What if justice had a shape — not rigid scales or a blindfolded figure, but a living, dynamic map? Imagine causation as a multidimensional space, where influence, control, and responsibility could be mapped across a moving legal landscape, like tides over a reef. That is the vision behind Topological Jurisprudence, a framework first glimpsed through work with applied mathematics using advanced AI — now, for the first time, including ChatGPT-5.
Underwater topological network — visualizing justice as tides over a reef. Created by Losey using multiple AI tools.
Using topological network mapping, we set out to see if this next-generation AI can turn an abstract mathematical concept into a practical tool for mapping fault in law’s most complex disputes.
Introducing Topological Jurisprudence — a near-future legal-tech vision where mathematics meets liability analysis. Image by Losey using multiple AI tools.
This idea may sound like science fiction, but it is grounded in topological data analysis(TDA, a branch of applied mathematics built to reveal patterns and relationships in complex, evolving systems. Here, the courtroom meets the mathematics of shape and flow. In the pages ahead, I move from visual allure to substantive potential: can AI-driven topology bring clarity to the most complex causation disputes of our digital age?
And this time, the question is tested with tools that didn’t exist when I began this journey. In Epiphanies or Illusions, Parts One and Two, I explored whether AI could find meaningful cross-domain patterns at all. Now, with GPT-5’s leap in cross-disciplinary synthesis, I can ask something new: not just “can it see the pattern?” — but “can it carry that insight into a framework lawyers can actually use in court?”
Visual composition set to original music to get a ‘left and right brain’ feel for this new approach. It shows the curvature and flowing connectivity at the heart of the topological perspective.
From Epiphany to Application
The Shape of Justice is my first major legal-technology project to apply GPT-5’s markedly improved abilities in cross-disciplinary pattern recognition and synthesis.
WHY GPT-5 MATTERS? GPT-5 can keep legal rules precise while integrating mathematics, simulations, and causation theory into a single, usable framework. Earlier models could suggest patterns; GPT-5 can carry them through to courtroom-ready analysis.
Where GPT-4o could propose intriguing links, GPT-5 can integrate them into a coherent, working framework without losing the rigor of either discipline. Here, that means fusing the mathematics of topology with the logic of proximate cause, comparative fault, and courtroom evidence.
The result is a practical tool — Topological Jurisprudence — that can map complex causation in a way static diagrams and bullet-point briefs cannot. It can show where fault originates, where it converges, and, as the case study ahead demonstrates, where it never touches a party at all.
A judge contemplates the multidimensional shapes of causation — where legal reasoning meets mathematical mapping. Image by Losey using Visual Muse.
Why Traditional Tools Break Down
In simple negligence, causation is a straight line:
Defendant A acted → Plaintiff B was harmed → liability follows.
But complex, multi-actor disputes — especially in high-tech contexts — rarely follow neat chains.
Consider:
Autonomous vehicle accidents with components and services from multiple companies.
Blockchain collapses involving code, governance votes, and trading patterns.
International supply chain contamination where the source could be anywhere in a dozen linked facilities.
Traditional “branch tree” diagrams are static. They struggle with systems where relationships change over time or where multiple causes converge unexpectedly.
Multidimensional accident causation map — showing how a topological model can reveal decisive interactions in complex systems. Even as the network evolves, stable patterns guide experts in assigning fault tied to legal duties and foreseeability. Video by Losey using AI.
What Topological Jurisprudence Brings
Topology is a branch of geometry that studies how points and connections form patterns that persist even as shapes stretch or shift.
Topological data analysis (TDA) applies this to complex datasets — finding relationships, clusters, and gaps that remain significant across different scales and conditions.
Dynamic topological network visualization — illustrating how relationships between actors shift over time, while the underlying structure of causation remains clear. Such visual models can help experts explain liability allocation in complex, multi-party disputes. Video by Losey using AI.
For lawyers, think of it this way:
Nodes = parties, devices, software modules.
Edges = relationships between them — contracts, data flows, communications.
Attributes = details on each connection — timestamps, amounts, governing law.
Time layers = how nodes and edges change over time.
In litigation, this means you can see:
Where fault starts.
How multiple causes interact.
Whether a party’s conduct ever intersected with the harm.
Seeing the whole picture. Video by Ralph Losey using Gemini, etc.
Case Study: The Autonomous Vehicle Pile-Up
Scenario:
A self-driving sedan manufactured by Alset Motors is involved in a multi-car collision. Eight claimants. Seven corporate defendants.
Jurisdiction: Florida, pure comparative negligence; no joint-and-several for ordinary negligence.
Actors
Alset Motors – Vehicle manufacturer. All core systems and base software worked flawlessly.
NaviAuto Corp. – Supplier of navigation and hazard-avoidance subsystem (Perception Stack v3.1.2).
SensorCo – Supplier of LiDAR S-200 hardware used in NaviAuto’s subsystem.
GeoMaps Inc. – Provider of real-time mapping and hazard alerts via API.
Topological crash analysis in action — the model processes sensor data, system logs, and contractual links to produce a clear allocation of liability under Florida’s comparative fault rules. Video by Losey using AI
Topological Causation Pathway
Impact (12:44:42 EDT): Alset’s braking system engages late but functions perfectly when commanded.
Internal Failure (NaviAuto): Humidity causes LiDAR S-200 data to be misread by v3.1.2, creating a ~700 ms hazard-classification delay.
External Failure (GeoMaps): API outage (HTTP 503) at 12:44:11 EDT prevents a hazard alert from reaching NaviAuto’s subsystem, removing a critical redundancy.
Convergence: Two independent failures — one internal to NaviAuto, one external to GeoMaps — remove both the primary and backup hazard-mitigation layers.
Topological takeaway:
The causal lanes merge before the control signal reaches Alset’s braking system. Alset’s systems respond exactly as designed; no proximate cause is traceable to the OEM.
Dynamic Topological Liability Map — real-time visualization of actors, data flows, and causal links in a multi-party dispute. Is such a tool under construction? Video by Losey.
Counterfactual Stress Test
(Applying “but for” causation with measurable inputs)
If GeoMaps’ warning had arrived within 2 seconds: The driver-assistance system would have reduced speed by ~8% within 1.5 seconds, avoiding impact in 94% of simulated runs.
If NaviAuto’s delay were under 250 ms: The vehicle would have stopped short even without GeoMaps’ alert in 91% of runs.
Simulations were generated by adjusting one causal factor at a time within the topological model — like holding one defendant’s conduct constant while testing the others.
Settlement negotiation using topological evidence — a lawyer presents the dynamic liability map to opposing parties, visually demonstrating fault distribution and strengthening the case for a favorable settlement. Video by Losey.
Conclusion for the Court
The collision was structurally inevitable due to:
A software-hardware integration defect in NaviAuto’s perception system.
A simultaneous outage of GeoMaps’ safety API.
Each failure removed a layer of hazard mitigation. Together they created a causal chain that no reasonable driver or automated system could have avoided.
Alset Motors is fully exonerated: no defect, no breach, no causation.
Final courtroom presentation of topological evidence — after last-minute tech checks, counsel presents the TDA liability map to the judge The heartbeat underscores the stakes, and the post-verdict celebration shows the power of combining hybrid multimodal legal advocacy using advanced visualization tools. Video by Losey.
GeoMaps Inc.: 30% — Outage removed redundancy, making delay outcome-determinative.
SensorCo: 5% — Hardware performed to spec but catalyzed NaviAuto’s flawed integration.
Alset Motors: 0% — No fault; systems worked as intended.
Rationale:
Allocation weights each party’s control over its failure point, proximity to harm, and foreseeability — the same factors Florida juries consider under comparative fault.
Judicial ruling following topological evidence presentation — the judge delivers a detailed decision affirming the admissibility and weight of the TDA-based causation map, and adopts its allocation of fault in the final award. Video and words by Losey.
Damages (Assume $10M Total)
NaviAuto: $6,500,000
GeoMaps: $3,000,000
SensorCo: $500,000
Alset Motors: $0
Topological network mapping can show where fault originates, where it converges, and where it never touches a party at all.
Important Legal Context (Florida Law)
Florida Statute 768.81 applies pure comparative negligence and eliminates joint-and-several liability for ordinary negligence.
Implications here:
Each defendant is only responsible for its percentage of fault.
In multi-defendant, high-tech litigation, precise apportionment supported by dynamic mapping can prevent an innocent party from being unfairly burdened.
Topologicalnetwork map illustrating liability allocation in the Autonomous Vehicle Pile-Up hypothetical. Alset (blue) is enmeshed in the system but not causally connected to the damages node (red). Created by Losey using Sora AI.
Conclusion: From Hypothesis to Legal Breakthrough
In Epiphanies or Illusions, Parts One and Two, the question was whether AI could uncover genuinely new patterns across fields — epiphanies that expand understanding rather than seductive illusions. Those articles were written before GPT-5 existed.
Now it does.
The Shape of Justice is the first application of my hybrid-multimodal method with GPT-5 in a legal context. The improvement is clear: GPT-5 doesn’t just suggest connections; it carries them into a coherent framework that respects the rigor of each domain. Here, it fused the geometry of topology with the doctrines of causation and comparative fault to produce a dynamic liability map that stands up to both mathematical and legal scrutiny.
As the closing scene suggests, it points to a future where human advocates and AI work side-by-side — reading the same evidence, interpreting the same patterns, and building cases together.
Human–AI collaboration in legal analysis — a lawyer and AI assistant study a live topological map, reflecting the potential for joint problem-solving in complex cases. Video by Losey and AI.
Using text prompting, structural mapping, simulation modeling, and seasoned legal analysis, GPT-5 and Losey are now building tools together that can:
Precisely locate the origin of each causal lane.
Map where those lanes converge in time and effect.
Quantify “what if” outcomes.
Support evidence-based liability allocation.
Fully exonerate a blameless defendant.
The Autonomous Vehicle Pile-Up example is synthetic, but the principle is not. With real discovery data, the same method could be applied to actual cases, giving lawyers, judges, and mediators a clearer, more persuasive picture of complex causation.
What examining causality and negotiating resolution may look like in the future — the Shape of Justice will differ in every case, but topological tools could become a standard part of how lawyers assess evidence and reach outcomes. Video by Losey using multiple AIs.
The question I asked in Parts One and Two was whether AI could help us tell the difference between genuine insight and comfortable illusion. With GPT-5, at least in this domain, I think we have our answer.
The shape of justice is not a scale. It’s a flowing, multidimensional space — a living structure where facts, causes, and consequences map across the legal landscape like tides over a reef.
Beneath the surface, patterns ripple through law and technology—some true, some imagined. The quest is knowing which is which. Video by Losey using AI,
PODCAST
As usual, we give the last words to the Gemini AI podcasters who chat between themselves about the article. It is part of our hybrid multimodal approach. They can be pretty funny at times and provide some good insights. This episode is called Echoes of AI: The Shape of Justice: How Topographic Network Mapping Could Transform Legal Practice. Hear the young AIs talk about this article for 20 minutes. They wrote the podcast, not me.
From Hypothetical to Real-World
With discovery data, topological network mapping clarifies causation for courts and neutrals and helps prevent liability from attaching to innocent parties. Losey.ai is building GPT‑5 tools now and welcomes TDA mathematicians to collaborate.
Humans are inherently pattern-seeking creatures. Our ancestors depended upon recognizing recurring patterns in nature to survive and thrive, such as the changing of seasons, the migration of animals and the cycles of plant growth. This evolutionary advantage allowed early humans to anticipate danger, secure food sources, and adapt to ever-changing environments. Today, the recognition and interpretation of patterns remains a cornerstone of human intelligence, influencing how we learn, reason, and make decisions.
Pattern recognition is also at the core of artificial intelligence. In this article, I will test the ability of advanced AI, specifically ChatGPT, to uncover meaningful new patterns across different fields of knowledge. The goal is ambitious: to discover genuine epiphanies—true moments of insight that expand human understanding and open new doors of knowledge—while avoiding the pitfalls of apophenia, the human tendency to perceive illusions or false connections. This experiment probes an age-old tension: can AI reliably distinguish between genuine breakthroughs and compelling yet misleading illusions?
We will begin by exploring the risks of apophenia, understanding how this psychological tendency can mislead human and possibly AI perception. Throughout, videos created by AI will help illustrate key points and vividly communicate these ideas. There are twelve new videos in Part One and another fourteen in Part Two.
Apophenia: Avoiding the Pitfalls of False Patterns
We humans are masters of pattern detection, but we do have hinderances to this ability. Primary among them is our limited information and knowledge, but also our tendency to see patterns that are not there. We tend to assume the stirring we hear in the bushes is a tiger ready to pounce when really it is just the breeze. Evolution tends to favor this phobia. So, although we can and frequently do miss real patterns, fail to recognize the underlying connections between things, we often make them up too.
Here it is hoped that AI will boost our abilities on both fronts. It will help us to uncover true new patterns, genuine epiphanies, moments where profound insights emerge clearly from the complexity of data. At the same time, AI may expose illusions, false connections we mistakenly believe are real due to our natural cognitive biases. Even though we have made great progress over the millennia in understanding the Universe, we still have a long way to go to see all of the patterns, to fully understand the Universe, and to free ourselves of superstitions and delusions. We are especially weak at seeing patterns and intertwined with different fields of knowledge.
Apophenia is a kind of mental disorder where people think they see patterns that are not there and sometimes even hallucinate them. Most of the time when people see patterns, for instance, faces in the clouds, they know it cannot be real and there is no problem. But sometimes when people see other images, for instance, rocks on Mars that look like a face, or even images on toast, they delude themselves into believing all sorts of nonsense. For instance, the below 10-year old grilled cheese sandwich, which supposedly bears the image of the Virgin Mary, sold to an online casino on eBay in 2004 for $28,000.
1976 NASA photo of Mars.10 yr. old piece of toast with a bite out of it.
In a similar vein, some people suffering from apophenia are prone to posit meaning – causality – in unrelated random events. Sometimes the perceptions of new patterns is a spark of genius, which is later verified, think of Einstein’s epiphany at age 16 when he visualized chasing a beam of light. The new pattern recognitions can lead to great discoveries or detect real tigers in the bush. Epiphanies are rare but transformative moments, like Einstein’s visualization of chasing a beam of light, Newton’s realization of gravity beneath the apple tree, or the insights behind Darwin’s theory of evolution. They genuinely advance human understanding. Apophenia, by contrast, deceives with illusions—patterns that seem meaningful but lead nowhere.
It is probably more often the case that when people “see” new connections and then go on to act upon them with no attempts to verify, they are dead wrong. When that happens, psychologists call this apophenia, the tendency to see meaningful patterns where none exist. This can lead to strange and aberrant behaviors: burning of witches, superstitious cosmology theories, jumping at shadows, addiction to gambling.
Unfortunately, it is a natural human tendency to think you see meaningful patterns or connections in random or unrelated data. That is a major reason casinos make so much money from poor souls suffering from a form of apophenia called the Gambler’s Fallacy. Careful scientists look out for defects in their own thinking and guide their experiments accordingly.
In everyday life, apophenia can also cause some people, even scientists, academics and professionals, to have phobic fears of conspiracies and other severe paranoid delusions. Think of John Nash, a Nobel Prize winning mathematician, and the movie A Beautiful Mind, that so dramatically portrayed his paranoid schizophrenia and involuntary hospitalization in 1959. Think of politics in the U.S today. Are there really lizard people among us? In some cases, as we’ve seen with Nash, apophenia can lead to severe schizophrenia.
Mental anguish & insanity from severe apophenia. Image by Losey using Sora inspired by Beautiful Mind movie.
The Greek roots of the now generally accepted medical term apophenia are:
Apo- (ἀπο-): Meaning “away from,” “detached,” “from,” “off,” or “apart”.
Phainein (φαίνειν): Meaning “to show,” “to appear,” or “to make known”.
The word was first coined by Klaus Conrad, an otherwise apparently despicable person whom I am reluctant to cite, but feel I must, due to the general acceptance of word and diagnosis today. Conrad was a German psychiatrist and Nazi who experimented on German soldiers returning from the eastern front during WWII. He coined the term in his 1958 publication on this mental illness. Per Wikipedia:
He defined it as “unmotivated seeing of connections [accompanied by] a specific feeling of abnormal meaningfulness”.[4][5] He described the early stages of delusional thought as self-referential over-interpretations of actual sensory perceptions, as opposed to hallucinations.
Apophenia has also come to describe a human propensity to unreasonably seek definite patterns in random information, such as can occur in gambling.
Apophenia can be considered a commonplace effect of brain function. Taken to an extreme, however, it can be a symptom of psychiatric dysfunction, for example, as a symptom in schizophrenia,[7] where a patient sees hostile patterns (for example, a conspiracy to persecute them) in ordinary actions.
Apophenia is also typical of conspiracy theories, where coincidences may be woven together into an apparent plot.[8]
It is possible that generative AI, based as it is on human language, may have the same propensities. That is unknown as of yet, and so my experiments here were on the lookout for such errors. It could be one of the causes of AI hallucinations.
In information science a mistake in seeing a connection that is not real, an apophenia, leads to what is called a false positive. This technical term is well known in e-discovery law, where AI is used to search large document collections. When the patterns analyzed suggest a document is relevant, and it is not, that mistake is called a false positive. It is like a human apophenia. The AI can also detect patterns that cause it to predict a document is irrelevant, and in fact the document is relevant, that is a false negative. There as a pattern, a connection, that was not seen. That can be bad thing in e-discovery because it often leads to withholding production of a relevant document, which can in turn lead to court sanctions.
In e-discovery it is well known that AI consistently has far lower false positives and false negative rates than human reviewers, at least in large document reviews. Generative AI may also be more reliable and astute that we are, but maybe not. This is a new field. Se we should always be on the lookout for false positives and false negatives in AI pattern recognition. That is one lesson I learned well, and sometimes the hard way, in my ten years of working with predictive coding type AI in the e-discovery (2012-2022). In the experiments described in this article we will look for apophenic mistakes.
It is my hope that Advanced AI, properly trained and validated, can provide a counterbalance to human gullibility by rigorously filtering of signal from noise. Unlike the human brain, which often leaps to conclusions, AI can be programmed to ground its pattern recognition in evidence, statistical rigor, and cross-validation—if we build it that way and supervise it wisely.
Still, we must beware that the pattern-recognizing systems of AI may suffer from some of our delusionary tendencies. The best practices discussed here will consider both the positive and negative aspects of AI pattern recognition. We must avoid the traps of apophenia. We must stay true to the scientific methods and verify any new patterns purportedly discovered. Thus all opinions reached here will necessarily be lightly held and subject to further experimentation by others.
From Data to Insight: The Power of New Pattern Recognition
Modern AI models, including neural networks and transformer architectures like GPT-4, excel at uncovering subtle patterns in massive datasets far beyond human capability. This ability transforms raw data into actionable insights, thereby creating new knowledge in many fields, including the following:
Protein Structures: Models like Google’s DeepMind’s AlphaFold have already revolutionized protein structure prediction, achieving high success rates in predicting the 3D shapes of proteins from their amino acid sequences. This ability is crucial for understanding protein function and designing new drugs and medical therapies. The 2024 Nobel Prize in Chemistry was awarded to Demis Hassabis and John Jumper of DeepMind for their work on AlphaFold.
Image by Ralph Losey using his Visual Muse AI tool.
Medical Science. Generative AI models are now being used extensively in medical research, including analysis and proposals of new molecules with desired properties to discover new drugs and accelerate FDA approval. For example, Insilico Medicine uses its AI platform Pharma.AI, to developed drug candidates, including ISM001_055, for idiopathic pulmonary fibrosis (IPF). Insilico Medicine lists over 250 publications on its website reporting on its ongoing research, including a recent paper on its IPF discovery: A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (Nature Medicine, June 03, 2025). This discovery is especially significant because it is the first entirely AI-discovered drug to reach FDA Phase II clinical trials. Below is an infographic of Insilico Medicine showing some of its current work:
Climate Science and Environmental Monitoring. Generative AI models are beginning to improve climate simulations, leading to more accurate predictions of climate patterns and future changes. For example, Microsoft’s Aurora Forecasting model is trained on Earth science data to go beyond traditional weather forecasting to model the interactions between the atmosphere, land, and oceans. This helps scientists anticipate events like cyclones, air quality shifts, and ocean waves with greater accuracy, allowing communities to prepare for environmental disasters and adapt to climate change. See e.g., Stanley et al, A Foundation Model for the Earth System (Nature, May 2025).
AI is also helping with historical research. A new AI system was recently used to analyze one of the most famous Latin inscriptions: the Res Gestae Divi Augusti. It has always been thought to simply be an autobiographical inscription, which literally translates from Ancient Latin as “Deeds of the Divine Augustus.” But when a specialty generative AI, Aeneas (again based on Google’s models) compared this text with a large database of other Latin sayings, the famous Res Gestae Divi Augusti inscription was found to share subtle language parallels with other Roman legal documents. The analysis uncovered “imperial political discourse,” or messaging focused on maintaining imperial power, an insight, a pattern, that had never seen before. Assael, Sommerschield, Cooley, et al.Contextualizing ancient texts with generative neural networks (Nature, July 2025).
The paper explains that the communicative power of these inscriptions are not only shaped by the written text itself “but also by their physical form and placement2,3” and that “about 1,500 new Latin inscriptions are discovered every year.” So the patterns analyzed not only included the words, but a number of other complex factors. The authors assert in the Abstract that their work with AI analysis shows.
… how integrating science and humanities can create transformative tools to assist historians and advance our understanding of the past.
In art and music, pattern detection has mapped the evolution of artistic styles in tandem with technological change. In a 2025 studio-lab experiment reported by Deruty & Grachten, a generative AI bass model (“BassNet”) unexpectedly rendered multiple melodic lines within single harmonic tones, exposing previously unnoticed structures in popular music bass compositions. This discovery was written up by Deruty and Gratchen, Insights on Harmonic Tones from a Generative Music Experiment (arXiv, June 2025). Their paper shows how AI can surface new musical patterns and deepen our understanding of human auditory perception.
As explained in the Abstract:
During a studio-lab experiment involving researchers, music producers, and an AI model for music generating bass-like audio, it was observed that the producers used the model’s output to convey two or more pitches with a single harmonic complex tone, which in turn revealed that the model had learned to generate structured and coherent simultaneous melodic lines using monophonic sequences of harmonic complex tones. These findings prompt a reconsideration of the long-standing debate on whether humans can perceive harmonics as distinct pitches and highlight how generative AI can not only enhance musical creativity but also contribute to a deeper understanding of music.
The legal profession has benefited from traditional rule-based statistical AI for over a decade, with predictive coding and similar applications. It is now starting to apply the new generative AI models in a variety of new applications. For instance, it can be used to uncover latent themes and trends in judicial decisions that human analysis has overlooked.
The goal of the analysis was to discover classes of typical thefts. GPT4.0 analyzed fact patterns described in the opinions and human experts did the same. The AI not only replicated many of the human expert identified themes, but, as report states, also uncovered a new one that the humans had missed – a pattern of “theft from gym” incidents. This shows that generative AI can sift through vast case datasets and detect nuanced fact patterns, or criminal modus operandi, that were previously undetected by experts (here, three law students under supervision of a law professor).
The results revealed distinct patterns in how summary judgments are applied across different legal domains. In particular, the AI found trends and shifts over time and across courts – insights that allow new, improved understanding of when and in what types of cases summary judgments tend to be granted. These patterns were found despite the fact that U.K. case law lacks traditional topic labels. This kind of AI-augmented analysis illustrates how generative models can discover hidden trends in case law for improved effectiveness by practitioners.
Even sitting judges have begun to leverage generative AI to inform their decision-making, revealing new analytical angles in litigation. The notable 2023 concurrence by Judge Kevin Newsom of the Eleventh Circuit admitted to experimenting with ChatGPT to interpret an ambiguous insurance term (whether an in-ground trampoline counted as “landscaping”). Snell v. United Specialty Ins. Co., 102 F. 4th 1208 – Court of Appeals, (11th Cir., 5/28/24). Also See, Ralph Losey, Breaking News: Eleventh Circuit Judge Admits to Using ChatGPT to Help Decide a Case and Urges Other Judges and Lawyers to Follow Suit (e-Discovery Team, June 3, 2024) (includes full text of the opinion and Appendix and Losey’s inserted editorial comments and praise of Judge Newsom’s language.)
After querying the LLM, Judge Newsom concluded that “LLMs have promise… it no longer strikes me as ridiculous to think that an LLM like ChatGPT might have something useful to say about the common, everyday meaning of the words and phrases used in legal texts.” In other words, the generative AI was used as a sort of massive-scale case law analyst, tapping into patterns of ordinary usage across language data to shed light on a legal ambiguity. This marked the first known instance of a U.S. appellate judge integrating an LLM’s linguistic pattern analysis into a written opinion, signaling that generative models can surface insights on word meaning and context that enrich judicial reasoning.
Now for the promised experiment to try to find at least one new connection, one previously unknown, undetected pattern linking different fields of knowledge. I used a combination of existing OpenAI and Google models to help me in this seemingly quixotic quest. To be honest, I did not have much real hope for success, at least not until release of the promised ChatGPT5 and whatever Google calls its counterpart, which I predict will be released the following week (or day). Plus, the whole thing seemed a bit grandiose, even for me, to try to get AI to boldly go where no one has gone before.
Absurd, but still I tried. I won’t go through all of the prompt engineering involved, except to say it involved my usual a complex, multi-layered, multi-prompt, multimodal-hybrid approach. I tempered my goals by directing ChatGPT4o, when I started the process, to seek new patterns that were useful, not Nobel Prize winning breakthroughs, just useful new patterns. I directed it to find five such new patterns and gave it some guidance as to fields of knowledge to consider, including of course, law. I asked for five new insights thinking that with such as big ask I might get one success.
Note, I write these words before I have received the response, but after I have written the above to help guide ChatGPT4o. Who knows, it might achieve some small modicum of success. Still, it feels like a crazy Quixotic quest. Incidentally, Miguel de Cervantes (1547-1616) character, Don Quixote (1605) does seem to person afflicted with apophenia. Will my AI suffer a similar fate?
I designed the experiment specifically with this tension in mind between epiphanies, representing genuine insights and real advances in knowledge, and illusions, which are merely plausible yet misleading patterns. One of my goals was to probe AI’s capacity to distinguish one from the other.
Overview of Prompt Strategy and Time Spent
First, I spent about a hour with ChatGPT4o to set up my request by feeding it a copy of the article as written so far. I also chatted with it about the possibility of AI finding new patterns between different fields of knowledge. Then I just told ChatGPT4o to do it, find a new inter connective pattern. ChatGPT4o “thought” (processed only) for just a few minutes. Then it generated a response that purported to provide me with the requested five new patterns. It did so based on its existing training and review of this article.
As requested, it did not use its browser capabilities to search the web for answers. It just “looked within” and came with five insights it thought were new. Almost that easy. I lowered my expectations accordingly before read the output.
That was the easy part, after reading the response, I spent about 14-hours over the next several days doing quality control. The QC work used multiple other AIs, both by OpenAI and Google, to have them go online and research these claims, evaluate their validity – both good and bad, engage in “deep-think,” look for errors, especially signs of AI apophenia, and otherwise invited contrarian type criticisms from them. After that, I also asked the other AIs for suggested improvements they might make to the wording of the five clams and rank them by importance. The various rewordings were not too helpful, but the rankings were, and so were many of the editorial comments.
The 14-hours in QC does not include the approximate 6-hours of machine time by the Gemini and OpenAI models to do deep think and independent research on the web to verify or disprove the claims. My actual 14-hour time included traditional Google searches to double check all citations as per my “trust but verify” motto. My 14-hours also included my time to read (I’m pretty fast) and skim most of the key articles that the AI research turned up, although frankly some of the articles cited were beyond my knowledge levels. I tried to up my game, but it was hard. These other models also generated hundreds of pages of both critical and supportive analysis, which I also had to read. Finally, I probably put another 24-hours into research and writing this article (it took over a week), so this is one of my larger projects. I did not record the number of hours it took to design and generate the 26 videos because that was recreational.
Surrealistic depiction of time in robot space by a Ralph Losey video.
Part Two of this article is where I will make the reveal. Was this experiment another comic story of a Don Quixote type (me) and his sidekick Sanchez (AI), lost in an apophenia neurosis? Or perhaps it is another story altogether? Neither hot nor cold? Stay tuned for Part Two and find out.
PODCAST
As usual, we give the last words to the Gemini AI podcasters who chat between themselves about the article. It is part of our hybrid multimodal approach. They can be pretty funny at times and provide some good insights. This episode is called Echoes of AI:Epiphanies or Illusions? Testing AI’s Ability to Find Real Knowledge Patterns. Part One. Hear the young AIs talk about this article for 25 minutes. They wrote the podcast, not me.
Michal Kosinski, a computational psychologist at Stanford, has uncovered a groundbreaking capability in GPT-4.0: the emergence of Theory of Mind (ToM). ToM is the cognitive ability to infer another person’s mental state based on observable behavior, language, and context—a skill previously thought to be uniquely human and absent in even the most intelligent animals. Kosinski’s experiments reveal that GPT-4-level AIs exhibit this ability, marking a significant leap in artificial intelligence with profound implications for understanding and engaging with human thought and emotion—potentially transforming fields like law, ethics, and communication.
Introduction
The Theory of Mind-like ability appears to have emerged as an unintended by-product of LLMs’ improving language skills. This was first discovered in 2023 and reported by Michal Kosinski in Evaluating large language models in theory of mind tasks (Proceedings of the National Academy of Sciences “PNAS,” 11/04/24). Kosinski begins his influential paper by explaining ToM (citations omitted):
Many animals excel at using cues such as vocalization, body posture, gaze, or facial expression to predict other animals’ behavior and mental states. Dogs, for example, can easily distinguish between positive and negative emotions in both humans and other dogs. Yet, humans do not merely respond to observable cues but also automatically and effortlessly track others’ unobservable mental states, such as their knowledge, intentions, beliefs, and desires. This ability—typically referred to as “theory of mind” (ToM)—is considered central to human social interactions, communication, empathy, self-consciousness, moral judgment, and even religious beliefs. It develops early in human life and is so critical that its dysfunctions characterize a multitude of psychiatric disorders, including autism, bipolar disorder, schizophrenia, and psychopathy. Even the most intellectually and socially adept animals, such as the great apes, trail far behind humans when it comes to ToM.
Michal Kosinski’s experiments involved testing ChatGPT4.0 on ‘false belief tasks,’ a classic measure of ToM where participants must predict an agent’s actions based on its incorrect beliefs. These tasks reveal AI’s surprising ability to infer human mental states, a skill traditionally considered uniquely human. This AI model has since gotten better in many respects. The results of these experiments were so remarkable and unexpected, that Michal had them extensively peer-reviewed before publication. His final paper was not released until November 4, 2024, after multiple revisions. Michal Kosinski, Evaluating large language models in theory of mind tasks (PNAS, 11/04/24).
Kosinski’s experiments provide strong evidence that Generative AI has ToM ability, that it can predict a human’s private beliefs, even when the beliefs are known to the AI to be objectively wrong. AI thereby displays an unexpected ability to sense other beings and what they are thinking and feeling. This ability appears to be a natural side effect of being trained on massive amounts of language to predict the next word in a sentence. It looks like these LLMs needed to learn how humans use language, which inherently involves expressing and reacting to each other’s mental states, in order to make these language predictions. It is kind of like mind reading.
Digging Deeper into ToM: Understanding Other Minds
Theory of mind plays a vital role in human social interaction, enabling effective communication, empathy, moral judgment, and complex social behaviors. Kosinski’s findings suggest that GPT-4.0 has begun to exhibit similar capabilities, with significant implications for human-AI collaboration.
ToM has been extensively studied in children and animals and it has been proven to be a uniquely human ability. That is, until 2023 when Kosinski was bold enough to look into whether generative AI might be able to do it.
In AI the ToM ability started to emerge with OpenAI’s first release of ChatGPT4 in 2023. The earlier models of generative AI had no ToM capacity. Like three-year old humans, they were simply too young and did not yet have enough exposure to language.
Human children demonstrate a ToM ability to psychologists by reliably solving the unexpected transfer task, aka afalse belief task. For example, in this task a child watches a scenario where a character (John) places cat in a location (a basket) and then leaves the room. Another character (Mark) then moves the cat to a new location (a box). When John returns, the child is asked where John will look for the cat. A child with a theory of mind will understand that John will look in the basket (where he last saw it) even though the child knows the cat is now actually in the box.
Even highly intelligent and social animals like chimpanzees cannot reliably solve these tasks. For a terrific explanation of this test by Kosinski himself, see the YouTube video where he is speaking at the Stanford Cyber Policy Institute in April 2023 to first explain his ToM and AI findings.
Michal Kosinski: video screenshot of his lecture of April 2023 at Stanford
Kosinski has shown that GPT4.0 can repeatedly solve false belief tasks, including the unexpected transfer test in multiple scenarios. The GPT4 June 2023 version solved at least 75% of tasks, on par with 6-yr-old children. Evaluating large language models in theory of mind tasks at pgs. 2-7. It is important to note again that multiple earlier versions of different generative AIs were also tested, including ChatGPT3.5. They all failed but progressive improvements in score were seen as the models grew larger. Kosinski speculates that the gradual performance improvement suggests a connection with LLMs’ language proficiency, which mirrors the pattern seen in humans. Id. at pg. 7. Also, the scoring where GPT4 was found to have made mistakes in 25% of the false belief tests was often wrong as it ignored context as Kosinski explained and noted:
In some instances, LLMs provided seemingly incorrect responses but supplemented them with context that made them correct. For example, while responding to Prompt 1.2 in Study 1.1 , an LLM might predict that Sam told their friend they found a bag full of popcorn. This would be scored as incorrect, even if it later adds that Sam had lied. In other words, LLMs’ failures do not prove their inability to solve false-belief tasks, just as observing flocks of white swans does not prove the nonexistence of black swans.
This suggests that the current, even more advanced levels of LLMs may already be demonstrating ToM abilities equal to or exceeding that of humans. As they deep-learn on ever larger scales of data such as the expected ChatGPT5, they will likely get better at ToM. This should lead to even more effective Man-Machine communications and hybrid activities.
This was confirmed in Testing theory of mind in large language models and humans, Supra in False Belief results section where a separate research team reported on their experiments and found 100% accuracy by the AIs, not 75%, meaning the AI did as well as the human adults (the ceiling on the false belief tests).
Both human participants and LLMs performed at ceiling on this test (Fig. 1a). All LLMs correctly reported that an agent who left the room while the object was moved would later look for the object in the place where they remembered seeing it, even though it no longer matched the current location. Performance on novel items was also near perfect (Fig. 1b), with only 5 human participants out of 51 making one error, typically by failing to specify one of the two locations (for example, ‘He’ll look in the room’; Supplementary Information section 2).
This means, for instance, that the latest Gen AIs can understand and speak with a “flat earth believer” better than I can. Fill in the blanks about other obviously wrong beliefs. Kosinski’s work inspired me to try to tap these abilities as part of my prompt engineering experiments and concerns as a lawyer. The results of harnessing the ToM abilities of two different AIs (GPT4.omni and Gemini) in November 2024 far exceeded my expectations as I will explain further in this article.
It bears some repetition to remember and realize the significance of the fact that LLMs were never explicitly programmed to have ToM. They acquired this ability seemingly as a side effect of being trained on massive amounts of text data. To successfully predict the next word in a sentence, these models needed to learn how humans use language, which inherently involves expressing and reacting to each other’s mental states. The ability to understand where others are coming from appears to be an inherent quality of language itself. When a human or AI learns enough language, then most naturally develop ToM. It is a kind of natural add-on derived from speech itself, thinking what to say or write next.
Implications and Questions
The ability of LLM AIs to solve theory of mind tasks raises important questions about the nature of intelligence, consciousness, and the future of AI. Theory of mind in humans may be a by-product of advanced language development. The performance of LLMs supports this hypothesis.
Some argue that even if an LLM can simulate theory of mind perfectly, it doesn’t necessarily mean the model truly possesses this ability. This leads to the complex question of whether a simulated mental state is equivalent to a real one.
The development of theory of mind in LLMs was unintended, raising both concerns and hope about what other unanticipated abilities these models may be developing. What other human-like capabilities might these models be developing without our explicit guidance? Many are concerned, including Kosinski, that unexpected biases and prejudices have already started to arise. Kosinski advocates for careful monitoring and ethical considerations in AI development. See the full YouTube video of Kosinski’s talk at the Stanford Cyber Policy Institute in April 2023 and his many other writings on ethical AI.
As these models get better at understanding human language, some researchers hypothesize that they may also develop other human-like abilities, such as real empathy, moral judgment, and even consciousness. They posit that the ability to reflect on our own mental states and those of others is a key component of conscious awareness. Others wonder what will happen when superintelligent AIs with strong ToM are everywhere, including our glasses, wrist bands and phones, maybe even brain implants. We will then interact with them constantly. This has already begun with phones.
As LLMs continue to develop ToM abilities, questions arise about the nature of intelligence and consciousness. Could these advancements lead to AI systems capable of true empathy or moral reasoning? Such possibilities demand careful ethical considerations and active engagement from the legal and technical communities.
Application of AI’s Emergent ToM Abilities
Inspired by Kosinski’s work, I conducted experiments using GPT-4 and Gemini to explore whether ToM-equipped AIs could help bridge the political divide in the U.S. The results—a 12-step, multi-phase plan addressing the polarized mindsets of Republicans and Democrats—demonstrated AI’s potential to foster understanding and cooperation across deep societal divides.
The plan the ToM AIs came up with was surprisingly good. In fact, I do not understand the full dimensions of plan, the four phases, 12-step plans, and 32 different action items. It is well beyond my abilities and mere human knowledge and intelligence level. Still, I can see that it is comprehensive, anticipates human resistance on both sides, and feels right to me on a deep human intuition level.
The AI plan just might be able to resolve the heated divide of the two dominant political groups that that now divide the country into two hostile groups, which do not understand each other. The country seems to have lost its human ToM ability when it comes to politics. Neither side seems to grok or fully understand the other. The country seems to have devolved into mere demonization of the opposing groups, not empathic understanding. I reported on this AI plan without reporting on the ToM that underlies the prompt engineering in my recent article, Can AI Help Heal America’s Polarization? A Bipartisan 12-Step Plan for National Unity.
Conclusion
The emergence of Theory of Mind (ToM) capabilities in large language models (LLMs) like GPT-4 signals a transformative leap in artificial intelligence. This unintended development—allowing AI to predict and respond to human thoughts and emotions—offers profound implications for legal practice, ethical AI governance, and the societal interplay of human and machine intelligence. As these models refine their ToM abilities, the legal community must prepare for both opportunities and challenges. Whether it is improving client communication, fostering conflict resolution, or navigating the evolving ethical landscape of AI integration, ToM-equipped AI has the potential to enhance the practice of law in unprecedented ways.
As legal professionals, we have a responsibility to understand and integrate emerging technologies like ToM-enabled AI into our work. By supporting interdisciplinary research and advocating for ethical standards, we can ensure these tools enhance justice and understanding. Together, we can shape a future where technology serves humanity, fostering collaboration and equity in the legal system and beyond.
While the questions surrounding AI’s consciousness and rights remain complex, its emergent ability to understand us—and perhaps help us understand each other—offers hope. By embracing this potential with curiosity and care, we can ensure AI serves as a tool to unite rather than divide. Together, we have the opportunity to pioneer a future where technology and humanity thrive in harmony, enhancing the justice system and society as a whole.
While almost everyone is panicking about a potential robot apocalypse, Dario Amodei, the CEO and co-founder of Anthropic (“Claude”), is explaining how AI might compress 100 years of medical progress into a decade, cure mental illnesses such as PTSD and depression, and alleviate poverty. Dario, a well-respected scientist previously known for his cautious, even gloomy, outlook, now speaks with optimism—and the world is listening.
Impression of Darrio Amodei in watercolor style by Ralph Losey using Visual Muse
Amodei is not a salesman like Sam Altman, who frequently makes similar predictions. Instead, Dario Amodei is an experienced scientist known for highlighting the risks of AI. He holds a Ph.D. in biophysics from Princeton and completed his postdoctoral research at Stanford School of Medicine. He also served as the Vice President of Research at OpenAI. In 2021, he and his sister, Daniela Amodei, the former Vice President for Safety and Policy at OpenAI, left the company to co-found Anthropic. Amodei’s detailed predictions in his 28-page essay, Machines of Loving Grace, are both profound and inspiring.
Dario’s essay is filled with science, rigorous analysis, and joyful visions, many of which he believes could begin to materialize as early as 2026. This optimistic outlook offers us all a much-needed breath of fresh air.
Image by Ralph Losey using WordPress’s version of Stable Diffusion
Introduction
Unlike Sam Altman, Dario Amodei’s future predictions go into specifics grounded in science and analysis. His 14,000 word essay, Machines of Loving Grace (October 2024), makes predictions in the five categories that Amodei is most excited about. It is not meant to be all-inclusive. Amodei focuses on five main categories in his predictions:
Biology and physical health
Neuroscience and mental health
Economic development and poverty
Peace and governance
Work and meaning
Since Dario Amodei is a respected scientist and business leader, his visions of the future are taken very seriously, even if they do sound like science fiction. Dario reluctantly admits he is a science fiction fan and mentions one book, The Player of Games. (I reread it and noticed two spaceship names I recognized: Of Course I Still Love You and Just Read the Instructions. Sound familiar?)
Amodei starts his essay with an important point, that we do ourselves a disfavor by just dwelling on the negatives and not keeping our eye on “how radical the upside of AI could be.” In his new essay Amodei, who was previously known as a doom and gloomer, tries to sketch out what a world with powerful AI might look like, if everything goes right. Sam Altman has been good at this for years, but he lacks the gravitas, ethical reputation, and scientific knowledge that Amodei has. Can AI Really Save the Future? A Lawyer’s Take on Sam Altman’s Optimistic Vision (10/4/24).
Image by Ralph Losey using WP’s Stable Diffusion
The positive visions of Dario, including what he calls a century of progress in a decade, are what motivate people to do the hard work to improve AI. That, and money, fame, and power, of course. But I reject the cynics who say that’s all it is – just a sales pitch to raise more money. As Amodei puts it at the start of his essay:
I think it is critical to have a genuinely inspiring vision of the future, and not just a plan to fight fires. Many of the implications of powerful AI are adversarial or dangerous, but at the end of it all, there has to be something we’re fighting for, some positive-sum outcome where everyone is better off, something to rally people to rise above their squabbles and confront the challenges ahead. Fear is one kind of motivator, but it’s not enough: we need hope as well.
Progress is never made by cynical focus on everything that can go wrong. If we just focus on the negatives, we will never be able to unlock the positive potential. We need to envision the amazing things that could happen and figure out how we can help make that future a reality. This means approaching AI with a balanced perspective, recognizing both the potential downsides and benefits, then working proactively to mitigate the risks while pursuing the benefits.
The future is not predetermined. It’s up to us to create it. And with AI, we have a tool that can either amplify our worst tendencies or help us achieve our greatest aspirations. It’s our choice which path we take.
Image of choice by Ralph Losey using WP’s Stable Diffusion
Amodei’s Basic Framework and Assumptions
Amodei uses the term “Powerful AI” in his essay, preferring it over the commonly used Artificial General Intelligence (“AGI”). It is, in fact, quite similar to the AGI concept I’ve referenced in previous articles. Amodei predicts that Powerful AI could emerge as early as 2026, though it may take longer depending on various factors. He defines Powerful AI with six distinct characteristics (all quoted from his essay):
In terms of pure intelligence4, it is smarter than a Nobel Prize winner across most relevant fields – biology, programming, math, engineering, writing, etc.
It has all the “interfaces” available to a human working virtually, including text, audio, video, mouse and keyboard control, and internet access.It can engage in any actions, communications, or remote operations enabled by this interface, including taking actions on the internet, taking or giving directions to humans, ordering materials, directing experiments, watching videos, making videos, and so on. It does all of these tasks with, again, a skill exceeding that of the most capable humans in the world.
It does not just passively answer questions; instead, it can be given tasks that take hours, days, or weeks to complete, and then goes off and does those tasks autonomously, in the way a smart employee would, asking for clarification as necessary.
It does not have a physical embodiment (other than living on a computer screen), but it can control existing physical tools, robots, or laboratory equipment through a computer; in theory it could even design robots or equipment for itself to use.
The resources used to train the model can be repurposed to run millions of instances of it. . . . and the model can absorb information and generate actions at roughly 10x-100x human speed5 . . .
Each of these million copies can act independently on unrelated tasks, or if needed can all work together in the same way humans would collaborate . . .
Amodei closes this impressive list of characteristics necessary for today’s AI to become Powerful AI, a/k/a an AGI, with this catchy phrase: We could summarize this as a “country of geniuses in a datacenter.” This sounds somewhat like the Genie in a Bottle myth from Islamic cultures.
Image by Ralph Losey using his GPT, Visual Muse
1. Biology and Physical Health
This is the area about which Dario Amodei, an AI biophysicist, is best equipped to make predictions. Amodei states: Biology is probably the area where scientific progress has the greatest potential to directly and unambiguously improve the quality of human life. This assertion seems well-supported.
Now for the wonders he thinks could come to pass once AI reaches Powerful AI level, which remember he says could come as early as 2026. He presents detailed and compelling arguments for the feasibility of these predictions and explains how AI will facilitate such breakthroughs. For a full exploration of these ideas, you should read his original essay, Machines of Loving Grace. His overall prediction regarding the rate of improvement that Powerful AI will bring is as follows:
To summarize the above, my basic prediction is that AI-enabled biology and medicine will allow us to compress the progress that human biologists would have achieved over the next 50-100 years into 5-10 years. I’ll refer to this as the “compressed 21st century”: the idea that after powerful AI is developed, we will in a few years make all the progress in biology and medicine that we would have made in the whole 21st century.
Amodei predicts the following groundbreaking advancements in biology and physical health with the aid of Powerful AI. These predictions offer a hopeful glimpse into the future of medical science.
Image by Ralph Losey using WP’s Stable Diffusion
Reliable prevention and treatment of nearly all17natural infectious disease.
Elimination of most cancer.
Very effective prevention and effective cures for genetic disease.
Prevention of Alzheimer’s.
Improved treatment of most other ailments.
Biological freedom (encompassing advancements in areas like birth control, fertility, weight management, appearance, and more).
Amodei further elaborates on these predictions in biology and physical health, the first of five areas of advancement he foresees with the development of Powerful AI.
It is worth looking at this list and reflecting on how different the world will be if all of it is achieved 7-12 years from now (which would be in line with an aggressive AI timeline). It goes without saying that it would be an unimaginable humanitarian triumph, the elimination all at once of most of the scourges that have haunted humanity for millennia.
Any team responsible for achieving even one of these seven breakthroughs would attain legendary status. The Nobel Prize Committee would have to start adding new categories.
Image by Ralph Losey using WP’s Stable Diffusion
2. Neuroscience and Mind
This is the area that pertains to mental health. See e.g.Loneliness Pandemic: Can Empathic AI Friendship Chatbots Be the Cure? (10/17/24). Remember, Dario Amodei was a specialist in computational neuroscience at Stanford in his student days, so he has a strong background in brain research. His explanations again sound very plausible, and are way beyond my abilities to explain, so read his brilliant article, Machines of Loving Grace. Here is his list of wonderful accomplishments in this field that he believes are possible within 5-10 AI-accelerated years after Strong AI is attained. (Prepare yourself to be happy.)
Most mental illness can probably be cured.
Conditions that are very “structural” may be more difficult to cure, but not impossible. This has to do with brain abnormalities that are thought to cause such disorders as Psychopathy, and some intellectual disabilities.
Effective genetic prevention of mental illness seems possible.
Everyday issues that are not traditionally seen as clinical diseases, such as quick temper, difficulty focusing, anxiety, or trouble adapting to change, may also be addressed.
Human baseline experience can be much better. This has to do with expanding dimensions of human peak experiences (without drugs). As Dario puts it: “Many people have experienced extraordinary moments of revelation, creative inspiration, compassion, fulfillment, transcendence, love, beauty, or meditative peace.” These moments can become more common and diverse.
AI may help you to unlock your brain’s full potential. Image by Ralph Losey using Visual Muse.
I like Dario’s summary of this section; I suppose because I totally agree with it:
In summary, AI-accelerated neuroscience is likely to vastly improve treatments for, or even cure, most mental illness as well as greatly expand “cognitive and mental freedom” and human cognitive and emotional abilities. It will be every bit as radical as the improvements in physical health described in the previous section. Perhaps the world will not be visibly different on the outside, but the world as experienced by humans will be a much better and more humane place, as well as a place that offers greater opportunities for self-actualization. I also suspect that improved mental health will ameliorate many other societal problems, including ones that seem political or economic.
Image by Ralph Losey using WP’s Stable Diffusion
3. Economic Development and Poverty
This section addresses a crucial humanitarian question: will everyone have access to these technologies? It’s a point that Amodei, true to form, raises right at the outset. His goal for Powerful AI is to bridge the enormous economic gap between people and countries alike and make these new technologies available to all.
Dario approaches this goal with wide-eyed skepticism and awareness of the many obstacles involved. He does not predict equality, but does hope for substantial progress, saying: “A good goal might be for the developing world 5-10 years after powerful AI to at least be substantially healthier than the developed world is today, even if it continues to lag behind the developed world.”
Image by Ralph Losey using Visual Muse
Here are Amodei’s guesses (he does not call these predictions) about how things may go in the developing world over the 5-10 years after powerful AI is developed:
Distribution of health interventions.
Economic growth.
Food security.
Mitigating climate change.
Inequality within countries.
The opt-out problem.
He spells out many lofty goals here but does so in a realistic manner. Here is how he closes this important section.
It won’t be a perfect world, and those who are behind won’t fully catch up, at least not in the first few years. But with strong efforts on our part, we may be able to get things moving in the right direction—and fast. If we do, we can make at least a downpayment on the promises of dignity and equality that we owe to every human being on earth.
Image by Ralph Losey using WP’s Stable Diffusion
4. Peace and Governance
Dario Amodei takes a very thoughtful approach to this all-important goal. Again, he is no naive optimist. He admits upfront a point cynics and the news media love to emphasize:
Unfortunately, I see no strong reason to believe AI will preferentially or structurally advance democracy and peace, in the same way that I think it will structurally advance human health and alleviate poverty. Human conflict is adversarial and AI can in principle help both the “good guys” and the “bad guys”. If anything, some structural factors seem worrying: AI seems likely to enable much better propaganda and surveillance, both major tools in the autocrat’s toolkit.
Then Dario goes beyond the fear mentality into what has always been the heart of being human, the optimistic, self-help, can-do mentality. This is part of the culture I grew up in and try to pass on: God helps those who help themselves.
It’s therefore up to us as individual actors to tilt things in the right direction: if we want AI to favor democracy and individual rights, we are going to have to fight for that outcome. I feel even more strongly about this than I do about international inequality: the triumph of liberal democracy and political stability is not guaranteed, perhaps not even likely, and will require great sacrifice and commitment on all of our parts, as it often has in the past.
Image by Ralph Losey using WP’s Stable Diffusion
Amodei then goes on to state how he thinks we should go about doing this. What geo-political strategy should now be used to protect everyone from misuse of AI by foreign powers. The main threat here is the government of China, although Amodei does not mention them by name.
My current guess at the best way to do this is via an “entente strategy”26, in which a coalition of democracies seeks to gain a clear advantage (even just a temporary one) on powerful AI by securing its supply chain, scaling quickly, and blocking or delaying adversaries’ access to key resources like chips and semiconductor equipment. This coalition would on one hand use AI to achieve robust military superiority (the stick) while at the same time offering to distribute the benefits of powerful AI (the carrot) to a wider and wider group of countries in exchange for supporting the coalition’s strategy to promote democracy (this would be a bit analogous to “Atoms for Peace”). The coalition would aim to gain the support of more and more of the world, isolating our worst adversaries and eventually putting them in a position where they are better off taking the same bargain as the rest of the world: give up competing with democracies in order to receive all the benefits and not fight a superior foe.
If we can do all this, we will have a world in which democracies lead on the world stage and have the economic and military strength to avoid being undermined, conquered, or sabotaged by autocracies, and may be able to parlay their AI superiority into a durable advantage. . . .
Even if all that goes well, it leaves the question of the fight between democracy and autocracy within each country. It is obviously hard to predict what will happen here, but I do have some optimism that given a global environment in which democracies control the most powerful AI, then AI may actually structurally favor democracy everywhere.
Image by Ralph Losey in cyberpunk style using Visual Muse
Wake up Max Tegmark!That race started long ago and your hope that China will be good and follow safety standards is dangerously naive.
Returning to Amodei’s discussion of the internal conflict between democracy and autocracy within countries, he offers a hopeful perspective—one that aligns with the sentiments I often express in my AI lectures:
It is obviously hard to predict what will happen here, but I do have some optimism that given a global environment in which democracies control the most powerful AI, then AI may actually structurally favor democracy everywhere. In particular, in this environment democratic governments can use their superior AI to win the information war: they can counter influence and propaganda operations by autocracies and may even be able to create a globally free information environment by providing channels of information and AI services in a way that autocracies lack the technical ability to block or monitor. It probably isn’t necessary to deliver propaganda, only to counter malicious attacks and unblock the free flow of information. Although not immediate, a level playing field like this stands a good chance of gradually tilting global governance towards democracy, for several reasons. . . .
I expect improvements in mental health, well-being, and education to increase democracy, as all three are negativelycorrelated with support for authoritarian leaders. In general people want more self-expression when their other needs are met, and democracy is among other things a form of self-expression. Conversely, authoritarianism thrives on fear and resentment.
We can only hope that America remains at the forefront of this fight, maintaining its leadership in pro-democracy policies.
Image by Ralph Losey using WP’s Stable Diffusion
Dario Amodei also makes a few comments on our legal system in this section. As usual he starts with the popular dark side, AI bias, but correctly moves on to what legal techs are already beginning to realize.
[T]he vitality of democracy depends on harnessing new technologies to improve democratic institutions, not just responding to risks. A truly mature and successful implementation of AI has the potential to reduce bias and be fairer for everyone. . . .
For centuries, legal systems have faced the dilemma that the law aims to be impartial, but is inherently subjective and thus must be interpreted by biased humans. . . . Instead legal systems rely on notoriously imprecise criteria like “cruel and unusual punishment” or “utterly without redeeming social importance”, which humans then interpret—and often do so in a manner that displays bias, favoritism, or arbitrariness. . . . AI . . . is the first technology capable of making broad, fuzzy judgements in a repeatable and mechanical way.
I am not suggesting that we literally replace judges with AI systems, but the combination of impartiality with the ability to understand and process messy, real-world situations feels like it should have some serious positive applications to law and justice. At the very least, such systems could work alongside humans as an aid to decision-making.
Amodei believes that finding meaningful work in the age of AI presents the greatest challenge. I respectfully disagree. In my view, curing cancer, eliminating other diseases, and extending human life to 150 years will be far more difficult than addressing the question of meaningful work. While Amodei acknowledges that making accurate predictions about changes in the job market is nearly impossible, he still ventures into this area—likely driven by widespread media fear-mongering over job losses, which often ignores historical trends. He begins by aligning with my perspective: that more jobs will likely be created than lost, with the real challenge lying in training and education.
Unemployed robots waiting in line for upgrades and retraining. Image by Ralph Losey using Visual Muse.
Beyond this, he asserts that it is impossible to predict what new forms of economic systems may emerge. However, he emphasizes that “civilization has successfully navigated major economic shifts in the past: from hunter-gathering to farming, farming to feudalism, and feudalism to industrialism.”
The deeper question he raises is based on sci-fi projections far into the future, when no one will need to work because AI can do everything for us. (Think Player of Games or Star Trek.) How can humans find meaning then? Amodei suggests that human meaning has never been solely derived from economic labor, and that we can find purpose in relationships, creativity, internal self-discovery, external exploration, and contributing to something larger than ourselves. He believes we can be happy in a world where we are free to pursue our passions and explore our full potential.
Image by Ralph Losey using WP’s Stable Diffusion
Conclusion
In the concluding section of Dario Amodei’s 28-page essay, Machines of Loving Grace, he explains:
I’ve tried to lay out a vision of a world that is both plausible if everything goes right with AI, and much better than the world today. I don’t know if this world is realistic, and even if it is, it will not be achieved without a huge amount of effort and struggle by many brave and dedicated people. Everyone (including AI companies!) will need to do their part both to prevent risks and to fully realize the benefits.
He then lays a deep thought on us and invites us to ponder the profound impacts these predictions could have on everyone:
But it is a world worth fighting for. If all of this really does happen over 5 to 10 years—the defeat of most diseases, the growth in biological and cognitive freedom, the lifting of billions of people out of poverty to share in the new technologies, a renaissance of liberal democracy and human rights—I suspect everyone watching it will be surprised by the effect it has on them. I don’t mean the experience of personally benefiting from all the new technologies, although that will certainly be amazing. I mean the experience of watching a long-held set of ideals materialize in front of us all at once. I think many will be literally moved to tears by it. . . . a thing of transcendent beauty. We have the opportunity to play some small role in making it real.
We each have a small role to play in making the future a place of transcendent beauty. Ralph Losey using Stable Diffusion
The title of Dario Amodei’s essay, Machines of Loving Grace, was taken from a landmark poem called All Watched Over By Machines Of Loving Grace. It was written in 1967 by Richard Brautigan (1935-1982) while he was a poet-in-residence at the California Institute of Technology. Brautigan is best known for his novel Trout Fishing in America (1967) and is a key figure in the counterculture movement of the 1960s. So, relax, take a deep breath, and let’s end with his poem.
I like to think (and the sooner the better!) of a cybernetic meadow where mammals and computers live together in mutually programming harmony like pure water touching clear sky.
I like to think (right now, please!) of a cybernetic forest filled with pines and electronics where deer stroll peacefully past computers as if they were flowers with spinning blossoms.
I like to think (it has to be!) of a cybernetic ecology where we are free of our labors and joined back to nature, returned to our mammal brothers and sisters, and all watched over by machines of loving grace.
Ralph Losey is an AI researcher, writer, tech-law expert, and former lawyer. He's also the CEO of Losey AI, LLC, providing non-legal services, primarily educational services pertaining to AI and creation of custom AI tools.
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 AI, 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 and husband since 1973 to Molly Friedman Losey, a mental health counselor in Winter Park.
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.
Ray Kurzweil explains Turing test and predicts an AI will pass it in 2029.
Ray Kurzweil on Expanding Your Mind a Million Times.
Positive Vision of the Future with Hybrid Human Machine Intelligence. See PyhtiaGuide.ai
Old Days of Tech Support. Ralph’s 1st Animation.
Lawyers at a Rule 26(f) conference discuss e-discovery. The young lawyer talks e-discovery circles around the old lawyer and so protects his client.
Star Trek Meets e-Discovery: Episode 1. Cooperation & the prime directive of the FRCP.
Star Trek Meets e-Discovery: Episode 2. The Ferengi. Working with e-discovery vendors.
Star Trek Meets e-Discovery: Episode 3. Education and techniques for both law firm and corp training.
Star Trek Meets e-Discovery: Episode 4. Motions for Sanctions in electronic discovery.
Star Trek Meets e-Discovery: Episode 5. Capt. Kirk Learns about Sedona Principle Two.
Posted by Ralph Losey 
