How can someone become a CTO without having any clue about tech?
In a big enough company where software isn't the product that just means they know the tech that applies to that company. The CIO is probably a fearful late career IT politician and the CTO could have spent 40 years progressively learning more about washing machines or planes.
Another bit that not many people know is that CTOs at big non-tech corps can just pay a consultant to literally tell them what to do (most of the big banks do this) and consulting firms make a ton of money providing this service (namely places like Deloitte etc).
I think a lot of people just don't realize that Machine Learning can be as simple as a linear regression with 2 variables. The hype and myth around ML are such that some employers just assume that they need to hire rockstar data scientists to do ML but in reality, a junior level data analyst or even a non-technical employee can do simple ML model in Excel spreadsheet assuming he/she has some background in Stats 101.
Yes but that is the easy part. The hard part is knowing what can be solved with ML, gathering the training data, validating the model and then explaining what the model does. Acually fitting the model is not the hard part. But yes, not all ML is deep learning and most ML problems should NOT use deep learning.
> Acually fitting the model is not the hard part.
Ideally you shouldn’t be fitting the model yourself. The idea behind ML is to use brute force computation.
If you’re fitting the model by hand, you’re a Statistician.
The ideal of the 6-pager is magnificent. The reality of the 6-pager in 2019 is that it has become boilerplate where style overwhelms substance. Similar things can be said of their OP1 and OP3 planning processes that are becoming increasingly cargo cult IMO. And that arises naturally from a static game whose most experienced players have become better at funding projects than they are at funding better projects. Thoug…
> The reality of the 6-pager in 2019 is that it has become boilerplate where style overwhelms substance. I think this varies by team. In my experience thus far, I've been receiving lots of feedback -- often actionable -- to improve my own narratives. If your reviewers aren't pushing back on you when your paper lacks the required substance, that definitely needs to be addressed. > OP1 and OP3 planning processes that a…
In my case, the project we wrote an OP1 for was judged to be too technically complex such that anyone capable of executing on it would be a "flight risk" capable of commanding higher pay elsewhere.
Therefore they decided to destaff the effort and wait for me to prove them right rather than give me a couple engineers and enough rope to hang myself (which I wouldn't have most likely given my track record that established me as a "flight risk" apparently).
> AMAZON’S SIX-PAGE memos are famous. Executives must write one every year, laying out their business plan. Less well known is that these missives must always answer one question in particular: how are you planning to use machine learning? Responses like “not much” are, according to Amazon managers, discouraged. I think company culture & talent make a big difference here. At most companies this would run the risk of…
Can anyone at amazon comment on whether this actually works? Edit: the ai part specifically.
I have never worked for Amazon so I'm just speculating but if you have a culture of being data driven, specifically in the ways you optimize things then I'm guessing you'll probably be pretty successful and it's not a giant leap to plugging that data in to some ML models or something. To plug that stuff in the some ML models requires an entire culture of data collection and some thought as to how you'd improve or benefit from it; it's sort of like the "no brown M&Ms" rider that Van Hallen used to have.
That's pretty much it. There is also a widespread belief that software development is "IT" which means that an initiative to implement cloud systems gets led by people who have experience with desktop deployments or firewall setup. There is a lot of politics around budgets so the IT department won't let go of that power.
I read in Werners Vogels blog (amazon CTO), that there are four types of CTOs [1] based on Berray and Sampath paper. [2] * Infrastructure Manager * Technology Visionary and Operations Manager * External Facing Technologist * Big Thinker [1] https://www.allthingsdistributed.com/2007/07/the_different_c... [2] http://www.brixtonspa.com/Career/The_Role_of_the_CTO_4Models...
That list is not exhaustive. One might also encounter "Peter Principle Beneficiary".
> AMAZON’S SIX-PAGE memos are famous. Executives must write one every year, laying out their business plan. Less well known is that these missives must always answer one question in particular: how are you planning to use machine learning? Responses like “not much” are, according to Amazon managers, discouraged. I think company culture & talent make a big difference here. At most companies this would run the risk of…
At most companies this would run the risk of poorly shoe-horning ML into a bunch of applications and making everything needlessly complex & expensive without seeing improved results (same goes for blockchain). Sure, and in Amazon, you get a few tweaks that get the company a few bucks. It suspect the article wildly overstates the value of ML to Amazon. Amazon's success seems to rest on: A. Attracting enough capital to…
Some specialty items should only be purchased on Ebay. You get the same products built in the same Chinese factories, without the markup for marketing and salaries required to involve USA middlemen. Inexpensive manufactured items of more general interest can be found lots of places: AMZN, WLMT, Harbor Freight, etc. Ebay is filling a niche for the less common stuff, though.
I ran Kindle software Product Management for 3 years. Left in 2014 AMA about the memo process or OP1/OP2 process.
What is it like being in the stack ranking system? Is it as stressful as it is reported or have its drawbacks been exaggerated in the media?
If you come prepared, it's not as bad as you think. Being stack ranked +you're not in the room for that) can lead to stress though. I would say my experiences at MSFT under Gates and Ballmer for stack ranking felt far more capricious than at AMZN.
Yes but that is the easy part. The hard part is knowing what can be solved with ML, gathering the training data, validating the model and then explaining what the model does. Acually fitting the model is not the hard part. But yes, not all ML is deep learning and most ML problems should NOT use deep learning.
> Acually fitting the model is not the hard part. Ideally you shouldn’t be fitting the model yourself. The idea behind ML is to use brute force computation. If you’re fitting the model by hand, you’re a Statistician.
Statisticians do not fit models by hand, they "fit" models in the same way machine learning pratictioners "train" models. "Fitting" in statistics lingo is completely synonymous with "training" in ML lingo. Statisticians were the original ML practitioners.
How can someone become a CTO without having any clue about tech?
I have worked with a lot of CTOs or similar. Sometimes CTO is a title that can approve large budgets and an effective CTO is actually their direct report. Being a CTO is less about technology and more about understanding the global technical landscape, accounting and legal implications to large budget decision making. If I, for example, choose Microsoft as a vendor will that impact our Pacific market and if so what i…
Sure but sometimes this lack of knowledge will cripple a company. I've had a VP(who had a role similar to the cto for the department) who picked what time series database we should use based on this technology being the best one from a business standpoint. Unfortunately the technology was so bad from a technical standpoint that every single project that used it had it's budget and timeline explode 4-6x. This seems like an exaggeration but it isn't. One literally wrote a working application in 6 months then was mandated to move to the new technology (basically rewriting the application) and they spent 2 years rewriting while still leverage a lot of the code they already wrote.
And I feel like 100s of these decision are made everyday by executives who oversee but don't grok software.