The Rise of Generative AI: Understanding Its Future in Business Processes
Despite huge strides and advancements with generative AI, there are still plenty of questions and uncertainties that remain.
Generative AI has been taking the world by storm lately and its impact on society is only expected to increase. The global Generative AI market size accounted for $7.9 billion in 2021 and is projected to occupy a market size of $110.8 Billion by 2030, marking a growth of 34% from 2022 to 2030. The latest game-changing innovation has been ChatGPT, but before digging deeper into that, there is another simple but important question that must be answered: what exactly is generative AI?
Generative AI encompasses different algorithms (artificially generated images and texts), usually based on neural network architectures like generative adversarial network (GANs), that are trained on massive amounts of data, images or texts, to produce similar looking images or texts, yet not the same.
Based on these techniques, we have since observed the creation of unlikely animals, inexistent people’s faces, deep fake news, songs, paintings, and even poetry, according to a selected artistic style. The results have often left us in awe of the incredible accuracy in representing the details and for simulating human creativity. From a more technical point of view, generative AI has also often left us in awe, with the incredibly well engineered experiments of humongous neural networks on massive amounts of data.
With new developments in Generative AI, come new questions
Despite huge strides and advancements with generative AI, there are still plenty of questions and uncertainties that remain. As it is easy to imagine, the biggest pitfall of generative AI is its credibility. Any answer produced by a machine might sound convincing, but how can we be sure that it is correct?
Realistic looking images might appear enlightening to the naked eye, but are they trustworthy? Undoubtedly, the next challenge for generative AI is to associate some degree of trustworthiness to all texts and images that are produced.
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From a technical point of view, the training of such gigantic models on such large amounts of data will require sufficient tools as well as adequately skilled data professionals to keep track of all the experiments, measure the quality of the results and maintain and constantly update the data. Thus, the next technical step will be defining the frameworks for fast creation, training, and deployment of experimental models. Moreover, the next technical challenge will be the adoption of adequate data engineering tools and the formation of skilled data engineers, to make generative AI affordable to every data science lab.
Artificial intelligence has become such a buzz word that it has distracted people from what the technology actually is. Today’s society is quick to look at the latest software that can solve complicated problems in cryptic ways and call it “artificial intelligence.” As a matter of fact, artificial intelligence makes predictions from knowledge obtained from observations. While the knowledge is extracted automatically, humans still have an influence over the learning and training processes used in the development of AI systems.
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