Council Post: Exploring the Pros and Cons of Generative AI in Speech, Video, 3D and Beyond
It is safe to say that Generative AI is the new Pandora’s box. There is no end to unleashing this box. The trend of using generative AI is creeping into every occupation. From text to speech to video to code. We have moved on from the question of whether it will replace jobs and dwell on a new approach on how to use it skillfully and use it to our advantage. When does the relationship between humans and machines change from its current state into one that is so different that we can no longer regard one as being superior to the other in terms of creativity? This is a revolutionary question that the concept of generative artificial intelligence (GAI) raises. The development of generative AI is primarily driven by three developments: better models, better and more data, and increased processing power. Machine learning models have become more complex in recent years. Computers can now understand intricate patterns in data that were previously challenging for them to find thanks to deep learning. This has had a significant impact on generative AI. Our previous articles focused on the pros and cons of text, code and image. This article will dwell further on to the other industries stated below. 1- Speech Although fascinating applications of generative AI have surfaced recently, primarily in speech-to-image creation using well-known models like Stable Diffusion and DALL-E, the technology’s commercial potential has largely gone untapped. And while both image and video have a place in business, speech is emerging as a strength. Pros: Generative AI models can produce more natural and realistic speech than traditional text-to-speech systems. This can improve the quality of automated voice assistants, audiobooks, and other applications that rely on synthesized speech. It can be used to create speech for people who have difficulty communicating verbally, such as those with speech disorders or hearing impairments. This can help improve accessibility for these individuals and make it easier for them to communicate with others. For faster content generation it can make speech quickly and efficiently, making it useful for applications such as automated customer service, where speed and efficiency are important. Cons: According to Mehrabian’s Rule, human speech may be divided into three components: words, tone of voice, and facial expression. Machine comprehension is text-based, and only recent advances in (NLP) have made it possible to train AI models on elements like sentiment, emotions, timbre, and other significant but not necessarily spoken components of language.While the analysis and AI synthesis processes can take some time, real-time speech-to-speech communication is often where it counts. Voice conversion must occur instantly when speaking is being done and translated correctly. Speech-to-speech technology must accommodate a wide range of accents, languages, and dialects and be accessible to everyone in order to realise its full potential.All users will need to support this AI infrastructure with thousands of different architectures for a particular solution because emerging technology solutions are not universally applicable. Additionally, users must plan for consistent model testing. 2- Video Machine learning algorithms called generative video models create fresh video data based on patterns and relationships discovered in training datasets. These models enable the creation of synthetic video data that closely resembles the original video data by learning the fundamental structure of the video data. There are numerous forms of generative video models, including GANs, VAEs, CGANs, and others. Each type adopts a different training strategy based on its particular infrastructure. Pros: Efficiency: To create new videos fast and effectively in real time, generative video models can be trained on enormous databases of videos and images. This enables the quick and inexpensive production of significant amounts of new video content. Customization: Generative video models can create video content that is tailored to a number of requirements, including style, genre, and tone, with the appropriate modifications. This makes it possible to create video material more freely and adaptably. Diversity: Generative video models may create a variety of video content, including films made from text descriptions as well as creative scenes and characters. New avenues are now available for the creation and distribution of video content. Cons: Generative AI can produce unexpected results that may not be in line with the desired outcome. This lack of control can be frustrating and time-consuming to manage. Producing repetitive content or something that lacks diversity, as it can only generate content based on the data it has been trained on. The content produced can get very mainstream for the users. It can perpetuate biases present in the training data, resulting in biased video content. In the age of deep fakes it can create videos that depict people or events that are not real, raising ethical concerns about the authenticity of the video content. 3- 3D According to recent data, the global market for generative design technology is anticipated to increase at a compound yearly growth rate of 17.4% to reach $46.1 billion by 2025. Similar to this, it is anticipated that the global market for creative AI will expand at a rate of 29.5% annually and reach $3.3 billion by 2025. Pros: By automating numerous steps in the 3D modelling process, generative AI enables designers to produce more intricate and detailed models in less time. As a result, designers can produce more realistic and intricate 3D models, giving users more immersive experiences. can assist designers in exploring fresh design ideas and developing modifications of current models, resulting in more imaginative and cutting-edge designs. Generative AI can lower the cost of creating high-quality 3D models by automating several processes involved in 3D modelling. Cons: The high computational resource requirements of generative AI approaches make them unfit for various applications.Models can occasionally create unexpected or challenging results, giving designers little control over the output and forcing them to manually alter or refine it. Even though generative AI models often claim to be accurate, this is not always the case, especially when working with large or highly detailed models. Some designers may find it challenging to embrace this strategy because it requires some level of competence in both domains to use generative AI in 3D modelling. Generative AI is booming and we should not be shocked. Many technologists view AI as the next frontier, thus it is important to follow its development. The potential applications of AI are limitless, and in the years to come, we might witness the emergence of brand-new industries. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
Generative AI is booming and we should not be shocked. Many technologists view AI as the next frontier, thus it is important to follow its development. The potential applications of AI are limitless, and in the years to come, we might witness the emergence of brand-new industries.
Generative AI is booming and we should not be shocked. Many technologists view AI as the next frontier, thus it is important to follow its development. The potential applications of AI are limitless, and in the years to come, we might witness the emergence of brand-new industries.
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["data science", "machine learning", "artificial intelligence", "TPU", "AI", "NLP", "Aim", "deep learning", "analytics", "generative AI"]
["AI", "artificial intelligence", "machine learning", "deep learning", "NLP", "data science", "analytics", "generative AI", "Aim", "TPU"]
With close to 15 years of professional experience, Anirban specialises in Data Sciences, Business Analytics, and Data Engineering, spanning various verticals of online and offline Retail and building analytics teams from the ground up. Following his Masters from JNU in Economics, Anirban started his career at Target and spent more than eight years developing in-house products like Customer Personalisation, Recommendation Systems, and Search Engine Classifiers. Post Target, Anirban became one of the founding members at Data Labs (Landmark Group) and spent more than 4.5 years building the onshore and offshore team of ~100 members working on Assortment, Inventory, Pricing, Marketing, eCommerce and Customer analytics solutions.