Brandon Jacobson

This is my first day on Morioh. I wish I knew about this sooner! Check out my Python Programming YouTube channel where I'm building my own digital assistant named S.H.A.N.E. like Jarvis from the Iron Man movies and comics.

#python #jarvis

 

https://www.youtube.com/channel/UCW34Ghe9-_TCA5Vy3-Agfnw

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Brandon Jacobson

Brandon Jacobson

This is my first day on Morioh. I wish I knew about this sooner! Check out my Python Programming YouTube channel where I'm building my own digital assistant named S.H.A.N.E. like Jarvis from the Iron Man movies and comics.

#python #jarvis

 

https://www.youtube.com/channel/UCW34Ghe9-_TCA5Vy3-Agfnw

Alfredo  Sipes

Alfredo Sipes

1618244160

AI, Analytics, ML, Data Science, Deep Learning Main Developments and Key Trends for 2021

2020 is finally coming to a close. While likely not to register as anyone’s favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.


ByMatthew Mayo ,KDnuggets.

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To the chagrin of absolutely no one, 2020 is finally drawing to a close. It has been a rollercoaster of a year, one defined almost exclusively by the COVID-19 pandemic. But other things have happened, including in the fields of AI, data science, and machine learning as well. To that end, it’s time for KDnuggets annual year end expert analysis and predictions. This year we posed the question:

What were the main developments in AI, Data Science, Machine Learning Research in 2020 and what key trends do you see for 2021?

#2020 dec opinions #2021 predictions #ai #ajit jaokar #analytics #brandon rohrer #daniel tunkelang #data science #deep learning #machine learning #pedro domingos #predictions #research #rosaria silipo

In Climate Conversations, Empathy is Everything (Brandon Leshchinskiy) S2E7

EPISODE 7: In Climate Conversations, Empathy is Everything (Brandon Leshchinskiy)
In our previous episode we met Professor Dava Newman, cofounder of the nonprofit group EarthDNA. Today’s guest is Brandon Leshchinskiy, a graduate student in Technology and Policy at MIT’s Institute for Data, Systems, and Society, who has helped Prof. Newman create the EarthDNA Ambassadors program, training young people in communication, negotiation, and storytelling to build support for individual and collective action on climate change. Leshchinskiy has crafted an engaging interactive presentation, called Climate 101, that creatively employs materials from various sources to examine climate change from scientific, economic, and civic perspectives. By teaching young people to deliver this presentation effectively, he is developing a cohort of trained climate educators who can in turn teach their peers to reach out to friends and family on one of humanity’s most pressing issues. In this episode, Leshchinskiy discusses why young people make effective climate ambassadors, how climate presentations can be made more powerful by customizing them with specific details that are relevant to people’s own communities, what we can learn from society’s response to the challenges of Covid-19, and how to avoid developing “doom fatigue” from exposure to negative news stories.

#developer

Sasha  Lee

Sasha Lee

1624593635

Principal Components: Alan Jacobson on Building Data Science Tools

Alan Jacobson, Chief Data and Analytics Officer at Alteryx, joined us on the Data Science Mixer podcast to talk about his work leading teams of data scientists who themselves build tools that are used by other data scientists, including the Alteryx platform and the open-source Python libraries EvalML, Featuretools, Woodwork and Compose.

Alan shared with us what makes a solid data science team, how he thinks about model interpretability, and how to communicate clearly about data science. Here are three “principal components” of our conversation that will get you thinking about these big issues in the field.

Diverse talent leads to better data science results.

Some of the best data scientists I’ve ever worked with have had incredibly different backgrounds.

Building a successful team — and this is not only true for data science; it’s true for, I’d say, most if not all teams — one of the arts of doing that well is building an extremely diverse group of people. And the science on that’s very clear: Diversity yields better results for teams.

There’s no doubt that when you’re dealing with the mix of problems that we deal with every day, having people with many different backgrounds certainly helps. Some of the best data scientists I’ve ever worked with have had incredibly different backgrounds: a geologist, an engineer, an English major. They all came from different experiences. I think that’s one of the keys to building great teams — having that diversity of talent to draw from.

Model transparency can come through understandable explanations.

Which plane are you interested in getting on?

Say you’re going to get on an airplane, and I can show you all the math of the model by which we’ve designed the airplane. I can show you completely transparently all the formulas and all the math. Great. Or, I can tell you that we flew the plane a million times, and we have a model that worked 100% of the time. Would you like to be on the 1,000,001st flight?

You can either pick the plane with the history of working a million times, and it never was wrong — or one that’s never flown before. It has no history, but I can show you all the math. Which plane are you interested in getting on? Personally, I would take the one that has flown a million times and has worked every time.

Machine learning is in some ways on that path of using lots of historical data, and building models that match the historical data, versus maybe more of a statistical econometric approach using formulas. So there are different approaches. But when it comes to the actual transparency, once you’ve built the model, it’s very easy with machine learning to understand how the model works and what’s in it. You can see the formulas if you want to see the formulas. I don’t know that seeing the formula is necessarily making it more understandable. I really think the art is not the transparency — can I see everything that’s in the box — but instead, have I made it understandable enough that you can really understand what’s going on?

#diversity #data-science #data-analytics #data #leadership #principal components

Anton Palyonko

Anton Palyonko

1619150520

Nabla Containers: A New Approach to Container Isolation - Brandon Lum & Ricardo Koller, IBM

Nabla Containers: A New Approach to Container Isolation - Brandon Lum & Ricardo Koller, IBM

Horizontal attacks are an important security concern for cloud providers and its tenants. Despite its many advantages, containers have not been accepted as isolated sandboxes, which is crucial for container-native clouds. The exposure of the syscall interface directly to untrusted workloads has greatly increased the number of exploits possible to the host. We present Nabla containers, which uses library OS/unikernel techniques to avoid system calls and thereby reduce the attack surface on the host kernel. Using our OCI runtime, runnc (https://github.com/nabla-containers/runnc), we show the running of popular applcations: Node.js, python, redis, etc. permitting use of 9 syscalls via seccomp. In this talk, we will discuss and demo how we have leveraged libOS ideas in a novel way and compare isolation and performance metrics against other technologies such as gvisor and Kata Containers.

#developer #kubernetes