Tag: mlweekly

Weekly ML drop #5

I’ve become more and more interested in machine learning during last year. This is my way of collecting and sharing interesting reads on the topic I stumble upon. Those posts are published each Friday, they are divided into few categories and the format is constantly evolving.

This week I gathered a lot of articles on the topic of AI for self-driving cars and this will be a theme for this edition.

News
In this part, I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about interesting applications,  predictions and controversies that AI causes.

Artificial Intelligence will enable jobs too
Usually, in the context of AI, you can hear predictions of people losing jobs. In this story about IBM “new collar” jobs, we can read that company is aiming at hiring people uneducated in cyber security who have necessary set of natural skills, and augment them with technology to fill lackings in professional skills.

Intel forms new AI division
Intel’s been behind Nvidia in developments of processing units that power AI revolution. Recent acquisitions mentioned last week and forming new group shows that they’re not gonna give up easily. In a similar move, YCombinator is gonna run experimental vertical group focused on AI in next batch of founded startups.

Andrew Ng leaves Baidu
He’ll now focus on “shepherding this important societal change”. He also wants to support ML community around the world. I love the quote from his blog.

Just as electricity transformed many industries roughly 100 years ago, AI will also now change nearly every major industry — healthcare, transportation, entertainment, manufacturing — enriching the lives of countless people. I am more excited than ever about where AI can take us. — Andrew Ng

Agents in OpenAI research develop their own language
In this blog post, OpenAi discusses some results from the research that aimed at teaching AIs developing a new language of communication “by dropping them into a set of simple words, giving them the ability to communicate, and then giving them goals that can be best achieved by communicating with other agents”.

Learning materials
Here I’m sharing material for learning ML that I found useful – online courses, blogs, books etc. This is usually rather technical stuff.

Algorithmia’s not very heavy intro to Deep Learning
This is not very heavy article explaining on a high level what Deep Learning is.

Convolutional Neural Networks
Recently in my learnings, I was exploring the topic of CNNs. This is a type of neural networks that are very good at image recognition tasks. This series of articles going through interesting papers on the field and this papers were especially interesting. I highly recommend them if you’re interested in the topic.

This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.

Weekly ML drop #4 – self-driving cars

I’ve become more and more interested in machine learning during last year. This is my way of collecting and sharing interesting reads on the topic I stumble upon. Those posts are published each Friday, they are divided into few categories and the format is constantly evolving.

This week I gathered a lot of articles on the topic of AI for self-driving cars and this will be a theme for this edition.

News
In this part, I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about interesting applications,  predictions and controversies that AI causes.

Some of those are not “news” anymore, as I have a long backlog of them saved. They are all very interesting, though!

Would you buy a car that’s programmed to kill you?
In this article, the author discusses some moral issues related to how self-driving cars will behave in death-threatening situations. If you want to test how your set of morals compare to others, check out this survey.

How Drive.ai Is Mastering Autonomous Driving With Deep Learning
This interesting piece takes a ride with drive.ai. This Silicon Valley company has a different approach than most others autonomous drive providers. Instead of using AI just for vision and understanding surrounding and using a rule-based approach to making decisions, they apply deep learning to the whole process of driving.

Self-driving trucks
I recently learned, that truck driver is the most common occupation in the United States. Over 1.7 million people drive all sizes of trucks to deliver goods across the country. The article looks into how knowledge gathered by testing self-driving cars translate to trucks and how the rise of this technology will affect the truckers.

Tesla Drivers Are Paying Big Bucks to Test Flawed Self-Driving Software
Looks like the reality of Tesla’s autopilot is slightly more complicated than promotional videos show. And you need to pay extra to take part in the test program. It may be due to fact that Tesla opted out from using Lidar technology and their autopilot is based solely on cameras. Also check out this youtube channel, that documents evolution of the Autopilot on the same local road.

Intel, Mobileye, and Smiling Curves
Read how recent Intel’s acquisition of Israeli self-driving technology startup fits into technology economy theory called “Smiling Curves”.

Learning materials
Here I’m sharing material for learning ML that I found useful – online courses, blogs, books etc. This is usually rather technical stuff.

Deep Learning for Self-driving Cars @ MIT
This winter MIT offered open lectures about Deep Learning for self-driving cars. If you were in the area, you could sign-up and come for free. Those lectures were recorded and successively published. Now the whole series is complete and available online.

This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.

Weekly Machine Learning drop #3

I’ve become more and more interested in machine learning during last year. This is my way of collecting and sharing interesting reads on the topic I stumble upon. Those posts are published each Friday, they are divided into few categories and the format is constantly evolving.

News
In this part, I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about interesting applications,  predictions and controversies that AI causes.

Some of those are not “news” anymore, as I have a long backlog of them saved. They are all very interesting, though!

Google acquires Kaggle
Kaggle is a platform for solving machine learning problems. As a company, you can publish your problem with sample dataset and pay people the or best solution of your problem. There are also practice tasks, for example analysing Titanic data. Last week Google bought Kaggle, most likely to access 800’000 community of machine learning developers.

Poker is another game, AI can beat humans in
Two independent researcher groups’ AIs managed to beat professional players in Texas Hold’em Poker, the most complicated version of poker. It’s another game at which computers are getting better.  Will those teams now play against each other?

Facebook wants to predict and prevent suicide attempts
Using machine learning and tens of thousands of reported posts, facebook build a model to predict people who plan to commit suicide. On a similar note, there is a map, that tries to predict crimes in London. Are we getting closer to Minority Report?

Is AI going to kill us all?
When you see a movie about AI, it usually tries to whipe out humanity. But there are also people thinking otherwise with solid philosophical arguments.

JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours
Big banks feeling more and more competition from fintech sector, are turning into ML to streamline their processes and help replace layers of redundant software.

Video
I pick one or two videos every week that touches an interesting subject in AI and ML field. Sometimes it’s more scientific and the other it’s about real life applications.

AI experts panel from Beneficial AI conference
Watch Elon Musk, Ray Kurzweil, Nick Bostrom and few others discuss their outlooks for incoming artificial intelligence.

Learning materials
Here I’m sharing material for learning ML that I found useful – online courses, blogs, books etc. This is usually rather technical stuff.

Google’s Depp Learning course on Udacity
This 3 months course goes through fundamentals of Deep Learning. Starting from building simple neural networks from a linear regression model, up to exploring Convolutional Neural Networks for image recognition and Long Term Short Memory algorithms for text analysis. It’s based on Tensorflow so basic knowledge of htis library will be helpful. It also assumes some basic knowledge in Machine Learning.

Pytorch tutorial on Github
Pytorch is another tensor-based python library for machine learning. It has its fans among the academic world and some upsides over Tensorflow.

This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.

Weekly Machine Learning drop #2

I’ve become more and more interested in machine learning in last year. This is my way of collecting and sharing interesting reads on topic I stumble upon. They will be arranged in few categories. Those posts will be published each Friday and format will evolve.

News
In this part I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about interesting applications,  predictions and controversies that AI causes.

Some of those are not “news” anymore, as I have a long backlog of them saved. They are all very interesting though!

The Great A.I. awakening.
Fascinating (and long) read on how Machine Learning drastically changed backends of Google services. If you are to read only one article from this week’s drop, pick this one.

Future of Finance
AI and Bitcoin are driving next generation of hedge funds. Read about how technology changes financial market.

“Ross” is AI Lawyer
Lawyers are expected to be one of the jobs to be replaced by AI. Algorightms are much better in going through complicated laws and precedences and drawing logical conclusions from set of rules. Ross is already doing great job as paralegal.

The AI Takeover Is Coming
Author is exploring more positives sides of incoming AI revolution. Instead of fearing the machine, let’s Embrace It.

Video
I pick one or two videos every week that touches interesting subject in AI and ML field. Some time it’s more scientific and the other it’s about real life applications.

Tensorflow Dev Summit 2017
This is whole playlist for this year’s Tensorflow Summit. It took place in mid February in Mountain View, CA. Talks on ecosystem, distributing calculation, building models or art generation. Pick what interests you :)

Learning materials
Here I’m sharing material for learning machine learning that I found useful – online courses, blogs, books etc. This is gonna be rather technical stuff.

MIT 6.034 Artificial Intelligence lectures
23 lectures from MIT, going through many AI algorithms and methodologies. Most of them are from Fall 2010, but some have been updated with versions from later years to include new advances in areas like Neural Networks.

Rules of ML
Google’s guidelines for developing Machine Learning systems. Slightly technical (but on high level) document presenting how Google approaches designing AI products.

Weekly Machine Learning drop #1

I’ve become more and more interested in machine learning in last year. This is my way of collecting and sharing interesting reads on topic I stumble upon. They will be arranged in few categories. Those posts will be published each Friday and format will evolve.

News
In this part I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about interesting applications,  predictions and controversies that AI causes.

Some of those are not “news” anymore, as I have a long backlog of them saved. They are all very interesting though!

AI builds it’s own encryption algorithm
Google wanted to see if AI can build encryption algorithm, that will be harder to break for humans. Looks like they succeeded.

Blizzard and DeepMind work on API for Starcraft II to build bots to play the game
It’s supposed to be research environment to build AI algorithms to play in complex game. DeepMind are authors of the AlphaGo that beat Lee Sedol in GO few months ago. Also similar concept of environment for testing AI algorithms on games is available through OpenAI Gym.

Does AI has First Amendment rights?
Amazon is claiming that Alexa has right to claim First Amendment rights in murder case to fight an order to hand over recordings from an Amazon Echo.

Bringing dead back to life with machine learning
Fascinating article about creating chatbot based on dead’s person text communication. If you’re into TV series, similar concept was presented in S02E01 episode of Black Mirror.

How to win election with machine learning
A bit scary article presenting how machine learning was used to to gather information and target individuals with customized advertisements to bias their views and alter their voting choices.

Video
I pick one or two videos every week that touches interesting subject in AI and ML field. Some time it’s more scientific and the other it’s about real life applications.

TED Talk – incredible inventions of intuitive AI
Interesting video about how helpful AI algorithms already are in engineering and design.

The Next Rembrandt
By analyzing previous painting of Rembrandt, group of data scientists and museum workers created new “Rembrandt’s” painting over 300 years after his death.

Learning materials
Here I’m sharing material for learning machine learning that I found useful – online courses, blogs, books etc. This is gonna be rather technical stuff.

Stanford University Machine Learning
What else could have landed in the first episode? This is a classic course on machine learning. Many people in the field recommend it for a start. It’s led by Baidu’s chief scientist, Coursera’s co-founder and Stanford professor – Andrew Ng. It’s quite intensive 3 months course, that will give you basics of mathematical concepts in ML and walk through basic algorithms. Highly recommended.