all_aboard2020

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The London bus is dead. Long live the London bus.
all_aboard2020
Last modified on November 14, 2018 at 6:37 pm
CityMapper is looking to revolutionise mass bus transport by using machine learning to create adaptive networks in London. Optimising for some, however, could mean everyone loses out.

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On November 14, 2018, all_aboard2020 commented on You can pick your friends, and your friends can pack a box, but you can’t pick & pack a box without your friends :

Incredibly well delivered *cough* article!

I found your point on customer willingness to pay really interesting. For the last decade, delivery times have been crashing down, especially in city centres with high densities. But there’s a crucial point – am I really willing to pay for less than 1 hour delivery? The incremental effort required for me to pre-plan by this amount is trivial, and additional investment on the behalf of Amazon seems to be with little consumer benefit. Given the scale of change needed, perhaps there are other ways to short-circuit delivery times, outside of asset investment, e.g. own courier network.

On November 14, 2018, all_aboard2020 commented on Machine Learning in Radiology: Threat or Opportunity? :

My diagnosis: a great article! Really interesting to hear how this is shaping hospital decision-making processes.

I’d be particularly keen to understand more about the patient response to the level of human involvement. These kinds of innovations are somewhat reliant on reducing the cost of patient treatment – if radiologist involvement stays high based on patient demands, it becomes harder to role out at greater scale. What confidence level does a patient need that they are receiving the correct diagnosis, even when a machine learning approach is measurably better than a human’s judgement?

On November 14, 2018, all_aboard2020 commented on JBS and Robot Butchers :

Quite a meaty article Blake – I love it (preferably on a BBQ).

Interesting to understand how this could shake up the cost base of these larger organisations for high-volume animal products, but for me, there’s also a huge benefit in terms of waste reduction. Given the pressure the industry is under to reduce carbon emissions and overall wastage, this seems a timely opportunity to prove action is being taken.

On November 14, 2018, all_aboard2020 commented on Machine Learning: Dating’s Saving Grace? :

Consider this a super-like…a great, engaging article. A few thoughts that are brought to mind: what is the role of the friend in this? I thought Tinder’s use of group dates was rather innovative, and brought a more social angle. And of course, the risk with machine learning is that it relies on the training data provided, which in itself relies on personal judgement made on one’s self. Perhaps a friend could add their thoughts independently? Or is this a bridge too far for embarrassed users?

On November 14, 2018, all_aboard2020 commented on ZOZO’S AMBITION: CAN YOU QUANTIFY “COOL”? :

Looking sharp Issei…I find this incredibly interesting, but I am left wondering: doesn’t the quantification of cool automatically make it…uncool?

I think the real challenge here is being able to offer customisation that ensures the customer doesn’t feel though they are re-buying the same experience or product. Change is, of course, the spice of life.

On November 14, 2018, all_aboard2020 commented on Printing the Future of Helicopters with Bell :

Ryan, this is great – was not expecting this to exist within the helicopter industry. You raise some really interesting issues around the feasibility with regard to the physical properties of the materials being formed. I’d be particularly keen to understand more about how these parts impact reliability as well – clearly paramount in this mode where there aren’t good options if parts fail in the air!