Prepare for the explosion of the global artificial intelligence (AI) market, one that will reach a staggering USD 1394.30 billion by 2029! According to Globe Newswire, the AI market is estimated to surge at a compound annual growth rate (CAGR) of 20.1% from 2022 to 2029, and AI now solidly owns the media and entertainment future. AI is a juggernaut in media and entertainment, boosting realism in games, detecting fake stories, identifying plagiarism, speeding up production schedules, providing content curation, helping with creativity, and improving sales and marketing.
Enthusiasm for Generative AI in Media
The arrival of the generative AI (Gen AI) revolution is being discussed in almost every media and entertainment meeting room you can think of. With Gen AI set to generate new artworks, enable more complex automation, and power new forms of creative expression, it’s hardly surprising that many creators and industry leaders are caught up in a frenzy of excitement. Here, we examine the beating heart of that exhilaration, asking what Gen AI is and how it’s changing the media landscape.
Leveraging AI to Enhance Creativity and Efficiency
AI tools are becoming increasingly useful to many artists to help them find new ideas, explore new directions, and refine their ideas visually. ML models will help creators break from the shackles of previous human-only creativity, letting them create an infinite number of images, designs, and stories. Although ML models may help to generate images, the artist remains indispensable for putting those images into an artistic context.
Recent research has also cast light on how artists are responding to AI. An Oxford study examining how AI has affected the arts in practice found that, overall, artists’ relationships with their work were essentially unchanged and that they tended to prioritize addressing human concerns over technical ones. Instead of replacing the entire workflow, the researchers found that artists are using machine learning in their practice as part of five different activities:
- Technical research.
- Using and building machine learning models.
- Using and creating datasets and training models.
- Combining models.
- Curating outputs.
Gen AI is changing creative practice, and this is not a passing fad – it’s a paradigm shift that will make creativity look very different in the future.
Automation of Things
Edward Ginis, one of OpenPlay’s co-founders, says: “One of the most exciting implications of Gen AI is the vast potential for automation, where the human is removed from the processing loop”.’ Take music. Currently, humans translate music metadata (titles, identifiers, lyrics, artists) into different languages. But soon, Gen AI could handle much of this work. It could also recommend music according to massively analysed data. Some of today’s leading music-streaming platforms, such as Spotify and Apple Music, are already using AI to curate content for users, which helps to expand musical horizons and find audiences for artists.
Furthermore, Gen AI, such as Amper Music’s Songwriter or AIVA, can help with writing melodies, chord progressions, and lyrics, and other composition tools, such as BandLab’s Band-in-a-Box and Presonus’ Notion, can help musicians arrange their songs.
With automation of the recording process, particularly of mixing, artists can spend more time on the creative process and less on the technicalities. In the highly visual-driven platforms of music consumption, this will end up being one of the most important things – how much of the creative process or the visuals could be automated so that artists can still focus on the recording process but not spend a lot of money on all the technicalities. And that’s what Gen AI is doing right now for the music industry. The future of the media industry will be defined by automation and creativity. It will give artists the tools to do this interactively and dynamically.
The Evolving Effect of AI Algorithms
Machine learning algorithms are at the heart of AI-created content. They analyze large amounts of data to see which pieces of content are connected and which appear similar to one another. By analyzing the user’s data and interests and suggesting relevant content, they try to create a personalized experience for each user. This process helps with the problem of discovering content in a digital world.
Gen AI tools and algorithms can open up spaces for users to access content across more diverse spectra, increasing creativity. Take the scenario of asking an LLM-powered chatbot to come up with three headlines for a particular article and then repeating the query a second time and a third time – each time; you’ll get a different set of answers. This variability speaks to the potential for Gen AI to help increase user choice and provide access to varied content. It could also help break the monopoly of the major social media platforms and its ability to funnel users to particular content. The result could be a media environment that operates in a more competitive, democratized manner than the one we experience today.
Generative AI: Overcoming Media Industry Hurdles
Creators and businesses are figuring out ways to use Gen AI to enhance and extend their products, but in broader use, there are real challenges here, too – from creating content from licensed material to the replication of voices and faces to the potential for misleading outputs. Navigating the lines between legitimate use and misuse requires careful and robust technical, legal, and regulatory frameworks and a nuanced awareness of the potential of the tools and any unintended consequences.
Data and Privacy Challenges
Personal and sensitive data holds significant commercial value for media and entertainment companies, which use it to optimize content recommendation engines, deliver targeted ads, develop interactive content, and design cutting-edge products and services. The dataset may contain names, addresses, financial information, social security numbers, and other sensitive details. Given the complexity of collecting and processing such data, there are valid concerns about the use and access of such data.
Data analytics algorithms allow businesses to track and make decisions about people’s viewing habits and tastes. Therefore, such companies must be extremely sensitive to the laws concerning data protection of people’s personal information. In the context of using AI to collect data, whatever the goal may be, GDPR compliance is of utmost importance. AI algorithms must be carefully designed to avoid the collection and processing of as much personal data as possible, and the data, once collected, must be kept as confidential as possible to allow the development of systems that respect privacy rules.
Copyright Problems
YouTube and UMG recently announced they are launching a first-of-its-kind music AI incubator, leading The Verge to speculate that what it’s really interested in doing is monetizing AI music. YouTube CEO Neal Mohan countered that narrative by publishing the company’s first principles for the use of AI in music, which he says will enable it to use AI for creators, not against them – to support artists, not undermine them.
Committed to the responsible deployment of AI, the collaboration strives to empower the creative process while also addressing copyright and AI-generated music questions. As the debate around copyright law and AI-generated music evolves, there are growing questions regarding the U.S. Copyright Office's existing policy that mandates human authorship for copyright eligibility.
Ensuring Authenticity in Synthetic Content Creation
The last two years have seen the rise of synthetic media, driven by the adoption of Gen AI, particularly generative deep learning. Tools such as MidJourney, RunwayML, Speechify, and even deepfake tools such as Reface are quickly becoming ubiquitous. This accessibility brings in a myriad of societal dilemmas, such as declining trust in digital content, the risk of misuse by bad actors, legislative restrictions, and an increasing need for authenticity.
As the boundaries between what is possible to realistically reproduce with an AI and what is not become more blurred – especially when it comes to depicting real individuals and places in made-up scenarios – we can expect trust in digital content to erode. Deepfakes are obscuring the truth about what is real and what is fake in news and social media.
Alongside the filmmaking, accessibility, artistic, personalization, and R&D acceleration potential of AI, the proliferation of synthetic media also poses some challenges. When placed in malicious hands, deepfakes can generate “facts” and statements that never took place and propagate them through the social networks that have come to control so much of our lives, blurring the line between truth and fiction within the public sphere.
Synthetic media has the potential to help us progress as a society, but without sensible protections, it will also facilitate cruelty. Only concerted action across government, industry, technology, and civil society can maximize the benefits and minimize the risks of a future in which much of what we see online isn’t real.
Gen AI's Role in Mass-Producing Low-Quality Content
Gen AI can be used to inundate the internet with drab, mediocre, lifeless, characterless, valueless content, offering nothing to companies or consumers. It’s concerning that even one person can use Gen AI to produce thousands of essays in just one online session with minimal effort. Imagine this scaled up across millions of users. We must take generative AI responses with a large pinch of salt, especially when it comes to supposedly factual information. We need to check the facts. Also, the best way to defend against generative AI content is a good dose of skepticism.
How to Recognise and Leverage the Power of Generative AI
This March, KPMG launched its 2023 Generative AI Survey to cut through the hype and see what practical insights emerged around how enterprises can use Gen AI. The key finding: 77% of executives across industries believe Gen AI is the most transformative emerging technology, and 71% plan to adopt their first Gen AI solution in the next two years.
Although Gen AI's potential uses in content creation, user engagement, software development, and data analytics seem limitless, a significant leap remains between hype and a clear path to business value in these and other areas. In the early stages of development, as Gen AI evolves quickly, a number of questions remain unanswered about security, reliability, jobs, and the potential value for organizations before they invest in AI.
IT managers need to adjust their expectations and strategies to maximize their employees' efficiency. Here’s how.
1. Enhance your understanding of generative AI opportunities by working closely with advisors and technology partners. As Gen AI is a new and powerful technology, it’s important to educate and showcase its potential to the various stakeholders of the media industry. It’s important to identify and prioritize the most relevant and valuable use cases. Don’t let Gen AI operate in isolation. Instead, work with subject-matter experts who are familiar with the business domain and the existing technologies, thereby ensuring Gen AI is integrated within the core of your operations to deliver the best ROI possible.
2. Taking a Solution Design-centric approach to platform development initiates a vital “discovery phase”. This phase broadens the vision by diving deep into the risks linked with the products or solutions that media companies plan to create. It encompasses a detailed examination of specific needs, workflows, and a comprehensive grasp of business goals. When integrating Gen AI technology, it's crucial to carefully assess and quantify risks and craft effective strategies to mitigate them before rolling out the solution. This method establishes a risk-conscious base, paving the way for successful platform development.
3. Upskill and reskill your team. By embracing a learning culture, you instill in your team the ability to be fast learners, which is your best insurance policy for the future. Ensure access to training courses geared towards AI technologies, data science, and advanced programming. This will ensure that your existing team members are equipped to deal with the AI wave in a proactive and effective manner.
4. Create an innovation-centric culture where experimentation is encouraged, and failure is seen as a necessary part of the innovation process. Encourage your team members to explore how generative AI could apply to their projects and recognise and reward members who come up with novel solutions. You’ll create a culture that takes a dynamic approach to new technologies.
Conclusion
Generative AI is transforming the media industry by enhancing automation and unlocking new creative possibilities for artists. Collaborations between major platforms and industry stakeholders are crucial for addressing privacy and copyright issues responsibly. As executives recognize the influence of generative AI, they must strategically adjust and enhance their teams' skills to convert the excitement around this technology into real business value. As challenges arise, individuals equipped with both technical and artistic skills are becoming key influencers, directing the future of the media and entertainment sectors. This new era calls for a focus on collaboration, skill-building, and innovation at the convergence of technology and art. The future belongs to those who are prepared to shape it.