I am glad to see more articles highlighting the difference between ML and AI. For example, this one: How machine learning is different from artificial intelligence – IBM Developer.
There is still lots to be done in the field of machine learning, but I think technologists and scientists need to break out of that tight circle and explore AI in general.
(Image: from the article)
Nope. And this piece, Machine Learning Vs. Artificial Intelligence: How Are They Different?, does a nice job of reviewing them at a non-technical level. At the end, you should see the differences.
(The image, via g2crowd.com, also shows this nicely).
Here’s an assortment of 42 links covering everything from Kubernetes to GCP and other cloud platforms to IoT to Machine Learning and AI to all sorts of other things. Enjoy! (Image from the last link)
- Prometheus Kubernetes | Up and Running with CoreOS , Prometheus and Kubernetes: Deploying – Kubernetes monitoring with Prometheus in 15 minutes – some good links on using Prometheus here
- Deploying a containerized web application | Container Engine Documentation | Google Cloud Platform – a good intro to using GCP
- How to classify workloads for cloud migration and decide on a deployment model – Cloud computing news – great insights for any IT Architects
- IP Address Locator – Where is this IP Address? – a handy tool, especially if you are browsing firewall logs
- Find a Google Glass and kick it from the network – Detect and disconnect WiFi cameras in that AirBnB you’re staying in– Good examples of how to catch spying devices
- The sad graph of software death – a great study on technical deby
- OpenTechSchool – Websites with Python Flask – get started building simple web sites using Python
- Build Your Own “Smart Mirror” with a Two-Way Mirror and an Android Device – this was something I wanted to do at some point
- Agile for Everybody: Why, How, Prototype, Iterate – On Human-Centric Systems – Medium – Helpful for those new or confused by Agile
- iOS App Development with Swift | Coursera – For Swift newbies
- Why A Cloud Guru Runs Serverless on AWS | ProgrammableWeb – If you are interested in serverless, this is helpful
- Moving tech forward with Gomix, Express, and Google Spreadsheets | MattStauffer.com – using spreadsheets as a database. Good for some
- A Docker Tutorial for Beginners – More Docker 101.
- What is DevOps? Think, Code, Deploy, Run, Manage, Learn – IBM Cloud Blog – DevOps 101
- Learning Machine Learning | Tutorials and resources for machine learning and data analysis enthusiasts – Lots of good ML links
- Machine learning online course: I just coded my first AI algorithm, and oh boy, it felt good — Quartz – More ML
- New Wireless Tech Will Free Us From the Tyranny of Carriers | WIRED – This is typical Wired hype, but interesting
- How a DIY Network Plans to Subvert Time Warner Cable’s NYC Internet Monopoly – Motherboard – related to the link above
- Building MirrorMirror – more on IT mirrors
- Minecraft and Bluemix, Part 1: Running Minecraft servers within Docker – fun!
- The 5 Most Infamous Software Bugs in History – OpenMind – also fun!
- The code that took America to the moon was just published to GitHub, and it’s like a 1960s time capsule — Quartz – more fun stuff. Don’t submit pull requests 🙂
- The 10 Algorithms Machine Learning Engineers Need to Know – More helpful ML articles
- User Authentication with the MEAN Stack — SitePoint – if you need authentication, read this…
- Easy Node Authentication: Setup and Local ― Scotch – .. or this
- 3 Small Tweaks to make Apache fly | Jeff Geerling – Apache users, take note
- A Small Collection of NodeMCU Lua Scripts – Limpkin’s blog – Good for ESP users
- Facebook OCP project caused Apple networking team to quit – Business Insider – Interesting, though I doubt Cisco is worried
- Hacked Cameras, DVRs Powered Today’s Massive Internet Outage — Krebs on Security – more on how IoT is bad
- Learn to Code and Help Nonprofits | freeCodeCamp – I want to do this
- A Simple and Cheap Dark-Detecting LED Circuit | Evil Mad Scientist Laboratories – a fun hack
- Hackers compromised free CCleaner software, Avast’s Piriform says | Article [AMP] | Reuters – this is sad, since CCleaner is a great tool
- Is AI Riding a One-Trick Pony? – MIT Technology Review – I believe it is and if AI proponents are not smart they will run into another AI winter.
- I built a serverless Telegram bot over the weekend. Here’s what I learned. – Bot developers might like this.
- Google’s compelling smartphone pitch – Pixel 2 first impressions | IT World Canada News – The Pixel 2 looks good. If you are interested, check this out
- Neural networks and deep learning – more ML
- These 60 dumb passwords can hijack over 500,000 IoT devices into the Mirai botnet – more bad IoT
- If AWS is serious about Kubernetes, here’s what it must do | InfoWorld – good read
- 5 Ways to Troll Your Neural Network | Math with Bad Drawings – interesting
- IBM, Docker grow partnership to drive container adoption across public cloud – TechRepublic – makes sense
Posted in IT
Tagged AI, cloud, computers, GCP, IOT, IT, Kubernetes, machinelearning, MEAN, ML, nodeJS
If you are looking to build AI tech, or just learn about it, then you will find these interesting:
- Artificial intelligence pioneer says we need to start over – Axios – if Hinton says it, it is worth taking note
- 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.
- Artificial Intelligence 101: How to Get Started | HackerEarth Blog – a good 101 piece
- 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.
- Now AI Machines Are Learning to Understand Stories – MIT Technology Review – and not just accelerating, but getting deeper.
- Robots are coming for your job. That might not be bad news – good alternative insight from Laurie Penny.
- Pocket: Physicists Unleash AI to Devise Unthinkable Experiments – not surprisingly, a smart use of AI
- 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
- A Neural Network Playground – a very nice tool to start working with AI
- Foxconn replaces ‘60,000 factory workers with robots’ – BBC News – there is no doubt in places like Foxconn, robots are taking jobs.
- 7 Steps to Mastering Machine Learning With Python – don’t be put off by this site’s design: there is good stuff here
- How Amazon Triggered a Robot Arms Race – Bloomberg – Amazon made a smart move with that acquisition and it is paying off
- 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
- How to build and run your first deep learning network – O’Reilly Media – more good stuff on ML/DL/AI
- 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
- Neural Evolution – Building a natural selection process with AI – more tutorials
- 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 🙂
- A Robot That Harms: When Machines Make Life Or Death Decisions : All Tech Considered : NPR – this is kinda dumb, but worth a quick read.
- Mathematics of Machine Learning | Mathematics | MIT OpenCourseWare – if you have the math skills, this looks promising
- Small Prolog | Managing organized complexity – I will always remain an AI/Prolog fan, so I am including this link.
- TensorKart: self-driving MarioKart with TensorFlow – a very cool application
- AI Software Learns to Make AI Software – MIT Technology Review – there is less here than it appears, but still worth reviewing
- 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.
- People want to know: Why are there no good bots? – bot makers, take note.
- Noahpinion: Robuts takin’ jerbs
- globalinequality: Robotics or fascination with anthropomorphism – everyone is writing about robots and jobs, it seems.
- Valohai – more ML tools
- 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.
- The Six Main Stories, As Identified by a Computer – The Atlantic – again, not a big deal, but interesting.
- A poet does TensorFlow – O’Reilly Media – artists will always experiment with new mediums
- How to train your own Object Detector with TensorFlow’s Object Detector API – more good tooling.
- Rise of the machines – the best – by far! – non-technical piece I have read about AI and robots.
- We Trained A Computer To Search For Hidden Spy Planes. This Is What It Found. – I was super impressed what Buzzfeed did here.
- The Best Machine Learning Resources – Machine Learning for Humans – Medium – tons of good resources here.
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.
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.
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.