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The Generative Workplace: How AI Is Already Disrupting Familiar Careers

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Introduction: how will generative AI change work?

00:00 The Learning With AI initiative

Introduced by Jon Ippolito.

01:54 Study: 9 out of 10 businesses want ChatGPT skills

02:44 Speaker biographies

Advantages for business (Matt James)

04:21 CourseStorm's needs

05:40 Simplifying an interface

Will AI be the death of web forms?

08:22 Programming shortcuts

09:38 Importing data

10:02 Generating text content

10:20 Learning new technologies

Will generative AI put coders out of a job?

12:19 Automating code tests

AI may put quality assurance (QA) jobs at risk, but coders who can think abstractly will gain superpowers.

13:29 Translating code between programming languages

Problems for business (Matt James)

14:06 Hallucinations

ChatGPT lies confidently.

15:42 Struggles with tabular data

ChatGPT can solve a word problem more easily than it can average numbers in a table.

17:23: Generic results

Prompt engineering will be in demand.

Learning more (Matt James)

18:00 Inspiration from OpenAI, Twitter, Copilot, and sample prompts

23:05 How should students be adapting to the future landscape

Generative or derivative? (John Bell)

24:21 Generative AI is really derivative AI

In generative art, creators set the rules, but in generative AI a machine reverse-engineers rules based on outputs.

26:19 Minesweeper metaphor

The industry is not predicting and steering around AI's failures but mitigating damage after encountering them.

"ChatGPT is essentially an interface without an application behind it."

Strengths and limits of large language models (John Bell)

28:02 Better code through dialogue

LLMs are good at making necessary components, less so at the plumbing required to fit them together.

29:20 Generic writing

LLMs can help with paperwork and overcoming "blank page" anxiety, but are not so good for literature review.

31:44 Language is lossy

Large language models don't inherit human context.

33:18 Derivative AI as meme generator

34:41 Aesthetics of convenience

Overuse of the Ken Burns Effect made it cliché.

35:33 Text personalized for specific audiences

36:12 Skepticism about extracting structured data from notes

37:13 Are smaller models better?

Best uses may be specific, especially visual, cases since imagery is less lossy than language.

AI can produce 3d landscape from photos or a 3d avatar from a 2D video.

We may soon use text prompts to add assets and textures to virtual worlds (Geppetto's Workshop).

Effect on journalism, healthcare, and trust (Pattie Reaves and audience)

41:00 Parsing notes fields into structured data may get better

Misuse in healthcare and journalism remains a real danger.

Reducing a complicated text for a specific audience may be a better use.

44:29 Do LLMs create a logical model of the world? (Mike Hanley)

45:45 Improvement from new models. (Scott Arndt)

46:27 Text customization as danger

Tailoring for audiences might be helpful but risks reinforcing echo chambers and privacy concerns.

49:58 How will salaries be affected by generative AI? (Fox Gleason)

Rote jobs may be replaced, but not the ability to apply more abstract concepts.

Implications for creators (Eryk Salvaggio)

52:28 History and social context matters

Before large language models, artists wielded more control over their models.

Precedents include rule-based generative art (Harold Cohen) and Generative Adversarial Networks (Anna Ridler and Daniel Hanley)

58:13 Today's indiscriminate data sets

By contrast, LLMs are based on "diffused" databases--they span almost every topic and image style.

Artists' contributions to the data set are like elections: "Your individual vote doesn't count, but without individual votes, there is no election."

60:10 The models can reproduce the original images in their training data

Professional creatives don't want to use someone else's work, both for legal cover and because they value originality.

61:44 "These tools are stereotype engines"

62:56 Who is responsible for context-less racist and sexist imagery?

"There are Nazis in the data set associated with the word hero."

Google performed a system-wide intervention to prevent stereotypic results for searches like "black girls," but AI systems generally do not.

"For our legal system to say these images are produced by AI is like saying the legal rights to a photograph belong to the camera."

Rights, responsibilities, and future solutions (Pattie Reaves and audience)

70:17 Rights over AI-generated art and music

What will it do to the music industry when anyone can generate a song with the voice of another artist? (Emily Brule)

Automation already plays a major role in producing Pop music. (Eryk Salvaggio)

When you use a "song extension" tool like OpenAI's Jukebox, are you a producer or just an active consumer?

74:23 Should intellectual property treat AI-generated images, languages, and code differently? (Vijayanta Jain)

"AI is not one thing." (Eryk Salvaggio)

Recent court cases have hinged on the difference between generic function and expressive code. (John Bell)

79:40 Can/are individual websites be disallowed from the dataset? (Aiden Fuller)

Guardrails are by definition put on after the fact.

"Prompt injection": DALLE-2 injects unseen modifiers to force its model to return more diverse images than, say, just white male doctors. (Eryk Salvaggio)

82:41 New standards for labeling web content (John Bell)

New HTML headers could help structure content, eg labeling AI-generated or diverse sources, to prevent self-reinforcing datasets.

84:23 Future solutions: outlaw, regulate, specialize, or reset the systems?

Some researchers are exploring "machine unlearning," looking for ways to remove specific sources from an AI data set. (Eryk Salvaggio)

We could go back to pre-statistical AI "expert systems," and use ChatGPT as their interface. (John Bell)

Advice for current students

89:46 Become a better overall writer (John Bell)

"Prompt engineering" syntax will change as the tools evolve, but natural language may become the standard for interacting with all digital systems.

92:57 Broaden your perspective (Eryk Salvaggio)

Rather than the details of today's tools, learn their purposes as well as social and environmental context.

This presentation was recorded by the University of Maine's New Media program. For more information, contact ude.eniam@otiloppij.

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Generative AI is expected to transform any career that depends on writing, coding, or audiovisual media--in other words, just about all of them. Who better to forecast how workers will adapt to these disruptions than New Media alumni already incorporating AI into their own planning and practice?

This New Media webinar offers reports from working professionals on ways that ChatGPT and Midjourney are having an impact today on business, software, and art.

23zoom Webinar Ai Alumni Grid Vga

Presenters include artist Eryk Salvaggio, whose research focuses on the impact of generative AI on visual art and design; software engineer and accessibility expert Pattie Reaves, who's worked for Variety and Rolling Stone magazine; John Bell, who directs augmented/virtual reality and digital humanities programs at Dartmouth; and CourseStorm co-founder Matt James, who's designed applications that hundreds of thousands of people across the country use to connect to education.

This is part of a series of free webinars on cutting-edge technologies offered by the University of Maine's New Media program, which teaches animation, digital storytelling, gaming, music, physical computing, video, and web and app development. These are not Powerpoint lectures but guided demonstrations that students can follow at school or at home on their laptops. Learn more about these webinars or UMaine's Learning With AI initiative.

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