#ise2024 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #ise2024, aggregated by home.social.
-
In the very last #ISE2024 lecture, we were discussing the limits of machine learning and #AI. The "Paperclip Maximizer Problem" is one of the dystopian scenarios that might occur if final goals and instrumental goals are diverging...
#dystopian #generativeAI #llms @fizise @fiz_karlsruhe #aiart
-
In the very last #ISE2024 lecture, we were discussing the limits of machine learning and #AI. The "Paperclip Maximizer Problem" is one of the dystopian scenarios that might occur if final goals and instrumental goals are diverging...
#dystopian #generativeAI #llms @fizise @fiz_karlsruhe #aiart
-
In the very last #ISE2024 lecture, we were discussing the limits of machine learning and #AI. The "Paperclip Maximizer Problem" is one of the dystopian scenarios that might occur if final goals and instrumental goals are diverging...
#dystopian #generativeAI #llms @fizise @fiz_karlsruhe #aiart
-
In the very last #ISE2024 lecture, we were discussing the limits of machine learning and #AI. The "Paperclip Maximizer Problem" is one of the dystopian scenarios that might occur if final goals and instrumental goals are diverging...
#dystopian #generativeAI #llms @fizise @fiz_karlsruhe #aiart
-
In our last #ISE2024 lecture before the summer break, we were focusing on artificial neural networks and deep learning. Their foundations already date back to the 1940s, when Warren McCulloch and Walter Pitts presented a mathematical model of the neuron.
W. McCulloch, W. Pitts (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bull. of Math. Biophysics. 5 (4): 115–133. https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf
#neuralnetwork #AI @fizise @fiz_karlsruhe @sourisnumerique @enorouzi
-
In our last #ISE2024 lecture before the summer break, we were focusing on artificial neural networks and deep learning. Their foundations already date back to the 1940s, when Warren McCulloch and Walter Pitts presented a mathematical model of the neuron.
W. McCulloch, W. Pitts (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bull. of Math. Biophysics. 5 (4): 115–133. https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf
#neuralnetwork #AI @fizise @fiz_karlsruhe @sourisnumerique @enorouzi
-
In our last #ISE2024 lecture before the summer break, we were focusing on artificial neural networks and deep learning. Their foundations already date back to the 1940s, when Warren McCulloch and Walter Pitts presented a mathematical model of the neuron.
W. McCulloch, W. Pitts (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bull. of Math. Biophysics. 5 (4): 115–133. https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf
#neuralnetwork #AI @fizise @fiz_karlsruhe @sourisnumerique @enorouzi
-
In our last #ISE2024 lecture before the summer break, we were focusing on artificial neural networks and deep learning. Their foundations already date back to the 1940s, when Warren McCulloch and Walter Pitts presented a mathematical model of the neuron.
W. McCulloch, W. Pitts (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bull. of Math. Biophysics. 5 (4): 115–133. https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf
#neuralnetwork #AI @fizise @fiz_karlsruhe @sourisnumerique @enorouzi
-
Summarizing our very brief #HistoryOfAI which was published here for several weeks in a series of toots , let's have a look at the popularity dynamics of symbolic vs subsymbolic AI put into perspective with historical AI hay-days and winters via the Google ngram viewer.
https://books.google.com/ngrams/graph?content=ontology%2Cneural+network%2Cmachine+learning%2Cexpert+system&year_start=1955&year_end=2022&corpus=en&smoothing=3&case_insensitive=false#ISE2024 #AI #ontologies #machinelearning #neuralnetworks #llms @fizise @sourisnumerique @enorouzi #semanticweb #knowledgegraphs
-
Summarizing our very brief #HistoryOfAI which was published here for several weeks in a series of toots , let's have a look at the popularity dynamics of symbolic vs subsymbolic AI put into perspective with historical AI hay-days and winters via the Google ngram viewer.
https://books.google.com/ngrams/graph?content=ontology%2Cneural+network%2Cmachine+learning%2Cexpert+system&year_start=1955&year_end=2022&corpus=en&smoothing=3&case_insensitive=false#ISE2024 #AI #ontologies #machinelearning #neuralnetworks #llms @fizise @sourisnumerique @enorouzi #semanticweb #knowledgegraphs
-
Summarizing our very brief #HistoryOfAI which was published here for several weeks in a series of toots , let's have a look at the popularity dynamics of symbolic vs subsymbolic AI put into perspective with historical AI hay-days and winters via the Google ngram viewer.
https://books.google.com/ngrams/graph?content=ontology%2Cneural+network%2Cmachine+learning%2Cexpert+system&year_start=1955&year_end=2022&corpus=en&smoothing=3&case_insensitive=false#ISE2024 #AI #ontologies #machinelearning #neuralnetworks #llms @fizise @sourisnumerique @enorouzi #semanticweb #knowledgegraphs
-
Summarizing our very brief #HistoryOfAI which was published here for several weeks in a series of toots , let's have a look at the popularity dynamics of symbolic vs subsymbolic AI put into perspective with historical AI hay-days and winters via the Google ngram viewer.
https://books.google.com/ngrams/graph?content=ontology%2Cneural+network%2Cmachine+learning%2Cexpert+system&year_start=1955&year_end=2022&corpus=en&smoothing=3&case_insensitive=false#ISE2024 #AI #ontologies #machinelearning #neuralnetworks #llms @fizise @sourisnumerique @enorouzi #semanticweb #knowledgegraphs
-
We are recruiting for the position of a PhD/Junior Researcher or PostDoc/Senior Researcher with focus on knowledge graphs and large language models connected to applications in the domains of cultural heritage & digital humanities.
Join our @fizise research team at @fiz_karlsruhe
@tabea @sashabruns @MahsaVafaie @GenAsefa @enorouzi @sourisnumerique @heikef #knowledgegraphs #llms #generativeAI #culturalHeritage #dh #joboffer #AI #ISE2024 #PhD #ISWS2024 -
We are recruiting for the position of a PhD/Junior Researcher or PostDoc/Senior Researcher with focus on knowledge graphs and large language models connected to applications in the domains of cultural heritage & digital humanities.
Join our @fizise research team at @fiz_karlsruhe
@tabea @sashabruns @MahsaVafaie @GenAsefa @enorouzi @sourisnumerique @heikef #knowledgegraphs #llms #generativeAI #culturalHeritage #dh #joboffer #AI #ISE2024 #PhD #ISWS2024 -
We are recruiting for the position of a PhD/Junior Researcher or PostDoc/Senior Researcher with focus on knowledge graphs and large language models connected to applications in the domains of cultural heritage & digital humanities.
Join our @fizise research team at @fiz_karlsruhe
@tabea @sashabruns @MahsaVafaie @GenAsefa @enorouzi @sourisnumerique @heikef #knowledgegraphs #llms #generativeAI #culturalHeritage #dh #joboffer #AI #ISE2024 #PhD #ISWS2024 -
We are recruiting for the position of a PhD/Junior Researcher or PostDoc/Senior Researcher with focus on knowledge graphs and large language models connected to applications in the domains of cultural heritage & digital humanities.
Join our @fizise research team at @fiz_karlsruhe
@tabea @sashabruns @MahsaVafaie @GenAsefa @enorouzi @sourisnumerique @heikef #knowledgegraphs #llms #generativeAI #culturalHeritage #dh #joboffer #AI #ISE2024 #PhD #ISWS2024 -
In 2022 with the advent of ChatGPT, large language models and AI in general gained an unprecedented popularity. It combined InstructGPT, a GPT-3 model complemented and fine-tuned with reinforcement learning feedback, Codex text2code, plus a massive engineering effort.
N. Lambert, et al. (2022). Illustrating Reinforcement Learning from Human Feedback (RLHF). https://huggingface.co/blog/rlhf
#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt #llms
-
In 2022 with the advent of ChatGPT, large language models and AI in general gained an unprecedented popularity. It combined InstructGPT, a GPT-3 model complemented and fine-tuned with reinforcement learning feedback, Codex text2code, plus a massive engineering effort.
N. Lambert, et al. (2022). Illustrating Reinforcement Learning from Human Feedback (RLHF). https://huggingface.co/blog/rlhf
#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt #llms
-
In 2022 with the advent of ChatGPT, large language models and AI in general gained an unprecedented popularity. It combined InstructGPT, a GPT-3 model complemented and fine-tuned with reinforcement learning feedback, Codex text2code, plus a massive engineering effort.
N. Lambert, et al. (2022). Illustrating Reinforcement Learning from Human Feedback (RLHF). https://huggingface.co/blog/rlhf
#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt #llms
-
In 2022 with the advent of ChatGPT, large language models and AI in general gained an unprecedented popularity. It combined InstructGPT, a GPT-3 model complemented and fine-tuned with reinforcement learning feedback, Codex text2code, plus a massive engineering effort.
N. Lambert, et al. (2022). Illustrating Reinforcement Learning from Human Feedback (RLHF). https://huggingface.co/blog/rlhf
#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt #llms
-
Higher, faster, farther... in 2021 Generative AI gains momentum with the advent of DaLL-E, a GPT-3 based zero-shot text2image model, and other major milestones, as e.g., GitHub CoPilot, Open AI Codex, WebGPT, and Google LaMDA.
Codex: Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code, https://arxiv.org/abs/2107.03374
DaLL-E: Ramesh, A.et al. (2021). Zero-Shot Text-to-Image Generation, https://arxiv.org/abs/2107.03374#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt
-
Higher, faster, farther... in 2021 Generative AI gains momentum with the advent of DaLL-E, a GPT-3 based zero-shot text2image model, and other major milestones, as e.g., GitHub CoPilot, Open AI Codex, WebGPT, and Google LaMDA.
Codex: Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code, https://arxiv.org/abs/2107.03374
DaLL-E: Ramesh, A.et al. (2021). Zero-Shot Text-to-Image Generation, https://arxiv.org/abs/2107.03374#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt
-
Higher, faster, farther... in 2021 Generative AI gains momentum with the advent of DaLL-E, a GPT-3 based zero-shot text2image model, and other major milestones, as e.g., GitHub CoPilot, Open AI Codex, WebGPT, and Google LaMDA.
Codex: Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code, https://arxiv.org/abs/2107.03374
DaLL-E: Ramesh, A.et al. (2021). Zero-Shot Text-to-Image Generation, https://arxiv.org/abs/2107.03374#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt
-
Higher, faster, farther... in 2021 Generative AI gains momentum with the advent of DaLL-E, a GPT-3 based zero-shot text2image model, and other major milestones, as e.g., GitHub CoPilot, Open AI Codex, WebGPT, and Google LaMDA.
Codex: Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code, https://arxiv.org/abs/2107.03374
DaLL-E: Ramesh, A.et al. (2021). Zero-Shot Text-to-Image Generation, https://arxiv.org/abs/2107.03374#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #llm #gpt
-
In 2020, GPT-3 was released by OpenAI, based on 45TB data crawled from the web. A “data quality” predictor was trained to boil down the training data to 550GB “high quality” data. Learning from the prompt (few-shot learning) was also introduced.
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS 2020, pp.1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
#HistoryOfAI #AI #ISE2024 #llms #gpt #lecture @enorouzi @sourisnumerique @fizise
-
In 2020, GPT-3 was released by OpenAI, based on 45TB data crawled from the web. A “data quality” predictor was trained to boil down the training data to 550GB “high quality” data. Learning from the prompt (few-shot learning) was also introduced.
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS 2020, pp.1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
#HistoryOfAI #AI #ISE2024 #llms #gpt #lecture @enorouzi @sourisnumerique @fizise
-
In 2020, GPT-3 was released by OpenAI, based on 45TB data crawled from the web. A “data quality” predictor was trained to boil down the training data to 550GB “high quality” data. Learning from the prompt (few-shot learning) was also introduced.
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS 2020, pp.1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
#HistoryOfAI #AI #ISE2024 #llms #gpt #lecture @enorouzi @sourisnumerique @fizise
-
In 2020, GPT-3 was released by OpenAI, based on 45TB data crawled from the web. A “data quality” predictor was trained to boil down the training data to 550GB “high quality” data. Learning from the prompt (few-shot learning) was also introduced.
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS 2020, pp.1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
#HistoryOfAI #AI #ISE2024 #llms #gpt #lecture @enorouzi @sourisnumerique @fizise
-
In 2019, OpenAI released GPT-2 as a direct scale-up of GPT, comprising 1.5B parameters and trained on 8M web pages.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.
https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
OpenAI blog post: https://openai.com/index/better-language-models/
GPT-2 on HuggingFace: https://huggingface.co/openai-community/gpt2#HistoryOfAI #AI #llm #ISE2024 @fizise @enorouzi @sourisnumerique #gpt
-
In 2019, OpenAI released GPT-2 as a direct scale-up of GPT, comprising 1.5B parameters and trained on 8M web pages.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.
https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
OpenAI blog post: https://openai.com/index/better-language-models/
GPT-2 on HuggingFace: https://huggingface.co/openai-community/gpt2#HistoryOfAI #AI #llm #ISE2024 @fizise @enorouzi @sourisnumerique #gpt
-
In 2019, OpenAI released GPT-2 as a direct scale-up of GPT, comprising 1.5B parameters and trained on 8M web pages.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.
https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
OpenAI blog post: https://openai.com/index/better-language-models/
GPT-2 on HuggingFace: https://huggingface.co/openai-community/gpt2#HistoryOfAI #AI #llm #ISE2024 @fizise @enorouzi @sourisnumerique #gpt
-
In 2019, OpenAI released GPT-2 as a direct scale-up of GPT, comprising 1.5B parameters and trained on 8M web pages.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners.
https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf
OpenAI blog post: https://openai.com/index/better-language-models/
GPT-2 on HuggingFace: https://huggingface.co/openai-community/gpt2#HistoryOfAI #AI #llm #ISE2024 @fizise @enorouzi @sourisnumerique #gpt
-
In 2018, Generative Pre-trained Transformers (GPT, by OpenAI) and Bidirectional Encoder Representations from Transformers (BERT, by Google) are introduced.
Radford, A. et al (2018). Improving language understanding by generative pre-training, https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
J. Devlin et al (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, ACL 2019, https://aclanthology.org/N19-1423
#HistoryOfAI #ISE2024 #AI #llm @fizise @enorouzi @sourisnumerique
-
In 2018, Generative Pre-trained Transformers (GPT, by OpenAI) and Bidirectional Encoder Representations from Transformers (BERT, by Google) are introduced.
Radford, A. et al (2018). Improving language understanding by generative pre-training, https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
J. Devlin et al (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, ACL 2019, https://aclanthology.org/N19-1423
#HistoryOfAI #ISE2024 #AI #llm @fizise @enorouzi @sourisnumerique
-
In 2018, Generative Pre-trained Transformers (GPT, by OpenAI) and Bidirectional Encoder Representations from Transformers (BERT, by Google) are introduced.
Radford, A. et al (2018). Improving language understanding by generative pre-training, https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
J. Devlin et al (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, ACL 2019, https://aclanthology.org/N19-1423
#HistoryOfAI #ISE2024 #AI #llm @fizise @enorouzi @sourisnumerique
-
In 2018, Generative Pre-trained Transformers (GPT, by OpenAI) and Bidirectional Encoder Representations from Transformers (BERT, by Google) are introduced.
Radford, A. et al (2018). Improving language understanding by generative pre-training, https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
J. Devlin et al (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, ACL 2019, https://aclanthology.org/N19-1423
#HistoryOfAI #ISE2024 #AI #llm @fizise @enorouzi @sourisnumerique
-
In 2014 Attention mechanisms were introduced by Bahdanau, Cho, and Bengio, which allow models to selectively focus on specific parts of the input. In 2017, the Transformer model introduced by Ashish Vaswani et al. followed, which learns to encode and decode sequential information especially effective for tasks like machine translation and #NLP.
Attention: https://arxiv.org/pdf/1409.0473
Transformers: https://arxiv.org/pdf/1706.03762#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #transformers
-
In 2014 Attention mechanisms were introduced by Bahdanau, Cho, and Bengio, which allow models to selectively focus on specific parts of the input. In 2017, the Transformer model introduced by Ashish Vaswani et al. followed, which learns to encode and decode sequential information especially effective for tasks like machine translation and #NLP.
Attention: https://arxiv.org/pdf/1409.0473
Transformers: https://arxiv.org/pdf/1706.03762#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #transformers
-
In 2014 Attention mechanisms were introduced by Bahdanau, Cho, and Bengio, which allow models to selectively focus on specific parts of the input. In 2017, the Transformer model introduced by Ashish Vaswani et al. followed, which learns to encode and decode sequential information especially effective for tasks like machine translation and #NLP.
Attention: https://arxiv.org/pdf/1409.0473
Transformers: https://arxiv.org/pdf/1706.03762#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #transformers
-
In 2014 Attention mechanisms were introduced by Bahdanau, Cho, and Bengio, which allow models to selectively focus on specific parts of the input. In 2017, the Transformer model introduced by Ashish Vaswani et al. followed, which learns to encode and decode sequential information especially effective for tasks like machine translation and #NLP.
Attention: https://arxiv.org/pdf/1409.0473
Transformers: https://arxiv.org/pdf/1706.03762#HistoryOfAI #AI #ISE2024 @fizise @sourisnumerique @enorouzi #transformers
-
In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
-
In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
-
In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
-
In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
-
In 1999, NVIDIA introduced the first Graphical Processing Unit (GPU) card Nvidia Geforce 256, enabling an unprecedented speedup for parallel computations as required for machine learning. This innovation paved the way for the rapid advancement of deep learning algorithms.
John Peddie, Famous Graphics Chips: Nvidia’s GeForce 256, IEEE Computer Society.
https://www.computer.org/publications/tech-news/chasing-pixels/nvidias-geforce-256#HistoryOfAI #ISE2024 #AI #deeplearning #machinelearning #lecture @sourisnumerique @enorouzi @fizise @fiz_karlsruhe
-
In 1999, NVIDIA introduced the first Graphical Processing Unit (GPU) card Nvidia Geforce 256, enabling an unprecedented speedup for parallel computations as required for machine learning. This innovation paved the way for the rapid advancement of deep learning algorithms.
John Peddie, Famous Graphics Chips: Nvidia’s GeForce 256, IEEE Computer Society.
https://www.computer.org/publications/tech-news/chasing-pixels/nvidias-geforce-256#HistoryOfAI #ISE2024 #AI #deeplearning #machinelearning #lecture @sourisnumerique @enorouzi @fizise @fiz_karlsruhe
-
In 1999, NVIDIA introduced the first Graphical Processing Unit (GPU) card Nvidia Geforce 256, enabling an unprecedented speedup for parallel computations as required for machine learning. This innovation paved the way for the rapid advancement of deep learning algorithms.
John Peddie, Famous Graphics Chips: Nvidia’s GeForce 256, IEEE Computer Society.
https://www.computer.org/publications/tech-news/chasing-pixels/nvidias-geforce-256#HistoryOfAI #ISE2024 #AI #deeplearning #machinelearning #lecture @sourisnumerique @enorouzi @fizise @fiz_karlsruhe
-
In 1999, NVIDIA introduced the first Graphical Processing Unit (GPU) card Nvidia Geforce 256, enabling an unprecedented speedup for parallel computations as required for machine learning. This innovation paved the way for the rapid advancement of deep learning algorithms.
John Peddie, Famous Graphics Chips: Nvidia’s GeForce 256, IEEE Computer Society.
https://www.computer.org/publications/tech-news/chasing-pixels/nvidias-geforce-256#HistoryOfAI #ISE2024 #AI #deeplearning #machinelearning #lecture @sourisnumerique @enorouzi @fizise @fiz_karlsruhe
-
In 1996, Long Short-Term Memory (LSTM) Recurrent Neural Networks are introduced by Sepp Hochreiter and Jürgen Schmidhuber, which efficiently enabled #neuralnetworks to process sequences of data (instead of single data points) being able to learn from data and to generate text.
Hochreiter, Sepp; Schmidhuber, Juergen (1996). LSTM can solve hard long time lag problems. Advances in NIPS, pp. 473–479.
https://dl.acm.org/doi/10.5555/2998981.2999048#HistoryOfAI #AI #ISE2024 #lecture @sourisnumerique @enorouzi @fizise
-
In 1996, Long Short-Term Memory (LSTM) Recurrent Neural Networks are introduced by Sepp Hochreiter and Jürgen Schmidhuber, which efficiently enabled #neuralnetworks to process sequences of data (instead of single data points) being able to learn from data and to generate text.
Hochreiter, Sepp; Schmidhuber, Juergen (1996). LSTM can solve hard long time lag problems. Advances in NIPS, pp. 473–479.
https://dl.acm.org/doi/10.5555/2998981.2999048#HistoryOfAI #AI #ISE2024 #lecture @sourisnumerique @enorouzi @fizise
-
In 1996, Long Short-Term Memory (LSTM) Recurrent Neural Networks are introduced by Sepp Hochreiter and Jürgen Schmidhuber, which efficiently enabled #neuralnetworks to process sequences of data (instead of single data points) being able to learn from data and to generate text.
Hochreiter, Sepp; Schmidhuber, Juergen (1996). LSTM can solve hard long time lag problems. Advances in NIPS, pp. 473–479.
https://dl.acm.org/doi/10.5555/2998981.2999048#HistoryOfAI #AI #ISE2024 #lecture @sourisnumerique @enorouzi @fizise
-
In 1996, Long Short-Term Memory (LSTM) Recurrent Neural Networks are introduced by Sepp Hochreiter and Jürgen Schmidhuber, which efficiently enabled #neuralnetworks to process sequences of data (instead of single data points) being able to learn from data and to generate text.
Hochreiter, Sepp; Schmidhuber, Juergen (1996). LSTM can solve hard long time lag problems. Advances in NIPS, pp. 473–479.
https://dl.acm.org/doi/10.5555/2998981.2999048#HistoryOfAI #AI #ISE2024 #lecture @sourisnumerique @enorouzi @fizise
-
In 1994, Tim Berners-Lee introduced the #SemanticWeb in his plenary presentation at the 1st WWW conference in Geneva, Switzerland.
“I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A Semantic Web, which makes this possible, has yet to emerge..."
Slides from TBL, 1994: https://www.w3.org/Talks/WWW94Tim/
#HistoryOfAI #AI #knowledgegraphs #lexture #ISE2024 @sourisnumerique @enorouzi
-
In 1994, Tim Berners-Lee introduced the #SemanticWeb in his plenary presentation at the 1st WWW conference in Geneva, Switzerland.
“I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A Semantic Web, which makes this possible, has yet to emerge..."
Slides from TBL, 1994: https://www.w3.org/Talks/WWW94Tim/
#HistoryOfAI #AI #knowledgegraphs #lexture #ISE2024 @sourisnumerique @enorouzi
-
In 1994, Tim Berners-Lee introduced the #SemanticWeb in his plenary presentation at the 1st WWW conference in Geneva, Switzerland.
“I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A Semantic Web, which makes this possible, has yet to emerge..."
Slides from TBL, 1994: https://www.w3.org/Talks/WWW94Tim/
#HistoryOfAI #AI #knowledgegraphs #lexture #ISE2024 @sourisnumerique @enorouzi
-
In 1994, Tim Berners-Lee introduced the #SemanticWeb in his plenary presentation at the 1st WWW conference in Geneva, Switzerland.
“I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A Semantic Web, which makes this possible, has yet to emerge..."
Slides from TBL, 1994: https://www.w3.org/Talks/WWW94Tim/
#HistoryOfAI #AI #knowledgegraphs #lexture #ISE2024 @sourisnumerique @enorouzi
-
In 1968, Terry Winograd introduced SHRDLU, a natural language understanding agent that was able to plan and execute directives in rudimentary 'block world'. In particular, SHRDLU emphasized the importance of user-friendly interfaces for HCI.
T. Winograd (1970). Procedures as a Representation for Data in a Computer Program for Understanding Natural Language", MIT AI Technical Report 235. https://web.archive.org/web/20201003212106/http://dspace.mit.edu/bitstream/handle/1721.1/7095/AITR-235.pdf
#HistoryOfAI #AI #lecture #ISE2024 @fiz_karlsruhe @sourisnumerique @enorouzi
-
In 1968, Terry Winograd introduced SHRDLU, a natural language understanding agent that was able to plan and execute directives in rudimentary 'block world'. In particular, SHRDLU emphasized the importance of user-friendly interfaces for HCI.
T. Winograd (1970). Procedures as a Representation for Data in a Computer Program for Understanding Natural Language", MIT AI Technical Report 235. https://web.archive.org/web/20201003212106/http://dspace.mit.edu/bitstream/handle/1721.1/7095/AITR-235.pdf
#HistoryOfAI #AI #lecture #ISE2024 @fiz_karlsruhe @sourisnumerique @enorouzi
-
In 1968, Terry Winograd introduced SHRDLU, a natural language understanding agent that was able to plan and execute directives in rudimentary 'block world'. In particular, SHRDLU emphasized the importance of user-friendly interfaces for HCI.
T. Winograd (1970). Procedures as a Representation for Data in a Computer Program for Understanding Natural Language", MIT AI Technical Report 235. https://web.archive.org/web/20201003212106/http://dspace.mit.edu/bitstream/handle/1721.1/7095/AITR-235.pdf
#HistoryOfAI #AI #lecture #ISE2024 @fiz_karlsruhe @sourisnumerique @enorouzi
-
In 1968, Terry Winograd introduced SHRDLU, a natural language understanding agent that was able to plan and execute directives in rudimentary 'block world'. In particular, SHRDLU emphasized the importance of user-friendly interfaces for HCI.
T. Winograd (1970). Procedures as a Representation for Data in a Computer Program for Understanding Natural Language", MIT AI Technical Report 235. https://web.archive.org/web/20201003212106/http://dspace.mit.edu/bitstream/handle/1721.1/7095/AITR-235.pdf
#HistoryOfAI #AI #lecture #ISE2024 @fiz_karlsruhe @sourisnumerique @enorouzi