Page 61 - Microsoft Word - The Future of Learning April 2017.docx

Making Learning Efficient 94
As a species, we have always been learning, but the amount and the rate at which we now need to learn is dramatically increasing. The learning we will require in our immediate future, let alone our long-term future, is unknowable right now. Purchasing a mobile phone compels us to learn how to use the camera function, send text messages, use Skype, synchronise our device with iTunes to download music, movies, video clips from YouTube, access TED talks, ‘WhatsApp’ and Snapchat, as well as setting up the phone to upload our photos to iCloud, G-DRIVE or Dropbox, make use of Google maps and learn how to configure it, synchronise our online banking, load our regular bill payments, and of course, we may also use it to make phone calls.
We also need to connect to the internet where we are, set up a hotspot for the unlikely situation where there is no free Wi-Fi access, download and use ‘Spotify’ as well as decide which friends we want to put on ‘find my friend’, and who we might follow on Twitter, Instagram, Facebook and LinkedIn. Then we must download and learn how to use Evernote, Uber, Kindle, eBay, Bump, Find My Phone, Dragon Dictate (just in case we don't have Siri on board), and one of the most useful apps ever - Torch. All of this for half the price of the digital camera that we bought in 2010.95
Our phone is just one of our many learning challenges. Learning how to learn as efficiently as possible, is now critical for everyone, due to the volume of learning we are now engaged in. Once we learn how to learn, and we achieve that efficiently, we can learn independently and anything becomes possible. Being able to learn anything, Just-In-Time and create a conceptual understanding of that, opens-up the door to innovation and ingenuity and we now have our ‘master key’ to open any creativity door we choose.
Artificial Intelligence: We’re missing something big. We’ve been making pretty fast progress, but it’s still not at the level where we would say the machine understands. We are still far from that [...] There are people who are grossly overestimating the progress that has been made. There are many, many years of small progress behind a lot of these things, including mundane things like more data and computer power. The hype isn’t about whether the stuff we’re doing is useful or not—it is. But people underestimate how much more science needs to be done. And it’s difficult to separate the hype from the reality because we are seeing these great things and also, to the naked eye, they look magical.96 MIT Technology Review
94 The chapter 9 summary video can be found here - https://youtu.be/0PVDcvKrAvQ if you are reading the book, otherwise click on the video icon at the top of the page
95 Fullan, M. & Donnelly, K. (2013). Alive in the Swamp: Assessing Digital Innovations in Education. Retrieved from http://www.nesta.org.uk/library/documents/Alive_in_the_Swamp.pdf
96 Knight, W. (2016). Will Machines Eliminate Us? MIT Technology Review. Retrieved from https://www.technologyreview.com/s/546301/will-machines-eliminate-us/
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