From chatbots to deepfakes, here is the lowdown on the current state of artificial intelligence
Barely a day goes by without some new story about AI, or artificial intelligence. The excitement about it is palpable – the possibilities, some say, are endless. Fears about it are spreading fast, too.
There can be much assumed knowledge and understanding about AI, which can be bewildering for people who have not followed every twist and turn of the debate.
Reinforcement learning
Perhaps the most basic form of training there is, reinforcement learning involves giving feedback each time the system performs a task, so that it learns from doing things correctly. It can be a slow and expensive process, but for systems that interact with the real world, there is sometimes no better way.
Large-language models
This is one of the so-called neural networks. Large-language models are trained by pouring into them billions of words of everyday text, gathered from sources ranging from books to tweets and everything in between. The LLMs draw on all this material to predict words and sentences in certain sequences.
Generative adversarial networks (GANs)
This is a way of pairing two neural networks together to make something new. The networks are used in creative work in music, visual art or film-making. One network is given the role of creator while a second is given the role of marker, and the first then learns to create things that the second will approve of.
Symbolic AI
There are even AI techniques that look to the past for inspiration. Symbolic AI is an approach that rejects the idea that a simple neural network is the best option, and tries to mix machine learning with more diligently structured facts about the world.
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From chatbots to deepfakes, here is the lowdown on the current state of artificial intelligence
Barely a day goes by without some new story about AI, or artificial intelligence. The excitement about it is palpable – the possibilities, some say, are endless. Fears about it are spreading fast, too.
There can be much assumed knowledge and understanding about AI, which can be bewildering for people who have not followed every twist and turn of the debate.
Reinforcement learningPerhaps the most basic form of training there is, reinforcement learning involves giving feedback each time the system performs a task, so that it learns from doing things correctly. It can be a slow and expensive process, but for systems that interact with the real world, there is sometimes no better way.
Large-language modelsThis is one of the so-called neural networks. Large-language models are trained by pouring into them billions of words of everyday text, gathered from sources ranging from books to tweets and everything in between. The LLMs draw on all this material to predict words and sentences in certain sequences.
Generative adversarial networks (GANs)This is a way of pairing two neural networks together to make something new. The networks are used in creative work in music, visual art or film-making. One network is given the role of creator while a second is given the role of marker, and the first then learns to create things that the second will approve of.
Symbolic AIThere are even AI techniques that look to the past for inspiration. Symbolic AI is an approach that rejects the idea that a simple neural network is the best option, and tries to mix machine learning with more diligently structured facts about the world. Continue reading…Technology | The Guardian