Earlier this year, I was publishing weekly, or sometimes semi-weekly drops with links to interesting news, videos and learning materials for Machine Learning. I wasn’t very consisted about it and at some point just stopped.
I’m sharing here sources I get my machine learning readings from:
DataTau (HackerNews-like data science-oriented service)
Machine Learnings (from creaters of MachineLearnings.co)
Have a great read!
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. I publish those posts every Friday. They are divided into few categories and the format is constantly evolving.
In this part, I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about applications, predictions and controversies around ML.
Inside Waymo’s Secret World for Training Self-Driving Cars
In this article, author visits Waymo’s facility for testing self-driving cars. He describes the operations there and how Waymo is using recorded real life situation, to rerun them in a virtual test environment. And this environment now evolved to simulate also previously “unseen” situation.
Microsoft unveils Project Brainwave for real-time AI
Microsoft presented new FPGA-based chip specifically designed for running high-performance machine learning computations. Together with it, they announced Project Brainwave, which (apart from the chip) consist of distributed system architecture and compiler and runtime for easy deployment of models.
How Big Data Mines Personal Information to Craft Fake News and Manipulate Voters
A darker side of Machine Learning revolution. With current computer powers and algorithms, it became very easy for evil actors to gather information about people, and create very tailored news that alters’s people opinions and decisions.
How does Physics connect to Machine Learning
Fascinating (and a bit math heavy) article on how concepts known from Physics find their translation into Machine Learning world.
This week I wanted to share with you some repositories with Tensorflow best practices and new Deep Learning course by Andrew Ng.
Machine Learning for humans
It’s a series of articles targeted at technical professionals wanting to understand ML or non-technical people who are happy to engage with technical content.
Practical Deep Learning for Coders
This 7 weeks course by Fast.ai is fun, project based way to learn Deep Learning. It’s pretty heavy in content – authors suggest to secure 10hrs a week to be able to succeed with it.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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. I publish those posts every Friday. They are divided into few categories and the format is constantly evolving.
In this part, I share interesting news from machine learning and artificial intelligence world. Those are mostly not very scientific articles about applications, predictions and controversies around ML.
Amazon’s move signals end of line for many cashiers
The recent Amazon’s acquisition of Whole Foods is another step for the company to dominate retail. When you put that together with their Amazon Go experiment, you may start thinking how grocery stores will look in the future.
Mars robot makes decisions on its own
NASA scientists installed 20’000 lines of code on the Mars Rover Curiosity to give it some intelligence. Now it can recognize which rocks are worth closer look, instead of beaming laser at any rock found.
Artificial intelligence beat human player in Dota 2
Another game with a set of complicated rules and a nonlinear way to win was beaten by an algorithm. This time it was a solution by Open AI, using solution based on reinforced learning.
DeepMind and Blizzard open Starcraft II as an AI research environment
On a similar topic – now Starcraft II also has an available platform for AI experiments. It contains, among others, an API and big (and constantly growing) set of recorded anonymized gameplays. The release contains also an open source version of DeepMind’s toolkit and access to mini games, that will allow training agents for specific tasks.
How Machine Learning is transforming drug creation
Machine learning algorithms that are good at pattern recognition, can go through new and existing genetic and medical information to find unknown previously connections, which will allow creating more targeted medication.
Tensorflow 1.3 released
The previous week, a new version of Tensorflow was released. Click through, to see the list of features and improvements.
This week I wanted to share with you some repositories with Tensorflow best practices and new Deep Learning course by Andrew Ng.
Tensorflow tutorials
Speaking of Tensorflow, this GitHub repository contains a bunch of tutorials, that are simple and easy to use and grasp basics of the library.
Tensorflow best practices
And this repository contains a bunch of good practices while developing with Tensorflow.
New Deep Learning specialization from Andrew Ng
After his recent departure from Baidu and forming Deeplearning.ai, there is more news from Andrew Ng. He recently published new Coursera specialization focusing on Neural Networks and Deep Learning. Many people started their adventures in Machine Learning with his previous Coursera course, and this is definitely a great continuation. I’m in the 3rd week of the first course and with a clear conscience, I can recommend it to you.
Also recently, Andrew raised $150M venture capital fund to invest in AI.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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. I publish those posts every Friday. They are divided into few categories and the format is constantly evolving. Last few weeks were a bit hectic, due to hosting changes, but I’m back to regular posting.
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.
Algorithms aren’t racist. Your skin is just too dark.
In this article, Joy shares her story on how facial recognition algorithms fail to recognise darker skin tones. You can also watch it in her Ted talk. This issue doesn’t cause just minor problems, like cameras not finding somebody’s face. With a widespread use of facial recognition by law enforcements, it can put innocent people into trouble. It’s part of the bigger problem, that algorithms can inherit human’s biases. She also calls to action She wants to collect example cases of biased algorithms to find a way to fix the problem.
Is China outsmarting American in A.I.?
China is rapidly increasing its support for AI-related projects, while the US is decreasing government spending in that area. The article looks how those changes can impact future of the technology. The problem for China may be its traditional top-down management and lack of open information exchange culture. But those things are also changing.
Software is Eating the world, but AI will eat software
Nvidia CEO, Jensen Huang, shares his opinion about industries that will be impacted by AI developments. Apart from obvious examples like automotive or healthcare, paradoxically he mentions software.
Apple is working on a dedicated chip to run AI on devices
Just another company is building custom designed chips to accommodate new processing needs of machine learning algorithms. But in contrast to Microsoft or Google, whose chips power their data centres, Apple is rumoured to plan to put a dedicated AI chip in their devices.
The next big leap in AI could come from warehouse robots
Kindred is a company, that has a different approach towards AI usage. In opposition to most of the tech companies that focus on software and build chatbots or recommender systems, they believe that the true AI innovation will come in a physical form of robots.
This week I have a little bit less technical and a bit more visual content here.
A visual introduction to machine learning
It’s a very nice visual presentation, which shows the process of building Machine Learning models. Starting with data analysis, through finding relevant features up to constructing the model. The algorithm used here is decision trees, which is pretty basic ML method but can be very effective with certain datasets. This looked like a website with a great educational potential, unfortunately, this “part 1” has no new follow ups.
A neural network playground
It’s another visualisation tool, that shows basic inner workings of neural networks. As input, you get to choose several datasets and you have control over the construction of the network. You get to set parameters like a number of hidden layers, a number of neurons or activation functions and see how they impact results. It puts a bit of light into rather mysterious ways, how neural networks work.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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. Last week I didn’t have time to prepare a weekly drop, so some “news” today may be a bit older.
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.
Data is the new oil
In this very interesting article in The Economist, author bring arguments how in the 21st century, data will be (and already is) the main resource to fuel the economy (compared to 20th-century oil)
Sent to Prison by a Software Program’s Secret Algorithm
A man charged with fleeing police in a car was sentence 6 years in prison. One of the information used against him was set of bar charts analysing risks and threats he’s posing to the society. It wasn’t the main proof, but it is a little bit unsettling.
The parts of America most susceptible to automation
Short article discussion which areas of US and why will be most hit by the advent of automation. There’s interesting map there also.
Harnessing automation for a future that works
In this article, on the other hand, the author claims we’re not quite there, and we’ll need close cooperation between people and machines to progress automation.
6 areas of AI and ML to watch closely
If you’re interested what’s really hot in the industry, this article lists 6 most interesting technology areas.
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 simplified
Series of short videos explaining basic concepts of Machine Learning. Recommended rather for total beginners.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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.
Neuralink and Brain’s Magical Future
Tim Burton from Wait But Why wrote another lengthy article on one of the Elon Musk’s project. This time it’s about Neuralink, some sort of direct interface to the brain. Tim also tackled other AI-related topics, like Superintelligence, which is also a worthy read.
The Myth of Superhuman AI
Kevin Kelly, on the other hand, thinks that we’ll never get to superintelligence for those five reasons. Kevin was also mentioned in two weeks ago edition. I am currently reading his book and if you’re interested in future, it’s a must-read.
Software Predicts Cognitive Decline Using Brain Images
We talked several times about advancements in medicine, that Machine Learning brings. Image recognition algorithms are getting or surpassing human levels of detecting various threats to our health on diagnostic imagery. This article treats the topic of using a neural network to early detection of Alzheimer’s disease.
The first wave of Corporate AI is doomed to fail
The author of the article compares current wave of AI-advances to first booms of the internet and cloud computing, that failed miserably. Only after backing off and some advances those technologies hit a home run.
Waymo’s Self-driving cars will take first raiders
Alphabet’s self-driving cars company is going to run tests in Phoenix, including people outside’s of Google. People can sign up, and the rides will be for free. The goal is to check how people use and react to self-driving cars.
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.
Videos from ICLR conference has been published
International Conference on Learning Representations which took place in Toulon, France in late April, just published their video on Facebook.
Learning AI if you suck at math
This is a good article for total beginners, who not only do not have much experience in Machine Learning but also feel they’re lacking in math. It links to several good resources to jump up your algebra and calculus.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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.
We need tools to track AI impact on jobs market
An expert panel composed mainly of economists and computer scientists said in a new report, that The World needs a way to measure how technology impacts job market. As when we started measured our economies in the 1930s, which greatly improved government’s awareness of issues to address.
Series of articles on how AI is used in biggest tech companies
Backchannel throughout last year visited major tech companies to interview them, how they use Machina Learning and AI. Apple, Google and Facebook.
Machine learning will be a great help in detecting cancer
The article from Google’s research blog shows how assistance of machine learning algorithms will greatly improve detecting cancer.
Will democracy survive Big Data and Artificial Intelligence?
This is a longer read from Scientific American that analyses various impacts of Big Data and growth of Machine Learning will have on future societies. It also tries to answer the question what we should do now, to secure our future.
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.
Intel’s Deep Learning 102
Continuation of the webinar I linked last week. In this part, they’re taking an overview of more advanced topics, like convolutional neural networks and recurrent neural networks.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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.
German retailer Otto allows algorithm to order their supplies
To shorter delivery times, Otto allows their Machine Learning based system to automatically resupply the stock, which led to shorter deliveries, fewer returns and less overall losses.
Nobody understands the Deep Learning
There are certain fields, where it is required, that algorithm used to find results must be explainable. Unfortunately, it’s close to impossible answer the question “what exactly caused this network to give this answer” given its complexity – hunders of layers with thousands of neurones. It also makes debugging and finding errors very hard.
AI can aquire biases against race, gender, etc.
There’s an old saying about computer systems: Garbage in, garbage out. It also works with ML systems. What kind of data we feed to learning algorithms, will impact how their models work. That’s why AI based on human generated data, can be not that democratic as some people promise.
Fast Drawing for everyone
This google post talks about the AutoDraw experiment, that figures out what you wanted to draw, and propose you a better representation of it. There are some limitations though. It won’t offer you a cat in similar shape, just bunch of predefined cats. And also a number of recognised objects is quite limited. Still, an interesting toy to play. And if you’re interested in science, behind it, there’s this article on research blog.
Google’s neural networks duel against each other
Most of today’s Machine Learning is so-called supervised learning. It does mean, that somebody needs to feed the algorithm with data, that’s supervised (for example labelled images). One overly simplified case could be that one network is generating cat pictures, and the other one is recognising cats and they get better by feeding data to each other.
Video
I pick one or two videos every now and then that touches an interesting subject in AI and ML field. Sometimes it’s more scientific and the other it’s about real life applications.
In the future, everything will be smart
In this short video Kevin Kelly, author of “The Inevitable“, talks how AI will be a commodity, as electricity became in XIX century and we’re on the brink of another revolution.
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 101 from Intel
This one hour webinar goes trough basic concepts of Deep Learning and how those type of algorithms perform on Intel’s stack.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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.
Exploring the mysteries of Go with AlphaGo and China’s top players
AlphaGo together with Chinese Go Association is bringing together the most talented Go players and computer scientist to explore deeper into the game. Turns out, that last year victory of AlphaGo over human player totally changed how humans play this game.
Who will pay insurance in the era of self-driving cars?
This article discusses impact self-driving cars may have on the insurance industry. As it is suggested currently by analysts, and some first legislation follows, car makers may be responsible for that cost.
Why deep learning is suddenly changing your life?
Just another article that discusses what exactly happened in recent years, that machine learning seems to be everywhere and changing everything. It also explains basic terms and methods used in the field.
AI based hedge fund created a new currency
The article, with a bit sensationalist title, discuss new fintech startup Numerai, that uses open market for AI algorithms to make its trading decisions and rewarding authors of the best algorithms.
How predictive AI will change shopping
The author of this texts brings up few examples how AI revolution impacts the retail world. Thanks to better data collections, connected devices and putting this data together, retail companies can offer better suggestions, increasing their profits.
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.
Artificial Intelligence and the Growing Importance of Soft Skills
Very interesting read about which skills are endangered and which not by AI. If you want to prepare for robots taking your job, that’s a good start.
Introduction to Machine Learning
This short video from Intel Nervana AI academy explains basic terms of Machine Learning and most popular types of it.
Stanford series on Natural Language Processing with Deep Learning
Another academic source of knowledge. I haven’t had time to dig into it, but quick skimming suggests, that’s another amazing 20hrs of the content of ML knowledge, this time targeted at NLP.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.
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.
Accidentally this week, I also have a theme. Most articles are related to impact on workplaces and how companies work.
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.
Axa is using ML to predict big car accident with 78% accuracy
In this article on Google Platform blog, the case of a big insurance company is discussed. What technology was used and how it worked.
How automation impacts economy?
This post discusses potential outcomes of more and more automation in modern economies.
Artificial intelligence could dramatically improve the economy and aspects of everyday life, but we need to invent ways to make sure everyone benefits.
How AI is transforming workplace
Discussion of multiple aspects of workplace and how machine learning and other AI-related technologies may change them.
How AI is changing the way companies are organised
This article brings up some examples how introducing ml tools may change how companies are structured and how internal communication is performed.
Machine learning used to smart compression of videos
Netflix uses new machine learning based algorithm to compress video scene by scene for better results.
Opinions of 17 experts, how worried we should be about AI
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.
In-depth, non-technical guide to machine learning
This five-part article goes through terms and techniques used in machine learning in human-friendly language. Very good first contact with anything machine-learning related.
This is it for today, thanks for reading. If you liked the post, let me know and please check other parts of the series.