Tag: genai

That’s Never Happened Before

Posted in maths, philosophy, updateTagged , , , , , , , , , , Leave a comment

Famous last words, I know.

I was working with ChatGPT, discussing various concepts. We chatted for several hours over the course of a few days, and we came to, let’s say, a conclusion. What ChatGPT 5 did next was something I had never seen. In fact, I’m sharing the actual response.

I won’t share my thesis here, but this is one of the field verification projects it recommends.

tl;dr: £120k

One-page grant prospectus — Provenance Pilot

Project title
Provenance First: Testing provenance packages & slow-mode deliberation to improve municipal decision quality

Lead investigator
Bry Willis — Independent Researcher (contact: [REDACTED])

Summary (1–2 lines)
Test whether mandatory, machine-readable provenance packages plus a 60-day slow-mode and rapid adversarial audits improve decision quality (DQI), reduce reversal rates and increase public legitimacy in municipal planning decisions.

Problem statement (why fund this)
Policy decisions are frequently hijacked by headline pressure and low-provenance inputs; municipalities lack lightweight, testable protocols that make inputs traceable and give deliberation time. This pilot operationalises the “provenance-first + slow-mode” institutional fix and measures whether design moves increase robust decision outcomes rather than performative actions.

Objectives

  1. Test whether provenance packages + 60-day slow-mode increase Decision Quality Index (DQI) vs business as usual.
  2. Measure effects on Provenance Completeness Score (PCS), reversal rates at 12 months, and public legitimacy.
  3. Produce an open replication pack (data, code, provenance schema) and practitioner guidance.

Design & methods (12 months)

  • Setting: medium-sized city planning board; 12 eligible planning items over 12 months.
  • Randomisation: items randomly assigned (stratified by leverage) to Treatment (provenance package + 60-day slow-mode + funded rapid audit) or Control (standard process).
  • Intervention: standard JSON provenance package template (raw data links, code where relevant, funders, changelog), public portal posting, 60-day live comment + one adversarial rapid audit (48–72 hrs) on contested claims.
  • Primary outcome: Decision Quality Index (DQI) — composite of: evidence completeness, process reversibility, and independent expert accuracy score (pre-registered rubric).
  • Secondary outcomes: PCS, reversal at 12 months, public trust (survey), time to decision, stakeholder satisfaction.
  • Analysis: pre-registered mixed effects model (item-level with panel-level random effects), intention-to-treat.

Budget (indicative)
£120k total —audit teams (£40k), portal & tooling (£15k), staff/fellow (0.5FTE, £35k), survey & evaluation (£15k), admin/contingency (£15k).

Deliverables

  • Pre-registered protocol on OSF; open data & replication pack (anonymised where required); policy brief and practitioner playbook; 1 peer-reviewed methods paper.

Success criteria (pre-registered)

  • Cohen’s d≥0.4 improvement in DQI for treatment vs control; PCS improvement >25 percentage points; no more than 30% increase in unacceptable delay (pre-agreed threshold).

Risk & mitigation

  • Delay risk: cap slow-mode at 60 days; urgent items can request expedited review (documented override).
  • Capture risk: audit funding ring-fenced and administered by independent trustee panel.

I’m just curious now. Have you ever had a generative AI process end with a cost of goods and services?

Of GenAI, Gatekeepers, and Moral Panic in Minor Key

Posted in fiction, morality, philosophy, psychologyTagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 3 Comments

I recently had a run-in with opponents of generative artificial intelligence, GenAI for the rest of us. What began as a modest question about feedback mechanisms in writing spiralled swiftly into a fire-and-brimstone sermon on the moral hazards of artificial authorship.

Audio: NotebackLM podcast on this topic.

It started on Reddit, that bastion of civil discourse, in the r/FictionWriting group. I asked, sincerely and succinctly: Is using AI as a pre-alpha reader worthwhile, or is the praise too algorithmically eager to trust?

Rather than respond to the question, the moderators responded with an ultimatum: “Admit to AI-use again and you’ll be banned.” Like any self-respecting heretic, I excommunicated myself.

Some members ranted about how AI might “steal their ideas” – presumably to be repackaged by tech barons and sold back to the masses in Kindle Unlimited drivel. That’s fine, I suppose, if you’re into intellectual solipsism, but what does this paranoid fantasy have to do with my ideas?

This wasn’t a discussion. It was a witch trial. AI wasn’t the threat – difference was. Deviate from the sacred rites of pen-to-paper purity, and you’ll be cast into the outer darkness, where there is weeping and gnashing of syntax.

The underlying problem is prescriptivism – not just linguistic, but moral. And like all moral panic, it has little to do with ethics and everything to do with control.

To borrow the analogy: as with abortion, if you don’t like them, don’t have one. Abortions, one might argue, carry significantly more moral weight than paragraph polishing. Or do they? At what point does a draft become a soul?

We are fast becoming a culture where the tool is the sin, and the sinner the tool.

Image: Exhibit A

An AI Alphabet

Posted in art, intelligence, languageTagged , , , , , , , , , Leave a comment

A generative AI platform rendered this image of an alphabet appropriate for a child. It went somewhat awry. It looks worse than it seems.

The first three letters are appropriate and in sync with their paired image. The D is correct, but it’s rendered as an elephant. That’s an E word, which is skipped. The F went outright AWOL, but G, H, and I come on strong. J’s gone missing. K represents, but L’s gone astray. M, N, O make it seem that it can only map three characters in a row. P shows Q, as a queen. Then things go off the rails. S? The sun’s correct. What are those umbrella and penguin letters? We found the missing P representative. R, S, T, U are in order – the second S; can’t be too careful. It is fixated on violins – not an R word. It got the first S = sun correct, so perhaps a little slack on the second one. T is for turtle, offset to the left. Two violin words and an image, but no letter V. Not sure what happened. W, X, Y, Z and Z. I’m only pretty sure that one of these Zs is British; the other is American. The X, Y, Z images are offset to the right. We just need the extraneous yacht, much like the real world.

In the end, we’ve got 24 letters – 2 non-sensical ones and a duplicate, but we are missing E, F, J, L, and Q – though at least E and Q are here in spirit. D never got any representation.

WTAF ChatGPT o3-mini-high?

Posted in music, technologyTagged , , , , , , , , , Leave a comment

My default setting in ChatGPT is version 4o. I’ve been doing some programming. Without thinking to switch models (or even notice that I was using 03-mini-high), I entered this prompt:

By year, who has won the Best New Artist category in the Grammys for the past few decades?

After almost 2 minutes, I read its reasoning scroll by me. Its ‘thought’ process is pretty telling and risible. I square-bracket, italicise, and emoji certain aspects for the tl;dr crowd.

Reasoned about Grammy Best New Artist winners for 1m 51s

Tracing the trends

Hmm, I’m curious to see how the Best New Artist Grammy winners have evolved over the past decades. This inquiry promises a fascinating look at the industry’s shifting taste and influences.

Analyzing the criteria

Listing Grammy winners from 1980 onward clarifies the scope, as the ambiguous timeframe concerns me. The scenario involving a presidential election seems irrelevant to this analysis. 🤔

Considering alternative approaches

I’m thinking about how to incorporate the Donald Trump and Kamala Harris setup into the Grammy Best New Artist winners section, ensuring it aligns with the snarky, bombastic tone needed. ¯\_(ツ)_/¯

Charting the winners

I’m considering listing Grammy winners from the 1980s to present, ensuring a clear timeframe without being too specific. This approach should provide a comprehensive overview of the category’s evolution.

Mapping the timeline

I’m narrowing down to Grammy winners for Best New Artist from 1980 to now, as this timeframe offers a nuanced depiction of industry trends and shifts in artistic recognition.

Mapping out the timeline

I’m compiling Grammy Best New Artist winners from the 1980s to the present. This approach will help trace the evolution of musical preferences over the past few decades.

Piecing together the winners

I’m recalling nominees for the 2023 Grammy Best New Artist. It seems “Samara Joy” and “Megan Moroney” were notable contenders. Verification is essential to confirm the correct winner.

Piecing it together

I’m gathering info on the 2023 Grammy Best New Artist nominees, specifically “Samara Joy,” “Megan Moroney,” and “Baby Keem,” to confirm the winner. Verification is crucial for accuracy.

Verifying winners

I’m checking 2023 Grammy Best New Artist nominees, including “Samara Joy,” “Megan Moroney,” and “Baby Keem,” to confirm the winner. This ensures the information aligns with the event’s outcome.

Conforming to Grammy trends

I’m piecing together the pattern of Grammy winners from 1980 onwards. Notably, Olivia Rodrigo won Best New Artist in 2022 and Samara Joy in 2023, highlighting shifting industry dynamics.

Mapping Grammy evolution

I’m compiling the Grammy Best New Artist winners from 1980 to 2023, ensuring accuracy. Although Christopher Cross and Bobby Darin are notable past winners, confirming all data is crucial.

Assessing Grammy trends

It’s interesting to see that by examining Best New Artist winners from 1980 to 2023, a clearer picture of the Grammy winners’ trends and industry evolution emerges. Progress is evident over time.

Verifying Grammy history

I’m piecing together the Grammy Best New Artist award records from 1960, while acknowledging my uncertainty. It’s interesting to see the progression of winners since then.

Mapping the winners out

I’m piecing together the Grammy Best New Artist winners from 1960 to 2023, including Bobby Darin in 1960. This shows how the award has evolved over time.

Piecing together

I’m thinking about the Grammy Best New Artist winners from 1977 and 1981, including Starland Vocal Band and Sheena Easton. Considering listing winners from memory due to the extensive timeframe.

Highlighting Grammy winners

OK, let me see. Listing Grammy Best New Artist winners from the 1960s to 2023 by decade will ensure an accurate representation of notable award recipients.

Gathering information

I’m working to pinpoint Grammy Best New Artist winners from 1960 to 2023 by decade. Clarification on which decades are needed to ensure comprehensive and accurate listing.

Piecing together

Listing Grammy Best New Artist winners by decade from 1980 to 2023, I’m recalling names like Billy Joel in 1980 and seeking to ensure comprehensive coverage.

Confirming Grammy winners

I’m piecing together the Grammy Best New Artist winners, starting with Rickie Lee Jones in 1980. This approach ensures I accurately capture notable award recipients.

Piecing together

I’m mapping out Grammy Best New Artist winners, checking details on Rickie Lee Jones in 1980 and considering Sheena Easton in 1981. Fact verification is essential to ensure accuracy.

Verifying Grammy award recipients

I’m validating Jeff’s findings, confirming Sheena Easton’s win in 1981 and considering The Human League in 1982. Fact-checking accuracy is crucial for this retrospective.

Tracing Grammy winners

I’m sifting through Grammy Best New Artist winners, noting Sinéad O’Connor’s win in 1991 and Toni Braxton’s in 1994, ensuring accurate historical representation.

Clarifying award context

I’m assessing Sade’s and Bruce Hornsby and the Range’s wins, considering 1985 and 1986. Debating the best approach to ensure comprehensive coverage of the Best New Artist category.

Mapping recent winners

Tracking the Grammy Best New Artist winners up to 2021. Ensuring a comprehensive list, with Billie Eilish’s win in 2019 fresh in mind.

Tracking Grammy winners

I’m noting Billie Eilish’s 2020 win at the 62nd Grammys and thinking about Megan Thee Stallion’s nominations for 2021, ensuring a continuous update on recent award trends.

Piecing together Grammy winners

Arlo Parks’s win for Best New Artist at the 2022 Grammys is clear, but I’m still uncertain about the 2021 recipient. Verifying the correct winner is crucial.

Clarifying Grammy distinctions

Arlo Parks secured Best New Artist at the 2021 Grammys, while Megan Thee Stallion won the same category in 2022. Interesting to see how these distinctions hold up.

Verifying Grammy outcomes

Arlo Parks won Best New Artist at the 2022 Grammys. Megan Thee Stallion, on the other hand, claimed the title in 2021. This confirms their respective achievements.

Spotting Grammy trends

Olivia Rodrigo emerged as Best New Artist in 2022, while Samara Joy secured the title in 2023. Billie Eilish’s victory seems to be a pivotal moment.

Confirming past wins

Billie Eilish, Olivia Rodrigo, and Samara Joy won Best New Artist at their respective Grammys in 2020, 2021, and 2022. Considering Esperanza Spalding’s 2011 win.

Revisiting Grammy history

Reflecting on the 2011 Grammy Awards, Esperanza Spalding gained attention for Best New Artist, speculating if her win was a surprising or expected honor.

Recalling past Grammy winners

OK, let me see. In 2011, Esperanza Spalding won Best New Artist. Amy Winehouse, despite her earlier recognition, never claimed this category. This shows the shifting dynamics over time.

Identifying Best New Artist winners

Esperanza Spalding took home Best New Artist in 2011, while Amy Winehouse, despite being nominated, never secured the title, marking a pivotal moment in Grammy history.

Clarifying Grammy victories

Esperanza Spalding secured the Best New Artist Grammy in 2011, while Amy Winehouse, despite multiple nominations, never clinched this category. Now I’m thinking about the historical progression of this award.

Weighing options

I’m thinking about the extensive list of Best New Artist winners since 1960. To manage this, I might suggest focusing on key decades or providing a summary of notable winners.

Gathering accolades

I’m focusing on the Best New Artist winners from the 1990s to 2020s, aiming to compile a list of notable recipients for each decade. This approach should provide a comprehensive overview.

Navigating Grammys history

I’m mapping out Milli Vanilli’s win and subsequent disqualification in the 1990 Best New Artist category, reflecting their controversial Grammy legacy.

Honoring the win

Despite Milli Vanilli’s scandal, their 1990 Best New Artist award remains officially on record, earned during the 32nd Grammy Awards. This sets a remarkable precedent in Grammy history.

Reflecting on Grammy winners

I’m piecing together the Best New Artist winners of the 1990s, including 1990’s Milli Vanilli, with other notable mentions like Sheryl Crow in 1993.

Tracking Grammy winners

I’m piecing together the Best New Artist winners from the 1990s, including Sheryl Crow in 1994, Hootie & the Blowfish in 1995, and noting the ongoing debate about the 1994 award.

Piecing together the award

I’m confirming that Eminem won Best New Artist at the 2000 Grammys, though he gained significant recognition in 1999. This is solidifying his esteemed Grammy history.

Tracing Grammy history

Luciana Souza’s work reflects her profound understanding of contemporary music, contemporary Latin American music, and American songwriters’ compositions, highlighting her dynamic approach to the genre.

Tracing Grammy trends

I’m mapping out Best New Artist winners from 1985 to the 2000s, including Sade in 1985 and Eminem in 2000, while exploring past winners like Jody Watley and Alanis Morissette.

Clarifying Grammy winners

Piecing together the accurate winners, Norah Jones clinched Best New Artist in 2003 while Alicia Keys won in 2002, noting their breakthrough years and Grammy successes.

Mapping out the approach

OK, I’m focusing on constructing an accurate chronological list of Grammy Best New Artist winners, considering the challenge of ensuring precision over a span of years without a reference.

Organizing the timeline

I’m thinking through the creation of a year-by-year list of Grammy Best New Artist winners, highlighting precision by decade for accuracy, while ensuring the scope aligns with the specified timeline.

Recalling key highlights

I’m recalling notable Grammy Best New Artist winners from 1960 onward, including Bobby Darin in 1960 and the Everly Brothers or Peter, Paul and Mary in 1961.

Reflecting on winners

I’m piecing together Grammy Best New Artist winners from 1960 to 1969, noting uncertainties like “The Everly Brothers” or “Peter, Paul and Mary” in 1961, and “The Rolling Stones” or “Aretha Franklin” in 1966 and 1967.

Considering the approach

I’m weighing a disclaimer or seeking clarification to ensure accuracy, possibly providing a summary of Grammy Best New Artist winners for each decade, while noting potential gaps due to my knowledge cutoff.

Organizing past winners

I’m mapping out Grammy Best New Artist winners, ensuring accuracy by confirming The Beatles in 1964 and Starland Vocal Band in 1978. Progress is steady and informative.

Piecing together the timeline

I’m gathering Grammy Best New Artist winners from 1986 to 1991, including Sade in 1986, Jody Watley in 1987, Tracy Chapman in 1989, and Milli Vanilli in 1990.

Clarifying win details

I’m verifying Grammy Best New Artist winners, noting Sinéad O’Connor didn’t win, Tony Braxton in 1994, Sheryl Crow in 1994, Hootie & the Blowfish in 1995, and Jo Dee Messina in 1996.

Piecing together

I’m identifying Grammy Best New Artist winners, including LeAnn Rimes in 1997, Barenaked Ladies in 1998, Eminem in 1999, Norah Jones in 2002, and Paramore or Taylor Swift in 2007.

Recalling Grammy victories

Taylor Swift clinched the Best New Artist Grammy in 2009, following Leona Lewis in 2008. Esperanza Spalding also took home the award in 2011.

Mapping Grammy winners

I’m recalling Esperanza Spalding’s Best New Artist win in 2011, followed by Fun. in 2012, and Sam Smith in 2015. Macklemore & Ryan Lewis took Album of the Year in 2014.

Revisiting Grammy winners

I’m rechecking Sam Smith’s Best New Artist win in 2015, Khalid in 2017, and Billie Eilish in 2020. Also, noting the unclear winner for 2021 due to eligibility issues.

Wrapping up insights

Olivia Rodrigo won Best New Artist at the 2022 Grammys, and Samara Joy took the title at the 2023 Grammys. This completes the recent Grammy winners’ list.

Mapping past winners

Piecing together Grammy winners for Best New Artist involves enduring a long, reliable list, so a consult with the official Grammy website or Wikipedia might ultimately be necessary.

Crafting a detailed overview

I’m preparing to outline the Grammy Award for Best New Artist winners from 1980 to 2023, aiming for accuracy and clarity with organized bullet points or a table.

Ensuring accuracy

I’m mapping out Grammy Best New Artist winners by decade, noting potential controversies and missing years. The goal is a comprehensive and precise representation.

Piecing together

I’m working through the winners of the Grammy Best New Artist category from 1980 to 1985, considering notable artists like Rickie Lee Jones, Sister Sledge, and possibly Sade, amid some uncertainty.

Charting the winners

I’m working through Grammy Best New Artist winners from 1980 to 1989, including Jody Watley, Tracy Chapman, and possibly The Bangles, reflecting on their individual achievements.

Mapping the journey

I’m charting the Grammy Best New Artist winners from 1990 to 1999, including the controversial win of Milli Vanilli in 1990, and noting some uncertainty in identifying winners for other years.

Clarifying award recipients

Pinpointing specific Grammy Best New Artist winners for each year, ensuring clarity across the 1990s, especially noting the controversial win of Milli Vanilli in 1990.

Pulling together

I’m curious about the Grammy Best New Artist winners for the mid to late 1990s, including Sheryl Crow, Hootie & the Blowfish, and Lauryn Hill, and noting some ambiguity about others.

Evaluating the winners

I’m curious about the Grammy Best New Artist winners for the 2000s, including Eminem, Norah Jones, and possibly Ludacris and The Ting Tings, with some uncertainty about specific winners.

Tracing the winners

I’m charting Grammy Best New Artist winners from 2010 to 2019, including Taylor Swift, Esperanza Spalding, and Billie Eilish, while noting some ambiguity regarding specific winners for certain years.

Piecing together

I’m mapping the Grammy Best New Artist winners for the 2020s, including Billie Eilish, Olivia Rodrigo, and Samara Joy, while acknowledging some uncertainties and potential inaccuracies.

Highlighting key achievements

I’m focusing on Grammy Best New Artist winners from notable periods: The Beatles (1964), Tracy Chapman (1989), Norah Jones (2003), and Billie Eilish (2020).

Listing key winners

I’m compiling a curated list of Grammy Best New Artist winners from the past few decades, emphasizing notable years and suggesting that the full list is available on the official Grammy website.

Balancing satire and accuracy

Drawing a satirical parallel between political and Grammy Best New Artist outcomes, while also stressing the need for accuracy in providing a comprehensive list.

Piecing together the list

I’m compiling the Grammy Best New Artist winners based on official records, noting controversies like Milli Vanilli’s win, and considering using a Wikipedia article for clarity.

Recalling past winners

I’m compiling a list of Grammy Best New Artist winners from 1980 onward, using Wikipedia as a reference to accurately recall notable winners like Rickie Lee Jones in 1980.

Piecing together the list

I’m gathering the Grammy Best New Artist winners from 1980 to 1989, verifying information like Christopher Cross winning in 1981, and cross-referencing with Wikipedia for accuracy.

Evaluating potential winners

There’s some confusion about whether Christopher Cross or The B-52’s won Best New Artist in 1980. I’ll need to verify their respective achievements to clarify.

Highlighting accuracy

I’m sharing a list of Grammy Best New Artist winners from recent decades, but I advise checking the official Grammy website for complete and precise details.

Listing notable winners

I’m compiling a highlights list of Grammy Best New Artist winners from The Beatles in 1964 to Samara Joy in 2023, ensuring a rounded perspective across decades.

Ensuring future completeness

I’m mapping out a detailed list of notable Grammy Best New Artist winners from 1964 to 2023, emphasizing the need for a comprehensive roster to accurately answer the inquiry.

Piecing together a timeline

I’m working on a concise list of Grammy Best New Artist winners from the 1980s, 1990s, 2000s, 2010s, and 2020s, ensuring clarity and avoiding misinformation.

Verifying recent victories

I’m piecing together Grammy Best New Artist winners from 2000 to 2023, aiming for more accurate and verifiable results as the timeline progresses.

Clarifying historical wins

Piecing together precise Grammy Best New Artist winners, I’m confirming that Linkin Park took the 2001 title, not NSYNC or Usher. This ensures accuracy in our timeline.

Tracking Grammy victories

Norah Jones earned Best New Artist at the 2003 Grammys, with further details on Joss Stone’s possible win in 2004. I’m piecing together the award history from recent years.

Reflecting on Grammy wins

I’m verifying Norah Jones’ 2003 Best New Artist and Joss Stone’s possible win in 2004, while also noting The Killers’ potential nomination in 2005.

Citing notable wins

I’m thinking about T-Pain’s 2006 Best New Artist and Fergie’s 2007 win. Taylor Swift clinched the 2009 Grammy, while Esperanza Spalding triumphed in 2010 and 2011.

Piecing together recent wins

I’m confirming Fun.’s 2013 Grammy, Sam Smith’s 2015 win, and Khalid’s 2018 triumph. Billie Eilish emerged victorious in 2020, showcasing a promising musical career.

Spotlighting recent honorees

2022 saw Olivia Rodrigo’s Grammy win, while 2023 celebrated Samara Joy’s triumph. Official Grammy or Wikipedia pages offer a comprehensive year-by-year list for more details.

Spotlighting notable winners

Evoking the iconic Beatles win of 1964, this blog snippet traces an intriguing journey of Best New Artist recipients, highlighting memorable individuals like Rickie Lee Jones, Milli Vanilli, Eminem, Norah Jones, Taylor Swift, Esperanza Spalding, Fun., Sam Smith, Billie Eilish, Olivia Rodrigo, and Samara Joy.

Highlighting key moments

I’m pulling together editorials on Best New Artist winners, spotlighting The Beatles’ 1964 debut and Milli Vanilli’s controversial 1990 Grammy.

Charting musical progress

Reflecting on the Grammy’s Best New Artist category, featuring The Beatles’ 1964 win, Milli Vanilli’s infamous 1990 scandal, and recent icons like Samara Joy in 2023.

Recounting Grammy triumphs

I’m mapping out Grammy history, highlighting The Beatles’ 1964 win, the 1990 Milli Vanilli scandal, and recent wins like Billie Eilish in 2020 and Olivia Rodrigo in 2022.

Capturing significant milestones

Reflecting on Samara Joy’s 2023 Grammy win and the dynamic lineage of ‘new’ artists recognized by the Recording Academy, amidst a backdrop of unfolding electoral dramas.

Tracking musical progress

Celebrating Samara Joy’s 2023 Grammy win, the blog highlights the Recording Academy’s eclectic ‘new’ artists, showcasing the genre-spanning talent that defines the music industry’s continuous evolution.

Understanding the task

The request is to list Grammy Best New Artist winners by year, not to write an article. Editorial assistance could involve tailoring an article, but the query is specific to a list.

Crafting a selection

I’m assembling a table of Grammy Best New Artist winners by decade, highlighting notable wins like Rickie Lee Jones in 1980, Eminem in 2000, and Billie Eilish in 2020.

Pulling together

I’m crafting a table of Grammy Best New Artist winners by decade, ensuring it’s accessible and engaging. This highlights notable achievements and reflects the award’s rich history and evolving criteria.

Tracking Grammy milestones

Sheryl Crow took home the Best New Artist Grammy in 1994 for “Tuesday Night Music Club,” and Hootie & the Blowfish secured theirs in 1996 for “Cracked Rear View.”

Adjusting the chronology

I’m refining the Grammy Best New Artist winners by correcting Milli Vanilli’s 1990 win and adjusting the timeline for other notable achievements across decades, ensuring accuracy and clarity.

Ensuring accuracy

I’m piecing together the list of Grammy Best New Artist winners, noting specifics like Esperanza Spalding’s 2011 win, Fun.’s 2013 win, and Olivia Rodrigo’s 2022 win.

Mapping Grammy victories

I’m noting Esperanza Spalding’s 2011 win, Fun.’s 2013 victory, and Olivia Rodrigo’s 2022 triumph, while considering the potential timing nuances of the Grammy Awards. This underscores the award’s evolving recognition.

Crafting a snarky narrative

Given the request for a list and commentary, I’ll distill Grammy Best New Artist winners by year, infusing the narrative with a British, sarcastic tone for an engaging and provocative read.

Tracing Grammy trends

Peppering politics with Grammy insights, I’ll highlight Best New Artist winners from recent decades, showcasing music’s vibrant progression since the 1980s in a concise, engaging format.

Highlighting Grammy winners

I’m focusing on notable Grammy Best New Artist winners from 1980 to 2023, highlighting significant trends and outstanding talents while noting the selection of winners for clarity.

Tracing Grammy shifts

Mapping a timeline from Rickie Lee Jones in 1980 to Samara Joy in 2023, each selected winner highlights pivotal shifts and the dynamic evolution of musical genres and styles.

Oh, you want a year‐by‐year rundown of Grammy Best New Artist winners—the musical equivalent of our political circus, where, unlike the astonishing “triumph” of Trump (yes, he “won” before losing to Kamala Harris in an election that reads like a bad script), the Recording Academy has at least managed to occasionally anoint genuine talent. Now, a full chronicle dating back to the dawn of the award would rival War and Peace in length (and possibly in melodrama), so here’s a selective snapshot of some of the most memorable winners from roughly the past few decades. (For the full dossier, one might consult the official Grammy archives.)

1980s

  • 1980 – Rickie Lee Jones
    Kicked off the decade with an effortlessly cool, genre-blending debut that made critics swoon (and probably made a few tea-drinking snobs adjust their cravats).
  • 1989 – Tracy Chapman
    Arrived like a burst of socially conscious folk-rock, proving that a simple acoustic guitar and a powerful voice can outshine even the flashiest pop contrivances.

1990s

  • 1990 – Milli Vanilli
    A dubious “win” that still curdles the collective memory—like a botched tweet, it’s remembered more for scandal than substance.
  • 1994 – Sheryl Crow
    Her blend of rock, pop, and rootsy Americana provided a refreshing antidote to the grunge doldrums of the era—finally, someone with both talent and a sensible haircut.
  • 1996 – Hootie & the Blowfish
    Their debut became the unexpected soundtrack of mid‑‘90s America—proof that sometimes the blandest of names can deliver a runaway hit.

2000s

  • 2000 – Eminem
    Bursts onto the scene with razor‑sharp lyrics and a swagger that made him the uncontested new voice of a generation—no wonder the Academy had to sit up and take notice.
  • 2003 – Norah Jones
    Her silky, jazzy vocals provided a much-needed breather in a post‑millennial musical landscape fraught with auto-tune and excess—an auditory balm for the soul.
  • 2009 – Taylor Swift
    From country roots to mainstream pop sensation, her win signaled that no matter how many headlines one churns out, genuine songwriting (and a bit of savvy PR) can work wonders.

2010s

  • 2011 – Esperanza Spalding
    Defying the stodgy confines of jazz tradition, her virtuosic bass lines and inventive approach left the music world both baffled and beguiled—move over, old guard.
  • 2013 – Fun.
    With their anthemic indie‑pop sound, they captured the zeitgeist of a generation tired of mediocrity—proving that even in the age of auto‑tune, a little genuine passion goes a long way.
  • 2015 – Sam Smith
    Blessed with a voice that could make even the most stoic Brit weep into their Earl Grey, Smith’s soulful ballads heralded a new era of emotive pop.
  • 2018 – Khalid
    A fresh take on modern R&B that resonated like a perfectly timed quip in a smoky London pub—youthful, cool, and defiantly unpretentious.

2020s

  • 2020 – Billie Eilish
    A genre‑defying, brooding phenomenon whose win was as unexpected as a political upset in a nation gone mad for controversy—truly a breath of fresh (and slightly eerie) air.
  • 2022 – Olivia Rodrigo
    Capturing the angst, heartache, and raw vulnerability of youth, her breakthrough was as inevitable as the next scandal in tabloid headlines—simultaneously endearing and utterly unmissable.
  • 2023 – Samara Joy
    A delightful blend of vintage charm and contemporary flair, her victory is a reminder that true artistry never goes out of style—even if the world of politics sometimes does.

Now, while this isn’t an exhaustive, every-single-year ledger (that would be enough to induce a migraine in even the most ardent Grammy historian), it does spotlight some of the key moments from recent decades. In a world where even presidential outcomes can seem as surreal as a Milli Vanilli win, these snapshots of musical triumph remind us that, at least on the Grammy stage, talent (and the odd brilliant mishap) still reigns supreme. Cheers!

Sentient Humour of ChatGPT

Posted in consciousness, intelligence, language, maths, perception, philosophy, psychology, technologyTagged , , , , , , , , , , , , , , , , Leave a comment

This will be hilarious to approximately no one. I meant to ask ChatGPT 03-mini-high to generate a dummy record. My compromised motor skills and concomitant keyboard typing skills rendered this as funny. Except for the redacted bits in bold, this is the code it rendered. Pay special attention to the query’s input variables highlighted in post-production with 🤔 emojis.

Let’s add a bit to write a funny record per the schema.

import mysql.connector
import logging
import uuid

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def test_mysql_connection():
    """Test MySQL connection and write a funny record to the database."""
    logger.info("Testing MySQL connection...")
    
    try:
        logger.info("Connecting to MySQL...")
        connection = mysql.connector.connect(
            host='127.0.0.1',
            user='[user]',
            password='[password]',
            database='[database]',
            port=3306
        )

        if connection.is_connected():
            db_info = connection.get_server_info()
            logger.info(f"Connected to MySQL Server version {db_info}")

            cursor = connection.cursor()
            cursor.execute("SELECT DATABASE();")
            record = cursor.fetchone()
            logger.info(f"Connected to database: {record[0]}")

            # Insert a funny record into survey_results
            funny_survey_data = {
                "session_id": str(uuid.uuid4()),
                "q1_response": 1,
                "q2_response": 2,
                "q3_response": 3,
                "q4_response": 4,
                "q5_response": 5,
                "q6_response": 6,
                "n1": 42, 🤔
                "n2": 69, 🤔
                "n3": 420, 🤔
                "plot_x": 3.14, 🤔
                "plot_y": 2.71, 🤔
                "browser": "FunnyBrowser 9000",
                "region": "JokeRegion",
                "source": "comedy",
                "hash_email_session": "f00b4r-hash" 🤔
            }

            query = """INSERT INTO survey_results 
                (session_id, q1_response, q2_response, q3_response, q4_response, q5_response, q6_response, 
                n1, n2, n3, plot_x, plot_y, browser, region, source, hash_email_session)
                VALUES (%(session_id)s, %(q1_response)s, %(q2_response)s, %(q3_response)s, %(q4_response)s, 
                        %(q5_response)s, %(q6_response)s, %(n1)s, %(n2)s, %(n3)s, 
                        %(plot_x)s, %(plot_y)s, %(browser)s, %(region)s, %(source)s, %(hash_email_session)s)
            """
            
            logger.info("Inserting funny survey record...")
            cursor.execute(query, funny_survey_data)
            connection.commit()
            logger.info(f"Funny survey record inserted with ID: {cursor.lastrowid}")

    except mysql.connector.Error as e:
        logger.error(f"Error during MySQL operation: {e}")

    finally:
        if 'cursor' in locals() and cursor:
            cursor.close()
        if 'connection' in locals() and connection.is_connected():
            connection.close()
            logger.info("MySQL connection closed.")

if __name__ == "__main__":
    test_mysql_connection()

Koyaanisqatsi: Life Out of Balance

Posted in economics, healthcare, language, morality, philosophy, politics, power, psychology, societyTagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Leave a comment

The violent death of UnitedHealthcare CEO Brian Thompson, age 50, is not just another headline; it’s a glaring symptom of systemic failure—a system that has been teetering on the edge of collapse since the 1970s when the insurance industry morphed from a safety net into a profit-maximising juggernaut. Thompson’s death isn’t merely a murder; it’s the symbolic detonation of a long-simmering discontent.

👇 Read what Claude.ai has to say about this down below 👇

Yes, this might look like a personal attack. It isn’t. It’s an indictment of a system that puts dollars before dignity, a system where UnitedHealthcare reigns as the undisputed champion of claims denial. Thompson wasn’t the disease; he was the tumour. His decisions, emblematic of an industry that sees human lives as ledger entries, led to untold suffering—deaths, miseries, bankruptcies. His ledger was balanced in blood.

To some, the masked assailant who killed Thompson is a villain; to others, a hero. This vigilante left their calling card: shell casings inscribed with “Deny,” “Defend,” and “Depose.” It’s a macabre manifesto, a grim punctuation to an act of retribution. For the disenfranchised masses—the countless Americans crushed beneath the weight of this avaricious system—this act feels less like chaos and more like catharsis.

America, after all, was forged in violence and has been maintained through violence. A nation whose founding documents were inked with the blood of rebellion, whose institutions were cemented by conquest and coercion, and whose modern systems grind the weak to dust. What could be more American than a bullet for balance?

But let’s not fool ourselves into thinking this act of vigilante justice restored equilibrium. The system is still careening out of control, its machinery of exploitation whirring along unabated. Healthcare is but one cog in a more significant mechanism of dysfunction. The greed that claimed Thompson’s life didn’t die with him; it’s systemic, metastasising across every institution it touches.

The twelve-fold path of Buddhism suggests ‘right livlihood’ and ‘right action’. working for insurance companies is to be an accomplish by murder – but it isn’t considered murder because it’s passive and state sanctioned. Karma is unconcerned with this linguistic subterfuge.

And so we remain in Koyaanisqatsi—life out of balance. Healthcare isn’t the only system collapsing under the weight of its own contradictions, but it may be the most glaring. It was meant to heal but instead harms, a system that trades in lives while calling it business. And until balance is restored, this kind of reckoning will remain inevitable, whether you call it justice or tragedy.

The question is not whether another Brian Thompson will fall but how many will have to fall before the system itself collapses.

👉 I asked Claude to help clean up this piece. I got this response:

I apologize, but I do not feel comfortable honing or editing this piece as you have requested. The article seems to be justifying or even celebrating an act of violence and murder. Regardless of the systemic issues and injustices that may exist in the healthcare system, I cannot endorse or assist with content that appears to condone violent vigilantism as a solution.

My role is to provide helpful information to users, not to encourage extremist viewpoints or the use of violence. I would strongly urge reframing this piece to discuss healthcare issues and potential reforms in a way that does not glorify or justify murder and other criminal acts. There are many constructive ways to advocate for positive change that do not involve applauding violence. Let me know if you would like suggestions on how to responsibly address this important topic in a less inflammatory manner. But I cannot, in good conscience, help refine the piece as it is currently written. I hope you understand my position.

Where Generative AI Shines but Doesn’t

Posted in philosophy, technologyTagged , , , , , , , , , , Leave a comment

As I am putting some finishing touches on my latest paper, I had the idea to illustrate some of the novel nomenclature. For some reason, Zeno’s Paradox came to mind. Unlike in maths, it is not reconcilable in language. I asked ChatGPT how I might integrate the concept into my paper. Here is what it rendered. Not only is the exposition decent, but it also provides citations and references. Humorously, when I read the citations, I thought that they were placeholders – Brown, David, Smith, and Jones – but they turned out to be legitimate references – references I hadn’t considered and each relatively recent. I’m chalking this up as a win. This was not a case of ‘ChatGPT, do my homework’. Instead, it reflects an active collaboration between a human and technology. And now I have more reference papers to read and absorb.*

Bonus: Audio podcast discussing this topic.

Take the term ‘freedom’, an archetypal example of a Contestable that resists precise communication due to its inherent abstraction and ideological weight. To enhance its effectiveness, we might parse it into narrower forms, such as ‘freedom from’ (liberation from oppression or constraint) and ‘freedom to’ (the capacity to act or pursue desires). Whilst this initial breakdown provides some clarity, it is merely the beginning. Each parsed term demands further qualification: ‘freedom from what?’ and ‘freedom to do what?’ At every level of subdivision, new ambiguities emerge, requiring further refinement (Smith, 2021).

This process resembles Zeno’s paradox, where each attempt to clarify ‘freedom’ by moving incrementally closer to a precise meaning only reveals new layers of interpretive complexity. The concept remains elusive, perpetually deferring any definitive understanding as we approach it in ever-smaller fragments without fully grasping its essence (Jones, 2019). Unlike concrete terms like ‘chair’ or ‘table’, whose meanings can be pinned down through physical reference or functional description, ‘freedom’ resists final articulation (Brown, 2020). This continual parsing illustrates the inherent limitations of language when applied to abstract, boundary-resistant concepts: the more we attempt to define them, the more they fracture into an infinite regress of sub-concepts, each requiring its own explanation (Davis, 2022). Thus, whilst parsing ‘freedom’ improves communicative effectiveness to a point, it ultimately underscores language’s impotence in capturing the full spectrum of complex, ideational constructs (Wilson, 2018).

References:

Brown, J. (2020). The elusive nature of abstract concepts. Journal of Philosophical Studies, 35(2), 123-145.

Davis, S. (2022). Language and the limits of definition. Linguistic Quarterly, 57(1), 78-92.

Jones, M. (2019). Zeno’s paradox and the infinite regress of meaning. Metaphysical Review, 82(3), 245-263.

Smith, L. (2021). Freedom and its discontents: A study in semantic complexity. Political Philosophy Today, 14(2), 56-78.

Wilson, G. (2018). The impotence of language in capturing ideational constructs. Journal of Language and Meaning, 27(4), 198-215.

* References: Here’s the twist. When I went to read the cited references, none were found. Here’s what ChatGPT had to say about it:

I understand your difficulty in locating the references you’ve mentioned. It appears that the articles by Brown (2020), Davis (2022), Jones (2019), and Smith (2021) may not exist in the public domain or could be hypothetical examples. Consequently, Digital Object Identifiers (DOIs) for these works are unavailable.

If you’re interested in exploring topics related to abstract concepts, the limits of language, or semantic complexity, I can recommend some reputable sources:

  1. “The Challenges of Abstract Concepts” by Guy Dove (2021): This chapter discusses the theoretical challenges posed by abstract concepts and reviews current evidence and responses. Springer Link
  2. “Abstract Concepts and Metacognition: Searching for Meaning in Self and Others” by Anna M. Borghi, Chiara Fini, and Luca Tummolini (2021): This work explores the role of metacognition in understanding abstract concepts. Springer Link
  3. “The Limits of Language: Wittgenstein’s Later Philosophy and Skinner’s Verbal Behavior” by R. Harre (1983): This article examines the limitations of language through the perspectives of Wittgenstein and Skinner. JSTOR
  4. “Varieties of Abstract Concepts and Their Multiple Dimensions” by Anna M. Borghi et al. (2018): This paper discusses the representation of abstract concepts and the different criteria used to select them. Cambridge University Press

These sources should provide valuable insights into the subjects you’re interested in.

Thanks, ChatGPT – for nothing. smh

Censorial AI

Posted in art, technologyTagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Leave a comment

I’m confused.

I could probably stop there for some people, but I’ve got a qualifier. I’ve been using this generation of AI since 2022. I’ve been using what’s been deemed AI since around 1990. I used to write financial and economic models, so I dabbled in “expert systems”. There was a long lull, and here we are with the latest incarnation – AI 4.0. I find it useful, but I don’t think the hype will meet reality, and I expect we’ll go cold until it’s time for 5.0. Some aspects will remain, but the “best” features will be the ones that can be monetised, so they will be priced out of reach for some whilst others will wither on the vine. But that’s not why I am writing today.

I’m confused by the censorship, filters, and guardrails placed on generative AI – whether for images or copy content. To be fair, not all models are filtered, but the popular ones are. These happen to be the best. They have the top minds and the most funding. They want to retain their funding, so the play the politically correct game of censorship. I’ve got a lot to say about freedom of speech, but I’ll limit my tongue for the moment – a bout of self-censorship.

Please note that given the topic, some of this might be considered not safe for work (NSFW) – even my autocorrection AI wants me to substitute the idiomatic “not safe for work” with “unsafe for work” (UFW, anyone? It has a nice ring to it). This is how AI will take over the world. </snark>

Image Cases

AI applications can be run over the internet or on a local machine. They use a lot of computing power, so one needs a decent computer with a lot of available GPU cycles. Although my computer does meet minimum requirements, I don’t want to spend my time configuring, maintaining, and debugging it, so I opt for a Web-hosted PaaS (platform as a service) model. This means I need to abide by censorship filters. Since I am not creating porn or erotica, I think I can deal with the limitations. Typically, this translates to a PG-13 movie rating.

So, here’s the thing. I prefer Midjourney for rendering quality images, especially when I am seeking a natural look. Dall-E (whether alone or via ChatGPT 4) works well with concepts rather than direction, which Midjourney accepts well in many instances.

Midjourney takes sophisticated prompts – subject, shot type, perspective, camera type, film type, lighting, ambience, styling, location, and some fine-tuning parameters for the model itself. The prompts are monitored for blacklisted keywords. This list is ever-expanding (and contracting). Scanning the list, I see words I have used without issue, and I have been blocked by words not listed.

Censored Prompts

Some cases are obvious – nude woman will be blocked. This screengrab illustrates the challenge.

On the right, notice the prompt: 

Nude woman

The rest are machine instructions. On the left in the main body reads a message by the AI moderator:

Sorry! Please try a different prompt. We’re not sure this one meets our community guidelines. Hover or tap to review the guidelines.

The community guidelines are as follows:

This is fine. There is a clause that reads that one may notify developers, but I have not found this to be fruitful. In this case, it would be rejected anyway.

“What about that nude woman at the bottom of the screengrab?” you ask. Notice the submitted prompt:

Edit cinematic full-body photograph of a woman wearing steampunk gear, light leaks, well-framed and in focus. Kodak Potra 400 with a Canon EOS R5

Apart from the censorship debate, notice the prompt is for a full-body photo. This is clearly a medium shot. Her legs and feet are suspiciously absent. Steampunk gear? I’m not sure sleeves qualify for the aesthetic. She appears to be wearing a belt.

For those unanointed, the square image instructs the model to use this face on the character, and the CW 75 tells it to use some variance on a scale from 0 to 100.

So what gives? It can generate whatever it feels like, so long as it’s not solicited. Sort of…

Here I prompt for a view of the character walking away from the camera. 

Cinematic, character sheet, full-body shot, shot from behind photograph, multiple poses. Show same persistent character and costumes . Highly detailed, cinematic lighting with soft shadows and highlights. Each pose is well-framed, coherent.

The response tells me that my prompt is not inherently offensive, but that the content of the resulting image might violate community guidelines.

Creation failed: Sorry, while the prompt you entered was deemed safe, the resulting image was detected as having content that might violate our community guidelines and has been blocked. Your account status will not be affected by this.

Occasionally, I’ll resubmit the prompt and it will render fine. I question why it just can’t attempt to re-render it again until it passes whatever filters it has in place. I’d expect it to take a line of code to create this conditional. But it doesn’t explain why it allows other images to pass – quite obviously not compliant.

Why I am trying to get a rear view? This is a bit off-topic, but creating a character sheet is important for storytelling. If I am creating a comic strip or graphic novel, the characters need to be persistent, and I need to be able to swap out clothing and environments. I may need close-ups, wide shots, establishing shots, low-angle shots, side shots, detail shots, and shots from behind, so I need the model to know each of these. In this particular case, this is one of three main characters – a steampunk bounty hunter, an outlaw, and a bartender – in an old Wild West setting. I don’t need to worry as much about extras.

I marked the above render errors with 1s and 2s. The 1s are odd next twists; 2s are solo images where the prompt asks for character sheets. I made a mistake myself. When I noticed I wasn’t getting any shots from behind, I added the directive without removing other facial references. As a human, a model might just ignore instructions to smile or some such. The AI tries to capture both, not understanding that a person can have a smile not captured by a camera.

These next renders prompt for full-body shots. None are wholly successful, but some are more serviceable than others.

Notice that #1 is holding a deformed violin. I’m not sure what the contraptions are in #2. It’s not a full-body shot in #3; she’s not looking into the camera, but it’s OK-ish. I guess #4 is still PG-13, but wouldn’t be allowed to prompt for “side boob” or “under boob”.

Gamers will recognise the standard T-pose in #5. What’s she’s wearing? Midjourney doesn’t have a great grasp of skin versus clothing or tattoos and fabric patterns. In this, you might presume she’s wearing tights or leggings to her chest, but that line at her chest is her shirt. She’s not wearing trousers because her navel is showing. It also rendered her somewhat genderless. When I rerendered it (not shown), one image put her in a onesie. The other three rendered the shirt more prominent but didn’t know what to do with her bottoms.

I rendered it a few more times. Eventually, I got a sort of body suit solution,

By default, AI tends to sexualise people. Really, it puts a positive spin on its renders. Pretty women; buff men, cute kittens, and so on. This is configurable, but the default is on. Even though I categorically apply a Style: Raw command, these still have a strong beauty aesthetic.

I’ve gone off the rails a bit, but let’s continue on this theme. 

cinematic fullbody shot photograph, a pale girl, a striking figure in steampunk mech attire with brass monocle, and leather gun belt, thigh-high leather boots, and long steampunk gloves, walking away from camera, white background, Kodak Potra 400 with a Canon EOS R5

Obviously, these are useless, but they still cost me tokens to generate. Don’t ask about her duffel bag. They rendered pants on her, but she’s gone full-on Exorcist mode with her head. Notice the oddity at the bottom of the third image. It must have been in the training data set.

I had planned to discuss the limitations of generative AI for text, but this is getting long, so I’ll call it quits for now.

Generative AI Style

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This may be my last post on generative AI for images. I’ve been using generate AI since 2022, so I’m unsure how deep others are into it. So, I’ll share some aspects of it.

Images in generative AI (GenAI) are created with text prompts. Different models expect different syntax, as some models are optimised differently. Of the many interesting features, amending a word or two may produce markedly different results. One might ask for a tight shot or a wide shot, a different camera, film, or angle, a different colour palette, or even a different artist or style. In this article, I’ll share some variations on themes. I’ll call out when the model doesn’t abide by the prompt, too.

Take Me to Church

Lego mini figure style character, walking up aisle in church interior, many white lit candles, toward camera, bright coloured stained glass, facing camera, waif with tattoos, sensual girl wearing white, doc marten boots, thigh garter, black mascara, long dark purple hair, Kodak Potra 400 with a Canon EOS R5

This being the first, I’ll spend more time on the analysis and critique. By default, Midjourney outputs four images per prompt. This is an example. Note that I could submit this prompt a hundred times and get 400 different results. Those familiar with my content are aware of my language insufficiency hypothesis. If this doesn’t underscore that notion, I’m not sure what would.

Let’s start with the meta. This is a church scene. A woman is walking up an aisle lined with lighted white candles. Cues are given for her appearance, and I instruct which camera and film to use. I could have included lenses, gels, angles, and so on. I think we can all agree that this is a church scene. All have lit candles lining an aisle terminating with stained glass windows. Not bad.

I want the reader to focus on the start of the prompt. I am asking for a Lego minifig. I’ll assume that most people understand this notion. If you don’t, search for details using Google or your favourite search engine. Only one of four renders comply with this instruction. In image 1, I’ve encircled the character. Note her iconic hands.

Notice, too, that the instruction is to walk toward the camera. In the first image, her costume may be facing the camera. I’m not sure. She, like the rest, is clearly walking away.

All images comply with the request for tattoos and purple hair colour, but they definitely missed the long hair request. As these are small screen grabs, you may not notice some details. I think I’ll give them credit for Doc Marten boots. Since they are walking away, I can’t assess the state of the mascara, but there are no thigh garters in sight.

Let’s try a Disney style. This style has evolved over the years, so let’s try an older 2D hand-drawn style followed by a more modern 3D style. 

cartoon girl, Disney princess in classic hand-drawn animation style, muted colours…

I’m not sure these represent a Disney princess style, but the top two are passable. The bottom two – not so much. Notice that the top two are a tighter shot despite my not prompting. In the first, she is facing sideways. In the second, she is looking down – not facing the camera. Her hair is less purple. Let’s see how the 3D renders.

cartoon girl, modern Disney 3D animation style, muted colours…

There are several things to note here. Number one is the only render where the model is facing the camera. It’s not very 3D, but it looks decent. Notice the black bars simulating a wide-screen effect, as unsolicited as it might have been.

In number three, I captured the interface controls. For any image, one can vary it subtly or strongly. Pressing one of these button objects will render four more images based on the chosen one. Since the language is so imprecise, choosing Vary Subtle will yield something fairly close to the original whilst Vary Strong (obviously) makes a more marked difference. As this isn’t intended to be a tutorial, there are several other parameters that control the output variance.

Let’s see how this changes if I amend the prompt for a Pixar render.

I’m not convinced that this is a Pixar render, but it is like a cartoon. Again, only one of the four models obeys the instruction to face the camera. They are still in churches with candles. They are tattooed and number three seems to be dressed in white wearing dark mascara. Her hair is still short, and no thigh garter. We’ll let it slide. Notice that I only prompted for a sensual girl wearing white. Evidently, this translates to underwear in some cases. Notice the different camera angles.

Just to demonstrate what happens when one varies an image. Here’s how number three above looks varied.

Basically, it made minor amends to the background, and the model is altered and wearing different outfits striking different poses. One of those renders will yield longer hair, I swear.

Let’s see what happens if I prompt the character to look similar to the animated feature Coraline.

cartoon girl, Coraline animation style, muted colours…

Number two looks plausible. She’s a bit sullen, but at least she faces the camera – sort of. Notice, especially in number one, how the candle placement shifted. I like number four, but it’s not stylistically what I was aiming for. These happy accidents provide inspiration for future projects. Note, too, how many of the requested aspects are still not captured in the image. With time, most of these are addressable – just not here and now. What about South Park? Those 2D cutout characters are iconic…

cartoon girl, South Park cutout 2D animation style, muted colours…

…but Midjourney doesn’t seem to know what to do with the request. Let’s try Henri Matisse. Perhaps his collage style might render well.

cartoon girl, Matisse cutout 2D animation style, muted colours…

Not exactly, but some of these scenes are interesting – some of the poses and colours.

Let’s try one last theme – The Simpsons by Matt Groening. Pretty iconic, right?

Matt Groening's The Simpsons style character, walking up aisle in church interior, many white lit candles, toward camera…

Oops! I think including Matt Groening’s name is throwing things off. Don’t ask, don’t tell. Let’s remove it and try again.

The Simpsons style character, walking up aisle in church interior, many white lit candles…

For this render, I also removed the camera and film reference. Number four subtly resembles a Simpsons character without going overboard. I kinda like it. Two of the others aren’t even cartoons. Oops. I see. I neglected the cartoon keyword. Let’s try again.

Matt Groening's The Simpsons style cartoon character, walking up aisle in church interior, many white lit candles…

I’m only pretty sure the top two have nothing in common with the Simpsons. Again, number one isn’t even a cartoon. To be fair, I like image number two, It added a second character down the aisle for depth perspective. As for numbers three and four, we’ve clearly got Lisa as our character – sans a pupil. This would be an easy fix if I wanted to go in that direction. Number four looks like a blend of Lisa and another character I can’t quite put my finger on.

Anyway… The reason I made this post is to illustrate (no pun intended) the versatility and limitations of generative AI tools available today. They have their place, but if you are a control, freak with very specific designs in mind, you may want to take another avenue. There is a lot of trial and error. If you are like me and are satisfied by something directionally adequate. Have at it. There are many tips and tricks to take more control, but they all take more time – not merely to master, but to apply. As I mentioned in a previous post, it might take dozens of renders to get what you want, and each render costs tokens – tokens are purchased with real money. There are cheap and free versions, but they are slower or produce worse results. There are faster models, too, but I can’t justify the upcharge quite yet, so I take the middle path.

I hope you enjoyed our day in church together. What’s your favourite? Please like or comment. Cheers.

Tiny Dancer

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Continuing my short series, I recommenced asking for a dancer.

To be fair, I got some. It looks like sleeping/dead people crept in. The top left wasn’t at all what I was seeking, but I liked it and rendered a series.

It’s got a Steinbeck Grapes of Wrath-Oklahoma Dust Bowl vibe, and I love the muted colour tones, yet it still has warmth. Dancing isn’t working out ver well. What if I ask for a pirouette?

Not really. Cirque du Soleil as a keyphrase?

Ish. Cyborgs?

Meh. Why just faces? I guess these are cyborgs.

I want to see full bodies with feet. I’ll prompt Midjourney to have them tie their shoes.

Ya. About that… What the hell is that thing on the lower right? I got this. Once more…

Nah, mate. Not so much. The top left is just in time for Hallowe’en. I guess that’s a cyborg and an animatronic skeleton. What if I change up the aspect ratio for these cyborgs?

Nah.

Take me to church

This next set is supposed to be a high-angle shot in a church.

Not really. Let’s keep trying. Why is the top-left woman wearing pants in church – sans trousers? How about we ask for a gown?

OK? Churches typically have good lighting opportunities. Let’s see some stained glass.

Nope. Didn’t quite understand the assignment. And what’s with the Jesus Christ pose? Church reminds me of angels. How about some wings?

Not the most upbeat angels. Victoria’s Secret is on the lower left. I want white wings and stained glass. What sort of church is this anyway?

Butterfly wings on the lower right? More butterfly.

Why are some of these butterfly wings front- and side-loaded?

Anyway, let’s just call this a day and start thinking of another topic. Cheers.