M.G's Profile

This is very interesting. For a company (especially a startup) to outsource the technological and commercial challenges to the crowd and create competitions that got so many people to participate. I think this worked in this case with having lots of participants just because these were Elon Musk’s startups.

Regarding your second question, I think that for the first stages it was a good idea to create these kinds of tournaments because they were searching for creative ways to solve the problem, and then choose between them, but for the future R&D challenges you need people who will be experts on the subject and who work for the company. Creating limited scope technical problems to solve using Hackathons is an interesting idea, but I am not sure that it is in the best interest of the company to expose its challenges in the longer run, because if the idea is financially lucrative this will entice others to enter this space, and by revealing your challenges to the outside you are exposing your R&D and providing your competition with an unnecessary advantage.

I didn’t really understand if the houses that are build using this technology can actually stand the test of time, how do they handle different climates? Does extreme sun and exposure, or intense cold for example make the houses that were build using this technology deteriorate much quicker? Because that will make this idea much less feasible in a lot of developing countries.

Regarding allocating resources to enhance the technology for development of 3D printed houses in space, I think this is bad idea. While it can lead to greater innovations in the long run, the amount of money that is needed to keep funding the project is huge. I would think that for a company perspective (especially a small one), it will be better to improve their technology to reduce their costs and work towards improving their bottom line.

I think that this is a very important topic, which lots of countries face. I think that retaining extremely talented cyber security specialists is a very hard thing to do, mainly because the salaries in the private sector are much higher, the bureaucracy is much lower, and the lifestyle is way less restrictive. So as a result, the number of people who do stay is limited, and in time, those people will become less creative in analyzing and detecting security risks because it is much harder to detect a flaw in systems you have designed yourself or help design.

This is why I think the move towards open innovation make a lot of sense, but as you mentioned, this is not feasible to expose your whole systems to the outside world. Also, replicating your internal systems in different context to try and produce a challenge to solve in these Hackathons is also not a good option, because you have no idea where your system is vulnerable (it’s usually where you expect it the least) and its time consuming to do.

In Israel for example, we have the privilege that everyone serves in the army and then have to serve in reserve duty until the age of 40. This helps tremendously, because you can keep the clearance of people who now work in the private sector (after they are discharged) to some extend and then bring them back for a few weeks every year to serve. This way you can actually greatly benefit from them working in the private sector as they meet more challenges and when they come back for reserve duty, can look at problems in different perspective.

So, my suggestion will be to consider this kind of model in the US, where people who left for the private sector, could serve as advisors, and come back for a few weeks every year (for a fee of course) and thus leveraging them as a source of control, but I am not really sure if this kind of model is feasible in the US, would love to hear your thoughts on that.

Regarding your 3’rd question, I think that this is the main issue, the ridiculously high price of the shoe will deter almost every customer to even try it, until the price drops significantly it will face a major challenge in persuading customers to buy them without any additional value. Therefore, a stronger emphasis needs to be on reducing the cost, maybe even consider selling at a loss until they can reach scale to support a much lower price?

Also, If the company could create more value than just perfect customization, I think that it could position itself to sell the shoes with the higher price tag to professional athletes who would value performance much over price.

Machine learning will definitely be a utilized much more in the legal industry, there is just so much manual labor work that can be automated, specifically as you stated due diligence and legal research. There is actually a company that claims it already reached more reliable analysis of NDA’s from its algorithm than from actual human lawyers [1].
But this is a good thing for the industry itself, right now lawyers spend much of their times on these daunting tasks. In the future, lawyers will just use the AI as an assisting tool and will be free for higher quality of work which requires more emotional intelligence (dealing with a customer, counseling, etc.) a key quality that is very hard to replicate by machines for the time being.

Regarding the fees in the industry, legal firms will have to adapt and significantly lower their prices if they want to stay competitive, sooner or later the companies that create these algorithms will gain the general public’s trust with better (or even good enough) results and will be able to charge much less money for the tasks such as of analyzing generic contract.

[1] https://www.lawgeex.com/AIvsLawyer/

Interesting article!

For the first question, in my opinion, machine “bias” is always going to be an issue, but the extent of it for the short and long term is virtually non-existent for several reasons: First, machine learning relays heavily on the data set provided, as the we feed it with more data, the correct predictions rate will increase, and as you stated, this is their main short term goal.
Secondly, although the prediction rate of the machine is lower than that of doctors, this is just temporarily, for example, an algorithm in detecting skin cancer (which is also done using image processing) has already outperforms doctors by a significant number (95% vs 85.5%) [1]. So, until the prediction rate of the machine is lower than the prediction rate of the doctors, it can serve merely as an addition to the arsenal of tools doctors use for their predictions.

Regarding your second question, I think this is an extremely important issue, especially for a company such as google that its own business model right now relies on “selling” its customer information to advertising companies. As a result, it will have to put a strong emphasis on persuading their users that the data will not be used by the company for any other purpose than improving the accuracy of the algorithms.

[1] https://www.theguardian.com/society/2018/may/29/skin-cancer-computer-learns-to-detect-skin-cancer-more-accurately-than-a-doctor