The Key to the Door of AGI: LLMs Applications
Sam Altman, the Co-founder and CEO of OpenAI, addressed the Imagination in Action Summit @ MIT on the dynamics and security concerns surrounding large language models (LLMs). According to Sam Altman, “I think we’re at the end of the era where it’s gonna be these giant models, and we’ll make them better in other ways.”
The LLM mentioned by Sam Altman is a massive natural language processing model launched by Google in 2019. With trillions of parameters, this model has excelled in various natural language processing tasks. Its emergence marks a significant advancement in the field of artificial intelligence and raises expectations for AI’s potential.
Sam Altman holds that OpenAI will decelerate its growth in the future though, this does not imply a cessation of AI technology development.
Generative AI is accelerating its penetration into people’s daily lives. When referring to Generative AI, people tend to mix up the various fields they’re involved in. In fact, each market has its own distinct underlying AI model architectures, computing power and scalability requirements, as well as quality standards and application areas.
In the field of image applications, the most prominent progress of Generative AI is currently in image generation and editing. Generative models can generate high-quality realistic images from noise vectors, which is crucial for computer graphics, game production, digital art, etc. In the future, Generative AI witnesses immense potential for development in image applications. It can enhance the quality and efficiency of image generation and editing to make it more natural and realistic while also integrating with reinforcement learning and language models to accomplish complex image tasks.
While image generation has the potential to bring about significant social changes in the future, its immediate impact may be less than that of text and language generation. Currently, most B2B applications focus on language (primarily text and speech), while at the consumer level, these applications take various forms combined with social media platforms like Twitter, Facebook, TikTok, YouTube, or with e-commerce platforms like Amazon and Airbnb. Image generation, despite a significant market potential, pales in comparison to the potential market value and revenue of language generation companies. From an economic standpoint, LLMs may be exponentially more valuable than image generation in the very near future.
In recent years, the size of LLMs has rapidly increased with the current state-of-the-art models containing over 100 billion parameters. These LLMs are used in various white-collar job fields such as search engines, B2B interactions, sales activities, data exchange systems, chat apps and professional areas like law, accounting and medicine.
The future trend in LLM technology will prioritize scaling up and promoting autonomous learning. LLMs with robust self-learning capabilities are expected to gain popularity. To achieve this, pre-training data diversity needs to be expanded across various fields so that the LLM can learn domain-specific knowledge autonomously during the pre-training process. As the model size continues to increase, many problems are being resolved.
Many far-sighted technology companies, not just OpenAI, are developing large language models (LLMs), including start-ups. As a result, LLMs are proliferating rapidly.
Developing a super-large-scale LLM requires exceptional engineering implementation capabilities from the technical team. OpenAI has invested years of effort in this field. Similarly, JUNLALA, a technology company deeply involved in the LLMs for decades, is constantly exploring and expanding the limits and boundaries of LLMs. This Silicon Valley-based AI company has over 50 proficient experts and employees who focus on creating cutting-edge artificial intelligence algorithms and products.
JUNLALA firmly regards the LLM as a necessary path to AGI. Just as ChatGPT cannot achieve breakthroughs in key technologies without increasing the scale of its LLM model, continuing to advance LLMs still holds important research value.
As AI technology advances continuously, we can see more applications being developed and more scenarios being expanded. In the future, AI technology will bring more convenience and innovation to people while also facing challenges such as data security and ethical risks. JUNLALA, as an essential player, will prioritize user experience through continuous product optimization and improvement towards sustainable development of AI technology.