생성형 인공지능, Generative AI
이용자의 특정 요구에 따라 결과를 생성해내는 인공지능을 말한다. 데이터 원본을 통한 학습으로 소설, 이미지, 비디오, 코딩, 시, 미술 등 다양한 콘텐츠 생성에 이용된다. 한국에서는 2022년 Novel AI의 그림 인공지능 등장으로 주목도가 높아졌으며, 해외에서는 미드저니, 챗GPT등 여러 모델을 잇달아 공개하면서 화제의 중심이 되었다.
보통 딥러닝 인공지능은 학습 혹은 결과 출력 전 원본 자료를 배열 자료형 숫자 데이터로 변환하는 인코딩 과정이 중요한데, 생성 AI의 경우 인공지능의 출력 데이터를 역으로 그림, 글 등의 원하는 형태로 변환시켜주는 디코딩 과정 또한 필요하다.
구글 vs MS, 챗GPT 열풍에 AI챗봇 경쟁
미국 오픈AI의 대화형 인공지능 모델인 '챗GPT' 열풍으로 시작된 챗봇 개발 경쟁이 구글과 마이크로소프트(MS)로 옮겨붙고 있다. 구글이 글로벌 검색 시장에서 91%의 점유율을 차지하고 있는 가운데 오픈AI와 계약을 맺은 MS가 시장판도를 뒤집을 수 있을지 관심이 쏠리고 있다.
영국 일간지 가디언은 10일(현지시간) "제임스웹 우주 망원경을 만드는 데 100억달러(약 12조7050억원)가 들었지만 구글의 새로운 챗봇이 제임스웹에 대한 질문에 잘못 대답해 구글의 손실이 1630억달러(약 207조915억원)이상에 달한다"며 "구글과 MS의 경쟁이 새로운 국면에 접어들었다"고 보도했다.
구글은 지난 8일(현지시간) 프랑스 파리에서 생성형 AI를 결합한 검색서비스 '바드'를 공개했으나 틀린 답을 내놓았다. 구글 바드에게 "9살 아이에게 제임스웹 우주망원경의 새로운 발견에 대해 어떻게 설명해줄 수 있을까"라는 질문을 던졌는데 바드는 "제임스웹 우주망원경은 최초로 태양계 밖의 행성을 찍었다"고 잘못 답한 것이다. 태양계 밖 행성을 최초로 촬영한 망원경은 유럽남방천문대가 칠레 남부 고도 2635ｍ 지점에 설치한 초거대 망원경 'VLT'이다.
MS가 검색엔진 빙(Bing)에 챗GPT를 접목하기로 하자 구글이 설익은 챗봇을 내놓은 것으로 분석됐다. MS는 수십억달러(약 수조원)를 투자해 오픈AI가 개발한 챗GPT 기술로 빙 검색 엔진과 엣지(Edge) 웹 브라우저를 향상시킨다고 지난 7일(현지시간) 발표했다.
MS는 "챗GPT를 기반으로 하는 이 기술은 관련성이 높은 최신 결과를 제공하며 쇼핑 또한 더 쉽게 하는 데 도움을 줄 것"이라고 밝혔다. 새로운 빙은 몇 주 후 공개적으로 사용할 수 있을 예정이다.
필립 옥켄덴 MS 윈도우·검색 부문 최고재무책임자(CFO)는 “검색 광고 시장에서 점유율이 1% 포인트 증가할 때마다 20억달러(약 2조5410억원)의 수익 기회가 발생한다”고 말했다. MS가 챗GPT를 탑재한 빙으로 검색 시장 점유율을 높일 수 있을 것으로 기대한다는 의미다.
그러나 MS는 여전히 넘어야 할 산이 많다. 구글은 여전히 강력한 위치에 있다. 인터넷데이터회사인 시밀라웹에 따르면 구글의 검색시장 점유율은 91%인데 MS 빙의 시장 점유율은 단 3%에 불과하다. 빙은 지난 2009년 출시된 뒤 단 한번도 구글의 검색시장 지배력에 영향을 미치지 못했다.
챗GPT는 구글 바드와 데이터 학습 시점에도 차이가 있다. 챗GPT는 2021년까지 생성된 데이터만을 학습했지만 바드는 구글 검색의 최신 정보도 종합해 답을 제공한다.
구글은 AI에 투자도 많이 했다. 대표적인 게 구글 번역기다. 알파벳은 또한 영국에 본사를 둔 선도적인 AI 조사 회사 딥마인드를 소유하고 있다.
마크 리들 미국 조지아 공대교수는 "구글은 오픈AI와 동등한 대규모 언어 모델 기술을 보유하고 있어 새로운 버전의 빙이 구글 검색 비즈니스에 심각한 위협이 될 것이라고 생각하지 않는다"면서도 "MS는 검색 기술을 거의 하룻밤 사이에 다시 양강 구도로 만든 큰 위업을 완수했다"고 밝혔다.
generative AI seems to have popped up everywhere in the mainstream—via the popularity of ChatGPT, the proliferation of text-to-image tools, and as avatars in our social media feeds. But beyond fun smartphone apps and handy ways for students to shirk essay-writing assignments, global adoption of AI will fundamentally change the way businesses operate, innovate, and scale in the near future.
Babson College Professor Thomas Davenport and Nitin Mittal, head of U.S. artificial intelligence growth at Deloitte, are the authors of All In on AI: How Smart Companies Win Big With Artificial Intelligence, which will be published in late January 2023. Their book examines how companies including Alphabet, Ping An, Airbus, Walmart, and Capital One leverage AI in business strategy, key processes, change management, and competition.
Here, Davenport and Mittal provide Fast Company with a primer on Generative AI along with an excerpt from their book offering an overview on AI archetypes, capabilities, and general principles.
What is Generative AI and how will most businesses and individuals use it in the near future?
Generative AI refers to artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data. Generative AI models produce text and images: blog posts, program code, poetry, and artwork. The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. In the shorter term, we see generative AI used to create marketing content, generate code, and in conversational applications such as chatbots.
What are some of the most useful capabilities of Generative AI?
Generative AI can already do a lot and are incredibly diverse. They can take in such content as images, longer text formats, emails, social media content, voice recordings, program code, and structured data. They can output new content, translations, answers to questions, sentiment analysis, summaries, and even videos. These universal content machines have many potential applications in business, and today marketing applications are among the most common uses of generative AI. In the future, there is potential for generative AI to make an impact in health care and life sciences—to make diagnoses, for example, or find new cures for disease.
What are some secondary or tertiary ways that Generative AI will manifest in our lives?
Not surprisingly, many of the early uses of generative AI began with large tech, or digital native, companies. Over the next several years, we see generative AI permeating traditional industries, like manufacturing, health care, and pharmaceuticals, for example. Once a generative model has been trained, it can be fine-tuned for specific content domains with much less data. We are now starting to see specialized generative models for biomedical content, legal documents, and translated text, which will give rise to additional use cases in those industries and domains. They may help organizations to manage their knowledge and content more effectively so that it can be easily accessed by employees and customers.
What are some concerns around Generative AI that businesses and individuals should be aware of?
There are some potential legal and ethical concerns related to generative AI. One is the ability to easily create “deepfakes”—images or video created by AI that appear realistic but are false or misleading. Additionally, generative AI raises questions about what is original and proprietary content and may have a significant impact on content ownership.
Reprinted by permission of Harvard Business Review Press. Excerpted from All In On AI: How Smart Companies Win Big With Artificial Intelligence by Thomas H. Davenport and Nitin Mittal. Copyright 2023 Deloitte Development LLC. All rights reserved.
The path to becoming all-in on AI is not particularly well trodden; we’ve estimated that fewer than 1 percent of large organizations would meet our definition of the term. However, there are capability maturity models for virtually every business capability, and we will describe a similar approach for AI. Advancing maturity in AI is based on a variety of factors, including:
Breadth of AI use cases across the enterprise
Breadth of different AI technologies employed
Level of engagement by senior leaders
The role of data in enterprise decision making
Extent of AI resources available—data, people, technology
Extent of production deployments, as opposed to AI pilots or experiments
Links to transformation of business strategy or business models
Policies and processes to ensure ethical use of AI
Capability maturity models tend to have five levels, and we see no reason to depart from that standard. They also tend to have low capabilities at Level 1 and high ones at Level 5, and we follow that pattern as well.
AI Fueled (Level 5). All or most of the components we’ve described above, fully implemented and functioning—the business is built on AI capabilities and is becoming a learning machine;
Transformers (Level 4). Not yet AI fueled but relatively far along in the journey with some of the attributes in place; multiple AI deployments that are creating substantial value for the organization;
Pathseekers (Level 3). Already started on the journey and making progress, but at an early stage—some deployed systems, and a few measurable positive outcomes achieved;
Starters (Level 2). Experimenting with AI—these companies have a plan but need to do a lot more to progress; they have very few or no production deployments;
Underachievers (Level 1). Started experimenting with AI but have no production deployments and have achieved little to no economic value.
We might also add a “Level 0” to describe companies that have no AI activity whatsoever, but this is certainly a minority category among large firms in sophisticated economies. The key difference with other maturity models is that we’re offering three alternative archetypes for the use of AI, but a company can be at various levels no matter what the primary focus of their efforts.
We would argue that in talking about AI-fueled enterprises, we are almost always describing Level 5 organizations. Like our examples, they are companies that have a wide variety of AI technologies and use cases in place, along with specialized technology platforms to support them. They do experiment, and companies striving to create may do more experimentation than those seeking operational improvements. The goal of all these organizations, however—usually achieved—is to actually do business with AI by putting AI systems into production deployment. New business processes are employed. New products and services are introduced to the marketplace and used by customers. Senior executives are engaged and active in identifying use cases and monitoring performance. They have established data science groups, modernized their digital infrastructures, and identified large volumes of data for training and testing models.
Perhaps most importantly, there are alternative archetypes for employing AI, and somewhat different versions of capability models for different strategies. As we noted earlier, our view is that the three major archetypes can be summarized as 1) creating new businesses, products, or services; 2) transforming operations; and 3) influencing customer behavior. While operational improvements are the most common objective for AI according to our survey research, it’s clear that at least some companies don’t just use AI to make their existing strategies, operations, and business models somewhat more efficient. Instead, they use it to enable new strategies, radically new business process designs, and new relationships with customers and partners. Those companies would assess their capabilities in terms of the degree to which they have successfully developed new strategies, business models, or products. Operationally focused AI objectives would involve achievement of substantial operational improvements, and customer behavior objectives would focus on how much actual customer behavior change has actually been achieved. Of course, that level of business transformation requires the active engagement and participation in strategic deliberations by senior management that Level 5 organizations typically display.