2023 is the most disruptive year in the field of artificial intelligence in a long time, with a large number ofGenerative AIProducts enter the mainstream. Continuing its transformative journey, generative AI is expected to move from exciting topics to real-world applications in 2024.
The field of generative AI is growing rapidly as tech companies continue to develop and fine-tune AI models, giving rise to a broad range of trends that will boost AI adoption across industries and its presence in our daily lives. Let’s take a deeper lookTopGenerative AI trends that will determine the true value of generative AI.
1. Small Language Model
After the huge success of ChatGPT, we see many companies releasing large language models in 2023. However, it is time to prepare for the surge of small language models (SLMs). SLMs are trained on massive datasets scrapped from various public online resources and are able to perform complex tasks that require human intelligence, from writing programming codes and logical reasoning to answering queries on almost every imaginable topic.
However, processing such large AI models with trillions of parameters requires massive computing resources and financial investment.
In contrast, small language models are trained on limited data for specific tasks and are more cost-effective. SLMs have fewer parameters and take up less storage space, making them suitable for running on cheaper hardware with less computational power. When trained with high-quality training data extracted from trusted sources such as textbooks, news sites, and magazines, the model can provide excellent performance. This will promote the adoption of these models.
To date, some popular SLMs include Meta's Llama-2, Microsoft's PHI-2, and Mistral7B.
2. Artificial Intelligence Generation
The current level of AI is not comparable to human intelligence. AI companies aspire to develop a model that can match or surpass human understanding and cognitive abilities, a breakthrough considered Artificial General Intelligence (AGI).
The AGI model is not limited to a specific field and can solve a variety of problems at the human cognitive level without human intervention. It can learn independently and solve unfamiliar problems without additional training. In short, AGI is the concept of complete artificial intelligence that reflects the broad cognitive ability of humans to understand and solve complex tasks.
In contrast, existing models rely on extensive training to understand and solve related problems within the same domain. For example, a pre-trained large language model (LLM) must be fed with a financial dataset to make investment-related decisions.
The concept of AGI is that machines can perform complex tasks with human-level cognition across domains without having to understand the background knowledge of these tasks.
3. Multimodal AI Models (Chatbots)
Generative AI models go beyond text creation by integrating multimodal versatility.Multimodal AI will beprogress and bring significant changes to the field of generative artificial intelligence.
Multimodal AI models are trained to learn and process multiple forms of data, such as text, photos, and even sound and video, through advanced algorithms to generate different types of content, such as text, images, sound, and video, based on prompts.
The combination of training datasets (including text, images, video, and audio) can train systems to learn the relationship between different types of media and enable them to recognize one type of media and respond to another. For example, if you input an image, the model will generate text as a response, and vice versa.
The transition to AI models will make the technology more intuitive and dynamic. Gemini, GPT4-V, Gen-2, ImageBind, etc. are popular among users for their multimodal capabilities.
4. Agent AI
While we have been able to chat with AI so far, by this year we will see chatbots operating as agents. Tech companies are working to turn AI models into autonomous software programs designed to achieve specific goals without direct human intervention.
These autonomous agents are designed using advanced algorithms and machine learning techniques. The development of such agents essentially requires multimodal artificial intelligence that integrates different technologies, including machine learning, computer vision, natural language processing, etc.
These agents are designed to use data to learn patterns, set new goals, and achieve those goals with little or no human intervention. They can predict, act, and interact effectively by analyzing different data types simultaneously and taking into account the current environment.
For example, financial AI agents can be trained to collect market data, analyze patterns, and adjust their investment strategies in real time based on changing market conditions.
5. AI governance
2024 will be a watershed year for AI regulation, reshaping the development and ethical risks of generative AI strategies to achieve safe and secure AI applications.
As generative AI quickly enters the mainstream, businesses are excited to use it to drive innovation and uncover new opportunities across industries and applications.Cutting EdgeThe technology is not without its challenges. The rapid development of AI has left regulators scrambling to keep up with the technology.
Despite its potential to generate or predict desired outcomes, generative AI raises concerns about hallucinations, spread of misinformation, deepfakes, etc. Furthermore, the susceptibility of these models to injection, poisoning, leakage of sensitive private information, copyright infringement, and generation of biased and racist content underscores the need for a swift regulatory response worldwide.
Regulators need to shape the future of AI governance, foster innovation, and ensure guardrails are in place to protect the rights and job opportunities of a diverse workforce.LeadersA coalition of governments, academic researchers, and civil society is necessary to create a successful regulatory framework for AI governance.
6. Customized enterprise-generated AI models
Large-scale language and image models like ChatGPT vs Bard and Midjourney have taken the world by storm. However, small, customized enterprise-generated AI models are emerging for commercial use cases. These models are designed by integrating proprietary data to meet niche market and user needs and ensure more accurate and relevant responses. The growth of customized enterprise AI applications shows that enterprises are moving towards more efficient and personalized AI-driven business solutions.
Enterprise generative AI can be customized to suit a variety of business needs, including customer support, document review, and even supply chain management. These models are particularly useful for the financial, legal, and healthcare sectors, where terminology and practices are highly specialized. Organizations that integrate customized models into their operations gain greater control over their data, resulting in increased levels of privacy and security.
Given the privacy and security risks posed by generative AI models, strict AI regulations may push companies to transition to using proprietary models in the coming years.
The generative AI landscape will continue to evolve rapidly in 2024, with a host of new trends emerging and new challenges for consumers and businesses. Generative AI has enormous potential, and its impact is only just beginning.