One such competitor, LaMDA, is the work of Google’s AI division. Built on Transformer – the company’s open-source neural network architecture – it can produce non-generic, open-ended dialogue after training on 1.56 trillion words of multi-content, public data and web text. By contrast, a typical chatbot is dependent on topic-specific datasets and has a limited conversation flow. LaMDA has 137 billion parameters, which can be thought of as the individual “synapses” combining to form the AI.
The sheer scale and complexity of models like LaMDA is leading some experts to ask profound questions about the nature of AI. In February, the Chief Scientist and Co-Founder of OpenAI, one of the leading research labs for artificial intelligence, claimed that the latest generation of neural networks are now large enough to be “slightly conscious”.
This month, another expert in machine learning has spoken out. Blake Lemoine, Senior Software Engineer at Google, believes that a form of self-awareness might be starting to emerge from the billions of connected parameters.
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