Chris Hartnoll

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On November 15, 2018, Chris Hartnoll commented on 3D Printing Straighter Smiles :

Dear Jane,
Thank you for the interesting article on the application of 3D printing to dentistry

I would have liked to hear more about how the company designs the moulds. The revolution in 3D printing is also being accompanied by new methods to scan 3D objects. In this case it would allow you to easily build virtual 3D models of patients teeth (I assume this is the method they are using). After patients visit their clinics, Align could up-sell these scans to patients to help monitor dental decay and during normal dental checkups. Building up a large database of these scans would address a strategy you identified at the end of the article.

Align could also diversify into 3D printing other dental products that are designed specifically for each customer. I’m not a dental expert, but how about false teeth? It would require acquiring different 3D printing technology (for example Laser Powder Deposition for metal items), but many of the same skills that Align already has.

Dear Mike,
Thanks for the article on an interesting and high stakes application of ML. Two things from me

How far up the decision making chain can ML move? Analysis of data from numerous sensors to identify potential subs is obviously an ideal application of ML. This could progress to prioritising those threats, integrating with other assets to create a search plan, and actually taking control of these assets. This is highly relevant as the US Navy develops new surface and sub-surface drones to track submarines. Can the US Navy adapt its decision making structure to integrate this, and where does the person manage the system? As machine learning relies on historic data to make predictions about the future, how quickly can it adapt to changing circumstances: new tacts, new guises, new decoys, new platforms? This is where working alongside a human may yield better performance.

To address your second point, I think the US Government needs to ask itself how it improve its processes to better engage with technology. Defence procurement is notorious for being inefficient and ineffective. How can it improve this situation, and would this attract more technology companies?

Dear RCTomUser555,
Thank you for your illuminating article on the application of 3D printing to building houses

I question the logic of using 3D printing in the developing world to solve a housing shortage. Referencing source 1, you identify the lack of economic resources to purchase houses driving a shortage of housing in the developing world. Reference 2 identifies several factors in the house building industry that increase the cost of housing. How relevant is this source though as it is not specifically referencing house building in developing countries

I’d like to share my own experiences of low cost housing in developing countries. Developing countries often have lots of cheap labour available. Houses are built with local materials (picture mud bricks, huts, corrugated iron, wood) that is cheaper than building materials used in the developed world (steel, concrete). This 3D printing technology is offering to reduce the labour cost (already cheap), while using standard developed world materials (expensive). A relevant question for me is whether the technology and designs can be adapted to local conditions. This would use local materials that are cheaper and more readily available. But also, the design and construction. For example, naturally ventilated houses in hot countries, insulated buildings in cold countries.

On November 15, 2018, Chris Hartnoll commented on Everything is Awesome: Product Innovation at LEGO :

Dear LxHuang,
Thank you for the fun and interesting article on innovation at Lego!

You have raised some interesting recommendations on improving the quality and quantity (of successful) innovation on Lego’s open innovation platform. However, I think another avenue for Lego to pursue is innovate with the teams and collaborators. Successful innovation is often a mix of different ideas developed by different people at different stages. Perhaps some of the most successful developers (who have the most skill and knowledge of how to create a product) are not the best idea generators. Why not promote collaboration between developers and idea generators? Lego’s current model up-votes ideas and then rewards the author if they are successful. How about creating an environment that is more collaborative and allows people to come onboard at different times?

Dear Tom Challenger,
Thank you for your informative article on distributed electric grids and how machine learning is benefitting Enel

I would like to address the final sections of your article: the changing utility business model and building on progress. You did not mention the role which Enel plays in electricity markets – is it an electricity wholesaler, retailer, transmitter, equipment manufacturer, trader or grid controller? Each of these roles has a different application for machine learning, and its ability to use and collect big data. For example, grid controllers (managing and controlling the market for trading electricity, for the benefit of consumers) are in a position to collect data on production, consumption and the impact of price on the market, and make decisions that benefit consumers.

However, certain agents could apply machine learning to game electricity markets for their benefit. To be able to transmit noticeable amounts of power between small distributed grids require upgraded infrastructure, that is mostly not in place currently. This gives distributors and producers incentives to manipulate their equipment (for example: deciding when to carry out maintenance) and influence the local electricity market. Machine learning will make this easier and smarter. Instead of democratising energy, we could be giving corporations the power to increase our electricity prices when we most need it. How will regulators and respond to this threat?

On November 15, 2018, Chris Hartnoll commented on Shooting for the Stars :

Dear E.T.,
Thank you for the informative article on NASA’s open innovation process – very interesting!

You mentioned that open innovation allows NASA to do 3 things: “learn about new technologies, crowdsource novel ideas, and tap into high skilled individuals”. Yet the example that you provided used “astronomers and space enthusiasts” to look at 100,000 images. Are astronomers and space enthusiasts really high skilled? I disagree that it is achieving any of the 3 aims stated earlier. Instead, it is outsourcing cheap repetitive work that saves some money, but isn’t creating a step change for NASA.

I think that open innovation can achieve these 3 aims by focusing on two areas of innovation: incremental and revolutionary change. Small scale issues – for example optimising a pump’s performance, designing a light weight gyroscope, overcoming a heating issues – requires innovative thinking from highly skilled people. (Speaking from experience of these tricky issues) often the more of them who see it, the better ideas you get. NASA’s budget limits the number of specialists it can employ. But why not create a network of technical specialists across the US (or, security permitting, other countries) who can view some of these issues, suggest ideas, and the winners get a small prize (it could be non-monetary)? My previous company did this to great effect.

For larger scale innovation – for example designing a new spacecraft architecture – requires thinking from multi-disciplinary groups. How can NASA best do this? Perhaps allowing teams to compete against each other, not just from schools/universities? Or maybe combining specialists from across the US in teams competing against each other?