Tom Dief

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Nice piece! As the CloudFlare product becomes more prevalent does that introduce some level of vulnerability in itself? It will be interesting to see the application in IoT devices given that one of the main vulnerabilities is out of date software on devices. Perhaps if one part of CloudFlare’s system is deemed vulnerable, does that make out of date devices a target? Alternatively, if the machine learning is at the network level and local hardware is irrelevant, that seems like a great opportunity for addressing that very criticism of IoT!

On November 15, 2018, Tom Dief commented on Data is the new oil: Rio Tinto builds new intelligent mine :

Interesting piece Gavriel. You point out some clear benefits of machine learning for Rio Tinto. The shortage of talent makes me wonder for a company like Rio Tinto, would they be better off outsourcing the AI work? Just as many other companies has decided to stick to what they are good at, is this an instance where Rio just doesn’t have the core competency and will they always be struggling to keep up? Also, is there some unfortunate trade offs to be made? Maybe only the most promising projects get precious AI resources?

On November 15, 2018, Tom Dief commented on Driving the Growth of Solar Energy Through Machine Learning :

Very interesting piece! It’s interesting to consider machine learning and the monetization of data collection in an industrial context since we see data proven to be a source of advantage and revenue for consumer facing companies, but in this context there are no advertisers to sell to or product offerings to optimize. In a business of efficiency, it will be interesting to see how they leverage their existing data to perhaps help producers, or if they pivot to a different model as you consider in your piece.

On November 15, 2018, Tom Dief commented on Stanford Health’s Vision of a “Smart Hospital” :

Nice piece Alec, very interesting topic. I think it will be very interesting to see whether this technology gets used to improve patient outcomes or reduce costs, or whether the two are inextricably linked, and what the economics involved look like. In the hand washing example, the technology requires investment while the incentivized action has no marginal cost and ultimately reducing infections has the $10B price tag. But who does the cost of the infection affect? The hospital itself or insurers? I think as that is borne out it will be interesting to see what technology get implemented and by whom.

On November 15, 2018, Tom Dief commented on I, Robot Meets Wall Street: The Age of the Robo-Advisor :

You pose an interesting question about whether or no machine learning can take over for human investors completely. I believe there are currently some risks to the market with robo investing, including increased volatility due to mass sell-offs when the market behaves in a certain way. I’m sure Schwab is aware of the shortcomings and I’m interested in how soon they will be able to counteract appropriately. You point out that currently they’re relying on humans to intervene in these instances – I wonder if Schwab is currently trying to understand that if even now the humans are actually doing more harm than good?


Great article and excellent analysis of acquisitions in order to infer what Boeing’s strategy and vision of the future is. In terms of regulation, I think the FAA and public will really grapple with whether or not advances in machine learning will ultimately allow for there to be one less pilot in the cockpit. Although the role of ML is intended to enhance and economize a pilot’s job, can it take over all flying functions in the event the sole human pilot has an issue like a medical emergency? I think that it will be interesting to see if Boeing take on the cost of building in the ability for the computer to fly even though it will be very rarely used. Beyond that, extensive simulating and testing on freight traffic, as you suggest, will need to prove that a computer can provide the redundancy that a co-pilot brings.