AIC1.1 AlphaGo
AlphaGo is a computer program that was developed by Google DeepMind to play the game of Go. In 2016, AlphaGo defeated a professional Go player, Lee Sedol, in a series of matches. This was a major breakthrough for AI, as Go is a very complex game that was previously thought to be too difficult for computers to master.
AIC1.2 AlphaFold
AlphaFold is a protein folding prediction program that was developed by Google DeepMind. It is able to predict the structure of proteins with unprecedented accuracy. This could have a major impact on the fields of medicine and biology, as it could help scientists to better understand how proteins work and how they can be used to treat diseases.
AIC1Z1 Transformers big takeovers (previous community models)
Transformers are a type of neural network that are used for natural language processing tasks. They are particularly good at tasks such as machine translation, text summarization, and question answering. By big takeovers, transformer designers are referring to the increasing use of transformer models in a variety of tasks. Transformer models are a type of neural network that are particularly good at natural language processing tasks, such as machine translation, text summarization, and question answering. They have been shown to outperform other types of neural networks on these tasks, and they are becoming increasingly popular.
The use of transformer models is having a major impact on a variety of industries. For example, transformer models are being used to improve the accuracy of machine translation systems, which is making it easier for people to communicate across languages. Transformer models are also being used to improve the performance of text summarization systems, which is making it easier for people to find the information they need in large documents. And transformer models are being used to improve the accuracy of question answering systems, which is making it easier for people to get the information they need from the internet.
The increasing use of transformer models is having a major impact on the way we interact with the world. Transformer models are making it easier for us to communicate, to learn, and to get the information we need. And as transformer models continue to improve, they will have an even greater impact on our lives.
Here are some specific examples of how transformer models are being used to "take over" different tasks:
- Machine translation: Transformer models have been shown to outperform other types of neural networks on machine translation tasks. For example, the transformer model T5 was able to achieve state-of-the-art results on the WMT 2019 machine translation benchmark.
- Text summarization: Transformer models have also been shown to outperform other types of neural networks on text summarization tasks. For example, the transformer model BART was able to achieve state-of-the-art results on the CNN/Daily Mail summarization benchmark.
- Question answering: Transformer models have also been shown to outperform other types of neural networks on question answering tasks. For example, the transformer model GPT-3 was able to achieve state-of-the-art results on the SQuAD question answering benchmark.
AIC1Z2 Word2vec
Word2vec is a method for learning the vector representations of words. These vector representations can be used for a variety of tasks, such as text classification, sentiment analysis, and natural language generation.
AIC1Z3 WaveNet
WaveNet is a neural network that can be used to generate realistic speech. It is used in Google Assistant and other voice-activated products.
AIC1Z4 Sequence to sequence models
Sequence to sequence models are a type of neural network that can be used to translate text from one language to another. They are also used for tasks such as machine translation, text summarization, and question answering.
AIC1Z5 Distillation
Distillation is a technique that can be used to improve the performance of a machine learning model. It works by transferring the knowledge from a large, complex model to a smaller, simpler model.
AIC1Z6 Deep reinforcement learning
Deep reinforcement learning is a type of machine learning that can be used to train agents to learn how to behave in complex environments. It is used in a variety of applications, such as robotics, game playing, and finance.
AIC1Z7 Distributed systems and software frameworks
Distributed systems and software frameworks are used to develop and deploy large-scale machine learning models. They provide a way to manage the resources required to train and run these models, and they make it easier to develop and deploy models across multiple machines.
TensorFlow : JAX
There are many ways that teachers can use these technologies in the classroom. For example, they can use AlphaGo to teach students about the game of Go, or they can use Transformers to help students learn about natural language processing. They can also use Word2vec to help students learn about the vector representations of words, or they can use WaveNet to help students learn about how to generate realistic speech.
---- further Q&A qirh bard
I understand that if you are in biotechnolgy now that alphafold has gifted you a huge leap forward - 200000 protein database -which Hassibis estmates might have taken a billion years if humans without ai had needed to do this. While I dont immediately expect any of the other innovations will be that big! can you indicate which sorts of practioner apart from bitechnolgists should be following which type of ai model that google deep mind is now in centre of?
