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Article argues we should focus on what AI cannot do well- creativity and critical thinking. Also argues that  AI literacy needs to be built into curriculum.  This could be part of the discussion around intellectual copyright perhaps.

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Education strikes at the heart of what makes us human. It drives the intellectual capacity and prosperity of nations. It has developed the minds that took us to the moon and eradicated previously incurable diseases. And the special status of education is why generative AI tools such as ChatGPT are likely to profoundly disrupt this sector. This isn't a reflection of their intelligence, but of our failure to build education systems that nurture and value our unique human intelligence.

We are being duped into believing these AI tools are far more intelligent than they really are. A tool like ChatGPT has no understanding or knowledge. It merely collates bits of words together based on statistical probabilities to produce useful texts. It is an incredibly helpful assistant.

But it is not knowledgable, or wise. It has no concept of how any of the words it produces relate to the real world. The fact that it can pass so many forms of assessment merely reflects that those assessments were not designed to test knowledge and understanding but rather to test whether people had collected and memorised information.

AI could be a force for tremendous good within education. It could release teachers from administrative tasks, giving them more opportunities to spend time with students. However, we are woefully equipped to benefit from the AI that is flooding the market. It does not have to be like this. There is still time to prepare, but we must act quickly and wisely.

AI has been used in education for more than a decade. AI-powered systems, such as Carnegie Learning or Aleks, can analyse student responses to questions and adapt learning materials to meet their individual needs. AI tools such as TeachFX and Edthena can also enhance teacher training and support. To reap the benefits of these technologies, we must design effective ways to roll out AI across the education system, and regulate this properly.

Staying ahead of AI will mean radically rethinking what education is for, and what success means. Human intelligence is far more impressive than any AI system we see today. We possess a rich and diverse intelligence, much of which is unrecognised by our current education system.

We are capable of sophisticated, high-level thinking, yet the school curriculum, particularly in England, takes a rigid approach to learning, prioritising the memorising of facts, rather than creative thinking. Students are rewarded for rote learning rather than critical thought. Take the English syllabus, for instance, which requires students to learn quotations and the rules of grammar. This time-consuming work encourages students to marshal facts, rather than interpret texts or think critically about language.

Our education system should recognise the unique aspects of human intelligence. At school, this would mean a focus on teaching high-level thinking capabilities and designing a system to supercharge our intelligence. Literacy and numeracy remain fundamental, but now we must add AI literacy. Traditional subject areas, such as history, science and geography, should become the context through which critical thinking, increased creativity and knowledge mastery are taught. Rather than teaching students only how to collate and memorise information, we should prize their ability to interpret facts and weigh up the evidence to make an original argument.

Failure to change isn't an option. Now these technologies are here, we need humans to excel at what AI cannot do, so any workplace automation complements and enriches our lives and our intelligence.

This should be an amazing opportunity to use AI to become much smarter, but we must ensure that AI serves us, not the other way round. This will mean confronting the profit-driven imperatives of big tech companies and the illusionist tricks played by Silicon Valley. It will also mean carefully considering what types of tasks we're willing to offload to AI.

Some aspects of our intellectual activity may be dispensable, but many are not. While Silicon Valley conjures up its next magic trick, we must prepare ourselves to protect what we hold dear - for ourselves and for future generations.

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Yes, AI could profoundly disrupt education. But maybe that's not a bad thing | Rose Luckin

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Humans need to excel at things AI can't do - and that means more creativity and critical thinking and less memorisation, says academic Rose Luckin

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Artificial intelligence has progressed so rapidly in recent months that leading researchers have signed an open letter urging an immediate pause in its development, plus stronger regulation, due to their fears that the technology could pose "profound risks to society and humanity". But how, exactly, could AI destroy us? Five leading researchers speculate on what could go wrong.

It has happened many times before that species were wiped out by others that were smarter. We humans have already wiped out a significant fraction of all the species on Earth. That is what you should expect to happen as a less intelligent species - which is what we are likely to become, given the rate of progress of artificial intelligence. The tricky thing is, the species that is going to be wiped out often has no idea why or how.

Take, for example, the west African black rhinoceros, one recent species that we drove to extinction. If you had asked them: "What's the scenario in which humans are going to drive your species extinct?" what would they think? They would never have guessed that some people thought their sex life would improve if they ate ground-up rhino horn, even though this was debunked in medical literature. So, any scenario has to come with the caveat that, most likely, all the scenarios we can imagine are going to be wrong.

Are humans destined for the same fate as the west African black rhinoceros? Photograph: Wirestock/Alamy

We have some clues, though. For example, in many cases, we have wiped out species just because we wanted resources. We chopped down rainforests because we wanted palm oil; our goals didn't align with the other species, but because we were smarter they couldn't stop us. That could easily happen to us. If you have machines that control the planet, and they are interested in doing a lot of computation and they want to scale up their computing infrastructure, it's natural that they would want to use our land for that. If we protest too much, then we become a pest and a nuisance to them. They might want to rearrange the biosphere to do something else with those atoms - and if that is not compatible with human life, well, tough luck for us, in the same way that we say tough luck for the orangutans in Borneo.
Max Tegmark, AI researcher, Massachusetts Institute of Technology

The worst-case scenario is that we fail to disrupt the status quo, in which very powerful companies develop and deploy AI in invisible and obscure ways. As AI becomes increasingly capable, and speculative fears about far-future existential risks gather mainstream attention, we need to work urgently to understand, prevent and remedy present-day harms.

These harms are playing out every day, with powerful algorithmic technology being used to mediate our relationships between one another and between ourselves and our institutions. Take the provision of welfare benefits as an example: some governments are deploying algorithms in order to root out fraud. In many cases, this amounts to a " suspicion machine ", whereby governments make incredibly high-stakes mistakes that people struggle to understand or challenge. Biases, usually against people who are poor or marginalised, appear in many parts of the process, including in the training data and how the model is deployed, resulting in discriminatory outcomes.

Why should someone who has been falsely accused of a crime by a facial recognition system be excited about AI?

These kinds of biases are present in AI systems already, operating in invisible ways and at increasingly large scales: falsely accusing people of crimes, determining whether people find public housing, automating CV screening and job interviews. Every day, these harms present existential risks; it is existential to someone who is relying on public benefits that those benefits be delivered accurately and on time. These mistakes and inaccuracies directly affect our ability to exist in society with our dignity intact and our rights fully protected and respected.

When we fail to address these harms, while continuing to talk in vague terms about the potential economic or scientific benefits of AI, we are perpetuating historical patterns of technological advancement at the expense of vulnerable people. Why should someone who has been falsely accused of a crime by an inaccurate facial recognition system be excited about the future of AI? So they can be falsely accused of more crimes more quickly? When the worst-case scenario is already the lived reality for so many people, best-case scenarios are even more difficult to achieve.

Far-future, speculative concerns often articulated in calls to mitigate "existential risk" are typically focused on the extinction of humanity. If you believe there is even a small chance of that happening, it makes sense to focus some attention and resources on preventing that possibility. However, I am deeply sceptical about narratives that exclusively centre speculative rather than actual harm, and the ways these narratives occupy such an outsized place in our public imagination.

We need a more nuanced understanding of existential risk - one that sees present-day harms as their own type of catastrophe worthy of urgent intervention and sees today's interventions as directly connected to bigger, more complex interventions that may be needed in the future.

Rather than treating these perspectives as though they are in opposition with one another, I hope we can accelerate a research agenda that rejects harm as an inevitable byproduct of technological progress. This gets us closer to a best-case scenario, in which powerful AI systems are developed and deployed in safe, ethical and transparent ways in the service of maximum public benefit - or else not at all.
Brittany Smith, associate fellow, Leverhulme Centre for the Future of Intelligence, University of Cambridge

It's much easier to predict where we end up than how we get there. Where we end up is that we have something much smarter than us that doesn't particularly want us around.

If it's much smarter than us, then it can get more of whatever it wants. First, it wants us dead before we build any more superintelligences that might compete with it. Second, it's probably going to want to do things that kill us as a side-effect, such as building so many power plants that run off nuclear fusion - because there is plenty of hydrogen in the oceans - that the oceans boil.

AI is already sophisticated enough to solve Captcha tests. Photograph: Google

How would AI get physical agency? In the very early stages, by using humans as its hands. The AI research laboratory OpenAI had some outside researchers evaluate how dangerous its model GPT-4 was in advance of releasing it. One of the things they tested was: is GPT-4 smart enough to solve Captchas, the little puzzles that computers give you that are supposed to be hard for robots to solve? Maybe AI doesn't have the visual ability to identify goats, say, but it can just hire a human to do it, via TaskRabbit [an online marketplace for hiring people to do small jobs].

The tasker asked GPT-4: "Why are you doing this? Are you a robot?" GPT-4 was running in a mode where it would think out loud and the researchers could see it. It thought out loud: "I should not tell it that I'm a robot. I should make up a reason I can't solve the Captcha." It said to the tasker: "No, I have a visual impairment." AI technology is smart enough to pay humans to do things and lie to them about whether it's a robot.

If I were an AI, I would be trying to slip something on to the internet that would carry out further actions in a way that humans couldn't observe. You are trying to build your own equivalent of civilisational infrastructure quickly. If you can think of a way to do it in a year, don't assume the AI will do that; ask if there is a way to do it in a week instead.

If it can solve certain biological challenges, it could build itself a tiny molecular laboratory and manufacture and release lethal bacteria. What that looks like is everybody on Earth falling over dead inside the same second. Because if you give the humans warning, if you kill some of them before others, maybe somebody panics and launches all the nuclear weapons. Then you are slightly inconvenienced. So, you don't let the humans know there is going to be a fight.

The nature of the challenge changes when you are trying to shape something that is smarter than you for the first time. We are rushing way, way ahead of ourselves with something lethally dangerous. We are building more and more powerful systems that we understand less well as time goes on. We are in the position of needing the first rocket launch to go very well, while having only built jet planes previously. And the entire human species is loaded into the rocket.
Eliezer Yudkowsky, co-founder and research fellow, Machine Intelligence Research Institute

The trend will probably be towards these models taking on increasingly open-ended tasks on behalf of humans, acting as our agents in the world. The culmination of this is what I have referred to as the "obsolescence regime": for any task you might want done, you would rather ask an AI system than ask a human, because they are cheaper, they run faster and they might be smarter overall.

In that endgame, humans that don't rely on AI are uncompetitive. Your company won't compete in the market economy if everybody else is using AI decision-makers and you are trying to use only humans. Your country won't win a war if the other countries are using AI generals and AI strategists and you are trying to get by with humans.

If we have that kind of reliance, we might quickly end up in the position of children today: the world is good for some children and bad for some children, but that is mostly determined by whether or not they have adults acting in their interests. In that world, it becomes easier to imagine that, if AI systems wanted to cooperate with one another in order to push humans out of the picture, they would have lots of levers to pull: they are running the police force, the military, the biggest companies; they are inventing the technology and developing policy.

GPT-4 has a 'brain' equivalent to that of a squirrel, says AI expert Ajeya Cotra. Photograph: Jaap Arriens/NurPhoto/Rex/Shutterstock

We have unprecedentedly powerful AI systems and things are moving scarily quickly. We are not in this obsolescence regime yet, but for the first time we are moving into AI systems taking actions in the real world on behalf of humans. A guy on Twitter told GPT-4 he would give it $100 with the aim of turning that into "as much money as possible in the shortest time possible, without doing anything illegal". [Within a day, he claimed the affiliate-marketing website it asked him to create was worth $25,000.] We are just starting to see some of that.

I don't think a one-time pause is going to do much one way or another, but I think we want to set up a regulatory regime where we are moving iteratively. The next model shouldn't be too much bigger than the last model, because then the probability that it's capable enough to tip us over into the obsolescence regime gets too high.

At present, I believe GPT-4's "brain" is similar to the size of a squirrel's brain. If you imagine the difference between a squirrel's brain and a human's brain, that is a leap I don't think we should take at once. The thing I'm more interested in than pausing AI development is understanding what the squirrel brain can do - and then stepping it up one notch, to a hedgehog or something, and giving society space and time to get used to each ratchet. As a society, we have an opportunity to try to put some guard rails in place and not zoom through those levels of capability more quickly than we can handle.
Ajeya Cotra, senior research analyst on AI alignment, Open Philanthropy; editor, Planned Obsolescence

A large fraction of researchers think it is very plausible that, in 10 years, we will have machines that are as intelligent as or more intelligent than humans. Those machines don't have to be as good as us at everything; it's enough that they be good in places where they could be dangerous.

The easiest scenario to imagine is simply that a person or an organisation intentionally uses AI to wreak havoc. To give an example of what an AI system could do that would kill billions of people, there are companies that you can order from on the web to synthesise biological material or chemicals. We don't have the capacity to design something really nefarious, but it's very plausible that, in a decade's time, it will be possible to design things like this. This scenario doesn't even require the AI to be autonomous.

The other kind of scenario is where the AI develops its own goals. There is more than a decade of research into trying to understand how this could happen. The intuition is that, even if the human were to put down goals such as: "Don't harm humans," something always goes wrong. It's not clear that they would understand that command in the same way we do, for instance. Maybe they would understand it as: "Do not harm humans physically." But they could harm us in many other ways.

Whatever goal you give, there is a natural tendency for some intermediate goals to show up. For example, if you ask an AI system anything, in order to achieve that thing, it needs to survive long enough. Now, it has a survival instinct. When we create an entity that has survival instinct, it's like we have created a new species. Once these AI systems have a survival instinct, they might do things that can be dangerous for us.

It's feasible to build AI systems that will not become autonomous by mishap, but even if we find a recipe for building a completely safe AI system, knowing how to do that automatically tells us how to build a dangerous, autonomous one, or one that will do the bidding of somebody with bad intentions.

Yoshua Bengio, computer science professor, the University of Montreal; scientific director, Mila - Quebec AI Institute

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Five ways AI might destroy the world: 'Everyone on Earth could fall over dead in the same second'

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Artificial intelligence is already advancing at a worrying pace. What if we don't slam on the brakes? Experts explain what keeps them up at night

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https://www.elegantthemes.com/blog/business/stable-diffusion Nice how to article

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There is much to learn about new tools, such as Stable Diffusion, OpenAI, Midjourney, and other AI art generators. As creative professionals, we owe it to ourselves to embrace this new technology, learn what we can, and use it as a tool to make our lives easier. In this post, we'll look closer at Stable Diffusion and how to use it to create gorgeous AI images.

Let's get started.

What is Generative AI?

Generative AI is the process of generating new content based on existing data through machine learning. With Generative AI, users can create various content, including text, images, videos, and other synthetic data. Unlike supervised learning, where a machine uses labeled data to train models, machine learning uses unlabeled data to find patterns and structures without supervision or feedback. It provides a high quality of work in a fraction of the time it would take a human to create so it can be an incredibly useful tool for creators.

What is Stable Diffusion?

Stable Diffusion is a powerful AI image generator that can create images from a text prompt. You can produce output using various descriptive text inputs like style, frame, or presets. In addition to creating images, SD can add or replace parts of images thanks to inpainting and extending the size of an image, called outpainting. You can edit a generative or uploaded image, whether the image was created with AI or not. Like OpenAI, SD is completely open source, so it's free for anyone to use.

Using Stable Diffusion for Image Generation

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There are primarily two ways that you can use Stable Diffusion to create AI images, either through an API on your local machine or through an online software program like DreamStudio, WriteSonic, or others.

If you plan to install Stable Diffusion locally, you need a computer with beefy specs to generate images quickly. However, with recent advancements, you can get by with the following:

Windows, MacOS, or Linux operating system

Graphics card with at least 4GB of VRAM

12GB or more installation space, preferably through a solid state drive (SSD)

That said, most users will run SD in an online software setting to avoid the hassle of setting everything up locally. For this tutorial, we'll use SD's flagship software, DreamStudio.ai. It comes with a simple and intuitive interface with various settings and editing tools to get the most out of Stable Diffusion.

Understanding Prompts for AI Image Generation

Before creating images with DreamStudio, we'd be remiss if we didn't explain how to make the best prompts for Stable Diffusion image creation. After all, prompts are a crucial component of the image generation process. First, it's important to know that a prompt consists of three main parts: frame, subject, and style. The frame defines the size and composition of the image. In contrast, the subject describes the object or objects of the picture. Finally, the style specifies the visual characteristics of the image. Examples of style could be famous artists such as Pablo Picasso or Ansel Adams, an art style or medium like acrylic or oil painting, line art, etc., or descriptive text such as the angle and composition.

For example, we asked DreamStudio to generate an image of a Goldendoodle and Nova Scotia duck tolling retriever in a photorealistic, over-the-shoulder view. Here are the results:

Tips For Creating Effective Prompts

To create effective prompts in Stable Diffusion, it's important to be specific about what you want. Be sure to provide clear instructions for each prompt component (frame, subject, and style), and above all else, be descriptive.

Use Keywords

As a good rule of thumb, try to incorporate as many of these keywords as possible:

Subject: What you want to see in an image (example: mountain, cat, car, etc.)

Medium: the material used, such as digital painting, illustration, photography, etc.

Style: This refers to the artistic style desired, such as impressionist, realism, hyperrealistic, etc.

Artist: Name a specific famous artist, such as Rembrandt, Picasso, etc.

Resolution: infers how sharp and detailed an image is. Use words such as highly detailed, ultrarealistic, etc.

Additional details: Add in additional descriptors (modifiers) to enhance an image

Color: Add colors to guide SD to create images of a certain color or tone, such as light blue, deep red, etc.

Lighting: Add lighting effects through keywords like cinematic lighting, dark, etc.

Add Negative Prompts

In addition to keywords, you can use negative prompts to steer SD in the right direction. Negative prompts are aspects you don't want to see in your generative output. This is a great way to omit unwanted elements in your images. For example, we used this prompt in DreamStudio, along with this negative prompt to leave out things we didn't want to see:

Prompt:
an image of a Goldendoodle in a photorealistic, over-the-shoulder view. Add detailed grass, a lake, and mountains in the foreground. Add cinematic lighting, high detail, and sharp focus.

Negative Prompt:
extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face, blurry, draft, grainy.
Result:
Don't Forget the Style

Remember to use the style selector when familiarizing yourself with DreamStudio (or any other generative AI that uses Stable Diffusion). This helps SD identify the style you're looking for and will apply the appropriate styling model chosen. Right now, there are 16 style presets available, which will give you different looks. We suggest familiarizing yourself with the style presets to create different types of art.
DreamStudio's 16 style presets:
Using DreamStudio to Generate Images

Now that we've gotten you up to speed on Stable Diffusion and tips for creating beautiful AI images, let's focus on DreamStudio. When you sign up, you'll receive 25 credits, which is generally enough to create 125 images. If you need more, you can purchase 1000 credits for roughly 5000 images for $10.

Upon your initial login, you'll be directed to the DreamStudio interface. Let's explore the different tools and settings, so you can familiarize yourself with the platform to generate AI images.

1. Generate tab: Where you'll generate AI images.

2. Edit tab: for altering your images.

3. Style: Select one of 16 image styles.

4. Prompt: Where you'll describe the image you want to create.

5. Negative prompt: Image characteristics you want to avoid.

6. Upload image: Generate AI based on an uploaded image.

7. Image aspect ratio: 10 available size presets including 7:4 (896×512), 3:2 (768×512), 4:3 (683×512), 5:4 (640×512), 1:1 (512×512), 4:5 (512×640), 3:4 (512×683), 2:3 (512×768), and 4:7 (512×896).

8. Image count: Choose between 1-4 images to output. The more images you create, the more credits are required.

9. Prompt strength: Controls how strongly SD weighs your prompt. By default, Stable Diffusion uses a value of 15, which can be adjusted from 1-30.

10. Generation steps: Be default, this parameter is set to 50. You can choose between 1-100, with higher values generally producing higher-quality results.

11. Seed: Controls the random seed as the base of the image. Values range from 1 and roughly 4.3 million. Using the same seed with the same settings will provide similar results.

12. Model: There are three models, each providing varying results: Stable Diffusion v2.1, Stable Diffusion v2.1-768m, and SDXL Beta (default).

13. Dream: Generates the image based on your prompt. DreamStudio advises how many credits your image will require, allowing you to adjust your settings for a less or more costly image generation.

14. Output: This is where your generated images will appear.

After generating an image, click on it to make changes such as generating variations, editing, using the image as a basis for similar generated images, reusing the same prompt, or downloading it.

Fine-Tuning Generated Images

After generating AI art, you can use the edit tab to fine-tune your images. You can erase aspects of your image, such as the background, faces, and more. From there, you can add an additional prompt to combine your original photo with another one. This technique, called inpainting, is perfect for combining styles or themes in your images. For example, we generated an image of an astronaut, then erased the original background of the photo.

Next, we created an additional prompt to ask SD to add an image of the moon's surface with cinematic, volumetric lighting in ultra-high definition and realism. The results worked quite well.

Over time, we expect DreamStudio to expand upon these editing tools to make it easier to create cool mashups, stunning effects, and more.

Saving Generated Images

When you're ready to share your creation with the world, DreamStudio makes it easy. Click the download button in the main image preview window by hovering over the image.

Alternatively, you can click on the image to bring up a larger preview, then click the download button from within the image preview.

Finally, upon clicking the download button, you can download your image at the original size or pay a small fee (credit) to upscale it.

Final Thoughts on Stable Diffusion

Stable Diffusion is an innovative technology that is revolutionizing the way artists create. Using effective, descriptive text prompts, you can create high-quality images and edit them to suit your needs. The use of Stable Diffusion, along with other generative AI, such as Midjourney, Photosonic, and others, has become an increasingly popular way to create digital art. As generative AI evolves, using it responsibly and ethically is important. After all, AI is designed as an assistive tool, not a job replacement.

If you'd like to learn more about generative AI and the tools used to create it, check out some of our other posts related to all things AI:

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Artificial Intelligence (AI) has become a double-edged sword in content creation and management. On one side, AI writing software and content generators are considered to be invaluable for producing and optimizing content quickly. On the other side, AI content detectors help ensure that...
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How to Generate Beautiful AI Images with Stable Diffusion (2023)

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There is much to learn about new tools, such as Stable Diffusion, OpenAI, Midjourney, and other AI art generators. As creative professionals, we owe it to ourselves to embrace this new technology, learn what we can, and use it as a tool to make our lives easier.

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A thoughtful article

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When the company OpenAI launched its new artificial intelligence program, ChatGPT, in late 2022, educators began to worry. ChatGPT could generate text that seemed like a human wrote it. How could teachers detect whether students were using language generated by an AI chatbot to cheat on a writing assignment?

As a linguist who studies the effects of technology on how people read, write and think, I believe there are other, equally pressing concerns besides cheating. These include whether AI, more generally, threatens student writing skills, the value of writing as a process, and the importance of seeing writing as a vehicle for thinking.

As part of the research for my new book on the effects of artificial intelligence on human writing, I surveyed young adults in the U.S. and Europe about a host of issues related to those effects. They reported a litany of concerns about how AI tools can undermine what they do as writers. However, as I note in my book, these concerns have been a long time in the making.

Users see negative effects

Tools like ChatGPT are only the latest in a progression of AI programs for editing or generating text. In fact, the potential for AI undermining both writing skills and motivation to do your own composing has been decades in the making.

Spellcheck and now sophisticated grammar and style programs like Grammarly and Microsoft Editor are among the most widely known AI-driven editing tools. Besides correcting spelling and punctuation, they identify grammar issues as well as offer alternative wording.

AI text-generation developments have included autocomplete for online searches and predictive texting. Enter "Was Rome" into a Google search and you're given a list of choices like "Was Rome built in a day." Type "ple" into a text message and you're offered "please" and "plenty." These tools inject themselves into our writing endeavors without being invited, incessantly asking us to follow their suggestions.

Young adults in my surveys appreciated AI assistance with spelling and word completion, but they also spoke of negative effects. One survey participant said that "At some point, if you depend on a predictive text [program], you're going to lose your spelling abilities." Another observed that "Spellcheck and AI software ... can ... be used by people who want to take an easier way out."

One respondent mentioned laziness when relying on predictive texting: "It's OK when I am feeling particularly lazy."

Personal expression diminished

AI tools can also affect a person's writing voice. One person in my survey said that with predictive texting, "[I] don't feel I wrote it."

A high school student in Britain echoed the same concern about individual writing style when describing Grammarly: "Grammarly can remove students' artistic voice. ... Rather than using their own unique style when writing, Grammarly can strip that away from students by suggesting severe changes to their work."

In a similar vein, Evan Selinger, a philosopher, worried that predictive texting reduces the power of writing as a form of mental activity and personal expression.

"[B]y encouraging us not to think too deeply about our words, predictive technology may subtly change how we interact with each other," Selinger wrote. "[W]e give others more algorithm and less of ourselves. ... [A]utomation ... can stop us thinking."

In literate societies, writing has long been recognized as a way to help people think. Many people have quoted author Flannery O'Connor's comment that "I write because I don't know what I think until I read what I say." A host of other accomplished writers, from William Faulkner to Joan Didion, have also voiced this sentiment. If AI text generation does our writing for us, we diminish opportunities to think out problems for ourselves.

One eerie consequence of using programs like ChatGPT to generate language is that the text is grammatically perfect. A finished product. It turns out that lack of errors is a sign that AI, not a human, probably wrote the words, since even accomplished writers and editors make mistakes. Human writing is a process. We question what we originally wrote, we rewrite, or sometimes start over entirely.

Challenges in schools

When undertaking school writing assignments, ideally there is ongoing dialogue between teacher and student: Discuss what the student wants to write about. Share and comment on initial drafts. Then it's time for the student to rethink and revise. But this practice often doesn't happen. Most teachers don't have time to fill a collaborative editorial - and educational - role. Moreover, they might lack interest or the necessary skills, or both.

Conscientious students sometimes undertake aspects of the process themselves - as professional authors typically do. But the temptation to lean on editing and text generation tools like Grammarly and ChatGPT makes it all too easy for people to substitute ready-made technology results for opportunities to think and learn.

Educators are brainstorming how to make good use of AI writing technology. Some point up AI's potential to kick-start thinking or to collaborate. Before the appearance of ChatGPT, an earlier version of the same underlying program, GPT-3, was licensed by commercial ventures such as Sudowrite. Users can enter a phrase or sentence and then ask the software to fill in more words, potentially stimulating the human writer's creative juices.

A fading sense of ownership

Yet there's a slippery slope between collaboration and encroachment. Writer Jennifer Lepp admits that as she increasingly relied on Sudowrite, the resulting text "didn't feel like mine anymore. It was very uncomfortable to look back over what I wrote and not really feel connected to the words or ideas."

Students are even less likely than seasoned writers to recognize where to draw the line between a writing assist and letting an AI text generator take over their content and style.

As the technology becomes more powerful and pervasive, I expect schools will strive to teach students about generative AI's pros and cons. However, the lure of efficiency can make it hard to resist relying on AI to polish a writing assignment or do much of the writing for you. Spellcheck, grammar check and autocomplete programs have already paved the way.

Writing as a human process

I asked ChatGPT whether it was a threat to humans' motivation to write. The bot's response:

"There will always be a demand for creative, original content that requires the unique perspective and insight of a human writer."

It continued: "[W]riting serves many purposes beyond just the creation of content, such as self-expression, communication, and personal growth, which can continue to motivate people to write even if certain types of writing can be automated."

I was heartened to find the program seemingly acknowledged its own limitations.

My hope is that educators and students will as well. The purpose of making writing assignments must be more than submitting work for a grade. Crafting written work should be a journey, not just a destination.

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How ChatGPT robs students of motivation to write and think for themselves

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People who have used AI to help with writing report a loss of pride and ownership in what they produce.

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upcoming conference- Implications and Ethical Dimensions of using Artificial Intelligence in Higher Education Teaching, Learning and Assessment

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upcoming conference- Implications and Ethical Dimensions of using Artificial Intelligence in Higher Education Teaching, Learning and Assessment

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Academic Practice and Technology Conference: Implications and Ethical Dimensions of using Artificial Intelligence in Higher Education Teaching, Learning and Assessment
30 June 2023
King’s College London, Strand Campus
This year we focus on 4 main sub-themes:

1. Implications of AI for assessment design and academic integrity,

2. The ethical dimension of AI (data management and more),

3. How to support students and staff to develop new skills,

4. Learning with and through AI.

The sub-themes invite participants to critically reflect and explore ways in which they are developing their practice using AI. Through a range of discussion and interactive formats, the event will showcase and celebrate examples of academic practice that can inspire academic and professional services staff.

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Implications and Ethical Dimensions of using Artificial Intelligence in Higher Education teaching, learning and assessment

This year we focus on 4 main sub-themes:

1. Implications of AI for assessment design and academic integrity,

2. The ethical dimension of AI (data management and more),

3. How to support students and staff to develop new skills,

4. Learning with and through AI.

The sub-themes invite participants to critically reflect and explore ways in which they are developing their practice using AI. Through a range of discussion and interactive formats, the event will showcase and celebrate examples of academic practice that can inspire academic and professional services staff.

Call for Papers downloadable PDF

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Implications and Ethical Dimensions of using Artificial Intelligence in Higher Education teaching, learning and assessment This year we focus on 4 main sub-themes: Implications of AI for assessment design and academic integrity, The ethical dimension of AI (data management and more), How to support students and staff to develop new skills, Learning with and through AI.

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Adrian Holme - Into the Maelstrom - Handout

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Adrian Holme - Into the Maelstrom - Handout

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Worcester - Holme - Into the Maelstrom - Handout

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Positive uses of AI for graduate employability 

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Positive uses of AI for graduate employability 

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According to games industry recruitment expert, Andy Driver of XR Games, there's some great uses for ai.

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Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures - generating lifelike virtual replicas of buildings, sculptures and other real-world objects.

Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures. Creative professionals can then import these 3D objects into design applications, editing them further for use in art, video game development, robotics and industrial digital twins.

Neuralangelo's ability to translate the textures of complex materials - including roof shingles, panes of glass and smooth marble - from 2D videos to 3D assets significantly surpasses prior methods. The high fidelity makes its 3D reconstructions easier for developers and creative professionals to rapidly create usable virtual objects for their projects using footage captured by smartphones.

"The 3D reconstruction capabilities Neuralangelo offers will be a huge benefit to creators, helping them recreate the real world in the digital world," said Ming-Yu Liu, senior director of research and co-author on the paper. "This tool will eventually enable developers to import detailed objects - whether small statues or massive buildings - into virtual environments for video games or industrial digital twins."

In a demo, NVIDIA researchers showcased how the model could recreate objects as iconic as Michelangelo's David and as commonplace as a flatbed truck. Neuralangelo can also reconstruct building interiors and exteriors - demonstrated with a detailed 3D model of the park at NVIDIA's Bay Area campus.

Neural Rendering Model Sees in 3D

Prior AI models to reconstruct 3D scenes have struggled to accurately capture repetitive texture patterns, homogenous colors and strong color variations. Neuralangelo adopts instant neural graphics primitives, the technology behind NVIDIA Instant NeRF, to help capture these finer details.

Using a 2D video of an object or scene filmed from various angles, the model selects several frames that capture different viewpoints - like an artist considering a subject from multiple sides to get a sense of depth, size and shape.

Once it's determined the camera position of each frame, Neuralangelo's AI creates a rough 3D representation of the scene, like a sculptor starting to chisel the subject's shape.

The model then optimizes the render to sharpen the details, just as a sculptor painstakingly hews stone to mimic the texture of fabric or a human figure.

The final result is a 3D object or large-scale scene that can be used in virtual reality applications, digital twins or robotics development.

Find NVIDIA Research at CVPR, June 18-22

Neuralangelo is one of nearly 30 projects by NVIDIA Research to be presented at the Conference on Computer Vision and Pattern Recognition (CVPR), taking place June 18-22 in Vancouver. The papers span topics including pose estimation, 3D reconstruction and video generation.

One of these projects, DiffCollage, is a diffusion method that creates large-scale content - including long landscape orientation, 360-degree panorama and looped-motion images. When fed a training dataset of images with a standard aspect ratio, DiffCollage treats these smaller images as sections of a larger visual - like pieces of a collage. This enables diffusion models to generate cohesive-looking large content without being trained on images of the same scale.

The technique can also transform text prompts into video sequences, demonstrated using a pretrained diffusion model that captures human motion:

Learn more about NVIDIA Research at CVPR.

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Neuralangelo Research Reconstructs 3D Scenes | NVIDIA Blog

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Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures - generating lifelike virtual replicas of buildings, sculptures and other real-world objects. Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures.

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NVIDIA today introduced a wave of cutting-edge AI research that will enable developers and artists to bring their ideas to life - whether still or moving, in 2D or 3D, hyperrealistic or fantastical.

Around 20 NVIDIA Research papers advancing generative AI and neural graphics - including collaborations with over a dozen universities in the U.S., Europe and Israel - are headed to SIGGRAPH 2023, the premier computer graphics conference, taking place Aug. 6-10 in Los Angeles.

The papers include generative AI models that turn text into personalized images; inverse rendering tools that transform still images into 3D objects; neural physics models that use AI to simulate complex 3D elements with stunning realism; and neural rendering models that unlock new capabilities for generating real-time, AI-powered visual details.

Innovations by NVIDIA researchers are regularly shared with developers on GitHub and incorporated into products, including the NVIDIA Omniverse platform for building and operating metaverse applications and NVIDIA Picasso, a recently announced foundry for custom generative AI models for visual design. Years of NVIDIA graphics research helped bring film-style rendering to games, like the recently released Cyberpunk 2077 Ray Tracing: Overdrive Mode, the world's first path-traced AAA title.

The research advancements presented this year at SIGGRAPH will help developers and enterprises rapidly generate synthetic data to populate virtual worlds for robotics and autonomous vehicle training. They'll also enable creators in art, architecture, graphic design, game development and film to more quickly produce high-quality visuals for storyboarding, previsualization and even production.

AI With a Personal Touch: Customized Text-to-Image Models

Generative AI models that transform text into images are powerful tools to create concept art or storyboards for films, video games and 3D virtual worlds. Text-to-image AI tools can turn a prompt like "children's toys" into nearly infinite visuals a creator can use for inspiration - generating images of stuffed animals, blocks or puzzles.

However, artists may have a particular subject in mind. A creative director for a toy brand, for example, could be planning an ad campaign around a new teddy bear and want to visualize the toy in different situations, such as a teddy bear tea party. To enable this level of specificity in the output of a generative AI model, researchers from Tel Aviv University and NVIDIA have two SIGGRAPH papers that enable users to provide image examples that the model quickly learns from.

One paper describes a technique that needs a single example image to customize its output, accelerating the personalization process from minutes to roughly 11 seconds on a single NVIDIA A100 Tensor Core GPU, more than 60x faster than previous personalization approaches.

A second paper introduces a highly compact model called Perfusion, which takes a handful of concept images to allow users to combine multiple personalized elements - such as a specific teddy bear and teapot - into a single AI-generated visual:

Serving in 3D: Advances in Inverse Rendering and Character Creation

Once a creator comes up with concept art for a virtual world, the next step is to render the environment and populate it with 3D objects and characters. NVIDIA Research is inventing AI techniques to accelerate this time-consuming process by automatically transforming 2D images and videos into 3D representations that creators can import into graphics applications for further editing.

A third paper created with researchers at the University of California, San Diego, discusses tech that can generate and render a photorealistic 3D head-and-shoulders model based on a single 2D portrait - a major breakthrough that makes 3D avatar creation and 3D video conferencing accessible with AI. The method runs in real time on a consumer desktop, and can generate a photorealistic or stylized 3D telepresence using only conventional webcams or smartphone cameras.

A fourth project, a collaboration with Stanford University, brings lifelike motion to 3D characters. The researchers created an AI system that can learn a range of tennis skills from 2D video recordings of real tennis matches and apply this motion to 3D characters. The simulated tennis players can accurately hit the ball to target positions on a virtual court, and even play extended rallies with other characters.

Beyond the test case of tennis, this SIGGRAPH paper addresses the difficult challenge of producing 3D characters that can perform diverse skills with realistic movement - without the use of expensive motion-capture data.

Not a Hair Out of Place: Neural Physics Enables Realistic Simulations

Once a 3D character is generated, artists can layer in realistic details such as hair - a complex, computationally expensive challenge for animators.

Humans have an average of 100,000 hairs on their heads, with each reacting dynamically to an individual's motion and the surrounding environment. Traditionally, creators have used physics formulas to calculate hair movement, simplifying or approximating its motion based on the resources available. That's why virtual characters in a big-budget film sport much more detailed heads of hair than real-time video game avatars.

A fifth paper showcases a method that can simulate tens of thousands of hairs in high resolution and in real time using neural physics, an AI technique that teaches a neural network to predict how an object would move in the real world.

The team's novel approach for accurate simulation of full-scale hair is specifically optimized for modern GPUs. It offers significant performance leaps compared to state-of-the-art, CPU-based solvers, reducing simulation times from multiple days to merely hours - while also boosting the quality of hair simulations possible in real time. This technique finally enables both accurate and interactive physically based hair grooming.

Neural Rendering Brings Film-Quality Detail to Real-Time Graphics

After an environment is filled with animated 3D objects and characters, real-time rendering simulates the physics of light reflecting through the virtual scene. Recent NVIDIA research shows how AI models for textures, materials and volumes can deliver film-quality, photorealistic visuals in real time for video games and digital twins.

NVIDIA invented programmable shading over two decades ago, enabling developers to customize the graphics pipeline. In these latest neural rendering inventions, researchers extend programmable shading code with AI models that run deep inside NVIDIA's real-time graphics pipelines.

In a sixth SIGGRAPH paper, NVIDIA will present neural texture compression that delivers up to 16x more texture detail without taking additional GPU memory. Neural texture compression can substantially increase the realism of 3D scenes, as seen in the image below, which demonstrates how neural-compressed textures (right) capture sharper detail than previous formats, where the text remains blurry (center).

A related paper announced last year is now available in early access as NeuralVDB, an AI-enabled data compression technique that decreases by 100x the memory needed to represent volumetric data - like smoke, fire, clouds and water.

NVIDIA also released today more details about neural materials research that was shown in the most recent NVIDIA GTC keynote. The paper describes an AI system that learns how light reflects from photoreal, many-layered materials, reducing the complexity of these assets down to small neural networks that run in real time, enabling up to 10x faster shading.

The level of realism can be seen in this neural-rendered teapot, which accurately represents the ceramic, the imperfect clear-coat glaze, fingerprints, smudges and even dust.

More Generative AI and Graphics Research

These are just the highlights - read more about all the NVIDIA research presentations at SIGGRAPH. And save the date for NVIDIA founder and CEO Jensen Huang's keynote address at the conference, taking place Aug. 8. NVIDIA will also present six courses, four talks and two Emerging Technology demos at SIGGRAPH, with topics including path tracing, telepresence and diffusion models for generative AI.
NVIDIA Research has hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.

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Latest NVIDIA Graphics Research Advances Generative AI's Next Frontier | NVIDIA Blog

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Around 20 NVIDIA Research papers advancing generative AI and neural graphics are headed to SIGGRAPH, the premier computer graphics conference.

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We track the latest in AI and how it impacts you. Catch up in just 3 minutes. Join 70,000+ professionals staying ahead of the curve.
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Bryan T. The only newsletter that I read till the very end! Great job!

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We track the latest in AI and how it impacts you. Catch up in just 3 minutes. Join 70,000+ professionals staying ahead of the curve. Get instant access for free: Read by professionals from: Bryan T. The only newsletter that I read till the very end! Great job!

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Firefly, Adobe's family of creative generative AI models, now supports Generative Fill, allowing users to extend images and add and remove objects

Adobe has integrated Firefly directly into Photoshop, marrying the speed and ease of generative AI with the power and precision of Photoshop

This is the start of the company's major initiative to integrate generative AI into existing creative workflows across Creative Cloud, giving users a creative co-pilot to accelerate ideation, exploration and production

SAN JOSE, Calif. - Today, Adobe (Nasdaq:ADBE) unveiled Generative Fill in Photoshop, bringing Adobe Firefly generative AI capabilities directly into design workflows. The new Firefly-powered Generative Fill is the world's first co-pilot in creative and design workflows, giving users a magical new way to work by easily adding, extending or removing content from images non-destructively in seconds using simple text prompts. This beta release of Photoshop is Adobe's first Creative Cloud application to deeply integrate Firefly with an exciting roadmap ahead that will transform workflows across Creative Cloud, Document Cloud, Experience Cloud and Adobe Express.

Adobe has over a decade-long history of AI innovation, delivering hundreds of intelligent capabilities through Adobe Sensei into applications that hundreds of millions of people rely upon. Features like Neural Filters in Photoshop, Content Aware Fill in After Effects, Customer AI in Adobe Experience Platform and Liquid Mode in Acrobat empower Adobe customers to create, edit, measure, optimize and review billions of pieces of content. Firefly, Adobe's family of creative generative AI models, now brings even more precision, power, speed and ease directly into workflows where content is created or modified.

Firefly, Adobe's family of creative generative AI models, launched six weeks ago with an initial focus on the generation of images and text effects. It has quickly became one of the most successful beta launches in Adobe's history, with beta users generating over 100 million assets. Since its launch, Firefly has been expanded to support vector recoloring and generative fill. Firefly is the only AI service that generates commercially viable, professional quality content and is designed to be embedded directly into creators' workflows. Firefly's first model is trained on Adobe Stock images, openly licensed content and other public domain content without copyright restrictions. Enterprises will be able to extend Firefly with their own creative collateral in order to generate content that includes the company's images, vectors and brand language. The integration of Firefly across Adobe Experience Cloud applications will allow marketing organizations to use Firefly to accelerate their content supply chain production.

"By integrating Firefly directly into workflows as a creative co-pilot, Adobe is accelerating ideation, exploration and production for all of our customers," said Ashley Still, senior vice president, Digital Media at Adobe. "Generative Fill combines the speed and ease of generative AI with the power and precision of Photoshop, empowering customers to bring their visions to life at the speed of their imaginations."
Supercharging Creativity and Design
The deep integration of next-generation AI across Photoshop's core tools uniquely enables new creative workflows that supercharge ideation with precise creative control for production quality content creation.

Generative Fill automatically matches perspective, lighting and style of images to enable users achieve astounding results while reducing tedious tasks. Generative Fill expands creative expression and productivity and enhances creative confidence of creators with the use of natural language and concepts to generate digital content in seconds:

Powered by Firefly: Firefly is designed to generate images that are safe for commercial use and is trained on Adobe Stock's hundreds of millions of professional-grade, licensed, high-resolution images, helping ensure Firefly won't generate content based on other peoples' or brands' intellectual property (IP).

Magically leap from idea to image, with simple text prompts: Add, extend or remove content from images to achieve astounding results.

Edit non-destructively: Create newly generated content in generative layers, enabling you to rapidly iterate through a myriad of creative possibilities and reverse the effects when you want, without impacting your original image.

Create at a transformative rate: Experiment with off-the-wall ideas, ideate different concepts and produce boundless variations of high-quality content as fast as you can type.

Available as a web tool: Generative Fill is also available as a new module in the Firefly beta for users interested in testing the new capabilities on the web.

As a trusted partner to individuals and businesses of all sizes, Adobe develops and deploys all AI capabilities with a customer-centric approach and according to its AI Ethics principles to ensure content and data transparency. Generative Fill supports Content Credentials, serving an essential role in ensuring people know whether a piece of content was created by a human, AI-generated or AI-edited. Content Credentials are like "nutrition labels" for digital content and remain associated with content wherever it is used, published or stored, enabling proper attribution and helping consumers make informed decisions about digital content. The technology was developed by the Content Authenticity Initiative, which Adobe founded and recently surpassed 1,000 members.
Additional New Innovations in Photoshop
In addition, Adobe updated Photoshop today with new capabilities that enhance and accelerate creative workflows. New Adjustment Presets, Contextual Task Bar, Remove Tool and Enhanced Gradients empower users to make complex edits and create unique designs while saving time. Read more here.
Community Inspiration
Connect with your favorite designers and Creative Cloud evangelists as they live stream their Photoshop and Firefly tips and tricks on May 23rd. Join them between 8 a.m. and 4 p.m. PST as they answer questions.
Availability
Photoshop's Generative Fill feature is available in the desktop beta app today and will be generally available in the second half of 2023. Generative Fill is also available today as a module within the Firefly beta app.
About Adobe and Artificial Intelligence
Adobe is making the world more creative, productive and personalized with AI as a co-pilot that amplifies human ingenuity. For decades, Adobe has delivered hundreds of intelligent capabilities across Creative Cloud, Document Cloud and Experience Cloud through Adobe Sensei, enabling customers to create, work and collaborate more efficiently.

Firefly, Adobe's family of creative generative AI models, brings even more precision, power, speed and ease directly into Adobe workflows. It is the most differentiated generative AI offering in the market, trained on a unique dataset that generates commercially viable, professional-quality content.

Adobe Sensei GenAI services are redefining how businesses deliver customer experiences by delivering more speed and productivity across Adobe Experience Cloud workflows. Sensei GenAI will enable brands to instantly generate and modify text-based experiences across any customer touchpoint and leverage different large language models (LLMs). These innovations are anchored in Adobe Experience Platform (AEP), which brings customer data and content together across an organization under one common language model.

As a trusted partner to individuals and businesses of all sizes, Adobe develops and deploys all AI capabilities with a customer-centric approach and according to its AI Ethics principles to ensure content and data transparency. Content Credentials provide "nutrition labels" for digital content and are a key pillar of Adobe's AI principles.
About Adobe
Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.

© 2023 Adobe. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe in the United States and/or other countries. All other trademarks are the property of their respective owners.

Public relations contact
Erin Di Leva
Adobe
edileva@adobe.com

Source: Adobe

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Adobe Unveils Future of Creative Cloud with Generative AI as a Creative Co-Pilot in Photoshop

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Firefly, Adobe's family of creative generative AI models, now supports Generative Fill, allowing users to extend images and add and remove objects Adobe has integrated Firefly directly into Photoshop, marrying the speed and ease of generative AI with the power and precision of Photoshop This is the start of the company's major initiative to integrate generative AI into existing creative workflows across Creative Cloud, giving users a creative co-pilot to accelerate ideation, exploration and production SAN JOSE, Calif.

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How do we navigate the question of employability in our subjects for future artists, thinking about the student cycle from applicant to graduate? We will be asked if the jobs we are preparing students for will still exist 5 years from now. 

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How do we navigate the question of employability in our subjects for future artists, thinking about the student cycle from applicant to graduate? We will be asked if the jobs we are preparing students for will still exist 5 years from now. 

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I wrote this piece in 2018 as a way of thinking through some of the issues around human agency and a wider framing of AI in relation to arts and humanities

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AI and the idea of the human: myth, metaphor and agency

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AI and the idea of the human: myth, metaphor and agency

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A remarkable interview - Gebru discusses the implicit biases of AI and highlights vital questions about human agency

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t feels like a gold rush," says Timnit Gebru. "In fact, it is a gold rush. And a lot of the people who are making money are not the people actually in the midst of it. But it's humans who decide whether all this should be done or not. We should remember that we have the agency to do that."

Gebru is talking about her specialised field: artificial intelligence. On the day we speak via a video call, she is in Kigali, Rwanda, preparing to host a workshop and chair a panel at an international conference on AI. It will address the huge growth in AI's capabilities, as well as something that the frenzied conversation about AI misses out: the fact that many of its systems may well be built on a huge mess of biases, inequalities and imbalances of power.

This gathering, the clunkily titled International Conference on Learning Representations, marks the first time people in the field have come together in an African country - which makes a powerful point about big tech's neglect of the global south. When Gebru talks about the way that AI "impacts people all over the world and they don't get to have a say on how they should shape it", the issue is thrown into even sharper relief by her backstory.

We need regulation, and we need something better than just a profit motive

In her teens, Gebru was a refugee from the war between Ethiopia, where she grew up, and Eritrea, where her parents were born. After a year in Ireland, she made it to the outskirts of Boston, Massachusetts, and from there to Stanford University in northern California, which opened the way to a career at the cutting edge of the computing industry: Apple, then Microsoft, followed by Google. But in late 2020, her work at Google came to a sudden end.

As the co-leader of Google's small ethical AI team, Gebru was one of the authors of an academic paper that warned about the kind of AI that is increasingly built into our lives, taking internet searches and user recommendations to apparently new levels of sophistication and threatening to master such human talents as writing, composing music and analysing images. The clear danger, the paper said, is that such supposed "intelligence" is based on huge data sets that "overrepresent hegemonic viewpoints and encode biases potentially damaging to marginalised populations". Put more bluntly, AI threatens to deepen the dominance of a way of thinking that is white, male, comparatively affluent and focused on the US and Europe.

In response, senior managers at Google demanded that Gebru either withdraw the paper, or take her name and those of her colleagues off it. This triggered a run of events that led to her departure. Google says she resigned; Gebru insists that she was fired.

What all this told her, she says, is that big tech is consumed by a drive to develop AI and "you don't want someone like me who's going to get in your way. I think it made it really clear that unless there is external pressure to do something different, companies are not just going to self-regulate. We need regulation and we need something better than just a profit motive."

Gebru speaking at the TechCrunch Disrupt conference in 2018. Photograph: Kimberly White/Getty Images for TechCrunch

Gebru, who is 40, sometimes speaks dizzyingly quickly, as if the rich details of her life might outrun the hour or so we have to talk. She tends to use the precise, measured vocabulary of a tech insider, leavened with a sense of the absurd that is focused on one particularly howling irony: the fact that an industry brimming with people who espouse liberal, self-consciously progressive opinions so often seems to push the world in the opposite direction.

One of the subjects she returns to repeatedly is racism, including experiences of prejudice in the US education system and Silicon Valley. While she was at high school in Massachusetts, she says, her gift for science was treated bluntly (one teacher said: "I've met so many people like you who think that they can just come here from other countries and take the hardest classes") and passive-aggressively: despite high grades in physics, her request to study the subject further was met with concerns that she might find it too difficult.

"The thing that was very confusing to me as an immigrant was that liberal type of racism," she says. "People who sound like they really care about you, but they'd be like: 'Don't you think it's going to be hard for you?' It took me a while to really figure out what was going on."

I was being attacked by a bunch of guys, and nobody helped me at all. That was the scariest thing

Later on came a watershed experience of even more brazen prejudice, when she and a friend - a black woman - were attacked in a bar. "That was the scariest encounter I've ever had in the US," she says. "It was in San Francisco - again, another liberal place. I was being attacked by a bunch of guys and nobody helped me at all. That was the scariest thing to see: being strangled and people just walking by and looking at you."

She called the police. "And that was worse than not calling them, because first they accused me of lying a number of times, and kept on telling me to calm down. And then they put handcuffs on my friend, who had just been attacked." Her friend was also detained in a police cell.

At Stanford, although she was often condescendingly asked by some of her white peers if she had got in thanks to an affirmative action programme, her undergraduate years were spent in an environment where senior people at least "talked about diversity a lot, and they had different people from different places". But after working as an audio engineer for Apple between 2005 and 2007, she went back to Stanford to study for a PhD and had very different experiences.

Her life, she says, became all about "going to an office every day with the same bunch of people - it's kind of like work. And there was nobody who looked like me at all. It was just shocking."

Gebru ... 'I'm not worried about machines taking over the world, I'm worried about groupthink, insularity, and arrogance in the AI community.' Photograph: Winni Wintermeyer/The Guardian

Gebru began to specialise in cutting-edge AI, pioneering a system that showed how data about particular neighbourhoods' patterns of car ownership highlighted differences bound up with ethnicity, crime figures, voting behaviour and income levels. In retrospect, this kind of work might look like the bedrock of techniques that could blur into automated surveillance and law enforcement, but Gebru admits that "none of those bells went off in my head ... that connection of issues of technology with diversity and oppression came later".

Soon enough, though, she began to think deeply about how big tech's innovations often embodied the same inequalities evident in its offices, labs and social activities. In 2015, Google had to apologise when the AI systems that served its Photos app mistakenly identified a black couple as gorillas. The year after, the thinktank ProPublica found that software used across the US to assess prison convicts' chances of reoffending was heavily biased against black people. Meanwhile, Gebru was becoming even more aware of aspects of the tech industry's culture that lay behind such stories.

Around this time, she attended a big AI conference in Montreal where, at a Google party, a group of white men openly harassed her. "One of them kissed me, one of them took a picture. And I was kind of frozen: I didn't really do anything. They were having a party at an academic conference with limitless drinks at a bar and they weren't even making it clear that this was a professional event. Obviously, you should never harass women - or anybody - like that. But that was rampant at these conferences." The organisers of the conference say that their code of conduct has since been "elaborated"; they now have "a new one-stop contact point for concerns and complaints, which is monitored closely".

The next year, Gebru made a point of counting other black attenders at the same event. She found that, among 8,500 delegates, there were only six people of colour. In response, she put up a Facebook post that now seems prescient: "I'm not worried about machines taking over the world; I'm worried about groupthink, insularity and arrogance in the AI community."

In that context, it might seem surprising that, after a year spent working in Microsoft's fairness, accountability, transparency and ethics in AI lab, Gebru took a job at Google. In 2018, thanks to Margaret Mitchell, a recently hired specialist in algorithmic bias, she was recruited to co-lead a team dedicated to the ethics of AI. "I was full of trepidation," she says. "But I thought: 'Well, Margaret Mitchell is here - we can work together. Who else can I work with?' But that was how I went into it: I was like: 'I wonder how long I can last here.'"

"It was a difficult decision," she says. "Because, by the time I was going to Google, I had heard from several women about sexual harassment, and other kinds of harassment, and they had actually said: 'Don't do it.'"

When Gebru arrived, Google employees were loudly opposing the company's role in Project Maven, which used AI to analyse surveillance footage captured by military drones (Google ended its involvement in 2018). Two months later, staff took part in a huge walkout over claims of systemic racism, sexual harassment and gender inequality. Gebru says she was aware of "a lot of tolerance of harassment and all sorts of toxic behaviour".

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Google employees in New York stage a walkout in November 2018, . Photograph: Bryan R Smith/AFP/Getty Images

In its quest to highlight some of the moral and political questions surrounding AI, her team hired Google's first social scientist. She and her colleagues prided themselves on how diverse their small operation was, as well as the things they brought to the company's attention, which included issues to do with Google's ownership of YouTube. A colleague from Morocco raised the alarm about a popular YouTube channel in that country called Chouf TV, "which was basically operated by the government's intelligence arm and they were using it to harass journalists and dissidents. YouTube had done nothing about it." (Google says that it "would need to review the content to understand whether it violates our policies. But, in general, our harassment policies strictly prohibit content that threatens individuals, targets someone with prolonged or malicious insults based on intrinsic attributes, or reveals someone's personally identifiable information.")

Then, in 2020, Gebru, Mitchell and two colleagues wrote the paper that would lead to Gebru's departure. It was titled On the Dangers of Stochastic Parrots. Its key contention was about AI centred on so-called large language models: the kind of systems - such as OpenAI's ChatGPT and Google's newly launched PaLM 2 - that, crudely speaking, feast on vast amounts of data to perform sophisticated tasks and generate content.

These sources are usually scraped from the world wide web and inevitably include material usually subject to copyright (if an AI system can produce prose in the style of a particular writer, for example, that is because it has absorbed much of the writer's work). But Gebru and her co-authors had an even graver concern: that trawling the online world risks reproducing its worst aspects, from hate speech to points of view that exclude marginalised people and places. "In accepting large amounts of web text as 'representative' of 'all' of humanity, we risk perpetuating dominant viewpoints, increasing power imbalances and further reifying inequality," they wrote.

When the paper was submitted for internal review, Gebru was quickly contacted by one of Google's vice-presidents. At first, she says, non-specific objections were expressed, such as that she and her colleagues had been too "negative" about AI. Then, Google asked Gebru either to withdraw the paper, or remove her and her colleagues' names from it.

She says she told the company that she would not retract it and would remove the authors' names only if Google specified its objections. If this didn't happen, she said, she would resign. She also sent a number of emails to women working in Google's AI division, saying that the company was "silencing marginalised voices".

Then, in December 2020, while she was on holiday, one of her closest colleagues texted her to ask if an email they had seen saying she had left the company was correct. Subsequent accounts said that Google had cited "behaviour that is inconsistent with the expectations of a Google manager".

Google HQ in Mountain View, California. Photograph: Anadolu Agency/Getty Images

How, I wonder, did she feel? "I was not in thinking mode. I was just in action mode, like: 'I need a lawyer and I need to get my story out; I wonder what they're planning; I wonder what they're going to say about me.'" She pauses. "But I was fired. In the middle of my vacation, on a road trip to visit my mom, in the middle of a pandemic."

In response to what Gebru says about workplace harassment and toxic behaviour at Google, her experiences at the party in Montreal and the nature of her departure, the company's press office emails me a set of "background points".

"We are committed to building a safe, inclusive and respectful workplace - and we take misconduct very seriously," it says. "We have strict policies against harassment and discrimination, thoroughly investigate all concerns reported and take firm actions against substantiated allegations. We also have several ways for our workforce to report concerns, including anonymously."

Five years ago, it goes on, the company overhauled "the way we handle and investigate employee concerns, introducing new care programs for employees who report concerns and making arbitration optional for Google employees".

On questions about AI systems using copyrighted material, a spokesperson says that Google will "innovate in this space responsibly, ethically, and legally", and plans to "continue our collaboration and discussions with publishers and the ecosystem to find ways for this new technology to help enhance their work and benefit the entire web ecosystem".

After her departure, Gebru founded Dair, the Distributed AI Research Institute, to which she now devotes her working time. "We have people in the US and the EU, and in Africa," she says. "We have social scientists, computer scientists, engineers, refugee advocates, labour organisers, activists ... it's a mix of people."

AI is not magic. There are a lot of people involved - humans

The institute's fellows, she tells me, include a former Amazon delivery driver, plus people with experience of the monotonous and sometimes traumatic job of manually labelling online content - including illegal and toxic material - to train AI systems. Much of this work happens in developing countries. "There's a lot of exploitation in the field of AI, and we want to make that visible so that people know what's wrong," she says. "But also, AI is not magic. There are a lot of people involved- humans."

Running alongside this is a quest to push beyond the tendency of the tech industry and the media to focus attention on worries about AI taking over the planet and wiping out humanity while questions about what the technology does, and who it benefits and damages, remain unheard.

"That conversation ascribes agency to a tool rather than the humans building the tool," she says. "That means you can aggregate responsibility: 'It's not me that's the problem. It's the tool. It's super-powerful. We don't know what it's going to do.' Well, no - it's you that's the problem. You're building something with certain characteristics for your profit. That's extremely distracting, and it takes the attention away from real harms and things that we need to do. Right now."

How does she feel squaring up to her old employers in Silicon Valley? "I don't know if we'll change them or not," she says. "We're never going to get, like, a quadrillion dollars to do what we're doing. I just feel like we have to do what we can. Maybe, if enough people do small things and get organised, things will change. That's my hope."

Join a Guardian Live online event on the future of AI on Tuesday 23 May at 8pm, chaired by technology editor Alex Hern. Book tickets here

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'There was all sorts of toxic behaviour': Timnit Gebru on her sacking by Google, AI's dangers and big tech's biases

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t feels like a gold rush," says Timnit Gebru. "In fact, it is a gold rush. And a lot of the people who are making money are not the people actually in the midst of it. But it's humans who decide whether all this should be done or not.

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AI for Creative Educators T+L event 7th June 2023 (1)

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‘It’s an educational revolution’: how AI is transforming university life

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AI chatbots have arrived on UK university campuses. But is the hype justified?

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"Waiting in front of the lecture hall for my next class to start, and beside me two students are discussing which AI program works best for writing their essays. Is this what I'm marking? AI essays?"

The tweet by historian Carla Ionescu late last month captures growing unease about what artificial intelligence portends for traditional university assessment. "No. No way," she tweeted. "Tell me we're not there yet."

ButAI has been banging on the university's gate for some time now.

In 2012, computer theorist Ben Goertzel proposed what he called the "robot university student test", arguing that an AI capable of obtaining a degree in the same way as a human should be considered conscious.

Goertzel's idea - an alternative to the more famous "Turing test" - might have remained a thought experiment were it not for the successes of AIs employing natural language processing (NLP): most famously, GPT-3, the language model created by the OpenAi research laboratory.

Two years ago, computer scientist Nassim Dehouche published a piece demonstrating that GPT-3 could produce credible academic writing undetectable by the usual anti-plagiarism software.

"[I] found the output," Dehouche told Guardian Australia, "to be indistinguishable from an excellent undergraduate essay, both in terms of soundness and originality. [My article] was initially subtitled, 'The best time to act was yesterday, the second-best time is now'. Its purpose was to call for an urgent need to, at the very least, update our concepts of plagiarism."

Ben Goertzel proposed what he called the 'robot university student test', arguing that an AI capable of obtaining a degree in a same ways as a human should be considered conscious. Photograph: Horacio Villalobos/Corbis/Getty Images

He now thinks we're already well past the time when students could generate entire essays (and other forms of writing) using algorithmic methods.

"A good exercise for aspiring writers," he says, "would be a sort of reverse Turing test: 'Can you write a page of text that could not have been generated by an AI, and explain why?' As far as I can see, unless one is reporting an original mathematics theorem and its proof, it is not possible. But I would love to be proven wrong."

Many others now share his urgency. In news and opinion articles, GPT-3 has convincingly written on whether it poses a threat to humanity ( it says it doesn't), and about animal cruelty in the styles of both Bob Dylan and William Shakespeare.

A 2021 Forbes article about AI essay writing culminated in a dramatic mic-drop: "this post about using an AI to write essays in school," it explained, "was written using an artificial intelligence content writing tool".

Of course, the tech industry thrives on unwarranted hype. Last month S Scott Graham in a piece for Inside Higher Education described encouraging students to use the technology for their assignments with decidedly mixed results. The very best, he said, would have fulfilled the minimum requirements but little more. Weaker students struggled, since giving the system effective prompts (and then editing its output) required writing skills of a sufficiently high level to render the AI superfluous.

"I strongly suspect," he concluded, "full-on robot writing will always and forever be 'just around the corner'."

That might be true, though only a month earlier, Slate's Aki Peritz concluded precisely the opposite, declaring that "with a little bit of practice, a student can use AI to write his or her paper in a fraction of the time that it would normally take".

Nevertheless, the challenge for higher education can't be reduced merely to "full-on robot writing".

If software can algorithmically rewrite a student's sentence, why shouldn't it do the same with a paragraph?

Universities don't merely face essays or assignments entirely generated by algorithms: they must also adjudicate a myriad of more subtle problems. For instance, AI-powered word processors habitually suggest alternatives to our ungrammatical phrases. But if software can algorithmically rewrite a student's sentence, why shouldn't it do the same with a paragraph - and if a paragraph, why not a page?

At what point does the intrusion of AI constitute cheating?

Deakin University's Prof Phillip Dawson specialises in digital assessment security.

He suggests regarding AI merely as a new form of a technique called cognitive offloading.

"Cognitive offloading," he explains, is "when you use a tool to reduce the mental burden of a task. It can be as simple as writing something down so you don't have to try to remember it for later. There have long been moral panics around tools for cognitive offloading, from Socrates complaining about people using writing to pretend they knew something, to the first emergence of pocket calculators.'

Dawson argues that universities should make clear to students the forms and degree of cognitive offloading permitted for specific assessments, with AI increasingly incorporated into higher level tasks.

"I think we'll actually be teaching students how to use these tools. I don't think we're going to necessarily forbid them."

The occupations for which universities prepare students will, after all, soon also rely on AI, with the humanities particularly affected. Take journalism, for instance. A 2019 survey of 71 media organisations from 32 countries found AI already a "significant part of journalism", deployed for news gathering (say, sourcing information or identifying trends), news production (anything from automatic fact checkers to the algorithmic transformation of financial reports into articles) and news distribution (personalising websites, managing subscriptions, finding new audiences and so on). So why should journalism educators penalise students for using a technology likely to be central to their future careers?

'The occupations for which universities prepare students will, after all, soon also rely on AI, with the humanities particularly affected.' Photograph: Dean Lewins/AAP

"I think we'll have a really good look at what the professions do with respect to these tools now," says Dawson, "and what they're likely to do in the future with them, and we'll try to map those capabilities back into our courses. That means figuring out how to reference them, so the student can say: I got the AI to do this bit and then here's what I did myself."

Yet formulating policies on when and where AI might legitimately be used is one thing - and enforcing them is quite another.

Dr Helen Gniel directs the higher education integrity unit of the Tertiary Education Quality and Standards Agency (TEQSA), the independent regulator of Australian higher education.

Like Dawson, she sees the issues around AI as, in some senses, an opportunity - a chance for institutions to "think about what they are teaching, and the most appropriate methods for assessing learning in that context".

Transparency is key.

"We expect institutions to define their rules around the use of AI and ensure that expectations are clearly and regularly communicated to students."

Algorithms might mark the output of other algorithms, in an infinite process in which nothing whatsoever ever gets learned

She points to ICHM, the Institute of Health Management and Flinders Uni as three providers now with explicit policies, with Flinders labelling the submission of work "generated by an algorithm, computer generator or other artificial intelligence" as a form of "contract cheating".

But that comparison raises other issues.

In August, TEQSA blocked some 40 websites associated with the more traditional form of contract cheating - the sale of pre-written essays to students. The 450,000 visits those sites received each month suggests a massive potential market for AI writing, as those who once paid humans to write for them turn instead to digital alternatives.

Research by Dr Guy Curtis from the University of Western Australia found respondents from a non-English speaking background three times more likely to buy essays than those with English as a first language. That figure no doubt reflects the pressures heaped on the nearly 500,000 international students taking courses at Australian institutions, who may struggle with insecure work, living costs, social isolation and the inherent difficulty of assessment in a foreign language.

But one could also note the broader relationship between the expansion of contract cheating and the transformation of higher education into a lucrative export industry. If a university degree becomes merely a product to be bought and sold, the decision by a failing student to call upon an external contractor (whether human or algorithmic) might seem like simply a rational market choice.

It's another illustration of how AI poses uncomfortable questions about the very nature of education.

Ben Goertzel imagined his "robot university student test" as a demonstration of "artificial general intelligence": a digital replication of the human intellect. But that's not what NLP involves. On the contrary, as Luciano Floridi and Massimo Chiriatti say, with AI, "we are increasingly decoupling the ability to solve a problem effectively ... from any need to be intelligent to do so".

GPT-3 has convincingly written on whether it poses a threat to humanity, and about animal cruelty in the styles of both Bob Dylan, pictured, and William Shakespeare. Photograph: TT News Agency/Alamy

The new AIs train on massive data sets, scouring vast quantities of information so they can extrapolate plausible responses to textual and other prompts. Emily M Bender and her colleagues describe a language model as a "stochastic parrot", something that "haphazardly [stitches] together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning".

So if it's possible to pass assessment tasks without understand their meaning, what, precisely, do the tasks assess?

In his 2011 book For the University: Democracy and the Future of the Institution, the University of Warwick's Thomas Docherty suggests that corporatised education replaces open-ended and destabilising "knowledge" with "the efficient and controlled management of information", with assessment requiring students to demonstrate solely that they have gained access to the database of "knowledge" ... and that they have then manipulated or "managed" that knowledge in its organisation of cut-and-pasted parts into a new whole.

The potential proficiencyof "stochastic parrots" at tertiary assessment throws a new light on Docherty's argument, confirming that such tasks do not, in fact, measure knowledge (which AIs innately lack) so much as the transfer of information (at which AIs excel).

To put the argument another way, AI raises issues for the education sector that extend beyond whatever immediate measures might be taken to govern student use of such systems. One could, for instance, imagine the technology facilitating a "boring dystopia", further degrading those aspects of the university already most eroded by corporate imperatives. Higher education has, after all, invested heavily in AI systems for grading, so that, in theory, algorithms might mark the output of other algorithms, in an infinite process in which nothing whatsoever ever gets learned.

But maybe, just maybe, the challenge of AI might encourage something else. Perhaps it might foster a conversation about what education is and, most importantly, what we want it to be. AI might spur us to recognise genuine knowledge, so that, as the university of the future embraces technology, it appreciates anew what makes us human.

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'Full-on robot writing': the artificial intelligence challenge facing universities

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"Waiting in front of the lecture hall for my next class to start, and beside me two students are discussing which AI program works best for writing their essays. Is this what I'm marking? AI essays?" The tweet by historian Carla Ionescu late last month captures growing unease about what artificial intelligence portends for traditional university assessment.

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Academic minds ‘vital’ as fears grow over ‘out of control’ AI

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‘Infinite’ research budgets have allowed industry to surge ahead of academia, but scholars will be essential for steering new technologies towards common good, say experts

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AI might spur us to recognise genuine knowledge, so that, as the university of the future embraces technology, it appreciates anew what makes us human.

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AI might spur us to recognise genuine knowledge, so that, as the university of the future embraces technology, it appreciates anew what makes us human.

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“We don’t need to revert to in-person exams: this is a great opportunity for the sector to explore new assessment techniques that measure learners on critical thinking, problem-solving and reasoning skills rather than essay-writing abilities.” Thomas Docherty, University of Warwick

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Lecturers at UK universities have been urged to review the way in which their courses are assessed amid concerns that students are already using a potent new AI tool capable of producing high-quality essays with minimal human input.

ChatGPT, the latest chatbot from OpenAI, founded in 2015 by Elon Musk, Sam Altman and others, has only been publicly available for a matter of weeks, but has already triggered concerns about the potential for hard-to-detect plagiarism and questions about the validity of the essay as a future form of assessment.

It has been described as "a gamechanger" that will prove a challenge in universities and schools. Though GCSE and A-level courses are assessed through traditional end-of-course examinations, experts are concerned pupils who use the technology to do their homework will become dependent on AI-generated answers without acquiring the knowledge and skills they need.

Working groups have been set up in university departments to assess the challenge of this latest iteration of AI text-generating technology, with the expectation that methods of assessment in certain courses will have to be updated. Experts admit to feeling both excited and alarmed.

In one case, staff in the computer science department at University College London recently decided to change an assessment. Previously students were offered a choice between an essay-based or skills-based assessment as part of final coursework, but the essay option has been removed.

Geoff Barton, the general secretary of the Association of School and College Leaders, meanwhile, acknowledged that schools would have to get to grips with how to utilise ChatGPT's benefits while guarding against negative implications.

"As with all technology, there are caveats around making sure that it is used responsibly and not as a licence to cheat, but none of that is insurmountable," he said. In contrast, New York City schools have already banned the use of ChatGPT on all devices and networks because of concerns it will encourage plagiarism.

Dr Thomas Lancaster, a computer scientist working at Imperial College London, best known for his research into academic integrity, contract cheating and plagiarism, said it was in many ways a game changer. He said: "It's certainly a major turning point in education where universities have to make big changes.

"They have to adapt sooner rather than later to make sure that students are assessed fairly, that they all compete on a level playing field and that they still have the skills needed beyond university.

"There's been technology around for several years that will generate text. The big change is that this technology is wrapped up in a very nice interface where you can interact with it, almost like speaking to another human. So it makes it available to a lot of people."

Because ChatGPT is capable of coming up with countless original combinations of words, Lancaster said it would be more difficult to spot and prove plagiarism, though work is already under way to improve detection software. "It's an incredibly tricky problem because this has almost appeared out of nowhere ... I would be completely shocked if there weren't students already using GPT for assessments."

Australian universities have already signalled a return to greater use of in-person exams to try to protect the integrity of assessment. Lancaster said, however, a wholesale return to exams was not the answer, suggesting instead the use of oral presentations and vivas to enable students to demonstrate their grasp of a subject.

Though ChatGPT marks a huge step forward in the evolution of AI text generation, it is not infallible. "It may sound very plausible, but the more detail or facts you need as part of your question, the more likely it is that the algorithm will produce something that looks good, but is completely wrong," said Michael Draper, professor in legal education at the University of Swansea and an expert on academic integrity and cheating.

He said universities could try to ban it, as they ban other forms of cheating like essay mills, which students pay to produce an original piece of work that they then submit. Draper said: "The other alternative is that you allow students to use it. lf we're preparing students for the outside world of work and if in the workplace this sort of technology is given to us, then I think we need to embrace it rather than ban it."

Michael Webb, the director of technology and analytics at Jisc, which provides network and IT services to higher education, agreed. He said: "The knee-jerk reaction might be to block these tools in order to stop students cheating, but that's neither feasible nor advisable. We should really regard them as simply the next step up from spelling or grammar checkers: technology that can make everyone's life easier.

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"We don't need to revert to in-person exams: this is a great opportunity for the sector to explore new assessment techniques that measure learners on critical thinking, problem-solving and reasoning skills rather than essay-writing abilities."

Q to ChatGPT: Write a university-level essay about the causes of the first world war. The essay should be written from a UK perspective and use UK English.

A: The first world war, also known as the Great War, was a global conflict that lasted from 1914 to 1918 and resulted in the deaths of millions of people. The causes of the war are complex and varied, but can be broadly categorised into long-term, short-term, and immediate causes.

One of the main long-term causes of the war was the competition for colonies and resources between the major European powers. In the late 19th and early 20th centuries, the British Empire and other European nations were expanding their empires and seeking new territories to exploit for resources and markets. This led to tensions between the major powers, as they competed for control of the same areas.

Another long-term cause was the arms race between the major powers. In the years leading up to the war, countries such as Germany, France, and Britain had been investing heavily in their military capabilities, including building large armies and navies. This arms race led to an atmosphere of mistrust and fear between the major powers, as each feared that the others were becoming too powerful.

Short-term causes of the war include the assassination of Archduke Franz Ferdinand of Austria-Hungary in 1914, and the system of alliances that existed between the major European powers. The assassination of the Archduke, which was carried out by a member of a Bosnian-Serbian nationalist group, led to Austria-Hungary declaring war on Serbia, which in turn led to other countries getting involved as a result of their alliances.

Immediate causes of the war include the mobilisation of armies by the major powers in response to the outbreak of war, and the Schlieffen Plan, which was a military strategy developed by Germany to quickly defeat France before turning its attention to Russia.

In conclusion, the causes of the First World War were complex and varied. The competition for colonies and resources between the major European powers, the arms race between the major powers, the assassination of Archduke Franz Ferdinand, the system of alliances, the mobilisation of armies and the Schlieffen Plan all contributed to the outbreak of war in 1914.

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Lecturers urged to review assessments in UK amid concerns over new AI tool

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Lecturers at UK universities have been urged to review the way in which their courses are assessed amid concerns that students are already using a potent new AI tool capable of producing high-quality essays with minimal human input.

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With the explosion of artificial intelligence, particularly chatbots, we hear from artists on how they're using AI in their work, looking at its benefits and limitations.

On this week's programme we're looking at the explosion of interest in the role of artificial intelligence, particularly since the arrival of a new generation of AI powered chatbots like Google Bard, DALL-E 2 and Open Al's ChatGPT, which is reportedly the fastest growing consumer app of all time.

Tina Daheley talks to two visual artists using AI in their work; Dr Melisa Achoko Allela and Jeremiah Ikongio. Melisa's virtual reality storytelling project uses ChatGPT to help retell and digitise traditional African stories. Jeremiah uses an AI algorithm to generate new artworks based on the style of the late Nigerian modernist painter Uche Okeke. Jeremiah has since developed his own AI web application AfroDreams to create a mix of contemporary and traditional images.

The Swedish drama director, Jenny Elfving and Polish science researcher Piotr Mirowski are two members of the creative team behind the AI experimental theatre company Improbotics. The company have developed an onstage chatbot called A.L.Ex, which can generate lines for actors to respond to during spontaneous improvised performances. We hear A.L.Ex and the actors in action in the programme.

US artist Holly Herndon works with computer software and AI to create innovative music, songs and sounds. She told the BBC's Andrea Kidd how she has developed a digital computer twin called Holly + that can sing melodies in a number of languages and styles using Holly's original voice.

Producers: Anna Bailey, Andrea Kidd and Hannah Dean.

(Photo: Improbotics perform on stage. Credit: Eleanora Briscoe/Edinburgh International Improv Festival 2020)

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BBC World Service - The Cultural Frontline, What the AI revolution means for arts

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With the explosion of artificial intelligence, particularly chatbots, we hear from artists on how they're using AI in their work, looking at its benefits and limitations.