Stargate – A joint venture between Oracle, Openai and Softbank of $ 500 billion. The goal is to build data centers and infrastructure needed to strengthen the development of it. Without connection with this announcement, there are many conversations and posts on the generation he, agents of him and agents’ work flows. Many SAAS organizations are launching agents within their product package. There are many predictions about the role of agents, with some suggesting that agents will soon represent individuals in discussions, leading to scenarios such as – make your agent talk to my agent and we can discuss after our agents join . The assistants of it are in every app and every platform. There are numerous models for consumption in LLM, very open source and very new that we need to consider every few weeks/day. There is so much emotion and the news, on the covers of magazines and advertising known by the big technology providers.
However, for most organizations I engage with, adoption is not widespread. Someone sees “micro -translations” of productivity largely driven by the benefits of repeated specific tasks. But 10 hours per month is not a change of play for anyone. This is not to say that specific organizational roles are not significantly affected. CEO of Meta Zuckerberg told Podkaster Joe Rogan how the company was looking to replace the “middle -level engineers” with him. Organizations are also realizing that this is expensive. Are returns in proportion to investments during commitments? Finally, hallucinations remain a real danger. Apple too recently returned the news collector.
The lack of broad adoption at this stage raises the question: Is the adoption of that generation in our daily professional and personal life inevitable? Or is this another technology that will adopt some roles within an organization because it has a significant impact, but this is unlikely to be widespread? The answer is that we expect large -scale effects from this technology. Throughout history, the combination of different technologies has been crucial for important progress, and the generator also follows this model.
Technological convergence refers to the integration of two or more unrelated technologies. This convergence can accelerate these unrelated technologies or sometimes join to form new ones. Some noticeable examples of this phenomenon include cell phones and the Internet. Cell phones were originally created for voice communication, but with the introduction of smartphones, they have evolved into multifunctional devices. One can call, send messages, browse the internet, take pictures, do business transactions and more. This convergence has prompted the adoption of cell phones and accelerated the development and use of internet services. Another example, to a lesser degree, are intelligent hours, such as Apple Watch. You can use the intelligent clock to keep track of time, trace your activity, track key health parameters, communicate and engage with different applications on a worn device.
What are the components that support the convergence for the generating one?
NEW– The movement of data in the cloud has been continuous and developing, strengthened by technological advances and powerful computer skills. This has happened for a while and it is appropriate for us to be in a situation where most organizations are in the mature stages of their journey.
Data—We are getting more data, which are also more accessible. We can consume data in many forms, structured and unstructured. Moreover, the fact that the generating can be used to engage with all this data in its current forms is an essential motivator for the use of this technology.
Digital reading—Organizations have increasingly better digital education. Further, these foundation models have democratized by data scientists sitting in a ivory tower, designing algorithms that the rest of the organization does not understand until the power of these models in the hands of all. Engaging with chatgpt does not require any special knowledge of how it works.
Multimodal—It that began as large linguistic models (LLM) that understand, generate and manipulate natural language has now become processing images, audio and video. So they are technically large multimodal models that are more natural in the way we make decisions.
Based on my conversations with leaders in organizations, here is my educated assumption of influence. Artificial generating intelligence will fully automate 20% of daily tasks, allowing more time for creativity by eliminating ordinary things. After all, we will adopt everything suitable for us. It will further increase efficiency and productivity to 60% of the tasks. The impact here can be wide – 25%-50%. Meanwhile, the remaining 20% of tasks will evolve and still need human supervision, mixing technology with human touch. All this to say that the issue of broad adoption can be premature. We need to understand that it is a trip and start with where adoption will bring the greatest value. Code writing, for example. And while identifying other sources of value, we will be sure we will design effective and economically sustainable solutions that work.
While the challenges remain, the prospect of the future of the generation is promising. We are ready to change the way we create, communicate and engage. However, we must balance this innovation with a responsible approach that will allow us to exploit the potential of the generating, creating efficient and fair tools for all users.