Interested in a full rolling history of Public Health England #Covid#Metrics as published each day during the entirety of the pandemic? DM for details.
In our view, these are the #Top 3 strong use cases for #LLMs like #ChatGPT
1️⃣ As a next generation 🔎 search engine
2️⃣ As a generator of scenarios 🌅 to “role play” or plan the future
3️⃣ To enhance content creation (not replace it entirely)
Information #overload in your daily life? You are not alone. Context switching between apps and views can be time and energy draining.
We are building apps that integrate with other apps and data, starting with email 📧
https://t.co/ZV3qh2hLhx
https://t.co/y9e7ieZrJU It’s just the start of copyright issues with #AI. The likeliness of so much intellectual property is embedded in the model training data
Let's go even deeper on LINQ (Language Integrated Query) and how it enables developers to write query expressions with a declarative query syntax that allow them to perform filtering, ordering, grouping, and so much more with minimal code. Jump in. 🎥 https://t.co/6w6dFf9DMK
"A normal data centre needs 32 megawatts of power flowing into the building. For an AI data centre it's 80 megawatts," says Mr Sharp. “All of this points to a problem. How can AI grow when it requires so much more power to function?” #AI#Energy https://t.co/ZHoKdSDrmm
#AI is all over social media, particularly generative AI. The future lies in the difference between supporting humans and automating the decisions they make https://t.co/QpEIAgMN6J
Let's repeat after me, LaMDA is not sentient. LaMDA is just a very big language model with 137B parameters and pre-trained on 1.56T words of public dialog data and web text. It looks like human, because is trained on human data.