The Future of Generative AI

- Overview

Generative artificial intelligence (AI) is a set of algorithms that can create new content, such as text, images, audio, and videos, using patterns in existing data. It can also perform tasks like summarization, classification, and Q&A. 

Generative AI is rapidly evolving to handle multiple input and output formats. This includes text, images, speech and video, making AI tools more versatile and integrated into a variety of applications.

Enterprises are increasingly adopting AI-as-a-service models to stay competitive. This shift allows companies to take advantage of advanced AI capabilities without requiring large infrastructure investments.

Generative AI is transforming the workforce by automating routine tasks. While this has boosted productivity, it has also raised concerns about job losses and the need for upskilling and reskilling.

However, generative AI can also introduce new risks, such as legal, financial, or reputational issues. For example, generative models can sometimes produce information that sounds authoritative but isn't true, or that is objectionable and biased. They can also inadvertently ingest information that's personal or copyrighted in their training data and output it later, creating challenges for privacy and intellectual property laws.

- Generative AI's Future

Generative AI can save time, reduce costs, and enhance efficiency across an organization. A June 2023 report from McKinsey & Company estimated that generative AI has the potential to add between $6.1 to $7.9 trillion to the global economy annually by increasing worker productivity. 

Generative AI can be used in many ways, including:

• Creative tasks: Generating prototypes, optimizing designs, and creating new content like video advertisements
• Improving efficiency: Automating manual tasks like writing emails or coding
• Personalizing experiences: Creating content tailored to a specific audience
• Supporting management: Generating reports, summaries, and projections
• Supporting marketing: Generating new sales scripts, email content, and blogs
• Analyzing data: Providing deeper visibility of data, such as customer transactions and software code, to help identify potential risks

Looking ahead, generative AI trends are expected to focus on three main areas: rapid and comprehensive technological advancements, faster-than-expected digital transformation, and an increasing focus on the social and global impact of AI. These specific predictions and growth trends are likely to be on the horizon.

- Growth in Multimodality

Multimodality - the idea that a generative AI tool is designed to accept inputs and generate outputs in multiple formats.

Multimodal AI is a remarkable advancement in AI that has improved how people interact with technology. It offers more advanced reasoning, problem-solving, and generation capabilities, which can change the way people work and live. According to Apptunix, the global multimodal AI industry is expected to reach $4.5 billion by 2028, with a compound annual growth rate of 35%.

Some research areas in multimodal AI include:

• Continual learning: Reduces information loss during retraining, which allows models to be updated for longer periods of time and become more adaptable.
• Few-shot learning: Improves AI accuracy with minimal data, eliminating the need for large datasets.
• Parameter efficiency: Lowers the computational resource requirements.