This year at CES, artificial intelligence (AI) took center stage, capturing the attention and imagination of attendees. However, amidst the hype, it became evident that many of the so-called AI features have been around for quite some time. What we witnessed at CES was not so much a revolution in AI technology but rather a rebranding of existing features under the AI umbrella.
While it is encouraging to see companies embracing AI branding, there is a risk of overpromising what AI can actually deliver. As AI continues to permeate various industries, it becomes crucial to differentiate between genuine AI solutions and those that simply leverage algorithms. By indiscriminately labeling everything as AI, companies may inadvertently mislead consumers. This conflation of different AI technologies has the potential to muddy the waters and create unrealistic expectations.
A prime example of this conflation is the widespread connotation of AI with generative AI, particularly the ChatGPT model. While generative AI is undeniably impressive, it is not the sole representation of AI. Many products and features showcased at CES rely on other forms of AI, such as robotics and computer vision, which are equally noteworthy but often overshadowed by their generative AI counterparts.
Furthermore, the industry’s focus on generative AI has led to a neglect of other essential AI components, such as machine learning. Machine learning forms the foundation of numerous pattern recognition features on display at CES. Despite its critical role, machine learning has become somewhat overlooked and labeled as traditional.
It is important to recognize that AI technology undergoes lifecycles, with periods of skepticism followed by breakthrough innovations. While CES may not have exhibited groundbreaking advancements in AI this year, it is only a matter of time before we witness the emergence of new and exciting use cases for AI.
Looking ahead, the future of AI at CES and beyond lies in the exploration of novel features and products that do not solely rely on chatbots or large language models. As AI continues to evolve, we can expect to see a diversification of AI applications, driven by advancements in robotics, computer vision, and other AI disciplines.
The concern is that companies are indiscriminately labeling various features as AI, potentially misleading consumers and creating unrealistic expectations about the capabilities of the products.
Generative AI, particularly models like ChatGPT, has gained significant attention and recognition. However, other AI technologies, such as robotics and computer vision, which are equally impressive, tend to be overshadowed by the prominence of generative AI.
Machine learning serves as a foundational component of AI and powers many of the pattern recognition features showcased at CES. Despite its importance, machine learning is often considered traditional and receives less attention than generative AI.
In the coming years, we can anticipate the emergence of features and products that showcase the diversity and breadth of AI, moving beyond chatbots and large language models. Technologies such as robotics and computer vision are expected to play a more prominent role in shaping the future of AI at CES.
