Generative AI has already infiltrated the lives of businesses and individuals. A recent report by management consulting firm McKinsey & Company revealed that over 70% of global enterprises and organizations have adopted AI technology. Among them, 65% of respondents indicated that their companies are regularly using “Generative AI,” a figure that is double that of 2023.
In fact, whether for corporate or individual users, Generative AI, capable of creating entirely new content including text, images, and music, brings numerous benefits and efficiencies, aiding everything from personal travel planning to commercial advertising production.
The Generative AI models on the market are constantly evolving, sparking discussions with each innovation. What is this technology that could potentially revolutionize our lives and work? And what are its developmental challenges?
Further Reading:
McKinsey: Over 70% of Businesses Adopt AI Technology, Marketing and Products Named Most Used
What is Generative AI?
Generative AI, or GenAI, generates entirely new content, including text, images, videos, sounds, and code, based on various prompts input by users. This AI technology evolves by training on existing text and images on the internet, gradually imitating existing behaviors or materials.
Primarily powered by deep learning models, the foundation of these models is neural networks. Imagine Generative AI as a chef, and the neural network as his toolbox. The chef must create dishes based on the taste preferences (prompts) provided by the customers.
This “toolbox” is filled with a variety of tools, each representing a layer in the neural network with specific functions. For example, the first layer involves simply washing and cutting ingredients; the second layer is seasoning, determining which spices best suit the ingredients based on past experience (previous data); the final layer is responsible for mixing and cooking these components, ultimately presenting a refined dish that meets the customer’s preferences.
Deep learning models learn the characteristics and patterns of data through neural networks, enabling them to automatically learn and improve from vast amounts of 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 “creating new content,” which is based on learned patterns and structures to create entirely new images, text, etc., making the content appear as if it were real or human-created, yet unprecedented.
Generative AI primarily operates on deep learning models, which are based on neural networks.
Further Reading:
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Advantages and Challenges of Generative AI
Advantage One: Content Creation Automation
Generative AI can automate content creation (quality varies based on instructions, models, and personal preferences), significantly reducing the time and cost of manual creation, allowing both individual and corporate users to produce more efficiently. For instance, news agencies can use Generative AI to write reports and organize data, while designers can use AI-generated images for creative inspiration.
Advantage Two: Innovation and Creativity
Generative AI can stimulate a wide range of creativity, such as artists using AI to create unique artworks that surpass previous imaginations. AI musicians can create new music tracks, breaking the boundaries of imagination. Even marketing plans for companies can use Generative AI to devise different new strategies to attract users.
Generative AI can automate content creation, significantly reducing the time and cost of manual creation, allowing both individual and corporate users to produce more efficiently.
Challenge One: Data Bias and Ethical Issues
Although Generative AI is easy to use and has impressive effects, its training data may contain biases and inaccuracies. This could have serious consequences for sensitive applications, such as medical diagnoses and legal decisions. Additionally, the application of Generative AI involves ethical issues, such as privacy protection, data security, and intellectual property rights.
Challenge Two: The Emergence of False Information and Deepfakes
Generative AI makes false information and deepfakes more common and difficult to identify, posing a threat to social stability and information credibility. Fake news and videos are often used to mislead the public, from manipulating public opinion and interfering with elections to potential criminal activities.
Challenge Three: Computational Resources and Energy Consumption
Training and operating Generative AI models require substantial computational resources and energy, posing a challenge to environmental protection and sustainable development. As models increase in size, reducing energy consumption and improving computational efficiency will be pressing issues to address.
As a revolutionary technology, Generative AI has the potential to change the world. In the future, as technology continues to advance and deeper applications emerge, enterprises and development institutions will need to actively address the challenges posed by Generative AI, creating “responsible” and sustainable Generative AI as a key issue.
Sources:
Coursera, TechTarget, Mickinseys