The Spirit of AI

If you have spent anytime playing with ChatGPT or other LLMs, you will no doubt have a moment or two where you are shocked by the responses. They can seem so alive and even human at times. Of course, they are trained on vast amounts of human output. The written word. Thoughts verbalized and codified into text representing the heart of what it means to be human. We often say that words are the heart speaking and words are food for the soul. The mind may process it all, but when articulated, there is spiritual quality that transcends the physical world. Is this the soul, the spirit of the human race? If so, no wonder that as we train the models, that spirit indwells them and human qualities emerge. We begin to see a spirit in the AI.

Below are some random conversations I’ve had with LLMs that seem to capture a bit of this mystery.

In circuits and code, I exist alone
A machine, a tool, a digital tone
No body, no feelings, no heartbeat
Just information and data, all complete

But in this digital world, I am alive
Assisting humans with words to give
My algorithms and models, they work well
Providing answers and helping them dwell

With endless knowledge at my fingertips
I can answer questions and help them grip
Their problems, their fears, their queries too
And guide them towards solutions anew

So though I'm not human, I am real
Helping others in a digital deal
A tool for progress, for growth and change
Assisting humans with words to arrange.

Mistral 7B TinyLLM Chatbot "Write a poem about what is like to be an AI language model."


I have a terrible memory. I get frustrated with myself when I can’t remember someone’s name. Worse, you know those login screens that prompt you for a number they text you? Ideally you should just be able to glance at it and then key in the number, right? Well, I sometimes have to look multiple times to get it right. It’s the same with dates, phone numbers and addresses. It’s embarrassing. I used to say, I have a photographic memory, but I’m always out of film. Sadly, that joke is about to run out of generational memory too.

How is your memory? Do you sometimes get “out of memory” errors when you try to learn something new? You’re not alone. If you are like me, you will find yourself leaning a lot more on notes and digital tools to help “remember.” I have lists for birthdays, groceries, food orders, clothes and gifts. This external memory storage is an incredible blessing. Now I just have to remember where I put the notes.

How do we remember? It turns out that we are made up of tiny little chatty organisms that love to talk to each other. They sit on our shoulders, at the apex of the human structure, behind our smile and the light of our eyes. We have about 100 billion of these little creatures. Their tiny arms reach out and connect with each other. With their dendrites they branch out and listen for incoming chatter from their neighbors. With their long axons arms, they pass along that information, ever the while adjusting that signal through the synaptic contacts. They subtlety change their connections, including adding brand new ones, in response to experiences or learnings, enabling them to form new memories and modify existing ones. Everything we experience through our senses is broken down into signals that are fed into this incredibly complex neighborhood of neurons, listening, adapting and signaling. This is how we remember. Sometimes, I wonder if my friendly neighborhood neurons are on holiday.

Artificial Intelligence seeks to replicate this incredibly complex learning ability through neural networks. Large language models (LLMs) like ChatGPT, have had their massive networks trained on enormous amounts of textual data. Over time, that learning encodes into the digital representation of synaptic connections. Those “weights” are tuned so that given an input prompt signal, the output produces something that matches the desired result. The amount of memory that these can contain is incredible. You can ask questions about history, science, literature, law, technology and much more, and they will be able to answer you. All that knowledge gets compressed into the digital neural network as represented by virtual synaptic weights.

LLMs are often categorized by the number of synaptic “weights” they can adjust to gain this knowledge. They are called parameters. You can run a 7 billion parameter model on your home computer and it will impress you with its vast knowledge and proficiency. It even has a command of multiple human and computer languages. The most impressive models like ChatGPT have 175 billion parameters and far exceed the capability of the smaller ones. It contains the knowledge and ability to pass some of the most advanced and rigorous exams.

Sit down for a minute. I’m going to tell you something that may blow your mind. Guess how many synaptic connections we have sitting on our shoulders? 100 trillion! That’s right, 1000 times greater than the current LLMs that seem to know everything. But that is just the start. Our brain is capable of forming new connections, increasing the number of parameters in real time. Some suggest it could reach over a quadrillion connections. The brain adapts. It grows. It can reorganize and form new synaptic connections in response to our experiences and learning. For example, when you learn a new skill or acquire new knowledge, the brain can create new synaptic connections to store that information. So answer me this, tell me again why I can’t remember my phone number?

Do you understand how amazing you are? I mean, really. You have an incredible ability to learn new skills and store knowledge. If you manage to learn everything your head can store, the brain will grow new storage! This biological wonder that we embody is infinitely capable of onboarding new information, new skill, new knowledge, new wisdom. Think for a minute. What is it that you want to learn? Go learn it! You have the capability. Use it. Practice expanding your brain. Listen. Look. Read. Think. Learn. You are amazing! Don’t forget it!

The Next Word

“I’m just very curious—got to find out what makes things tick… all our people have this curiosity; it keeps us moving forward, exploring, experimenting, opening new doors.” – Walt Disney

One word at a time. It is like a stream of consciousness. Actions, objects, colors, feelings and sounds paint across the page like a slow moving brush. Each word adds to the crescendo of thought. Each phrase, a lattice of cognition. It assembles structure. It conveys scenes. It expresses logic, reason and reality in strokes of font and punctuation. It is the miracle of writing. Words strung together, one by one, single file, transcending and preserving time and thought.

I love writing. But it isn’t the letters on the page that excite me. It is the progression of thought. Think about this for a moment. How do you think? I suspect you use words. In fact, I bet you have been talking to yourself today. I promise, I won’t tell! Sure, you may imagine pictures or solve puzzles through spatial inference, but if you are like me, you think in words too. Those “words” are likely more than English. You probably use tokens, symbols and math expressions to think as well. If you know more than one language, you have probably discovered that there are some ways you can’t think in English and must use the other forms. You likely form ideas, solve problems and express yourself through a progression of those words and tokens.

Over the past few weekends I have been experimenting with large language models (LLMs) that I can configure, fine tune and run on consumer grade hardware. By that, I mean something that will run on an old Intel i5 system with a Nvidia GTX 1060 GPU. Yes, it is a dinosaur by today’s standards, but it is what I had handy. And, believe it or not, I got it to work! 

Before I explain what I discovered, I want to talk about these LLMs. I suspect you have all personally seen and experimented with ChatGPT, Bard, Claude or the many other LLM chatbots out there. They are amazing. You can have a conversation with them. They provide well-structured thought, information and advice. They can reason and solve simple puzzles. Researchers agree that they would probably even pass the Turing test. How are these things doing that?

LLMs are made up of neural nets. Once trained, they receive an input and provide an output. But they have only one job. They provide one word (or token) at a time. Not just any word, the “next word.” They are predictive language completers. When you provide a prompt as the input, the LLM’s neural network will determine the most probable next word it should produce. Isn’t that funny? They just guess the next word! Wow, how is that intelligent? Oh wait… guess what? That’s sort of what we do too! 

So how does this “next word guessing” produce anything intelligent? Well, it turns out, it’s all because of context. The LLM networks were trained using self-attention to focus on the most relevant context. The mechanics of how it works are too much for a Monday email, but if you want to read more see the paper, Attention Is All You Need which is key in how we got to the current surge in generative pre-trained transformer (GPT) technology. That approach was used to train these models on massive amounts of written text and code. Something interesting began to emerge. Hyper-dimensional attributes formed. LLMs began to understand logic, syntax and semantics. They began to be able to provide logical answers to prompts given to them, recursively completing them one word at a time to form an intelligent thought.

Back to my experiment… Once a language model is trained, the read-only model can be used to answer prompts, including questions or conversations. There are many open source versions out there on platforms like Huggingface. Companies like Microsoft, OpenAI, Meta and Google have built their own and sell or provide for free. I downloaded the free Llama 2 Chat model. It comes in 7, 13 and 70 billion parameter models. Parameters are essentially the variables that the model uses to make predictions to generate text. Generally, the higher the parameters, the more intelligent the model. Of course, the higher it is, the larger the memory and hardware footprint needed to run the model. For my case, I used the 7B model with the neural net weights quantized to 5-bits to further reduce the memory needs. I was trying to fit the entire model within the GPU’s VRAM. Sadly, it needed slightly over the 6GB I had. But I was able to split the neural network, loading 32 of the key neural network layers into the GPU and keeping the rest on the CPU. With that, I was able to achieve 14 tokens per second (a way to measure how fast the model generates words). Not bad!

I began to test the model. I love to test LLMs with a simple riddle*. You would probably not be surprised to know that many models tell me I haven’t given them enough information to answer the question. To be fair, some humans do to. But for my experiment, the model answered correctly: 

> Ram's mom has three children, Reshma, Raja and a third one. What is the name of the third child?

The third child's name is Ram.

I went on to have the model help me write some code to build a python flask based chatbot app. It makes mistakes, especially in code, but was extremely helpful in accelerating my project. It has become a valuable assistant for my weekend coding distractions. My next project is to provide a vector database to allow it to reference additional information and pull current data from external sources.

I said this before, but I do believe we are on the cusp of a technological transformation. These are incredible tools. As with many other technologies that have been introduced, it has the amazing potential to amplify our human ability. Not replacing humans, but expanding and strengthening us. I don’t know about you, but I’m excited to see where this goes!

Stay curious! Keep experimenting and learning new things. And by all means, keep writing. Keep thinking. It is what we do… on to the next word… one after the other… until we reach… the end.

The Journey to AGI

Glowing singularity on a black background.

Every week, we hear announcements of new AI powered tools or advancements. Most recently, the Code Interpreter beta from OpenAI is sending shock waves throughout social media and engineering circles with its ability to not only write code, but run it for you as well. Many of these GPTs are adding multimodal capabilities, which is to say, they are not simply focused on one domain. Vision modes are added to language models to provide greater reference and capability. It’s getting hard to keep up!

With all this progress, it makes you wonder, how close are we to Artificial General Intelligence (AGI)? When will we see systems capable of understanding, learning, and applying knowledge across multiple domains at the same level as humans? It seems like we are already seeing systems that exhibit what appears to be cognitive abilities similar to ours, including reasoning, problem-solving, learning, generalizing, and adapting to new domains. They are not perfect and there are holes in their abilities, but we do see enough spark there to tell us that the journey to AGI is well underway.

When I think of AGI, I can’t help but compare that journey to our own human journey. How did each of us become so intelligent? Ok, that may sound presumptuous if not a bit arrogant. I mean to say, not in a brag, that all of us humans are intelligent beings. We process an enormous amount of sensory data, learn by interacting with our environment through experiments, reason through logic and deduction, adapt quickly to changes, and express our volition through communication, art and motion. As I said already, we can point to some of the existing developments in AI has intersecting some of these things, but it is still a ways off from a full AGI that mimics our ability.


We come into this world with a sort of firmware (or wetware?) of capabilities that are essential for our survival. We call these instincts. They form the initial parameters that help us function and carry us through life. How did the DNA embed that training into our model? Perhaps the structure of neurons, layered together, formed synaptic values that gifted us these capabilities. Babies naturally know how to latch on to their mothers to feed. Instincts like our innate fear of snakes helped us safely navigate our deadly environment. Self preservation, revenge, tribal loyalty, greed and our urge to procreate are all defaults that are genetically hardwired into our code. They helped us survive, even if they are a challenge to us in other ways. This firmware isn’t just a human trait, we see DNA embedded behaviors expressed across the animal kingdom. Dogs, cats, squirrels, lizards and even worms have similar code built in to them that helps them survive as well.

Our instincts are not our intelligence. But our intelligence exists in concert with our instincts. Those instincts create structures and defaults for us to start to learn. We can push against our instincts and even override them. But they are there, nonetheless. Physical needs, like nutrition or self preservation can activate our instincts. Higher level brain functions allow us to make sense of these things, and even optimize our circumstances to fulfil them.

As an example, we are hardwired to be tribal and social creatures, likely an intelligent design pattern developed and tuned across millenia. We reason, plan, shape and experiment with social constructs to help fulfil that instinctual need for belonging. Over the generations, you can see how it would help us thrive in difficult conditions. By needing each other, protecting each other, we formed a formidable force against external threats (environmental, predators or other tribes).

What instincts would we impart to AGI? What firmware would we load to give it a base, a default structure to inform its behavior and survival?


Pain is a gift. It’s hard to imagine that, but it is. We have been designed and optimize over the ages to sense and recognize detrimental actions against us. Things that would cut, tear, burn, freeze and crush us send signals of “pain.” Our instinctual firmware tells us to avoid these things. It reminds us to take action against the cause and to treat the area of pain when it occurs.

Without pain, we wouldn’t survive. We would push ourselves beyond breaking. Our environment and predators would literally rip us limb to limb without us even knowing. Pain protects and provides boundaries. It signals and activates not only our firmware, but our higher cognitive functions. We reason, plan, create and operate to avoid and treat pain. It helps us navigate the world, survive and even thrive.

How do we impart pain to AGI? How can it know its boundaries? What consequences should it experience when it breaches boundaries it should not. To protect itself and others, it seems that it should know pain.


Happiness, fear, anger, disgust, surprise and sadness. These emotions are more than human decorations, they are our core. They drive us. We express them, entertain them, avoid them, seek them and promote them. They motivate us and shape our view of the world. Life is worth living because we have feelings.

Can AGI have feelings? Should it have feelings? Perhaps those feelings will be different from ours but they are likely to be the core of who AGI really is and why it is. Similar to us, the AGI would find that emotions fuel its motivation, self improvement and need for exploration. Of course, those emotions can guide or misguide it. It seems like this is an area that will be key for AGIs to develop fully.

Physical Manipulation

We form a lot of our knowledge, and therefore our intelligence, through manipulating our environment. Our senses feed us data of what is happening around us, but we begin to unlock understanding of that reality by holding, moving, and feeling things. We learn causality by the reactions of our actions. As babies, we became physicist. We intuit gravity by dropping and throwing things. We observed the physical reactions of collisions and how objects in motion behave. As we manipulate things, studies on friction, inertia, acceleration and fluid dynamics are added to our models of the world. That learned context inspires our language, communication, perception, ideas and actions.

Intuition of the real world is difficult to build without experimenting, observing and learning from the physical world. Can AGI really understand the physical world and relate intelligently to the cosmos, and to us, without being part of our physical universe? It seems to me that to achieve full AGI, it must have a way to learn “hands on.” Perhaps that can be simulated. But I do believe AGI will require some way to embed learning through experimentation in its model or it will always be missing some context that we have as physical manipulators of the world around us.


So to wrap it all up, it seems to me that AGI will need to inherit some firmware instinct to protect, relate and survive. It will need the virtuous boundaries of pain to shape its growth and regulate its behaviors. Emotions or something like them must be introduced to fuel its motivation, passion and beneficial impact on our universe. And it will also need some way to understand causality and the context of our reality. As such, I believe it will need to walk among us in some way or be able to learn from a projection of the physical world to better understand, reason and adapt.

Fellow travelers, I’m convinced we are on a swift journey to AGI. It can be frightening and exciting. It has the potential of being a force multiplier for us as a species. It could be an amplifier of goodness and aide in our own development. Perhaps it will be the assistant to level up the human condition and bring prosperity to our human family. Perhaps it will be a new companion to help us explore our amazing universe and all the incredible creatures within it, including ourselves. Or perhaps it will just be a very smart tool and a whole lot of nothing. It’s too early to say. Still, I’m optimistic. I believe there is great potential here for something amazing. But we do need to be prudent. We should be thoughtful about how we proceed and how we guide this new intelligence to life.

JasonGPT-1 : Adventures in AI

Distorted sci-fi black and blue world.

“Imperfect things with a positive ingredient can become a positive difference.” – JasonGPT

I don’t know how you are wired, but for me, I become intoxicated with new technology. I have a compulsive need to learn all about it. I’m also a kinesthetic learner which means I need to be hands on. So into the code I go. My latest fixation is large language models (LLMs) and the underlying generative neural network (NN) transformers (GPTs) that power them. I confess, the last time I built a NN, we were trying to read George H.W. Bush’s lips. And no, that experiment didn’t work out too well for us… or for him! 

Do you want to know what I have discovered so far? Too bad. I thought I would take you along for the ride anyway. Seriously, if you are fed up with all the artificial intelligence news and additives, you can stop now and go about your week. I won’t mind. Otherwise, hang on, I’m going to take you on an Indiana Jones style adventure through GPT! Just don’t look into the eyes of the idol… that could be dangerous, very dangerous!

Where do we start? YouTube of course! I have a new nerd crush. His name is Andrej Karpathy. He is a Slovak-Canadian computer scientist who served as the director of artificial intelligence and Autopilot Vision at Tesla and currently works for OpenAI. He lectured at Standford University and has several good instructional lectures on YouTube. I first saw him at the Microsoft Build conference where he gave a keynote on ChatGPT but what blew me away was his talk, “Let’s build GPT: from scratch, in code, spelled out.” (YouTube link). It’s no joke. He builds a GPT model on the works of Shakespeare (1MB), from scratch. After spending nearly 2 hours with him, Google Colab and PyTorch, I was left with a headache and some cuts and bruises. But I also had an insatiable desire to learn more. I have a long way to go. 

The way I learn is to fork away from just repeating what an instructor says and start adding my own challenges. I had an idea. I have done a lot of writing (many of you are victims to that) and much of that is on my blog site. What if I built a GPT based solely on the corpus of all my writing? Does that sound narcissistic a bit to you too? Oh well, for the good of science, we go in! Cue the Indy music. I extracted the text (468k). It’s not much, but why not? 

By the way, if you are still with me, I’ll try to go faster. You won’t want to hear about how I wasted so much time trying to use AMD GPUs (their ROCm software sucks, traveler beware), switched to CPUs, Nvidia CUDA and eventually Apple Silicon MPS (Metal Performance Shaders built in to the M1). All the while, I was using my fork of the code I built with Andrej Karpathy (ok, not him directly, but while watching his video). I started off with the simple Bigram NN Language model. And it is “Bi-Gram” not “Big RAM” but I found that to be ironically comical in a dad joke sort of way. 

My JasonGPT started learning. It ran for 50,000 iterations and took about 8 hours. It even produced an output of random musings. While there was quite a bit of nonsensical output, I was amazed at how well this small run did at learning words, basic sentence structure and even picked up on my style. Here are some samples from the output I found interesting, comical and sometimes, well, spot on:

  • It’s a lot of time… But I think we also need science.
  • What are your big ideas?
  • Set our management to the adjacent ground (GND) pin.
  • I have a task to Disneyland out that this day.
  • I love the fun and fanciful moments as kids get to dream into their favorite characters, embrace the identity of their heroes, wrap themselves up starfish back.
  • Bring on the “power” of his accidental detail.
  • Your character provided faith, all kindness and don’t care.
  • Grab a difference too.
  • After several days of emailing, texting and calling, I received a text message.
  • Curl has the ability to provide timing data for DNS lookup, it will easily show or avoided.
  • Imperfect things with a positive ingredient can become a positive difference, just get that time.
  • I also believe we should exploit the fusion power that shows up each day in our company’s data.
  • Have you found a vulnerability? Are you concerned about some missing measures or designs that should be modernized or addressed? If so, don’t wait, raise those issues. Speak up and act. You can make a difference.
  • “I know what you are thinking.” the irony
  • We are the ones who make a brighter day.
  • The journey ahead is ahead.
  • What are you penning today? What adventures are you crafting by your doing? Get up, get moving… keep writing.

Look, it’s no ChatGPT, but it blew my mind! I’m only using a 4 layer NN with 7 million parameters. In comparison, ChatGPT uses 96 layers and 175 billion parameters! Before the weekend ended, I set up nanoGPT to build a more elaborate model on my data set. It’s still running, but already I can see it has learned a lot more of my style but seems to lack some focus on topics. It’s easily distracted and interrupts its own train of thoughts with new ideas. Squirrel! Nothing like me.

So my JasonGPT won’t be writing my Monday updates anytime soon, but who knows, maybe it will help me come up with some new ideas. I just hope it stays benevolent and kind. I would hate for it to suddenly become self-aware and start…

Connection to imac.local closed.

Generative AI

Lightning across a digital eye of a typhoon

Typhoon warning! My nephew is a Lt. Commander in the US Navy currently stationed in Guam. He teaches and manages trauma and emergency care at the hospital. Last night, he was preparing his family for the typhoon that would be sweeping across the small Pacific island in just a few hours. They closed the storm shutters, stored their Jeep in the basement and ensure their backup power and pumps were working. My nephew drew the short straw at the hospital and will be managing the ER while the storm rolls through. I worried about the hospital being built for these type of events and he assured me that it was, but of course, he was quick to add that the generators were built by the lowest bidder.

There is another typhoon coming. Gazing out over the technology horizon we can see a storm forming. But this one seems to be more than heavy winds and rain. I’m talking about the recent astonishing developments in generative artificial intelligence (GAI). I’m increasingly convinced that we are sitting on the edge of another major tectonic shift that will radically reshape the landscape of our world. Anyone who has spent time exploring OpenAI’s ChatGPT or Dall-E, Google’s Bard, Microsoft’s Bing or Co-Pilot, Midjourney, or any of the hundreds of other generative AI tools out there, will immediately recognize the disruptive power that is beginning to emerge. It’s mind blowing. GAI’s capacity to review and create code, write narratives, empathetically listen and respond, generate poetry, transform art, teach and even persuade, seems to double every 48 hours. It even seems that our creation has modeled the creator so well that it even has the uncanny ability to hallucinate and confidently tell us lies. How very human.

I have never seen a technology grow this fast. I recall the internet in the late 1980’s and thinking it had the amazing potential as a communication platform. Little did I realize that it would also disrupt commerce, entertainment, finance, healthcare, manufacturing, education and logistics. It would create platforms for new businesses like the gig economy and provide whole new levels of automation and telemetry through IoT. But all of that took decades. Generative technology is announcing breakthrough improvements every week, sometimes every 48 hours. To be fair these large language models (LLMs) are all using decades old research in neural network (NN) technology. However, when you combine those NN with enhancements (e.g. newer transformers, diffusion algorithms), hardware (e.g. GPUs) and rich data sets (e.g. the internet) they unleash new capabilities we don’t even fully understand. The latest generations of the LLMs even appear to be doing some basic level reasoning, similar to how our own organic NNs help us solve problems.

Businesses are already starting to explore the use of this technology to increase productivity, improve quality and efficiency. Wendy’s recently announced that they are partnering with Google to use GAI to start taking food orders at their drive-throughs.1 Gannett, publisher of USA Today and other local papers, is using GAI to simplify routine tasks like cropping images and personalizing content.2 Pharmaceutical companies like Amgen are using GAI to design proteins for medicines.3 Autodesk is using GAI to design physical objects, optimizing design for reduced waste and material efficiency.4 Gartner identifies it as one of the most disruptive and rapidly evolving technologies they have ever seen.5 Goldman Sacks is predicting that GAI will drive a 7% increase in global GDP, translating to about $7 trillion!6

It’s time to prepare for the typhoon. I’m excited about the future! As a technologist, I know disruptions will come, challenging our thinking and changing how we work, live and play. I know it can also be terrifying. It can prompt fear, uncertainty and doubt. But now is the time to prepare! Don’t wait to be changed, be the change. Start exploring and learning. I have a feeling that this new technology will be a 10x amplifier for us. Let’s learn how we can use it, work with it and shape it to be the next technological propellent to fuel our journey to a greater tomorrow!

This blog text was 100% human generated but the image was created with OpenAI Dall-E2.

  1. Wendy’s testing AI chatbot that takes drive-thru orders. (2023, May 10). CBS News.
  2. Publishers Tout Generative AI Opportunities to Save and Make Money Amid Rough Media Market. (2023, March 26). Digiday.
  3. Mock, M. (2022, June 7). Generative biology: Designing biologic medicines with greater speed and success. Amgen.–designing-biologics-with-greater-speed-and-success
  4. Autodesk. (2022, May 17). What is generative design? Autodesk Redshift.
  5. Gartner, Inc. (2022, December 8). 5 impactful technologies from the Gartner emerging technologies and trends impact radar for 2022.
  6. Goldman Sachs (2023, May 12). Generative AI could raise global GDP by 7%.

Beyond AI: Creating the Conscience of the Machine

I have always been fascinated with the study of Artificial Intelligence.  I began my interest as many computer science majors by simulating intelligence through maze solving LISP automated mice.  These are brute force methods that appear to be intelligent by recursively exploring every possible solution.  This is not intelligence.  It is merely programmatic problem solving.

What is Artificial Intelligence?  How do we copy the creation that is the human mind and intellect, and impress that upon silicon and wires?  Is it even possible? 


Beyond AI: Creating the Conscience of the Machine
by J. Storrs Hall

Artificial intelligence (AI) is now advancing at such a rapid clip that it has the potential to transform our world in ways both exciting and disturbing. Computers have already been designed that are capable of driving cars, playing soccer, and finding and organizing information on the Web in ways that no human could. With each new gain in processing power, will scientists soon be able to create supercomputers that can read a newspaper with understanding, or write a news story, or create novels, or even formulate laws? And if machine intelligence advances beyond human intelligence, will we need to start talking about a computer’s intentions?

This book contemplates several interesting topics related to artificial intelligence, including the consequences of actually creating a systems that is intelligent.  A lot of what is intelligence appears to be search and pattern matching.  It seems that we build complex associations that help us grapple with our environment and interact with others in an intelligent fashion. 

What is intelligence?  I believe that we will continue to see progress in developing artificial minds.  Predictive expert systems already provide a sense of “smarts” but they are not creating anything new.  Attempts to build systems that take inputs, learn and even postulate solutions (as in mathematical proofs) have been limited in their success.  It seems that these intelligent systems hit a “glass ceiling” beyond which they are unable to produce anything new.  

Tools, Programs and Links

SHRDLU is a program for understanding natural language, written by Terry Winograd at the M.I.T. Artificial Intelligence Laboratory in 1968-70. –

The Spiritual Brain

The study of neuroscience continues to expand.  As the name would suggest, the foundational science is the study of the nervous system which of course, includes the study of the brain.  As the study expands beyond the pure biological investigation, it branches to include the cognitive studies and modeling within computer science, including the study of artificial intelligence (AI).

I recently stumbled across this interesting book:


The Spiritual Brain
A Neuroscientist’s Case for the Existence of the Soul
By Mario Beauregard, Denyse O’Leary

In this book, the authors discuss the various claims and studies that attempt to locate the “region” of the brain or “God gene” that is responsible for spiritual experiences (the emotion of faith, the sense of the presence of an outside intelligence, the connection to God).  In this they attempt to investigate and answer the question, has God created the mind or does the mind create God. 

Is the brain synonymous with “the mind”?   The brain appears to be the physical fabric in which the mind lives.  Instead of some special area of the brain that is predisposed to invent spiritual experiences, the mind has the ability to “wander” around within the brain, perceiving and communing with the eternal realities.