Matti Lyra

@DanielReichman6 @tallinzen Wouldn’t that have made it longer? 🤣🤣

Some thoughts about the NYT GPT-3 article (I haven't read the responses to the article yet):

… and a valid legal document then why would I accept the common language texts as being generated by “an intelligence”? It is, as pointed out out by @emilymbender, me doing the work interpreting the texts, where I can.

The only reason I’m willing to posit some “intelligence” to the LLM creating the language output is because those texts have a familiar context to them, broadly, the human experience. BUT, if I can’t differentiate between random legalese garbage from a LLM … /3

“When we encounter something that seems to be speaking our language, … we use the skills associated with using that language to communicate with other people. Those skills involve … joint attention so we imagine a mind behind the language even when it is not there.”

@dark_matter88_ @tttthomasssss My team at Zalandoin Berlin is looking for applied scientists jobs.zalando.com/en/jobs/390769…. Zalando is also hiring applied scientists across Europe at least in Berlin, Helsinki, Dublin and Zürich

As many Ukrainian IT engineers right now I was left without a job 😔I am a Data Scientist with 5 years of experience in full-cycle ML models development (including deployment) and in managing data science teams. If anyone sees job positions I could fit, ping me 🙏❤️

We're looking for a software engineer to join our brand new NLP center of excellence at Zalando. jobs.zalando.com/en/jobs/228264…

Quite simply the best talk I've seen about writing in an academic context: "your readers don't care about your problem, you need to care about theirs" youtu.be/vtIzMaLkCaM

Love #PyData talks? Want to bring your vision to the the next biggest PyData? Sign up to review proposals for #PyDataGlobal2020! forms.gle/4NxP6cyipEDbCi…

You have 🚨10 days🚨 left to submit a proposal for #PydataGlobal2020 global.pydata.org/pages/cfp.html

Some folks over at @Rasa_HQ also got to play with GPT-3 so we wrote a blogpost about some of our first findings. There were impressive moments but algorithmic bias is definately a problem. Here's some careful first impressions. blog.rasa.com/gpt-3-careful-…

A "tell-all" account of why improving @SemanticScholar #search is not as simple as you might think... Dealing with dirty data, feature engineering, proper evaluation, posthoc correction, and more in this article by @SergeyFeldman today on the AI2 Blog: medium.com/ai2-blog/build…

@SashoSavkov As soon as you think about deploying ML. If you don't know what to measure from a business perspective then you haven't thought through what the system should be doing, which puts you in the research phase. That ought to be communicated to manage expectations.

@mattilyra I think burning money is exactly what it is but many companies do exactly that not just with ML teams. The problem is that it does not make sense in the long term and companies are struggling with recognising when it is time to invest in detailed evaluation.

Are you struggling with ML system monitoring after an initial deployment? This is one of the best articles I've seen on the topic (it's long though) #why" target="_blank" rel="nofollow noopener">christophergs.com/machine%20lear…

@SashoSavkov ... and you're not even heating the office but some data center somewhere

@SashoSavkov Depending on the business somewhere between burning through your funding, losing customer trust and being fined or shutdown by a regulator for non-compliance. Funding an ML team that's not measured against business metrics (unless their explicit purpose is to do research):

PyData Global CFP deadline is August 2nd! global.pydata.org//pages/cfp.html

AI == compute. Here's a couple more in a similar vein arxiv.org/abs/2005.04305 and arxiv.org/abs/1806.00610. @Miles_Brundage has written quite a bit about measuring progress in AI.