Difference between revisions of "History of Artificial Intelligence (AI)"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
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[https://www.youtube.com/results?search_query=history+of+ai+artificial+intelligence YouTube] | [https://www.youtube.com/results?search_query=history+of+ai+artificial+intelligence YouTube] | ||
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[https://www.bing.com/news/search?q=history+of+ai+artificial+intelligence&qft=interval%3d%228%22 ...Bing News] | [https://www.bing.com/news/search?q=history+of+ai+artificial+intelligence&qft=interval%3d%228%22 ...Bing News] | ||
| − | + | * [[Creatives]] ... [[History of Artificial Intelligence (AI)]] ... [[Neural Network#Neural Network History|Neural Network History]] ... [[Rewriting Past, Shape our Future]] ... [[Archaeology]] ... [[Paleontology]] | |
| − | * [[Creatives]] | + | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]] |
| − | * [[Gaming]] | ||
* [[Benchmarks#Turing Test|Turing Test]] ... test of a machine's ability to exhibit intelligent behavior | * [[Benchmarks#Turing Test|Turing Test]] ... test of a machine's ability to exhibit intelligent behavior | ||
* [https://en.wikipedia.org/wiki/History_of_artificial_intelligence History of Artificial Intelligence] ...[https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence Timeline] ...[https://en.wikipedia.org/wiki/Timeline_of_machine_learning Timeline of machine learning] | Wikipedia | * [https://en.wikipedia.org/wiki/History_of_artificial_intelligence History of Artificial Intelligence] ...[https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence Timeline] ...[https://en.wikipedia.org/wiki/Timeline_of_machine_learning Timeline of machine learning] | Wikipedia | ||
| + | * [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Animal Language]] | ||
| + | * [[Archaeology|Using AI to reveal historical mysteries]] | ||
* [https://www.aaai.org/ojs/index.php/aimagazine/article/view/1848/1746 A (Very) Brief History of Artificial Intelligence | Bruce G. Buchanan] | * [https://www.aaai.org/ojs/index.php/aimagazine/article/view/1848/1746 A (Very) Brief History of Artificial Intelligence | Bruce G. Buchanan] | ||
* [https://www.techrepublic.com/article/how-china-tried-and-failed-to-win-the-ai-race-the-inside-story/ How [[Government Services#China|China]] tried and failed to win the AI race: The inside story | Alison Rayome] | * [https://www.techrepublic.com/article/how-china-tried-and-failed-to-win-the-ai-race-the-inside-story/ How [[Government Services#China|China]] tried and failed to win the AI race: The inside story | Alison Rayome] | ||
| − | * [ | + | * [https://arxiv.org/abs/2302.09419 A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT | C. Zhou, Q. Li, C. Li, J. Yu, Y. Liu, G. Wang, K. Zhang, C. Ji, Q. Yan, L. He, H. Peng, J. Li, J. Wu, Z. Liu, P. Xie, C. Xiong, J Pei, P. Yu, L. Sun - arXiv - Cornell University] |
| − | * [[ | + | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
| − | * [[ | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]] |
| − | * [https://www.technologyreview.com/2023/02/08/1068068/chatgpt-is-everywhere-heres-where-it-came-from/ ChatGPT is everywhere. Here’s where it came from | Will Douglas Heaven - MIT Technology Review] | + | ** [https://www.technologyreview.com/2023/02/08/1068068/chatgpt-is-everywhere-heres-where-it-came-from/ ChatGPT is everywhere. Here’s where it came from | Will Douglas Heaven - MIT Technology Review] |
| + | ** [https://www.techtarget.com/searchenterpriseai/tip/History-of-generative-AI-innovations-spans-9-decades History of generative AI innovations spans 9 decades | George Lawton - TechTarget] | ||
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| + | <hr><center> <b><i> | ||
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| + | Never give up on a dream just because it will take time to accomplish it. The time will pass anyway.</i></b> | ||
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| + | </center><hr> | ||
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In AI, there are four generations. | In AI, there are four generations. | ||
| − | # First Generation AI - is the Good Old-fashioned AI, meaning that you handcraft everything and you learn nothing. These were simple programs that could only do one task really well. They were like little robots that were programmed to do a specific thing, like adding numbers or sorting data. | + | # <b>First Generation AI</b> - is the Good Old-fashioned AI, meaning that you handcraft everything and you learn nothing. These were simple programs that could only do one task really well. They were like little robots that were programmed to do a specific thing, like adding numbers or sorting data. |
| − | # Second Generation AI - is shallow learning — you handcraft the features and learn a classifier. This was when people started teaching computers how to learn by giving them lots of data and letting them figure out patterns on their own. These programs were called "machine learning" programs, and they could do things like recognize images or translate languages. | + | # <b>Second Generation AI</b> - is shallow learning — you handcraft the features and learn a classifier. This was when people started teaching computers how to learn by giving them lots of data and letting them figure out patterns on their own. These programs were called "machine learning" programs, and they could do things like recognize images or translate languages. |
| − | # Third Generation AI - is deep learning. Basically you handcraft the algorithm, but you learn the features and you learn the predictions, end to end. This is when computers started to get really good at things that only humans used to be able to do, like understanding language and making decisions based on what they know. These programs are called "neural networks" because they're modeled after the way our brains work. | + | # <b>Third Generation AI</b> - is deep learning. Basically you handcraft the algorithm, but you learn the features and you learn the predictions, end to end. This is when computers started to get really good at things that only humans used to be able to do, like understanding language and making decisions based on what they know. These programs are called "neural networks" because they're modeled after the way our brains work. |
| − | # Fourth Generation AI - This is the most advanced kind of AI we have so far - “learning-to-learn.”. These programs can understand things like emotions and creativity. They can learn from experience and get better at things over time, just like we do. They're often called "artificial general intelligence" because they're almost as good as humans at thinking and learning. | + | # <b>Fourth Generation AI</b> - This is the most advanced kind of AI we have so far - “learning-to-learn.”. These programs can understand things like emotions and creativity. They can learn from experience and get better at things over time, just like we do. They're often called "artificial general intelligence" because they're almost as good as humans at thinking and learning. |
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| + | <youtube>qpoRO378qRY</youtube> | ||
| + | <b>Full interview: "Godfather of artificial intelligence" talks impact and potential of AI | ||
| + | </b><br>[[Creatives#Geoffrey_Hinton|Geoffrey Hinton]] is considered a godfather of artificial intelligence, having championed machine learning decades before it became mainstream. As chatbots like ChatGPT bring his work to widespread attention, we spoke to Hinton about the past, present and future of AI. CBS Saturday Morning's Brook Silva-Braga interviewed him at the Vector Institute in Toronto on March 1, 2023 | ||
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| + | <youtube>bzgU_I_wx60</youtube> | ||
| + | <b>The history and future of AI | ||
| + | </b><br>The history and future of AI | ||
| + | * 3:30 What killed neural network research for decades | ||
| + | * 6:30 Holy trinity of AI/ML | ||
| + | * 07:00 Overview of all modern ML/Deeplearning | ||
| + | * 10:00 Why Agent modelling is so powerful | ||
| + | * 15:00 About Transformers | ||
| + | * 17:20 Modern breakthoughs in conversational models | ||
| + | * 23:00 How about autonomous driving. Not limited to L3 | ||
| + | * 26:00 How less "strong" general deep learning system beat specialized "stronger" chess AI. Alphago | ||
| + | * 32:00 AlphaFold (to predict protein) | ||
| + | * 35:00 Next gen ML models. Multitask Unified Model (MUM) | ||
| + | * 37:00 Q&A. Political and technical questions from Central Asia developers to Murat | ||
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| − | == [https://en.wikipedia.org/wiki/ | + | == <span id="The Turk"></span>The Turk == |
| + | * [https://en.wikipedia.org/wiki/Mechanical_Turk The Turk] | ||
| + | ** [https://www.mturk.com/ Amazon Mechanical Turk (MTurk)] - [https://blog.mturk.com/using-mturk-with-amazon-sagemaker-for-supervised-learning-ml-bc30f94e1c0d Using MTurk with Amazon SageMaker for Supervised Learning (ML)] | ||
| + | * [[Math for Intelligence#Analog Computers | Analog Computers]] | ||
https://rosfilmfestival.com/wp-content/uploads/2016/05/truco-Turco-ROS-821x335.png | https://rosfilmfestival.com/wp-content/uploads/2016/05/truco-Turco-ROS-821x335.png | ||
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<youtube>7W_kQsO6MPc</youtube> | <youtube>7W_kQsO6MPc</youtube> | ||
<b>Mechanical Marvels—Automaton: The Chess Player "Android," 1769 | <b>Mechanical Marvels—Automaton: The Chess Player "Android," 1769 | ||
| − | </b><br>Touted as an android that could defeat chess masters, Wolfgang von Kempelen's famed illusion debuted at the court of Empress Maria Theresa during wedding celebrations for her daughter in 1769. Over the course of the eighteenth century, the chess player (known in its time as The Turk for its costume) won games against Catherine the Great and Benjamin Franklin. When Napoléon Bonaparte tried to cheat, it wiped all the pieces from the board. The mysterious machine sparked discussions of the possibilities and limits of artificial intelligence, and it inspired the development of the power loom, the telephone, and the computer. The original and its secrets were destroyed in a fire in 1854. The subject of more than eight hundred publications attempting to uncover its secrets, Kempelen's illusion also inspired a 1927 silent movie, The Chess Player, directed by Raymond Bernard. In the sequence shown here, the inventor presents his creation at court. The year of its release, this early science-fiction drama attracted more attention than Fritz Lang's Metropolis, a now-legendary film that also involves an android. Featured Artwork: The Chess Player (The Turk), Original ca. 1769. Wolfgang von Kempelen (1734–1804). Austrian, Vienna. Wood, brass, fabric, steel. Collection of Mr. John Gaughan, Los Angeles | + | </b><br>Touted as an android that could defeat chess masters, Wolfgang von Kempelen's famed illusion debuted at the court of Empress Maria Theresa during wedding celebrations for her daughter in 1769. Over the course of the eighteenth century, the chess player (known in its time as The Turk for its costume) won games against Catherine the Great and Benjamin Franklin. When Napoléon Bonaparte tried to cheat, it wiped all the pieces from the board. The mysterious machine sparked discussions of the possibilities and limits of artificial intelligence, and it inspired the [[development]] of the power loom, the telephone, and the computer. The original and its secrets were destroyed in a fire in 1854. The subject of more than eight hundred publications attempting to uncover its secrets, Kempelen's illusion also inspired a 1927 silent movie, The Chess Player, directed by Raymond Bernard. In the sequence shown here, the inventor presents his creation at court. The year of its release, this early science-fiction drama attracted more attention than Fritz Lang's Metropolis, a now-legendary film that also involves an android. Featured Artwork: The Chess Player (The Turk), Original ca. 1769. Wolfgang von Kempelen (1734–1804). Austrian, Vienna. Wood, brass, fabric, steel. Collection of Mr. John Gaughan, Los Angeles |
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Latest revision as of 11:41, 6 November 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Creatives ... History of Artificial Intelligence (AI) ... Neural Network History ... Rewriting Past, Shape our Future ... Archaeology ... Paleontology
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Games - Metaverse ... Quantum ... Game Theory ... Design
- Turing Test ... test of a machine's ability to exhibit intelligent behavior
- History of Artificial Intelligence ...Timeline ...Timeline of machine learning | Wikipedia
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Animal Language
- Using AI to reveal historical mysteries
- A (Very) Brief History of Artificial Intelligence | Bruce G. Buchanan
- How China tried and failed to win the AI race: The inside story | Alison Rayome
- A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT | C. Zhou, Q. Li, C. Li, J. Yu, Y. Liu, G. Wang, K. Zhang, C. Ji, Q. Yan, L. He, H. Peng, J. Li, J. Wu, Z. Liu, P. Xie, C. Xiong, J Pei, P. Yu, L. Sun - arXiv - Cornell University
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
Never give up on a dream just because it will take time to accomplish it. The time will pass anyway.
In AI, there are four generations.
- First Generation AI - is the Good Old-fashioned AI, meaning that you handcraft everything and you learn nothing. These were simple programs that could only do one task really well. They were like little robots that were programmed to do a specific thing, like adding numbers or sorting data.
- Second Generation AI - is shallow learning — you handcraft the features and learn a classifier. This was when people started teaching computers how to learn by giving them lots of data and letting them figure out patterns on their own. These programs were called "machine learning" programs, and they could do things like recognize images or translate languages.
- Third Generation AI - is deep learning. Basically you handcraft the algorithm, but you learn the features and you learn the predictions, end to end. This is when computers started to get really good at things that only humans used to be able to do, like understanding language and making decisions based on what they know. These programs are called "neural networks" because they're modeled after the way our brains work.
- Fourth Generation AI - This is the most advanced kind of AI we have so far - “learning-to-learn.”. These programs can understand things like emotions and creativity. They can learn from experience and get better at things over time, just like we do. They're often called "artificial general intelligence" because they're almost as good as humans at thinking and learning.
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