생성적 AI와 예측적 AI: 운영 가치에 대한 이해

### 비즈니스 애플리케이션에서 생성 AI와 예측 AI의 현실 비교
* 🤖 chatGPT와 같은 생성형 AI는 인간과 유사한 기능으로 주목을 받았지만, **실제 비즈니스에 적용하는 데는 한계**가 있습니다.
* 🔮 반면 **예측 AI는 기존의 대규모 운영을 크게 개선**할 수 있는 검증된 기술입니다.
* 💼 엔터프라이즈 머신 러닝이라고도 하는 **예측 AI는 데이터를 학습**하여 다양한 산업 분야에서 의사 결정을 개선하는 예측을 수행합니다.
* 🚚 예를 들어, UPS는 예측 AI를 사용하여 배송 경로를 최적화함으로써 **연간 3억 5천만 달러를 절약**하고 배출량을 줄이고 있습니다.
* 🧠 연사는 비즈니스 애플리케이션에서 구체적인 가치에 집중하는 것이 중요하다고 강조하면서 **제너레이티브 AI를 둘러싼 과대 광고**에 대해 경고합니다.
* 👨 생성형 AI는 **초안 작성과 같은 작업**에는 유용할 수 있지만, 사람의 감독이 필요하고 프로세스를 완전히 자동화할 수는 없습니다.
* 🌐 비즈니스에서 AI를 성공적으로 활용하기 위한 핵심은 **기술이 실질적인 가치를 제공할 수 있는 구체적이고 신뢰할 수 있는 사용 사례**를 파악하는 것입니다.
* 📉 궁극적으로 연사는 AGI나 '컴퓨터가 살아 움직이는' 시나리오라는 환상을 쫓는 것보다 **실용적이고 결과 지향적인 접근 방식**이 더 효과적이라고 제안합니다.

생성형 AI는 인상적이지만 대규모 운영을 개선하기에는 예측형 AI의 자율성과 신뢰성이 부족합니다.

There's kind of an illusion with generative AI. This promises to be the viral sensation that could completely reset how we do things. According to all the headlines, it's on the brink of solving all business problems automatically with the slight side effect of displacing huge amounts of the workforce. It seems so amazing. It's potentially a panacea. No. It's hyperbole. It's hype. What we get with generative AI is extremely impressive, but it's not going to run the world. It does have the ability to create efficiencies, but it's more limited. Whereas predictive AI, which is older, very much still has great amounts of untapped value. I'm Eric Siegel. I'm the co-founder and CEO of Goodr AI, the founder of the Machine Learning Week conference series, and the author of The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. I became fascinated with the concept of artificial intelligence as a kid in the late

seventies and then in the early eighties. Eventually, my education led me to machine learning, and I've been in the field since nineteen ninety-one. Whoa, Kasparov, after the move c4, has resigned. Now I was sort of semi-horrified with the AI hype for a few decades, and it just got a lot worse in recent years because of generative AI. It's going to feed that frenzy because it's so seemingly human-like. Generative AI, something like chatGPT, a large language model, it is capable of communicating about any topic and often giving responses that seem to understand what you're saying. And I grant that on some level, it has captured understanding and the meaning of words and phrases and sentences and paragraphs. But I believe that the difference between what it can do and what humans can do is going to become increasingly apparent. Generative AI is sort of correct often

only as a side effect. When people say hallucinate, they're like, Well, look. It just makes things up. What impresses me is that it actually gets things right sometimes because it's only working on that low level of detail, the per-word level, which results in that sort of seemingly human-like capability. There's a big difference between that impressive capability and the potential value. It's certainly valuable for writing first drafts. So it'll produce a first draft of a letter you need to write or a syllabus or something like that. But you can't trust it blindly. You have to proofread everything that it gives you. That actually, in a way, makes it less potentially autonomous. The whole point of computers is to automate. Right? It does things really fast. And to the degree that we can actually trust it well enough to do things automatically, that ultimately helps the economy. It helps the efficiencies of the world.

Predictive AI, that's the technology you turn to when you want to improve your existing largest scale operation. It does have the potential to enjoy the benefits of autonomy. So predictive AI or enterprise machine learning, that's the technology that learns from data to predict in order to improve any and all of the millions of decisions that make up large-scale enterprise operations. And these are the things that make the world go round. So predict who's going to buy in order to decide who to contact with marketing, which transaction is most likely to be fraudulent to decide which transactions to block or audit, which train wheel is most likely to fail in order to decide which one to inspect. It's not just train wheels. The New York Fire Department does that to predict which buildings are at most risk of fire to triage and prioritize inspections, or which healthcare patient should we take another look at before discharging because they're predicted very likely to be readmitted to the hospital?

All of these predictive applications are a form of prioritization or triage, and the computer is systematically making those decisions over and over again real fast, fully autonomous. So we have data. We give it to machine learning, which is the underlying technology. It generates models that predict, and those predictions improve all the large-scale operations that we conduct. Predictive AI is so applicable across industries. Let's take the delivery industry. UPS is one of the biggest three delivery companies in the United States, and they actually streamlined the efficiency of their deliveries by predicting tomorrow's deliveries. That makes such a big difference that in combination with another system that actually prescribes the driving directions, to this day, UPS enjoys savings of three hundred and fifty million dollars a year and hundreds of thousands of metric tons of emissions. So this is how it works. When they have to start planning and then loading the trucks in the late afternoon or early evening so that it'll be ready the next

morning, they have incomplete information. What they don't know is some of the packages that are still coming in later that night. So what they do is they augment the known information, which is that they already have a bunch of packages in hand that they know are meant to go out tomorrow morning for their final deliveries. And they'll augment that with tentatively presumed predicted deliveries by applying a predictive model for each potential delivery address and saying, Hey, what are the chances that there'll be a delivery there tomorrow? Now they have a more complete picture of all the deliveries needed for tomorrow. They can do a better job planning and loading the packages overnight so that when the trucks go out in the morning, they'll have relatively optimal routes that don't take too many miles of driving, too much gasoline, too much time of the drivers. Now some of those predictions will be wrong, but they're confident enough that the completeness now actually overweighs

some of that uncertainty. This is what you need to do if you want to improve existing large-scale operations. You need to work with probability. Assign a number. How likely is this outcome? Here's the thing. It doesn't make a difference how good the number crunching is unless you act on it. It's not intrinsically valuable. The value only comes if you actually deploy it and change your existing operations. We have this incredible seemingly human-like capability of generative AI, which in one sense, I think is the most amazing thing I've ever seen. But underlying the excitement is the idea that we are moving steadily towards and potentially very near AGI, Artificial General Intelligence, which is a computer that can do anything a person can do. It's this feeling of a computer, kind of, coming alive, like Frankenstein, which we see over and over again in science fiction movies.

In the real world, I do not believe we're going to fully replicate humans anytime soon or that we're actively making progress in that direction. That is a recipe for mismanaged expectations, otherwise known as hype. The antidote to hype is simple. Focus on concrete value. Discover whether you're using generative AI or predictive AI. Determine a very specific, concrete, credible use case of exactly how this technology is going to improve some kind of operation in the enterprise and deliver value. If you want to just sort of explore how close is it to the human mind and why you think it might be getting there, that's kind of a philosophical conversation, and that's great. But if you're talking about, sort of, improving efficiencies of operations that make the world go around, I think we should be a lot more practical and less pie in the sky.

Generative AI is not the panacea we’ve been promised | Eric Siegel for Big Think+

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제너레이티브 AI의 과대광고와 한계

제너레이티브 AI는 비즈니스의 판도를 바꿀 것으로 선전되고 있지만, 그 영향력은 과장되어 있으며 세상을 지배하지는 못할 것입니다.

효율성을 높일 수는 있지만 예측 AI의 미개척 가치에 비하면 그 기능은 제한적입니다.

chatGPT와 같은 생성형 AI의 인간과 유사한 반응은 잘못된 이해감을 불러일으킬 수 있지만, 시간이 지나면 그 능력과 인간 능력의 차이가 더욱 분명해질 것입니다.

인간과 유사한 반응을 보이는 인공지능의 능력은 인상적이지만, 이를 맹목적으로 신뢰해서는 안 됩니다.

초안을 작성하는 데는 유용하지만 교정은 필수입니다.

예측 AI와 엔터프라이즈 머신러닝은 데이터를 사용하여 대규모 운영에서 의사 결정을 개선하고 마케팅에서 의료에 이르기까지 다양한 산업에 도움이 되는 자율성의 잠재력을 가지고 있습니다.

배달 업계에서 예측 AI가 미치는 영향

예측 AI는 자율적으로 의사 결정을 내리고 산업 전반의 효율성을 개선하는 모델을 생성하여 운영을 혁신하고 있습니다.

UPS는 예측 모델을 성공적으로 구현하여 배송을 간소화함으로써 연간 3억 5,000만 달러를 절감하고 탄소 배출량을 크게 줄였습니다.

예측 배송으로 알려진 정보를 보강함으로써 UPS는 보다 효과적으로 포장물을 계획하고 적재할 수 있어 최적의 배송 경로와 전반적인 배송 성과를 개선할 수 있습니다.

기존 운영을 개선하려면 확률을 가지고 작업하고 제공된 데이터에 따라 조치를 취하는 것이 중요합니다.

제너레이티브 AI와 AGI의 잠재력에 대한 흥분은 현실적인 기대치로 완화되어야 합니다.

구체적인 가치와 구체적인 사용 사례에 초점을 맞추는 것은 기업에서 AI 기술을 효과적으로 배포하는 데 필수적입니다.