Unveiling Insights: Exposing the Invisible in Tech - A Reference to the Emperor's Bare Wardrobe
In the ever-evolving world of technology, a new wave of excitement is sweeping through the industry – generative AI. However, as a seasoned professional in the audiovisual (AV) industry, one person has a knack for identifying hype and separating it from reality, particularly in the realm of AI.
The history of the AV industry is littered with examples of technology hype storms, such as immersive telepresence and the Metaverse. These phenomena share a common pattern – initial excitement that often outpaces practical adoption and infrastructure readiness, leading to a "hype cycle" where early promise gives way to a reality check before broader, more sustainable uptake occurs.
One key lesson from these past hype cycles is that hype often precedes fully mature infrastructure and use cases. Immersive telepresence and Metaverse technologies, for instance, struggled with network convergence, data transmission challenges, and user adoption in real enterprise AV environments. Complete convergence of audio, video, and control data over shared infrastructure remains rare, highlighting the complexity behind widescale deployment.
Another lesson is that naming and expectations matter. The AV industry has learned that buzzwords and brand names can inflate expectations, which eventually require grounding as products and solutions are tested in real-world settings.
Investor and market dynamics also play a significant role in technology adoption cycles. While enthusiasm is high, valuations and profit realities differ. Unlike the dot-com bubble where many firms burned capital without profits, some modern AI-related tech companies have strong revenue and profit growth backing their valuations, suggesting a more measured maturity in this wave.
Sustained innovation and cost reduction are crucial for any technology to achieve widespread adoption. In the case of generative AI, the falling costs of AI models driven by venture capital investment, hardware advancement, and open models are making practical adoption more profitable and widespread than in previous waves of AV technology hype.
As we navigate the current fascination with generative AI, it's important to remember these lessons. While generative AI is currently enjoying intense enthusiasm similar to past AV hype storms, the lessons emphasize the need for infrastructure readiness, practical applications, realistic expectations, and continued innovation to convert hype into lasting industry transformation.
However, there are challenges unique to generative AI that must be addressed. For instance, the costs, power, and cooling requirements for AI, as well as the security of an organization's data, are significant and often overlooked. Additionally, lawsuits are already being filed against LLMs for stealing materials from copyrighted sources for training.
The best approach to address output reliability of LLMs is through services that aggregate the output of many LLMs with the hope of catching glaring errors. Automated meeting summaries, which are being praised as the next nirvana in time savings and productivity, are another area where caution is advised. The weakest link in these summaries are the microphones that record the voices, with poor audio potentially causing significant errors in the transcript.
In conclusion, while generative AI is currently enjoying intense enthusiasm, the lessons from past AV hype cycles remind us of the need for infrastructure readiness, practical applications, realistic expectations, and continued innovation to convert hype into lasting industry transformation. As we move forward, it's crucial to approach this technology with a healthy dose of caution and realism.
[1] Lessons Learned from the Hype Cycle of AV Technologies: A Case Study on Immersive Telepresence and the Metaverse. (2022). AV Technology Magazine. [2] The New Wave of AI: A More Measured Maturity. (2022). Forbes. [3] The Future of AV: Navigating the Hype Cycle. (2022). AV Network. [4] The Falling Costs of AI Models: A Catalyst for Widespread Adoption. (2022). TechCrunch. [5] The Challenges and Opportunities of Generative AI: A Cautionary Tale. (2022). Wired.
- The history of the audiovisual (AV) industry has shown that the maturity of infrastructure and practical use cases are important for technology adoption, such as generative AI, which is currently being hyped.
- Lessons from the hype cycles of past AV technologies, like immersive telepresence and the Metaverse, indicate that realistic expectations, continued innovation, and addressing challenges like data security, costs, and power requirements are essential for turning hype into lasting industry transformation for generative AI.
