“AI” has become a “hot” topic in both technical publications and general newspapers. There have been multiple stories of ChatGPT 3.5 and 4.0 and other Chatbots [Ref 1]. The addition of AI to search engines by Google (BARD) and Microsoft’s Bing (ChatGPT+) [Ref. 2]. Browsers, like Chrome, have been adding AI capable features [Ref. 3]. The has been news from the medical field of AI being employed by medical professionals and improving the diagnosis of patients [Ref. 4]. On the negative side, there was a publication about Chaos-GPT with very negative connotations [Ref. 5]. How do these chatbots apply to nano technology, or does the technology even apply?
There have been many attempts to produce tools that can assist humans in making decisions or even make the decisions itself. Automation of equipment is an obvious example. In the lumber industry, equipment has been developed that inspects a segment of a harvested tree, calculates the orientation to get the maximum lumber from the tree, and then does the actual cutting [Ref. 6]. The ongoing work for self-driving vehicles is another application of AI. The millions of lines of code keep increasing as new options have to be allowed depending on the circumstances that the vehicle is encountering.
ChatGPT was developed by OpenAI as a chatbot [Ref. 7] and is different from previously released chatbots. While it was released in November of 2022, it was not until later March 2023 that its applications started making headlines because the responses did not require accessing a restricted set of data, but permitted unstructured assembling on the response data to random questions in a manner and format that provides the appearance of a knowledgeable response. Algorithms are developed to guide the collection and organization of data relevant to the subject under investigation. A proper arrangement of algorithms can make it appear as being answered by a person.
In the 1980s, there was significant work on expert systems, which are a precursor to today’s algorithm driven chatbots. Computing power and the cost of storage were orders of magnitude less capable than today. The amount of data available was significantly less and the speed of the computations were much slower. Still there were interesting developments. One of the observations from that work was that each expert system had to have a starting base of data. As the system encountered additional data of choices and the outcomes, the database changed the probabilities of the possible outcomes. So, the system “evolved” based on its environment, i.e. machine learning. A system for farming in colder climes would provide different answers from one in the tropics. Understandable, due to the two systems being distinct.
Today’s computing power is orders of magnitude greater than the early 1990s. The memory capacity has also increased greatly. But so has the data. It the author’s opinion that there is more data created and stored on line in a single day now than there was in an entire year in the 1990s. This raises the question of where will and how will the chatbots get their information. One of the recent reviews indicated that it is possible that some chatbots have information that was current in 2018. A lot happens in five years.
A recent article [Ref. 8] express the concerns of an AI ethicist. The development of machine-learning algorithms to assist in the responses of a chatbot could lead to replacing judgement on situations with the chatbots’ output. She is quoted as saying “Using chatbots in search engines . . . is a bonkers idea that everyone is now racing to do to.”
It is too early to decide how the machine learning chatbots will evolve and assist in developing new materials or technologies in the nano realm. In the late 1990s, Text Mining was the next computer driven technology that would provide a very widespread application. It has evolved to applications that are focused, e.g., evaluating customer databases to determine produce or service issues or similar evaluation of structured word evaluation. A report from Stanford states: “A lot of inefficiencies and errors that happen in medicine today occur because of the hyper-specialization of human doctors and the slow and spotty flow of information” [Ref. 9]. Hopefully, nanotechnology will witness something that can evaluate research similarities and provide a database as appears to be happening in medicine that researchers nano realm can utilize to move toward the future more quickly. Chatbots can apply to nanotechnology given the proper access to relevant data.
References:
- Top 25 Chatbot Case Studies & Success Stories in 2023 https://research.aimultiple.com/top-chatbot-success/
- https://www.pcmag.com/news/chatgpt-alternatives-ai-chatbots-ready-to-answer-your-burning-questions
- https://www.digitaltrends.com/computing/best-ai-chatbots/
- The AI Will See You Now, Wall Street Journal, Saturday, 04/08/2023 Page .C001
- https://decrypt.co/126122/meet-chaos-gpt-ai-tool-destroy-humanity
- https://www.innovating-automation.blog/why-a-sawmill-needs-automation/
- Https://chatgpt.pro
- Weekend Confidential with Timnit Gebru, Emily Bobrow, Wall Street journal, Saturday, 02/25/2023 Page .C006
- https://news.stanford.edu/press-releases/2023/04/12/advances-generalizable-medical-ai/
