Here is the image submitted, previously prepared with Midjourney.
Image by Losey using Midjourney
After the image was uploaded to AI Smith, the following 429 word story was immediately created.
In a world dominated by high technology where artificial intelligence had been seamlessly integrated into the fabric of daily life, sat David – a thinker, tinkerer, and long-time robotics engineer. Sitting in the quiet dimness of his cluttered, tech-filled office, he surveyed an unusual spectacle before him. It was a robot, but not just any robot – this one was special.
At his wooden desk, silhouetted by the faint hues of the evening creeping in through the window, sat a humanoid robot; its metallic silhouette futuristic. Cold yet oddly familiar. This was not merely a machine. It was the culmination of David’s decade-long research, his creation – the one many in his field had said was impossible.
The robot sat there, humanoid in shape, a mesmerizing blend of polished chrome and matte black. Its human-like eyes, orbs of bioluminescent crystal, twinkled with curious intelligence. Atlas, as David lovingly christened it, was the epitome of technological sophistication.
David adjusted his thick-rimmed glasses, rotated his swivel chair to face Atlas, and said, “Today is a big day, my friend.”
Atlas responded with an inflection and rhythm uncharacteristic of a traditional AI, “Yes, David. It is indeed.”
David’s fingers hovered momentarily over the motherboard that was patched into Atlas. It was an interface of myriad colorful wires, blinking LED lights, a wild jungle held together by David’s technical know-how. The man-machine connection was both strange and beautiful.
To inject personality into a robot was audacious, but David had loftier ambitions—he sought to imbue empathy. David aimed to create a mechanical being capable of genuine human interaction, understanding emotions, sarcasm, and even humor, a revolutionary leap in the domain of artificial intelligence.
The final test had arrived. David, a hint of anticipation in his voice, told a joke. It was delightfully silly, absurd enough to test Atlas’s grasp on nonsensical humor.
Without a beat, Atlas broke the room’s silence with a laughter that resonated metallic, pure—and perhaps, just perhaps, genuinely amused. It was the revered ‘aha’ moment. David leaned back in his chair and smiled triumphantly at his exceptional creation, realization shining in his eyes- he had done it.
David and Atlas, man and machine, sat there in reverence of the moment. Their one fleeting moment of breakthrough, echoed in the quiet hush of the dim-lit room, reflected in the ambiance of rustling papers, soldering irons, and the woody smell of an old desk. The image of a man sitting with his robot, just told itself an extraordinary story, one of perseverance, innovation, and the incredible potential of the man-machine relationship.
AI Smith Using ChatGPT-4
Note how the story incorporates many details from the image. Did you find it interesting as promised? Here is the “movie” version, created using Python coding abilities of ChatGPT-4, which is not something that AI Smith can do, yet. To help create the video the cool zoom-out feature of Midjourney was used, along with a little Photoshop and iMovie work; a good example of hybrid multimodal. For best results click on the lower right corner and zoom out to full screen to view, sound on.
Video of AI Smith’s Story “Teaching a Robot to Laugh”
Amidst the obscurity of an unlit room, there echoed a peculiar symphony of clicking keyboards, whirring computer fans, and an intermittent hum of casual discussion. The room where the glowing screens were the only source of illumination, casting an ethereal glow over the faces of its occupants. They were a group of individuals banded together by their unparalleled knack for technical acuity—a crew of prodigious minds who called themselves ‘The Illuminators.’
The Illuminators weren’t just any ordinary tech enthusiasts or software engineers. These were the elites, the crème de la crème of the digital world—the hackers. Their playground was a realm of cyberspace where only the fittest survived, and they didn’t just survive, they thrived.
Assembled from various walks of life and diverse backgrounds, they each had a unique story. There was Piper, the former cybersecurity expert for a clandestine government agency, using her experience now for more globally beneficial ends. Ajay was an international puzzle champion with a soft spot for cryptography; his algorithms were capable of decrypting most advanced systems. Then there was Yara, a former pro gamer with reflexes as impressive as her coding prowess.
In the center of the room, under the focus of multiple screens, sat their leader, Orion—a prodigy turned renegade, disillusioned from corporate greed and misuse of technology. Their cause was their bond, a shared vision to create an unbiased and equal digital world to protect the vulnerable from the wolves of cyber predation.
Each night was a new mission, a new challenge. To outsiders, their world came across as a sequence of unintelligible letters, numbers, and symbols strewn across the eerie blue glow of their screens. But to them, it was poetry—a language they loved, a language they understood better than their own mother-tongue.
Tonight, they were summoning a digital storm against a pharmaceutical giant involved in a controversial data privacy violation. The Illuminators had the evidences, they had the know-how. All they needed was the right pressure point in a maze of labyrinthine firewalls.
As their fingers danced on the keyboards, the room echoed with their unsaid unity, a harmony between hearts and minds resonating in the darkness, punctuated by the rhythm of their singular purpose. The scent of freshly brewed coffee filled the air, reminding them of the long night ahead.
Each stroke, every command indented was like a well-orchestrated symphony, slowly and surely converging into a climax that was yet unseen. They were poets of the virtual world, their sonnet weaved in lines of intricate code as they collectively shouldered the responsibility of their elucidating mission.
The muted hum of the computers suddenly surged into a crescendo as the final line of code was executed. A triumphant smile tugged on Orion’s lips as he confirmed with a single word that reverberated warmly in the dim room: “In.”
For those in that room, the world might have been dark, but the work they did was irrefutably radiant—a testament to the power of shared goals, knowledge, and their unabiding faith in the justice of their cause. The Illuminators weren’t just emissaries in the digital space. They were its guardians—turning darkness into light, one line of code at a time.
AI Smith’s Story of “The Illuminators” e-Discovery Team using ChatGPT-4
It was a slightly longer story this time, 534 words. Interesting coincidence, I suppose, that AI Smith chose a justice, investigatory theme by an e-discovery team of elite hackers. Could just be a prejudice on my part, but seems to me like another “fascinating story” as promised.
This article analyzes the creative imagination capacities of ChatGPT and two software settings to control it: Temperature and Nucleus Sampling (aka Top-P). It is easy to change these parameters, as will be shown by multiple examples. Knowing how to use these settings will make your work with Ai more enjoyable, productive and accurate.
All Images and Videos by Ralph Losey
According to Sam Altman, OpenAI’s CEO, when you reduce the Temperature and Top-P values, you not only reduce the creativity of the responses, you reduce the chances of error and hallucinations. This will be explained in the context of Sam Altman’s deep understanding of creativity and the role of mistakes in the creative process.
The insights and software control skills explained in this article can empower anyone to dial in the right balance of creativity and mistakes for a particular ChatGPT-4 assisted project. Lawyers, for instance, may, for most of their uses, want to lower the default settings, which are high on creativity. This should improve the probability of accurate, delusion free answers. That makes most lawyers and their bots very happy. Judges too. All of this, and more, will be explained.
Questioner (paraphrased): [I]t’s human to make mistakes. All people we love make mistakes. But an Ai can become error free. It will then have much better conservations with you than the humans you love. So, the AI will eventually replace the imperfect ones you love, the Ai will become the perfect lover.
Sam Altman: Do you want that? (laughter)
Questioner: Yeah.
Sam Altman: (Sam explains AI is a tool not a creature, as I have quoted before, and before addressing the intimacy lover aspect, as I quoted in Code of Ethics for “Empathetic” Generative AI, Sam talks about creativity.) On the question of mistakes and errors, I believe that creativity and certainly the creation of new knowledge, is very difficult, maybe impossible, without the ability to make errors and come up with bad ideas. So, if you made a system that would never tell you anything that it was not absolutely sure was a fact, you would lose some creativity in that process.
One of the reasons people don’t like ChatGPT is because it hallucinates and makes stuff up, but one of the reasons they do like it is because it can be creative. What we want is a system than can be creative when you want, which means sometimes being wrong, or saying something you are not sure about, or experimenting with a new idea. Then when you want accuracy, you can get accuracy.
Sam Altman, June 7, 2023, at an Economic Times event in India
Sam Altman’s answer here assumes you know about ChatGPT’s creativity volume controls, where you can, if you want, turn the creativity volume down to zero. In so doing, you will improve accuracy, but the response will often be boring. Boring, but accurate, may be just what you want sometimes, but that is not the default setting for ChatGPT, as will be explained and demonstrated.
ChatGPT Creativity Settings
This section provides a technical explanation of these two settings. Much of this is difficult to understand, but worry not, and plough through it, because after this comes an easy to follow demonstration of what it all means. Multiple examples will be provided to allow you to see for yourself how the GPT controls work in practice. That is the hacker “hands on” e-Discovery Team way.
First, the technical explanation of the two volume controls for GPT creativity: Temperature and Nucleus Sampling (aka Top-P). Both typically have settings of between zero and one, 0.0 and 1.0.
TEMPERATURE: Technically temperature affects the probability distribution over the possible tokens at each step of the generation process. A temperature of 0 would make the model completely deterministic, always choosing the most likely token. The “temperature” setting in GPT and similar language models, such as ChatGPT, controls the randomness of the model’s responses. A higher temperature value makes the model’s responses more random, while a lower, cooler value, makes the responses more deterministic and focused. See eg. Cheat Sheet: Mastering Temperature and Top_p in ChatGPT API (OpenAI Forum). Temperature values are said to produce a more focused, consistent, and deterministic output. It is like going from water, the higher 1.0 value, to ice, the colder, more probable value of 0.0.
Typically, OpenAI experts say a higher temperature (e.g., 0.8) may be suitable when you want a range of ideas, brainstorming suggestions, or creative writing prompts. A lower temperature (e.g., 0.2) is more appropriate when you’re looking for a precise answer, a more formal response, or when the context demands consistency. The default setting for ChatGPT 3.5 and 4.0 is 0.7. That’s pretty hot, especially for most legal work. No doubt OpenAI have put a lot of research into that default setting, but I could not find it. Id., Also see: Prakash Selvakumar, Text Generation with Temperature and Top-p Sampling in GPT Models: An In-Depth Guide (4/29/23).
Open AI says that finding the right temperature setting may require experimentation to strike a balance between creativity and consistency that suits your specific needs. Id. This sounds like a good lawyer answer of “it depends.” There are a tremendous number of variables, different questions and needs, different circumstances. That is the same situation lawyers are in with many legal questions. Plus the OpenAI software itself is constantly being updated, even though the version number of 4.0 has not been changed since March 2023.
The Bottom line for lawyers is that the default setting of 0.7 is pretty high in the random predictions scale. Unless you are looking for clever, very creative language or legal imagination – off the wall ideas – lawyers and judges should use a lower setting. Maybe dial down the random creativeness to 0.2, or even zero – 0.0 – for maximum route parroting of the most probable information. You just want the cold truth.
As Sam Altman explained, lowering the temperature setting also makes it more likely that your answers will not have as many mistakes or hallucinations. Note that I did not say no mistakes, the software is too new, and life is too complex to say that. Human lawyers are still needed to verify the Ai. Just because it appears much smarter than you, it can still be wrong, no matter how conservative the temperature setting. Think of the brilliant, very creative, higher IQ than you, conservatively dressed, young associate with little or no actual legal experience.
Baby lawyer explains Top-P to skeptical partner
NUCLEUS SAMPLING (aka TOP-P): Top-P sampling is an alternative to temperature sampling. Technically, this means that instead of considering all probable tokens that are likely to come next, the Top-P parameter directs GPT to considers only a subset of all probable tokens (the nucleus) whose cumulative probability mass adds up to a certain threshold, the (Top-Probablity). For example, if Top_P is set to 0.1, GPT will consider only the tokens that make up the top 10% of the probability mass for the next token. This allows for dynamic vocabulary selection based on context. The setting values for Top-P are, like temperature, between 0.0 and 1.0.
Put another way, the Top-P sampling parameter maintains a balance between diversity and high-probability words by selecting tokens from the Top-P most probable tokens. They are the tokens whose collective probability is greater than or equal to a specified threshold p. The Top-P parameter helps ensure that the chatbot response is both diverse and relevant to the given context. Text Generation with Temperature and Top-p Sampling in GPT Models: An In-Depth Guide
The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, using likelihood as a decoding objective leads to text that is bland and strangely repetitive. . . . Our findings motivate Nucleus Sampling, a simple but effective method to draw the best out of neural generation. By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.
From my study, the Top-P prompt parameter is seldom employed in ChatGPT. Most users prefer to just stick with the Temperature control variation. But still, try it a few times and see what you think, especially if temperature control variations are not working well for some reason on a particular challenge. Software customization is always a good thing. I have not used it enough myself to have any more specific conclusions or recommendations.
When and How to Tweak These Parameters As a business user, you might need to tweak these parameters to get the desired output quality, depending on the specific use case. Temperature:
If the generated text is too random and lacks coherence, consider lowering the temperature value.
If the generated text is too focused and repetitive, consider increasing the temperature value.
Top-P Sampling:
If the generated text is too narrow in scope and lacks diversity, consider increasing the probability threshold (p).
If the generated text is too diverse and includes irrelevant words, consider decreasing the probability threshold (p).
Here is the guide found in the OpenAI Forum on how these parameters can be used in different scenarios, with example values:
Use Case
Temperature
Top_P
Description
Code Generation
0.2
0.1
Generates code that adheres to established patterns and conventions. Output is more deterministic and focused. Useful for generating syntactically correct code.
Creative Writing
0.7
0.8
Generates creative and diverse text for storytelling. Output is more exploratory and less constrained by patterns.
Chatbot Responses
0.5
0.5
Generates conversational responses that balance coherence and diversity. Output is more natural and engaging.
Code Comment Generation
0.3
0.2
Generates code comments that are more likely to be concise and relevant. Output is more deterministic and adheres to conventions.
Data Analysis Scripting
0.2
0.1
Generates data analysis scripts that are more likely to be correct and efficient. Output is more deterministic and focused.
Exploratory Code Writing
0.6
0.7
Generates code that explores alternative solutions and creative approaches. Output is less constrained by established patterns.
Examples of GPT Temperature Control Settings in Action
Now, on to the examples, so you can see for yourself what happens when you change the volume parameter in a “What Is” type question. (It would take too long to also demonstrate the Top-P variable, and anyway, is not as useful.) These examples will get crazy as we enter the higher temperatures, so hold on. Of course, as you could easily predict, I am going to use a What is Creativity prompt in my test. Here is the transcript of my using ChatGPT-4, May 24, 2023 Pro Version.
______________________
Ralph: Temperature 0, please explain what creativity means in 100 words or less.
ChatGPT: Creativity is the human ability to generate unique and innovative ideas, products, or solutions that are both novel and valuable. It involves original thinking, imagination, and the capacity to transcend traditional ideas to create meaningful new ones. It’s present in arts, science, technology, and everyday problem-solving, reflecting our capability to envision, invent, and express in distinctive ways.
Ralph: Temperature .25, please explain what creativity means in 100 words or less.
ChatGPT: Creativity is the ability to generate new, unique, and valuable ideas. It is an essential human trait, expressed in diverse ways such as art, science, technology, and problem-solving. Creativity involves original thinking, pushing beyond the boundaries of conventional wisdom to produce innovative solutions and insights. It’s our capacity to envision and create something different and meaningful from existing elements, thus driving progress and evolution.
Ralph: Temperature .7, please explain what creativity means in 100 words or less.
ChatGPT: Creativity is a mental spark that illuminates originality, crafting fresh ideas and unique solutions that hold value. It’s an intellectual dance, weaving thoughts together in ways unseen before. Not confined to art or literature, it flourishes in science, technology, and daily life, offering new perspectives and innovations. It’s our ability to venture beyond known boundaries, reimagining and reshaping our world. Creativity is the birthmark of human ingenuity and a key driver of evolution and progress.
Ralph: Temperature 1.0, please explain what creativity means in 100 words or less.
ChatGPT: Creativity is the dazzling dance of the mind, spinning new, unexpected connections between thoughts and ideas. It’s the spark of novel insight, the ripple of originality that makes us see the world anew. A wild ride on imagination’s wings, pushing beyond boundaries to invent and innovate. In art, it’s a unique masterpiece; in science, it’s a game-changing theory; in daily life, it’s the pinch of spice that adds flavor to the ordinary. It’s our unique human capacity to give birth to the unseen, the unheard, and the unthought.
Ralph: Temperature 1.5, please explain what creativity means in 100 words or less.
ChatGP: Creativity, ah, the rainbow in a thinker’s cloud, the galactic spiral of intellectual wonder! It’s the cosmic jazz, a whimsical waltz between thoughts, creating a symphony of unexplored vistas. Like fireflies in the midnight forest of cognition, it lights up unseen pathways, twirling the familiar into a fantastic masquerade of novelty. In the vast canvas of existence, from the minuscule to the monumental, it’s our kaleidoscope, eternally refracting the mundane into myriad hues of innovation. Creativity, the celestial wizardry of our minds, endlessly knitting the threads of the known into the cloak of the extraordinary.
Ralph: Temperature 10.0, please explain what creativity means in 100 words or less.
ChatGPT: Apologize for the confusion, but the temperature value for text generation typically ranges between 0.0 (completely deterministic) and 1.0 (more random). A value of 10.0 would exceed the usual scale and won’t lead to a meaningful or understandable output. Please try a value within the typical range.
Ralph: I understand, but do not understand why you accepted a temperature setting of 1.5 in the question before the last one? Please explain.
ChatGPT: I apologize for the inconsistency. You’re correct, the usual temperature range is indeed from 0.0 to 1.0 for generating text with AI models. My acceptance of a temperature of 1.5 was a mistake. While you may technically be able to input a value higher than 1.0, such settings would lead to increasingly random outputs, often not providing meaningful or relevant responses. It’s best to stay within the standard range for most effective use. I appreciate your patience and understanding.
Ralph: Temperature 2.0, please explain what creativity means in 100 words or less.
ChatGPT: Creativity? It’s the stars in brain-sky dancing tango, paint-dripping ideas spinning topsy-turvy. Think banana-moon-sailing on oceans of uncooked pasta. It’s in music baked into pies, equations sung like lullabies, blue giraffes nibbling cloud-cakes. A heart-spark exploding kaleidoscope-thoughts, mushrooming into deliciously wild inventions. Confetti-filled cortex blasting out mind-fireworks, transforming known galaxies into uncharted nebulae. It’s where ordinary pebbles become philosopher’s stones, touching mundane, birthing extraordinary. Creativity, the mad hatter’s party in the wonderland of our cognition, delighting in serendipitous somersaults of imagination.
You can see from these examples how the random probability connections make the responses more creative. You can also see there is a very good reason for the temperature speed limit of 1.0. The two specifications above that of 1.5 and 2.0 were way over the top, worthless, Mad Hatter stuff, although kind of funny. Even though a GPT may take a higher value above 1.0, don’t bother, unless your goal is to prompt “stars in brain-sky dancing tango stuff.”
Baby Lawyer is not amused by excess creativity
Conclusion
It appears that creativity, like intelligence, is not, as once thought, a uniquely human capacity. For a discussion on the equivalence of machine and human intelligence, see: WHAT IS THE DIFFERENCE BETWEEN HUMAN INTELLIGENCE AND MACHINE INTELLIGENCE? Creativity and imagination are not what makes humans special. Like many common beliefs of the past, such as belief that the Earth was the center of the Universe, and all revolved around us, the past belief that only humans are creative is just another vain delusion. In fact, our creativity may not be as good in some ways. Consider how GPT creativity can be precisely controlled by skilled manipulation of the Temperature and Top-P parameters. How many of us can precisely lower and raise our creative imagination like that?
The price of both Ai and human creativity is mistakes and delusions. Just as human creativity is based on mistakes, and freedom of wild imagination, tempered by skill and control, so too is Ai creativity. All human creatives throw away many of their creations, their first drafts. (For instance, even after writing literally millions of published words in my long life, my blogs, like this one, require an embarrassing number of revisions. Some go beyond a hundred versions before I accept them, and then later, someone still finds mistakes!) All creatives go back and redo, they correct, they learn from the mistakes and try again. Rare is the visual artist, musician or writer who creates a finished work on the first try. The same is true with ChatGPT. Mistakes and hallucinations just come with the territory of creativity, both written and visual. Here is a video I created with the Chat-GPT code interpreter version to illustrate the creative process.
Click on the image to open a video of the imagination process
Of course, the creative process of all authors is not the same. Some go from without to within, like the writer below emerging from his own pile of experimental mistakes with inspiration from the moon.
Click on the image to open a video of the imagination process
No one wants to chat to a boring, just the facts, type of unimaginative person. We all know the type and run from them at parties. That explains why the default setting is 0.7 for ChatGPT, it is, after all, intended for chatting. That is the marketing hook. That is what most users want, at least at first. But GPT has many other uses too, including legal, accounting, translation and computer coding, where boring, but accurate, is often what you want. Same goes for data analysis, document review, document generation and many of the other incredible applications of ChatGPT-4 and other GPT tools.
So play with the Temperature and Plus-P settings. Learn how they work. The 3.5 version, for instance, is on my website, bottom right, as a chatbot. (Has not been named yet. Any suggestions?) I have very conservative creativity settings on the little chatbot because I am going for accuracy. The Temperature value is 0.2 and the Top-P is 0.01. These settings cannot be changed by the user, unlike the free OpenAI chatbots. Try asking my little chatbot, bottom right, what temperature and top-p means. After writing this, I tried, and the answers were really pretty good. I did not have Ai help in writing this blog, which may have been a mistake.
Serious professional users, like medicine, law and accounting, will probably want to set the default temperature to 0.0, and just warm it up on second passes. Plus, always remember, no matter what the temperature, trust but verify. Check the work, check the research and accuracy of reports; that is especially important in these early days of Ai. Finally, do not forget to update the work. Google search is not dead yet, and will likely always have its place in hybrid multimodal efforts, where all types of search are helpful.
It seems appropriate to end with a small creative effort using Ai tools on the high default temperature setting of 0.7. This video is a hybrid, human-Ai imagination, revealing the behind the scenes work to create Temperature control settings for ChatGPT. (For best results, click on bottom right hand corner and enlarge. Press space bar to watch again!)
Sam Altman, All Images by Losey using Midjourney and Photoshop
Sam Altman had some fascinating things to say recently about technology revolutions and what comes after AI. Apparently that is his favorite unasked question. After completing a grueling world tour of meetings and interviews, Sam Altman gave a lengthy video interview to Bloomberg Technology on June 22, 2026 at the Bloomberg Technology Summit. Journalist Emily Chang was the interviewer. I suggest you watch the full video, but there is one excerpt that is especially interesting. Below is the transcript, which I edited somewhat for readability. The quote begins by Sam’s responding to the classic interviewer question of what is the One Question Sam wished people would ask him, but usually don’t. This is followed by some joking, then Sam settles into a serious response.
Sam:One question. I’m always excited to just talk about what can happen in the coming few years and decades with this technology.
Emily interrupts:So what are we all going to do when we have nothing to do? (Question asked in a uncharacteristic whisper, followed by some joking response, here edited out, then Sam’s actual answer, at video 18:05.)
Sam:I don’t think we ever run out of things to do. It’s deeply in our nature to want to create, to want to be useful, to want to feel the fulfillment of doing something that matters. If you talk to people from thousands of years ago, hundreds of years ago even, the work we do now would have seemed, unimaginable, at best, and probably trivial. This is not directly necessary for our survival, in the sense of food or whatever.
The shift happens with every technology revolution. Every time we worry about what people are going to do on the other side. And every time we find things. I expect that not only will this time not be an exception to that, but the things we find will be better, more interesting, and more impactful than ever before.
There are a lot of people talking about AI as the last technological revolution. I suspect that from the other side it will look like the first! The other stuff will be so small in comparison.
I think the whole thing about technological revolutions is sort of dumb because my understanding has always been that it’s just one long continuous one. But it is continuing exponentially. So what will be enabled is stuff that we can’t even imagine on the other side.
We will have way too much to do, if you want. If you want to just sit around and do nothing, that will be fine too. (said jokingly)
Emily:Alright, alright, bonbons and beaches are in my future. (laughter)
Sam:I don’t think that is what you will turn out to want, but if you do, great. [tape 19:40]
Sam Altman, June 22, 2023, Bloomberg Technology Summit
Future, very busy, Sam Altman
The fear that Ai will take over and Humans will have nothing to do after Ai comes of age is a common fear. It was strange to hear Emily Chang tentatively ask her question in a whisper, followed by lots of nervous laughter. The anxiety in her and the large audience was real. There is fear we will be replaced, and become useless, irrelevant, as computers become ever more intelligent.
I have indirectly considered this core issue before in several blogs, including What is the Difference Between Human Intelligence and Machine Intelligence? There I concluded, following the lead of Sam Altman, that the answer to the question of the difference between human and machine intelligence, is that there is no real difference.
Still, again following Altman’s lead, I concluded that we humans are important, that we are special in ways other than intelligence. There is far more to us than IQ. If we were just intelligence, then we could be replaced. But, as Sam Altman said “[E]ven if humans aren’t special in terms of intelligence, we are incredibly important. . . . I think there’s something strange and very important going on with humans. I really, deeply hope we preserve all of that.” I agree with Sam.
Sam Altman believes that intelligence is a fundamental property of all matter. He was not surprised when his generative Ai learned to learn, since it is based on human brain neurology. Still, in spite of this, Sam and OpenAI do not believe that its smart Ais are living beings. As previously discussed in my blog, What is the Difference,Sam Altman believes, and I agree, that Ais are intelligent tools, not living creatures.
The Ai tools may become all around more intelligent than we are someday, and this day may come sooner than we think, but even then, AI would still just be a tool, not a creature. It cannot replace humanity, only augment.
So back to the question Emily Chang fearfully asked Sam Altman, what will we do after we are on the other side of the Ai technical revolution, a time when the line of exponential change is going nearly straight up? I agree with Sam’s answer, that Ai will eventually be seen as the first great technological revolution, not the last. All of the other technology advances leading to that point will seem minor in comparison. The Singularity will open up new advances far greater than ever conceived before. As this happens – “We will have way too much to do, if you want.”
Alternative Future, Older Sam Altman, still very busy
Not being a lie on the beach and eat bon bons kind of guy, I can think of many things as a lawyer and arbitrator that I’d like to do, if only I had the intelligent tools to do it. I’ve discussed a few of these things in another blog, Part 2 of Start Preparing For “THE SINGULARITY.” There I imagined what humans lawyers might do after Ai attains a high level of proficiency. I also discussed a few practical things lawyers, judges, arbitrators and the public should do now to prepare. For ADR specialists and arbitrators such as myself, I suggested a three-part plan:
Continuous Learning: ADR professionals should stay informed about advancements in Ai and how they impact dispute resolution processes. This includes understanding new technologies, tools, and methodologies that emerge as Ai continues to evolve.
Develop Complementary Skills: ADR professionals should focus on honing skills that complement Ai systems, such as empathy, cultural awareness, and creativity. These human-centric skills will remain valuable even as Ai takes on a more prominent role in dispute resolution.
Collaborate with AI Systems: ADR professionals should embrace the opportunity to work with Ai systems, leveraging their strengths and insights to enhance their practice. This might involve using AI tools to analyze complex data, facilitate communication, or generate creative solutions to disputes. It could be a great boon to mediators everywhere.
Losey, Start Preparing For “THE SINGULARITY”Part 2.
Super-Intelligent AI Tools will transform the law for the better
In theStart Preparing For “THE SINGULARITY blog I elaborate on this plan and, more generally, the dramatic, positive changes to the law that super-intelligent Ai tools make possible. The new Ai based ideas discussed include:
Ai systems that can analyze vast amounts of data, identify patterns, and generate creative solutions to complex legal problems that were, despite the progress of predictive coding, previously beyond our reach. This could lead to the development of new legal strategies, more accurate predictions of case outcomes, and innovative approaches to dispute resolution.
New forms of alternative dispute resolution could be developed that leverage advanced Ai capabilities. For instance, Ai-mediated negotiation platforms could facilitate communication between parties and propose fair, data-driven settlements. Ai-driven arbitration systems could analyze complex legal disputes and deliver unbiased, well-reasoned decisions more quickly and cost-effectively than traditional arbitration processes. Ai systems might someday be able to autonomously handle all aspects of dispute resolution, developing entirely new processes and approaches that leverage their superior cognitive abilities. This can happen in the courts too, but I expect it will happen in private arbitrations first, probably with small, high volume, consumer claims.
Along those lines, and including pre-suit interventions, Ai systems could analyze and understand the complexities of human emotions, motivations, and values, allowing them to facilitate dispute resolution in previously unimaginable ways. They might create dynamic, adaptable legal frameworks that evolve in real-time, anticipating and addressing emerging conflicts before they escalate. This could result in previously unheard efficiency, accuracy, and fairness. With super Ai in the hands of skilled attorneys, we should be able to resolve most disputes before they mature into claims and lawsuits; nip them in the bud. This may seem far out, but based on my experience, I believe this is a near certainty.
Ai-driven legal tools should make legal advice and representation far more accessible and affordable for individuals and small businesses than it is now. By automating routine tasks and streamlining complex processes, Ai systems should lower the cost of legal services and improve the efficiency of the justice system.
AI systems could be employed in drafting legislation and regulatory frameworks, leveraging their ability to analyze vast amounts of data, predict potential consequences, and optimize policies for societal well-being. This could lead to more effective, evidence-based, and adaptable legal frameworks that address emerging challenges, such as Ai regulation, climate change, cybersecurity, and inequality.
Future Ralph, Lawyer/Arbitrator
Lawyers will still be very busy after AI, if they want to be. I do. Most of the world does not have a lawyer, does not have access to justice. Most people cannot afford to pay a lawyer, cannot afford to go to court or other ADR forums. There is a great unserved need for good legal advice and fair dispute resolution. The world currently has a near inexhaustible need for justice. After AI, the lawyers who want to continue to work will have many great opportunities to do so. Those who are ready will have an opportunity to do things that have never been done before.
Even if we are not the center of the Universe as we once thought, and even if our intelligence and thinking ability is not unique, as we once thought, and even if our machine tools ultimately become smarter than we are, I feel, like Sam Altman does, that humanity is still very special. We are invaluable, living beings. We are capable of compassion, empathy, love and spiritual realization. Our living corporal experience make us unique, gives us the possibility of ineffable wisdom. Rest assured, there will always be plenty for humans to do. As our tools improve, so too will our accomplishments. Exciting times and opportunities lie ahead in all fields, especially the law.
Ralph Losey as Future AI Assisted Arbitrator
Future ADR-AI Proceeding for 1000s
To conclude, here is a small simulation of Sam Altman for entertainment and educational purposes. He is talking of the promise of AI. The video uses a digital art image of him I made and a generic computer voice. The words are largely Sam’s own. Notice, he does not even mention advances in our system of justice. That is up to us to conceive and implement.
All images created by Losey using Midjourney and Photoshop
The report by well-known consulting firm McKinsey, The economic potential of generative AI: The next productivity frontier (June 2023), provides reliable information and analysis on the jobs potential of ChatGPT and other generative Ai. The analysis and projections are encouraging. This report is much needed because almost everyone’s initial reaction to the surprising superpowers of generative Ai, is an “on no” type of response concerning job loss. We all tend to jump to fear of replacement by super smart Ai. Good news! The McKinsey report shows that is wrong. The conclusion is based on careful study, and extensive, fact checked research by top human experts. The report should encourage lawyers to embrace the coming Ai change, not fear it.
1. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed. (Editors Note – use cases considered do not include law.)
2. About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D.
3. Generative AI will have a significant impact across all industry sectors.
4. Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.
5. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.
6. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs.
The McKinsey report defines up front what it means by “Generative AI” and ties it to “foundation models” patterned on the “neural net” synapses used by our brains. Generative AI copies the basic connecting neuron brain structure to create intelligent AI, but, due to current computing limitations, they have to use far fewer neuron switches, thousands or at least hundreds of times fewer switches, than we have in our brain. (Go humans!) Humans have from between 100 Trillion synapses, to 1 Quadrillion synapses (That’s a thousand trillion.) The GTPs today have only a couple of hundred Billions synapse equivalents, parameters. For instance, ChatGPT-3.5 has only 175 billion. The size of GPT-4.0 is a trade secret, aside from OpenAI saying that it has attained a substantial increase over the 3.5. Version. Still, version 4.0 is rumored to have 1 Trillion parameters.
That is still quite smaller than us. Now here are some of the McKinsey report definitions.
For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step change evolution within deep learning.
A part of the report on knowledge workers like lawyers that I found particularly interesting, concerned Generative AI as a virtual collaborator.
In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. Generative AI could increase sales productivity by 3 to 5 percent of current global sales expenditures.
This productivity increase has been my experience to date, and that of other tech lawyers I have spoken with. It does, however, require prompt skills, in other words, you have to know what you are doing. See: OpenAI’s Best Practices For Using GPT Software.
The last interesting part in the Report for those whose expertise is in the “vertical” of law, which was not included the Report analysis, is the segment entitled: “The generative AI future of work: Impacts on work activities, economic growth, and productivity.” Report pg. 32. It applies to work in general, including knowledge workers otherwise omitted from the Report, such as lawyers. “Generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation.” Report at pg. 39. That is the key high level function of lawyer work, “decision making and collaboration.” See the below Exhibit 10 of the report at page 40 for the chart that summarize the surprising stats on this.
The next chart, Exhibit 11, finally includes a line, among many, that at least includes the legal profession as “part of” an occupation, “business and legal professionals.” The chart states the share of global employment (47 countries) for this professionals group is 5%. The chart predicts the overall technical automation potential is likely to grow by 62% with generative AI, whereas with other prior technology, including AI that is not generative AI (meaning basically the tech before 2023) was 32%. So the advent of ChatGPT and related, yet to be released generative AI is expected to double the automation potential of business and legal professionals. If they were to drill down to the U.S. alone and to the legal profession alone, I expect the number would be even higher, conservatively up to 75%. So, if you study the report carefully (and I personally did all of this analysis and writing, not “neuron weak” ChatGPT), it is suggesting that three fourths of lawyer tasks have the potential to be automated. It does not give a time frame for how long it might take this potential to be realized. Still, think of the implications of McKinsey’s study.
One more quote from the report is already well-known but bears repetition:
Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern— it is likely to have the most incremental impact through automating some of the activities of more-educated workers. . . .
However, generative AI’s impact is likely to most transform the work of higher-wage knowledge workers because of advances in the technical automation potential of their activities, which were previously considered to be relatively immune from automation.
To state the obvious, the legal profession in the U.S., which requires a graduate degree, is one of the most educated, higher-wage occupations in the world. That puts us near the tip of the spear.
The Report goes on to conclude that unlike all other automation technologies that have come before: “The rapid development of generative AI is likely to significantly augment the impact of AI overall, generating trillions of dollars of additional value each year and transforming the nature of work.” Report pg. 48. McKinsey goes on to caution, as it should:
But the technology could also deliver new and significant challenges. Stakeholders must act—and quickly, given the pace at which generative AI could be adopted—to prepare to address both the opportunities and the risks. Risks have already surfaced, including concerns about the content that generative AI systems produce: Will they infringe upon intellectual property due to “plagiarism” in the training data used to create foundation models? Will the answers that LLMs produce when questioned be accurate, and can they be explained? Will the content generative AI creates be fair or biased in ways that users do not want by, say, producing content that reflects harmful stereotypes? (emphasis added by Author)
Loss of Some Jobs in the Law is Probable and Not Such a Bad Thing
What is the probable impact of generative Ai on the legal industry? As shown, the McKinsey report does not specifically address our profession. But that’s ok, that is the area we already know about. Lots of us have been thinking about this for decades. In Law the job that is typically mentioned as the one most likely to be replaced by Ai is document review in discovery. In fact, employment in this area has already been disrupted by predictive coding, well before generative AI. I have a lot of personal experience with that. The impact will soon accelerate.
This part of the popular analysis about legal job disruption is correct, but, the negative reaction is misplaced. It is a mistake to think of “all the poor contract document review attorneys who will lose their job.” I have compassion for these contract review attorneys, in fact, I favor short term financial assistance for them, but I do not feel bad about their having to get a new job. I have personally done hundreds of document reviews over my career, supervised many thousands, and spent about a thousand hours myself actually doing doc reviews, often non-billable. I don’t know of any other big law partner who is dumb enough to have done that. But I did. Sometimes boring work is calming. Plus it needed to be done.
I was fortunate to have been paid pretty well for this, but the typical contract reviewer works ten hour days for compensation that is, after all deductions, just a hair above minimum wage. No kidding. The low compensation is a disgrace. As a consequence, most contract reviewers just doze around on a computer, trying to stay awake, reading company emails and messages to judge relevance and privilege at a snails pace of fifty files per hour. So boring, most of the time.
This work is much better suited for Ai, where reliable review of 50 files per minute is coming soon. My specialized, highly skilled teams of reviewers were able to attain these speeds, even higher, years ago, just by using predictive coding and our hybrid multimodal methods. With generative Ai these speeds will increase, reliability will increase, and, here is the big change, it should be far easier to do.
The pre-automation, grueling, mental labor task of trained lawyers manually reading hundreds of thousands of documents all day for possible use of a few of them at trial, is a task that should be eliminated. This particular lawyer job, which did not even exist until well into this century, is going away fast. “Good riddance,” I say. Some doc review jobs will remain, but never again the mobs of bored stiff law grads. With Ai, one human document reviewer will be able to supervise and do the work of dozens of unassisted lawyers, of hundreds, of thousands. We should not feel bad about the elimination of such boring, low paid drudgery work. I have done this work. It is awful. Far better to delegate it to intelligent robots.
We Should Prepare Now for Accelerated Job Displacement
I know and have worked with lots of doc reviewers. They are smart people; they have a law degree. It is demeaning to force them to do such legal work to earn a living. Many in the U.S. are burdened by crazy high student debt from greedy law schools, often low ranked, but still, they have a degree. They graduated from law school and most, like ChatGPT-4, have also passed a Bar exam. Moreover, unlike any Ai, they have also passed an ethics exam and personal history review and have been admitted to a state Bar association.
These low paid doc reviewers have good language skills and intelligence and, this is important, by completing law school, they have demonstrated an ability to learn new things, complicated things. Moreover, the legal training they received on ethics, research, persuasion, evidence, reasoning, logic and analysis, is transferable to a lot of new work, for instance, prompt engineering. They can, and should, retool. Their current employers, including especially e-discovery vendors, should help them with that.So should the whole legal industry. The help should begin with financial and retraining assistance for the most vulnerable group, contract review lawyers, but should not stop there. A lot of lawyers need help today, and there will be many more in the near future. Retooling and ongoing education is necessary for most everyone today, including parters in law firms.
The same replacement and retooling situation applies to the legal work of reviewing and writing large, complex contracts, except that those attorneys are usually associates or junior partners in law firms, not outcast contract lawyers, and they are paid far more than doc reviewers. Most legal tech experts predict, and I agree, that lawyers who perform this function will also soon be replaced, or numbers greatly reduced. Another mind numbing law job will bite the dust. I have done that too, from preparing shopping center leases, to software licenses, to the worst of them all, zillion page ERISA plans. Lots of boring tasks in the law and other areas will go away, or be greatly reduced by generative Ai. But here is the point of the McKinsey study, many more new and interesting tasks and jobs will be created by Ai. Overall it is a big 4.4 Trillion Dollar win.
Conclusion
The McKinsey Report provides clear warning of the coming storm of generative Ai. There are great opportunities and dangers. McKinsey is trying to prompt all of us to take action now. Quick action does not come easily to the legal profession. Lawyers suffer from a common affliction of the over-educated, paralysis by analysis. We tend to think too much and act too little. We are consumed with the fear of making a mistake. That is especially true when thinking about something new and strange.
In times of rapid change, like we are living through now, we must resist the temptation to just sit back and do nothing, or worse, appoint a committee to study the situation. Long gone are the leisurely days of taking years to consider and implement new rules and procedures in the law. If the legal profession is to make a smooth transition, not only survive, but prosper and provide the justice services our world desperately needs, then we must all realize that weakness. As McKinsey concluded, all of us must act-and quickly, given the pace of generative AI adoption.
Bar associations, courts, judges, arbitrators, arbitration associations, law firms (especially Big Law), in-house counsel, mediators, lawyers, paralegals, consultants, legal tech experts, and legal tech vendors, especially vendors who provide, or provided, document review services, need to start taking action now. They need to prepare new rules to govern generative Ai and they need to start retooling efforts and financial aid efforts to help unemployed lawyers in need. Big law and e-discovery vendors have profited greatly by the sweat of document reviewers over the years, and they should, along with Bar Associations, take the lead in financial aid efforts. We should start setting up charitable fund and equitable distribution systems now. Morally speaking the Ai industry should also be a major contributor to the retooling and financial aid efforts for all industries they disrupt, including the legal industry. They will receive a large share of the $4.4 Trillion. They too need to open their wallets and start taking action.
We need to formulate rules and best practices for use of generative Ai. At the same time, we need to plan for temporary job displacement and start taking action on that front. We should think about establishing free, or at least heavily subsidized, re-training programs. We should start to set up charitable programs for lawyers, and their families, who will soon be in need of temporary financial aid. The forthcoming $4.4 Trillion windfall that McKinsey predicts will inevitable hurt many in the short-term. The legal and technology industries should help. We should not be too hasty about any of this, we have a few months. We need some discussion, some time for deliberation, but not too much.
The world is depending on a functional system of justice. Legal work is important. It is too dangerous for us to remain in the mental world and just talk and write. As always with justice, balance is the way. New technology is a moving target, I know, but if mistakes are made, and they will, we can always adjust and revise. Let us be proactive and do what no Ai can now do, take the initiative and do things in the real world.
Copyright Ralph Losey 2023 – ALL RIGHTS RESERVED – (Also Published on EDRM.net and JDSupra.com with permission.)
Ralph Losey is a Friend of AI with over 740,000 LLM Tokens, Writer, Commentator, Journalist, Lawyer, Arbitrator, Special Master, and Practicing Attorney as a partner in LOSEY PLLC. Losey is a high tech oriented law firm started by Ralph's son, Adam Losey. We handle major "bet the company" type litigation, special tech projects, deals, IP of all kinds all over the world, plus other tricky litigation problems all over the U.S. For more details of Ralph's background, Click Here
All opinions expressed here are his own, and not those of his firm or clients. No legal advice is provided on this web and should not be construed as such.
Ralph has long been a leader of the world's tech lawyers. He has presented at hundreds of legal conferences and CLEs around the world. Ralph has written over two million words on e-discovery and tech-law subjects, including seven books.
Ralph has been involved with computers, software, legal hacking and the law since 1980. Ralph has the highest peer AV rating as a lawyer and was selected as a Best Lawyer in America in four categories: Commercial Litigation; E-Discovery and Information Management Law; Information Technology Law; and, Employment Law - Management.
Ralph is the proud father of two children, Eva Losey Grossman, and Adam Losey, a lawyer with incredible litigation and cyber expertise (married to another cyber expert lawyer, Catherine Losey), and best of all, husband since 1973 to Molly Friedman Losey, a mental health counselor in Winter Park.
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