Key takeaways
- LLM: Large Language Models predict next token in the sentence
- Output is not deterministic
- the output will always be different
- Have turned out to be highly versatile in its application
- No lift and shift technology for businesses
- Presonalisation of daily activities for employees
- Personalisation of support and communication with clients
- Main responsibility when checking the output of AI
- Accuracy & trustworthiness
- Common sens
- Ambiguity
- Bias
- Moving from creating to assessing the outcome of the AI
- The AI can do what we do when checking out on how to do things, save the time when we not necessarily create from scratch
- We can look outside, learn from those already moving fast. Not necessarily need to lead the ways, rather learn from others. But make sure we are moving.
- Move to Gen AI has to bring value
- A question to see how useful it is was asked by asking if people would pay a portion of their salary to keep the tool. A high number of people said they would
- Huge potential in the game industry with real complex time rendering for example
- What is necessary to be successful?
- You do nee to do a lot to prepare the output of AI with data training etc…
- An issue is that we don’t really know how the AI is biased up front
- We should focus on transition and how to u se AI as a mean to a better world
- There are big ethic concerns.
- Do we want to require to add a watermark to inform when something was generated with AI
- Sustainability question in the use of AI and its energy consumption.
- The scene is evolving
- Bing Chat Enterprise | copilot pro as an alternative to ChatGPT, can also easily export to Excel
- Github Copilot for developing. Code generation, Code explanation, code conversion
- Bot training on internal data for specific business task to get for example information out of a massive FAQ or How to in a more efficient way