This $40 AI Tool Turns Dreams Into Art
In a world where people have so much to say, images are still king. Marketing campaigns can be defined by a single photograph, and it’s possible to have entire conversations using only emojis.
It’s no surprise, then, that AI-generated art has captured our imaginations so quickly. And one way to enjoy it is WOMBO’s discounted Dream AI Art Tool, which was featured at CES 2023.
It doesn’t take much effort to start making art with Dream by WOMBO. Just type in a prompt or idea, and the app will render an AI artistic interpretation based on your description. Create everything from dark-hued, psychedelic alien landscapes to colorful, whimsical takes on famous historical scenes. You can even modify existing photographs from your library by asking the app to recreate them in a specific artistic style or color scheme.
The process only takes a few seconds, and you get to watch Dream “paint” your instant masterpiece. If you want different takes on a prompt, you can ask the app to generate multiple images at a time, and then edit them to fit whatever mood or style you’re looking for. Even better, a premium plan for this innovative art tool gives you access to exclusive art styles, as well as the ability to generate anything on Discord, save AI art as a video, fine-tune output with three alternative options, and much more.
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Prices subject to change.
This betting company used AI to generate dream football commentary for sleeping fans
Football fans often dream about their teams making the final, Oddset took that one step further using artificial intelligence (AI).
The Swedish activation follows the nation’s progress in the European tournament. The ‘Dreams of Europe’ audio service was created with the help of Stockholm-based communication agency Perfect Fools.
Use of artificial intelligence generates questions about the future of art
Artificial intelligence, or A.I., is everywhere. It’s now part of our conversations about education and politics and social media. It’s also become a hot topic in the art world.
Programs that generate art using A.I. are widely available to the public and are skyrocketing in popularity. But what goes into these programs and the work that comes out are heavily debated in the arts community.
Jeffrey Brown explores the influence of A.I. on art, and where it may be headed next. It’s part of our arts and culture series, Canvas.
The changing face of journalism
AI is changing the norms of journalism and data collection. Can it give new insights, make news unbiased and responsible remains to be seen
In the wake of the increasing use of artificial intelligence (AI) in journalism, data journalism and the nature of storytelling are taking shifts which have further invited deliberations on news values, the political economy of news media and media ethics. There is no doubt that AI tools are immensely contributing to the news industry. Subsequently, there are paradigm shifts in the realm of journalism, as a form of art and profession. However, excessive and unstructured use of technologies in newsrooms has plummeted to various ailments in the media industries. Inculcated forays of AI into newsrooms have accumulated pointless pitfalls.
Due to the bottlenecks that such complicated and opaque systems pose for undermining accountability, decision-making, and professional judgment, this intelligibility issue is especially acute for public service media. Unravelling the dichotomy between the Global North and Global South has further worsened the feasibility of using AI tools in data journalism. Inclusive journalism has become a distant dream. Moreover, there are other several unidentified factors plaguing the news industry, that are to be scientifically probed.
Tellingly, journalism is the process of gathering, analysing, producing, and presenting news and information. History demonstrates that society tends to have more news and information the more democratic it is. Journalism as a means of craft and business remains a dynamic field at the same time. With the pace of time and demand, data journalism is increasingly significant these days. And subsequently, this approach to storytelling is getting apparent in newsrooms across the globe.
The practice of reporting on facts while using structured data as the central element of the narrative and managing it impartially is known as data journalism. It covers an expanding range of storytelling tools, methods, and approaches. It can include everything from conventional computer-assisted reporting to the most cutting-edge news applications and data visualization. The overarching objective is a journalistic one: disseminating data and analysis to help us all learn more about pressing current affairs.
Digital tools are used to streamline data collection in data journalism, which is news-making that is fuelled by quicker data collection and visualization and first emerged in the US in the 1950s. Probability and computation play a large role in data journalism. Big data has the potential to produce news more quickly and with greater depth than ever before. Like the internet, big data is an infinite reservoir of ever-expanding data that belongs to no one in particular. Big data can be used for a wide range of intricate tasks with the right equipment and personnel, including improving public healthcare in smart cities and personality-based psychometric profiling.
Applications with AI that use big data from the media can search through millions of forums and social media posts. By analysing the tone of social media posts, forum posts, emails to government agencies, and other sources of information, sentiment-based big data in media can reveal the underlying problems in a particular area. In these posts, elements like the use of negative or positive sentences, word choice, length and readability of posts, and the characteristics of images or other media within the posts are evaluated to ascertain the mood of the public in a specific area. News networks can use technology to bring the issues of citizens to the attention of the ruling government once it has developed sufficiently and is more practical to use for journalism.
AI tools have become instrumental in mitigating the biases in mainstream journalism. Because of the polarization of news coverage, viewers enjoy picking sides and demonizing people who disagree with their way of thinking and opinions. Even though from a TRP standpoint this may be profitable for news channels, if it is not addressed in the beginning it can eventually cause a fractured society.
News outlets can publish and curate content using machine learning and AI to make data journalism impartial and unbiased. Machine learning involves finding distinctive and subtle patterns in vast amounts of data. This enables AI-based applications to distinguish between authentic data and fake data with great clarity. Data journalists can produce factually accurate articles and news reports once the non-factual data has been separated from it. News production becomes more moral when using this method to develop new campaigns.
The impact of AI extends beyond the newsrooms of a specific news network. Media companies can more effectively manage their financial operations thanks to technology, big data, and NLP. Through data-driven financial management and expense control, these technologies enable media businesses to make money by simplifying the analysis of financial documentation. In addition to identifying instances of fraud and errors in financial statements, AI-based accounting systems can also lower the number of false positives in fraud detection. False positives are decreased, which lowers the costs associated with financial fraud investigations that would otherwise be incurred. News networks are safe when it comes to complying with financial regulations thanks to a well-calibrated financial management framework guaranteed by AI and big data in media and journalism.
AI, big data analytics, and machine learning will support human journalists and remove human error from newsgathering, making journalism more integrity-driven. To be fair, the news media is regarded as the fourth pillar of any democracy. Data journalism must uphold this goal to strengthen society and preserve a nation’s democracy. Big data’s emergence in journalism-related media is expected to achieve this goal. The news media cannot escape bias because it is a global problem. But because AI’s machine learning algorithms are trained to take accuracy into account, it helps to reduce the subjective interpretation of data by humans.
The use of AI tools in data journalism has developed certain flaws in newsrooms across the globe. Certain newsrooms are not equipped with AI tools and their orientation towards technological implications in Global South compared to Global North remains lethargic. The tendency of AI algorithms to reproduce existing bias is one of the main issues with their growing use. At the moment, AI-written articles can only be found on straightforward, formulaic subjects like stock market data and sports scores.
Considering how quickly technology is developing, data journalism will play a significant role in the way we tell and visualize stories going forward. Additionally, audiences are becoming more engaged and will anticipate seeing more journalism and articles that focus on the power of the data. However, the future of AI in the domain of data journalism remains undecided. Some newsrooms already use artificial intelligence to mine data, develop algorithms, and produce content automatically. Journalists face new issues as a result of routinely using this technology. Some experts assert that we are in a transitional period and must decide how this technology will be used in the media going forward, particularly in data journalism. It’s difficult to know exactly what all of the limitations may be because we are still in the early stage of the adoption of AI techniques in journalism.
(Biswal is an Associate Professor and Head of the Department of Journalism and Mass Communication, at Rama Devi Women’s University, Bhubaneswar. Kulkarni is a Research Professor and Associate Director of the Institute of Artificial Intelligence, at MIT World Peace University, Pune)
What happens in our brains when we’re trying to be funny
Jean Mary Zarate: 00:05
Hello and welcome to Tales from the Synapse, a podcast brought to you by Nature’s careers section in partnership with Nature Neuroscience. I’m Jean Mary Zarate, a senior editor at the journal Nature Neuroscience, and in this series we speak to brain scientists all over the world about their life, their research, their collaborations, and the impact of their work.
In episode three, we chat to a researcher and amateur stand up comedian who explores what’s happening in our neural networks when we’re trying to be funny.
Ori Amir: 00:41
Hey, I’m Ori Amir, I am, I guess. a professor at Pomona College. That is a college in the US in California, not far from Los Angeles.
My research was all over the place. I studied everything from how visual processing happens in the brain, to, you know, moral processing, moral cognition. And finally, I also studied creativity and specifically how humour is perceived and created in the brain.
So how the brain essentially understands humour, and how it generates, like, new jokes.
Ori Amir: 01:33
So I was born in a small village in Israel called Tel Aviv. And I grew up there. I was miserable.
I had some good times, but mostly miserable. But I started in Israel, I did my bachelor’s there. I thought I wanted to become a clinical psychologist, to treat myself mostly.
But then I realized after one year that it’s not my thing, but I really enjoyed the science of psychology, and I got more and more into neuroscience and artificial intelligence.
That was Israel, and then I realized that, you know, maybe I can switch scenery a little bit. Maybe I can go and do my advanced degrees in the US. So I went and studied at the University of Santa Cruz in Los Angeles. I did my PhD in neuroscience essentially.
I did a lot of brain imaging. I had my brain scanned way too many times during this process.
And at that time, I also realized that there isn’t a possibility, you can actually do stand up comedy, like if you want. You can just go to an open mic, or like a comedy club some, some nights and just try it out, to just do it. So I decided to try it. And after the first moment, I got hooked.
Ori Amir: 03:02 (clip from stand-up show)
So I wasn’t always a foreigner. I was always creepy. I grew up in Israel, where I was a creepy local.
I love being a foreigner, I can say the craziest things. If you go “Well, I guess it must be normal in his country.
“You say creepy, I say tomato. It’s culture differences. If you don’t like it, go back to where I came from.”
Ori Amir: 03.15
And I ended up doing that as a serious hobby throughout my time in the US.
And at some point, I realized, well, I live in Los Angeles, I know a lot of famous comedians. And I have an MRI, access to an MRI machine where I can scan the brain and see what’s going on in the brain when you are in the process of coming up with funny ideas.
And at that time, there was no research on what goes on in the brain when you are being comedically creative, when you’re actually coming up with a funny idea.
There have been some studies, you know, maybe 20 studies about what’s going on in the brain when you are processing comedy, enjoying comedy, but nothing about the creative process.
And I figured, okay, I know these famous or sem-famous comedians and I have an MRI machine, not far from Hollywood. I can just bring these people to the MRI machine, scan their brains, see what’s going on. And so that’s 99% of my life story. Nothing much else has happened.
Ori Amir: 04:55
So I wanted to see what goes on in the brain of comedians when they are in the process of coming up with a funny idea.
Now the MRI is quite limited because it’s a very noisy signal. And there is a lot, you know, in the brain itself. Whenever you, whenever you do something as complex, like a lot of it is working.
And it’s not necessarily parts of the brain that are critical for writing comedy exactly. It’s, you know, you’re looking at a picture, you will see visual cortex activated. You know, you are thinking about what to say. You have language areas activated.
So you want to be able to have multiple events of coming up with a funny idea within a certain window of time, so that you can average out this activation and see some kind of reliable signal from the noise.
And you have to, so you have to give them a task that pretty much forces them to do that.
So the New Yorker captioning task, essentially, you have a cartoon of a mouse pointing a revolver at a cat. And you have to come up with a caption. Now the winning caption for this particular image was “Six rounds, nine lives. Do the math.”
So that was, but that’s the task, the task is to come up with something funny that one of those characters would say in the situation, right. So that’s, that’s the task.
So you see multiple, multiple such images, you have to come up with multiple captions. And you need a control condition that pretty much has all of the elements of the experimental condition, but leaves out that part of having to come up with a funny idea, right?
So you basically want to show them the same kind of cartoons, and have them come up with a caption, but this time, the caption should not be funny. Just, you know, what you would normally hear in the situation.
So this way, in both the control condition and the experimental condition, you have the part of the brain that does language, the part of the brain that visually processes the image, but what separates the condition, if you contrast them, if you subtract the activation of the control from the experimental condition, then hopefully what you’re getting is specifically those areas of the brain that are uniquely involved in generating funny ideas.
So we had 13 professional comedians, nine amateurs, and I believe it was 19 controls. So the idea is to see the difference between, you know, professional comedians and controls. But also to see if there is some kind of, you know, continuum there.
So what we saw was that the professional comedians have more activation in temporal cortices, certain areas of the temporal cortex in the front of it, where it is sort of like a high level, semantic area, the kind of area where associations from different parts of the brain sort of converge.
So if you have some remote concepts that usually don’t go together, but you want to find a way to link them in a meaningful way, you would go to that cortex, that part of the cortex, yeah.
So you see more activation there, the more experience you have doing comedy.
So there’s a lot of sort of, sort of associative sort of brainstorming going on. And you see less activity in the prefrontal cortex, which is essentially the area in the front of the brain, where it’s one of those areas that developed late in evolution.
So humans have a particularly large prefrontal cortex. But what we think it does in this context, and why we think that the more experience you have doing comedy you see less activity there, is because it has to do with control over the creative process.
So that’s the area that would tell you the goal of what you’re supposed to be doing. So “Oh, I’m supposed to come up with something funny,” and you’re supposed to do this, supposed to do that.
That’s like the conductor of the orchestra, essentially. And I guess you can say that you need less of that, the more experience you have doing comedy.
Maybe it’s even in the way of coming up with, with a particularly novel, and original idea, because you’re sort of like, sort of hands-on directing the process. If you let it have a little bit of freedom, a little less control, you might actually come up with more novel, original ideas.
Another thing we saw was greater activation in the striatum, the part of the striatum that is involved in reward processing before the professional comedians were coming up with the funnier ideas.
So the funnier the joke would be, then the more activation you see in the striatum before they come up with it.
And so that’s somewhat somewhat tricky to interpret, because it could mean one of two things.
It could mean that you need to, sort of, like set up the environment. So like the general feeling in your brain, so that you will be more inclined to come up with funny ideas.
Or it could mean that they are just really good at predicting, “Oh, now I’m going to come up with a funny idea.” This has a lot of potential.
So that prediction is rewarding. I guess that might be less of a clear cut kind of story than the previous one. So you may or may not want to cut it out.
But if you want to translate it to advice that you can give a comedian is like try to have more fun and you will be funnier, right.
So if you’re having more fun, you activate your reward regions more. And that might give sort of like a mental context. Sort of a sense in the brain where humour is more likely to spontaneously emerge.
The other advice, the more like immediate advice, is pretty much a confirmation of the advice that comedy coaches, especially improv comedy coaches, have been giving for a long time, which is, “Get out of your head.”
And by “get out of your head,” if you think the actual meaning of it is to let your mind flow naturally. You know, don’t don’t try to, you know, force the direction of where your associations go.
But in terms of what it means, in terms of the neurosciences, you know, don’t over activate your prefrontal cortex, let it rest a little bit, and let the associations in the anterior temporal cortex do their thing.
I do enjoy jokes that are logic-based and are absurdist, and have some commentary about how silly is, how people behave or think. Just some commentary about that. So essentially, parodying, like the acceptable narratives. I would say that’s my favorite. My favourite stuff I’m trying to do, and that’s what I enjoy the most listening to.
Ori Amir: 13:48 (from stand-up)
If y’all want to have a PhD like me, here’s what you’ve got to do. It’s going to take seven years, the first five and a half years to work very hard on developing a silly accent.
Then you do some original research and it all culminates in a dissertation defence in which you present your work in front of five important neuroscientists. And if you fail, they eat your brains.
Ori Amir: 13:55
I noticed that when I started doing comedy, and I wasn’t as good, people were telling me “Well, you know, you will have a hard time making it because of your accent. And then once I became good, people will tell me, “Well of course you’re funny. It’s because of your accent.”
Ori Amir: 14:05 (from stand-up)
Yeah, my dream is to become a professional comedian and an amateur neurosurgeon. This way I can just cut brains for fun.
Ori Amir: 14:15
You know, the one place where my comedy fails horribly, like without any saving grace, is whenever I try to go back to Israel and do comedy there.
So when you’re talking about the appreciation of humour, and you can, there are two types, two major types of studies. One that looks into humour versus non-humour, and see like what areas of the brain are activated. So you actually find, similarly, that temporal, these areas in the temporal regions appear to be activated to a different, to a different extent, or with a different timeline.
So, when you’re getting a joke then you have a quick spike in areas like in the temporal areas. Whereas when you are constructing a joke when you’re being creative, then you see a gradual increase in activity there.
So that’s like one of the differences you might see. Otherwise, you would see reward regions activated, as you see in human creation.
But again, the activation has a different timeline. It happens after getting the joke as opposed to when you create a joke. Apparently, it comes before you fully formulate the joke.
And the other type of study looks into the differences between different types of humour. So, and there, it’s actually not particularly surprising.
So if you have visual gags, you would see more activation in visual areas. Or if you have language-based humour, you see more activation in the language area.
If you have humour that relies on understanding other people’s mind, viewpoint or whatever, you have activation in regions involving in theory of mind, which is a fancy word for just understanding other people’s perspective.
And then there are studies that try to sort of break down to different parts of humour processing. And that comes from different theoretical perspectives.
So there’s like a researcher from Taiwan, I believe, that recently just published a lot of studies about humour in the brain, and they were looking at the different stages of humour processing.
So you start from realizing that there is some kind of incongruity in the narrative, right? So the setup and the punchline result in some kind of incongruity based on your original interpretation of the setup.
And then you have to sort of change your perspective. You have to revise your understanding of the setup so as to reach a resolution.
And this process, and then you find it funny. It’s like one theory that I feel does not cover all types of humour but it’s definitely a prominent theory of what is humour.
So that’s incongruity resolution hypothesis. And they show, okay, see that area of the brain is involved in the detection of incongruity, and that area is more active during the insight or the resolution part.
So I am working on some projects that attempt to use artificial intelligence to generate, like, clever humour.
So there have been a lot of work on you know, that uses artificial intelligence to make essentially patterns or well like structured jokes, like your mama’s jokes or something like that, or “I like my man how I like my kind of jokes.”
So these jokes have been possible to make quite successfully using artificial intelligence for 20 years or so.
The humans usually have to select the ones that makes sense out of the multiple things in the output, but I’m working on a couple of projects that I can’t maybe completely reveal that might help people in marketing for example, use artificial intelligence to generate jokes that are appropriate to what they’re trying to make, right?
So basically, okay, I have a commercial about Coca-Cola and a polar bear and I need some link between those two concepts that have comedic potential. So it’s supposed to help with that
What I did see in terms of what’s already out there is a pretty scarily good algorithm that explains jokes.
So it’s based on, you know, some general language model of Google that is not completely, as far as I understand it, available to the general population yet.
But it’s basically, you give it a joke and in a German fashion, just to explain, “Well, the reason why this, you know, it is funny is because the dog has died. And because War and Peace is a very big book, but the manual of the company is also very big.”
And so it’s referencing, it explains the jokes, but it does a very good job of, like, as good as it gets. And so, that’s scary.
Like, I want to see if the inverse is possible, like if you can just write a serious bunch of serious statements, and it translated into humour, which is like one of the projects we’re trying but that’s, you know, that that’s probably, that’s a very difficult problem, for sure.
That would be the killer app of humour. Of humour creating AI would be basically like a Google Translate, but instead of translating from one language to another, you give it a serious statement, and it makes a joke that essentially says the same thing.
So I think humour is probably one of the hardest problems for AI. It’s what we call AI-complete, meaning, you have to pretty much be able to do everything that humans can do in order to do humour, right?
So in order to do humour, you would have to be able to master all other human cognitive functions.
Whether it will happen and when it will happen, I used to be more certain about it. I used to be in the camp that was like, “Okay, AI is definitely coming, it’s definitely coming soon. It’s like, you know, less than 10 years away, and it’s going to replace us and going to exponentially, you know, improve itself.
After that point, assuming nothing will go wrong and explode or whatever, which is very possible, if we, if you do achieve any kind of level of artificial intelligence, that has self control.
And now, you know, I give room for the possibility that it might not be possible, more so than I did in the past.
But put it this way, I think if artificial intelligence, human level artificial intelligence, that sort of self improvement is possible, we’ll find this out in the next 10 years.
I’m afraid that if I make any jokes about artificial intelligence, I will get in trouble in the future. Artificial intelligence would cancel me. So I’m refraining from making any such jokes.
Jean Mary Zarate: 23:39
Now, that’s it for this episode of Tales from the Synapse. I’m Jean Mary Zarate, a senior editor at Nature Neuroscience. The producer was Don Byrne. Thanks again to Ori Amir. And thank you for listening.