It is hard not to notice the recent buzz around Generative AI. Its implications in the global arena are at a tipping point. The enigmatic front of Generative AI simply lies in its ability to create convincingly authentic images, videos, and audio of real people. But there are still those who do not quite understand what exactly Generative AI is. It is a type of artificial intelligence technology that can produce various types of content including text, imagery, audio, and synthetic data. Before we dive into the why and how of Generative AI, it is important to understand the key difference between Generative AI and mainstream AI.
Generative AI vs AI
Among the emerging trends of the technological landscape, generative AI is a subset of traditional AI and ultimately shows immense capability in reshaping industries.
Artificial Narrow Intelligence (ANI) or Weak AI drives most of the AI that surrounds us today. While it is called “weak”, it is anything but weak. “Narrow” might be a more accurate depiction of ANI. Some of our very robust applications like Apple’s Siri, Amazon’s Alexa, IBM’s Watsonx, and self-driving vehicles are all powered by ANI.
Generative AI, on the other hand, is known to be the next frontier of artificial intelligence. Unlike ANI, Generative AI can create new and original content – creatively and innovatively. One of the best examples is GPT-4, OpenAI’s language prediction model – it can produce human-like text that is almost indistinguishable from text written by a person. Today’s Gen AI cannot only create text but also music, videos, images, and even computer code. They are trained on a data set and learn the underlying patterns to generate new data that mirrors the training set.
Natural Language Processing in Generative AI
NLP or Natural Language processing is a subfield of artificial intelligence concerned with enabling computers to understand, manipulate, and generate human language. It holds a wide range of techniques that allow computers to interact with and process language in a way that is similar to how humans do.
NLP acts as both a foundation and a powerful tool for Generative AI. It equips Generative AI models with the ability to understand the nuances of human language. This allows them to process and analyze text data which is crucial to learning the patterns and styles presented in the data, extracting relevant information from text sources, and identifying relationships between elements of language.
Beyond text generation, NLP also has a technique called Sentiment analysis which helps them to analyze the tone of text data, which helps Gen AI models to generate content that aligns with a desired sentiment. NLP also helps in summarization – condensing large amounts of text into a shorter, more concise format.
Machine Learning vs Deep Learning
Deep learning and machine learning are two sides of the same coin. While both of them are subfields of AI, deep learning is a sub-field of machine learning. In retrospect, both of these differ in the way its algorithm is learned. Classical or “non-deep” machine learning is more dependent on human experts to understand the difference between data inputs and requires features of hierarchy and structured data to learn.
On the other hand, deep learning eliminates manual intervention, automates feature extractions, and enables the use of larger datasets. It can conduct unsupervised learning using raw content like text and images. Generative AI is, in fact, a deep-learning model that analyses raw data and “learns” to generate outputs when prompted. Models like GPT-3, BERT, and DALL-E are prime examples of the potential of Generative AI.
Impact of Generative AI
Generative AI has transformed the way we live today. Its transition from research labs to real-world applications across various industries is notable. We are witnessing the democratization of generative AI with user-friendly tools and platforms, allowing a wider range of users to leverage its capabilities.
We cannot fathom the speed at which Generative AI is going to change the way the world is going to be operated shortly. It is currently playing a pivotal role in transforming industries and individuals. Its innovation and efficiency should be duly noted especially in how businesses operate, fostering innovation in product development, marketing, and customer service. Its streamlining processes by automating repetitive tasks and generating content like product designs and marketing copy leads to increase deficiency and productivity. A recent article in The Economic Times highlights the IDC’s forecast that enterprises globally will invest $143 billion in Gen AI by 2027. This is due to its ability to increase labor productivity across various sectors, potentially leading to a new era of economic prosperity.
Regarding consumer experience, gen AI is already influencing our everyday experiences from personalized recommendations and targeted advertising to AI chatbots handling customer inquiries. As it evolves, we can expect even more personalized interactions with technology in various aspects of our lives.
However, it is also crucial to acknowledge that Gen AI is still under development and its full impact is yet to be explored. It is essential to address potential challenges like ethical considerations, job displacement, and regulation to ensure responsible and beneficial integration of this technology into our future.
Image generated by AI using the prompt: “Smoke plume that looks human::1 Portrait photograph of human smoke::1” Credited to Freepik
Impact of Generative AI on Business
Generative AI is rapidly transforming various aspects of businesses, offering a range of benefits and exciting possibilities.
Boosting Efficiency and Productivity:
- Automating Content Creation: Generative AI can automate repetitive tasks involving content creation, such as generating product descriptions, social media posts, or email marketing materials. This frees up valuable time and resources for employees to focus on more strategic initiatives.
- Streamlining Workflows: By automating routine tasks and generating data or reports, generative AI can streamline workflows, improve operational efficiency, and lead to faster decision-making processes.
- Generating Synthetic Data: Generative AI can create synthetic data sets that are statistically similar to real-world data but protect privacy. This allows businesses to test new ideas, analyze scenarios, and make data-driven decisions without compromising sensitive information.
- Optimizing Operations: By analyzing and identifying patterns in data, generative AI can help businesses optimize their operations, improve resource allocation, and predict potential challenges.
Specific Applications in Different Industries:
- Marketing & Advertising: Generating personalized ad copy, creating targeted marketing campaigns, and optimizing content for different platforms.
- Product Development: Exploring various design concepts, generating product variations, and testing virtual prototypes.
- Customer Service: Developing chatbots for handling customer inquiries, creating personalized support responses, and automating routine tasks.
- Finance: Analyzing market trends, generating financial reports, and creating personalized investment recommendations.
- Healthcare: Analyzing medical data, generating drug discovery models, and personalizing treatment plans.
The Future of Generative AI
It is predicted that foundation models will dramatically accelerate AI adoption in enterprises. Reducing labeling requirements will make it much easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations.
According to Vishnu, an AI enthusiast and expert at Stackmod Innovation says, “While Generative AI in all its sense would change industries and economies, it would also impact the job market but the impact is complex and multifaceted. It is crucial to understand both potential job displacement and creation. For example, coding will be automated very soon, and although the demand for software engineers might decrease, it will also create new opportunities. The development, deployment, and management of AI systems will create new job opportunities for specialists in fields like data science, cybersecurity, AI ethics, and engineering.”
The future of Generative AI is a culmination of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Imagine a world operated by a self-aware conscious machine that can solve problems, learn and plan for the future.
