Artificial Intelligence and its Potential and Threat's it Possess

 

Artificial Intelligence is being used in every aspect of human life. Let us understand the use of AI through an example of a cake factory and how AI is helpful in the growth of the business.

Let, for example, a cake factory has 10 varieties of cake for sale. It is taking orders through AI-powered tools and is asking for suggestions on its product from its consumers. Suggestions can be categorised as taste, aesthetics, value for money, and on-time delivery, and there is a column for complaints. The consumer can rate this product and also leave suggestions for its better delivery. Now a product that has a global reach will have a huge amount of data for every product it sells, and this data could be processed through AI-powered tools. The cake factory would have an outline to make its product more consumer-specific, which could increase its productivity and also help in grasping the taste of its consumers.

A huge industry such as the cake factory would have detailed data on every aspect of its business such as Financial assessment, efficiency of its staff in cake preparation, its supply partner, wastage of its product,, etc. All these assessments could help in the growth of its business and also in improving its product.

Cake Factory was a small example to elaborate on the use of AI in this globalised world. The term AI, coined in 1950's (a 1956 seminar organised in Dartmouth, USA, is widely believed to be the founding moment of modern AI research and development), refers to the simulation of human intelligence by machines. AI, Machine Learning, and deep learning are common terms and are sometimes used interchangeably. But there are distinctions. AI covers an ever-changing set of capabilities as new technologies are developed. Technologies that come under the umbrella of AI include machine learning and deep learning. Machine learning enables software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. This approach became vastly more effective with the rise of large data sets to train on. Deep learning, a subset of machine learning, is based on our understanding of how the brain is structured. Deep learning's use of artificial neural network structure is the underpinning of recent advances in AI, including self-driving cars and ChatGPT.

The Rashmika Mandanna "Deepfake" controversy has broken the lavender haze around generative artificial intelligence. The wonder about the magic of ChatGPT has given way to a shock that has urban Indian women feeling more insecure online than ever before.

A side-effect is that it is forms of moral panic that are driving the discourse on the topic, demanding legal and regulatory interventions. Such measures, when based on reactivity rather than considered thought, are likely to be short-sighted, providing only superficial relief while increasing government censorship.