Personally, I didn’t find the Minecraft demo very impressive, aside from the speed of inference. 20fps is quite an achievement, generating Minecraft screenshots, not as much.
Yeah… that’s Atlassian for you. Why sell just Jira when you can sell jira, confluence, bitbucket, opsgenie, and atlas as part of the Atlassian cloud platform.
This is the company that (already having owned jira) bought trello and did nothing with it for 5 years.
B) it can manage pipelines through those services. Each card change would kick off pipelines whose status updates would cause jira to further change cards potentially kicking off more ci/cd steps.
C) you absolutely can use it as a database, calendar, and a wiki.
It has an api and can store data. You can query cards by label, search by content, and extract structured information from them.
Nested and linked cards provide the tools needed to build wikis.
I’m not 100% sure what the difference between CRMs and support ticketing system are, but Jira instances have been used as support ticketing systems in order to give devs view into what customers want.
It’s not the best at any of those things, but those are all doable (and are being done) with jira.
people also use jira as a support ticket system.
Sprint planning system.
Kanban system.
I’ve seen it set up so that it can track work across kanban teams and scrum teams.
Jira has deep integration with bitbucket, confluence, and GitHub.
It can manage your CI pipelines as well.
Jira is an anything app with a bend towards project management.
Setting up jira workflows is a whole career.
Source: I worked at Atlassian for 5 years and they use jira as the backbone for _everything_. It all flows into jira.
Using ReLU instead of sigmoid is a significant departure with regards to how closely it models actual neurons.
Using non fully connected layers is as well. Our brains likely aren’t fully connected, but the connections that matter are made stronger through living life and learning.
If you squint, it’s kind of like training a dense series of linear layers, but that’s not what we’re doing anymore (for the better)
Comparing NNs to organic brains is an apples to oranges comparison, is what I’m saying.
precision wrt translation, especially when the translation is not 1-to-1, is not excellent with LLMs.
In fact, their lack of precision is what makes them so good at translating natural languages!