interesting analysis @hwchase17 - though you've forgotten your friends at Haystack 😉
We seem to share the view that blending "AI workflows" with "agentic" capabilities gives best results right now, and we extended Haystack with many carefully chosen agentic capabilities over the last year.
As the thread here indicates, there are plenty of directions agent frameworks can focus and evolve – how about we get together with @jerryjliu0@samuelcolvin@MarcKlingen on a live panel to discuss?
Would for sure be a panel with many hot takes and those are typically the most interesting ones 😅
Excited about machine learning? Want to support the energy transition?
We have two openings for PhD students/Postdocs:
https://t.co/6SxNTDoa9c
(Application Deadline: 30.11.2022; early applications highly encouraged) #machinelearn…https://t.co/p66O3zIF9p https://t.co/EeF6i9RKP7
. @MarcTimme, @David_Storch_ and colleagues (@cfaed_TUD, @tudresden_de) introduce a demand-driven approach for the generation of bike path networks that simultaneously balances cyclist demand and safety preferences (https://t.co/utMFa5EHHt). https://t.co/5LZUyfqcA1
How can we design ride-sharing services for more sustainable urban mobility? In our new paper, we study collective interactions among ride-sharing users to find answers 🚌 Joint work with H. Wolf, @MarcTimme, M. Schröder from @cfaed_TUD via @PhysRevE
https://t.co/Ej4cCdkrXo
Research published in @NatureComms suggests that a moderate increase in financial incentives for people to use ride-sharing services may have a substantial effect on their adoption. https://t.co/UxWlaLizYq