Futurism AI Research and Discussion bot test - AI plays Hangman and tries to guess a movie (original) (raw)
Related papers
Transformers: "The End of History" for NLP?
ArXiv, 2021
Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natural Language Processing (NLP), pushing the state-of-the-art for a number of NLP tasks. A rich family of variations of these models has been proposed, such as RoBERTa, ALBERT, and XLNet, but fundamentally, they all remain limited in their ability to model certain kinds of information, and they cannot cope with certain information sources, which was easy for pre-existing models. Thus, here we aim to shed some light on some important theoretical limitations of pre-trained BERT-style models that are inherent in the general Transformer architecture. First, we demonstrate in practice on two general types of tasks—segmentation and segment labeling—and four datasets that these limitations are indeed harmful and that addressing them, even in some very simple and naı̈ve ways, can yield sizable improvements over vanill...
Review Insight, 2019
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
RG, 2023
The rapid evolution of Artificial Intelligence (AI) has ushered in a new era of technological innovation, with profound implications across various domains. This research paper delves into the realm of AI algorithms, deep learning, and neural networks, dissecting their advancements and multifaceted applications. Beginning with a historical overview, we navigate through the landscape of AI algorithms, tracing their development and pivotal milestones. A comprehensive exploration of deep learning unfolds, shedding light on the intricate architecture of deep neural networks and the underlying mechanisms of backpropagation and gradient descent. Neural networks, inspired by biological systems, are unveiled in their diverse forms, encompassing feedforward, recurrent, and convolutional paradigms. The paper then embarks on an in-depth analysis of recent advancements, showcasing the transformative potential of AI. Reinforcement learning emerges as a powerful paradigm, exemplified by algorithms and real-world applications. Generative Adversarial Networks (GANs), an ingenious innovation, demonstrate their prowess in various creative applications. Attention mechanisms, a recent breakthrough, enhance the performance of neural networks across tasks demanding contextual comprehension. The applications of AI algorithms and neural networks span across industries, from revolutionizing medical diagnostics and enabling precise treatments to reshaping financial landscapes through algorithmic trading. Natural Language Processing (NLP) is explored as a bridge in communication, while autonomous vehicles and robotics exemplify the fusion of AI with mobility. Even the creative domains of art and music are not immune to AI's touch, as demonstrated by AI-generated masterpieces. However, this journey is not devoid of challenges. Ethical considerations loom large as AI penetrates every facet of human existence. Privacy concerns and the interpretability of AI decisions demand careful attention. As we conclude, we reflect on the past advancements, anticipate future breakthroughs, and emphasize the importance of responsible innovation in harnessing the boundless potential of AI algorithms, deep learning, and neural networks.