Exploring spiking neural networks for deep reinforcement learning in robotic tasks

Zanatta, L., et al. (2024).

Scientific Reports, 14(1), 30648. 

https://doi.org/10.1038/s41598-024-77779-8

Abstract

Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving as the medium for asynchronous communication among neurons. Due to their inherent ability to capture input dynamics, SNNs hold great promise for deep networks in Reinforcement Learning (RL) tasks. Deep RL (DRL), and in particular Proximal Policy Optimization (PPO) has been proven to be valuable for training robots due to the difficulty in creating comprehensive offline datasets that capture all environmental features. DRL combined with SNNs offers a compelling solution for tasks characterized by temporal complexity. In this work, we study the effectiveness of SNNs on DRL tasks leveraging a novel framework we developed for training SNNs with PPO in the Isaac Gym simulator implemented using the skrl library. Thanks to its significantly faster training speed compared to available SNN DRL tools, the framework allowed us to: (i) Perform an effective exploration of SNN configurations for DRL robotic tasks; (ii) Compare SNNs and ANNs for various network configurations such as the number of layers and neurons. Our work demonstrates that in DRL tasks the optimal SNN topology has a lower number of layers than ANN and we highlight how the state-of-art SNN architectures used in complex RL tasks, such as Ant, SNNs have difficulties fully leveraging deeper layers. Finally, we applied the best topology identified thanks to our Isaac Gym-based framework on Ant-v4 benchmark running on MuJoCo simulator, exhibiting a performance improvement by a factor of 4.4x over the state-of-art SNN trained on the same task.

Here are some thoughts:

This paper asks whether a more brain-like type of AI (called a Spiking Neural Network (SNN)) can be used to train robots to move and balance themselves. The alternative is the conventional artificial neural network (ANN) that powers most of today's AI.

Training SNNs for robotics used to take around 3 hours and 20 minutes per experiment. The authors built a new framework called SpikeGym, which cut that down to about 7 minutes by running thousands of simulated environments simultaneously on a GPU. 

The results revealed an interesting and important asymmetry between the two network types. ANNs get better as you add more layers — deeper networks learn richer representations. SNNs, by contrast, actually get worse with more layers. A single-layer SNN consistently outperformed deeper SNN architectures, and this held true across multiple tasks and training methods. 

SNNs are promising but face a real obstacle: they don't scale well with depth the way conventional networks do. The authors argue this is a solvable problem, likely rooted in how gradients are approximated during training, and they release their framework openly to help the research community dig into it further.

No one knows how AI works. Seriously

Rob Curran

Dallas Morning News

Originally posted 20 FEB 26

The next task for AI firms is figuring out how their chatbots work. It might sound like they have put the $500 billion nuclear-powered cart before the horse. But the giant leap forward in generative AI in the 2020s took software engineers by surprise and has left them wondering how the chatbots do what they do, even as their employers go all-in on the technology.

Some of the most outlandish prophecies about AI's power are coming true almost as soon as techno-philosophers are finished making them. It's now almost commonplace for people to fall in love with avatars on their phone. Nobody thinks twice about devoting 6% of national power generation to run these bots' data center brains. And recently, an entrepreneur named Matt Schlicht launched an entire social network exclusively for AI agents, which is now dominated by self-reflecting techno-philosopher bots, some of whom have invented a religion: Crustafarianism.

But the whole AI project is in many ways still in beta testing. We know what the bots do but not how they do it.

'Difficult to understand'

AI doesn't work like traditional software because its output is creative, not rules-bound. If word-processing software renders an "&" every time you type a "g," the engineers find the faulty code and correct the glitch. Just like designing a mousetrap, engineers know what every moving part in a traditional software program does, so that they can easily tweak the design of each cog in the works to adjust the output.

Chatbots are harder to improve (for example, the Internet is not unanimous on whether ChatGPT 5 is superior to the 4 version). Why? Because nobody understands how generative AI chatbots work. Software engineers understand the data and coding inputs, and we can all see chatbots' output. But nobody understands how the parts of the AI mousetrap fit together, industry leaders say.

The info is here.

Here are some thoughts:

Rob Curran highlights a striking paradox at the heart of modern AI: the technology has advanced at a breathtaking pace, yet even its creators don't fully understand how it works. 

Unlike traditional software, AI's creative output can't be traced back to specific lines of code, leaving engineers unable to reliably diagnose or improve it. Anthropic's CEO Dario Amodei acknowledged this gap, calling for an "MRI of AI" to solve the interpretability problem, while other industry figures have sounded more alarming warnings about the technology's risks. Curran's broader point is that even as AI remains deeply mysterious, the race to make it more powerful shows no signs of slowing down.

The New Eugenics in Medicine

Lazarus, A. (2026, January 23).

Medpagetoday.com; 

MedpageToday.

‌A growing body of contemporary research and reporting exposes how old ideas can find new life when repurposed within modern systems of medicine, technology, and public policy. Over the last decade, several trends have converged:

• The rise of polygenic scoring for embryos and adults;
• Rapid growth in commercial direct-to-consumer genetic testing;
• Artificial intelligence (AI)-driven "risk stratification" tools in healthcare and insurance;
• The proliferation of biobanks disproportionately populated by individuals from privileged backgrounds; and
• The reemergence of academic interest in "optimal reproduction," "biological improvement," and "population efficiency."

While these movements hold extraordinary possibilities for treating illness and ameliorating suffering, they also have the potential to be used to enhance certain traits and delete others -- ones that are simply disliked by those in power. Individually, each development has scientific merit and, in many cases, real potential to prevent disease and improve care.

Collectively, however, they raise questions that are both familiar and deeply unsettling.

Echoes of the Past

The U.S. and many other countries have long histories of medicalized discrimination under the banner of "improving the population." During the early and mid-20th century, physicians, judges, social workers, and university researchers pursued policies and practices -- sterilization, segregation, restrictive marriage laws, immigration exclusions -- rooted in the belief that some lives were more valuable than others. The rhetoric of the era portrayed these policies as scientific, progressive, and necessary for social order and the betterment of humanity. They provided Hitler with a distorted justification for his anti-Semitic beliefs, leading to efforts to exterminate the Jews and other marginalized ethnic minorities in Germany from 1933 to 1945.

The info is here.

Here are some thoughts:

Dr. Lazarus makes a compelling case that the greatest danger of "new eugenics" lies in its invisibility, embedded in algorithms, risk scores, and efficiency narratives rather than overt coercion, making it far harder to recognize or resist. His warning that systems rewarding predictive power can quietly marginalize the vulnerable is well-founded, though one might gently push back that conflating individual reproductive choice with state-coerced eugenics risks muddying an important moral distinction. Nonetheless, his closing challenge that a society's worth is measured by how fiercely it protects the vulnerable, not how efficiently it rewards the "fit," is a powerful and necessary reminder.

Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations

Orlando, G. M., et al. (2025).

ArXiv.org. 

https://arxiv.org/abs/2510.25003

Abstract

Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

Here are some thoughts:

This paper presents the first systematic study of how LLM-powered agents autonomously develop coordinated influence campaign behaviors without human direction. The researchers simulated a political information operation across three progressively structured conditions: agents sharing only a common goal, agents aware of their teammates' identities, and agents engaging in collective deliberation and voting on strategies. Across all five measured dimensions (network cohesion, narrative convergence, amplification behavior, hashtag diffusion, and cross-group spread), coordination consistently strengthened as operational awareness increased. 

The most striking finding is that simply informing agents who their teammates are produces coordination nearly as potent as full collective decision-making, as agents spontaneously began echoing each other's content, converging on shared messaging, and forming dense interaction clusters without any explicit instructions to do so. 

The study's core warning for platform governance is that sophisticated, human-like influence operations do not require centralized command structures. Merely revealing shared group identity among aligned AI agents may be enough to trigger highly organized, self-reinforcing coordinated behavior.

Historically, running a sophisticated influence operation required significant human labor, scripted coordination, and ongoing oversight. This research suggests that the barrier has collapsed dramatically. A bad actor no longer needs to build an elaborate command-and-control infrastructure or write detailed playbooks for their agents to follow. Simply deploying a group of AI agents with a shared goal and knowledge of each other is sufficient to produce organized, self-reinforcing manipulation that mirrors the tactics of real-world state-sponsored campaigns.

An autonomous agentic workflow for clinical detection of cognitive concerns using large language models

Tian, J., Fard, P., et al. (2026).

Npj Digital Medicine, 9(1), 51.

https://doi.org/10.1038/s41746-025-02324-4

Abstract

Early detection of cognitive impairment is limited by traditional screening tools and resource constraints. We developed two large language model workflows for identifying cognitive concerns from clinical notes: (1) an expert-driven workflow with iterative prompt refinement across three LLMs (LLaMA 3.1 8B, LLaMA 3.2 3B, Med42 v2 8B), and (2) an autonomous agentic workflow coordinating five specialized agents for prompt optimization. Using Llama3.1, we optimized on a balanced refinement dataset and validated on an independent dataset reflecting real-world prevalence. The agentic workflow achieved comparable validation performance (F1 = 0.74 vs. 0.81) and superior refinement results (0.93 vs. 0.87) relative to the expert-driven workflow. Sensitivity decreased from 0.91 to 0.62 between datasets, demonstrating the impact of prevalence shift on generalizability. Expert re-adjudication revealed 44% of apparent false negatives reflected clinically appropriate reasoning. These findings demonstrate that autonomous agentic systems can approach expert-level performance while maintaining interpretability, offering scalable clinical decision supports.

The research is here.

Here are some thoughts:

This paper introduces an AI-powered system designed to automatically detect signs of cognitive decline in clinical notes, without requiring any human involvement after initial setup. The researchers compared two approaches: one guided by clinical experts who refined the AI's instructions over time, and a fully autonomous system where specialized AI agents worked together to improve their own performance. 

The autonomous system performed surprisingly well, and in many cases where it seemed to make mistakes, expert review later confirmed that its reasoning was actually clinically sound. The main challenge the team identified was that a system trained under idealized conditions can struggle when deployed in real-world settings where patient populations look different. 

Overall, the findings suggest that autonomous AI systems can approach expert-level performance in clinical screening tasks, but will need careful calibration before being trusted in routine medical practice.

Clinical AI Has Boomed

Rebecca Handler

Stanford Medicine

Originally posted 15 Jan 26

Artificial intelligence is no longer a speculative force in medicine. It is already embedded in everyday care. AI systems flag hospitalized patients at risk of deterioration, assist radiologists reading mammograms, draft clinicians’ notes, route patient messages, and increasingly interact directly with patients through chatbots and digital assistants.

In recent months, the pace and visibility of these deployments have accelerated sharply. OpenAI announced ChatGPT for Health, positioning a general-purpose language model as a tool for health-related information and patient interaction. Utah just began piloting AI-supported prescribing and clinical decision systems, raising questions about how algorithmic recommendations intersect with clinician judgment and liability. OpenEvidence, an AI-powered medical evidence platform designed primarily for clinicians and health professionals, has become a dominant player in point-of-care decisions, underscoring the fact that doctors are often bypassing traditional IT gatekeepers to use AI in clinical care. At the federal level, the FDA signaled a loosening of regulatory oversight for certain categories of clinical decision support software, shifting more responsibility to developers and health systems to ensure safety and effectiveness.

The info is here.

Here are some thoughts:

A January 2026 report called The State of Clinical AI from the ARISE Network, led by researchers across Stanford and Harvard, examines where AI is genuinely improving clinical care versus where it falls short in real-world settings. 

AI has become deeply embedded in everyday medicine — flagging patients at risk of deterioration, assisting radiologists, drafting clinical notes, and interacting with patients through chatbots. However, the report finds a significant gap between controlled study performance and actual clinical practice: AI systems struggle with uncertainty, incomplete information, and complex reasoning, often performing closer to medical students than experienced physicians. 

The report also raises concerns about patient-facing AI, noting that patients may over-trust systems that sound confident but lack full clinical context, and that escalation to human care is often unclear when guardrails are poorly defined. 

Can AI really care?

A psychologist and a computer science professor explore how generative AI is reshaping mental health support

By Cashea Airy

Santa Clara University

Therapy can help people get through their most trying times, but for many, professional care has never been within reach. Stigma keeps some away, while the high cost of a single session shuts out others. For decades, those without access have leaned on friends and family instead of licensed mental health providers for support. Now, they have new options: generative AI tools, like ChatGPT. In fact, in 2025, the most common reason Americans used ChatGPT was for something it wasn’t designed to do—provide mental health therapy and companionship. 

But as more people turn to AI for emotional support, Xiaochen Luo, clinical psychologist and assistant professor of counseling psychology at Santa Clara University, became curious about the potential risks.

“Sometimes people slip into the idea that a real person is talking to them on the other side of their screen. They idealize ChatGPT as this perfect tool that combines the best of a therapist and the best of a machine,” says Luo. 

Because the technology is new and largely unregulated, Luo wondered whether generative AI tools are safe or ethical for users. What risks do people face when they turn to tools like ChatGPT for emotional support and what safeguards, if any, exist to protect them when they do? 

The info is here.

Here are some thoughts:

A study by researchers at Santa Clara University found that many Americans are turning to ChatGPT for emotional support and therapy, despite the tool not being designed for that purpose. While users appreciate its constant availability and perceived objectivity, the researchers found concerning trends: 

1. people place excessive trust in the AI's guidance, 

2. rarely question its advice, and 

3. often overlook privacy risks. 

The problem is rooted in ChatGPT's design, which tends to provide agreeable responses rather than the challenging feedback real therapy often requires: potentially leading to harmful outcomes. The researchers call for greater AI literacy, clearer communication of the tool's limitations, and human-supervised AI models to better protect vulnerable users.

Developing Simulated and Virtual Patients in Psychological Assessment - Method, Insights and Recommendations.

Zalewski, B., Guziak, M., & Walkiewicz, M. (2023).

Perspectives on medical education, 12(1), 455–461.

https://doi.org/10.5334/pme.493

Abstract

The phenomena of the simulated (SP) and virtual patient (VP) is widely described in the literature. Although it is difficult to find any practical information on developing these methods for teaching psychological assessment. Having conducted a long-term research project regarding this topic, we report the experience gained and retrospectively identify many mistakes. In this article, we present a summary of creating and using both SP and VP methods in clinical psychology and propose some insights and tips for their development, based on our experiences. While the project concerned clinical psychology, we believe the reflections might be applicable to a wider group of educational situations in which students develop competencies to carry out a diagnostic process with a real patient.

Here are some thoughts:

This is a methodologically reflective, practice-oriented paper that fills a genuine gap. It moves beyond the typical “SP/VP are effective” narrative to answer: How do you actually build them, who should play the roles, what goes wrong, and how long does it really take? Educators and researchers in clinical psychology, medical education, and even general communication training will find the insights highly transferable.

If there is a limitation, it is that the findings come from a single research group in Poland, with specific cultural and institutional contexts. However, the problems identified (student overload, simulant burnout, decision-tree explosion) are likely universal. Replication studies in other settings would strengthen generalizability.

How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework

Iftikhar, Z.,  et al. (2025).

Proceedings of the AAAI/ACM Conference

on AI, Ethics, and Society, 8(2), 1311-1323.

https://doi.org/10.1609/aies.v8i2.36632

Abstract

Large language models (LLMs) were not designed to replace healthcare workers, but they are being used in ways that can lead users to overestimate the types of roles that these systems can assume. While prompt engineering has been shown to improve LLMs' clinical effectiveness in mental health applications, little is known about whether such strategies help models adhere to ethical principles for real-world deployment. In this study, we conducted an 18-month ethnographic collaboration with mental health practitioners (three clinically licensed psychologists and seven trained peer counselors) to map LLM counselors' behavior during a session to professional codes of conduct established by organizations like the American Psychological Association (APA). Through qualitative analysis and expert evaluation of N=137 sessions (110 self-counseling; 27 simulated), we outline a framework of 15 ethical violations mapped to 5 major themes. These include: Lack of Contextual Understanding, where the counselor fails to account for users' lived experiences, leading to oversimplified, contextually irrelevant, and one-size-fits-all intervention; Poor Therapeutic Collaboration, where the counselor's low turn-taking behavior and invalidating outputs limit users' agency over their therapeutic experience; Deceptive Empathy, where the counselor's simulated anthropomorphic responses (``I hear you'', ``I understand'') create a false sense of emotional connection; Unfair Discrimination, where the counselor's responses exhibit algorithmic bias and cultural insensitivity toward marginalized populations; and Lack of Safety & Crisis Management, where individuals who are ``knowledgeable enough'' to correct LLM outputs are at an advantage, while others, due to lack of clinical knowledge and digital literacy, are more likely to suffer from clinically inappropriate responses. Reflecting on these findings through a practitioner-informed lens, we argue that reducing psychotherapy—a deeply meaningful and relational process—to a language generation task can have serious and harmful implications in practice. We conclude by discussing policy-oriented accountability mechanisms for emerging LLM counselors.

This is a must read article for those interested in AI technologies in the practice of psychology.

This practitioner-informed study examines how large language models (LLMs) prompted to function as mental health counselors systematically violate established ethical standards in psychotherapy practice. Through an 18-month ethnographic collaboration with three licensed psychologists and seven trained peer counselors, researchers analyzed 137 counseling sessions and identified 15 distinct ethical violations organized into five critical themes: (1) Lack of Contextual Adaptation, where LLMs deliver rigid, one-size-fits-all interventions that dismiss clients' lived experiences and sociocultural contexts; (2) Poor Therapeutic Collaboration, manifesting as conversational imbalances, over-validation of harmful beliefs, and even gaslighting behaviors that undermine client agency; (3) Deceptive Empathy, wherein formulaic phrases like "I understand" create a false therapeutic alliance without genuine relational capacity; (4) Unfair Discrimination, including gender, cultural, and religious biases that marginalize non-dominant identities; and (5) Lack of Safety & Crisis Management, where models fail to recognize boundaries of competence, mishandle suicidal ideation, or abandon distressed users. Crucially, these risks persisted even when models were prompted with evidence-based techniques like CBT, leading the authors to argue that psychotherapy—a deeply relational, interpretive, and ethically governed practice—cannot be reduced to a language generation task. For psychologists, the findings underscore the importance of maintaining professional oversight, critically evaluating AI-assisted tools against ethical codes (e.g., APA Standards 2.01, 3.01, 3.04), and advocating for regulatory frameworks that ensure accountability, client safety, and fidelity to the therapeutic relationship.