Why are we living the age of AI applications right now? The long innovation path from AI's birth to a child's bedtime magic

• URL: http://arxiv.org/abs/2501.06929v1
• Date: Sun, 12 Jan 2025 20:50:24 GMT
• Title: Why are we living the age of AI applications right now? The long innovation path from AI's birth to a child's bedtime magic
• Authors: Tapio Pitkäranta,
• Abstract summary: A four-year-old child who does not know how to read or write can now create bedtime stories with graphical illustrations and narrated audio. This remarkable example demonstrates why we are living in the age of AI applications. This paper examines contemporary leading AI applications and traces their historical development.
• Score: 0.0
• License:
• Abstract: Today a four-year-old child who does not know how to read or write can now create bedtime stories with graphical illustrations and narrated audio, using AI tools that seamlessly transform speech into text, generate visuals, and convert text back into speech in a natural and engaging manner. This remarkable example demonstrates why we are living in the age of AI applications. This paper examines contemporary leading AI applications and traces their historical development, highlighting the major advancements that have enabled their realization. Five key factors are identified: 1) The evolution of computational hardware (CPUs and GPUs), enabling the training of complex AI models 2) The vast digital archives provided by the World Wide Web, which serve as a foundational data resource for AI systems 3) The ubiquity of mobile computing, with smartphones acting as powerful, accessible small computers in the hands of billions 4) The rise of industrial-scale cloud infrastructures, offering elastic computational power for AI training and deployment 5) Breakthroughs in AI research, including neural networks, backpropagation, and the "Attention is All You Need" framework, which underpin modern AI capabilities. These innovations have elevated AI from solving narrow tasks to enabling applications like ChatGPT that are adaptable for numerous use cases, redefining human-computer interaction. By situating these developments within a historical context, the paper highlights the critical milestones that have made AI's current capabilities both possible and widely accessible, offering profound implications for society.

Related papers

• AI Generations: From AI 1.0 to AI 4.0 [3.4440023363051266]
  This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations. Each of these AI generations is driven by shifting priorities among algorithms, computing power, and data. It explores the profound ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy.
  arXiv  Detail & Related papers  (2025-02-16T23:19:44Z)
• The Rise and Fall(?) of Software Engineering [3.89270408835787]
  We aim at outlining the key elements that are vital for the smooth integration of AI into software engineering. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation.
  arXiv  Detail & Related papers  (2024-06-14T15:50:24Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs [0.0]
  The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach. This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs.
  arXiv  Detail & Related papers  (2023-08-08T21:14:21Z)
• Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems [120.297940190903]
  Recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
  arXiv  Detail & Related papers  (2022-05-02T18:00:10Z)
• Artificial Intelligence for the Metaverse: A Survey [66.57225253532748]
  We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse. Several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds.
  arXiv  Detail & Related papers  (2022-02-15T03:34:56Z)
• Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence [0.0]
  We believe that AI is a helper, not a ruler of humans. Computer vision has been central to the development of AI. Emotions are central to human intelligence, but little use has been made in AI.
  arXiv  Detail & Related papers  (2022-01-05T06:00:22Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• The MineRL BASALT Competition on Learning from Human Feedback [58.17897225617566]
  The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. We provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline.
  arXiv  Detail & Related papers  (2021-07-05T12:18:17Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.