Test 10 Helped Invent Generative AI. Now He Wants to Save It
In 2016, Google engineer Illia Polosukhin had lunch with a colleague, Jacob Uszkoreit. Polosukhin had been frustrated by a lack of progress in his project, using AI to provide useful answers to questions posed by users, and Uszkoreit suggested he try a technique he had been brainstorming that he called self-attention. Thus began an 8-person collaboration that ultimately resulted in a 2017 paper called “Attention Is All You Need,” which introduced the concept of transformers as a way to supercharge artificial intelligence. It changed the world.
What is generative AI?
For a more technical generative AI definition, Forrester describes it as “a set of technologies and techniques that leverage a very large corpus of data, including large language models (LLMs) like GPT-3, to generate new content.”
- Generative artificial intelligence (GenAI) is an AI-powered technology that uses extensive libraries of information to generate new things, like stories, pictures, videos, music, and software code
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| wdaw | dwadw | dawd | dadaaw |
| dwadawd | fsesfefsefsefs | dsefes esfes sefse sef | fegsseg s8e4 89se 9se948 98se |
| Traditional AI | Generative AI |
|---|---|
| Task-specific and rule-based | Task-specific and rule-based |
| Task-specific and rule-based | Task-specific and rule-based |
The difference between traditional AI and generative AI is that traditional AI uses machine learning, predefined rules, and programmed logic to perform specific tasks, whereas generative AI learns from large datasets to create human-like content. For example:
- Traditional AI can make ticketing systems more efficient by identifying the customer sentiment, intent, and language of service requests, automatically routing them to the right agent based on predetermined criteria (such as expertise, capabilities, and availability).
- Generative AI boosts agent productivity by providing intelligent writing tools, allowing teams to address requests more efficiently and provide consistent support.
Traditional AI can make ticketing systems more efficient by identifying the customer sentiment, intent, and language of service requests, automatically routing them to the right agent based on predetermined criteria (such as expertise, capabilities, and availability).
Generative AI boosts agent productivity by providing intelligent writing tools, allowing teams to address requests more efficiently and provide consistent support.
Traditional AI can make ticketing systems more efficient by identifying the customer sentiment, intent, and language of service requests, automatically routing them to the right agent based on predetermined criteria (such as expertise, capabilities, and availability).
Generative AI boosts agent productivity by providing intelligent writing tools, allowing teams to address requests more efficiently and provide consistent support.