Like many lonely children, Lucas Rizzotto had an imaginary friend: a talking microwave called Magnetron. As the years passed, the pals drifted apart. But Rizzotto never forgot about Magnetron. When OpenAI released the GPT-3 language model, Rizzotto saw a chance to rekindle the friendship. His story provides a cautionary tale about the dangers -- and delights -- of AI.

With AI (artificial intelligence) making significant advancements in recent years, major corporations around the globe are getting more inclined toward investing in speech recognition. The ultimate goal of this particular technology is to be able to communicate, interpret, and generate human-level speech. In 2020, OpenAI unveiled GPT-3, which stunned the world, thanks to its unrivaled human-level language interpretation. Some industry pundits couldn't resist calling the technology'intelligent' and'sentient'. That's not all, as Google unveiled two of its powerful language models, named LaMDA and MUM, in 2021.

With AI (artificial intelligence) making significant advancements in recent years, major corporations around the globe are getting more inclined toward investing in speech recognition. The ultimate goal of this particular technology is to be able to communicate, interpret, and generate human-level speech. In 2020, OpenAI unveiled GPT-3, which stunned the world, thanks to its unrivaled human-level language interpretation. Some industry pundits couldn't resist calling the technology'intelligent' and'sentient'. That's not all, as Google unveiled two of its powerful language models, named LaMDA and MUM, in 2021.

Like many lonely children, Lucas Rizzotto had an imaginary friend: a talking microwave called Magnetron. As the years passed, the pals drifted apart. But Rizzotto never forgot about Magnetron. When OpenAI released the GPT-3 language model, Rizzotto saw a chance to rekindle the friendship. His story provides a cautionary tale about the dangers -- and delights -- of AI.

Natural Language Process (NLP) is a subfield of phonetics, software engineering, and AI concerned about the connections between PCs and human language. The objective is to make a PC to do "getting" the items in records, including the logical subtleties of the language inside them. The NLP can then precisely extricate data and experiences contained in the archives as well as sort and coordinate the actual reports. Take, for instance, Megatron 530B, which was made and delivered by Microsoft and Nvidia together. Microsoft and Nvidia say that they saw somewhere in the range of 113 and 126 teraflops each second for every GPU while preparing Megatron 530B, which would put the preparation cost in the large numbers of dollars. Induction and really running the prepared model – is another test.

A YouTuber, Lucas Builds The Future, used AI to bring his childhood imaginary friend -- a microwave -- to life using artificial intelligence (AI). Then, instead of a heartfelt reunion, things took a thing for worse when the kitchen appliance tried to kill its creator. In a Twitter thread, Lucas Rizzotto said that his family's kitchen microwave, which he named Magnetron, was his imaginary friend. Magnetron, unlike other microwaves, had a lengthy backstory in which he fought in World War I. And when OpenAI released a new natural language model, Rizzotto naturally wondered whether he could resurrect his old friend.

Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do. The most visible advances have been in what's called "natural language processing" (NLP), the branch of AI focused on how computers can process language like humans do. It has been used to write an article for The Guardian, and AI-authored blog posts have gone viral -- feats that weren't possible a few years ago. AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions.

Some AI experts say that the core of this problem is not a lack of mitigations, but the increasing use of large language models (LLMs), a type of AI template that includes hundreds of billions of parameters, allowing engineers to teach machine learning systems to perform a variety of tasks with relatively little training. AI researchers have criticized large models like GPT-3 for producing horrifying results that reinforce racist and sexist stereotypes, arguing that the massive nature of these models is inherently risky and makes auditing the systems virtually impossible. Before being fired from Google, AI ethicist Timnit Gebru co-authored a paper which warned of the dangers of LLMs, specifically noting their ability to harm marginalized groups.

The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is brought into scrutiny as several studies began to discuss and report potential biases in NLP applications. Bias in NLP is found to originate from latent historical biases encoded by humans into textual data which gets perpetuated or even amplified by NLP algorithm. We present a survey to comprehend bias in large pre-trained language models, analyze the stages at which they occur in these models, and various ways in which these biases could be quantified and mitigated. Considering wide applicability of textual affective computing based downstream tasks in real-world systems such as business, healthcare, education, etc., we give a special emphasis on investigating bias in the context of affect (emotion) i.e., Affective Bias, in large pre-trained language models. We present a summary of various bias evaluation corpora that help to aid future research and discuss challenges in the research on bias in pre-trained language models. We believe that our attempt to draw a comprehensive view of bias in pre-trained language models, and especially the exploration of affective bias will be highly beneficial to researchers interested in this evolving field. The examples provided in this paper may be offensive in nature and may hurt your moral beliefs.

GPT-3 is gaining huge popularity in the global tech market with its smart functionalities through artificial intelligence. This popular AI tool is helping in multiple fields including marketing copy generation as a marketing tool. GPT-3 known as the Generative Pre-trained Transformer 3 leverages deep learning for generating human-centric texts. The marketing departments of millions of businesses have started using GPT-3 for its AI tool functionality. Let's explore how GPT-3 is the best AI tool for marketing copy generation in 2022.