Tuesday, May 3, 2022

Access All of My Writings at jared-research.com

I just created a new website where people can download all of my articles and books for free. Each of my peer reviewed scientific journal articles as well as the three books I have written are available there in PDF form. There are also links to my other writings, websites, and social media profiles. You can access this at:


www.jared-research.com



You can find out about my model of working memory and consciousness, my design for superintelligent AI, and my system for self-care, Program Peace. There are links to my Google Scholar, Academia, and Research Gate. 

Monday, May 2, 2022

The Program Peace Book and App are Available Now

 



After over two decades of work, my book on self-care and healthy living, Program Peace, is now available. You can get your copy for free from the website: www.programpeace.com On the site it is available as a PDF or as a series of webpages. 



Alternatively, you can get it on Amazon as a hardcover, softcover, or ebook.  



The mobile app is also available now. You can get it for iPhone on the Apple App Store, or for Android on the Google Play Store. Download your copy today.

https://play.google.com/store/apps/details?id=com.program_peace.app

 


 

I have also launched the YouTube page which you can find here:

https://www.youtube.com/channel/UCdnX093ce5PMbvHC705vt_w



Take advantage of Program Peace and see how you can transform you breathing pattern and numerous other habits to support a stress-free life. 

Thursday, February 17, 2022

Video Games Can Contribute to Anxiety, Here is What You Can Do About It

I believe that video games cause more anxiety than people realize. I also think that playing video games regularly for years can drive chronic anxiety, otherwise known as hyperarousal. After years of frantic button pressing and thousands of “digital deaths,” the fight or flight branch of the nervous system can be turned up relative to the resting and digesting branch resulting in what researchers call “sympathetic dominance” and “autonomic dysregulation.”

Videogames have been shown to increase stress hormone levels, bolster aggressive affect, and reduce prosocial behavior. Most of my friends who play video games act breathless and panicked afterward, driving them to smoke cigarettes and drink alcohol during gaming breaks. Playing an hour of competitive, online “deathmatches” would strip almost anyone of their composure. Over time, this undermines emotional balance. In my twenties I didn’t realize that loud, violent entertainment was turning me into the stereotype of the high-strung geek.

This blog post describes how and why videogames can negatively affect us and how to address it.


Video Games Can Create Stress

Most video games create suspenseful situations in which players must react quickly to keep their character from being hurt or killed. The simulated danger and time pressure recruits fast-acting areas of the brain, such as the amygdala and basal ganglia, to help perform the hazardous button pressing. These unconscious brain areas don’t know they are just playing a game. They assume the actions are dire, and so involuntarily activate the stress system. This is why playing real-time video games can be emotionally draining. Each reduction in the character’s life bar results in the release of stress hormones and an acceleration of your heartbeat. In this way, every play session contributes ever so slightly to one’s background hum of anxiety.

Despite your full attention and best efforts, you watch as your character is torn to shreds before your eyes. Seeing your character die and having to start a level from the beginning over and over is frustrating. This acute stress is the reason many people act like they are going to throw the controller after a loss. Are you having trouble seeing how a game can be traumatic? Just watch the expressions of a toddler or an elderly person as they play and you will see how negative an experience it can be.

Even game developers have long recognized that games produce stress. In fact, the Japanese version of the video game that ended up becoming super Mario Bros 2 in the United States was originally titled “Heartbeat Panic” in Japanese. And today we play more games than ever. Globally people play more than three billion hours of videogames every week. In America digital media consumes around 11 hours of the average person’s day. Gaming disorder is a recognized mental health issue.

But stress feels bad, wouldn’t we recognize this and stop playing? Well.. Because of the way our dopamine systems work, we have a tendency to seek out overstimulation. We are willing to engage in upsetting interactions as long as they produce novelty and reward. Seeing the new graphics, interacting with a pixelated world, and beating the levels produces lots of reward. Drug addicts are willing to destroy their bodies for the same reason, because the dopamine rush is incredibly psychologically compelling. Like an addict, we can become hooked on the dopamine and adrenaline produced by games. The fleeting thrills cause us to ignore the persistent, low-level panic.


Playing Video Games Pushes us Into Distressed Breathing

When most people play video games, they tense their respiratory diaphragm, immobilizing it. This pushes them from breathing diaphragmatically into distressed breathing. During distressed breathing the diaphragm is held in partial contraction and the thoracic muscles of the chest do the work of breathing. This places the diaphragm muscle in a state of partial contraction which, over time, causes it to become stiff and frail.

I created a system called Program Peace that you can access entirely for free at www.programpeace.com. There you can learn about how to optimize your breathing pattern and then combine optimal breathing with other exercises and activities. You can then combine optimal breathing with videogame playing and detraumatize your orientation toward it.

The best way to counteract distressed breathing and engage the diaphragm is to belly breathe. As you inhale, imagine the diaphragm emerging from the bottom of your ribs and pushing down on the contents of your belly. This should cause your belly to protrude outward. It also helps to breathe long, deep, slow breaths through your nose. Whenever you are playing video games it is important to be aware of your breath and not to let it become too shallow. While you increase your awareness of your breathing as you play video games, you should have dozens of breakthrough moments where, little by little, you are able to encourage your diaphragm to participate more in the effort of inhalation.

At a certain level, when we play video games, we assume that if we don’t breathe shallowly, we are putting our character in danger. It takes time and effort to counteract this. If you convince yourself that it is safe to breath deeply while you play, you can totally transform your psychological orientation toward the game.

Extending the duration of your breath can help too. Using a paced breathing app like the one I created, Program Peace, can help you do this. While on your couch, pull out your paced breathing app, dim the screen, and override your tendency to breathe shallowly. While you witness the character experience peril, breathe long, slow breaths. You will be amazed by how this allows you to detach from and become desensitized to the nail-biting worriment.

If you are playing video games while breathing 2 second inhalation and 2 second exhalations you are going to be traumatized by them. If you can extend your breaths to 5 second inhalations and 7 second exhalations trauma cannot enter the equation. Performing paced breathing while engaging in these activities is an excellent way to learn to retain your composure as you are inundated. If you practice, you will get to the point where you can breathe through your nose at five breaths per minute in a digital firefight.


Playing Video Games Creates Tension in Our Bodies

But it is not just your diaphragm that you brace when you play video games. It is also your face, voice box, spine, digestive tract, and genitals. Any muscles that are braced for long periods of time become stuck in partial contraction causing pain and accelerated aging. So, while you play, it is very important to be aware of your tension patterns and to try to sense the state of your breath, heart, and gut. Let them relax.

Many people have terrible posture when they play games. They breathe through their mouth, sneer, startle, raise their eyebrows, curve their lower backs, and hunch their necks. This bad posture causes tension – tension that is often held for hours at a time. Moreover, as you hold the controller (as with your phone or keyboard), most of your major postural muscles are completely braced and immobilized. This is why it is important to be aware of your posture, take regular gaming breaks, and if those include stretching, all the better.


Reducing the Volume of Your TV Can Reduce the Stress Response

Many studies have shown that merely reducing television volume can vastly reduce the sympathetic stress response to violent videos and games. In general, the louder the TV, the more frequently and intensely the amygdala is triggered, and the more cortisol is released. Turn your speakers down a few decibels, and you should notice that you feel far less uneasy after a play session.



Real Exercise Can Counteract the Stress Caused by Video Games

Overstimulating media tricks our bodies into thinking we are preparing for tremendous amounts of exercise, even though we usually consume it while sitting on our backsides. Rather than stewing in them, use up your stress hormones by engaging in physical activity. Decades of research have shown that regular exercise is one of the best ways to counteract stress.


Video Games Cause Us to Startle and Tremble

When you trip while walking, it is usually because you didn’t see the uneven surface on the ground. It came out of nowhere. For a few seconds your heart races and your adrenaline spikes as you try to recover from the fall without injuring yourself. Whenever threats come out of nowhere, as they do in videogames, they cause us to startle. The more time you spend startling, the stronger your startle response becomes. Startle plays a key role in fear and anxiety, and it also increases trembling. Now that I am middle aged, it has become very clear to me that I startle and tremble after playing video games.

Startle is also a sign that your adrenal glands have released adrenaline. Elevated levels of adrenaline cause the stress hormone cortisol to go up. Elevated cortisol is caustic to the body and has negative repercussions for most of the cells in your body. It also contributes to a large number of diseases and disorders. Cortisol also increases hair loss, greying, and premature aging in general. There is also good reason to assume that the startling and anxiety caused by video games, could over time, decrease testosterone (lowering muscle mass and assertiveness), and serotonin (lowering confidence and increasing susceptibility to depression).


We Weren’t Designed to Play Videogames

Our bodies were not built for video games. Traditional hunter-gatherers had exceptionally low stimulation levels 95% of the time. They were out in nature all day. Today, we plug into many streams of overstimulation that were designed to assault our senses. Our ancestors would not have had access to movies, television, and video games and so they would’ve been forced to spend more time in relative boredom, where they were fully exposed to any unease in their bodies. Having this exposure allowed them to confront any anxiety, negotiate with it, and subdue it. We on the other hand, use chaotic media to try to drown it out.

A hunter gather should be able to go through their day in a state of flow where they’re not making mistakes and they’re not constantly getting adverse feedback from their world. To be happy, confident, and healthy we need to give our body the signals and cues that tell it that we are capable of getting through our day without upsetting impediments. We want our bodies to feel like winning is easy for us. Our cells and the DNA within them listen to environmental inputs and use them to determine our level of relaxation or upset. But inevitably our cells will interpret the feedback that we get from video games as negative.

When we are fighting another character on screen and taking damage randomly, we are not actually experiencing pain, but clearly things are going wrong in our world. When you are hit by simulated gunfire hundreds of times every week, your body is likely to assume that things are not working out well for you. Your body assumes that you don’t have much control of your world because intractable, flich-inducing situations keep popping up unexpectedly.

Video games prove to our body that we’re not able to go into a relaxed state of flow. This, as a way of life, does not support confidence, elegance, dominance, or power. Rather it causes the unconscious motor systems of the brain (such as the basal ganglia) to assume that they must be continually failing. Every time your health bar is decremented they assume that they have not learned the right motor patterns to master the tasks necessary for survival. When they feel like they are failing they assume that the only way is to use anxiety and stress to power through.


It Can Help to Go Cold Turkey

Once I realized that video games were strongly adversely affecting me around my late 20s, I stopped playing fighting and shooting games altogether. I cut my videogame playing down from two to three hours a week to less than ten minutes and I only played nonviolent games. I was still interested in games from a creative and technological standpoint, so I mostly just watched friends play.

Ten years later in 2021, Grand Theft Auto Trilogy the Definitive Edition came out and I felt compelled to beat the games. They were heavily nostalgic, I loved the colors, locales, and music and I was strongly motivated to finish the story missions so that I could unlock all of the maps. I beat GTA Vice City in 12 hours, and then spent several hours getting through San Andreas. I could feel the games taking a toll. Lots of shooting, lots of deaths, lots of being forced to restart frustrating missions from the beginning. I wanted to get over it, but I felt compelled to finish what I had started.

I woke up early one Saturday morning after only two hours of sleep and all I could think about was finishing GTA San Andreas. I wanted to get it out of the way so I could get back to my life. I calculated that I could finish the remaining 15 missions in four hours and decided to crawl out of bed and try. I ended up having to play 12 hours straight just to get to the final mission. I did it without sleeping and without any food at all. I spent all twelve hours trying to monitor my bracing patterns, and breathe diaphragmatically, while I analyzed the game’s effect on my breathing. By the end of the first hour I was breathing 5 breaths per minute. However, it wasn’t until about 10 hours in that I began genuine belly breathing for the first time while playing a video game. It was a great experience and may have strengthened my ability to belly breathe under stress. But it also had lasting repercussions. It was mildly traumatic for me. My mind was racing, my gut was aching, and I startled and trembled slightly for weeks. I also stuttered and had trouble looking people in the eye for a few days.

After that experience I would recommend that people not play while sleep deprived, not play while fasting, and not play for more than a couple of hours at a time. It is also worth mentioning that studies show that violent media before bed can increase stress hormone levels, so I would also recommend that you stop playing at least two hours before bed time.


Use the Play/Challenge Mindset not the Fight/Threat Mindset

Another important thing to mention is your mindset. Mice that wrestle each other can become stronger and happier if they interpret the wrestling as play. But if they interpret it as fighting, their stress levels can shoot through the roof. Interpret the conflict as rough and tumble play and you are much less likely to be traumatized by it. Studies have shown that when you take something as play it changes the nature of the stress response from the “threat response” to the “challenge response” which is much healthier for the mind and body. Look it up. I believe that some people’s limbic systems are more likely to respond to intense media stimulation with the challenge response, but I think most respond to them with the threat response. Mine certainly does.

When mammals are playful they’re not concerned about reputation, they’re not concerned about getting hurt, they’re not concerned about making an enemy, they’re learning, enjoying themselves, exerting themselves, and bonding. When mammals play they are also more likely to breathe diaphragmatically and less likely to engage in distressed breathing. Another reason why breathing deeply can help.

Play your videogames, don’t fight them.


Play Peaceful/Nonviolent Video Games

Game designers should create more games that encourage a state of flow but do not constantly punish players using arbitrary rules. In many games, you can reduce the difficulty setting. But many major game developers avoid creating a “freeplay” or “creative” mode because they are concerned that if the game is not challenging enough, they will lose sales. This is unfortunate. Every game should have a punish-free setting, and digital worlds should be places to relax, explore, and fantasize. Nonviolent, friendly videogames do exist, though, and many are worth trying.

 

Here are some fun, nonviolent games that I recommend:

 

Tetris

Beat Sabre

Astroneer

Rez

Minecraft

Assassin’s Creed Discovery Tours

The Turing Test

Katamari

Fez

Portal

Abzu

Rime

Journey

Flower

Firewatch

Last Guardian

Captain Toad Treasure Tracker

Stardew Valley

Just Dance

Spirit of the North

Civilization


These games are more likely to put us into a healthy state of flow and stimulate dopamine without stimulating cortisol and the startle response.

 

Conclusion

Taking up videogames is not enough to give the average person an anxiety disorder, but it is enough to mildly and noticeably increase anxiety. And remember, anxiety is a ratchet. This means the effects are cumulative. It is easy to become more anxious, but difficult to become less anxious, so why would you want to expose yourself to anything that is anxiogenic?

The intense stimulation of video games can cause us to completely disregard the panic signals our body is sending us. Instead, pay attention to them. Start with the breath. As you play, gain awareness of your breath. Try to breathe slowly and deeply with your belly and through your nose. Breathe long breaths that last for more than five seconds. And stay aware of any unnecessary bracing that is going on for too long. Let it go, relax, and have fun.


Thursday, October 7, 2021

A Language Model like GPT Could Serve as Broca's Area in a Larger AGI System

Cutting edge AI language software is more powerful than ever, and what it can do today might surprise you. I'm sure you have heard of Siri, Alexa, and Google Assistant. These AIs can be really helpful but they are not even near the cutting edge of computer language generation. Some of the best language AIs (also known as models) today can respond so well to queries that most people assume there is a human at the other end typing the answers. However, even the best of them still have a way to go before achieving full human-level performance.


The limitations of these AI language models are very similar to those of our brain's language area. Isolated from the rest of the brain, our brain's language area could potentially still produce speech, but it would be rote, mechanical, and unplanned, much like the language produced by contemporary AI language models. But if this language software was integrated into a larger network with a form of working memory, as our brain's language area is, it could potentially produce much more coherent language.

 

Our brain's language region is called Broca's area and helps us with the fine details of piecing together our sentences. These are mostly the tedious details that we can't be bothered by. We are unconscious of most of the work Broca's area performs, but we wouldn't be able to speak without it. It does many things, including helping us find words that are on the tip of our tongue. Broca's area could at least keep us talking if the more generalist brain areas like the prefrontal cortex (involved in both consciousness and larger overarching language concerns) were gone. The speech generated from Broca's alone might sound grammatically correct and have proper syntax, but the semantics would have issues. We see this in people with prefrontal injuries today. They can talk indefinitely, and at first, what they are saying might sound normal, but it doesn't take long to realize that it is missing some intellectual depth. 

 

As you will learn here, modern AI language software is also missing depth because it does not plan its speech. These systems literally put their sentences together one word at a time. Given the series of words that have come so far, they predict which word is most likely to come next. Then they add their prediction to the list of words that have come before and use this new list to make the following prediction. They repeat this process to string together sentences and paragraphs. In other words, they have no forethought or real intention. You could say that modern AI language generation systems "think" like someone who has a serious brain injury or who has been lobotomized. They formulate speech like the TV show character Michael Scott from The Office. Here is a telling quote from Michael:

 

"Sometimes I'll start a sentence, and I don't even know where it's going. I just hope I find it along the way. Like an improv conversation. An improversation." 

-      Michael Scott

 

Michael is a parody. He is a manager with an attention deficit, that has no real plan and does everything on the fly. As you can see on the show, his work doesn't lead to productivity, economic or otherwise. We need AI that is structured to do better than this.

 

The question becomes, how can we create an AI system that does more than just predict the next best word, one at a time? We want a system that plans ahead, formulating the gist of what it wants to say in its imagination before choosing the specific words needed to communicate it. As will be discussed, that will never emerge by using the same machine learning architectures we have been using. Additional architectural modifications are needed.

 

This entry will espouse taking the current neural network language architecture (the transformer neural network model introduced in 2017) and attaching it to a larger, generalist system. This larger system will allow word choice to be affected by a large number of varied constraints (not just the words that came earlier in the sentence). The diagram below shows a multimodal neural network made up of several different interfacing networks. You can see the language network on the far right, in the center, attached directly to a speaker.

 

 


 

It would be a tremendous engineering feat to get multiple neural networks to interface and work together, as depicted above. But research with neural networks has shown that they are often interoperable and can quickly adapt to each other and learn to work cooperatively. AI pioneer Marvin Minsky called the human brain a “society of minds.” By that he meant that our brain is made up of different modular networks each contribute to a larger whole. I don’t think AI will truly become intelligent unless it works in this way. Before we go any further, the next section will briefly explain how neural networks (the most popular form of machine learning in AI) work.

 

How Neural Networks Chose Their Words

 

I explain how neural networks work in detail in my last entry, which you can read here

 

A quick recap: Most forms of machine learning, including neural networks, are systems with a large number of interlinked neuron-like nodes. These are represented by the circles in the diagram below. The nodes are connected via weighted links. These links are represented as the lines connecting the circles. The links are considered “weighted” because each has a numerical strength that is subject to change during learning. As the AI software is exposed to inputs, those inputs flow through the system (from left to right), travelling from node to node until they reach the output layer on the far right. That output layer contains a node for every word in the dictionary. Whichever node in the output layer is activated the most will become the network’s next chosen word.

 

Language generating neural networks are exposed to millions of sentences, typically from books or articles. The system can learn from what it is reading because it adapts to it. The weight’s values are strengthened when it is able to correctly guess the next word in a sentence it is reading, and the values are weakened when it chooses any other word. Given a broad attention span and billions of training sessions, they can get really good at internalizing the structure of the English language and piecing sentences together word by word.

 

The diagram below shows the words “this is my pet…” being fed into an AI neural network. The final word “cat” is hidden from the network. As “this is my pet” passes through the network from left to right, the words travel from node (circle) to node, through the weighted links, toward the full dictionary of nodes at the output layer. The pattern of inputs caused a pattern of network activity that then selected a single output. You can see the network converging on the word “cat” as its best prediction. It got it right! This network could then continue, rapidly writing sentences in this way for as long as you tell it to.

 


Broca's area in your brain works similarly. It takes inputs from many different systems in the brain, especially from the system that recognizes spoken language. These inputs activate select neurons out of a network of millions of them. The activation energy in the form of neural impulses travels through the network and toward something analogous to an output layer. This happens every time you speak a word. In both the brain and AI software, inputs work their way through a system of nodes toward an existing output. That output represents the decision, and in this case, it's the next word. 

 

AI that Generates Natural Language

 

Broca's area is a patch of cortical tissue in the frontal lobe designed by evolution to have all the right inputs, outputs, and internal connectivity to guide the involuntary, routinized aspects involved in speech production. Neuroscientists still don't know much about how it works, and reverse engineering it completely would be nearly impossible with today's technology. Lucky for us, AI probably doesn't need an exact equivalent of Broca's to develop the gift of gab. In fact, it may already have something even better.  

 

There are many state-of-the-art natural language systems we could discuss, but here we will focus on one called GPT-3 (which arrived in May 2020). It is an exciting new AI project that has proven to be highly adept at natural language processing (NLP). It can answer questions, write computer code, summarize long texts, and even write its own essays. However, keep in mind that as discussed above, it has no plan. The next word it chooses is just the word that it "predicts" should come next. This is called "next word prediction."

 

You can feed it the first two sentences of a news article, and it will write the rest of the article convincingly. You can ask it to write a poem in a certain author's style, and its output may be indistinguishable from an actual poem by that author. In fact, one blogger created a blog where they only posted GPT-3 text as entries. The entries were so good that people were convinced it was written by a human and started subscribing to the blog. Here is an example of a news article that it wrote:

 


Traditionally AI does poorly with common sense, but many of GPT-3’s responses are highly logical. I want to urge you to use an online search to find out more about the fantastic things that it can do. However, keep in mind that it sometimes makes fundamental mistakes that a human would never make. For example, it can say absurd things, completely lose coherence over long passages, and insert non-sequiturs and even falsehoods. Also, as rational as its replies may seem, GPT-3 has no understanding of the language it creates, and it is certainly not conscious in any way. This becomes clear from its responses to nonsense:

 

 


 

I don’t think that tweaking or expanding GPT-3’s architecture (which many in AI are discussing) is ever going to produce a general problem solver. But it, or a language system like it, could make a valuable contribution to a larger, more general-purpose AI. It could even help to train that larger AI. In fact, I think GPT-3 would be a perfect addition to many proposed cognitive architectures, including one that I have proposed in an article in the journal Physiology and Behavior here. The rest of this blog post will describe how a language model like GPT-3 could contribute meaningfully to a conscious machine if integrated with other specialized systems properly.

 

Playing the Role of Broca’s

 

When we are born, our language areas are not blank slates. They come with their own instincts. Highly complex wiring patterns in the cerebral cortex set us up in advance to acquire language and use it facilely. AI should also not be a blank slate, like an undifferentiated mass of neurons. It needs guidance in the form of a wiring structure. GPT-3s existing lexicon, and record of dependencies between words could help bootstrap a more extensive blank-slate system. Taking a pretrained system like GPT-3 and embedding it within a much larger AI network (that starts with predominantly random weights) could provide that AI network with the instincts and linguistic structure it needs to go from grammatical, syntactic, and lexical proficiency to proper comprehension. In other words, an advanced NLP system will provide Noam Chomsky and Steven Pinker’s “language instinct.”

 

When GPT-3 chooses the next word, it is not influenced by any other modalities. There is no sight, hearing, taste, smell, touch, mental imagery, motor responses, or knowledge from embodiment in the physical world influencing what it writes. It is certainly not influenced by contemplative thought. These are major limitations. By taking the GPT neural network and integrating it with other functionally specialized neural networks, we can get it to interact with similar systems that process information of different modalities resulting in a multimodal approach.  This will give it a broader form of attention that can keep track of, not just text but a variety of other incentives, perceptions, and concepts. GPT-3 already prioritizes strategically chosen words, but we want it also to prioritize snapshots, audio clips, memories, beliefs, and intentions.

 

Determining priority should be influenced by real-world events and experiences. Thus, the system should be able to make its own perceptual distinctions using cameras, microphones, and other sensors. It should also be able to interact using motors or servos with real-world objects. Actual physical interaction develops what psychologists call “embodiment,” crucial experiences that shape learning and understanding (GPT-3, on the other hand, is very much disembodied software). Knowledge about perception and physical interaction will influence the AI’s word choice, just like our real-world experiences influence the things we say. For instance, by applying embodied knowledge to the events that it witnesses at a baseball game, an AI should understand what it is like to catch or hit a ball. This understanding coming from experience would influence how it perceives the game, what it expects to happen, and the words it uses to talk about the game. This kind of embodied knowledge could then interact with the millions of pages of written text that it has read about baseball from sports news and other sources.

 

For me, the end goal of AI is to create a system that can help us accomplish things we cannot do on our own. I am most interested in creating an AI that can learn about science, by doing things like reading nonfiction books and academic articles, and then make contributions to scientific knowledge by coming up with new insights, innovations, and technology. To do this, an AI must think like a human, which means it must have the equivalent of an entire human brain and all of its sensory and associative cortices, not just its language area. 

 

I think that to build superintelligence or artificial general intelligence, the system must be embodied and multimodal. You want it to be interacting with the world in an active way, watching events unfold, watching movies and youtube videos, interacting with people and animals. As it does this it should be using the words that it has to describe its experience as psychological items (higher-order abstractions) to make predictions about what will come next and how to interact with the world.

 

GPT-3 uses its attention to keep track of long-term dependencies. It selectively prioritizes the most relevant of recent words so that it can refer back to them. This is how it keeps certain words “in mind” so that it doesn’t stray from the topic as it writes. GPT-3 is 2048 tokens (think words) wide. That is its “context window” or attention span. In my opinion, this may be more than large enough to serve as an equivalent of Broca’s area. GPT-3 must have an attention of thousands of tokens because it is compensating for the fact that it doesn’t have the equivalent of an overarching, hierarchical, embodied, multimodal, global working memory.

 

The architecture for GTP-3 is very similar to that of GPT-2 and GPT-1. They all use the same algorithm for attention. GPT-3 performs much better than its earlier iterations, mostly because it is much larger. It contains more layers, wider layers, and was trained on more data. Some people think that using this architecture and continuing to scale it up could lead to artificial general intelligence, which is AI that can do anything a human can do. Some even speculate that it could lead to conscious AI. I am highly convinced that GPT-3, or other neural networks like it, will never lead to consciousness.

 

Continuing to scale up this system will lead to improved performance but also diminishing returns. Although it could lead to many general abilities, it will never lead to true understanding or comprehension. Trying to do so would be like creating an Olympic sprinter by building an intricately complex robotic foot. The foot may be necessary, but you will need all the other body parts to come together for it to run competitively. GPT-3 must be linked together with other specialized modules into a shared workspace for it to really shine. Before we talk about this workspace in the last section, let’s look at Broca’s in a little more detail.

 

Broca's and Wernike's Areas

 

Broca's area is a motor area in the frontal lobe responsible for speech. Patients with damage to Broca's have trouble speaking. If the damage is sufficient, they may be disfluent, aphasiac, or mute. Much like GPT-3, Broca's selects the next word to be spoken based on the words that came before. Once it chooses the word that fits in context, it hands the word down to lower-order motor control areas (like the primary motor area) that coordinate the body's actual muscular structures (voice box, tongue, lips, and mouth) to say the word. The neurons in your motor strip constitute your output layer, and there is a dictionary in there in some shape or form. To continue to explain the role of Broca's in language, we must introduce its sister area, Wernicke's.

 

Wernike's area is a cortical area that helps us process heard speech. It is found in the temporal lobe and takes its inputs from early auditory areas that get their inputs straight from the ears. Neurological patients with damage to this area can hear most nonspeech sounds normally as their auditory areas are still intact, but they have a specific deficit in recognizing language. In other words, your Wernicke's area will not try to analyze the sound of a car but will try to analyze the voice of your friend. It acts as a pattern recognizer specifically for spoken words. 

 

Wernicke's and Broca's are specialized modules whose (mostly unconscious) outputs affect how we perceive and use language and even how we think. It is interesting to note that Broca's is about 20% larger in women than in men, and this may be responsible for women's greater fluency with language and higher verbal abilities.

 

The diagram below shows how we can go from hearing our friend say something to us to responding to them with our own words. First, the primary auditory area takes sounds heard by the ears, processes them further, and sends its output to Wernicke's area. Wernicke's then picks out the words from these sounds and sends those to Broca's. Broca's, in turn, sends the words that should be spoken in response to the motor area, which will then send the appropriate instructions to the tongue, jaw, mouth, and lips. This seems like a tight loop, but keep in mind that several other loops involving various brain areas contribute to our verbal responses (loops that NLP systems such as GPT-3 don't have).

 

 


It is worth mentioning that GPT-3 handles both the input of text and its output, so in this sense, it serves as an analogue of both Broca's and Wernicke's areas. However, it cannot hear or speak. This is easily fixed, though, by connecting a speech to text program to its input to allow it to hear. Allowing it to speak is as easy as connecting its output to a text to speech program. 

 

In the brain, Broca's and Wernicke's have a continual open circuit connecting them at all times. They are constantly working together. Your Wernicke's area also listens to the speech you generate, which helps provide real-time feedback about the words coming out of your mouth. This allows language perception to be constantly linked to language generation. Not only does it allow us to hear the words that we say out loud, but it also gives a voice to the subvocal inner speech that we create. In other words, the loop between these two areas is responsible for the voice in your head, your internal monologue. Your Broca's area allows its outputs to be sent to your auditory area even when actual speech is suppressed, and this is why you can hear your own voice in your head even when you are not speaking aloud. We basically hallucinate our inner voice. Inner speech may be an essential aspect of consciousness, so we should give our AI system this kind of circuit. 


The circuit connecting Broca's to Wernicke's is also responsible for the "phonological loop", which is a form of short-term sensory memory that allows us to remember a crystal-clear version of the last 2.5 seconds of what we just heard. This is why you can remember someone's last sentence word for word or remember a seven-digit phone number. Judging from the fact that all humans have one and that it is very useful to us day to day, the phonological loop may also make substantial contributions to consciousness. For this reason, Broca's and Wernicke's analogues may be essential ingredients for superintelligent AI.

 

GPT-3 may be ready as-is to serve as an equivalent of Broca's area in a larger system that is designed to interface with it. However, it is not ready to handle the long-term conceptual dependencies necessary for true cognition. To do this, it needs to interact with a global workspace.

 

What is a Global Workspace?

 

The Global Workspace is a popular model of consciousness and brain architecture from brain scientist Bernard Baars. It emphasizes that what we are conscious of is broadcast globally throughout the brain ("fame in the brain"), even to unconscious processing areas. These unconscious areas operate in parallel, with little communication between them. They are, however, influenced by the global information and can form new impressions of it, which in turn can be sent back to the global workspace. 

 

The diagram below, adapted from Baars' work, shows five lower-order systems separated from each other by black bars. Each of these systems is hierarchical, and only at the top of their hierarchy can they communicate with one another. The location where they meet and exchange information is the global workspace. Here in the workspace, the most critical elements are activated and bound together into a conscious perception.   

 

 


This neurological model can be instantiated in a computer in the form of interacting neural networks. The diagram below shows six different neural networks, which all remain separate until their output layers are connected in a shared global workspace. The black letters represent items held active in working memory.

 

 


The global workspace is like a discussion between specialists who share their most important ideas. If Broca's converges strongly on a series of words, those will be shared with the global workspace. From there, they are shared with other brain areas and modules. For example, if when you read about a "pink rhino in a bathing suit" the words in this phrase are translated to words you hear in your "mind's ear." From there they are broadcast to the global workspace where you become conscious of them. From there they are shared with your visual processing areas so that you can form a mental picture in your "mind's eye."

 

It would probably be helpful to train the AI language model (or module) by itself first before it is dropped into a larger global architecture. This is similar to the way our Broca's and Wernicke's areas come with genetically determined wiring patterns that have been selected over tens of millions of years of evolution (it is worth mentioning that even apes and monkeys have analogues of these two areas, and they generally perform the same functions). Once the language area is dropped in it can play a hand in training the rest of the system by interacting with it. Over time, the two systems will help to fine-tune each other.

 

Broca's area is always running in the background, but its processing does not always affect us. It only has access to consciousness when what it is doing is deemed important by dopaminergic centers. Similarly, the language it produces is only broadcast to the larynx and thus spoken aloud when other brain areas grant this. Our AI system should work this way too. It should have three levels of natural language generation activity: it should be able to speak, produce subvocal speech that only it can hear, and have speech generation going on in the background that unconsciously influences it (and the global workspace). 

 

Even if the system is not speaking or printing text to a console, its language generation should be running in the background. Like us, it may or may not be conscious of the words its Broca's area is stringing together. In other words, its output may not be taking center stage in the global workspace. However, whether subliminal or not, the language it generates should still influence the behavior of other modules. And just as you can hear the words coming out of your mouth, this larger system would be able to analyze GPT -3's outputs and provide it with feedback about what to say next. We would want it to be able to self-monitor its own language output.

 

Broca's area takes conceptual information from the global workspace and turns it into a stream of words. It translates, fills in the blanks, and finds the appropriate words to express what is intended. When you are approached by a stranger that seems to have a sense of urgency, Broca's area turns your intentions into words: "Hi, how can I help you?" We don't have the mental capacity to pick and choose all the words we use individually. Much of it is done completely unconsciously by this system.

 

 At first, the word selections made by the AI system would be almost entirely determined by the language model. This is analogous to how our language areas and their inherited architecture shape how we babble as infants. Slowly the weights and activation from the global workspace would start to influence the word selection randomly and subtly. Errors and reward feedback would alter the weights in various networks and slowly tune them to perform better. Over time, the language model will gradually relinquish control to the higher-order demands and constraints set by the larger system.

 

The diagram below shows a large system made of rectangles. Each rectangle represents a neural network. The largest network on the left (more of a square) contains semantic representations that can be held in either the focus of attention (FOA), the short-term memory store (STM), or in inert long-term memory. The letters in this square show that this system is updated iteratively. This means that the contents of the system's working memory have changed from time one (t1) to time two (t2). But significantly, it hasn't changed entirely because these two states overlap in the set of concepts they contain. This kind of behavior would be important for the language module, but also for the other modules in our AI system as well.

 


It is important to mention that GPT-3 is also updated iteratively. Its attention span for the words that it just read is limited. Once it is full it is forced to drop the words that have been there the longest. We can assume that Broca's area is also updated iteratively. But unlike Broca's, GPT-3 does not connect to a larger system that prioritizes its working memory by using an FOA and an STM.

 

The network of neural networks described here should utilize nodes that fire for extended periods to simulate the sustained firing of cortical pyramidal neurons to create a focus of attention. Neurons that drop out of sustained firing should then remain primed using a form of synaptic potentiation amounting to an STM. This larger system should also use SSC, icSSC, iterative updating, multiassociative search, and progressive modification, as explained in my article here. This architecture should allow the system to form associations and predictions, formulate inferences, implement algorithms, compound intermediate results, and ultimately create a form of mental continuity.


Rather than relying on “next word prediction” truly intelligent systems need a form of working memory that and a global workspace. Linking the modern natural language generation models with the major mechanistic constructs from cognitive neuroscience could give us the superintelligence we want.


Sorry this entry is SO fragmented. I spent weeks on this but just couldn't seem to pull it together.