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JULY 2023 I SMT007 MAGAZINE 9 As an undergrad studying computer science in the early 1980s, I recall interacting with a ver- sion of ELIZA as a laboratory exercise. At the time, I was unconvinced and lost interest in the conversation pretty quickly. Of course, I knew in advance that ELIZA was a program, not a person. I remember sharing in class that I found "her" rather droll and boring. Intelligent? Per- haps by Turing's standards in the 1950s, when UNIVAC could perform a whopping 1,095 instructions per second (IPS), but it was insuf- ficiently convincing in 1983 when an Apple II computer averaged 300,000 IPS. Nowadays, ELIZA is nothing more than a quaint exercise. In fact, in a recent article published on INSIDER , DeepMind co-founder Mustafa Suleyman is proposing a new test for artifi- cial intelligence. Rather than the Turing test, Suleyman suggests in his book, e Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma, that AI "should be tested on its ability to turn $100,000 into $1 million." 1 Yes, the Turing Test seems quite quaint, indeed. Like so many other media channels, we couldn't resist the urge to test the AI engines. I asked some of our contributors to play with generative AI and see what they got back. Happy Holden's article on business strategies, for example, includes an extended AI passage. It's somewhat useful and makes for a good start- ing point. It demonstrates that the program can respond to your natural language request with a much more sophisticated reply than ELIZA, but is it different enough? I would argue that the current artificial intel- ligence engines likewise aren't actually "intelli- gent." All that these generative AI engines are capable of is collating or concatenating content and automating the data gathering process; but it's still based on calculations. Yes, generative AI tools can create program code, art images, prose, or song lyrics upon demand. But at the core of these functions is the fact that the task is simple data collection, recalculation, and trans- formation of existing data as applied to lan- guage. What's missing is "heuristics," which is defined as "encouraging a person to learn, dis- cover, understand, or solve problems indepen- dently, as by experimenting, evaluating possible answers or solutions, or by trial and error." Personally, I think the AI fearmongering craze misses the point. To my way of thinking, adaptive machine learning is much more inter- esting, much closer to heuristic thinking. e algorithmic research into sophisticated pat- tern identification has led to a wide number of advances in our body of knowledge. Comput- ers running these cutting-edge analyses on big data have recognized patterns as wide ranging as retinal eye diseases, to a potential message from another planet located closer to the center of our galaxy. is last one may be far-fetched, but I will suggest that if and when we locate sig- nals from space aliens, an AI pattern matching tool will be at the center of that discovery. It's this sort of work that is truly powerful. So, how do we tackle this topic? Well, mostly by talking to real human beings who are topic experts. We intentionally scoped this conversa- tion down to the EMS manufacturing process. Where does AI (machine learning) fit today? Where might it be going soon? is will be an ongoing topic, undoubtedly. We're only just entering a brave new world where compute power and database capacity allows for more than number crunching—or should I say, allows number crunching to apply to linguistic com- munication as well? SMT007 References 1. "DeepMinds' co-founder suggested testing an AI chatbot's ability to turn $100,00 into $1 million to mea- sure human-like intelligence," by Sawdah Bhaimiya,, June 20, 2023. Nolan Johnson is managing editor of SMT007 Magazine. Nolan brings 30 years of career experience focused almost entirely on electronics design and manufacturing. To contact Johnson, click here.

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