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The Path to Robust deAGI | Ben Goertzel SCaLE 23x

The Path to Robust deAGI asks what it would take to build artificial general intelligence that is both powerful and structurally aligned with human flourishing—not just steered by after‑the‑fact safety patches. Ben Goertzel, CEO of SingularityNET and a founding member of the Artificial Superintelligence (ASI) Alliance, will outline how a decentralized, token‑coordinated ecosystem—combining ASI: Chain, Hyperon AGI, and community‑owned GPU clouds—can prevent AGI from being captured by any single corporation or state.

Goertzel will contrast centralized AGI roadmaps with a deAGI approach that bakes openness, diversity of values, and economic inclusion into the architecture itself, drawing on ideas like pluralistic training data, interoperable agent networks, and on‑chain governance of key system upgrades. He will also discuss technical milestones toward “robust” deAGI—modular cognitive architectures, decentralized marketplaces for AI services, and verification mechanisms that let communities audit and constrain AGI behavior—framing them as concrete steps toward an AGI that advances joy, growth, and choice for all rather than amplifying existing power imbalances.

Overview of Kwaai.
Kwaai is a registered 501©3 non-profit organization and open source AI research and development lab. Its mission is to democratize artificial intelligence by building open source Personal AI systems that prioritize user privacy, data ownership, and transparency. Kwaai operates as a volunteer-based initiative and invites technologists, researchers, policy experts, and community members to join its efforts.

What is Personal AI?
Kwaai’s vision of Personal AI is an assistant that users own and control. This AI:

Is trained on the user’s own data and experiences.

Runs locally on personal devices or on a peer to peer fabric, without requiring a SaaS subscription.

Continue reading “The Path to Robust deAGI | Ben Goertzel SCaLE 23x” | >

Lab-on-a-chip platform shows how immune cells attack cancer cells

Immunotherapies are a promising approach in the fight against cancer. Researchers at the Technical University of Munich (TUM) have developed a lab-on-a-chip system called CellTrap. It makes it possible to observe the interactions between immune cells and cancer cells at the single-cell level. The method is intended to reveal fundamental processes in cancer immunology and answer key questions. The technology is described in the journal RSC Advances.

Established laboratory tests mainly capture average values across many cells and show, for example, how many cancer cells survive after contact with immune cells. What happens in detail—how each cell reacts and interacts with others—remains hidden. However, to better understand the effectiveness of immunotherapies, the precise timing of a cell-cell interaction is often crucial: when contact, activation and, ultimately, the killing of the cancer cell occur.

Gene tied to energy production in brain could lead to new treatment for cognitive disorders

Researchers in the Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo have discovered a connection between a specific gene and healthy brain function. “The hope is that this discovery could eventually lead to expanded treatment for psychiatric and neurological disorders such as schizophrenia, bipolar disorder and autism,” explains Mikhail V. Pletnikov, MD, Ph.D., professor and chair of the Department of Physiology and Biophysics, the senior author of the study with Kateryna (Kate) Murlanova, Ph.D., the first lead author and a research scientist in the department.

They discovered that the NPAS3 gene expressed in astrocytes—the cells that help with brain chemistry—regulates the energy production required to support thinking and memory. NPAS3 is a transcription factor, which means it directs how certain genes work and influences how cells function. Their findings are published in Science Advances.

“Previous studies have linked NPAS3 to conditions involving cognitive problems, such as schizophrenia, but scientists didn’t know exactly how it might be involved,” Pletnikov says.

The Role of Tau Pathology in Alzheimer’s Disease and Down Syndrome

Background: Individuals with Down syndrome (DS) exhibit an almost complete penetrance of Alzheimer’s disease (AD) pathology but are underrepresented in clinical trials for AD. The Tau protein is associated with microtubule function in the neuron and is crucial for normal axonal transport. In several different neurodegenerative disorders, Tau misfolding leads to hyper-phosphorylation of Tau (p-Tau), which may seed pathology to bystander cells and spread. This review is focused on current findings regarding p-Tau and its potential to seed pathology as a “prion-like” spreader. It also considers the consequences of p-Tau pathology leading to AD, particularly in individuals with Down syndrome. Methods: Scopus (SC) and PubMed (PM) were searched in English using keywords “tau AND seeding AND brain AND down syndrome”

Gene therapy shows promise in ARC syndrome, a deadly childhood liver disease

A new gene therapy has been used to successfully treat a deadly childhood liver disease in mice that model the disease, according to researchers at UCL and Great Ormond Street Hospital. Arthrogryposis, renal dysfunction and cholestasis (ARC) syndrome is a lethal genetic disorder usually caused by a lack of the VPS33B protein, with children diagnosed with the condition rarely living beyond their first year of life.

Now, in a study published in Nature Communications, the UCL-GOSH team found that by injecting a healthy version of the gene into the body, they can treat the condition in mice lacking VPS33B. Crucially, the final version of the treatment, which specifically targeted the liver cells, caused no harm. In the earlier versions, the genes became abnormally activated and caused cancerous cells to grow and expand in some cases.

While more tests must be done before the treatment can be tested in humans, the researchers’ breakthrough offers hope to babies with this devastating disorder and their families. In the UK, as many as six pregnancies per year might be affected by ARC syndrome. Furthermore, the findings may promote improved understanding of why some treatments may cause cancer.

ChartNet trains AI to read charts, boosting smaller models past commercial rivals

To accelerate and refine decision-making in a fast-paced, global marketplace, enterprises may deploy generative artificial intelligence models to help summarize and interpret the charts that often fill market summaries and financial reports.

But even the latest vision-language models sometimes struggle with this task, since it requires a model to integrate visual, numerical, and linguistic understanding. A company that invests in a state-of-the-art model might still receive inaccurate or incomplete information.

To fill this performance gap, researchers from MIT and the MIT-IBM Computing Research Lab developed a multifaceted resource for AI users that is specifically designed to teach vision-language models (VLMs) how to effectively interpret charts.

Corrected microbial family tree offers statistically sound model for how earliest life forms evolved

In this era of Big Data, the prevailing wisdom is that more information leads to better answers. However, a new Canadian study shows that in the hunt for life’s ancient ancestors, more data can actually lead to less truth. Published in the Proceedings of the National Academy of Sciences, the research by UdeM associate professor of computer science Miklós Csűrös reveals that standard methods for reconstructing the genomes of ancient microbes are being overwhelmed by an explosion of information.

This paradox causes current models to “hallucinate” evolutionary events—specifically, an implausibly high number of horizontal gene transfers—that are actually just statistical ghosts, the study shows.

In it, Csűrös identifies a crisis point in evolutionary biology: As researchers try to reconcile thousands of gene sequences across the entire tree of life, the actual evolutionary signal begins to vanish, replaced by mathematical noise.

Moral inconsistency is based on the vmPFC’s insufficient representation across tasks and connectedness

A new Cell Reports study looked at why people sometimes judge others harshly for dishonest behavior while excusing similar behavior in themselves. The researchers call this moral inconsistency: a mismatch between the moral standards someone uses to judge others and the standards they apply to their own behavior. The study used an honesty-versus-profit task, where participants could gain money by being dishonest, and then judged both their own behavior and other people’s behavior.

The main finding was that people who were more morally inconsistent showed weaker involvement of the ventromedial prefrontal cortex, or vmPFC, a brain region involved in value-based decision-making, social judgment, emotion regulation, and moral evaluation. In morally consistent participants, the vmPFC seemed to represent moral judgment more similarly across “judging myself” and “judging others.” In morally inconsistent participants, that cross-task representation was weaker, especially when they were making choices for themselves.

Liu. V, et al. find that moral inconsistency arises from a reduced ability of the vmPFC to form a cross-task representation of moral principles and its connectedness during the moral behavior task. This indicates that individuals with higher moral inconsistency consider moral principles less often to guide their own behavior.

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