Tag Archives: ML

34 good links on AI, ML, and robots (some taking jobs, some not)

If you are looking to build AI tech, or just learn about it, then you will find these interesting:

  1. Artificial intelligence pioneer says we need to start over – Axios – if Hinton says it, it is worth taking note
  2. Robots Will Take Fast-Food Jobs, But Not Because of Minimum Wage Hikes | Inverse – true. Economists need to stop making such a strong link here.
  3. Artificial Intelligence 101: How to Get Started | HackerEarth Blog – a good 101 piece
  4. Deep Learning Machine Teaches Itself Chess in 72 Hours, Plays at International Master Level – MIT Technology Review – the ability of tech to learn is accelerating.
  5. Now AI Machines Are Learning to Understand Stories – MIT Technology Review – and not just accelerating, but getting deeper.
  6. Robots are coming for your job. That might not be bad news – good alternative insight from Laurie Penny.
  7. Pocket: Physicists Unleash AI to Devise Unthinkable Experiments – not surprisingly, a smart use of AI
  8. AI’s dueling definitions – O’Reilly Media – this highlights one of the problems with AI, and that it is it is a suitcase word (or term) and people fill it with what they want to fill it with
  9. A Neural Network Playground – a very nice tool to start working with AI
  10. Foxconn replaces ‘60,000 factory workers with robots’ – BBC News – there is no doubt in places like Foxconn, robots are taking jobs.
  11. 7 Steps to Mastering Machine Learning With Python – don’t be put off by this site’s design: there is good stuff here
  12. How Amazon Triggered a Robot Arms Race – Bloomberg – Amazon made a smart move with that acquisition and it is paying off
  13. When Police Use Robots to Kill People – Bloomberg this is a real moral quandary and I am certain the police aren’t the only people to be deciding on it. See also: A conversation on the ethics of Dallas police’s bomb robot – The Verge
  14. How to build and run your first deep learning network – O’Reilly Media – more good stuff on ML/DL/AI
  15. This expert thinks robots aren’t going to destroy many jobs. And that’s a problem. | The new new economy – another alternative take on robots and jobs
  16. Neural Evolution – Building a natural selection process with AI – more tutorials
  17. Uber Parking Lot Patrolled By Security Robot | Popular Science – not too long after this, one of these robots drowned in a pool in a mall. Technology: it’s not easy 🙂
  18. A Robot That Harms: When Machines Make Life Or Death Decisions : All Tech Considered : NPR – this is kinda dumb, but worth a quick read.
  19. Mathematics of Machine Learning | Mathematics | MIT OpenCourseWare – if you have the math skills, this looks promising
  20. Small Prolog | Managing organized complexity – I will always remain an AI/Prolog fan, so I am including this link.
  21. TensorKart: self-driving MarioKart with TensorFlow – a very cool application
  22. AI Software Learns to Make AI Software – MIT Technology Review – there is less here than it appears, but still worth reviewing
  23. How to Beat the Robots – The New York Times – meh. I think people need to learn to work with the technology, not try to defeat it. If you disagree, read this.
  24. People want to know: Why are there no good bots? – bot makers, take note.
  25. Noahpinion: Robuts takin’ jerbs
  26. globalinequality: Robotics or fascination with anthropomorphism – everyone is writing about robots and jobs, it seems.
  27. Valohai – more ML tools
  28. Seth’s Blog: 23 things artificially intelligent computers can do better/faster/cheaper than you can – like I said, everyone is writing about AI. Even Seth Godin.
  29. The Six Main Stories, As Identified by a Computer – The Atlantic – again, not a big deal, but interesting.
  30. A poet does TensorFlow – O’Reilly Media – artists will always experiment with new mediums
  31. How to train your own Object Detector with TensorFlow’s Object Detector API – more good tooling.
  32. Rise of the machines – the best – by far! – non-technical piece I have read about AI and robots.
  33. We Trained A Computer To Search For Hidden Spy Planes. This Is What It Found. – I was super impressed what Buzzfeed did here.
  34. The Best Machine Learning Resources – Machine Learning for Humans – Medium – tons of good resources here.
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It’s not because most developers are white that AI has hard time with non-white faces. It’s this….

An example of a neural net topology
This piece, Most engineers are white — and so are the faces they use to train software – Recode, implies that AI software doesn’t do a good job recognizing non-white faces because most engineers (i.e. software developers) are white. I’d argue that the AI does a poor job because of this: the developers aren’t very good.

Good software developers, in particular the lead developers, take an active role in ensuring they have good test data. The success of their software when it goes live is dependent on it. Anyone using training data  (i.e. using test data) in AI projects that is not using a broad set of faces is doing a poor job. Period. Regardless of whether or not they are white.

If the AI is supposed to do something (i.e. recognize all faces) and it does not, then the AI sucks. Don’t blame it on anything but technical abilities.

 

 

Facebook shows why we need augmented intelligence (Artificial and Human Intelligence)

Because if you don’t have augmented intelligence, and if you solely depend on AI like software, you get problems like this, whereby automated software triggers an event that a trained human might have picked up on.

AI and ML (machine learning) can be highly probabilistic and limited to the information it is trained on. Having a human involved makes up for those limits. Just like AI can process much more information quicker than a limited human can.

See the link to the New York Times story to see what I mean.

Want to understand what artificial intelligence and machine learning is?

If you want a better understanding of artificial intelligence or if you want to gain some insight into the future of machine learning, I recommend these two free reports, found here: Free AI Reports from  O’Reilly Media. There’s so much hype and speculation about AI: these reports cut through all that noise and they will give you a better understanding of what A.I. really is and where it is going.

P.S. If you like them, check out the many great non-A.I. related reports as well. You don’t have to be a technologist to be able to read them.