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What is Gen AI?

 





Generative Artificial Intelligence or Gen AI in short, is a type of of artificial intelligence that can create content, generate ideas, and reuse its knowledge and produce new content, such as text, images, videos, and even speech. Gen AI can learn and adopt by processing large sums of data and use machine learning and deep learning techniques to simulate human learning and decision making processes, identify patterns, and refine its outputs based on the feedback and additional information. This exciting ability benefits it to create more accurate, authentic and tailored outputs in forms of text, video, image and code.

Its impact is profound. According to Goldman Sachs, its adoption could contribute to an astonishing $7 trillion in global GDP.

Gen AI and AI became a hot topic recently after the release of ChatGPT, OpenAI’s chat model and other AI models, however, AI has been around since the 1960s. In 2014, that Generative Adversarial Networks (GANs), a machine learning algorithm, consisting of two networks —generator and discriminator— came around. This breakthrough in AI industry and technological realm, marked a significant milestone to enable Gen AI to produce highly authentic images and media content.

According to International Business Machines Corporation (IBM), there are three phases of Gen AI development: training, tuning, and generation evaluation. The first phase, training, involves around creating a foundational model as the base for various Generative AI applications. In the second phase, tuning, the model is refined and customized to be used for specific tasks or industries. Thirdly, in the generation evaluation phase, the model’s output is assessed, and improvised based on feedback and performance analysis. These continuous cycles of evaluation allow Gen AI to evolve over time.

Today, Generative AI is widely used in various applications, such as in text and speech generation, video creation, software coding, forms of art and more. In education sector for example, AI-powered tools are utilized to enhancing and personalization of education industry and making it more accessible to learners. Similarly, in content creation, AI can assist in writing articles, generating scripts, summaries, reports and more.

However, the rise of Generative AI also presents new challenges and risks. One of the most concerning issues is the rise of deepfakes, which are AI-generated images and/or videos manipulated to create false or misleading content, and target individuals, businesses and celebrities. This issue has raised serious implications for spreading misinformation, cybersecurity, safety, privacy and public trust. Also cyberattacks, and AI-driven frauds represent alarming threats that must be managed and taken care of. Furthermore, it is not wise to accept outputs of any from of AI as hundred percent accurate. AI can make mistake, and this factor must be considered; information and outputs should be double checked and verified before use and publishing. There is also the possibility of biased content as AIs are trained from large sums of datasets and they can contain biased data.

Looking ahead, Generative AI continues to evolve and mark bigger milestones in the history of technology and science. As its capacities expand, so too its application in every industry. While challenges remain, particularly in addressing the ethical implications and potential misuse of the technology, the future of Generative AI is yet to come and be shaped.

 

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