable features to the semantic processes of memory usage. This is an important aspect if we need a human-in-the-loop workflow. We separate memory usage algorithms from vector space methods, which, while powerful, offer only a limited "flavor" of retrieval for agentic reasoning. #Aiagens #agenticmemory #generativeAI #AI #system2 https://lnkd.in/gcbGSr7t

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Brazilian AI research company Neurogram™ has raised $3M in a seed round to expand its EEG data platform. Neurogram is a digital health platform focused on neurological health that makes electroencephalogram (EEG) reporting faster and easier. The secure, LGPD-compliant platform automates data collection, exam uploads, reporting, and delivery, allowing doctors to work from anywhere and send reports instantly with just one click. The seed round was led by Headline, the venture capital firm of Romero Rodrigues. The capital will be used to expand its EEG data platform, grow its technology and research teams, and strengthen its position in the neurological diagnostics market. Congratulations to founders Daniele De Mari, Heitor Ettori, and the Neurogram team. LatamList: https://lnkd.in/dmB26Kfw #HealthTech #AI #MedTech #VentureCapital #Funding #SeedRound #LatAm #Brazil #DigitalHealth

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Over the holiday break, I spent some time revisiting some of the foundational work shaping the field of scientific AI. One paper that stands out—published back in 2021—was co-authored by Damien Guého, now a Senior R&D Engineer at Geminus. The paper tackles a timeless challenge: how do you uncover the true physics of a system when all you have is sparse, noisy, imperfect data? Damien and his co-author's approach is both clever and elegant. Instead of a brute force deep learning strategy, they leveraged clever math to uncover a physics-grounded structure with interpretable dynamics—from messy, nonlinear signals. The result is a robust, reliable and generalizable solution that uses a fraction of the computational effort. They first solved the problem using a deep learning approach that required 7,720 parameters. They then applied their method that used just 3 terms and was 10,000× more accurate. This mindset is core to Geminus' mission to simplify engineering and operational decision making in complex systems where uncertainty abounds and data is sparse and messy. Work like that of Damien and his colleagues continues to inspire where we’re headed next. Proud of the team at Geminus Labs—and excited for what’s coming. Check out his paper here. https://lnkd.in/eHt-5tXX Watch this space - be sure to follow Geminus to be part of the conversation.

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AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence On a recent episode of the Dr. Data Show, my co-host Luba Gloukhova and I tackled a new paper authored by AI luminary Yann LeCun alongside other researchers. We had been tipped off by another co-author of the paper, AI researcher Philippe Wyder, who reached out on social media to say the paper related to our prior episode since it “shows a path out of our muddled AGI discourse by embracing specialization.” The paper proposes a much-needed pivot for the industry: Because the ever-popular notion of artificial general intelligence offers only a hazy, overzealous goal, these researchers have proposed a new North Star they call “superhuman adaptable intelligence.” Defining Superhuman Adaptable Intelligence What is SAI? The authors’ definition breaks down into two parts. First, SAI is capable of adapting to exceed humans at any task humans can do – but crucially, it espouses tackling one task at a time. Second, SAI can also adapt to useful tasks outside the human domain. SAI establishes a new goal for the industry. Instead of trying to build a singular, all-capable solution that can do everything a human can do, which is the goal represented by AGI, this framework champions a return to specialized, narrow AI. These researchers advocate leveraging massive amounts of data through self-supervised learning, as for example LLMs do, but they also advise that such tech then be adapted to solve specific problems. One of the most consequential insights highlighted by the authors is... Read the full article, my latest in Forbes: https://lnkd.in/gQqaExTs

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An important article I wrote that differentiates LLM assistance and agentic AI. Please read. 🔗 https://lnkd.in/eb8mJyjJ A note for those applying to jobs: This matters for your career too. When you're in an interview and someone asks what you built, be precise. If you used an LLM to help you write code faster, say that, don't call it an agent. If you built a RAG pipeline, call it retrieval-augmented generation, don't call it agentic. If you built a chatbot with a system prompt, own it for what it is. Hiring managers who know the difference will respect your precision. Hiring managers who don't know the difference need you to teach them. Either way, you win by naming it correctly. Overselling what you built doesn't just hurt your credibility when someone digs deeper, it tells the room you don't actually understand the architecture you're describing. Know what you built. Know what it's called. Know why it matters at that level. That's what gets you hired. #AI #AgenticAI #LLM #NLP #SportsAnalytics #DataScience #CareerAdvice #MachineLearning

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