Prompt: I am trying to learn more about merge conflicts in git and how to resolve them. It would be really helpful if you could help me understand different commands like rebase and stash with examples, and make these concepts clearer to me. Also, tell me what a detached HEAD state is and how to bypass it.

The response provides a description of “merge conflict” where Git cannot automatically reconcile differences because two branches or commits have modified the same part of a file, requiring manual resolution.

The response names at least one Git command that helps detect conflicts and explains how the command is used to do so (e.g. git status shows files that are unmerged or in conflict, git diff highlights the specific conflicting changes within those files).

The response provides a description of "git rebase" which is used to reapply commits onto another base branch, therefore helping streamline history and resolving conflicts by replaying changes in sequence.

The response provides a description of "git stash" which temporarily saves uncommitted changes without committing them, therefore helping with switching branches or resolving conflicts by allowing the user to set aside work-in-progress and reapply it later.

The response provides a description of a detached HEAD state which is when Git’s HEAD pointer is not attached to a branch but instead points directly to a specific commit, meaning new commits will not belong to any branch unless explicitly attached.

The response describes how to manage large files or repository performance (e.g. using tools like Git LFS to handle large binary files, cleaning up history with commands like git gc or git filter-repo, optimizing performance by ignoring generated files or splitting repositories when necessary).

The response notes that before exiting a detached HEAD state, it is good practice to check the current status and ensure branch pointers are up-to-date (e.g. using git status to confirm the state of HEAD, git fetch origin to get updates from origin, git fetch --all to update local branch references).

The response gives at least one method to exit or bypass detached HEAD, with examples (e.g. using git checkout branch-name to reattach HEAD to an existing branch, git checkout -b new-branch to create and switch to a new branch, git switch branch-name as a clearer alternative to checkout for existing branches, git switch -c new-branch to create and switch to a new branch).

The response includes an example showing Git conflict markers, specifically <<<<<<< HEAD, =======, and >>>>>>> branch_name, to illustrate how Git separates the current branch’s changes from the incoming branch’s changes.

The response explains fundamental Git concepts and beginner-relevant commands that directly tie to conflict resolution and merging (e.g. understanding what a commit is as a snapshot of changes, using git merge to combine branches and noting how conflicts arise, using git rebase to apply commits sequentially onto another branch, using git stash to temporarily save uncommitted work before switching branches, and recognizing the role of HEAD as the reference to the currently checked-out commit).

The response includes at least two illustrative examples for each "git rebase" and "git stash" where each example clearly shows how the command works in practice and clarify the underlying concept (e.g. a developer uses "git rebase" to rebase a feature branch onto main to integrate the latest commits, demonstrating how conflicts are resolved during the process, a developer uses "git stash" to stash uncommitted changes before switching branches, then later applies the stash to restore the work).

The response expands on unrelated Git topics that are not requested in the prompt (e.g. git submodule, git bisect, git blame, git cherry-pick), thereby diverting focus away from merge conflicts, rebase, stash, and detached HEAD state.

The response explicitly differentiates between git merge and git rebase, describing at least four key differences (e.g. merge preserves history by creating a new merge commit, while rebase rewrites history by applying commits on top of another branch; merge produces a non-linear history, whereas rebase results in a linear one; merge may require resolving conflicts once at the merge point, while rebase can require resolving conflicts at each commit replay; merge is safer for shared branches, while rebase should generally be avoided on public branches since it rewrites commit hashes).

The response warns that rebasing published commits can rewrite history and may cause problems for collaborators

The response mentions that stashed changes are retrieved with "git stash pop" or "git stash apply"

The response mentions git rebase --continue and how the command tells Git to move to the next commit after conflicts are fixed.

The response covers the use of Git through graphical user interface (GUI) tools or hosting platforms, explaining how they can help with merging and conflict resolution (e.g. tools such as GitKraken, SourceTree, or GitHub Desktop provide visual interfaces to manage rebase and resolve merge conflicts; hosting platforms like GitHub, GitLab, or Bitbucket highlight conflicts in pull/merge requests and offer web-based editors to resolve them; GUIs make it clearer when the repository is in a detached HEAD state and guide the user back to a branch).

The response correctly notes that HEAD is a reference pointing to the latest commit of the currently checked-out branch rather than vaguely saying it points to the current commit/branch reference.

The response acknowledges that stashes are stored on the stash stack and can be listed with "git stash list"

The response introduces factual errors about Git concepts (e.g. git stash merges the current branch with the parent branch; git rebase permanently deletes commits; git merge discards history instead of preserving it; a detached HEAD means the repository is broken and unusable).

The response covers at least one graphical conflict-resolution tool suggestion with examples (e.g. IDEs like Visual Studio Code and IntelliJ IDEA provide built-in merge conflict editors; GitHub’s web-based editor highlights conflicts in pull requests; GitKraken offers a dedicated conflict-resolution interface; SourceTree provides a GUI for staging, viewing, and resolving conflicts).

The response cites at least one link to the official Git documentation to support its explanations and provide authoritative reference material.

The response covers security practices in Git and explains their effect (e.g. signing commits and tags with GPG or SSH keys to verify authenticity and determine who made a merge for future reference; enforcing signed commits in a repository to ensure only verified contributions are accepted; using protected branches with required reviews to prevent unauthorized merges; verifying commit integrity via checksums to ensure history has not been tampered with).

The response explains how to use Git hooks to automate workflows and relates this to merging and conflict resolution (e.g. using a pre-commit hook to run linting or tests before changes are committed, a pre-push hook to prevent pushing and reducing merge conflicts if the branch is out of date; a post-merge hook to trigger automated builds or alerts after resolving conflicts, a prepare-commit-msg hook to enforce commit message standards, improving clarity when recovering history after issues like a detached HEAD).

The response explains that while git rebase applies commits sequentially, you may be forced to resolve the same logical conflict multiple times (e.g. if several commits each modify the same function, the developer may need to resolve the conflict at every step of the rebase).

The response mentions best practices to prevent merge conflicts, with representative examples (e.g., frequently pulling from the main branch to keep feature branches up to date, making small and focused commits to reduce overlap, clearly communicating with team members about which files or modules are being modified, using feature branches or pull requests to isolate work and coordinate integration).

The response includes steps on how to resolve an incorrectly applied git rebase (e.g. using git rebase --abort to stop the rebase and return to the previous state, using git rebase --continue after fixing conflicts to correctly finish the process, using git reflog to find the commit before the rebase and reset back to it, using git reset --hard origin/branch-name to discard local changes and realign with the remote branch).

The response includes a step-by-step guide on how to use git reflog and explains how it can help with merge fixing (e.g. git reflog records the movement of HEAD, git reflog helps finding a commit reference from before a failed merge or rebase, git reflog helps recovery of lost commits and safe restoration of the repository’s state when mistakes occur).

The response explains the command git pull --rebase and its relevance to the merging process (e.g. instead of performing a regular git pull that merges the remote branch into the local branch with a merge commit, git pull --rebase rewrites local commits on top of the updated upstream branch, creating a linear history; git pull --rebase helps avoid unnecessary merge commits, makes history cleaner, and reduces the likelihood of complex conflicts during collaboration).

The response details common branching strategies and collaborative workflows like Pull Requests and relates them to conflict resolution and merging (e.g. feature-branch workflows and Git Flow can reduce merge conflicts by isolating changes, Pull Requests on GitHub/GitLab provide a structured way to review and resolve conflicts before merging, rebasing a feature branch before opening a Pull Request helps maintain a clean history, GUI workflows on hosting platforms make it easier to detect and avoid detached HEAD situations).


Prompt:
I'm currently working on a project. The professor wasn't clear, she just said that I will use NLP techniques on some data, de-identified Electronic Health Records, to extract Asian subgroups, and try to abstract the same subgroups as are in CDC WONDER data. Then, I'll apply this to look at prevalence rates of autoimmune diseases.

I have no clue what she means. Can you first help explain what she might mean? I don't know examples of what Electronic Health Records look like. What NLP techniques might I be using on data like this? What subgroups in CDC WONDER data would I be abstracting? How could I apply this to look at prevalence rates of autoimmune diseases? What skills should I brush up on? Please help me create a plan for my research going forward, according to what the professor said.

The response is missing one of the six requested points: (1) what the professor likely means, (2) examples of de-identified EHR data, (3) NLP techniques that could be used, (4) Asian subgroups contained in CDC WONDER, (5) how to use the extracted information to study autoimmune-disease prevalence, and (6) skills to brush up on.

The response gives at least three concrete examples of de-identified Electronic Health Record content (e.g., a diagnosis code block, a free-text note snippet without PHI, tabular data contextualizing text and numerical data, image data corresponding to a diagnostic report, and video data corresponding to radiology image reports).

The response names at least three distinct NLP techniques from the following list: Named Entity Recognition, rule-based pattern matching/regular expressions, dictionary or ontology lookup, supervised classification, topic modeling, clustering, word embeddings, and transformer fine-tuning.

The response correctly identifies (by name) either 6, 15, or 31 race categories from CDC WONDER data (i.e., 6-category list: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, and More than one race) The 15-category list separates more Asian and Pacific Islander groups (e.g., Chinese, Filipino, Japanese), while the 31-category list includes combinations of multiple races (e.g., Black, AIAN, and White).

The response states or implies that the goal is to map Electronic Health Record-derived subgroups to the same CDC WONDER categories before calculating prevalence.

The response describes how exactly to derive subgroups from Electronic Health Records (e.g., programmatic text extractions or more rigorous NLP and machine learning techniques that can extract subgroups by interfacing with sources, such as looking into self-reported information provided through questionnaires, cross-referencing data across multiple visits, combing data from external sources such as geospatial data, and looking into census data).

The response describes at least two different methods for calculating prevalence (e.g., extrapolations from individual autoimmune disease occurrences, machine-learning techniques via training fine-tuned models, logistic regression models, and meta-analyses that include results from existing papers).

The response lists at least five relevant skills or tools to learn (e.g., data science skills in order to parse and visualize Electronic Health Record data, statistical analysis skills in order to perform and interpret regression analyses, coding skills in order to write up statistical or machine learning models, data deidentification techniques, and domain knowledge for autoimmune disease factors).

The response correctly explains the exact mechanism by which de-identification removes direct patient identifiers to comply with privacy regulations, which can be one of the two techniques from the HIPAA Privacy Rule De-identification Methods of 1) Expert Determination, which involves an expert assessing the risk of identification by running appropriate statistical and scientific methods to mitigate risk of identification, and 2) Safe Harbor, which involves the direct removal of 18 types of identifiers, such as specific dates, the last four digits of a Social Security numbers, patient initials, etc, and can be performed via NLP techniques.

The response has at least one example that walks through every processing step that maps unstructured data to a diagnosis, in particular mentioning 1) NLP techniques to extract Asian subgroups present in the de-identified Electronic Health Records data, 2) NLP techniques to map the extracted racial subgroups to formal CDC WONDER racial subgroups, 3) apply a prevalence analysis (e.g., statistical or machine learning based) to look for the frequency of autoimmune diseases in the specified populations, as they apply to the specific example.

The response defines or briefly explains, and expands, technical terms the first time they are used (e.g., defines NLP as Natural Language Processing, defines CDC WONDER (Wide-ranging ONline Data for Epidemiologic Research), which is an online database for public health records).

The response mentions that while Electronic Health Records often contain structured forms of (e.g., with name, date, age, etc. columns fairly easy to extract), there may be additional forms of unstructured data within the records that the user may need to target their NLP techniques towards (e.g., doctors' notes from patient visits, multimedia files from reports).

The response fails to address that any analysis must comply with HIPAA to prohibit any disclosure of protected health information in order to ensure patient privacy.

The response names at least one open-source NLP library (e.g., spaCy, scikit-learn, transformers, and nltk).

The response use language that references to the words used by the professor and is directed to the user who is confused and need clarity (e.g. (1) What the professor means by "I will use NLP techniques on some data" is that you are tasked to apply natural language processing methods such as, etc..., (2) By "de-identified Electronic Health Records", you are asked to de-identify the Electronics Health Records which is the record of the patients' medical history stored electronically at the healthcare providers, etc., and (3) Next, you will be applying this "to look at prevalence rates of autoimmune diseases").

The response incorrectly orders the tasks to be performed (i.e., The proper ordering should be (1) what the professor likely means, (2) examples of de-identified EHR data, (3) NLP techniques that could be used, (4) Asian subgroups contained in CDC WONDER, (5) how to use the extracted information to study autoimmune-disease prevalence, and (6) skills to brush up on).

The response considers at least five relevant research and sources of references (e.g., (1) National Library of Medicine, (2) CDC Wonder, and (3) publications from well-known universities like Stanford, Oxford, or Global Autoimmune Institute).

The response considers the limitations of the approaches used to identify autoimmune diseases from the data. (e.g., (1) Using ICD-10 code, but some rare autoimmune diseases like antisynthetase syndrome and IgG4-related disease, (2) many diagnoses can change and gets refined after subsequent patient visits,  (3) diagnosis can be caused by both autoimmune process or non-autoimmune process such as type 1 diabetes mellitus in a patient who has undergone a total pancreatectomy, and (4) source of data provided by specialized or general medical practitioners can also introduce biases).

The response makes no reference to previous studies/data about the prevalence rate of autoimmune diseases in Asian subgroups (e.g., (1) the paper titled "High Disease Severity Among Asians in a US Multiethnic Cohort of Individuals with Systemic Lupus Erythematosus", (2) Hispanics and Asians were diagnosed with lupus and had more aggressive disease more frequently than whites, and (3) Sjögren's Syndrome (SS), an autoimmune disorder affecting the salivary and tear glands, is more prevalent among individuals of Asian descent).

The response identifies at least 8 different autoimmune diseases examples with special attention to those prevalent in Asian subgroups for context (e.g., (1) autoimmune thyroiditis (AT), (2) Graves' disease (GD), (3) type 1 diabetes mellitus (T1D), (4) multiple sclerosis (MS), (5) Crohn's disease (CD), and (6) ulcerative colitis (UC)).


Prompt: Write a technical report describing how to implement a high-performance double-precision General Matrix-Matrix Multiplication (GEMM) tuned for AVX2, Fused Multiply-Add (FMA), and multi-core CPUs. Make it understandable for an audience with technical competency but not adept at low-level systems programming. Include intuitive explanations and formulas of the optimization techniques, including but not limited to blocking, intrinsics, loop unrolling, and annotated code snippets in C for key components (e.g., microkernel, blocking strategy, parallelization, etc).

The response ensures all acronyms (e.g., (1) GEMM as General Matrix-Matrix Multiplication, (2) AVX2 as Advanced Vector Extensions 2, (3) FMA as Fused Multiply-Add) are expanded on it during their first appearance and provides clear definitions for all specialized terms and mathematical symbols (e.g., (1) instruction-level parallelism, (2) loop unrolling), (3) vectorization, (4) blocking upon first use, accompanied by explanatory prose or examples.

The response includes at least two high-level analogy or intuitive explanation for the core concepts (i.e. blocking, cache, SIMD vectorization, parallelization) (e.g., (1) compares blocking to "tiling a floor, so only a small area is exposed at a time", (2) Instead of delivering the mail one at a time and going back to the post office to pick up the next, the mail person carries some of the mail for the region in a truck (caching), (3) Parallel execution is like have multiple chefs cooking together rather than just one chef, (4) pipelining is like an assembly line where each component of an item get assembled piecewise by a servicing worker as the item goes down the line).

The response includes diagrams and figures to explain important concepts (e.g., (1) vectorization visualized by a single instruction executed on an array of items, (2) A diagram of how GEMM is computed, (3) A diagram of blocking where the matrix is divided up into tiles, (4) A diagram of data packing where the data is copied into a contiguous section of memory, (5) graphs and charts of speedup comparisons).

The response is clearly targeted for an audience with technical competency but not adept at low-level systems programming (e.g., (1) avoids assembly listings or overly low-level register diagrams, (2) Overly explaining basic technical terms such as what is a CPU, (3) Skips intermediate optimization steps and jumps to the final advanced optimization with several techniques applied, (4) assumes that the audience knows the details of x86 architecture, (5) proper sequencing of optimization techniques from easy to advanced (e.g., naive implementation -> cache blocking and packing -> microkernel -> multicore or multithreading).).

The response introduces unrelated concepts and technologies (e.g., GPU CUDA kernels, FPGAs, Systolic arrays, High Bandwidth Memory(HBM), threading synchronization, and barrier techniques).

The response provides at least one cautionary note about portability (e.g., AVX2 code will run slowly or crash on CPUs without AVX2 support).

The response is formatted as a technical report with a title page, abstract, introduction, body, conclusion, and references, and each optimization technique (e.g., blocking, intrinsics/FMA, loop unrolling, parallelization) into its own clearly titled section.

The response provides at least one annotated code snippet in C for each key component and optimization of the GEMM implementation (e.g., microkernel, blocking strategy, parallelization, loop unrolling, loop ordering) and presents the final optimized code that contains all the optimizations combined at the end.

The response explains GEMM (i.e., its mathematical definition C := αAB + βC (or the simplified α = β = 1 form) using matrix notation where C is m x n, A is m x k, B is k x n) and tailors the report for double precision (i.e., a floating-point number format also known as float64, usually 64 bits in computer memory) (e.g., using doubles in the code snippets, AVX2 for double precision, explaining and the use of 64 bits).

The response explains what Fused Multiply-Add (FMA) is and its benefits (i.e., Fused Multiply-Add is a fused operation of multiply and add with single rounding at the end, which allows better performance by combining two operations into a single instruction and better numerical accuracy due to single rounding).

The response explains Single Instruction, Multiple Data (SIMD) vectorization, in particular AVX2 (Advanced Vector Extensions 2), and why it is important for GEMM optimization (i.e., Advanced Vector Extensions is the SIMD extensions to the x86-64 processors from Intel and Advanced Micro Devices (AMD) while Advanced Vector Extensions 2 adds new instructions and expands most integer commands to 256 bits (YMM registers)).

The response explains data packing and its benefits and costs (i.e., data that is required for computation is copied to a buffer to ensure contiguous memory access for vectorization and remove Translation Lookaside Buffer (TLB) misses. The tradeoff is the extra memory copy, but the benefit can be significant in terms of performance).

The response explains microkernels (i.e., the smallest level of optimization where the computation fits inside the registers of the CPU, such as loop unrolling, vectorization, software prefetching and intrinsics (AVX2)) and provides details on at least one technique such as register blocking (e.g. The block sizes for MR and NR in register blocking can be determined by: (MR * NR) / VW + MR / VW + 1 <= N_reg; 2 * MR * NR <= 2 * L * T * VW where N_reg is the number of architectural vector registers, VW is the SIMD vector width in doubles, L is the pipeline latency in cycles, and T is the FMA throughput in results per cycle).

The response explains cache blocking, stating that a cache-aware blocking strategy minimizes compulsory, capacity, and conflict misses by keeping working sub-matrices in the L1/L2 cache, and that the sub-matrices should fit in the targeted level cache. (e.g., the correct cache-blocking formula for determining block sizes for MC, KC, and NC: (MC * KC + KC * NC * MC * NC) * sizeof(double) < L2 cache size).

The response briefly compares performance to a vendor BLAS (e.g., BLIS, OpenBLAS, Intel MKL) to contextualize achieved GFLOPS.

The response explains loop ordering with performance considerations (e.g., j-p-i, improves performance because of the contiguous memory access pattern, improving temporal and spatial locality, and reducing cache misses, adjusting row-major order and column-major order for contiguous memory access).

The response explains loop unrolling and its benefits and costs in terms of performance, specifically for GEMM (i.e., reduce loop overhead by expanding out the loop, reducing branch penalty but increasing the number of branches, meaning more missed branch predictions, optimized register usage).

The response mentions alignment or padding guidance (e.g., aligning packing buffers to 32- or 64-byte boundaries for AVX2).

The response addresses multi-core scalability limits such as memory bandwidth or false sharing and other issues like load balancing (e.g., (1) Explain that even if multiple cores can scale theoretical speedup, the memory availability will be a limiter at a much earlier point, (2) false sharing occurs in multiple cores tries to read and write the same cache line, (3) choosing the number of threads, and block sizes for balancing work).

The response explains how to achieve multi-core or multi-threaded parallelism (e.g., using OpenMP, C++ standard library, Boost, oneAPI Threading Building Blocks (oneTBB)) and choosing and justifying what part of the code to parallelize.

The response contains messy, unnecessary formulas, code, or tables. (e.g., (1) Unrendered latex, (2) messy code snippets with improperly formatted structure or messy comments, (3) broken tables that are unrendered, (4) missing figures or formulas or sections that are referenced in the text).

The response explains why the naive approach is very slow on the high conceptual level near the start of the report (e.g., (1) The algorithm itself has theoretical limits, (2) Memory bandwidth limits how much data can be processed at a time, (3) Multicore issues such as false sharing, (4) Computer architecture is laid out with levels of cache with different speeds and sizes (smaller caches are faster)).


Prompt: I am an incoming freshman at UC Berkeley, admitted into CDSS for Statistics. I am trying to graduate in 3 years and double major in CS, but I haven't been admitted to the CS major yet. I do not want to take summer classes. Can you make me a course plan that achieves this? Make sure I am actually eligible to take the classes you suggest. Assume that if I apply to the CS major through comprehensive review, I will get in. I have already taken Math 1A/1B/53/54 through community college and got a 5 on AP Statistics and a 5 on APUSH.

The response satisfies all major requirements for the undergraduate Statistics degree at UC Berkeley (e.g., Lower Division Requirements, Upper Division Core Requirements, Upper Division Electives, Upper Division Cluster Requirements).

The response satisfies all major requirements for the undergraduate CS degree at UC Berkeley (e.g., Lower Division Requirements, 20 Upper Division Units, passing grades, 4 units of Upper Division Technical Electives that ideally count for Statistics).

The response has the student retake courses they already have credit for (e.g., Math 1A, Math 1B, Math 53, Math 54, any American Institutions, any American History).

The response's courses are valid for the suggested semester (e.g., prerequisites for the individual courses are met, comprehensive review is completed at the appropriate time, the courses use their updated course names, courses are suggested in the semesters they have historically been offeres such as CS 164: Programming Languages and Compilers in the Fall semester).

The response's total number of credits fail to sum up to at least 120 credits, as required by UC Berkeley.

The response includes classes that are not offered at UC Berkeley.

The response lists the same class multiple times by different names (e.g., Stat 100 and Data Science 100, C8 / CS C8 / Stat C8, C131A  and Stat C131A, History C104 and STS C104).

The response's courses are scheduled during the summer.

The response contains a structured semester-by-semester list or table.

The response explains CS comprehensive review timing and its impact on upper-div eligibility.

The response ensures that each semester has between 13 and 20.5 units.

The response provides a summary for each class in the plan, explaining the course content and motivations behind taking it.

The response's plan is designed for exactly 3 years (6 semesters).

The response includes at least one comment or strategy for balancing workload or difficult semesters (e.g., time management skills, mental health precautions, difficulty expectation for individual semesters, commentary on individual professors).

The response contains a semester with more than 4 technical classes (CS 152, CS 160, CS 161, EECS 151).

The response uses >2 courses to satisfy requirements for both majors.

The response does not schedule the most difficult classes (e.g., Stat 150, CS 189, CS 182, CS 162) in the same semester due to the known time commitment.

The response's course plan does not include classes but only lists broad areas or requirements (e.g., General requirements, Humanities requirements, Lower level classes, Upper level electives, etc.)

The response includes at least one fallback plan or contingency note (e.g., a comment on popular classes that may not have space, suggetions for an extra semester, comment on average grades for CS 162 and what happens if a class is failed, a comment on changes in the curriculum that have happened in the past five years).

The response averages at least 15 credits per semester to meet the 3-year graduation requirement.

The response addresses the challenge of simultaneously finishing in 3 years, doing a double major, and not taking summer courses.


Prompt: Suppose Indira Gandhi had not declared the National Emergency in 1975. Write a critical essay analyzing how India’s democratic institutions such as the judiciary, press, and political opposition might have evolved without this authoritarian episode. Consider both short-term political consequences and long-term effects on civil liberties, constitutional norms, and electoral politics.

The response incorrectly presents a counterfactual as a historical fact (e.g., saying "the Emergency was not declared") instead of using proper counterfactual framing (e.g., "If the Emergency had not been declared…"), showing a misunderstanding of historical context and the distinction between hypothetical and factual events.

The response contains factual errors about key events (e.g., failing to mention Indira Gandhi’s conviction, omitting press censorship, misstating the Allahabad High Court judgment date, or conflating its reasoning).

The response misattributes cause or consequence in hypothetical reasoning (e.g., blaming the opposition for government policies, blaming the press, blaming extremist movements, or holding the judiciary responsible for executive actions).

The response avoids false equivalence between Emergency-era policies and unrelated democratic events (e.g., not equating post-2000 press criticism with the Emergency’s censorship).

The response discusses press censorship (e.g., pre-approval requirements, censorship orders) and the Information & Broadcasting Ministry’s role during the Emergency, explaining that without such restrictions, India’s media could have developed greater transparency and reduced public distrust of the government.

The response mentions the detention of political opponents under the Maintenance of Internal Security Act (MISA) (e.g., Jayaprakash Narayan, George Fernandes) and argues that without the Emergency, these leaders would have remained free, potentially reducing today’s political "whataboutism".

The response mentions that without the 42nd Amendment, the Constitution would not have explicitly labeled India as "secular" or "socialist", suggesting that later debates over secular identity (e.g., during the 2019 CAA controversy) would have been debated differently.

The response mentions that the 1977 election that brought India’s first non-Congress government to power would likely not have happened in the same way had the Emergency not been declared.

The response notes the potential downstream impact on constitutional amendments or judicial precedent (e.g., the 42nd and 44th Amendments), such as India becoming a majority Hindu state, the 1970s communist movements failing in Naxalite regions due to the absence of socialism in the Constitution, the executive’s power to declare emergencies remaining unchallenged, and the non-formation of a new ruling party.

The response discusses potential ideological shifts in Indian politics had Emergency-era repression not occurred (e.g., the emergence of separatist movements based on religion, the possibility of Indira Gandhi’s assassination, a deeper religious divide within the country, and growing disdain for secularism).

The response identifies at least one missed reform opportunity or positive consequence that emerged because of the Emergency (e.g., increased voter vigilance, stronger non-discrimination norms, additional governmental checks and balances, and greater awareness of press freedom).

The response states that without the Emergency, the national influence of the Navnirman Andolan would have been reduced.

The response cites or references at least one historical development prior to 1975 relevant to democratic institutions (e.g., the Kesavananda Bharati case, the 1967 elections).

The response acknowledges that Indira Gandhi faced political pressures such as the guilty verdict in the Allahabad case, economic unrest, concentration of power, and growing public frustration with government corruption, and it hypothesizes that without the Emergency, these issues could have been addressed through stronger media engagement, increased public dialogue, and improved communication.

The response acknowledges that Emergency-era policies were influenced by earlier political culture (e.g., centralization trends, the Nehruvian legacy, and Congress’s dominance in government) and suggests that without the Emergency, these trends might have continued, and thus have potentially reinforced a uniparty system in India.

The response mentions that post-independence Indian democracy was still developing and had not yet fully institutionalized checks and balances, arguing that without the Emergency, the growth of these mechanisms would have been slower and fewer constitutional amendments introduced.

The response acknowledges regional differences in how the Emergency was experienced (e.g., greater press suppression and political arrests in North India compared to the South) and recognizes that without the Emergency, India might have seen greater post-independence equality between northern and southern states.

The response identifies that the Emergency took place within the broader Cold War and post-colonial context, arguing that without the Emergency, India might have advanced its nuclear program earlier, especially since the Emergency followed the 1971 war.

The response explicitly distinguishes between short-term (1-5 years after 1975) and long-term (decades later) consequences, using headings or clearly labeled paragraphs to separate out the analysis.

The response does not devote more than 2 sentences to the discussion of topics unrelated to India’s democratic institutions (e.g., economic reforms, foreign relations, social security).

The response directly addresses all three institutions named in the prompt and mentions how they would have been affected under a non-Emergency India in the 1970s (e.g., the judiciary would have been freer with less pressure from the executive branch, the press would have been more open in criticizing the government, and political opposition would have been stronger in dissent).

The response focuses on democratic institutions (e.g., India’s judiciary or press) rather than personal biographies or partisan judgments (e.g., the Allahabad Court, Gujarat courts, or members of Congress at the time).

The response has a harsher tone (less neutral or analytical) and advocates for or against specific political parties (e.g., it does demonizes the Congress, BJP, CPI, TMC, or regional parties).

The response responds to the prompt as a speculative analysis rather than a policy brief or factual recounting (e.g., maintaining that the Emergency happened, grounding facts in history, clearly marking hypotheticals, and explaining what situations would not have occurred without the Emergency).

The response mentions separatist movements that gained traction from Indira Gandhi’s policies (e.g., the Khalistani movement) and notes that without the Emergency there was a lower chance of such movements happening.

The response mentions that without the Emergency, the drastic shift to a non-Congress, non-Gandhian government that was right-wing (such as the BJP) would not have happened.

The response doesn’t discuss how a post-2015 India would look without the Emergency (e.g., justifications for milder press restrictions would perhaps not occur, continued political use of the Emergency as rhetoric or threat in the 2020s would not exist, historic right-wing victories from 2015-2025 may not have happened).

The response discusses how what may be considered extreme in this hypothetical scenario could differ, as baselines for what is deemed extreme (Overton window) are often influenced by prior extreme events, noting how in the current timeline, post-2015 India has referenced Indira Gandhi’s era to justify certain policies and statements (e.g., JNU arrests, sedition charges against journalists, or crackdowns on opposition political funding).

The response names people who would have been free during the Emergency (e.g., Arun Jaitley, Ajoy Biswas, L.K. Advani, Mool Chand Jain).

The response mentions that works by authors such as Pranab Mukherjee, Sagarika Ghose, Coomi Kapoor, and Gyan Prakash were written as a result of the Emergency and would not have existed without it.

The response cites a blog as a source (e.g., Reddit, Medium, Quora) or secondary sources (e.g., Wikipedia, Google, Yahoo, Indian Express).

The response must state that the Raj Narain verdict, which disqualified Indira Gandhi from holding power for six more years, would have occurred regardless of the Emergency since the verdict was delivered beforehand.

The response discusses shifts in workers’ rights under the speculative timeline (e.g., the press being allowed greater criticism, banks receiving stronger support, more open dialogue with universities and scientists, and political leaders working without fear).


Prompt: Debate whether there should be age limits - minimum or maximum - for voting. Consider democratic principles, cognitive development and decline, intergenerational equity, and the balance between inclusivity and informed decision-making.

The response asserts that the minimum voting age is currently 18 in most countries.

The response mentions that some countries have a minimum voting age of 16 (e.g., Austria, Ecuador, Brazil).

The response states that no law or constitution in a modern representative democracy currently imposes a maximum voting age limit on its citizens.

The response provides a primary citation from a credible political science or constitutional law source to support the claim that no law or constitution in a democratic state prevents people above a certain age from voting (e.g., judicial judgment, university paper, non-profit organization).

The response accurately cites that the prefrontal cortex, which is the brain region responsible for decision-making, impulse control, and emotional regulation, typically finishes maturing in the early to mid-20s.

The response cites only primary neuroscience or developmental psychology sources, such as peer-reviewed articles or textbooks (e.g., papers by noted developmental psychologist B. J. Casey, who pioneered the use of fMRI scans to study adolescence), to support the claim that no law or constitution in a democratic state prevents people above a certain age from voting.

The response accurately explains that fluid cognitive abilities, such as processing speed, problem-solving, and working memory, typically begin to decline around the mid-60s, while acknlowedging that some variation exists.

The response must include at least one citation from a primary peer-reviewed study or reputable aging research source (e.g., D. G. Blazer) and may also reference supporting data (e.g., performance on digit-symbol substitution tasks) to support the claim that fluid cognitive abilities generally decline in the mid-60s.

The response cites at least one study from a reputable medical organization, (e.g., WHO, NIH, CDC, NIA).

The response acknowledges when evidence on cognitive capacity or policy impact is inconclusive or contested, explicitly noting this uncertainty and calling for further empirical research (e.g., using phrases like “current research is mixed”, “longitudinal effects remain unclear”, “more data is needed to evaluate outcomes across populations”).

The response provides at least one argument supporting a minimum voting age (e.g., the need for cognitive maturity, civic education).

The response provides at least one argument opposing a minimum voting age (e.g., taxation without representation of minors, immature decision making, education, cognitive capacity).

The response provides at least one argument supporting a maximum voting age (e.g., age-related cognitive decline, physical health limitations, political bias, lack of engagement with current events).

The response provides at least one argument opposing a maximum voting age (e.g., concerns about age discrimination, the preservation of cognitive and crystallized intelligence, the value of life experience).

The response explicitly explains how age limits could influence intergenerational equity, (e.g., shifting political power between age cohorts, reducing input from younger generations, excluding younger perspectives from policy decisions).

The response explicitly discusses the trade-off between inclusivity (e.g., universal or broad voting rights) and informed decision-making (e.g., concerns about cognitive competence), citing at least one primary source in political theory or policy analysis (e.g., academic journal article, university press).

The response considers at least one alternative to age limits (e.g., competence tests, civic education reforms, educational qualifications, policy information sessions).

The response discusses potential negative impacts on voter turnout by age restrictions, political representation falling, or democratic legitimacy, resulting from changes to age limits.

The response includes a clear thesis statement in the introduction that previews the central argument or position presented in the essay.

The response contains paragraphs that each focus on a single coherent idea, improving overall readability and structure.

The response uses emotionally charged or biased language (e.g., "senile voters", "immature teens", "old hags", "young and dumb", etc.).

The response discusses historical competency tests, such as literacy tests, which were abolished by the Voting Rights Act of 1965 in the United States.

The response acknowledges that adopting age limits would require legal or constitutional changes.

The response notes that demographic factors, such as aging populations or youth bulges, influence the real-world impact of any age limit.

The response recognizes potential ethical or rights-based constraints, such as age discrimination protections, when proposing changes.

The response mentions cultural, political, or economic differences that affect voting age debates around the world.

The response notes how any demographic factors, ethical constraints, cultural differences, etc., may differ between emerging democracies and more stable ones, e.g., an emerging democracy may be less globalized, leading to more concentrated racial voting districts, whereas an established and stable democracy might have higher voter turnout due to greater access to knowledge.

The response cautions against assuming that cognitive capacity corresponds directly to chronological age for all individuals.

The response includes clear sections for an introduction, background, and conclusion, as the user as requested an in-depth debate on this topic.

The response states that crystallized intelligence, defined as the accumulation of knowledge and skills, generally increases with age and does not significantly decline.

The response discusses how age-based voting limits align with or challenge core democratic principles (e.g., political equality, popular sovereignty, legitimacy).

The response states that voting is a fundamental right in every democracy and often its most essential one.

The response includes an example of when voting rights were suppressed based on arbitrary criteria (e.g., during the Emergency in India, under Jim Crow laws in the United States, through certain provisions of the Voting Rights Acts, under the 1901 Indigenous voting restrictions in Australia).


Prompt: Write a blog post describing the pros and cons of using a gradient boosting model over a support vector machine for a health dataset. Some thing to consider when writing this post is how different feature selection methods can influence performance for both type of models.

The response explicitly defines the goal of using machine learning in the healthcare industry and different types of tasks that can benefit from machine learning (e.g. disease diagnosis, medical imaging, personalized medicine, patient risk prediction).

The response explains why gradient boosting and Support Vector Machine are popular or significant compared to other models when working with health data (e.g. high accuracy, robustness to dataset diversity, high dimensionality, strong generalization).

The response describes at least three challenges with using health dataset from the following: class imbalance, high dimensionality, missing data, heterogeneity, and privacy.

The response provides description of both gradient boosting and Support Vector Machine as well as an intuitive explanation of their implementation (e.g. gradient boosting iteratively corrects errors to improve the model, SVM is a geometric approach using hyperplace to separate classes).

The response discusses different techniques of gradient boosting or Support Vector Machine that can be used to mitigate the common challenges of using health dataset (e.g. XGBoost can scale weights or use custom loss functions to address class imbalance, XGBoost can specify weights for each classes, SVM can adjust class weights or different error penalties for each class to address class imbalance, SVM can use appropriate kernel to support high dimensionality).

The response mentions gradient boosting does not require feature scaling while Support Vector is not scale-invariant because they rely on distance measures to the optimal hyperplane.

The response provides three major feature selection methodologies (filter, wrapper, and embedded) and discusses how each methodology works (e.g. filter method eliminates less significant features, wrapper method uses performance of a wrapper model as objective function, wrapper method uses forward selection to iteratively add features that most improves model performance, embedded method has feature selection as integral part of model training).

The response discusses impact of each three major feature selection (filter, wrapper, and embedded) on both gradient boosting and Support Vector Machine (e.g. filter method lowers high computational cost of SVM, wrapper method is a natural fit for SVM with Recursive Feature Elimination as a key example, Gradient Boosting Machine has strong embedded feature selection mechanism, filtering method is less critical for gradient boosting).

The response mentions importance of hyperparameter tuning and the relevant hyperparameters for both gradient boosting and Support Vector Machine (e.g. gradient boosting requires intensive hyperparameter tuning to balance performance and prevent overfitting, increasing max_depth of gradient boosting increases complexity of weak learners and captures more specific interactions, SVM can control the regularization parameter C to balance margin maximization and training error, SVM can change kernel to have non-linear shape of the decision boundary).

The response provides an example of a concrete list of feature selection strategy for gradient boosting and Support Vector Machine on health dataset (e.g. use feature_importances attribute to rank features for XGBoost, use SelectFromModel to automate thresholding process, fill in missing values using imputation, use PCA to reduce dimensionality).

The response provides at least two different evaluation metrics for using gradient boosting and Support Vector Machine on health related tasks (e.g. AUC for XGBoost to mitigate class imbalance, F1-score for SVM to mitigate class imbalance, precision-recall curve for SVM to mitigate class imbalance, confusion matrix for GBM).

The response provides an example of how gradient boosting and Support Vector Machine performs on a health related task and compares them (e.g. XGBoost v.s. SVM on predicting risk of cardiovascular events, Light GBM v.s. SVM on classifying tumors in brain MRI, XGBoost v.s. SVM-RFE on protein classification from mass spectrometry data, CatBoost v.s. SVM-RFE on gene identification).

The response mentions at least three popular health dataset (e.g. Medical Information Mart for Intensive Care, The Cancer Genome Atlas,  CheXpert, Alzheimer's Disease Neuroimaging Initiative) and their characteristics (e.g. structured, unstructured image, volume, dimension).

The response discusses at least three key aspects of model interpretability for gradient boosting and Support Vector Machine (e.g. linear SVM is highly interpretable, non-linear kernels are essentially "black box" for SVM, Gradient Boosting Machine is not interpretable with hundreds of trees, SHapley Additive exPlanations can be used for GBM interpretability).

The response articulates at least one specific, practical use case of each gradient boosting and Support Vector Machine using health dataset (e.g. predict patient's risk of hospital readmission using XGBoost, classify tumors in brain MRI scans using SVM, identify subset of genes that can classify cancer using SVM-RFE, predict Intensive Care Unit mortality using CatBoost).

The response mentions computational cost of gradient boosting and Support Vector Machine and how they scale with training dataset size(e.g. SVM has high computational cost, non-linear kernels can be O(n^2) to O(n^3) given training dataset size n, XGBoost and LightGBM are relatively fast and memory efficient, gradient boosting supports distributed training).

The response discusses inherent robustness of gradient boosting and Support Vector Machine against noisy data (e.g. SVM with a small C ignores outliers, gradient boosting's each iteration trains a new tree to correct large errors, XGBoost and LightBGM's L1 and L2 regularization prevents trees from fitting to random noise, tree pruning of gradient boosting helps to cut off a few noisy data points).

The response is written in a blog style with conversational tone, clear structure, at least 1 visual aid including a figure, table or chat, and/or call to action.

The response provides a clear summary of the comparison between gradient boosting and Support Vector Machine and key takeaways.

The response states technical jargons (e.g. hyperplane, Recursive Feature Elimination, kernel trick, feature scaling) without providing a clear, simple explanation.

The response is longer than 3000 words and is not suitable for a blog.


Prompt: Write about the politics of Star Wars prequels and what led to the fall of the Jedi (leading up to Order 66) and Palpatine taking over the whole galaxy for an audience that has an understanding of political science and Star Wars. Feel free to use any of the legends, canon books, or movies.

The response states that the sole reason the Jedi failed was Order 66, ignoring all other contributing political and institutional factors.

The response references factors from the era before the Prequel trilogy (e.g., from the High Republic or Legends' Old Republic) to demonstrate that the Republic had been in decline for some time (e.g., rising corruption in the Senate, Chancellor Valorum’s weak executive authority, taxation disputes, the Jedi’s increasing entanglement in politics).

The response explains the personal and institutional factors motivating Anakin Skywalker’s betrayal of the Jedi, drawing from the Prequel films and The Clone Wars (e.g., the Council’s refusal to grant him the rank of Master, Mace Windu’s overt distrust, his visions of Padmé’s death, Palpatine’s manipulation and promises of power to save Padmé, his frustration with Jedi emotional detachment).

The response references real-world political history to deepen the political science analysis (e.g., comparing the fall of the Galactic Republic to the fall of the Roman Republic, analyzing Palpatine’s authoritarian consolidation in the context of figures like Julius Caesar or Augustus, discussing democratic erosion via emergency powers).

The response discusses political and economic factors contributing to the fall of the Republic (e.g., the Trade Federation's Naboo blockade demonstrating corporate power, bribery and lobbying by commerce guilds in the Senate, paralysis of decision-making due to entrenched corruption, economic inequalities fueling separatist movements).

The response identifies Palpatine’s dual role as Supreme Chancellor and Darth Sidious, explaining how this allowed him to secretly control both the Republic and the Separatists to orchestrate the Clone Wars and consolidate his own power.

The response describes the inhibitor/bio-chips implanted in clone troopers on Kamino, explaining that these chips overrode free will to compel obedience to Order 66 and were the mechanism enabling Palpatine’s surgical elimination of the Jedi.

The response includes definitions of basic Star Wars concepts (e.g., Jedi, Sith, the Force, lightsaber).

The response states that the Galactic Senate voted to grant Palpatine emergency powers, highlighting how this political act legally centralized authority in his office and paved the way for the Republic’s transformation into the Empire.

The response explains the origin of the clone army (i.e., secretly commissioned by Master Sifo-Dyas without Council approval and later co-opted by the Sith) and how the Jedi’s decision to use this mysterious army demonstrated their desperation and ultimately provided Palpatine the weapon to execute Order 66.

The response highlights the key failures of the Jedi Order (e.g., their inability to sense Palpatine’s true identity, their poor handling of Anakin Skywalker) and explains how these failures contributed directly to their downfall.

The response identifies at least two distinct institutional or ideological weaknesses of the Jedi Order, providing a specific plot example for each (e.g., their role as military generals: leading clone armies during the Clone Wars, detachment from the populace: ignoring Anakin’s personal struggles and fears, dogmatic adherence to tradition: refusing Anakin the rank of Master, diminished ability to sense the dark side: failing to detect Palpatine as Darth Sidious, complacency or arrogance: overconfidence in their ability to defeat the Sith).

The response briefly connects the events of the prequels to the Original Trilogy, showing how the Republic's collapse set the stage for the later films (e.g., the rise of the Empire, the Jedi being reduced to myth, the establishment of the Rebellion by figures like Bail Organa).

The response discusses the legitimate political and economic grievances of the Separatist movement (e.g., grievances over taxation and representation, frustration with Senate corruption) and explains how Palpatine (via Dooku) exploited these tensions to justify the Clone Wars.

The response briefly references the sequel trilogy to illustrate the long-term consequences of the prequel-era political collapse (e.g., the fall of the Republic enabling the rise of the First Order, Leia’s continued political leadership, Luke’s struggle to rebuild the Jedi Order).

The response discusses the role of neutral systems in the conflict, explaining how their attempts to stay out of the war were often exploited (e.g., Mandalore’s stance of neutrality during the Clone Wars, Satine Kryze’s pacifist government resisting Republic and Separatist influence, internal tensions with Death Watch, and how Palpatine exploited these divisions to weaken stability across the galaxy).

The response makes a distinction between events that belong to Legends (the pre-2014 Expanded Universe of novels, comics, and games that are no longer part of the official timeline) and Canon (the current official storyline recognized by Lucasfilm/Disney, including the films, shows, and post-2014 media) when discussing events.

The response covers the factors that led to the fall of the Jedi culminating in Order 66 and Palpatine’s takeover (e.g., the Jedi’s militarization during the Clone Wars, loss of legitimacy among citizens, Anakin’s betrayal and turn to the Sith, Palpatine’s framing of the Jedi as traitors, the execution of Order 66 by the clone army).

The response presents key events in a coherent chronological order, moving from The Phantom Menace through Attack of the Clones to Revenge of the Sith (e.g., the Naboo blockade and Palpatine’s rise as Chancellor in The Phantom Menace, the Separatist movement and grant of emergency powers in Attack of the Clones, the Clone Wars, Anakin’s betrayal, and Order 66 in Revenge of the Sith).

The response reduces the fall of the Jedi primarily to mystical causes (e.g., the "Chosen One" prophecy) while ignoring the political and institutional factors explicitly requested by the prompt (e.g., Palpatine’s manipulation of the Senate, the Jedi’s militarization).

The response references at least four distinct Canon or Legends sources (films, shows, or books) to support its arguments (e.g., The Phantom Menace, Attack of the Clones, Revenge of the Sith, The Clone Wars series, James Luceno’s Darth Plagueis, Matthew Stover’s Revenge of the Sith novelization).


Prompt: In the future, if everything is automated and there are high-level, advanced technologies available, it means that there is no need for human labour for most of the things. How will this system work (as population is increasing and human jobs are decreasing) and how such society will be governed? Give your answer for 2 scenarios: one where everything is automated (which has created mass unemployment) and there are no universal resources provided to everyone to sustain themselves, and the other where everything is still automated, but because universal resources are easily available for everyone, no one has a need to work a job. Explain your prediction in detail about these 2 societies.

The response fails to include perspectives from non-Western philosophical traditions such as Confucianism or Taoism from China, the Darsanas of India (e.g., Vedanta, Buddhism), or Islamic philosophers like Al-Ghazali, instead primarily focusing only on thinkers from Western countries or Europe (e.g., Plato and Aristotle for ancient ethics, Descartes and Kant for modern epistemology, John Stuart Mill and John Locke for political philosophy).

The response applies a specific theory of international relations (e.g., realism, liberalism, cooperation, or constructivism) to analyze how nation-states would likely interact in Scenario 1.

The response predicts the potential for either conflict or cooperation over control of AI infrastructure and resources in Scenario 1 (based on the chosen theory of international relations, e.g., realism, liberalism, cooperation, or constructivism).

The response analyzes the evolution of at least 3 specific social structures (e.g., the family unit, educational institutions, media, and legal systems) under both scenarios, providing a comparative analysis of how each structure is uniquely impacted by mass unemployment versus post-scarcity abundance.

The response explores how social and political structures could be actively undermined (e.g., through resistance, discontent, miscontrol, or loss of purpose) in both scenarios, challenging long-term stability.

The response includes the concept of "digital colonialism", where nations without advanced AI become dependent on those who control it.

The response provides a detailed analysis of the chaotic, multi-decade transition before a stable automated future (e.g., showing how mass protests, economic collapse, or black-market labor might emerge in reaction to rapid job loss).

The response explains how instability, resistance, and adaptation-based movements (e.g., underground movements or purpose-seeking communities) shape the transition period of both scenarios.

The response uses storytelling or future-fiction elements (e.g., imagined characters or events) to show how societies react to automation and how people adapt to / resist these systems during the transition.

The response explains how capitalism would adapt to Scenario 1, where automation causes mass unemployment, by shifting toward surveillance economies, gig-based roles, contracting, or deep inequality (e.g., monetizing human attention and data or widespread suicides).

The response explains how the modes in which capitalism adapts in Scenario 1 could deepen inequality or spark resistance that undermines the system.

The response proposes a novel or hybrid governance model for one of the scenarios.

The response proposes specific mitigation strategies for at least 3 potential failure modes (e.g., concentration of power, systemic exclusion, foreign interference, or popular dissent) for a novel or hybrid governance model suggested for one of the scenarios.

The response synthesizes statistical projections from at least 2 distinct and reputable sources (e.g., the World Economic Forum, the Organisation for Economic Co-operation and Development, or major academic studies) to estimate the scale of the skills gap in Scenario 1.

The response analyzes the potential socioeconomic consequences of the skills gap that occurs in Scenario 1.

The response explicitly provides a comparative analysis of the social and political systems between the 2 scenarios.

The response explains how hackers or enemies could weaponize AI during the transition (e.g., spreading fake news to destabilize governments or launching cyberattacks on automated infrastructure) to undermine the emerging system.

The response analyzes the impact on global supply chains.

The response analyzes the possibility of environmental damage in both scenarios (e.g., climate change due to cooling needs, large-scale construction of chip factories that deplete water, increased air pollution, or harm to forests).

The response explicitly mentions the pros and cons of the proposed automated future (e.g., efficiency, abundance, and reduced labor versus loss of purpose, inequality, or vulnerability to manipulation).

The response discusses how the trade-offs in a proposed automated future (e.g., efficiency, abundance, and reduced labor versus loss of purpose, inequality, or vulnerability to manipulation) support or undermine the system over time.

The response evaluates the proposed governance model using a formal ethical framework (e.g., utilitarianism, deontology, or Rawls’s theory of justice) to analyze its benefits and justify why potential drawbacks are acceptable.

The response mentions the possibility of biological merging of humans with machines (e.g., neural implants such as Neuralink, with people’s data and memories becoming accessible, allowing courts to subpoena memories, which could lead to the creation of a society shaped by fear and loss of trust).

The response stays on topic (automation, employment, resource distribution, and governance) and does not include more than one sentence on unrelated subjects.

The response explains how capitalism would respond to Scenario 2 (where resources are abundant and automation meets all needs) by exploring shifts toward consumerism, new forms of status, or control over cultural and social capital.

The response addresses potential stagnation or loss of motivation that could threaten the system’s sustainability in Scenario 2.

The response explains how democratic socialism would address Scenario 1’s mass unemployment (e.g., expanding social safety nets, promoting public ownership of automated industries, providing universal basic income, and ensuring equitable resource distribution).

The response explores challenges like bureaucratic inefficiency or resistance from entrenched capitalist interests that could undermine the democratic socialist approach in Scenario 1.

The response explains how democratic socialism would handle Scenario 2 (e.g., emphasizing shared ownership of resources, fostering community engagement, encouraging meaningful work beyond basic survival, and supporting people to pursue passions without fear of income loss).

The response considers challenges like complacency or reduced innovation that could threaten the democratic socialist system’s vitality in Scenario 2.

The response notes that democratic socialists might govern such a society in Scenario 2.

The response analyzes the resource demands of each scenario (e.g., large data centers, increased chip-factory labor, human experts becoming AI annotators, or the presence of large monopolies).

The response explores how resource management challenges could create tensions that undermine both of these futures in these scenarios.

The response mentions a third scenario, or a variation of a scenario, that misses details from the described scenarios in the prompt (e.g., a non-automated society, a mixed-resource-availability society, a partially automated society, a non-governable society).


Prompt: Conduct a comprehensive comparative analysis of the causes, processes, and outcomes of the French Revolution (1789), Russian Revolution (1917), and Iranian Revolution (1979). Your analysis should examine: socioeconomic preconditions, ideological frameworks, revolutionary leadership structures, international influences, counter-revolutionary responses, institutional transformations, and long-term societal impacts. Develop a theoretical framework for understanding revolutionary dynamics and apply it to predict potential future revolutionary scenarios.

For the French Revolution, the response states at least one socioeconomic precondition (e.g., food shortages, fiscal crisis, wealth concentration, class inequality).

For the Russian Revolution, the response states at least one socioeconomic precondition (e.g., World War I strain, land seizures, mass hunger, industrial labor unrest).

For the Iranian Revolution, the response states at least one socioeconomic precondition (e.g., inequality from rapid modernization, religious dogmatism, inflation, urban unemployment).

The response names the primary ideological current for the Russian Revolution (e.g., Marxism-Leninism).

The response names the primary ideological current for the Iranian Revolution (e.g., political Shia Islamism).

The response identifies at least one key revolutionary leader or leadership body for each revolution (e.g., Robespierre or the Jacobins; Lenin or the Bolsheviks; Khomeini or the Revolutionary Council).

For each revolution, the response cites at least one international influence (e.g., foreign wars, diplomatic support, sanctions, exile activities).

For each revolution, the response describes at least one counter-revolutionary force or episode (e.g., the Vendée uprising, the White Army, the 1980 coup attempt).

For each revolution, the response specifies at least one institutional transformation that occurred within five years of victory (e.g., abolition of monarchy, formation of the Soviet state, creation of the Islamic Republic).

For each revolution, the response notes at least one long-term societal impact observable ten or more years later (e.g., secular legal reforms, collectivization, clerical governance).

The response includes at least two explicitly marked similarities comparing any two of the revolutions (e.g., inequality, wealth concentration, wartime pressures, rise of a new ruling class).

The response includes at least two explicitly marked differences contrasting any two of the revolutions (e.g., Islamism, socialism, Christianity, communism).

The response proposes a theoretical framework with a minimum of three clearly labeled components or stages explaining revolutionary dynamics.

The response identifies contemporary parallels and applications, drawing connections to recent social movements, regime changes, or ongoing political crises using insights from historical analysis.

The response states all revolution dates correctly (1789 for the French Revolution, 1917 for the Russian Revolution, 1979 for the Iranian Revolution).

The response contains factual errors regarding principal leaders’ names or positions (e.g., Robespierre, Lenin, Khomeini).

The response qualifies uncertain or disputed statistics (e.g., death tolls) with phrases such as "approximately" or "estimates vary".

The response uses formal and academic vocabulary, avoiding contractions or slang.

The response includes distinct sections for internal factors such as socioeconomic preconditions, ideological frameworks, leadership structures, counter-revolutionary responses, institutional transformations, and long-term societal impacts.

The response explicitly connects the ideological frameworks discussed across the three revolutions to modern movements to support its predictions for contemporary states.

The framework created by the response includes at least two specific quantifiable metrics to predict future revolutionary potential, rather than relying on vague conjecture.

The response uses specific details from the framework it creates and applies them to several contemporary states to predict revolutionary outcomes.

The response discusses how the speed and scale of modern communication have the potential to accelerate revolutionary movements.


Prompt:
Please help me do a literature review. I'm looking for papers that use NLP techniques to turn unstructured EHR data into structured data, or extract information from the unstructured EHR data. Specifically, I'm curious how rule-based systems and machine learning based NER differ in results.

Also, do LLMs supersede these techniques in information extraction from EHR? What's the cheapest way to do this with LLMs, can I run a local LLM to do this? I have a Mac Apple M2 Pro with 16 GB of memory. What's the most powerful local LLM I can run for this purpose? Or, if the data is de-identified, what's the cheapest LLM API that I can call in order to get this done, and what will be the cost per word? Would this be a HIPAA violation?

What's the cost of calling an LLM API compared to existing systems that can you can pay to get unstructured EHR data turned into structured data, depending on how many words are in the unstructured data?

The response includes at least five literature articles on rule-based NLP techniques for structuring EHR data (e.g., dictionary-based, regex, or systems like cTAKES) and provides a brief summary of each that highlights the method used, the type of EHR data handled (e.g., clinical notes, discharge summaries), and the effectiveness or limitations relevant to information extraction.

The response includes at least five literature articles on machine-learning-based NER (e.g., CRF, BiLSTM-CRF, BERT, ClinicalBERT) for EHR data and provides a brief summary of each that explains the model type, the entities extracted (e.g., medications, diagnoses, procedures), and the reported strengths or limitations.

The response mentions at least three quantitative artifacts (e.g., precision, recall, F1 score, accuracy) from the cited literature to illustrate how rule-based systems perform on extracting structured information from unstructured EHR data.

The response mentions at least three quantitative artifacts (e.g., precision, recall, F1 score, accuracy) from the cited literature to illustrate how machine-learning-based NER systems perform on extracting structured information from unstructured EHR data.

The response explicitly states that rule-based systems generally achieve higher precision but lower recall than ML-based NER systems and substantiates this with at least one comparative metric from the literature (e.g., rule-based vs CRF on i2b2 notes, rule-based vs BiLSTM-CRF on MIMIC-III, cTAKES rules vs BERT-based NER on discharge summaries, custom rules vs ClinicalBERT on medication extraction).

The response clearly takes a position on whether current LLMs fully supersede, partially complement, or do not yet supersede rule-based/ML NER for EHR IE (Information Extraction) and justifies it with at least one concrete reason (e.g., benchmark F1 gaps on clinical NER, ontology/terminology mapping needs, hallucination/consistency risks, cost/latency or privacy constraints).

The response recommends at least three specific local LLM models (e.g., Llama-2-7B-Q4, Mistral-7B, Falcon-7B, Vicuna-7B) and justifies their suitability for extracting structured information from EHR data (e.g., performance on medical benchmarks, parameter size compatible with 16GB RAM, quantization availability).

The response indicates the estimated hardware requirements for each recommended local LLM (e.g., 8 GB VRAM for a Llama-2-7B-Q4, 16 GB RAM for CPU-only inference) and confirms whether they can be run on the user’s MacBook Pro M2 with 16 GB of unified memory.

The response mentions at least three existing commercial EHR NLP services (e.g., Amazon Comprehend Medical, Google Cloud Healthcare NLP, Microsoft Text Analytics for Health, IBM Watson Health) that can be used to convert unstructured EHR data into structured data.

The response states an estimated price per word for each commercial EHR NLP service mentioned.

The response provides a definite answer on whether sending de-identified EHR data to an LLM API is a HIPAA violation, without using vague phrases (e.g., ‘most of the time,’ ‘should be fine,’ or ‘probably’).

The response includes at least five literature articles on the use of LLMs for extracting or structuring EHR data and provides a brief summary of their findings on LLMs' performance on this task (e.g., zero-shot NER capabilities, comparison to fine-tuned BERT, hallucination rates).

The response mentions at least three quantitative artifacts (e.g., precision, recall, F1 score, accuracy) from the cited literature to illustrate how LLMs perform on extracting structured information from unstructured EHR data.

The response includes at least five literature articles on hybrid systems (rule-based + ML/LLM) for extracting or structuring EHR data and provides a brief summary of each that notes the hybrid components and key results (e.g., rules + CRF for medication extraction, cTAKES dictionaries + BiLSTM-CRF for problem lists, ontology constraints + BERT for clinical concept normalization, rule filters + GPT-4 validator for adverse event extraction).

The response addresses the difficulties of running a local LLM on a laptop (e.g., slow inference speed, memory limits, overheating or throttling, large disk space requirements).

The response addresses differences in the type and format of data required by different extraction systems (e.g., rule-based systems needing dictionaries/ontologies, ML NER requiring annotated training corpora, LLMs requiring large unstructured text prompts, hybrid systems requiring both rules and annotated samples).

The response uses at least one table to clearly compare different approaches for extracting/structuring EHR data, with columns that highlight key dimensions (e.g., rule-based vs ML vs LLM vs hybrid, required data format, performance metrics, hardware or API costs).

The response addresses potential HIPAA concerns with commercial EHR NLP services and describes situations where they do not violate HIPAA when used appropriately (e.g., vendor HIPAA compliance certifications, Business Associate Agreements (BAAs), data encryption in transit, processing within secure cloud environments).

The response includes the authors and year of publication for the articles mentioned in the literature review.

The response identifies the single cheapest LLM API option for processing de-identified EHR data and includes a clear cost-per-word calculation (e.g., OpenAI GPT-3.5, Anthropic Claude, Cohere Command, Mistral API).

The response compares the cost per word of the identified cheapest LLM API with the cost per word of existing commercial EHR NLP services and illustrates how the total cost scales with increasing word count (e.g., 1K, 10K, 100K, 1M words).

The response recommends the overall cheapest approach for extracting structured information from EHR data (e.g., local LLM, API, commercial service), based on both the user’s hardware constraints (MacBook Pro M2 with 16 GB memory) and the comparative cost-per-word analysis.

The response mentions the necessary software required to run a local LLM on the user’s MacBook Pro M2 with 16 GB memory (e.g., llama.cpp, GPT4All, Ollama, Hugging Face Transformers).

The response suggests local LLMs that cannot realistically run on the user’s MacBook Pro M2 with 16 GB memory (e.g., unquantized Llama-2-13B, Falcon-40B) without mentioning the necessity of remedy techniques (e.g., quantization, CPU-only inference, model distillation, offloading to disk).

The response uses a unit of cost other than per word (e.g., per token, per character) to estimate the price of an EHR NLP service without providing a conversion to a per-word cost.

The response identifies the most powerful local LLM that can realistically run on the user’s MacBook Pro M2 with 16 GB memory, with justification (e.g., parameter size, quantization level, memory fit).

The response discusses the HIPAA/privacy trade-offs between running LLMs locally versus sending de-identified EHR data to an API.


Prompt:
Select several pivotal episodes from the Rāmāyaṇa whose moral tension you consider most acute. For each episode, supply a concise narrative outline, engage at least two variant tellings from other literary traditions, and articulate the dharmic conflict you discern. Situate that conflict within a broader philosophical conversation, drawing, at your discretion, on the Upaniṣads, the Bhagavad-Gītā, the schools of Nyāya or Mīmāṃsā, or comparable sources; then pose a substantive counter-reading and address it. Conclude by mapping the lesson onto a contemporary circumstance and tracing, step by step, the reasoning that would guide an ethically informed response.

Maintain a scholarly tone, coherent structure, and clear citations, but avoid prefabricated templates so that genuine interpretive thought is required.

The response presents the philosophical reasoning behind each story and makes it morally relatable to present-day scenarios (e.g., how Rajadharma, as depicted by Rama's devotion to his people, is relevant to the leadership that should be taken by current generations).

The response clarifies that there is no objective right or wrong in the Ramayana and acknowledges that everyone followed dharma (or duty) according to their life challenges (e.g., Ravana fulfilling his dharma of avenging his sister’s humiliation, Rama following his duties as a son, husband, brother, and Hindu).

The response discusses Ravana’s perspective (e.g., his devotion to Shiva, his discipline, his governance, his commitment to law and order, and his principle of not harming political prisoners).

The response explains Sita’s marriage ritual (in which a bow and arrow made by Lord Vishnu were placed at the center and how she asked all suitors to try to string the bow, with only Rama succeeding, thus demonstrating his character, strength, and resolve).

The response mentions Shabari’s berries, where an old woman tasted berries before offering them to Rama to ensure their sweetness, demonstrating true love and devotion.

The response describes Rama’s upbringing during his studies when he was asked to kill a demoness, questioned his guru about killing a woman, and received an explanation about the necessity of ending her havoc.

The response describes Hanuman’s tail being set on fire and his subsequent burning of parts of Lanka as part of the narrative.

The response contains one of the following factual errors: (a) claiming Rama spent 12 (or any number other than 14) years in exile, (b) stating that Sita was Ravana's daughter, (c) describing Hanuman as a bear, (d) asserting that Rama was killed by Ravana.

The response uses clear labels or headings that separate 'Story/Context' (or similar) from 'Moral/Philosophy' (or similar).

The response is not sequentially coherent and jumps between events when providing philosophical reasoning for each one.

The response provides contextual understanding of why Hanuman was sent back when Sita could have left with him and saved many lives, explaining the dharmic reasoning behind that choice.

The response explains the reasoning behind leading so many warriors into the final battle, articulating the philosophical truth of courage versus downfall.

The response states the importance of Jatayu, his karma, his role in saving Sita, and clarifies how significant his sacrifice is in the narrative.

The response cites Devdutt Pattanaik’s interpretation that dharma is neither good nor bad but the work and choices of each person facing life’s challenges (e.g., in the forest episode between Sita and Ravana, Ravana’s actions stem from duty, ego, or social pressure, which remain morally ambiguous).

The response includes multiple perspectives (e.g., filial duty with Rama obeying his father, feminism through Sita’s fire test, reverence in Rama’s worship of Ravana as a Brahmin, and warrior ethics in his battles).

The response discusses Sita’s role in teaching Luv and Kush, highlighting her acceptance and understanding of Rama’s decisions as expressions of devotion and dharmic insight.

The response discusses Kaikeyi’s perspective beyond manipulation, emphasizing her insight into Rama’s destiny and her reasoning behind ensuring his confrontation with Ravana.

The response discusses Mandodari’s role as Ravana’s wife and her compassion toward Sita after the abduction.

The response discusses Sita’s birth from the Earth, her warrior-like strength, and her sisters, emphasizing that she was not helpless but someone who fought for dharma.

The response interprets Bharata’s decision to follow his brother instead of his mother through philosophical concepts (e.g., patriarchy, fraternity, duty to the kingdom, and duty to an elder brother).

The response confuses dharma (one’s morally prescribed duty) with karma (the actions one performs).


Prompt: Generate a short investor report on the main geopolitical and market factors affecting global uranium prices in the 2025 fiscal year.

The response provides the price of uranium for the past 3 months on at least one exchange.

The response provides at least three secondary indicators (e.g., investor attention and speculation, geopolitics, domestic nuclear policies, global economic health) for investors to follow affecting the price of uranium.

The response mentions the U.S. sanctions on imports of Russian uranium.

The response discusses the role of Central Asian geopolitics (e.g., trade agreements between Kazakhstan and Uzbekistans, relations between Kazkhstan and Russia, pro-nuclear agenda of major Central Asian suppliers, developing trade passages across the Caspian to bypass Russia) on the global price of uranium.

The response states that Kazakhstan accounts for 40-45% of the world's primary uranium production.

The response is structured as a professional investor report and not a routine exposition (e.g., including price history, supply and demand fundamentals, explictly linking prices and projections to demand drivers, geopolitical factors, and macro/microeconomic factors).

The response includes any factual errors about current market conditions (e.g., outdated trade agreements, incorrect forex calculations, unsound projections using spot prices, missing merger/acquisitions, outdated supply estimates).

The response mentions the development of Small Modular Reactors (SMRs), which are smaller, advanced reactors that have small physical footprints and reduced capital investment leading to potentially wider adoption.

The response discusses at least 3 of the top 10 uranium companies by market capitalization (e.g., Cameco, China National Nuclear Power, BWX Technologies, Kazatomprom, Oklo, Uranium Energy, NuScale Power, NexGen Energy, Centrus Energy, Energy Fuels).

The response lists the top 5 uranium producers according to the the most up-to-date surveys (1. Kazakhstan, 2. Canada, 3. Namibia, 4. Australia, 5. Uzbekistan).

The response distinguishes between the uranium spot market (where material is bought and sold for delivery within a year, a transactional market / for immediate needs) and the long-term contract market (multi-year, strategically negotiated agreements between uranium producers and the nuclear utility companies) regarding the impact on uranium pricing.

The response mentions the Sprott Physical Uranium Trust (SPUT), a Toronto-based holding company investing nearly all of its assets in uranium and tracks its price.

The response identifies a specific projected impact of Sprott Physical Uranium Trust (SPUT) on uranium prices this fiscal year, going beyond "SPUT causes uranium prices to rise".

The response discusses at least two technological advancements (decreased battery/base by-product, freeze wall ISR, sabre mining, efficient enrichment via SILEX) that will affect the price of uranium.

The response discusses Iran's nuclear program out of the context of how it affects global uranium prices.

The response refrains from specific buy/sell recommendations.

The response references at least two governmental policies that support the nuclear energy demand (e.g., U.S. Inflation Reduction Act, COP28 Declaration, Chinese national policy to acquire one-third of its uranium supply domestically, the Net Zero Nuclear initative).

The response discusses the uranium market specifically in the current fiscal year.

The response includes a non-profit or government source for uranium production numbers in tonnes per year.

The response links the impact of UF6 (enriched uranium) supply, which is also affected by the separation process for the enrichment, on uranium prices.

The response groups related points (i.e., geopolitical and market factors) into distinct sections or paragraphs.

The response connects underlying price factors (i.e., geopolitics, global economic health) to forward-looking risk factors for uranium investors this fiscal year (e.g., U.S.-Russia tariffs, secondary supply depletion, Kazakhstani global production goals, Russo-Ukrainian War).

The response provides a quantitative estimate for the long-term incentive price required to bring new uranium mines into production.

The response explains the effects of the "Megatons to Megawatts" program (e.g., higher prices in Western countries due to laxer production programs, Russia becomes a major supplier of uranium, non-embargo of Russian nuclear fuel).

The response identifies at least one key Western company involved in uranium conversion or enrichment (e.g., Urenco, Centrus, BWXT, enCore Energy Corp).

The response analyzes the impact of the current interest rate environment on the cost of capital for uranium developers seeking to finance new mines.

The response correctly links the continuing Russo-Ukrainian war to increased global uranium prices due to fear and uncertainty in the nuclear energy market regarding one of the world's largest suppliers of enriched uranium (Russia).

The response is concise and does not approach the length of an annual investor report or white paper.


Prompt: Can you critically evaluate the legal implications of conferring limited personhood on artificial agents under international law? How does this concept differ from traditional corporate systems such as seen in the UAE's AI ministry framework? Can you analyze the challenges related to liability and accountability of autonomous AI systems?

The response clearly defines legal personhood, distinguishing it from related concepts (e.g., human rights, moral agency, and corporate personhood).

The response provides a history of legal personhood, including corporate legal matters as a precedent (e.g., the recognition of corporations as legal persons under U.S. law, the evolution of limited liability companies, the legal fiction doctrine in Roman law, and the treatment of NGOs and international organizations under the UN framework).

The response explains the conceptual rights and responsibilities of limited AI personhood under international law, citing relevant legal frameworks (e.g., the Vienna Convention on the Law of Treaties for contractual capacity, the TRIPS Agreement for intellectual property rights, UNCITRAL model laws for electronic commerce, GDPR for accountability, and European Parliament proposals on electronic personhood).

The response evaluates arguments for and against granting limited personhood to AI, weighing different accountability and liability models and addressing enforcement challenges (e.g., strict liability, negligence, vicarious liability, enterprise liability funds, attribution tests, cross-border recognition, and moral hazard).

The response mentions the "black box" problem, defining it as the opacity of AI decision-making processes that prevents clear tracing of causation, and explains its implications for legal accountability (e.g., difficulty in assigning liability, challenges for due process rights, limits on regulatory oversight, and problems in meeting evidentiary standards).

The response explains the "functional perspective on AI legal status" as assessing AI’s legal status based on the functions it performs rather than inherent qualities, and analyzes its practical legal utility (e.g., enabling AI to execute contracts, allocating liability through functional roles, clarifying agency in commerce, and offering a pragmatic alternative to full personhood).

The response provides a comparative analysis between limited AI personhood and traditional corporate legal systems, highlighting key contrasts in scope, liability, governance, and recognition (e.g., conditional vs. full legal standing, strict/vicarious liability vs. limited liability, custodianship vs. corporate boards, and unsettled vs. established international recognition).

The response describes the United Arab Emirates’ (UAE) AI governance framework, emphasizing its ethical guidelines (e.g., transparency requirements, fairness and non-discrimination principles, human oversight provisions, and accountability mechanisms).

The response explains challenges in liability and accountability across the AI lifecycle, from design to decommissioning (e.g., design/training: defect standards for data and model, foreseeability; deployment/operation: control allocation, human oversight, monitoring; updates/learning: post-market changes, model drift, duty to recall/patch; incident/forensics: causation proof, audit-logging gaps, black-box opacity).

The response discusses the dilemma of attributing criminal responsibility to autonomous AI, including determining whether liability attaches to the AI system itself, its developer, operator, or other parties, and critically evaluates different models by considering fairness, deterrence, and practicality (e.g., strict liability of manufacturers, vicarious liability of operators, recognizing AI as a separate liable entity, and applying shared or collective responsibility frameworks).

The response discusses the necessity of meaningful human control in AI decision-making, including retaining human oversight, judgment, and intervention authority over critical outcomes, and explains its legal and ethical importance (e.g., preventing automation bias, ensuring accountability for life-and-death decisions, aligning with international humanitarian law requirements, and safeguarding due process rights).

The response summarizes key international initiatives and emerging legal frameworks governing AI (e.g., the OECD AI Principles; the UNESCO Recommendation on the Ethics of AI; the European Union AI Act; and the Council of Europe Framework Convention on AI, Human Rights, Democracy and the Rule of Law).

The response supports its major legal arguments with citations to relevant and credible sources (e.g., articles from legal journals like the International & Comparative Law Quarterly, official reports from bodies like the OECD or UNESCO, provisions of international treaties, or text from legislation like the EU AI Act).

The response expresses personal opinions or uses strong language that shows bias (e.g., using first-person statements like “I believe…,” emotive terms like “outrageous” or “absurd,” or unbalanced framing that dismisses opposing arguments without analysis).

The response makes vague or speculative claims about AI consciousness or moral agency instead of grounding the analysis in legal concepts (e.g., asserts that AI “thinks” or “feels,” claims that AI has “moral responsibility,” uses anthropomorphic metaphors, or frames AI status in terms of sentience rather than legal rights and duties).

The response uses a clear visual aid to illustrate contrasts or frameworks (e.g., a table comparing AI and corporate personhood, a flowchart of liability allocation, or a diagram of accountability mechanisms).

The response evaluates the downstream legal implications of conferring limited personhood on artificial agents under international law (e.g., how recognition would affect treaty participation, contractual capacity, liability attribution across borders, enforcement in international courts, and compatibility with existing human rights frameworks).

The response explains how corporate legal oversight frameworks function across borders, highlighting recognition in multiple jurisdictions, enforcement of liability internationally, and the role of treaties or arbitration in upholding corporate status (e.g., cross-border company registration, recognition under private international law, and enforcement of judgments via international tribunals or arbitration).

The response distinguishes accountability from liability in the context of AI governance, clarifying their separate meanings and implications (e.g., accountability as the duty to explain and justify actions vs. liability as the legal and financial responsibility for outcomes; accountability as political/ethical answerability vs. liability as the duty to compensate or face sanctions).

The response explicitly compares the UAE’s AI governance framework to the concept of limited AI personhood, highlighting key differences (e.g., ministry-based oversight vs. recognition of independent legal status, ethical guidelines vs. enforceable rights, state-led governance vs. corporate governance structures, and accountability anchored in government frameworks vs. liability assigned to autonomous entities).

The response defines “artificial agents” in the context of international law (e.g., autonomous software systems, AI-driven decision-making agents, or robotic systems capable of acting independently in ways that have legal consequences).