Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

1. OpenAI'sChatGPT: OpenAI created the modern AI language model ChatGPT. It is based on the GPT architecture, often called the "Generative Pre-trained Transformer." ChatGPT can carry on sophisticated context-aware discussions since it is built to comprehend and produce text that resembles human speech. It can be used for many jobs and sectors, including customer service, content creation, translation, and virtual assistance. The model is regularly improved and added with new features based on a sizable dataset. However, it occasionally produces illogical or erroneous but plausible answers. It may also be susceptible to the wording of user input and leading questions. ChatGPT involves ethical questions about content generation and potential abuse while requiring a robust computational infrastructure for optimal performance. 2. Google's DeepMind AlphaFold: An artificial intelligence program called AlphaFold was created by DeepMind, an Alphabet Inc. subsidiary, to forecast protein shapes. By predicting the three-dimensional structure of proteins based on their amino acid sequences with unprecedented levels of precision, it has revolutionized the study of structural biology. This has significant ramifications for researching the effects of genetic changes on protein structure, discovering new drugs, and understanding diseases. Based on a deep learning architecture, AlphaFold uses cutting-edge methods, including attention mechanisms and distance geometry. Its open- source codebase enables scientists and programmers to expand upon its capabilities. However, its use is restricted to making predictions about protein folding and requires significant computing power and resources. Access to and control over breakthroughs in biotechnology are likewise ethically problematic. 3. Tesla's Autopilot: Tesla Inc. created Tesla Autopilot, an advanced driver-assistance system (ADAS), for its electric automobiles. It has semi-autonomous driving features like self-parking, adaptive cruise control, and lane-keeping assistance. Autopilot combines cameras, radar, ultrasonic sensors, and neural networks to process the environment and make driving judgments. By assisting with navigation and collision avoidance and lowering driver tiredness and stress on lengthy travels, the technology is intended to increase safety. In order to continuously enhance its perfor- mance, it learns from the data gathered. Autopilot, however, still needs the driver's attention and participation and is not entirely autonomous. Drivers may feel uneasy as a result, which encourages abuse and accidents. Its functionality is constrained in various driving situations and environments, and there are legal and moral issues regarding accountability and safety. 4. IBM Watson: IBM created Watson, an AI platform with sophisticated machine learning and natural language processing capabilities. It is adaptable to a variety of industries and applications and has a high capacity for processing and analyzing massive datasets. Watson has been used in various industries, including healthcare, banking, and customer service. The system effectively concludes unstructured data since it is built to comprehend and respond to complex questions in natural language. However, Watson's implementation and upkeep can be expensive and challenging. For best outcomes, it needs domain- specified help and fine-tuning, but it needs to be clarified, able to clarifications that are unclear or ambiguous. Concerns over data privacy and probable job loss are also ethical issues. h) The Impact of Artificial Intelligence on Work: In a lecture at Northwestern University, AI specialist Kai-Fu Lee highlighted the potential impact of AI on job displacement, particularly for the bottom 90% of the population. He emphasized that routine and quantifiable tasks are more likely to be replaced by AI, such as sorting items, customer service calls, and manual labor. Companies like Amazon are already utilizing AI-powered robots in their warehouses, leading to concerns about job reduction goals. Lee stressed that AI lacks creativity and compassion, serving as a tool to amplify human creativity. He suggested that individuals in repetitive roles should acquire new skills to remain competitive and that investing in education and retraining is crucial for AI's success. However, the transition to new jobs may not be as seamless as some anticipate. Experts like Klara Nahrstedt emphasize the need for widespread education in programming and coding to adapt to the future demands. While there is optimism that people will eventually find new opportunities, the immediate effects of job displacement can be significant. Nonetheless, there is a growing recognition among programmers to identify AI problems within their domains. i) Possibilities of AGI Simulating the complexity of the human brain remains a significant challenge in the pursuit of AGI. John Laird, a computer science professor, emphasizes the need for a cognitive architecture that goes beyond simple neuron models and incorporates elements like procedural, semantic, and episodic memory. Laird's lab conducts experiments teaching robots games and puzzles using natural language instructions to improve their planning abilities. Progress in this field has been slow, as each advancement reveals the difficulty of the task. Concerns have been raised about the collection of personal data for AI purposes, with Apple CEO Tim Cook emphasizing the importance of respecting human values and privacy in AI development. Research suggests that responsible implementation of AI can benefit society, but there is a risk of negative impacts on © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 28 ( ) Year 2023 D Journey of Artificial Intelligence Frontier: A Comprehensive Overview