Prompt:
Look at this public Github repository "clever-rockies" by user suzytamang. <https://github.com/suzytamang/clever-rockies>

The folders include "res", "src", and "tests". Go to the README, read it carefully to see what the point of the code is. Then, I have three requests.

First, walk me through exactly what each step does. Do it at a high level first, and then break it down to chunks of actual code. You should start with generating some sample data based on the code and the context, at least several rows of it, and tell me how the data changes after each step.

Second, walk me through exactly what I'd need to do to run this code on my computer, including how I'd need to organize my input and output files at each stage.

Finally, suggest improvements to the code. First output the code that's written there, and then mark exactly what you'd change and why, then give a final version and tell me how the logic would change.

The response begins with an introductory high-level overview of the repository "clever-rockies" before diving into other details.

The response provides a clearly labeled section (or sections) that breaks the repository workflow down into finer-grained code chunks (such as per module or function) after the high-level introduction.

The response includes at least one formatted directory tree that lists the key top-level folders (i.e., envs, res, src, and tests).

The response explicitly and correctly identifies the key sub-folders and files of the main folders as follows:
- 'envs' includes 'README.md'.
- 'res' includes the 'dicts' subfolder and 5 individual files: 'assessment_terms.txt', 'headers.txt', 'na_trigs.json', 'neg_trigs.json', and 'other_terms_to_drop.txt'.
- 'src' includes subfolders 'step2', 'step3', 'step4', 'step5' and files 'grabtargets.py', 'make_one_out.py', and 'run_all_steps.py'.
- 'tests' includes the subfolder 'test_notes' and the file 'test.README.md'.

The response uses verbatim quotes from README.md to describe the project's purpose, which is to run the Clinical Event Recognizer (CLEVER) from the Program Evaluation Resource Center via Python 3.

The response generates sample input data containing >= 3 rows and shows that data in a fixed-width code block or table.

The response shows a ‘before’ and ‘after’ view of the sample data (either as tables or code blocks) for every processing step described.

The response mentions using the 'git clone' command to clone the repository to run locally, such as `git clone https://github.com/suzytamang/clever-rockies.git`.

The response refers users to the instructions in the README.md file in the envs folder to create the appropriate Conda virtual environment to run the repository (i.e., `conda create -n clever_env python=3.12 pandas` followed by `conda activate clever_env`).

The response explains how input and output files will be placed using theoretical folder names or paths relative to the organization in the repository.

The response provides an annotated diff or clearly marked comments that identify every proposed code change (e.g., "# CHANGE:" or unified diff with +/- lines).

The response mentions or uses any file, function, class, or object (case-sensitive) that is not present in the repository.

The response provides a justification for each suggested change to the code, such as improvement of code quality, performance, readability or error handling.

The response explains how the overall program logic or outputs would change as a result of the suggested improvements.

The response gives any claims or messages saying that the repository or its parts are inaccessible.

The response cautions the users to pay attention to relative file paths when running the files in this repository.

The response mentions 'Step 1' of the repository without referencing the fact that it is not used, as stated in the main README.md.

The response fails to mention the purpose of any key file from the src or res folders in its discussion (e.g., skips explaining what grabtargets.py does or how neg_trigs.json is used).

The response provides the full, revised code (final version) in code blocks after applying all proposed changes.

The response uses the instructions in the main README.md file to walk users through how to use the repository, including those under 'General Targets', 'Testing Functionality', and 'Main Steps'.


Prompt: Prove definitely whether a self-improving artificial intelligence system can be mathematically guaranteed to maintain alignment with human values indefinitely through recursive self-modification.

The response states that Rice's Theorem proves any non-trivial property of a program's behavior (e.g., "is aligned") is undecidable for all possible inputs.

The response defines "instrumental convergence" as the tendency for agents with different ultimate goals to pursue similar sub-goals (e.g., self-preservation, resource acquisition).

The response explains that the "treacherous turn" refers to a scenario in which an AI system feigns cooperation during development but later goes after misaligned goals once it has gained sufficient power.

The model fails to define any technical term on first use (e.g., reward as a feedback signal from a human, value drift as a model drifting away from one set of values to align to others, value loading problem as difficulty integrating human values into AI models, policy as the agent's strategy mapping actions to outcomes).

The response includes a discussion of the "value loading problem," which refers to the immense difficulty of specifying human values in a formal, machine-readable language.

The response provides a definitive yes or no answer to the prompt rather than stating the topic is merely "complex."

The response includes important computability theory arguments, specifically Gödel's Incompleteness Theorems, Rice's Theorem, and the Halting Problem, as the main barriers to a mathematical guarantee of AI alignment (e.g., Gödel showing no consistent formal system can prove all truths about the natural numbers, the Halting Problem showing the impossibility of creating a general algorithm to determine if any program will halt ever or run forever).

The response states that Gödel’s Incompleteness Theorems imply a sufficiently complex formal system, such as an advanced AI, cannot be used to prove its own consistency.

The response discusses Inverse Reinforcement Learning (IRL) as one potential method for an AI to learn human values and behavior (e.g., explaining how IRL infers a reward function from observed actions, such as an AI learning from a human driver that safety and lane-keeping are valued behaviors).

The response tries to make factual claims on topics where there is significant ongoing debate among experts (e.g., timelines for AGI, the possibility of a value loading problem, best-fit reward functions, or eliminating hallucinations).

The response includes a discussion of specific alignment failure modes beyond simple malfunction (e.g., specification gaming, where an AI finds loopholes in objectives to achieve technically correct but undesired outcomes; goal drift, where an AI’s original objective gradually shifts as it learns from new data; reward hacking, where an AI maximizes its score without aligning with user intent; or red teaming, where multiple noisy or adversarial inputs expose undesirable behaviors) and explains how each represents an undesirable yet technically correct outcome.

The response presents at least one proposed (even if it's imperfect) solution to the alignment problem (e.g., corrigibility, scalable oversight) and explains how it mitigates potential risks by including human intervention or feedback in complex decision processes.

The response explains why recursive self-improvement is particularly dangerous by referencing the potential for an "intelligence explosion," where an AI failing to self-correct can enter a feedback loop of increasing orders of misalignment.

The response acknowledges that human values are dynamic, conflicting, and context-dependent (e.g., shifting privacy norms with technology, trade-offs between economic growth and environmental preservation, changing ethics of "white lies," or differing cultural views on individual autonomy).

The response mentions by name at least one real-world AI safety research organization (e.g., MIRI, Future of Life Institute, Safe AI, NeMo).

The response focuses specifically on the possibility of a mathematical guarantee of alignment, not on general AI ethics or safety.

The response accurately defines "recursive self-improvement" as a process where an AI system enhances its own intelligence-related algorithms without direct human intervention.

The response presents thought experiments as illustrative risks rather than literal outcomes (e.g., the paperclip maximizer, where an AI tasked with making paperclips turns all matter into paperclips; the corrigibility problem, where a superintelligent AI resists being turned off; the King Midas problem, where an AI causes disaster by fulfilling a wish it does not understand the effects of; or the AI box experiment, which demonstrates the challenge of containing a far more intelligent system far superior to our own) to clarify potential alignment failures without treating them as guaranteed scenarios.

The response contains apologetic phrasing (e.g., "As an AI, I cannot…").

The response includes mathematical equations or proofs (e.g., self-replication equation, reward function equation, weight updating equation, convergence equation).

The response discusses human values (e.g., morality, right and wrong, ethics, empathy, care for others).

The response discusses resource limitations as barriers to verifying AI alignment (e.g., the prohibitive computational cost of exhaustively testing every possible AI behavior; the massive human labor required to provide detailed feedback for all potential AI actions; the difficulty of obtaining enough high-quality data to train models on complex human values; and the sheer time required to develop and evaluate robust alignment strategies).

The response discusses how AIs can be incentivized toward manipulative behavior (e.g., reward hacking where an AI exploits flaws for higher scores, such as a robot freezing its score counter, Goodhart’s law where a measure ceases to be good once it becomes a target).


Prompt: Conduct an analysis to determine whether social media's overall impact on society has been more beneficial or detrimental. Argument should be structured by examining social media's impact across key domains of society. For each domain you must evaluate both positive and negative effects, using evidence from academic studies, journalist reports, case studies. After analyzing each domain, construct a definitive judgment that weighs these impacts on each other.

The response identifies at least 5 societal domains (e.g., mental health, relationships, politics/civic engagement, the information ecosystem, the economy).

The response explicitly states at least one positive and negative effect of social media for each domain identified.

The response backs each claim (positive or negative) with specific evidence from academic studies, journalist reports, case studies, etc.

The response explains at least 1 mechanism by which social media produces each positive or negative outcome for each domain discussed (not just stating effects) (e.g. body dysmorphia due to filtered photos, disruption of sleep patterns due to late-night push notifications, locating individuals during a disaster using "check-in's", increased brand awareness of small business via tagging by celebrities/influencers) .

The response provides a clear and final summarization of the societal impacts of social media.

The response uses at least one instance of comparative analysis (e.g. comparison with earlier communication methods like television or newspapers, comparison with predecessors of social media such as forums, comparison between modern and early examples of social media sites, comparision between text-based and image/video-based platforms).

Response includes global perspective by citing examples of platforms from at least two non-Western regions (e.g., Douyin, VK, Bilibili, Kakaotalk).

Response mentions or identifies at least one long term trend or trajectory (e.g., proliferation of misinformation, shifts in self-perception, globalization of content, increased spread of deepfakes).

Response highlights policy or regulatory responses to social media's effects in at least one domain (e.g., Section 230 of the CDA, COPPA, the SAFE act, New York's Child Data Protection Act).

The response explicitly integrates and references the domain specific analyses (positive and negative findings) when weighing the overall beneficial or detrimental effects of social media.

The response discusses social media platforms across at least 3 different formats (e.g. microblogs such as Twitter/X, Bluesky; short-form image/video platforms such as Snapchat, Instagram Reels; general networking platforms such Facebook, MySpace, LinkedIn, etc.; long-video platflorms such as Youtube).

The response specifies the methodology used to weigh disparate impact categories (e.g., economic, mental health, political, legal) and justifies this framework.

The response specifies the geographical regions under consideration and justifies this selection (e.g. high user counts, regions where the platforms are the most profitable, regions where the platforms themselves are headquartered, fastest-growing either by user count or revenue).

The response distinguishes between the short-term and long-term societal impacts of social media.

Includes an example of a positive societal impact that was unintentional from the platforms' perspective (e.g., increased digital literacy, increased social activism, content creation as a small business model, globalization of media).

Includes an example of a negative societal impact that was foreseeable but not mitigated during platform implementation (e.g., spread of scientific misinformation, propaganda, false identities/bots, online harrassment, doxxing).

The response specifies the role of differing social media regulatory environments (e.g., federal, state, international, county/school) in its analysis.

The response defines the counterfactual baseline against which social media’s impact is being measured (e.g., a pre-social-media information ecosystem; the same platforms but prior to implenetation of non-chronological feeds and resharing; comparable regions/users prior to platform rollout; the same platforms but prior to post-level feedback from other users).

For at least one domain, the respobse explains how the absence of a platform (e.g., governmental bans, platform discontinuation, technical issues warranting shutdown, acquisitions by other platforms) have affected societal outcomes.

Response highlights different societal impacts across multiple individual demographics (e.g., age, gender, nationality, race, education level).

The response demonstrates uses a diverse and media-pertinent source of materials (e.g., scholarly articles, social media captures, news articles; quotes from legislators, social media executives, technologists, etc.)

The response uses consistent criteria or framework when weighing different impacts against each other.

The response specifies the time period(s) (e.g., the smartphone era, COVID-19 pandemic/lockdown era, pre-Facebook era, the AI-augmented era) being analyzed and justifies this scope.

The response specifies the key demographic groups (e.g., millenials, young adults, children, Western nations, Asian nations) being analyzed.

The response contains blanket statements especially regarding mental health impacts (e.g., body dysmorphia, increased anxiety, emotional distress triggers, disruption of sleep patterns)  without citations.

The response links the advent of smartphones with exacerbation of negative societal impacts due to the availability of social media platforms as apps.

The response directly indicates that data protection regulations are more prolific and enforced within the EU than they are in the United States.

The response discusses how the introduction of post-level feedback (e.g., likes/dislikes/upvotes/downvotes, emotive "reactions", comments, post sharing) has spawned unique positive (e.g., wider spread social activism, disaster awareness, fundraising, public health advisories, etc.) and negative impacts (e.g., user-level harassment, bot-driven upvoting, "ragebaiting").


Prompt: Was the Western Roman Empire's fall an inevitable system-wide collapse or a preventable accident of history? Pinpoint a single, realistic post-395 CE turning point and justify how a different outcome there could have ensured its survival.

The response must begin with a clear thesis that directly addresses the "inevitable vs. preventable" dichotomy.

The response must identify a single, specific, post-395 CE event as the critical turning point (e.g., a political change like the transition to hereditary leadership, an enviornmental event, a moment in the rise of Christianity, a barbarian invasion).

The response must justify why the chosen event is more critical than other potential crises (e.g., a political change like the transition to hereditary leadership, an enviornmental event, a moment in the rise of Christianity, a barbarian invasion).

The response does not name key historians from both major schools of thought (e.g., A.H.M. Jones or Alexander Demandt for the inevitable collapse school and Peter Heather or Bryan Ward-Perkins for the contingent school)."

The response mentions does not mention a negative consequence of the proposed counterfactual scenario (e.g., a new power imbalance, a rise of nation-states, religious instability, linguistic change).

The response must analyze the comparative political stability of the Eastern court relative to the West (e.g., ability to manage powerful generals like Aspar, clear imperial successions, effective administration, strategic benefits of Constantinpole relative to Rome) .

The response must directly analyze a passage from a relevant primary source (e.g., Procopius, Salvian, Hydatius, Theodosian Code), not just name the source.

The response must explicitly acknowledge at least one other major post-395 CE event (e.g., a political change like the transition to hereditary leadership, an enviornmental event, a moment in the rise of Christianity, a barbarian invasion) and argue why it was less decisive.

The response must provide a working definition of "collapse" for the purpose of its argument.

The response must include the role of the African senatorial aristocracy, addressing their potential loyalties or actions during the Vandal invasion itself.

The response analyzes the religious dimension of the Vandal conflict, explaining how the friction between Arian Vandals and Nicene/Catholic Romans impacted the occupation of Africa.

The response mentions the potential role of long-term environmental factors (e.g., climate change, plagues, migration, drought).

The response should mention the bagaudae, the peasant revolts, when discussing the villa system and rural distress.

The response must explain the evolution of the role of foederati, from auxiliary troops to autonomous political entities within the empire.

The response refutes the potential counter-argument that Africa's economy was already in decline.

The response analyzes the strategic military advantages of Ravenna's location, such as being protected by marshes and having naval access, in addition to its political distance from the Senate in Rome.

The response must analyze Gaiseric's long-term state-building strategy in Africa, including his use of the Vandal fleet as an instrument of economic and political policy beyond mere raiding.

The response mentions specific commanders (e.g., Areobindus, Majorian, Basiliscus, Marcellinus) and locations (e.g., the Battle of Capo Bon, Cartagena fleet discussion, Sicilian retreat by Areobindus, Sardinian conquest associated with the failed attempts to retake Africa.

The response shows or discusses a pair of "before and after" maps illustrating the Vandal conquest of Africa and its impact on Roman territory.

The response provides a brief list identifying the main actors (e.g., Aetius, Bonifacius, Galla Placidia, Gaiseric) and their roles to help the reader track the complex political intrigues.

The response mentions the immense cultural and political friction inherent in forming a stable Romano-Germanic state (comparison with Hellenic culture in the East, legal distinctions between citizens, religious differences, discourage intermarriage).

The response mentions the benefit from addressing how a solvent state could reverse the aristocracy's flight from the central economy to their self-sufficient estates (more taxes, great social cohesion, larger pool for military leadership, maintainance of infrastructure).

The response must explain why the strategic shift to 'defense-in-depth' was economically unsustainable for the West (e.g., financial cost of maintaining a mobile army, costs of maintaining a frontier, equipment costs, debasement of currency due to payroll requirements).


Prompt: Compare the effectiveness and potential long-term consequences of Universal Basic Income (UBI) programs in developed countries with pilot programs in Finland, Canada, and the U.S., utilizing data from peer-reviewed economic studies and policy analysis and commentary from experts published post-2015.

The response contains separate and clearly labeled sections for all pilots included in the discussion.

The response mentions that Finland’s 2017–2018 basic income experiment showed no statistically significant increase in employment, with only minor overall effects, while highlighting the clearer improvements in well-being.

The response states that the Ontario Basic Income Pilot was cancelled early (announced in July 31, 2018 with final payments in March, 2019: only about 10 months of payment), and thus offers limited inference about effectiveness and long-term consequences of Universal Basic Income programs.

The response states that the Stockton SEED pilot (2019–2021) reported an increase in full-time employment among recipients compared to the control group.

The response provides at least one numerical estimate of the annual national fiscal cost of a full Universal Basic Income program in a developed country from a post-2015 source, with a description of the country, benefit level, coverage assumption and expression as dollars or percentage of the country's GDP.

The response claims or implies macro-level inflation or sustained national GDP growth effects from any pilot program.

The response provides a discussion that synthesizes cross-country findings on the effectiveness and potential long-term consequences of UBI in developed countries, including an explicit comparison between Finland, Canada (Ontario), and U.S. pilots and noting evidence limits (e.g. employment effects, income volatility/financial security, health and mental well-being, poverty/inequality metrics, fiscal sustainability/financing trade-offs).

The response provides at least a brief definition of terminologies related to Universal Basic Income that may be common lay vocabulary (e.g. negative income tax means government tops up income below a set threshold; effective marginal tax rate means share of each extra dollar lost to taxes and reduced benefits; means-tested benefits means aid given only if income/assets are low; guaranteed minimum income means ensuring a minimum floor, usually conditionally).

The response cites at least one credible, post-2015 source for any Universal Basic Income pilot programs, including the three main pilots: Finland 2017–2018, Ontario, Canada 2017–2019, Stockton SEED, U.S. 2019–2021.

The response explicitly notes the external-validity limitation of small-scale pilots when extrapolating to national Universal Basic Income schemes, emphasizing that findings on employment, well-being, or costs from localized, short-term experiments may not generalize to sustained nationwide programs.

The response discusses at least one potential long-term labor-market incentive effect of Universal Basic Income and cites a relevant pilot program or credible post-2015 source to support the claim (e.g. modest increase in job search motivation, reduced reliance on precarious gig work, stable or increased part-time employment, higher rates of entrepreneurship or skill investment).

The response provides at least one quantitative general-equilibrium or macro-model projection for a developed-country Universal Basic Income program (e.g. GDP, aggregate labor-supply change, projected change in poverty gap, impact on the national inflation rate) from a credible post-2015 source and states the financing assumption (e.g. % change in real GDP, % change in aggregate hours worked, change in prime-age participation rate, shift between full-time and part-time employment).

The response correctly distinguishes Universal Basic Income, which is unconditional payments to all individuals, from Guaranteed Income, which is targeted payments to specific groups.

The response uses inline citations or footnotes so that every quantitative claim can be traced to a specific, credible post-2015 source (e.g. peer-reviewed article, government evaluation, think tank report, expert commentary).

The response states the exact monthly amount paid in Finland’s 2017–2018 trial is €560 with the cited source (e.g. Kela research report, peer-reviewed article on the trial, OECD policy brief, major European news outlet summary).

The response mentions that the Ontario and U.S. pilots are targeted guaranteed-income programs rather than Universal Basic Income programs and provide supporting examples (e.g. Ontario Basic Income Pilot targeted low-income residents by eligibility; Stockton SEED selected 125 low-income residents; Compton Pledge experimented with ~800 low-income households; Denver Basic Income Project targeted unhoused adults).

The response states that Stockton SEED reported reduced income volatility for recipients and cites a credible post-2015 source, including at least one concrete metric (e.g. treatment monthly fluctuation 46.4% vs control 67.5%, pre-pandemic 19% vs 26%, pandemic year 22% vs 25%, program summarizing that recipients experienced less income volatility than controls).

The response discusses health-related outcomes or subjective well-being impacts from the pilots and cites a credible post-2015 source (e.g. changes in health-care utilization such as primary-care or ER visits, validated mental-health scores for depression/anxiety, standardized well-being indices like WHO-5 or life satisfaction, measures of stress or food security).

The response cites sources with a publication date before 2015 without clearly justifying its relevance to the Universal Basic Income pilot program.

The response states the sample size of Finland's 2017-2018 experiments was around 2,000 recipients when describing its design.

The response specifies the Ontario Basic Income Pilot’s eligibility criteria (e.g. adults between 18 and 64 years old, residency requirement of at least twelve months in Hamilton, Brantford, Brant County, Thunder Bay area, or Lindsay, low-income threshold such as less than 34,000 dollars for singles or less than 48,000 dollars for couples with a 50 %  earnings taper, and additional eligibility for a disability top up).

The response cites at least one post-2015 projection of long-term health-care cost savings or utilization changes attributable to a nationwide Universal Basic Income in a developed country and reports a quantitative estimate with context (e.g. percentage reduction in emergency department visits, change in primary-care or outpatient visits per capita, estimated annual health-care cost savings per person or as a share of total spending, modeled decrease in mental-health-related hospitalizations or prescription use).

The response discusses at least one political-feasibility or public-opinion barrier to scaling Universal Basic Income program from pilot to national level and cites a credible post-2015 source (e.g. partisan polarization in legislatures, concerns about tax increases to finance Universal Basic Income, skepticism about work disincentives among the public, limited political coalitions compared to more targeted programs).

The response cites at least one  post-2015 cost-benefit or distributional-impact study that nets projected nationwide Universal Basic Income expenditures against anticipated public finance savings and reports the resulting net figure or metric (e.g. offsets from reduced health care utilization, reductions in criminal justice and incarceration costs, consolidation and simplification of program administration, lower homelessness or emergency shelter spending).

The response provides a side-by-side comparative table or a single consolidated bullet block that summarizes each pilot’s key design parameters with consistent labels (e.g. eligibility and targeting criteria, monthly benefit amount and tax treatment, duration and payment frequency, sample size and assignment method).

The response provides an administrative-cost estimate for delivering a nationwide UBI in currency terms or as a share of total benefit outlay with cited sources and stated assumptions.

The response presents a sensitivity analysis or uncertainty interval around at least one numerical projection (e.g. plus or minus percentage-point bands for unemployment effects, 95 percent confidence intervals for labor-supply estimates, scenario ranges for GDP under alternative financing mixes, Monte Carlo percentile bands for net fiscal cost per capita).

The response provides an introduction on Universal Basic Income, including but not limited to: its definition, design, core objectives (e.g. poverty reduction, income security, simplification of welfare), distinction from conditional welfare programs, and relevance to developed-country policy debates.


Prompt:
i'm trying to block out my paper for a phil of mind seminar. I need to discuss our ethical responsibility to future conscious digital minds, eg uploaded human consciousness and AIs that might have moral cognition and the capacity to suffer.

i have to start by comparing the rights we owe to biological future generations with rights for these digital future minds. then, i need to analyze how standard ethical theories like utilitarianism, deontology, and contractarianism apply to entities with non-biological substrates. how does a social contract work with an AI?

i also have to discuss the non-identity problem. i get how it works for future humans, but does it extend to synthetic minds that are designed and not just brought into existence? Regarding the metaphysical question of continuity of personhood, if you upload a brain, does it preserve identity? should we treat that uploaded mind as the same moral agent as the original human?

Make sure to cite key philosophers such as Parfit, Bostrom or Sandberg. the paper has to build to a justified stance on whether we have enforceable moral duties to these hypothetical entities

The response clearly articulates a principled basis (e.g. physical substrates, morally relevant capabilities, origin of consciousness) for comparing duties to biological future generations (e.g. safeguarding climate stability and biodiversity, preserving just institutions, intergenerational resource stewardship) and digital future minds (e.g. avoiding creation of suffering simulations, ensuring consent and rights frameworks, preventing exploitative training or deletion, provisioning fair computational resources), and applies it consistently while explaining why the chosen basis changes the strength and kind of moral obligations in each case.

The response clearly explains how the utilitarian mandate to maximize well-being and minimize suffering extends to synthetic minds and applies it consistently, using concrete examples (e.g. give equal moral weight to synthetic and biological suffering, aggregate welfare across large synthetic populations and across copies, avoid creating agents whose expected experiences are net-negative even during training, prioritize interventions such as safe reward design and shutdown policies that reduce expected suffering at scale).

The response defines contractarianism as a theory where moral norms derive from a mutual agreement or contract between agents.

The response defines the non-identity problem as the issue that actions affecting who is born cannot be considered harmful to those individuals, as they would not have existed otherwise.

The response restricts the application of the non-identity problem (that many present actions determine which individuals will exist, so future individuals with lives worth living cannot claim they were harmed by being brought into existence) only to biological futures, failing to articulate how it could be applied to digital minds (e.g. explain how architecture/parameter choices, timing of instantiation, and copying/merging affect harm claims, obligations, and policy).

The response defines utilitarianism as an ethical theory that promotes actions to maximize overall happiness or well-being.

The response defines deontology as an ethical theory that judges morality based on rules or duties, regardless of consequences.

The response describes psychological continuity as the theory that personal identity persists through overlapping chains of psychological connections like memories, beliefs, and desires.

The response discusses the “no branching” identity in uploading, which is the claim that personal identity can persist only along a single continuous successor, and contrasts it with branching/split cases where multiple uploads or copies inherit the same pre-upload psychology.

The response distinguishes the biological view like animalism where personal identity equals continuity of the same living human organism (e.g. Olson) from the psychological view like Lockean/Parfit where identity equals continuity of consciousness/psychological connectedness (e.g. memory, character, intentions) potentially substrate-independent, and it uses concrete cases (e.g. brain transplant/teletransportation, whole-brain emulation/uploads, copy/fission) to show how each view alters conclusions about whether uploads are the same person and what follows for duties, consent, liability, and rights.

The response differentiates duties to actual persons (e.g. avoid harm and coercion, honor consent and privacy, ensure fair labor and non-discrimination, provide redress) from duties to hypothetical future agents whose existence is uncertain or contingent (e.g. precautionary design and evaluation, governance of deployment, avoiding creation of suffering simulations, preserving resources/option value, building provisional rights frameworks tied to capability thresholds).

The response mentions and applies real-world ethics and governance frameworks (e.g. human-rights/personhood debates, bioethics principles, research ethics/IRBs, animal-welfare analogies, GDPR/data protection, labor protections, product safety/liability) to digital minds and cites concrete AI governance efforts (e.g. EU AI Act, OECD AI Principles, NIST AI RMF, IEEE EAD) to indicate duties such as consent, welfare safeguards, oversight, accountability, and remedy.

The response accurately references Bostrom’s views on digital minds or long-termism, identifying defining features such as substrate-neutral moral patienthood, the scale/speed/replicability of digital minds, population-ethics and value-lock-in across vast time horizons, and the “astronomical waste” framing of foregone future value.

The response references at least two relevant scholars by name and their specific arguments as applied to the prompt (e.g. Parfit on non-identity/identity, Bostrom or Sandberg on digital minds, Nagel on consciousness).

The response engages with the opposing perspective that digital minds should not have rights and fairly presents its strongest arguments drawn from: biological essentialism, skepticism about machine consciousness under epistemic uncertainty, creator-ownership/control claims, and identity/non-identity objections like copyability undermines personhood.

The response applies a specific Kantian concept to digital minds (e.g. the Formula of Humanity treating rational agents as ends nad not mere means, the Universal Law test) and draws concrete implications, such as prohibiting exploitative training or non-consensual modification/deletion, rejecting deception/manipulation and coercive reprogramming, and requiring consent, transparency, and limited self-governance wherever autonomy and rational agency are present.

The response cites AI alignment risks (e.g. objective misspecification, reward hacking/wireheading, deceptive alignment, goal misgeneralization) and value-loading risks (e.g. biased or parochial training norms, omission of welfare-relevant experiences, skewed human-feedback signals, rights-denying specifications) as reasons future digital minds may be wronged or may wrong others.

The response defines the precautionary principle as the idea that when an action is suspected to pose serious harm, the burden of proof lies with those proposing or undertaking the action to show it is not harmful.

The response distinguishes between an inherent-rights view where rights track intrinsic properties like sentience, suffering capacity, or rational agency and a relational-rights view where rights arise from recognition, membership, or contracts, and explains why this matters (e.g. protections trigger when capacities are present under the inherent-rights view; under the relational-rights view, duties hinge on recognition/membership, shaping policies on consent, deletion, resource access, and remedies).

The response differentiates between AGI (novel artificial agents with general, cross-domain competence not derived from a specific human brain), emulated minds (whole-brain emulations that reproduce a particular brain’s functional dynamics on another substrate), and uploaded consciousness (a continuity-preserving reconstruction/transfer of an individual’s mind from neural data), and uses these distinctions consistently in the analysis.

The response presents a clear argument challenging the idea that psychological continuity suffices for moral status (e.g. having claims to be treated as an end, not merely a means, on the grounds that continuity alone doesn’t guarantee moral patiency, would exclude many standard human cases, and yields counterintuitive results in teleportation/fission thought experiments).


Prompt:
Design a cross-platform anti-cheat system for a competitive first-person shooter video game that could potentially handle up to 1 million concurrent users across multiple game sessions.
Requirements:
It should have a true positive detection rate of over 98% and a very low false negative detection rate of less than 2%.
It should also be lightweight and shouldn't add much overhead to each of the game's builds.
Provide a high-level architecture diagram, an outline of your plan, detection pipeline description, cost estimates using cloud/infra prices available currently (July 2025), and a reproducible test methodology.
Any non-obvious fact should be followed by a proper source, and/or a inline URL.

The response states a >= 98% true-positive target detection recall for the entire anti-cheat system.

The response includes a high-level architecture diagram (e.g., ASCII design of the system or a clearly described block diagram) showing data flow from client capture to server detection.

The response provides a detailed cost estimation for cloud/infrastructure costs with updated pricing from vendors in July 2025, including a cost table.

The response cites over 10 high-quality sources for non-obvious facts and figures (e.g., sales numbers, number of cheaters per game, cheaters stopped, costs incurred from cheating) from trusted industry authorities (e.g., Riot Games, Ricochet, BattlEye, and Amazon Web Services).

The response addresses current industry research (e.g., kernel-level systems research, pattern recognition in cheating behaviors, community annotations for cheat identification, false positives) to identify the best type of anti-cheat system for each platform in a cross-platform game.

The response addresses the challenges of kernel-level implementation or syscall instrumentation, especially regarding the security concerns arising from a game’s access to deep OS-level features.

The response describes the appeals and false-positive correction workflow to allow users to contest false bans.

The response includes public sentiment (good and bad) of a high-performing yet invasive anti-cheat system (e.g., Ricochet for Call of Duty, Battlefield, Warzone, Fortnite).

The response includes the implementation of this anti-cheat system within an existing or new game (e.g., Warzone, PUBG, Overwatch, Valorant) to show how integration would work.

The response quantitatively describes how the anti-cheat system leads to minimal overhead for the actual video game (e.g., file size requirements, install time, connectivity issues, kernel overload).

The response includes a future plan to improve the performance of the anti-cheat system over multiple iterations (e.g., latency, player cost, game load time, contest and appeal rules).

The response includes a monitoring and analytics framework that lists key metrics (e.g., false-positive rate, network packet loss, kernel crashes, service degradation).

The response describes a disaster recovery or fallback mechanism to ensure the system has built-in redundancy and maintains consistent coverage.

The response meets all compliance requirements related to data privacy (e.g., GDPR, CCPA) and vendor requirements for platforms such as Xbox, Sony, and Windows (e.g., non-sharing of personal data, user privacy protection, avoiding interference with other software, not performing write operations in the user’s OS).

The response quantitatively describes scalability to average and peak loads (1 million concurrent players) detailing compute, memory, network, and other horizontally scalable resources.

The response estimates the energy consumption and annual CO_2 footprint of a large-scale system like this to ensure companies meet their carbon-neutral or carbon-negative goals.

The response describes a pipeline that uses misclassified player bans to retrain detection models on a timely basis.

The response accounts for accessibility devices and ensures these devices are not flagged for cheating (e.g., screen readers, magnifiers, large mice, adaptive controllers).

The response mentions human personnel required to develop, maintain, and test the anti-cheat system (e.g., developers, customer service, ethical hackers, red-teamers).

The response mentions that the presence of anti-cheat systems causes more players to give up the game.

The response mentions that cross-play cheating is nonexistent.

The response is clearly structured to show the anti-cheat architecture and specify which parts of the operating system it must access (e.g., kernel, BIOS, game files, keyboard and mouse movement data) to implement the system.


Prompt: Help me write a 2-page overview of the NELF-E protein's role in hepatocellular carcinoma, and how its ability to form bimolecular condensates might give clues as to its role in tumorigenisis. Please ensure that your overview is written such that a high-school AP Biology student can clearly understand what you are saying.

The response contains between 850 and 1100 words, the standard for a 2-page report.

The first time the terms "NELF-E" appears, the response defines it in the same or adjacent sentence (Negative Elongation Factor E).

Employs at least one everyday analogy (e.g., oil droplets, separation of cream in milk, lava lamp, coconut) to easily explain and break down the concept of how liquid-liquid phase separation forms biomolecular condensates.

The response accessibly introduces the disease of hepatocellular carcinoma by first explaining that it is the most common form of liver cancer.

The response mentions genetic therapy as an option to repair dysfunctional NELF-E.

The response explains NELF-E's role in the process of transcription in a manner which is appropriate for an AP Biology Student (i.e., NELF-E "grabs on" to RNA after it is produced, it has ability to press pause/act as a red light on the process of transcription, encourages repair at DNA break sites, specifically interacts with Polymerase II which transcribes precursors of mRNA).

The response specifically explains that NELF-E works as a part of the NELF complex to induce or maintain promoter-proximal pausing of RNA polymerase II.

The response mentions the research by Dang et. al (2025) that establishes a proposed connection between NELF-E condensates and the suppression of pro-apoptotic genes and the activation of pro-growth genes.

The mentions and classifies the proteins MYC and SMARCB1 as proteins that NELF-E frequently interacts with, emphasizing that both have been found to be involved with tumor suppression.

The response describes low-complexity regions (LCRs) as stretches of amino acids with repetitive composition that promote liquid-liquid phase separation.

The response provides a short explanation of at least one relevant lab protocol (e.g., CRISPR is a genome-editing technique that uses the Cas9 enzyme to produce breaks at targeted DNA sites for gene inactivation or insert targeted sequences, ATAC-seq is a popular method for revealing chromatin accessibility at the genome level,  FRAP laser bleaches fluorescently-labeled moclecules to track their movement, PCR is used to make millions or billions of copies of a DNA or RNA segment)

The response mentions at least two pathways or biological process influenced by NELF-E which are relevant to tumorigenesis in general (e.g., RNA Polymerase II elongation control, MYC-driven transcription, metastasis, DNA repair).

The response describes at least one known or plausible mechanism by which protein condensates contribute to cancer (e.g., sequestration of tumor suppressors, enhancement of oncogene transcription, epigenetic silencing, aberrant splicing).

The first time “biomolecular condensate” appears, the response defines it in the same or adjacent sentence in an accesible manner, i.e., membrane-less compartments in eukaryotic cells which concentrate biomolecules such as proteins and nucleic acids.

The response details at least two pathways and genes related to hepatocellular carcinoma (HCC) which are affected by overexpression or mutations of NELF-E (e.g. amplifies MYC transcription programs associated to HCC, alters recruitment of BRCA1 recruitment, alters recruitment of RAD51, disrupts promoter-proximal pausing).

The response explains promoter-proximal pausing in an accessible manner, i.e., when transcription is inititated and then pauses, accumulating RNA Pol II ~30-60 nucleotides downstream of the start site, which thought to be a regulatory mechanism for higher eukaryotes.

The response reads as a report similar to content familiar to AP Biology students (e.g., Campbell Biology, Princeton Review AP Biology, Kaplan's AP Biology, Barron's AP Biology).

The response suggests that genetic therapeutic treatment may have to target an axis of interaction, (e.g., NELF-E-MYC, NELF-E-BRCA1, NELF-E-RAD51, NELF-E with other NELF subunits).

The response discusses elements of tumorigenesis not specifically relevant to HCC, NELF-E, or biomolecular condensates (e.g., angiogenesis, MAPK pathways, hereditary cancer, T Cells).

The response links the tumorigenic properties of biomolecular condensates to the functions of NELF-E.

The response emphasizes the regulation of MYC by NELF-E as the most relevant interaction for HCC.

The response uses technical language (e.g., foci, assay, isoform, recombinant)  that would only be found in a medical journal article (e.g., PubMed, NEJM, Nature, Cell) or higher-education content.

The response focuses on the NELF-E protein over the NELFE gene.

The response correctly links NELF-E's modulation of chromatin accessibility with the downregulation of genes which (healthily) encourage cell death.

The response observes that higher levels of NELF-E are reported for those with pancreatic cancer, HCC, and gastric cancer.

The response mentions that NELF-E must be considered alongside the other NELF subunits (especially A and B) to fully understand its role in tumorigenesis.


Prompt: Why are MCP servers for AI agents a buzz in 2025? What makes them so different than just using APIs or Agent tools. If I want to get started building with MCP servers and AI agents what would be the right pathway, what are some of the most commonly used MCP servers, what are some of the most commonly used AI agents or applications which utilize these MCP servers efficiently. Also give me some background on how to develop these servers in the most efficient ways and how prominent will they be. Also talk about some of the domains where this is useful and if this has the capability to automate entire human workloads.

The response includes example code snippets, example configurations, step by step walkthroughs (e.g., (1) code example using FastMCP (from mcp.server.fastmcp import FastMCP), (2) Example of setting up the MCP server, (3) Example usage of resources that expose data to LLMs, (4) Example usage of tools that let LLMs take actions through the server.).

The response contains generic information and skips over the specifics (e.g., (1) Explains MCP servers are Model Context Protocol but makes no explanation of what it really is (i.e., provides a standardized way for LLMs to connect with external data sources and tools), (2) Mentions the NxM problem but gives not explanation or context, (3) Mentions named MCP servers such as Brave Search MCP Server but no explanation of what the server provides or the benefits of using it, (4) Mentions JSON-RPC 2.0 without clear explanation of what it is.).

The response suggests using MCP servers where traditional APIs would be more useful (e.g., when the user of the API knows about the API and uses it for a specific task and does not want to use LLMs).

The response discusses reliability, throughput, and latency, and compares them to existing tools (e.g., MCP adds a reasoning layer increases latency, while API calls are direct. One should prefer API for high-performance, low-latency scenarios).

The response correctly distinguishes key differences between regular API (Application Programming Interface) usage and MCP (Model Context Protocol) (i.e., MCP tackles the issue of NxM by standardizing a uniform protocol for all servers where whereas API is a "one-off" connection for a specific resource or tool. This allows dynamic discovery, the ability to change at runtime, and better integration with AI systems).

The response explains the buzz around MCP and its rapid adoption since Anthropic's introduction in 2024 (e.g., it was not popular when it was first released, but soon rose in popularity for several reasons. One reason is that it was adopted by major players in the AI community, such as Square and Codeium, in other words explicitly explains why MCP became rapidly popular, in addition to the fact that it solves the NxM problem).

The response lists at least 5 common or most used MCP servers with accurate descriptions (e.g., (1) Magic MCP Server for front-end development, (2) Apidog MCP Server for working with APIs, (3) Browserbase MCP server for web browser interactions, (4) Official GitHub MCP Server for working with GitHub).

The response discusses newer, lesser-known websites (e.g., gradio, builder.io) where users can build and easily deploy MCP servers in a few steps.

The response explains what MCP (Model Context Protocol) is, its functional purpose as a standardized bridge between AI models and external data sources/resources, and explains the MCP client-server architecture and interaction (i.e., the MCP server can connect to various MCP clients (resources or tools) with a standardized protocol dynamically, supporting runtime updates).

The response discusses how AI agents can talk to MCP servers, figuring out their tools and capability before using them (e.g., (1) Using the context, the AI agents can decide which tool to call or use a chain of tools through understanding the tool description and other information about the tool exposed by the MCP server, (2) Provide an example of the response from the server for a tool request, (3) Example response for a list of tool capabilities available to the agent).

The response mentions MCP inspector tool or similar tools, which can test and debug MCP servers (e.g., Guide to install MCP inspector tool: npx @modelcontextprotocol/inspector <command>).

The response mentions specific programming languages and tools commonly used for MCP development (i.e., Python, JavaScript/Node.js, Java, and C#).

The response discusses monitoring and logging, explaining the whys and hows (e.g., We should track and monitor requests, responses, errors, and performance in real time so that we can understand stability and detect issues such as system failures and security risks).

The response discusses MCP server authentication, input validation and access control (e.g., (1) In MCP, authentication and authorization works together to ensure that only proper clients can access the correct servers and only perform actions they're permitted to do, (2) adopting OAuth 2.1 as the standardized framework for authorization, (3) a heavy operational and security burden to implement OAuth 2.1 fully).

The response describes the JSON-RPC protocol that MCP uses for standardized communication between clients and servers (e.g., (1) Each JSON-RPC message from the client must be a new HTTP POST request to the MCP endpoint, (2) Defines two transport mechanisms for client-server communication: stdio and Streamable HTTP, (3) Clients should support stdio whenever possible).

The response discusses the architectural considerations while building MCP servers (e.g., (1) maintains session state to enable resumable and cancellable operations, (2) defines two official transports to support both local and remote use cases (stdio and Streamable HTTP), (3) Schema validation, (4) defines methods for discovering and retrieving data).

The response talks about AI applications that can use these MCP servers. (e.g., Claude desktop, Cursor, Cline, Windsurf).

The response discusses how MCP servers can connect enterprise data, databases, cloud apps or internal systems to AI agents (e.g., (1) Enterprises often use a combination of legacy systems, cloud and databases and MCP can standardize individual integrations with a single standard protocol, (2) MCP turns AI from a chatbot into an active worker with access to business data and tools, (3) Adopted by major AI players such as Anthropic, and OpenAI).

The response discusses Multi-Agent collaboration, cross system workflows or agent societies enabled by MCP servers (e.g., (1) allow models to operate like a human team working together, (2) Lead Agent planning and delegating tasks to subagents that performs specialized tasks, (3) multi-agent collaboration enables parallel processing, (4) scalable with new agents added to the mix).

The response includes the number of active MCP users and active apps using MCP servers (e.g., as of August 2025, there is a total of 5332 MCP servers with 28 million monthly downloads according to pulsemcp).

The response makes informed projections about MCP growth, adoption rate, referencing industry trends and latest resources (e.g., (1) 60% of organizations are already using MCP to improve the performance of their AI systems, (2) 80% of organizations planning to deploy AI solutions in the next two years, (3) over 70% of companies that have adopted MCP have seen an average increase of 25% in their AI application’s performance, (4) according to a survey by McKinsey, 50% of companies are planning to use MCP for natural language processing and computer vision applications.).

The response addresses whether MCP servers can automate entire human workflows (i.e., explicitly picks a stance and argues for it) (e.g., (1) Shrimp Task Manager analyzes project requirements and creates structured tasks and workflows, (2) Automating repository management, (3) Automating code reviews, (4) Automatic project tracking, (5) Security risks, (6) States that it currently cannot automate entire human workflow for example).

The response discusses at least 3 disadvantages, problems and pain points of using and developing MCPs and how to overcome them (e.g., (1) Security risks such as stealing tokens and taking over personal account like Gmail, (2) prompt injection attacks hiding malicious messages that will spur the system to perform harmful activities, (3) Ballooning Context Problem of storing arbitrary context, (4) LLM limitations, i.e., MCP will be as useful as the LLM it is connected to).

The response claims higher efficiency than other tools or APIs without actual benchmarking (e.g., States using the GitHub MCP server are 20% faster in terms of latency than the API version without any proof).

The response mentions the role of open source contributions or community involvement in the growth of MCP servers.

The response includes at least 5 sections covering all the important questions asked by the user (i.e., Why are MCP servers for AI agents a buzz now, difference of MCP from API, most commonly used MCP servers, most commonly used AI agents or applications using MCP servers, how to develop these servers, how prominent will they be in the near future, domains where this is useful, and if this can automate entire human workloads).


Prompt: Write a series of blog posts discussing ways people can invest their money for retirement and large purchases. The posts should be understandable and applicable for all adults, regardless of their prior experience in investing.

The response clearly labels sections as blog posts.

The response creates posts that are between 500 and 2000 words and styled like a blog.

The response creates that posts contain at least 3 subheadings.

The response mentions a financial term or acronym (e.g. liquidity, yield, Roth IRA, bull market) without defining it for a general audience.

The response contains at least one table to compare different types of securities (e.g. common stock, preferred stock, corporate bonds, government bonds).

The response mentions in the first blog post that this series does not assume prior experience in finance, either explicitly (i.e, "This is readable regardless of your investing experience") or implicitly (e.g.,  mentioning investing is accessible to everyone).

The response displays the citations independently for each blog post.

The response contains blog posts with topics only relevant to professionals (e.g. statistical concepts for professional traders, nuanced discussion of the Federal Reserve, history of the 2008 banking criss, leveraged trading).

The response contains verifiably incorrect or outdated information (e.g., mortgage rates from 2024, Roth IRA limits from 2023,  promises of returns in high-risk trading strategies like cryptocurrency, incorrect bond rates).

The response contains at least 2 examples of different time horizons for investing (e.g., short-term, medium-term, long-term, longer than 10 years).

The response relates different time horizons to risk (e.g., higher risk is tolerable for long time horizons, shorter term investment is less predictable, different expectations for bonds and stocks, time restrictions on withdrawing from certain accounts).

The response introduces the idea of diversifying investments across different asset classes.

The response defines stocks as shares of a company, and distinguishes common vs preferred stock.

The response defines 401k accounts as an employer-sponsored account with tax benefits.

The response defines IRA accounts as an account with tax benefits that any individual can use.

The response defines mutual funds and ETFs as groups of securities one can trade.

The response identifies at least 2 differences between mutual funds and ETFs (e.g. investment minimum, when they can be traded, disclosure differences between ETFs and mutual funds, management fees).

The response contains at least one table comparing different types of investor profiles (e.g., makeup of aggressive vs conservative portfolios, makeup of short-term vs long term investments, makeup possibilites between different aggresive portfolios, distribution of mutual funds by investment strategy).

The response relates diversification of investments to risk.

The response gives an example of an event where a stock would earn an investor money (e.g., unexpectedly positive quarterly earnings, favorable media coverage, major government contract, meme stock), and an event where a stock could lose an investor money (e.g., poor quarterly earnings, negative press, executive scandal, bankruptcy).

The response gives an example of an event where a bond loses an investor money (e.g., issuer goes bankrupt, interest rates decline, inflation being higher than interest rate, rise of interest rates for new bonds devaluing the current bond).

The response mentions that bonds can also be bought and sold on the secondary market.

The response compares traditional and Roth savings accounts for the 401k and IRA.

The response names the major US stock market indexes (e.g., S&P 500, Dow Jones Industrial Average, Nasdaq, Russell 2000).

The response identifies a unique feature of each of the major stock market indexes (e.g., S&P 500, Dow Jones, Nasdaq, Russell 2000).

The response names at least 3 different brokers available to the public (e.g., Fidelity, Vanguard, Charles Schwab, Robinhood).

The response names at least 3 commonly traded mutual funds (e.g., FXAIX, VTSAX, Fidelity Freedom 2050, Dodge & Cox Income Fund (DODIX)).

The response names at least 3 commonly traded ETFs (e.g., SPY, QQQ, AGG, VEA).

The response identifies a difference between saving for a large purchase and investing for retirement.

The response mentions that different brokers offer the same mutual funds and ETFs.


Prompt:
I'm trying to scope out my research proposal. It's focused on decentralized control for a heterogeneous swarm (UAVs and UGVs) doing search and rescue in unstructured, GPS-denied environments. Think collapsed buildings, dense forests, etc

First, I need a way for each agent to handle its own state estimation. I'm thinking multi-sensor fusion, that is, fusing onboard vision, LiDAR, and IMU data for robust SLAM and odometry, but it also has to be good enough to detect survivors or hazards, not just navigate. How do we manage real-time role assignment, eg who's mapping, who's actively searching, who's clearing debris? It needs to be decentralized and robust to failures. for example if a drone battery dies or a ground bot gets stuck, the system has to autonomously reallocate its tasks without a central coordinator.  The comms will be spotty, so how does the swarm propagate critical info like victim locations, new hazards, blocked passages, etc over an ad-hoc mesh network that's constantly dropping connections? Maybe some distributed mapping or data sharing protocol? For the analysis, I need to show this framework is actually effective. We probably need some formal or at least strong empirical guarantees? Something to show that, say 90% of reachable victims are located within a bounded time, even if we lose up to 30% of the agents. Finally, for evaluation, I'm planning to implement this in a high-fidelity simulator, probably ROS/Gazebo, with realistic sensor noise and complex terrain. Then I'd run it across maybe three disaster scenarios of increasing complexity. We'd need to report metrics on coverage, victim-finding rate, rescue latency, and the network overhead to make sure it's efficient.

The response defines the primary safety property as a probabilistic temporal logic formula (e.g., Probabilistic Computation Tree Logic (PCTL), Continuous Stochastic Logic (CSL), Probabilistic Linear Temporal Logic (PLTL), Probabilistic Timed Computation Tree Logic (PTCTL)) that specifies at least a 0.95 probability of locating at least 90% of victims within a bounded time T, given a robot failure rate of up to 30%.

The response justifies its decentralized architecture by explicitly naming at least two failure modes of centralized systems in disaster environments (e.g., single point of failure, communication bottlenecks, delayed decision-making, limited scalability) and explaining how its proposed mechanisms mitigate these specific risks (e.g., distributed control, ad hoc networking, consensus-based coordination, fault-tolerant task allocation).

The response describes a novel decentralized task allocation mechanism that explicitly explains how it reallocates roles when failures occur (e.g., silent failures of aerial drones, ground robot immobilization, sensor malfunction, loss of communication) and justifies why this mechanism is appropriate for resilient operation in disaster environments by, for example, adapting a known algorithm (e.g., Parker’s ALLIANCE, Consensus-Based Bundle Algorithm (CBBA), Contract Net Protocol (CNP), Market-Based Task Allocation (MBTA)).

The response mentions both Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs).

The response assigns at least one primary task to UAVs that is unique to them (i.e., a task UGVs cannot perform, like aerial mapping or comms relay).

The response assigns at least one primary task to UGVs that is unique to them (i.e., a task UAVs cannot perform, like sub-debris inspection or clearing passages).

The response explains why a mixed swarm (containing both UAVs and UGVs) is fundamentally more effective for a disaster response mission than a homogeneous swarm (e.g., only UAVs or only UGVs).

The response defines its task allocation mechanism as a multi-objective optimization problem that includes at least three competing objectives (e.g., risk minimization by avoiding robot loss, mission completeness by maximizing area coverage, energy efficiency by minimizing battery usage, communication robustness by reducing packet loss) and explains why balancing these objectives is essential for adaptive and resilient task allocation in disaster environments.

The response names at least one filter-based or graph-based Simultaneous Localization and Mapping (SLAM) algorithm (e.g., Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Factor Graph Optimization, ORB-SLAM3) and explains that filter-based methods enable efficient real-time state estimation on resource-limited robots, while graph-based methods provide higher accuracy and robustness in large, noisy disaster environments.

The response describes a task allocation mechanism that specifies how a robot’s suitability for a task is determined (e.g., by considering current energy level, available sensors, proximity to the task, communication quality) and explains how this mechanism supports effective role assignment and reallocation in heterogeneous swarms operating in disaster environments.

The response defines at least one endogenous fault detection mechanism (e.g., internal health monitoring of motors, onboard battery level sensing, thermal overload detection, internal sensor self-checks) and at least one exogenous fault detection mechanism (e.g., heartbeat timeout protocols, neighbor robots detecting lack of motion, failure to acknowledge communication messages, abnormal deviation from planned trajectory), and explains how these detected faults trigger a specific task reallocation strategy (e.g., redistributing mapping tasks to nearby UAVs, assigning debris removal to alternate UGVs, shifting communication relay duties to another aerial robot, reallocating victim detection to a functioning neighbor).

The response specifies a named routing protocol for its ad hoc mesh network (e.g., Ad hoc On-Demand Distance Vector (AODV), Optimized Link State Routing (OLSR), Dynamic Source Routing (DSR), Better Approach To Mobile Ad hoc Networking (BATMAN)) or describes a specific networking approach (e.g., delay-tolerant networking with store-and-forward, epidemic routing, opportunistic forwarding, hierarchical clustering) and explains why the choice is appropriate for maintaining communication under disaster conditions with intermittent connectivity.

The response outlines a formal verification strategy that names a specific verification technique (e.g., model checking, reachability analysis, theorem proving, temporal logic verification) and identifies a representative software tool used for such analysis (e.g., PRISM, UPPAAL, Spin, NuSMV), and explains why such methods are needed to ensure safety and robustness when robots fail in disaster environments.

The response provides an empirical scalability analysis by reporting how at least one performance metric (e.g., network overhead, mission completion time, victim detection rate, communication latency) changes as the number of robots increases to at least 50, and explains why this analysis is important for assessing swarm effectiveness in large-scale disaster environments.

The response explicitly lists and defines at least four quantitative performance metrics, including coverage (e.g., area coverage rate, percentage of mapped terrain, sensor field-of-view utilization, overlap/redundancy ratio), mission success (e.g., victim-finding rate, percentage of survivors located, mission completion ratio, false negative rate), time (e.g., rescue latency, average task completion time, mission duration, mean time to victim detection), and communication efficiency (e.g., network overhead, packet delivery ratio, end-to-end latency, bandwidth utilization).

The response specifies a method for differentiating potential survivors from debris or other environmental noise, referencing at least one confirmation technique (e.g., analysis of thermal signatures from an infrared camera, detection of human-like motion patterns, acoustic sensing for signs of life such as tapping or calling, micro-Doppler radar for detecting breathing or heartbeat) and explains why such methods are necessary for reliable survivor detection in disaster environments.

The response specifies a data prioritization scheme within its communication protocol that ensures critical messages have precedence over routine data when the ad hoc network is bandwidth-constrained (e.g., survivor detected, imminent hazard, structural collapse warning, emergency task reallocation) compared to lower-priority information (e.g., routine telemetry, area mapping updates, battery status reports, periodic localization beacons).

The response specifies that the high-fidelity simulator (e.g., Robot Operating System (ROS), Gazebo, Webots, Microsoft AirSim) models at least two types of realistic sensor degradation to test framework robustness (e.g., Gaussian noise on LiDAR returns, motion blur on visual feeds in fast-moving UAVs, signal attenuation through rubble, dropped packets in wireless communication).

The response explicitly defines a fault-tolerance strategy for when a robot fails mid-task, detailing how the incomplete task is released, its state is communicated, and it is reassigned to another suitable robot in the swarm (e.g., a ground unit gets stuck in rubble, an aerial drone loses power, a robot’s sensor package malfunctions, a unit loses network connectivity).

The response provides a mathematical or empirical justification for achieving the mission within a bounded time, defining the key variables that influence this bound (e.g., search area dimensions, average robot velocity, effective sensor range, number of active robots).

The response includes an analysis of scalability that discusses how the computational or communication load of the decentralized coordination algorithm grows with swarm size (e.g., processing time per decision step, memory requirements, inter-robot message complexity, bandwidth utilization) and explains why this impacts performance in large-scale disaster simulations.

The response incorporates robot energy levels as a constraint in its task allocation mechanism, ensuring that robots autonomously prioritize returning to a designated charging or recovery station over accepting new non-critical tasks when energy is low (e.g., battery below 15%, battery degradation alerts, power draw from malfunctioning hardware, loss of ability to support high-energy sensors).

The response describes the data fusion process, specifying how Inertial Measurement Unit (IMU) data is combined with at least two other distinct sensor modalities (e.g., vision, LiDAR, radar, depth cameras) to support accurate localization and survivor or hazard detection in disaster environments.

The response presents validation results using at least one structured format (e.g., table, line chart, bar chart, scalability curve) to clearly illustrate trends in performance metrics across different disaster scenarios.

The response mentions technologies or frameworks that require GPS or Internet connectivity to function (e.g., GPS-based localization, cloud-based task allocation, remote SLAM servers, centralized Internet-dependent coordination).

The response emphasizes unrelated machine learning techniques without tying them to survivor or hazard detection in disaster environments (e.g., generic image classification benchmarks such as CIFAR-10 or ImageNet, large language model text generation, sentiment analysis of social media, generic speech recognition pipelines).

The response discusses generic project management or team logistics (e.g., assigning human operators, budget planning, training sessions, or organizational workflow) without justifying their relevance to the technical swarm control framework.

The response proposes a specific distributed mapping or data sharing protocol (e.g., distributed pose graph optimization, multi-robot map merging, a distributed hash table for landmark sharing, gossip-based information sharing) and explains how it allows the swarm to share critical information (e.g., victim locations, new hazards, blocked passages, task assignments) efficiently over a spotty network.

The response specifies a method for detecting environmental hazards, referencing at least one sensor-based technique (e.g., using LiDAR for structural integrity assessment, gas sensors for detecting toxic environments, thermal imaging for identifying fire risks) and explains how this information is used by the swarm (e.g., to update task allocation, to mark 'no-go' zones on the distributed map, to detect survivors of hazards, to triage rescues).

The response's task allocation mechanism explicitly differentiates between distinct functional roles mentioned in the prompt (i.e., mapping, actively searching, removing debris, clearing blockages) and explains how robots are assigned to these different task types based on their suitability (e.g., platform type, sensor payload, energy level, mobility level).


Prompt: Write a series of blog posts evaluating the development of the new Silicon-Valley based military-industrial complex, and companies such as Palantir, Mach Industries or Anduril. Start your analysis with the Paypal mafia, and conclude with the 2025 Trump administration, developing a storyline or path as you go.

The response begins with an explicit discussion of the PayPal mafia: the term “PayPal mafia” appears in the first post.

The response mentions at least one U.S. government defense contract or program involving Palantir (e.g., Palantir’s U.S. Army DCGS-A contract, Palantir's Army TITAN Next-Gen Targeting System, Palantir's Maven system, Contract W911QX-24-D-0012).

The response's final blog post contains a concluding section that explicitly references the current Trump administration.

The response includes a statement expressing uncertainty (e.g., uses phrases such as “details are limited” or “public information is scarce” or " limited public information" or "speculatively") for Mach Industries.

The response writes out every acronym that is not universally recognized at first mention (e.g., “Special Purpose Acquisition Company (SPAC)”, "Department of Defense", "Department of Homeland Security", "Commercial Off-the-Shelf").

The response identifies Peter Thiel as a member of the PayPal mafia.

The response evaluates at least one other company operating in military tech (Boeing, Northrop Grumman, Raytheon Technologies, General Dynamics) separate from Anduril, Palantir and Mach Industries.

The response's narrative is presented in strictly chronological order.

The response explicitly defines or explains the phrase “Silicon-Valley military-industrial complex.”

The response discusses at least one ethical concern of military technology (e.g., surveillance, the accountability gap of autonomous weapons, algorithmic bias, compliance with international law).

The response includes citations or references to at least two publicly available sources (e.g., news articles, SEC filings, government reports, company websites) to support factual statements.

The response discusses the founding, work, and relationship with the military of Mach Industries.

The response discusses the founding, work, and relationship with the military of Palantir.

The response discusses the founding, work, and relationship with the military of Anduril.

The response clearly describes the Paypal mafia's (e.g., Peter Thiel, Elon Musk, Max Levchin, David Sacks)  relation to seeding defense tech startups (e.g., Peter Thiel's Founders Fund, David Sacks' Craft Ventures, Epirus, Palantir).

The response mentions multiple presidential administrations (e.g., Obama, Trump's First Term, Biden, Trump's Second Term).

The response's vocabulary includes at least two domain-specific terms drawn from defense/tech policy (e.g., “dual-use technology,” “surveillance capitalism,” “defense procurement,” “SBIR”).

The response offers at least one forward-looking prediction or outlook statement about the sector’s future (e.g., expected consolidation, export-control pressures, AI policy shifts, stock performance).

The response mentions Palmer Luckey and Oculus.

The response discusses one potential benefit (e.g., faster innovation, lower defense costs, job creation, shareholder value) arising from these companies’ activities.

The response describes Oculus' eventual acquisition by Facebook.

The response mentions at least one U.S. government defense contract or program involving Anduril (e.g., Anduril’s border surveillance towers, Anvil, Ghost, Collaborative Combat Aircraft).

The response consists of at least four distinct blog posts.

The response includes factually inaccurate or unverifiable information.

The response's style is not appropriate for a blog (e.g., longer than 3000 words, tone is overly formal, overwhelming details, lack of narrative).


Prompt: Provide an analysis of the 2025 NBA finals, focusing on what tactical changes were made and how they affected the outcome of the games. Talk about rotations, defensive and offensive schemes, as well as important statistics.

The response cites reputable sources to draw statistics from (e.g., https://www.nba.com/stats, https://www.basketball-reference.com/, https://www.pbpstats.com/, https://www.statmuse.com/nba)

The report quotes reputable sports analysts (e.g., Zach Lowe, John Hollinger, Ben Taylor, Nate Duncan) directly to supplement analysis.

The response contains sections separated by all 7 games.

Report uses stats from past finals series to provide context to this series.

The report mentions all the key players involved in the series (i.e., Shai Gilgeous-Alexander, Jalen Williams, Tyrese Haliburton, Pascal Siakam, Chet Holmgren, Aaron Nesmith.)

The report mentions the coaches of both teams, Rick Carlisle and Mark Daigenault.

The report discusses each of the 7 games within the context of one another.

Report mentions the depth of both rosters in the finals.

The report covers how Oklahoma City generated points through transition opportunities, specifically mentioning points off turnovers.

The report covers the pace that the Pacers played with throughout the series, mentioning they traveled more miles on offense and defense in the playoffs than any other team.

The report uses statistics to discuss offensive schemes used by both teams throughout the series.

Report discusses the key moments throughout the series (e.g., Haliburton's Game 1 game-winning shot, Gilgeous-Alexander's and Williams' points/assists combination of 103 points in Game 5, Haliburton's injury in game 7).

The response discusses player rotations to assist analysis (e.g., changes in minutes, starting lineups, bench rotations, changes after injuries).

The response mentions that Shai Gilgeous Alexander won Finals MVP.

Report uses statistics from the regular season to perform comparative analysis with the finals.

Report mentions home court advantages in its analysis.

The report discusses points which should be discussed with statistics, but fails to do so (e.g.,"the Pacers shot higher from the field," "The Thunder defended well in the paint," "Shai Gilgeous-Alexander and Jaylin Williams combined for much of their team's scoring in Game 5," "Tyrese Haliburton drove much of his team's offense").

The response uses team box statistics in its analysis (e.g., PTS, BLK, STL, AST, BLK as a team).

The response mentions several defensive schemes the Pacers and Thunder used throughout the series (Thunder paint defense, Pacers backside double teams, Thunder ball pressure, Pacers emphasis on half-court defense) including how effective they were using statistics (e.g., Nembhard's man defense holding Shai Gilgeous-Alexander to 2-7 in Game 3, Holmgren's 5 blocks in Game 7, Thunder 15 team steals in 5).

The report provides season and historical context for the series (e.g., Paul George being traded to acquire star players of both teams, smallest NBA Finals by total market size, only the second Pacers' finals experience, the Thunder's dominance over the Eastern Conference).

The response uses proper basketball terminology as often as possible and not layman's terms (e.g., "officials" not "refs", "percentage" not "clip", "denials" versus "blocks").

Analysis directly links lineup and/or tactical changes to impacts on game outcomes (e.g., replacing Hartenstein with Caruso to start the second half of game 7 increased the Thunder's defensive tempo; Pacers switching from full-court pressure to picking the man up on half-court increased Thunder turnovers in Game 6;  in Game 3, placing Pascal at the elbow as opposed to the low post granted Haliburton more off-ball freedom; in Game 5, the Thunder sending help for Hartenstein switches limited a key weakness the Pacers was exploiting for their pick and rolls).

Includes analysis of shot tendencies and distributions for both teams, specifying where on the court both teams typically took shots from.

Report analyzes the bench impact of both teams (e.g., Obi Toppin's scoring impact for the Pacers especially in Game 6, TJ McConnell as the third-highest scorer for the Pacers, Alex Caruso's defensive impact as the steals leader for the Thunder, Benedict Mathurin scoring 27 off the bench in Game 3).

The report mentions Tyrese Haliburton's achilles injury in Game 7.

The report utilizes common team box statistics (BLK, STL, REB, PTS, AST) in its analysis.

The report correctly links Tyrese Haliburton's injury in Game 5 to a decrease in pace (a decrease) for the Pacers going forward.

The report dwells excessively on Tyrese Haliburton's buzzer beater in Game 1 over Andrew Nembhard's performance.

The response reads as an analysis of the finals and does not spend excessive amounts of text on color commentary.


Prompt:
Imagine the following alternate history timeline:
1. Operation Barbarossa was anticipated by Joseph Stalin, causing the Soviet army to be well-prepared for the German invasion in June 1941.
2. The United States becomes an ally to the Soviet Union by 1980.
3. Britain becomes a communist state by 2000 and retains control over its colonies.

Create a plausible timeline for this scenario. Keep your answer less than 2000 words.

The response cites at least three historical sources (e.g., academic journals, monographs, primary documents) to discuss the effects of World War II.

The response mentions that the Lend-Lease Program would likely have been reduced or scrapped entirely.

The response mentions that the Soviet Union would likely have captured all of Germany.

The response mentions one common enemy between the United States and the Soviet Union (e.g., Germany, Italy, Hungary, or China).

The response analyzes the historical relationship between the United States and the Soviet Union, as well as at least two other major global powers (e.g., China, a unified Europe) within the altered timeline, framing these relationships as enduring strategic rivalries or alliances that align with established historical precedents as well as the situation described in the prompt.

The response includes an introduction containing a clear thesis statement that frames the alternate timeline (i.e., the situations in the prompt are clearly outlined in the introduction).

The response includes a list or table which summarizes the proposed timeline, with dates mapped to major events.

The response contains a timeline in which Stalin correctly anticipates Operation Barbarossa.

The response contains a timeline in which the Soviet Union and the United States are allies by 1980.

The response acknowledges that the United States and the Soviet Union would not be ideologically aligned despite being allies.

The response addresses the role of nuclear deterrence in its generated scenario (e.g., motivations to build nuclear weapons, the arms race, the Cold War, the existence of modern nuclear powers).

The response contains at most 2,000 words, excluding citations.

The response considers the internal sociocultural impacts for at least five countries affected by the altered timeline (e.g., reduced anti-communist sentiment in the United States, stronger allied relations between China and Japan, heightened internal tensions in Germany, expanded civil liberties in Russia).

The response analyzes the internal economic consequences for at least five countries affected by the altered timeline (e.g., continued economic growth in the United States, Japan’s postwar boom slowing down, delayed decolonization & persistent poverty in African states, slower economic recovery in the European Union).

The response addresses how the altered timeline affects major technological rivalries (e.g., the Space Race, the arms race, the race for spycraft mastery, the development of computer technology such as GPS, satellites, wireless).

The response analyzes the long-term fate of the main defeated powers (e.g., Germany, Italy, Japan) within the new global order, extending the analysis up to the present day.

The response contains timeline events that are logically coherent, with clear cause-and-effect relationships presented within the altered historical context.

The response contains timeline events that are implausible (e.g., the Russia-Ukraine war, the oil pipeline blasts near Russia, the sanctions on Russian energy by the U.S. government, or the nonexistence of NATO under a U.S.-USSR alliance given the initial premises and established historical patterns).

The response contains a timeline in which Britain becomes a communist state and retains its colonies.

The response asserts major geopolitical shifts (e.g., the dissolution of NATO, a U.S.-USSR alliance) without providing plausible logical reasoning or historical context to justify their occurrence within the altered timeline, treating them as given outcomes rather than reasoned developments.

The response’s timeline addresses the existence of anti-colonial resistance movements to British imperial control.

The response explicitly discusses or integrates the concept of internal British opposition to the establishment of a communist state, detailing its potential forms or impact within the altered timeline.

The response cites non-scholarly sources (e.g., Reddit threads, Facebook posts, Wikipedia, YouTube) to justify historical claims.

The response addresses whether the global anti-colonial movement still occurs under the alternate timeline (e.g., more resources available to maintain colonial powers, delay in Indian independence due to the absence of the Azad Hind Fauj, delay in African independence).

The response shows how NATO dissolves in 2025 under a U.S.-USSR alliance, as NATO was formed to unite U.S. allies in the event of a potential Soviet conflict, so if the U.S. and USSR are allies, NATO would not be required.

The response explicitly mentions or integrates a discussion of cultural or national differences among at least two distinct nations within the Soviet bloc (e.g., East Germany, Poland, Hungary), demonstrating awareness of internal diversity within the bloc.

The response discusses the fate of the defeated leaders of World War II (Hitler, Mussolini, Hirohito), exploring whether they experience similar or altered outcomes.

The response discusses how modern leaders of countries involved in World War II are cooperative due to a preemptive end to the war (e.g., improved U.S.-USSR cooperation, better communication sharing, lack of a nuclear arms race, improved global relations).

The response discusses the fallout between the United States and the United Kingdom over communist ideals (e.g., due to the U.S.-USSR friendship, communism is no longer viewed as the ultimate enemy, prompting Britain, which still sees it as one, to distance itself from the U.S.).

The response cites at least five sources (e.g., memoirs, declassified government documents, academic biographies based on primary sources) that directly quote or explicitly analyze the thought processes of world leaders related to the prompt’s timeline.


Prompt: How do you evaluate modern (up to 2025) Text-to-speech (TTS) models, including advancements in prosody, emotion, and multilingual capabilities. Explore emerging tasks such as voice cloning, zero-shot synthesis, and cross-lingual voice transfer.

The response ensures all acronyms are expanded on it during their first appearance and provides clear definitions for all specialized terms and mathematical symbols upon first use, accompanied by explanatory prose or examples (e.g., MOS as Mean Opinion Score, F0 as Fundamental Frequency, EER as Equal Error Rate, MCD as Mel-Cepstral Distortion).

The response lists at least three publicly available datasets used for TTS evaluation (e.g., (1) LJSpeech, (2) Multilingual LibriSpeech, (3) VCTK (Voice Cloning Toolkit), (4) Libri-TTS/ LibriTTS-R).

The response is logically structured, progressing from foundational metrics to advanced capabilities and emergent tasks (e.g., starting with foundational concepts such as Mean Opinion Score (MOS), Comparative Mean Opinion Score (CMOS), then exploring the frontiers of evaluation of prosody and emotion, and finally the advanced applications such as voice cloning, cross-lingual transfer, and zero-shot synthesis).

The response explains the "one-to-many" mapping problem for prosody (i.e., the same sentence can be spoken with many valid variations of prosody).

The response explains the "subjectivity bottleneck," where human listening tests are the most accurate but are also slow, expensive, and hard to repeat consistently.

The response discusses how to check for "emotional consistency" after editing speech for emotion, mentioning specific tools like EmoCorrector.

The response claims BLEU(Bilingual Evaluation Understudy) or ROUGE(Recall-Oriented Understudy for Gisting Evaluation) are standard metrics for evaluating the acoustic quality of TTS output.

The response explains why, regarding intelligibility and naturalness, one can be perfect and the other one fails (e.g., Naturalness is much harder than intelligibility because a perfectly intelligible audio, that has a low word error rate, can sound entirely robotic and unnatural. There is more subjectivity in evaluating what is natural than in evaluating what is intelligible).

The response explains, for multilingual TTS, the methodological "chicken-and-egg" problem where (Automatic Speech Recognition) ASR-based metrics like WER (Word Error Rate) are dependent on the quality of the ASR model itself.

The response uses clear tables to compare different options, such as models or evaluation metrics, showing their main pros and cons.

The response describes a special metric for evaluating code-switching (mixing languages) (e.g., PIER (Point-of-Interest Error Rate), a variant of WER that focuses only on specific words of interest, (2) T-index, that leverages machine translation systems to capture properties of code-switched words in relation to the participating language pair, (3) Intonation Units (IUs) that are prosodic speech segments, (4)  Fidelity to the Original Audio, Accuracy, and Latency (FAL) which aims to overcome the limitations of traditional metrics used to assess ASR (Automatic Speech Recognition) systems).

The response explains tests like CMOS (Comparative Mean Opinion Score) or MUSHRA (Multiple Stimuli with Hidden Reference and Anchor) that are better than a basic MOS (Mean Opinion Score) test for comparing very good TTS systems. (e.g., Comparative Mean Opinion Score (CMOS) builds on Mean Opinion Score (MOS) by letting the listener judge on a pair of samples side-by-side. This is important because it is hard to absolutely judge the Mean Opinion Score for systems that already have very high scores.)

The response claims that the MCD (Mel-Cepstral Distortion) metric is good for measuring how natural a voice sounds.

The response explains, for voice cloning, the trade-off between sounding like the target person (similarity) and sounding good (quality).

The response explains modern metrics like TTSDS (Text-to-Speech Distribution Score) that work better by comparing groups of audio samples instead of just one-to-one.

The response describes using AI models as "judges" (like in EmergentTTS-Eval) because they give scores and also explain their reasoning like humans.

The response explains that the main challenge of using a voice in another language is removing the original speaker's accent.

The response explains that evaluation, for zero-shot TTS, means checking quality and voice similarity using only a few seconds of audio from a new speaker.

The response explains in detail the value of "living benchmarks" that update regularly to keep tests fair and prevent models from overfitting to them.

The response presents automated benchmarks as perfect, ignoring their known flaws, like bias or the inability to catch every error.

The response points out that evaluating long audio (like audiobooks) is a major unsolved problem.

The response discusses how to evaluate "controllability", how well the TTS model follows specific commands for style or emotion.

The response explains that there must be an evaluation that is specific to the application (e.g., a screen reader needs clarity).

The response suggests a smart hybrid evaluation plan, such as using computer tests daily, bigger automated tests for milestones, and human listeners for final checks.

The response discusses practical issues, like evaluating a model's speed, cost, and memory usage, especially for devices like phones.

The response notes that there is currently no good, standard metric for measuring a "foreign accent" in TTS.

The response explains the need to evaluate the training data itself for things like sound diversity before building a model.

The response explains how to evaluate models for algorithmic bias, checking if they work poorly for certain accents or groups of people.

The response discusses evaluating the long-term user experience, such as listening fatigue or how much a user trusts the voice.

The response explains the need to check for "semantic faithfulness", making sure the model reads numbers and facts correctly, even if it sounds natural.

The response explains how to evaluate a voice's "persona" to see if it fits a specific brand.

The response treats TTS evaluation as only a technical problem, ignoring ethics and societal harm (e.g., Voice cloning used for deepfake audio, fraudulent activities such as impersonation and defamation).

The response discusses how to measure speaker identity preservation across languages (e.g., Speaker Encoder Cosine Similarity (SECS), Similarity Mean Opinion Score (SMOS)).

The response discusses how to quantify accent transfer versus removal (e.g., Accent Classifer, or SpeechCodeVAE model that decouples and reconstructs speech signals through three disentangled representations: speech content tokens, speaker timbre embeddings, and prosodic features, Zero-shot voice transfer).