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AI (Artificial Intelligence) towardsdatascience.com

A response to OpenAI's new dangerous text generator

Those of you following AI related things on Twitter have probably been overwhelmed with commentary about OpenAI’s new GPT-2 language model, which is “Too Dangerous to Make Public” (according to Wired’s interpretation of OpenAI’s statements). Is this discussion frustrating or confusing for you? Well, Ryan Lowe from McGill University has published a nice response article. He discusses the model and results in general, but also gives some perspective on the ethical implication and where the AI community should go from here. According to Lowe: “The machine learning community really, really needs to start talking openly about our standards for ethical research release”

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Claire Jaja talent.works

You only need 50% of job “requirements”

Claire Jaja (Manager of Data Science) at TalentWorks was curious about how many job requirements are actually required, so they analyzed job postings and resumes for more than 6,000 applications across 118 industries from their database. The results are quite interesting… Your chances of getting an interview start to go up once you meet about 40% of job requirements. and… You’re not any more likely to get an interview matching 90% of job requirements compared to matching just 50%. For women… …these numbers are about 10% lower i.e. women’s interview chances go up once they meet 30% of job requirements, and matching 40% of job requirements is as good as matching 90% for women.

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link Icon spectrum.ieee.org

LinkedIn reports dramatically increasing shortage of data scientists across U.S.

What a difference a few years makes. In 2015, a LinkedIn snapshot of what it calls the skills gap—a mismatch between the skills workers have and the skills employers seek—showed a national surplus in the United States of people with data science skills; as of August 2018, LinkedIn data shows a dramatic shortage. It’s a good time to be alive a Practical AI listener. 😉

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Kubernetes blog.openai.com

Scaling Kubernetes to 2,500 Nodes

Are you really pushing Kubernetes? No? OpenAI is… We’ve been running Kubernetes for deep learning research for over two years. While our largest-scale workloads manage bare cloud VMs directly, Kubernetes provides a fast iteration cycle, reasonable scalability, and a lack of boilerplate which makes it ideal for most of our experiments. We now operate several Kubernetes clusters (some in the cloud and some on physical hardware), the largest of which we’ve pushed to over 2,500 nodes. This cluster runs in Azure on a combination of D15v2 and NC24 VMs.

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Machine Learning varianceexplained.org

What's the difference between data science, machine learning, and AI?

We’ve needed this post for a very long time. Thank you David Robinson. When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” But that overlap, tho. The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable. Most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words. Here’s the break down…

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link Icon blog.floydhub.com

Turning Design Mockups Into Code With Deep Learning

How do you you teach a neural network to code? One screenshot with matching HTML at a time. 😂 Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code. Currently, the largest barrier to automating front-end development is computing power.

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