That is, it needs to be rechecked. Once again, we come to the conclusion that you need to be an expert in the topic in order to evaluate the correctness of the content and use it. And sometimes it takes even more time than doing everything from scratch and by yourself.
– Emotions, ethics and responsibility
A Gen AI without a proper query will tend to simply reproduce information or create content, without paying attention to emotions, context, and tone of communication. And from the series of articles about communication, we already know that communication failures can occur very easily. As a result, in addition to all the problems above, we can also get a huge number of conflicts.
There are also questions about the possibility of determining the authorship of the created content, as well as the ownership rights to the created content. Who is responsible for incorrect or malicious actions performed using the GII? And how can you prove that you or your organization is the author? There is a need to develop ethical standards and legislation regulating the use of GII.
– Economic feasibility
As we’ve already seen, developing high-end generative AI yourself can be a daunting task. And many people will have the idea: “Why not buy a ‘box’ and place it at home?” But how much do you think, this solution will cost? How much will the developer’s request?
And most importantly, how big should the business be to make it all pay off?
What should I do?
Companies are not going to completely abandon large models. For example, Apple will use ChatGPT in Siri to perform complex tasks. Microsoft plans to use the latest Open AI model in the new version of Windows as an assistant. At the same time, Experian from Ireland and Salesforce from the United States have already switched to using compact AI models for chatbots and found that they provide the same performance as large models, but at significantly lower costs and with lower data processing delays.
A key advantage of small models is the ability to fine-tune them for specific tasks and data sets. This allows them to work effectively in specialized areas at a lower cost and easier to solve security issues. According Yoav Shoham, co-founder of Tel Aviv-based AI21 Labs, small models can answer questions and solve problems for as little as one-sixth the cost of large models.
– Take your time
You should not expect the AI to decline. Too much has been invested in this technology over the past 10 years, and it has too much potential.
I recommend that you remember the 8th principle from the Toyota DAO, the basics of lean manufacturing and one of the tools of my system approach: “Use only reliable, proven technology.” You can find a number of recommendations in it.
– Technology is designed to help people, not replace them. Often, you should first perform the process manually before introducing additional hardware.
– New technologies are often unreliable and difficult to standardize, and this puts the flow at risk. Instead of an untested technology, it is better to use a well-known, proven process.
– Before introducing new technology and equipment, you should conduct real-world testing.
– Reject or change a technology that goes against your culture, may compromise stability, reliability, or predictability.
– Still, encourage your people not to forget about new technologies when it comes to finding new paths. Quickly implement proven technologies that have been tested and make the flow more perfect.
Yes, in 5—10 years generative models will become mass-produced and affordable, meticulously smart, cheaper, and eventually reach a plateau of productivity in the hype cycle. And most likely, each of us will use the results from the GII: writing an article, preparing presentations, and so on ad infinitum. But to rely on AI now and reduce people will be clearly redundant.
– Improve efficiency and safety
Almost all developers are now focused on making AI models less demanding on the quantity and quality of source data, as well as on improving the level of security-AI must generate safe content and be resistant to provocations.
– Master AI in the form of experiments and pilot projects
To be prepared for the arrival of really useful solutions, you need to follow the development of the technology, try it out, and form competencies. It’s like digitalization: instead of, diving headlong into expensive solutions, you need to play with budget or free tools. Thanks to this, by the time the technology reaches the masses:
– you and your company will have a clear understanding of the requirements that need to be laid down for commercial and expensive solutions, and you will approach this issue consciously. A good technical task – 50% success rate.
– you will already be able to get effects in the short term, which means, that you will be motivated to go further.
– the team will improve its digital competencies, which will remove restrictions and resistance due to technical reasons.
– incorrect expectations will be eliminated, which means, that there will be less useless costs, frustrations, and conflicts.
– Transform user communication with AI
I am developing a similar concept in my digital advisor. The user should be given ready-made forms where they simply enter the necessary values or mark items. And already give this form with the correct binding (prompt) to the AI. Or deeply integrate solutions into existing IT tools: office applications, browsers, answering machines in your phone, etc.
But this requires careful study and understanding of the user’s behavior, requests, and whether they are standardized. In other words, either this is no longer a stumpy solution that still requires development costs, or we lose flexibility.
– Develop highly specialized models
As with humans, teaching AI everything is very labor-intensive and has low efficiency. If you create highly specialized solutions based on the engines of large models, then training can be minimized, and the model itself will not be too large, and the content will be less abstract, more understandable, and there will be fewer hallucinations.
Visual demonstration – people. Who makes great progress and can solve complex problems? Someone who knows everything, or someone who focuses on their own direction and develops in depth, knows various cases, communicates with other experts, and spends thousands of hours analyzing their own direction?
An example of a highly specialized solution:
– expert advisor for project management;
– tax consultant;
– lean manufacturing advisor;
– a chatbot for industrial safety or an assistant for an industrial safety specialist.
– chat bot for IT technical support.
Resume
Although