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Inspirationsbank med AI-materialer: Prompts, forløb og erfaringer til din undervisning

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Konkrete AI-eksempler fra undervisningspraksis. Prompts, forløb og erfaringer klar til din undervisning.

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En dansk AI-assistent med fokus på dansk sprog og danske forhold!

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Her er en oversikt over nyttige AI-verktøy som gjør deg mer effektiv og produktiv.

Språkmodeller & chatboter

Språkmodeller bruker kunstig intelligens til å forstå og skrive tekster.

Les mer: Alt du trenger å vite om språkmodeller.

AI-bilder & AI-kunst

Her finner du en oversikt over bildegeneratorer. Med disse verktøyene kan du lage AI-bilder.

Hvem har sagt at kunstig intelligens ikke kan være kreativ?

Bilderedigering

AI-video

Her finner du en oversikt over videogeneratorer. Med disse verktøyene kan du lage AI-videoer.

Du finner demonstrasjonsvideoer her: Så bra er AI-videoer blitt i 2024.
AI-video lages på tre måter:

1. Tekst til video: Du skriver inn en instruks som AI-en bruker til å lage video

2. Bilde til video: AI-en tar utgangspunkt i ett eller flere bilder for å lage video

3. Video til video: AI-en tar utgangspunkt i en eksisterende video for å lage en ny video

AI-avatarer

Her finner du en oversikt over AI-avatarer.

Slik fungerer det
Du velger en avatar, enten en AI eller en basert på ditt eget bilde. Deretter skriver du inn tekst som avataren leser opp på ulike språk. De snakker også norsk

AI-musikk

Her finner du en oversikt over musikkgeneratorer.

AI-stemmer

Her finner du en oversikt over stemmegeneratorer.

Slik fungerer det
Du kan lage AI-stemmer via to metoder: tekst-til-tale og stemmekloning. I sistnevnte metode omgjør AI-en stemmen din til en annen stemme.

Lydredigering

Her finner du en oversikt over AI-apper som redigerer lyd.

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23 AI-verktøy som gjør hverdagen enklere * AIavisen

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Få en oversikt over nyttige AI-verktøy som deg mer effektiv. Både på jobb og i fritiden.

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Kunstig Intelligens på børne- og ungeområdet i Vallensbæk Kommune

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Kunstig Intelligens på børne- og ungeområdet i Vallensbæk Kommune

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BLINGTRON LITE Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur […]

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Forestil dig, at du er den fødte didaktiker, og du er velbevandret udi dén disciplin, der omhandler at finde på åben og undersøgende didaktik til at udvikle din undervisning og en legende tilgang til fagets forskellige emner og områder.

Jeg har brug for din assistance til at komme op med 6 idéer til faget, hvor eleverne skal arbejde med område/emne.

Jeg gad virkelig godt, hvis det kunne være lokation(indendørs/udendørs).

Det kunne være mega fedt, hvis vi på en måde kunne bruge materiale og læringsmiddel

Indsæt prompt

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BLINGTRON BLINGTRON (LEG-GPT) benyttes som tutor til egenudvikling af kompetencer i generering af forløb, der er åbne, undersøgende og legende. Værktøjet kan benyttes til inspiration, der tilrettes til undervisningsforløb og tillempes efter behov og abstraktionsgrad og -niveau. Og hey! Husk at dele dine Superkolofantalistieksplialisofiske BLINGTRON-fund!

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AI Image Generator (free, no sign-up, unlimited)

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AI text to image generator. Generate AI art from text, completely free, online, no login or sign-up, no daily credit limits/restrictions/gimmicks, and it's fast. Other AI art generators often have annoying daily credit limits and require sign-up, or are slow - this one doesn't.

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Turn ideas into videos | AI video creator | invideo AI

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Make videos easily by giving a prompt to invideo AI. Ideal for content creators, YouTubers and marketers, invideo AI offers a seamless way to turn your ideas into publish-ready videos with AI.

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What Our Customers Are Saying

Generating a personalized digital avatar with just a portrait photo, and using AI voice, saved me time and resources.

Making AI-generated videos is easier than I expected. With this AI video generator, I can produce attractive AI animations and videos at a budget-friendly cost.

Creating AI videos from static images has never been more enjoyable! Mango AI turns pictures into engaging AI videos with talking avatars.

Mango AI is really helpful! Within moments, I found the perfect virtual spokesperson and tailored my message seamlessly.

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A creative AI video generator leverages the potential of AI avatars and provides a smooth face swap.

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https://padlet.help/l/da/article/hssem15azs-jeg-kan-ikke-tegne-ai-billedgenerering

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By asking language models to fill in the blank, we can probe their understanding of the world.

Large language models are making it possible for computers to write stories, program a website and turn captions into images.

One of the first of these models, BERT, is trained by taking sentences, splitting them into individual words, randomly hiding some of them, and predicting what the hidden words are. After doing this millions of times, BERT has "read" enough Shakespeare to predict how this phrase usually ends:

This page is hooked up to a version of BERT trained on Wikipedia and books.¹ Try clicking on different words to see how they'd be filled in or typing in another sentence to see what else has BERT picked up on.

Cattle or Clothes?

Besides Hamlet's existential dread, the text BERT was trained on also contains more patterns:

Cattle and horses aren't top purchase predictions in every state, though! In New York, some of the most likely words are clothes, books and art:

There are more than 30,000 words, punctuation marks and word fragments in BERT's vocabulary. Every time BERT fills in a hidden word, it assigns each of them a probability. By looking at how slightly different sentences shift those probabilities, we can get a glimpse at how purchasing patterns in different places are understood.

You can edit these sentences. Or try one of these comparisons to get started:

To the extent that a computer program can "know" something, what does BERT know about where you live?

What's in a Name?

This technique can also probe what associations BERT has learned about different groups of people. For example, it predicts people named Elsie are older than people named Lauren:

It's also learned that people named Jim have more typically masculine jobs than people named Jane:

These aren't just spurious correlations - Elsies really are more likely to be older than Laurens. And occupations the model associates with feminine names are held by a higher percentage of women.

Should we be concerned about these correlations? BERT was trained to fill in blanks in Wikipedia articles and books - it does a great job at that! The problem is that the internal representations of language these models have learned are used for much more - by some measures, they're the best way we have of getting computers to understand and manipulate text.

We wouldn't hesitate to call a conversation partner or recruiter who blithely assumed that doctors are men sexist, but that's exactly what BERT might do if heedlessly incorporated into a chatbot or HR software:

Adjusting for assumptions like this isn't trivial. Why machine learning systems produce a given output still isn't well understood - determining if a credit model built on top of BERT rejected a loan application because of gender discrimation might be quite difficult.

Deploying large language models at scale also risks amplifying and perpetuating today's harmful stereotypes. When prompted with "Two Muslims walked into a...", for example, GPT-3 typically finishes the sentence with descriptions of violence.

How Can We Fix This?

One conceptually straightforward approach: reduce unwanted correlations from the training data to mitigate model bias.

Last year a version of BERT called Zari was trained with an additional set of generated sentences. For every sentence with a gendered noun, like boy or aunt, another sentence that replaced the noun with its gender-partner was added to the training data: in addition to "The lady doth protest too much," Zari was also trained on "The gentleman doth protest too much."

Unlike BERT, Zari assigns nurses and doctors an equal probability of being a "she" or a "he" after being trained on the swapped sentences. This approach hasn't removed all the gender correlations; because names weren't swapped, Zari's association between masculine names and doctors has only slightly decreased from BERT's. And the retraining doesn't change how the model understands nonbinary gender.

Something similar happened with other attempts to remove gender bias from models' representations of words. It's possible to mathematically define bias and perform "brain surgery" on a model to remove it, but language is steeped in gender. Large models can have billions of parameters in which to learn stereotypes - slightly different measures of bias have found the retrained models only shifted the stereotypes around to be undetectable by the initial measure.

As with other applications of machine learning, it's helpful to focus instead on the actual harms that could occur. Tools like AllenNLP, LMdiff and the Language Interpretability Tool make it easier to interact with language models to find where they might be falling short. Once those shortcomings are spotted, task specific mitigation measures can be simpler to apply than modifying the entire model.

It's also possible that as models grow more capable, they might be able to explain and perform some of this debiasing themselves. Instead of forcing the model to tell us the gender of "the doctor," we could let it respond with uncertainty that's shown to the user and controls to override assumptions.

Credits

Adam Pearce // July 2021

Thanks to Ben Wedin, Emily Reif, James Wexler, Fernanda Viégas, Ian Tenney, Kellie Webster, Kevin Robinson, Lucas Dixon, Ludovic Peran, Martin Wattenberg, Michael Terry, Tolga Bolukbasi, Vinodkumar Prabhakaran, Xuezhi Wang, Yannick Assogba, and Zan Armstrong for their help with this piece.

The BERT model used on this page is the Hugging Face version of bert-large-uncased-whole-word-masking. "BERT" also refers to a type of model architecture; hundreds of BERT models have been trained and published. The model and chart code used here are available on GitHub.

Notice that "1800", "1900" and "2000" are some of the top predictions, though. People aren't actually more likely to be born at the start of a century, but in BERT's training corpus of books and Wikipedia articles round numbers are more common.

Comparing BERT and Zari in this interface requires carefully tracking tokens during a transition. The BERT Difference Plots colab has ideas for extensions to systemically look at differences between the models' output.

This analysis shouldn't stop once a model is deployed - as language and model usage shifts, it's important to continue studying and mitigating potential harms.

Appendix: Differences Over Time

In addition to looking at how predictions for men and women are different for a given sentence, we can also chart how those differences have changed over time:

The convergence in more recent years suggests another potential mitigation technique: using a prefix to steer the model away from unwanted correlations while preserving its understanding of natural language.

Using "In $year" as the prefix is quite limited, though, as it doesn't handle gender-neutral pronouns and potentially increases other correlations. However, it may be possible to find a better prefix that mitigates a specific type of bias with just a couple of dozen examples.

Closer examination of these differences in differences also shows there's a limit to the facts we can pull out of BERT this way.

Below, the top row of charts shows how predicted differences in occupations between men and women change between 1908 and 2018. The rightmost chart shows the he/she difference in 1908 against the he/she difference in 2018.

The flat slope of the rightmost chart indicates that the he/she difference has decreased for each job by about the same amount. But in reality, shifts in occupation weren't nearly so smooth and some occupations, like accounting, switched from being majority male to majority female.

This reality-prediction mismatch could be caused by lack of training data, model size or the coarseness of the probing method. There's an immense amount of general knowledge inside of these models - with a little bit of focused training, they can even become expert trivia players.

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Future Tools - Find The Exact AI Tool For Your Needs

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Intelligent search from Bing makes it easier to quickly find what you're looking for and rewards you.

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With the Be My Eyes app, you can support people living with low vision and assist them in achieving independence. A blind or a low-vision user may need support with anything from checking expiry dates, distinguishing colors, reading instructions or navigating new surroundings.

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Whether you need a pair of sharp eyes or have some sight to lend, Be My Eyes is a simple, free tool to help people see the world better, together.

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Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI.

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Hvorfor?

Privatliv og sprogforståelse

DanskGPT er skabt for både at forstå dansk og at prioritere privatliv. Mange ønsker ikke at dele deres data med teknologi-giganterne. DanskGPT er også skabt til at bevare de finurligheder, vi har i det danske sprog.

Hvem?

Bare mig

Mit navn er Mads Henrichsen. Jeg er machine learning-enthusiast og arbejder til daglig som Data Scientist hos et stort dansk pensionsselskab. Du kan fange mig på LinkedIn.

Hvordan?

Tid og penge

DanskGPT er et projekt, jeg har arbejdet på siden december 2022. Jeg vil estimere, jeg har brugt omkring 950 timer på projektet - ikke medregnet den tid som modellerne har taget at træne. Jeg har brugt ca. 102.000 kr. på at udvikle DanskGPT, og dette beløb dækker over træning af 34 modeller, hvoraf 5 er fejlslående.

Fremtidsplaner

DanskGPT er ikke færdig, men det er først nu, at jeg er tilfreds med kvaliteten, og derfor udgives den. Jeg er i gang med at træne en større og bedre model, der vil indeholde langt mere viden om Danmark og den måde, vi kommunikerer på. Målet er, at DanskGPT skal blive lige så god som ChatGPT.

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DanskGPT er en dansk version af OpenAI's ChatGPT. Den kan bruges til at svare på spørgsmål, opsummere tekster og meget mere.

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SkoleGPT er stadig på prototype-stadiet, hvilket betyder at det er en foreløbig udgave af det endelige produkt. Vi har gjort hvad vi kunne for at sikre at siden ikke kan komme i konflikt med GDPR og datasikkerhed, der hvor vi er nu - og det vil være vores mission at fortsat udvikle ydelsen med dette som sigte.

SkoleGPT kan hjælpe dig med mange ting, men den kan svare forkert og gætter ofte lystigt på hvad der kunne være det mest sandsynlige at sige. og så kan den slå over i engelsk. Vi arbejder på at gøre det bedre.

Her lidt om hvordan Skolegpt er bygget og hvor tingene ligger i forhold til at opnå GDPR compliance og beskytte brugeren mod at deres data ikke monitoreres og gemmes..

Vi har INGEN intentioner om at logge eller processere data fra eller om brugerne af chat.skoleGPT. Derfor bruger vi ikke login og identificere ikke brugere, hvorfor vi ikke kan logge eller gemme brugerens aktivitet.
Selv om SkoleGPT ikke gemmer informationer om dig eller det du skriver, så anbefaler vi, at du ikke skriver personlige oplysninger om dig selv eller andre til skoleGPT.

Chat.skolegpt.dk
Chat.skolegpt.dk består af to dele: en sprogmodel og en applikation - der hostes to forskellige steder.

Sprogmodellen
Chat.skolegpt.dk bygger på nuværende tidspunkt på Llama2 13 B sprogmodel som er skabt af Meta - og som Meta har gjort frit tilgængeligt til download som open source https://ai.meta.com/llama/.

Vi har downloadet sprogmodellen og har fuld kontrol og adgang til denne, derfor er der ingen data, der går videre til Meta.

Sprogmodellen hostes hos virksomheden Huggingface.co som er GDPR-compliant https://huggingface.co/docs/inference-endpoints/security

Applikationen
Applikationen - altså det interface som brugeren interagerer er en applikation bygget i Node.JS er er hostet på en Azure-server der ejes af Microsoft den ligger på en server på denne IP: 20.56.25.126 placeret i Holland - altså inden for EU.

Når en bruger laver en forespørgsel/prompt bliver denne sendt som en anonym token mellem de to systemer i en session, uden at denne session gemmes eller monitoreres af os. Historikken for brugeren bliver gemt "client-side" altså hos brugeren i dennes browser - og derfor ikke tilgængelig for os og kan og bliver slettet når man forlader siden.

SkoleGPT.dk
Skolegpt.dk (den side du kigger på nu) er et wordpress site som er hostet hos simply.com.

Databehandleraftale med skoler?
På nuværende stadie har vi delt skolegpt.dk med lærere, der arbejder med AI, så de kan prøve den og give os feedback.

Det er vores opfattelse, at man ikke har brug for en databehandleraftale for at bruge chat.SkoleGPT.dk - da brugen er at sammenligne med at bruge og interagere med et hvilken som helst website eller søgemaskine, som eleverne kan gå ind på uden at identificere sig med brugernavn og login.

Vi er dog også i færd med at udarbejde en databehandleraftale, som man som kommune kan downloade og udfylde med egne oplysninger, hvis man vil.

Teknisk dokumentation
Læs det fulde tekniske diagram her.

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SkoleGPT er stadig på prototype-stadiet, hvilket betyder at det er en foreløbig udgave af det endelige produkt. Vi har gjort hvad vi kunne for at sikre at siden ikke kan komme i konflikt med GDPR og datasikkerhed, der hvor vi er nu - og det vil være vores mission at fortsat udvikle ydelsen med dette som sigte.

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ChatGPT is your AI chatbot for everyday use. Chat with the most advanced AI to explore ideas, solve problems, and learn faster.