Generating AI has infiltrated businesses and individuals’ lives
A recent report by management consulting firm McKinsey & Company revealed that over 70% of global companies and organizations are adopting AI technology. Among the respondents, 65% stated that their companies regularly use “generative AI,” a figure that has doubled since 2023.
In fact, whether for businesses or individuals, generative AI, which can create new content including text, images, and music, offers many benefits and advantages, from personal travel planning to commercial advertising production.
The market is constantly introducing new generative AI models, each one sparking discussions. What is this technology that has the potential to revolutionize our lives and work? And what development challenges does it face?
Further reading:
McKinsey: Over 70% of companies adopt AI technology, marketing and products are the most commonly used
What is Generative AI?
Generative AI, also known as GenAI, uses various prompts provided by users to generate new content, including text, images, videos, audio, and code. This AI technology trains on existing text and images available on the internet, gradually evolving and imitating established behaviors or materials.
Generative AI primarily relies on deep learning models, with neural networks forming the foundation of these models.
Imagine generative AI as a chef and neural networks as their kitchen toolbox. The chef uses prompts from customers to create dishes.
The “toolbox” is filled with various tools, representing different layers within the neural network, each with its specific function. For example, the first layer handles basic tasks like cleaning and cutting ingredients, the second layer handles seasoning based on previous experiences (past data), and the final layer combines and cooks these ingredients to create a refined dish that aligns with the customer’s preferences.
Deep learning models learn the features and patterns of data through neural networks, automatically learning and improving from extensive data.
In the application of generative AI, these deep learning models can identify patterns in data, such as recognizing colors and shapes when processing images. The most important aspect is “generating new content,” creating completely new images, text, etc., based on learned patterns and structures, making the content appear authentic or human-created but previously unseen.
Generative AI primarily relies on deep learning models, with neural networks forming the foundation of these models.
Further reading:
Did AI do a good job with the 66-second “Toys R Us” creative video?
Advantages and Challenges of Generative AI
Advantage 1: Automated Content Creation
Generative AI automates content creation (quality varies based on instructions, models, and personal preferences), significantly reducing the time and cost of human creativity, enabling individuals or business users to be more efficient. For example, news organizations can use generative AI to write reports and organize data, while designers can use AI-generated images for inspiration.
Advantage 2: Innovation and Creativity
Generative AI stimulates various forms of creativity, such as artists using AI to generate unique artworks that go beyond imagination. AI musicians can create new music tracks, pushing the boundaries of imagination. Even marketing campaigns can use generative AI to plan different new strategies to attract users.
Generative AI can automate content creation, significantly reducing the time and cost of human creativity, enabling individuals or business users to be more efficient.
Challenge 1: Data Bias and Ethical Issues
While generative AI is easy to use and yields astonishing results, the training data it relies on may contain biases and inaccurate information. This can have serious consequences for sensitive applications such as medical diagnosis and legal decision-making. Additionally, generative AI applications involve ethical issues such as privacy protection, data security, and intellectual property rights.
Challenge 2: Emergence of Fake News and Deepfakes
Generative AI makes the prevalence and identification of fake news and deepfakes more challenging, posing threats to social stability and the credibility of information. Fake news and manipulated videos are often used to mislead the public, manipulate public opinion, interfere with elections, or even commit crimes.
Challenge 3: Computing Resources and Energy Consumption
Training and running generative AI models require significant computing resources and energy, posing challenges to environmental protection and sustainable development. As models continue to grow in size, reducing energy consumption and improving computational efficiency become urgent issues.
Generative AI is a revolutionary technology with immense potential to change the world. In the future, as technology advances and deeper applications emerge, businesses and development organizations need to actively address the challenges posed by generative AI and focus on creating “responsible” and sustainable generative AI, which will be a key issue.
Sources:
Coursera, TechTarget, McKinseys
