Love the post, especially after our case on this. Think the concept of what matters more, idea generation from scratch or to improve an existing idea, is crucial. Ideo have clearly done a wonderful job at enabling idea generation through open facilitation however what I struggle with is when to know how to draw the line? Equally if all ideas are generated so freely, how do you know when you have a hit a good idea vs an unrealistic idea, beside through gut instinct? I really value your suggestion of evaluating a person on other individual performance beside idea generation as this helps move past simply the plethora of ideas someone may have.
Such an important use of machine learning given it touches most of us everyday, thanks for raising it. I totally agree with how you describe the circle of benefits as more consumers listen, then more artists are inclined to join the site. It was really eye-opening to read about a future whereby songs are written based off popular beats. As consumers we often don’t notice the similarity in beats, but it is really interesting to know that music is so simmilar. I had not realised machine learning could help impact this. You raise very important questions for the future of music- should they be written based off an algorithm or use human creativity? I totally agree with your consensus that human input is still valuable because the essence of music itself is that its creative.
Really fascinating to see how machine learning can be used within the oil and gas industry, as use case I had not yet thought of! I particularly liked your evidence for its importance in helping optimise for weather impacts as I can imagine this mitigates a lot of frustration from employees. Your initial question of how to use large datasets and not reduce headcount raised some thoughts. Is this something which you believe could cause negative buy-in from employees should there be a potential to reduce headcount? Are there other ways in which large datasets and machine learning could help in order to make decisions about future plants and linking in changing historical oil prices? A couple of thoughts but overall thought this is a very impactful piece, thank you.
I found this piece to be really thought-provoking. We hear a lot of news about machine learning in medicine but not as much about additive manufacturing. I think your questions are really important- and my gut instinct would say diagnosis and “speed of printing” would be important for the consumer as they seek to get the issue sorted. You mention that customisation is why this hearing aid is to particular and only possible through 3D printing, this leads me to wonder what else could they customise on the hearing aid, such as size, flexibility, waterproof capabilities etc?
Thank you Melina. So fascinating to read about a new “super app” and all the many things it can offer consumers (from pizza to mopeds!). With the huge growth in number of consumers it is clear that they have a lot of data they can use machine learning for to help improve things like consumer interface. I wonder what else machine learning could improve within the website? Perhaps more importantly, as someone who hasn’t used the app, I also wonder whether all 18+ activities are useful to consumers (great if so!) and also whether they could add any more and expand further to become the “jack of all trades”. How could machine learning help them make these decisions and priorities certain categories?
Really interesting to see an apparel producer enter the 3D printing field and provide customisable footwear. It begs the question, do you think UA should also use additive manufacturing for apparel, something they have equal if not more expertise in? One question I am grappling with myself is how much do consumers truly value this technology and how can brands like UA, Nike or Adidas prove win over customers who are currently quite happy with their current way of purchasing? Really thought-provoking piece about the future of shoes, thank you!