The concept of generative artificial intelligence (GAI) poses a groundbreaking question that has until recently not been contemplated: at what stage does the relationship between humans and machines evolve from its present-day form into one that is so fundamentally changed that we can no longer regard one as being superior to the other when it comes to creative terms?
As generative AI becomes ever more advanced, media expressed in a purely synthetic form will radically accelerate the process of content creation and delivery. Its accessibility and interactivity will usher in an exciting new era of digital media, one in which creativity, insight, and imagination determine content dissemination instead of the limitations imposed by physical space. Although generative AI will unleash a form of creativity not seen before, it will also pose dangers as the recent failure of Meta’s Galactica only showed so well.
The large language model was meant to be a tool to create scientific-looking articles and content using a string of text as input. However, similar to the chatbot Tay launched by Microsoft in 2016, Galactica quickly turned sour when users started to input it with biased and racial text. Within three days, the generative AI was pulled by Meta because of racially biased and inaccurate information being published as scientific content.
Generative AI is a branch of computer science that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code. It is all about creating authentic-looking artifacts that are completely original. Its output can be synthetic content also known as synthetic media, but also synthetic data that can be used to train large language models and other machine learning models.
Synthetic media is a new form of virtual media produced with the help of artificial intelligence (AI). It is characterized by a high degree of realism and immersiveness. Furthermore, synthetic media tends to be indistinguishable from other real-world media, making it very difficult for the user to tell apart from its artificial nature. It is possible to generate faces and places that don’t exist and even create a digital voice avatar that mimics human speech.
Synthetic media is created using Generative Adversarial Networks (GANs). These deep neural networks are more powerful than ever before and have helped to make synthetic media possible by learning from existing content while also being able to produce entirely new content. Since GAN outputs look natural and indistinguishable from their input, for example, photos, they enable synthetic media that is difficult to distinguish from real media, particularly in computer vision and image processing applications.
Synthetic media tools are reinventing our work with more intelligent, efficient methods that produce quality media experiences as never thought possible.
The main advantages of synthetic media include the following:
We are entering a new age where more people will be exposed to synthetic media. It’s a mass social experiment, and we have no idea what the consequences of this medium might be. If we cannot predict or study its impact accurately, there is little hope of protecting ourselves against its dangers.
While synthetic media can be compelling, it does come with risks. Since the system is in charge of creating meaningful and appropriate content for users, there is less control over what is created. While AI is generally considered a positive technology, some drawbacks are associated with it, especially when it comes to synthetic media. In the years to comen, generative AI will undoubtedly influence many professions, but there are several challenges associated with generative AI:
1. Security issues: Due to its ability to generate fake photos and images closely resembling the real thing, generative AI may increase identity theft, fraud, and counterfeiting cases. Deepfakes rely on artificial intelligence to generate realistic videos and photos that can be used to impersonate people or make them appear to be doing things they didn’t do. In the past, deepfakes were known for putting celebrities into movies and TV shows. But now, the technology has become more accessible to everyday users, who use it to create fake celebrity porn and other types of content. The problem is that this technology could be used for malicious purposes: to create fake news stories about politicians or celebrities, for example, or even to embarrass an enemy or bully someone online.
2. Concern over data privacy: Data privacy issues can arise from using generative AI in different industries, such as healthcare since it involves collecting private information about individuals.
3. Limitations in creativity: The neural net mindlessly uses past data as a template for future work, meaning that the output it produces is usually based on something that has already happened rather than anything genuinely creative. In short, AI systems lack creativity and originality. They cannot generate new ideas by themselves—they can only make associations based on the data fed into them by humans.
4. Copyright issues: The main copyright issues in generative AI are the same as those in traditional creative works. In fact, Getty Images has banned the publication of AI-generated content over concerns that it could be held legally liable for copyright infringement. Also, a number of stock libraries have banned AI images after artists and photographers raised concerns related to the reasons mentioned above.
The most relevant concerns regarding copyright in generative AI are:
This threat (and ultimately truth) about generative AI and synthetic media stands as a sort of warning and caution for those taking advantage of generative artificial intelligence generatively to produce even more artificially intelligent output. It is a bit mind-bending, but we should not forget we are still in the learning phase of it and many mishaps will happen in the years to come.
It has been ten years of AI’s golden era. It is still the early days for meaningful AI.
The field of generative AI is still developing but is promising as a creative approach. It has been one of the past decade’s most successful machine-learning frameworks.
To understand how important it will be in the future, let’s see where it stands today:
Generative AI may seem trivial today, but it could dramatically improve AI efficiency and reduce bias in the future. With the help of artificial intelligence, Google has developed a tool that can turn text prompts into high-definition videos. Another Big Tech company, Meta, recently announced its own text-to-video system, which is doing considerably better than its Galactica model.
Google AI updated an existing function, Imagen, to support video with its Make-A-Video generator. Meta’s Make-A-Video generator replaces Make-A-Scene, which was a text-to-image tool.
Investors Nathan Benaich and Ian Hogarth, who authored the State of AI report 2022, said research on the subject had only just begun.
“Meta and Google announced a more rapid, quality jump in the DALL-E moment of text-to-video generation in September,” the pair wrote in the report. Despite the early stages of the process of generating video, AI-produced images are becoming mainstream with tools like Stable Diffusion and OpenAI’s DALL-E.
Apart from images, the technique can also be used to generate text through chatbots, automated articles, and speech.
Generative artificial intelligence is gaining significant traction among all large tech companies and many startups. AI, in this form, creates something new rather than simply analyzing what already exists.
It is expected that in the future, these kinds of generative models could be adapted to, for example, allow architects to describe a building and have an AI model provide a walkthrough of the building in seconds.
Do algorithms make the decisions morally correct?
This question has been at the center of much debate in recent years. This is because algorithms are not only models of a particular aspect of the world but also, increasingly, models of complex social interactions.
Ethical issues are not only computational problems but also sociocultural ones. For example, they may result from algorithms (such as biases and discriminatory outcomes) or data collection and use. As a result, they require a much deeper understanding of the social impact of AI-based technologies, past experiences with similar innovations, and their ethical implications, especially when it comes to synthetic media.
As synthetic media is getting more realistic and easier to handle, it will require scrutiny from various angles to understand how generative AI has been created and whether it is biased or not. Otherwise, many new models will go down the route of Galactica.
Over the next few years, we expect that users, content creators, and companies will continue to embrace generative AI in ways that might seem unbelievable today. However, with the speed of change, companies need to be kept up-to-date on the latest technologies and its impact, including tools such as generative AI, or risk going the route of Nokia, Kodak or Blockbuster.
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