- Calling itself The Future of Life Institute, its founders included Jaan Tallinn – who helped create Skype – and a physicist from Massachusetts Institute of Technology.
- That physicist was Professor Max Tegmark.
- With a mission to help safeguard life and develop optimistic visions of the future, the Institute has focused largely on Artificial Intelligence (AI).
- Of particular concern is the potential for AI to leapfrog humans and achieve so-called “superintelligence” – something discussed in depth in Tegmark’s latest book Life 3.0.
- This week Ian Sample asks the physicist and author what would happen if we did manage to create superintelligent AI?
Ian Sample speaks with Prof Max Tegmark about the advance of AI, the future of life on Earth, and what happens if and when a ‘superintelligence’ arrives
Continue reading “Being human in the age of artificial intelligence”
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In 2016 Klaus Schwab, founder and chairman of the World Economic Forum, wrote: “We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another.”
- This fourth Industrial Revolution, he said, will fuse the physical, digital and biological worlds, and affect all corners of society – even challenging ideas of what it means to be human.
- Andrew McAfee and Erik Brynjolfsson of the the Massachusetts Institute of Technology think not.
- And in their latest book Machine, Platform, Crowd, they tell us why.
- Joining Ian Sample in the studio, Andrew and Erik lay out their blueprint for the future of the workplace, including the role big data will play, how some processes involving decision-making could be automated, and how minds and machines can come together to cancel out each other’s errors.
Ian Sample sits down with Andrew McAfee and Erik Brynjolfsson to discuss the future of the workplace and the role artificial intelligence will play
Continue reading “Minds and machines: can we work together in the digital age?”
- From First Principles With Pure Python and
Use them on Real-World Datasets
You must understand algorithms to get good at machine learning.
- In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
- I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
- (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
- I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets
You must understand algorithms t…
Continue reading “Book: Machine Learning Algorithms From Scratch”
- About 8,300 articles related to Python have been posted on Data Science Central, according to Google.
- Below is a small sample — the 12 most useful and popular articles to get started with Python and data science.
- The Guide to Learning Python for Data Science has been moved here.
- To receive updates about Python and any other data science topics, sign-up with DSC
About 8,300 articles related to Python have been posted on Data Science Central, according to Google. Below is a small sample — the 12 most useful and popular…
Continue reading “12 Python Resources for Data Science”
- A rather comprehensive list of algorithms can be found here.
- Many are posted and available for free on Github or developers with over 800 algorithms, though you have to pay a fee to access them.
- You can find the original article, here.
- For other articles about algorithms, click here.
A rather comprehensive list of algorithms can be found here. Many are posted and available for free on Github or Stackexchange. Algoritmia provides developers…
Continue reading “12 Algorithms Every Data Scientist Should Know”
- This cheat sheet, along with explanations, was first published on DataCamp.
- To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deel Learning, Data Science, and so on) click here.
- To view a better version of the cheat sheet and read the explanations, click here.
This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Lea…
Continue reading “Data Science in Python: Pandas Cheat Sheet”
- With new neural network architectures popping up every now and then, it’s hard to keep track of them all.
- RNNs sometimes refer to recursive neural networks, but most of the time they refer to recurrent neural networks.
- That’s not the end of it though, in many places you’ll find RNN used as placeholder for any recurrent architecture, including LSTMs, GRUs and even the bidirectional variants.
- Many abbreviations also vary in the amount of “N”s to add at the end, because you could call it a convolutional neural network but also simply a convolutional network (resulting in CNN or CN).
- Composing a complete list is practically impossible, as new architectures are invented all the time.
This article was written by Fjodor Van Veen.
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing…
Continue reading “Concise Visual Summary of Deep Learning Architectures”