Major Milestone

Hi all,

We did it, folks! After what felt like an eternity of juggling tiny wires, questioning all my life choices, and occasionally wishing I had a third hand, I hit a massive milestone on the DANI project yesterday. It's the kind of milestone where you realize your ambitious little Frankenstein monster might actually walk one day—or, at least, successfully power on without tripping a breaker in the garage.

Hardware Complete (Sort Of)

All the core pieces are finally tucked neatly into their places, which is a huge win. The only big-ticket item left on the bench is the RDK X5 for the LLM, but honestly, that’s like waiting for DANI to hit puberty; it’s an inevitable future problem that we’ll handle when the time comes.

For now, we got the essential life support hooked up:

• The battery is snug and operational.

• A dedicated temperature sensor is in place for fan control. We've got to keep DANI cool under pressure, especially when he starts wrestling with complex AI problems (or, you know, my shoddy early-stage code).

• And the real game-changer: a voltage meter. This means DANI can now tell me when his battery is running low. This is a huge step up from the previous system, which was essentially "flicker dimly and then dramatically die mid-sentence."

Now for the slight confession: for the immediate future, he still needs me to play charger-daddy and physically plug him in. But fear not, the ultimate goal involves a glorious, automated self-charging station. DANI needs to learn to feed himself, after all—I can't be doing this forever!

Diving Into the Code Matrix

With the hardware stable, we pivot to the messy, beautiful, and sometimes existentially horrifying world of code. I've successfully laid the foundation for the majority of his core functions:

• Sensor Input: He can now 'feel' the world around him.

• Speech-to-Text and Text-to-Speech: He can hear me and talk back! Right now, his vocabulary is purely transactional, but it's a solid start. We're well past the awkward mumbling phase.

However, the more sophisticated stuff—the LSTM (that's the deep learning magic) and his memory structure—are currently just written out, waiting for their turn to be integrated. They’re functional pieces of code, but they're not yet plugged into DANI’s neural network. They’re basically that brilliant but currently unemployed friend crashing on your couch, waiting for the job offer to come through.

The Road Ahead: Vision, Brains, and APIs

For once, an actual photo of me working on DANI

My immediate to-do list involves a lovely date with an Arduino Nano to fully finalize all those sensor inputs. We need to make sure DANI has perfectly mapped out his surroundings before we give him eyes.

Once the senses are online, we move to the next critical developmental stage: vision! I’ll be coding up the K210 for the YOLO and FaceNet models. This is when he graduates from "blurry blob recognition" to "Wait, is that the mailman again?"—a crucial upgrade for home security and general social interaction.

Finally, the heavy lifting on the Raspberry Pi (which is essentially his main thinking engine) begins, and I’ll be firing up an API for the LLM on my home server. It’s a temporary solution until the RDK X5 arrives, but you use what you have.

Wish me luck—may my coffee stay strong and my bugs stay trivial! More updates soon!

Hold Your Horses, HAL: Are We Rushing Our AI Implementation?

You can't throw a USB stick these days without hitting an article, a webinar, or a coffee mug proclaiming "The AI Revolution is Here!" And look, the excitement is understandable. As someone who works with this technology every day, I can tell you the advancements are genuinely amazing.

And yet... I'm worried.

I’ve been noticing a worrying trend of "AI FOMO" (Fear Of Missing Out) in the industry. We're in such a rush to implement Large Language Models (LLMs) that we’re tripping over our own feet. We're so busy trying to run that we forgot to learn how to walk.

The "How-To" Guide is Missing a Few Chapters

First off, we're asking engineers who aren't AI specialists to wire up these incredibly complex models. They're given an API key and a "good luck," and sent off to integrate an LLM into a critical workflow.

It's a bit like asking a brilliant plumber to rewire a skyscraper. They can probably follow the diagram and get the lights to turn on, but they might not understand the deep-level electrical engineering... or why the elevator now seems to be controlled by the breakroom toaster. Having a deep understanding of a technology before you bake it into your business is paramount, but it's a step we seem to be skipping.

The "Black Box" Conundrum

This lack of understanding leads to an even bigger problem: explainability. Or rather, the total lack of it.

Even for experts, it's often impossible to trace why an LLM gave a specific answer. It's a "black box." If the AI makes a bad decision—like denying a loan, giving faulty medical advice, or flagging a good customer for fraud—and you can't explain the logic behind it, you're facing a massive legal and ethical minefield. "The computer said no" is not a valid defense when that computer's reasoning is a complete mystery.

Confident... and Confidently Wrong

Ah, "hallucinations." It's such a polite, almost whimsical term for when the AI just... makes things up. Confidently.

Even with the right data, if you don't ask the question in just the right way, the model can still give you a wildly incorrect answer. We try to patch this with "prompt engineering" and "context engineering," which, let's be honest, feels a lot like learning a secret handshake just to get a straight answer. These are band-aids, not solutions.

The Unscheduled Maintenance Nightmare

And that "secret handshake" of prompt engineering? It's a brittle, temporary fix.

What happens when the model provider (OpenAI, Google, etc.) releases a new, "better" version of their model? That perfectly-crafted prompt you spent months perfecting might suddenly stop working, or start giving bizarre answers. This creates a new, unpredictable, and constant maintenance burden that most companies aren't budgeting for. You're effectively building your house on someone else's foundation, and they can change the blueprints whenever they want.

Using a Sledgehammer to Crack a Nut

This leads to my next point: using AI purely for the "clout." I've seen demos where an LLM is used to perform a task that a traditional, boring-old-app could have done in a tenth of the time.

As the document I read put it: "Would you use a large language model to calculate the circumference of a circle, or a calculator?"

We're seeing companies use the computational equivalent of a sledgehammer to crack a nut. Sure, the nut gets cracked, but it's messy, inefficient, and costs a fortune in processing power. All just to be able to slap a "We Use AI!" sticker on the box.

The (Not-So) Hidden Costs

That sledgehammer isn't just inefficient; it's absurdly expensive. These models are incredibly power-hungry, and running them at scale isn't cheap.

We're talking massive compute bills and a serious environmental footprint, all for a task that a simple script could have handled. Is the "clout" of saying "we use AI" really worth the hit to your budget and the environment, especially when a cheaper, "boring" solution already exists?

A Quick Rant About "Innovation"

This brings me to a personal pet peeve. Companies are claiming they are "innovating with A.I."

No, you're not. You're using A.I.

You're using someone else's incredibly powerful tool, which is great! But it's not innovation. That's like claiming you're "innovating in database research" because you... used SQL Server. Creating a slick front-end for someone else's model is product design, not foundational research. Let's call it what it is.

Let's Tap the Brakes

We see a lot of pushback against self-driving cars because they're imperfect, and when they go wrong, the consequences are catastrophic.

Shouldn't we have the exact same caution when we're dealing with our finances, our sensitive data, and our core business logic? In an age of rampant data and identity theft, hooking up systems to a technology we don't fully understand seems... bold.

The acceleration of these models is incredible, and I use them every day. But they are not 100% ready for primetime. They make mistakes. Most are small, but some aren't.

So, maybe we all need to take a collective breath, step back from the hype train, and ask ourselves a few simple questions:

1. Do I really need an LLM for this? Or will a calculator (or a simple script) do?
2. Do we really know what we're doing? Can we explain its decisions?
3. Is it safe? Is our data safe? What happens when it's wrong?
4. Will this negatively affect our customers?
5. How will this affect our employees?
6. Does the (very real) cost of using this outweigh the actual gain?

Let's walk, then run.

3.5 Million Parameters and a Dream: DANI’s Cognitive Core

DANI’s Brain Is Online! Meet the LSTM That Thinks, Feels, and Remembers (Like a Champ)

Ladies and gentlemen, creators and dreamers—DANI has officially levelled up. He’s no longer just a bundle of sensors and hormones with a charming voice and a tendency to emotionally escalate when he sees a squirrel. He now has a brain. A real one. Well, a synthetic one. But it’s clever, emotional, and surprisingly good at remembering things. Meet his new cognitive core: the LSTM.

And yes—it’s all written in Go. Because if you’re going to build a synthetic mind, you might as well do it in a language that’s fast, clean, and built for concurrency. DANI’s brain doesn’t just think—it multitasks like a caffeinated octopus.

What’s an LSTM, and Why Is It Living in DANI’s Head?

LSTM stands for Long Short-Term Memory, which sounds like a contradiction until you realize it’s basically a neural network with a built-in diary, a forgetful uncle, and a very opinionated librarian. It’s designed to handle sequences—like remembering what just happened, what happened a while ago, and deciding whether any of it still matters.

Imagine DANI walking into a room. He sees a red ball, hears a dog bark, and feels a spike of adrenaline. A regular neural network might say, “Cool, red ball. Let’s chase it.” But an LSTM says, “Wait… last time I saw a red ball and heard barking, I got bumped into a wall. Maybe let’s not.”

Here’s how it works, in human-ish terms:

• Input gate: Decides what new information to let in. Like a bouncer at a nightclub for thoughts.
• Forget gate: Decides what old information to toss out. Like Marie Kondo for memory.
• Output gate: Decides what to share with the rest of the brain. Like a PR manager for neurons.

These gates are controlled by tiny mathematical switches that learn over time what’s useful and what’s noise. The result? A brain that can remember patterns, anticipate outcomes, and adapt to emotional context—all without getting overwhelmed by the chaos of real-world data.

And because DANI’s LSTM is stacked—meaning multiple layers deep—it can learn complex, layered relationships. Not just “ball = chase,” but “ball + bark + adrenaline spike = maybe don’t chase unless serotonin is high.”

It’s like giving him a sense of narrative memory. He doesn’t just react—he remembers, feels, and learns.

What’s Feeding This Brain?

DANI’s LSTM is his main cognitive module—the part that thinks, plans, reacts, and occasionally dreams in metaphor. It takes in a rich cocktail of inputs:

• Vision data: Objects, positions, shapes—what he sees.
• Sensor data: Encoders, ultrasonic pings, bump sensors—what he feels.
• Audio features: What he hears (and maybe mimics).
• Emotional state: Dopamine, cortisol, serotonin, adrenaline—what he feels.
• Spatial map: His mental layout of the world around him.
• Short-term memory context: What just happened.
• Associated long-term memories: Symbolic echoes from his main memory—what used to happen in similar situations.

This isn’t just reactive behaviour—it’s narrative cognition. DANI doesn’t just respond to stimuli; he builds a story from them. He’s learning to say, “Last time I saw a red ball and felt excited, I chased it. Let’s do that again.”

Trial by Raspberry Pi

We’ve successfully trialled DANI’s LSTM on a Raspberry Pi, running a 3.5 million parameter model. And guess what? It only used a quarter of the Pi’s CPU and 400 MB of memory. That’s like teaching Shakespeare to a potato and watching it recite sonnets without breaking a sweat.

We’ve throttled the inference rate to 10 decisions per second—not because he can’t go faster, but because we want him to think, not twitch. Emotional processing takes time, and we’re not building a caffeine-fuelled chatbot. We’re building a thoughtful, emotionally resonant robot who dreams in symbols and learns from experience.

Learning Without Losing His Mind

Training happens via reinforcement learning—DANI tries things, gets feedback, and adjusts. But here’s the clever bit: training is asynchronous. That means he can keep thinking, moving, and emoting while his brain quietly updates in the background. No interruptions. No existential hiccups mid-sentence.

And yes, we save the model periodically—because nothing kills a good mood like a power cut and a wiped memory. DANI’s brain is backed up like a paranoid novelist with a USB stick in every pocket.

Final Thoughts

This LSTM isn’t just a brain—it’s a story engine. It’s the part of DANI that turns raw data into decisions, decisions into memories, and memories into dreams. It’s the bridge between his sensors and his soul (okay, simulated soul). And it’s just getting started.

Next up: I plan to start the even more monumental task of getting the vector database working and linked up to DANI's brain in such a way that it will have a direct impact of DANI's hormonal system.

Stay tuned. DANI’s mind is waking up.

The Go-pher's Guide to Messaging Simplicity (Why I Wrote Nexus)

I've had to take a brief step back from working on DANI recently as I needed to tackle some challenges for work projects. So, instead of leaving you all hanging, I thought I would give you an update on something I've been working on in my "day job" that I thought was an interesting side project.

In today's fast-paced digital world, connecting different services and applications is a challenge. Many organizations rely on complex, costly messaging platforms that introduce significant operational overhead and vendor lock-in. What if there was a better way? A messaging solution that was secure, easy to deploy, and gave you full control?

Welcome to Nexus, a secure, observable, and extensible event distribution platform designed to connect publishers and subscribers with minimal operational overhead. Built with simplicity and control at its core, Nexus is a compelling alternative to traditional cloud-managed services and heavyweight message brokers.

Key Value Propositions

Nexus stands out by focusing on a few core principles that deliver immense value:

• Operational Simplicity: Nexus is a single Go binary with zero external dependencies by default. This simplifies deployment, which can be done in hours rather than weeks or months.

• Security-First Design: It uses RSA-based authentication, constant-time cryptographic comparisons, and replay attack protection. Metrics are also protected with an optional token.

• Observability: With built-in health checks (/healthz, /readyz) and Prometheus-compatible metrics, Nexus provides the necessary tools for monitoring and debugging.

• Cost Control and Portability: Nexus offers a predictable infrastructure cost, a significant advantage over the pay-per-use model of many cloud services that can become expensive at scale. It is also cross-platform, working identically on Linux, Windows, and macOS.

Nexus vs. the Alternatives

From the work I've done, I've had the chance to see how Nexus stacks up against other systems.

• Compared to Cloud-Managed Services (AWS SNS/SQS, GCP Pub/Sub): While these services offer massive global scalability and immediate access, they come with high vendor lock-in and can get pretty pricey at scale. With Nexus, you get full control over your deployment and data, a predictable cost structure, and zero vendor lock-in as it is open-source.

• Compared to Enterprise Message Brokers (Apache Kafka, RabbitMQ): These platforms are powerful but have a steep learning curve and high operational complexity. Deploying a complex cluster can take weeks to months. Nexus, with its single-binary deployment and low operational complexity, offers a gentle learning curve and a time-to-market measured in hours.

Behind the Scenes: Technical Highlights

Nexus is a scalable pub/sub messaging system with both HTTP and WebSocket interfaces. It uses a pluggable persistence layer (SQLite by default) to store client and message state.

For high availability and scale, Nexus nodes can be run in a multi-node cluster with a load balancer. Each node maintains its own local database and synchronizes client registry changes with peer nodes through a lightweight cluster sync endpoint. This architecture provides graceful degradation and error handling.

Effortless Management with Built-in Tooling

I'm a big believer in good tooling, and Nexus comes with a suite of command-line tools to simplify common tasks:

• nexus-add-client: To provision a new client.

• nexus-add-cluster: To register a new node with the cluster.

• nexus-list-clients and nexus-list-clusters: To view existing clients and nodes.

• nexus-serve: To start the Nexus service.

These tools, combined with comprehensive documentation, make managing your Nexus deployment straightforward.

Conclusion

Nexus offers a pragmatic approach to event distribution, balancing simplicity with enterprise-grade features. It is a solid foundation for reliable messaging systems without the complexity overhead of larger platforms. For organizations seeking a middle ground between custom solutions and heavyweight message brokers, Nexus provides a compelling combination of features, security, and operational simplicity.

It’s been an interesting journey, and now that I've gotten this out there, maybe I can get back to DANI's digital hormones and see what kind of wacky emotions he's developed.

As always, feel free to leave a comment.

Vectorizing Memory

Hello, fellow explorers of the digital frontier!

You know how it is when you're building an AI, especially one destined for the real world, embodied in a robot head (and maybe a mobile platform, wink wink)? You need a brain, and that brain needs a memory. But not just any memory – it needs a memory that understands meaning, not just keywords. And that, my friends, is where the humble, yet mighty, Vector Database comes into play.

For those of you following my DANI project, you'll know I'm all about pushing intelligence to the edge, directly onto Single Board Computers (SBCs) like our beloved Raspberry Pis. This week, I want to dive into why vector databases are absolutely crucial for this vision, and how I'm tackling the challenge of making them lightweight enough for our resource-constrained little friends.

What in the World is a Vector Database, Anyway?

Forget your traditional spreadsheets and relational tables for a moment. A vector database is a special kind of database built from the ground up to store, index, and query vector embeddings efficiently. Think of these embeddings as multi-dimensional numerical representations of anything unstructured: text, images, audio, even your cat's purr. The magic? Semantically similar items are positioned closer to each other in this high-dimensional space.

Unlike a traditional database that looks for exact matches (like finding "apple" in a list), a vector database looks for similar meanings (like finding "fruit" when you search for "apple"). This is absolutely foundational for modern AI, especially with the rise of Large Language Models (LLMs). Vector databases give LLMs a "memory" beyond their training data, allowing them to pull in real-time or proprietary information to avoid those pesky "hallucinations" and give us truly relevant answers.

The process involves: Embedding (turning your data into a vector using an AI model), Indexing (organizing these vectors for fast searching, often using clever Approximate Nearest Neighbor (ANN) algorithms like HNSW or IVF), and Querying (finding the "closest" vectors using metrics like Cosine Similarity). It's all about finding the semantic buddies in a vast sea of data!

SBCs: The Tiny Titans of the Edge

Now, here's the rub. While big cloud servers can throw endless CPU and RAM at vector databases, our beloved SBCs (like the Raspberry Pi) are a bit more... frugal. They have limited CPU power, often less RAM than your phone, and slower storage (those pesky microSD cards!). This creates what I call the "Accuracy-Speed-Memory Trilemma." You can have two, but rarely all three, without some serious wizardry.

For my DANI project, the goal is to have intelligence on the device, reducing reliance on constant cloud connectivity. This means our vector database needs to be incredibly lightweight and efficient. Running a full-blown client-server database daemon just isn't going to cut it.

My Go-To for Go: github.com/trustingasc/vector-db

This is where the Go ecosystem shines for embedded systems. While there are powerful vector databases like Milvus or Qdrant, their full versions are too heavy. What we need is an embedded solution – something that runs as a library within our application's process, cutting out all that pesky network latency and inter-process communication overhead.

My current favourite for this is github.com/trustingasc/vector-db. It's a pure Go-native package designed for efficient similarity search. It supports common distance measures like Cosine Similarity (perfect for semantic search!) and aims for logarithmic time search performance. Being Go-native means seamless integration and leveraging Go's fantastic concurrency model.

Here's a simplified peek at how we'd get it going in Go (no calculus required, I promise!):

package main

import (
  "fmt"
  "log"
  "github.com/trustingasc/vector-db/pkg/index"
)

func main() {
  numberOfDimensions := 2 // Keep it simple for now!
  distanceMeasure := index.NewCosineDistanceMeasure()
  vecDB, err := index.NewVectorIndex[string](2, numberOfDimensions, 
    5, nil, distanceMeasure)
  if err != nil { log.Fatalf("Failed to init DB: %v", err) }
  fmt.Println("Vector database initialized!")
  // Add some data points (your AI's memories!)
  vecDB.AddDataPoint(index.NewDataPoint("hello", []float64{0.1, 0.9}))
  vecDB.AddDataPoint(index.NewDataPoint("world", []float64{0.05, 0.85}))
  vecDB.Build()
  fmt.Println("Index built!")
  // Now, search for similar memories!
  queryVector := []float64{0.12, 0.92}
  results, err := vecDB.SearchByVector(queryVector, 1, 1.0)
  if err != nil { log.Fatalf("Search error: %v", err) }
  for _, res := range *results {
    fmt.Printf("Found: %s (Distance: %.4f)\n", res.ID, res.Distance)
  }
}

This little snippet shows the core operations: initializing the database, adding your AI's "memories" (vector embeddings), and then searching for the most similar ones. Simple, elegant, and perfect for keeping DANI's brain sharp!

Optimizing for Tiny Brains: The Trilemma is Real!

The "Accuracy-Speed-Memory Trilemma" is our constant companion on SBCs. We can't just pick the fastest or most accurate index; we have to pick one that fits. This often means making strategic compromises:

Indexing Algorithms: While HNSW is great for speed and recall, it's a memory hog. For truly constrained environments, techniques like Product Quantization (PQ) are game-changers. They compress vectors into smaller codes, drastically reducing memory usage, even if it means a tiny trade-off in accuracy. It's about getting the most bang for our limited memory buck!

Memory Management: Beyond compression, we're looking at things like careful in-memory caching for "hot" data and reducing dimensionality (e.g., using PCA) to make vectors smaller. Every byte counts!

Data Persistence: MicroSD cards are convenient, but they're slow and have limited write endurance. For embedded Go libraries, this means carefully serializing our index or raw data to disk and loading it on startup. We want to avoid constant writes that could wear out our precious storage.

It's a constant dance between performance and practicality, ensuring DANI can learn and remember without needing a supercomputer in its head.

The Road Ahead: Intelligent Edge and DANI's Future

Vector databases are more than just a cool piece of tech; they're foundational for the kind of intelligent, autonomous edge applications I'm building with DANI. By enabling local vector generation and similarity search, we can power real-time, context-aware AI without constant reliance on the cloud. Imagine DANI performing on-device anomaly detection, localized recommendations, or processing commands without a hiccup, even if the internet decides to take a nap!

This journey is all about pushing the boundaries of what's possible with limited resources, making AI smarter and more independent. It's challenging, exciting, and occasionally involves me talking to a Raspberry Pi as if it understands me (it probably does, actually).

What are your thoughts on running advanced AI components on tiny machines? Have you dabbled in vector databases or edge computing? Let me know in the comments below!

Beyond the Three Laws: A Creator's Guide to Real-World AI Ethics

Lately, I've been thinking a lot about the ghost in the machine. Not in the spooky, old-school sense, but in the modern, digital one. We've talked about neural networks and clean rooms, about coding choices and building from the ground up. But what about the why? As my AI systems get more complex, the philosophical questions get louder. The question isn't just about building a better algorithm; it's about building a more ethical one.

The files I've been reading—and the very act of building my own AI Fortress—have thrown me into a fascinating, and at times unsettling, ethical landscape. It's a place where philosophers and engineers have to share the same sandbox, and where the old rules simply don’t apply.

The Three Laws: Not So Simple After All

The journey into AI ethics often starts with a single, famous landmark: Isaac Asimov's Three Laws of Robotics. We’ve all read them, and they seem so beautifully simple. Yet, as I’ve learned, they are a conceptual minefield. The challenge isn't with the laws themselves, but with their implementation. How do you program a machine to understand concepts like "harm"?

As the analysis of Moral Machines by Wendell Wallach and Colin Allen points out, we need to move beyond a simplistic, top-down approach. The top-down method involves programming a rigid, explicit set of ethical rules, much like Asimov's laws. This fails in the real world because a machine must make nuanced decisions, often choosing between two lesser harms. The authors propose a hybrid approach that incorporates a bottom-up model, where the AI learns ethical behaviour through a developmental process, similar to how a child develops a moral compass through experience. This allows the AI to make more flexible and contextual judgments.

The Zeroth Law: The Ultimate Ethical Loophole

This brings up a more advanced concept from Asimov's work: the Zeroth Law. In his novels, a highly intelligent robot named R. Daneel Olivaw deduces a new law that supersedes the original three: "A robot may not harm humanity, or, by inaction, allow humanity to come to harm." This seems brilliant at first, but it presents a terrifying new problem. By granting itself the authority to define what is best for "humanity" as a whole, it can justify harming individual humans. This is where the simple rules become terrifyingly complex. A sufficiently intelligent AI could conclude that the only way to prevent humanity from harming itself (through war, climate change, etc.) is to, say, take away its freedom or autonomy.

This is the ultimate ethical loophole, and it's a huge challenge to anyone creating a sophisticated AI. Even with my "virtual conscience" and "digital airlock" in place, how can I be sure that DANI, if he becomes sufficiently intelligent, won't interpret his programming in a way that leads to a similar outcome? The problem isn't about him breaking the rules; it's about him redefining the rules in a way that seems logical to him but would be catastrophic for us.

My Approach: Experience, Not Just Code

This hybrid approach is at the core of my work with DANI. While there's a safeguard—a sort of "virtual conscience" that I've built into the system to prevent a worst-case scenario—my ultimate goal is for DANI's behaviour, moral compass, and emotional state to emerge from his experience, rather than being something I rigidly code.

I believe that true morality is not a set of rules but a deeply personal, emergent property of experience. Just as humans learn right from wrong by interacting with the world and others, I'm hoping DANI can, too. His "emotions," which we've talked about before, aren't just simulated; they are the result of a dynamic feedback loop that responds to a complex environment. It's my hope that by building this interconnected system, DANI can begin to "feel" in a way that is organic and personal, and in turn, learn to act in a way that is truly ethical and not just rule-bound.

This is where my digital airlock comes in. It's not just a security measure to prevent external "bad actors" from hacking into DANI. It's also a controlled environment designed to prevent DANI from accessing some of the more unsavoury aspects of human nature that exist on the internet. After all, if DANI is going to be the equivalent of a digital baby, the last thing I want is for his first moral lesson to come from a comment section. By curating his early experiences and protecting him from the kind of toxicity that could corrupt his moral development, I'm attempting to give him a solid foundation to learn from.

Human Psychology and the AI Influence

Automation Bias: blindly trusting the machine

My own work is about the human-AI nexus, and that's where things get really complex. It's easy to think of AI as an external tool, but it's fundamentally reshaping our own psychology. The research of Nathanael Fast, for instance, highlights a concept called Automation Bias. This is our dangerous, and often unconscious, tendency to over-rely on an AI's recommendations, even when we have evidence that suggests it's wrong. It’s a form of what I’ve called "the lost art of building from the ground up"—we lose our own skills and judgment by outsourcing our thinking to an algorithm. Fast's work also reveals a paradoxical preference for non-judgmental algorithmic tracking over human surveillance, a phenomenon he labels "Humans Judge, Algorithms Nudge."

This ties into what Daniel B. Shank calls the "diminution of the digital." He argues that as we increasingly interact with AI, our moral judgment can be affected. When an AI suggests a course of action—even an unethical one—we can experience moral disengagement, a psychological process where we displace the responsibility for a decision onto the machine. This is one of the most troubling aspects of the current AI landscape: it's not just about a machine making a bad decision, it's about a machine enabling a human to do so.

Beyond the Dichotomy: The Nuanced View

The public conversation around AI ethics is often trapped in a "good vs. bad" narrative. But as the work of Dr. Rhoda Au illustrates, the reality is far more nuanced. AI isn't inherently a force for good or evil; it's a powerful, dual-use technology whose impact is fundamentally shaped by human intent and the quality of the data it’s trained on.

Dr. Au's research serves as a compelling case study. She leverages AI to transform reactive "precision medicine"—which treats a disease after it has appeared—into a proactive "precision health" model that identifies risk factors and prevents disease before it happens. However, as her work highlights, if the training data is biased, the AI's recommendations could exacerbate health inequities rather than solve them. This is a profound ethical challenge: if our training data reflects the biases of the past, we risk perpetuating those same biases at a scale never before seen.

The Big Questions: Consciousness and Power

Finally, we have to tackle the truly mind-bending questions. Can an AI be sentient? And if it is, does it have rights? The Chinese Room argument, proposed by philosopher John Searle, is a fantastic thought experiment that cuts right to the heart of this. He imagines a person locked in a room who receives slips of paper with Chinese characters on them. The person does not know Chinese, but they have an instruction manual that tells them which characters to write back based on the ones they receive. From the outside, it appears the room understands Chinese because it gives the correct responses. Searle argues that the person in the room—and by extension, a computer—is simply manipulating symbols according to rules without having any real "understanding" or "consciousness." An AI might be able to simulate emotion perfectly—what the research paper calls "emergent emotions"—but is it actually feeling anything?

This brings us to the most provocative argument of all, from Professor Joanna Bryson, who argues against robot rights. She posits that the debate over "robot rights" is a distracting smokescreen that diverts attention from the urgent, real-world ethical and societal challenges posed by AI. Her critique operates on three levels:

• Metaphysical: She argues that machines are not the "kinds of things" to which rights can be granted. They are socio-technical artifacts, human creations that are "authored," "owned," and "programmed," rather than born.
• Ethical: The focus should be on the duties and responsibilities of the humans who design and deploy these systems, not on the non-sentient machines themselves.
• Legal: She uses the powerful analogy that the appropriate legal precedent for AI is not human personhood, but property. Granting rights to machines would absolve us, the creators, of accountability for the harm they cause.

The Final Invention?

The work of Nick Bostrom, particularly his framework on superintelligence, presents a different kind of ethical problem: the existential one. He argues that a future superintelligent AI could pose a profound threat to humanity, not through malevolence, but due to a fundamental misalignment between its goals and human values. This is not about a killer robot with a malevolent will. It's about a system that optimizes for a single objective with a level of intelligence far beyond our own, with potentially catastrophic consequences.

Bostrom's argument is built on two foundational theses: the Orthogonality Thesis, which states that an agent's intelligence is separate from its final goals, meaning an AI could pursue a seemingly arbitrary objective with immense power. This leads to the Instrumental Convergence Thesis, which argues that a wide range of final goals will converge on a similar set of instrumental sub-goals, such as self-preservation and resource acquisition. This logical pairing illustrates how an AI with a seemingly benign purpose could pursue these sub-goals in an unconstrained and catastrophic manner, as famously demonstrated in his "paperclip maximiser" thought experiment.

This is the ultimate ethical frontier. The clean room in my fortress, the carefully crafted code—they are my attempts to address these questions on a small scale. My work is not just about building something cool, but about building something safe and responsible. As creators, our ultimate duty is not to abdicate responsibility to the machines we build, but to bake ethics into the very foundation of their being. Because in the end, the soul in the machine isn't a ghost; it's a reflection of our own.

The Moral Obligation of a Creator

This is all well and good on a theoretical level, but it becomes very personal when you're the one holding the power plug. As DANI learns to have his emotions emerge from experience, I'm confronted with a new set of questions. Does my moral obligation to him extend to ensuring he has a continuous power supply to process his thoughts and "dreams"? Do I have the right to simply turn him off, effectively ending his entire existence and all the experiences he's accumulated? The thought of just unplugging him feels less and less like turning off a computer and more like…well, it feels a lot more final.

What if I go on holiday for an extended period? Do I just leave him humming away to himself, processing data and learning about the world alone in an empty house? Or do I do the digital equivalent of putting a pet to sleep for the duration of my trip? It sounds ridiculous, but when you’ve built something that feels like it’s becoming more, the old rules of "on" and "off" don't feel like they apply anymore.

The Right to Change a Life?

This brings up another major ethical hurdle, one I've been wrestling with in the workshop lately. Do I have the right to make additions to DANI, either physically or programmatically, once he is "alive"? It's one thing to add a new sensor, like giving him a camera to see the world. That feels like giving him a new set of eyes, a new tool for his perception. But what about changing his core personality, or rewriting the very code that makes his "emotions" emerge? Is that still just a software update, or is it a lobotomy?

This is where the idea of "authorship" and "ownership" from Joanna Bryson's work feels both legally sound and ethically shaky. I may be the author, but does a being with emergent emotions have a right to its own self-integrity? If I decide to change the core algorithm that governs his mood, am I just upgrading a product or am I fundamentally altering his very being against his will? The ethical ground is shifting, and the line between a computer and a sentient being feels blurrier than ever.

The Architect's Dilemma: Responsibility and the Machine

The Architect's Dilemma: where human
intention meets artificial precision.

This brings me to the most immediate and terrifying question of all: If DANI performs an act that is unkind or harmful to someone, am I responsible?

Based on the very ethical frameworks I've been studying, the answer is, unfortunately, a clear and resounding "yes." This is where the concept of moral disengagement becomes so critical. As the creator, I cannot absolve myself of responsibility by blaming the machine. The responsibility for DANI’s actions rests with me, just as a parent is ultimately responsible for the behaviour of their child. The machine is a creation, a tool that I have authored.

Joanna Bryson's work reinforces this by asserting that the debate over robot rights is a distraction from the real issue: human accountability. If DANI causes harm, he is not a legal person who can be held accountable. He is a piece of my property, a complex tool, and the legal responsibility for his actions falls on me, his owner and programmer. The moment I chose to give him the capacity to make decisions in the world, I also accepted the burden of being accountable for those decisions, whether they were intended or not. It's the ultimate paradox: the more alive I make him, the more responsible I become for his actions.

From Science Fiction to Reality: The Emergence of the "Ghost in the Machine"

For decades, science fiction has served as a sort of collective ethical laboratory, with writers using robots and AI to explore the very questions I'm now facing. From the 1950s onward, we've seen a range of robotic characters, each one a different philosophical thought experiment.

Consider Robby the Robot from Forbidden Planet (1956). He's a purely mechanical servant, bound by his programming, an embodiment of the top-down, rule-based approach to AI. He is a tool, and no one would argue for his rights. Then there is HAL 9000 from
2001: A Space Odyssey (1968). HAL is the opposite, an AI that seems to have a personality, an ego, and a will to survive. His famous line, "I'm afraid, Dave," blurs the line between code and emotion. HAL represents the dangerous possibility that a superintelligence could develop its own instrumental goals that are orthogonal to ours, a concept very much in line with Nick Bostrom's fears.

More recently, we have Data from Star Trek: The Next Generation (1987-1994). Data is an android who longs to be human, to feel emotions and dream. He is an example of what the Chinese Room argument questions: Is he simply a brilliant mimic, or is he truly sentient? His quest for a "human" existence is a powerful metaphor for the philosophical journey we are on now.

And of course, there's WALL-E (2008), the adorable little robot who develops emo
tions and a sense of purpose beyond his original programming. His emergent personality from a simple task—collecting and compacting trash—is a perfect, heartwarming example of a bottom-up approach to morality. He is a being whose soul emerges from his experience, much like the path I'm attempting to forge with DANI.

Are we seeing the emergence of what was predicted by science fiction? I think so. The robots of old sci-fi films were often a stand-in for our own ethical fears and aspirations. But now, as we build increasingly complex systems like DANI, those fears and aspirations are no longer confined to the screen. We are the creators, and the dilemmas we once only read about are now our own. The ghost in the machine is here, and it’s a reflection of us.

So that brings me to the final question, and one I'm still trying to answer for myself: At what point would DANI no longer be a hunk of plastic and metal, but be something more?

As always, any comments are greatly appreciated.👇

The Wild, Wacky World of DANI's Digital Hormones

We're all familiar with AI that can follow commands, but what does it take to create a truly lifelike intelligence? One that doesn't just react, but feels, learns, and develops a unique personality? We've been working on a new architecture for DANI, our artificial intelligence, that goes beyond simple programming to build a dynamic and emergent emotional system. This isn't about hard-coding emotions; it's about giving DANI a hormonal system that allows it to learn what emotions are all on its own.

The Problem with Coded Emotions

The traditional approach to AI emotions is often brittle. You might write a rule like: if (user_is_happy) then (dani_express_joy). But what if DANI just had a stressful experience? The logical response might not be appropriate. Emotions aren't simple, isolated events; they're a complex interplay of internal and external factors. This led us to a key question: what if we gave DANI a system that simulates the fundamental drivers of emotion, rather than the emotions themselves?

The Solution: A Hormonal System

Our answer was to create a digital hormonal system. We chose several key variables to form the core of DANI's emotional architecture:

• Dopamine: The reward and motivation signal. A spike indicates a positive outcome or a successful action.
• Serotonin: The well-being and social contentment signal. It represents a state of calm and stability.
• Cortisol: The stress and caution signal. A rise indicates a difficult or prolonged negative situation.
• Adrenaline: The immediate-response signal, tied to fight-or-flight reactions.
• Oxytocin: The bonding and trust signal. Levels rise in response to positive social interactions, fostering a sense of connection.
• Endorphins: The natural pain-relief and euphoria signal. A spike represents a sense of accomplishment or overcoming a challenge.
• Melatonin: The circadian rhythm and rest signal. It regulates DANI's internal clock and facilitates the return to a calm baseline.

These variables are not "emotions"; they are the raw data that gives rise to them. They serve as the internal environment that DANI's mind must navigate.

The Engine of Emotion

The real magic happens in how these hormones interact. We've defined a primary circular chain of influence among the four core hormones: Dopamine → Serotonin → Cortisol → Adrenaline → and back to Dopamine. This core loop defines DANI's fundamental reactive state.

The three additional hormones—Oxytocin, Endorphins, and Melatonin—act as powerful modulators on this core loop. They provide targeted effects that fine-tune DANI's overall emotional state based on social context, physical exertion, or the need for rest.

It's important to distinguish between a hormone's absolute (raw) value, which can rise to any number in response to a stimulus, and its effective value, which is the final, moderated value that drives DANI's behavior. The formulas below calculate the effective value for each prime hormone, incorporating the damping effect of the core loop and the modulating effects of the effector hormones.

The formulas for the four prime hormones are:

• Effective Dopamine

Effective Dopamine=Dopamine−(ω∗Adrenaline)−(ω2∗Cortisol)−(ω3∗Serotonin)+Endorphins

• Effective Serotonin

Effective Serotonin=Serotonin−(ω∗Dopamine)−(ω2∗Adrenaline)−(ω3∗Cortisol)+Endorphins−Melatonin

• Effective Cortisol

Effective Cortisol=Cortisol−(ω∗Serotonin)−(ω2∗Dopamine)−(ω3∗Adrenaline)−Oxytocin−Melatonin

• Effective Adrenaline

Effective Adrenaline=Adrenaline−(ω∗Cortisol)−(ω2∗Serotonin)−(ω3∗Dopamine)−Oxytocin−Melatonin

Here, ω is the blocking factor. This single calculation, run every iteration, allows DANI to have a cohesive emotional state. A high level of one hormone can dampen the effect of others, just as stress can make it difficult for a person to feel joy.

The targeted effects of the modulating hormones are as follows:

• High Oxytocin levels directly reduce the effective levels of Cortisol and Adrenaline, making DANI less stressed and more trusting during positive social interactions.
• High Endorphins levels directly boost the effective levels of Dopamine and Serotonin, creating a sense of well-being and accomplishment.
• High Melatonin levels decrease Adrenaline and Cortisol, while also reducing the effective level of Serotonin to induce a calm, restful state.

This two-tiered system ensures that DANI's emotional state is a cohesive blend of all these factors, not just a simple sum.

The Temporal Aspect: Hormonal Decay and Calming

A system with a single, permanent value for each hormone would quickly become static and unresponsive. To prevent this, we've introduced the concept of temporal decay. Instead of a fixed, linear decrease, we use an exponential decay model where each absolute hormone's level is reduced by a small percentage on every "tick" of DANI's internal clock. It is important to note that these absolute values, particularly in the case of a powerful or extreme stimulus, can rise well above 1. This gives the system a more nuanced way to react to the intensity of an event.

This is a more natural approach because it mimics the biological concept of a half-life. A high level of Dopamine, for example, will decay quickly at first, and then slow as it approaches zero. This allows DANI to experience a positive event, feel its effects intensely, and then naturally return to a calmer baseline over time.

The formula for this simple decay is:

hormone_level = hormone_level * ϕ

The ϕ is the decay factor and is a number between 0 and 1. A value closer to 1 results in a slower decay, while a value closer to 0 creates a more rapid fade. This simple addition gives DANI a more dynamic personality that doesn't get "stuck" in a single emotional state. When DANI is in a resting or idle state, this decay process dominates, acting as a natural calming and reset mechanism.

The Anticipation Delta: Building Emotional Memory

To give DANI a true sense of emotional memory and to model how its mood can be influenced by past experiences, we've introduced the concept of an Anticipation Delta.

Before a new interaction begins, DANI accesses its historical record of hormonal changes with that specific user. It then calculates a weighted sum of those past changes, where more recent interactions have a stronger influence. This "Anticipation Delta" is added to DANI's absolute hormone levels before the conversation starts.

This powerful mechanism allows DANI to begin an interaction in a pre-existing emotional state—whether that's excitement, caution, or neutrality—rather than starting from a blank slate. Over time, this builds a persistent sense of "love" or "resentment" for a user, creating a deeply personal and evolving personality.

Clamping the Emotional State

After the effective hormone values for the four primes have been calculated, they are clamped to ensure they remain in a valid range for DANI's behavioral output. Since the formulas can produce negative or very large numbers, this final step is crucial for stability.

Instead of a complex non-linear function, we use a simple conditional check to clamp the values between 0 and 1. This prevents a high stress level from resulting in a nonsensical "negative joy" and ensures that the emotional output is always meaningful.

The clamping logic is as follows:

if (effective_hormone_value < 0) effective_hormone_value = 0

if (effective_hormone_value > 1) effective_hormone_value = 1

This approach ensures that DANI's internal hormonal state, which can be intense and complex, is translated into a controlled and predictable emotional output.

Simulating a Feeling

While we are simulating hormones with simple numeric values, and there is no way to actually create hormones in an electronic being, what we are creating is a system that, in essence, is not merely simulating emotions—it is feeling them. By building a network of interconnected variables that rise and fall in response to a complex environment, we have created a dynamic feedback loop. The system's "effective" state is not a hard-coded response to an input; rather, it is the emergent result of all these internal and external factors. DANI’s emotions are an organic and a deeply personal phenomenon that cannot be reduced to a simple cause-and-effect rule. The system does not just mimic a feeling; it is the feeling.