Brian Cohen, champion of NYC tech, entrepreneur, angel investor, and author, was honored with the first ER Community Award on January 22, 2020. We’re fortunate that he’s been part of the Entrepreneurs Roundtable Community since we began hosting events in 2007.

As Founding Partner, New York Venture Partners and Chairman Emeritus, New York Angels, Brian’s impact on NYC tech is extensive. He’s a lifelong New Yorker who has championed startups and entrepreneurship, guiding young startups and helping them to understand the complexities of building venture businesses.

Our Host, DigitalOcean’s Hollie Haggans, Opens the Evening

Nearly 100 attendees mingled and shared stories of Brian’s insights, expertise, and endless energy.  Our host, Hollie Haggans, DigitalOcean’s Global Partnerships head, opened the evening. After a welcome from Murat and Jon, Murat MC’d the evening. Speakers honoring Brian included Owen Davis, Partner at Contour Ventures Partners, who delivered remarks about Brian’s many contributions to NYC tech; Comixology’s David Steinberger, who reflected on the immense value of receiving investment from Brian; and David Rose, Founder of New York Angels who provided a history of New York tech all the way from New Amsterdam to Brian Cohen. Murat and Brian closed the evening with a Q&A, followed by the presentation of the award.

Comixology’s David Steinberger

Does Homeowners Insurance Cover Falling Trees? 

You’ve probably heard the old adage

If a tree falls in a forest, and no one hears it, does it make a sound?

But what if a tree falls on your house – are you covered? What about if it falls in your yard or falls on your fence? The answer is not straightforward.

Many Variables Come Into Play

This is one of those homeowners insurance answers where whether or not you’re covered does depend on the precise situation. And with 5% of homeowners filing at least one claim every year, you need to know what to do if you have to deal with a fallen tree. There are a lot of scenarios. So, let’s look at a few.

What if…

Your tree falls on your home or any other structure during a storm?
The national average for repairs after a storm is $7,296. The low end for damages may only be a few hundred while the high end can go to the tens of thousands. Wind damage is also the most frequent homeowners claim at roughly 25 percent of all claims.

Storms happen. And with any storm, debris removal comes with the cost of damages. If your tree falls on any roofed structure and causes damage to your property, then you’ll be covered by your homeowners insurance policy. Debris removal can be included on your policy, but sometimes carriers put a limit on debris removal (of $500, $1,000, or $2,000 per occurrence). Check your policy details for your exact coverage limit.

Your tree falls in your driveway?
Because you cannot use your driveway and because the tree may have damaged the driveway, homeowners insurance would cover its removal (in most states). However, check your deductible. Do you want to file a $1,200 claim with a $1,000 deductible – probably not.

A tree falls on your car?
Ouch. Your homeowners insurance would not cover it. Your auto insurance policy would under comprehensive or other-than-collision coverage.

Your tree fell down because of rot?
Homeowners insurance companies are a huge fan of responsible homeowners. Any time damage happens that could have been prevented, it gets classified as negligence. And when that happens, they most likely won’t cover damages. Something to keep in mind if you see a tree start to look like it could be a problem exercise some preventative maintenance to restore the tree or remove the tree before damage occurs. Supporting the tree with cables is risky, because if the cables break in a storm, the insurance company will say you were negligent in not removing the tree (given you put the cables up, you knew it had issues).

Your tree decides to fall into the street?
This is a tricky one. Either you or the city would be responsible for cleaning it up, depending on local laws in your county.

Your tree falls into your yard? 
While annoying, unfortunately, cleaning it up is on you. Homeowners insurance generally doesn’t cover trees falling over in your yard. They have to damage a structure for coverage to kick in. Some policies do, however, offer coverage for debris removal assistance.

Your tree fell because of…other causes?
If there was a riot, car accident, or fire that caused the tree to fall, then it would be classified under that peril and covered. On the other hand, if a tree fell over because of ground movement and damaged your house, it could be excluded if ground movement is an exclusion on your policy. (most common)

Your pesky neighbor’s tree fell into your yard?
This is where it gets unfortunate: where the tree lands determines responsibility. So, if the tree falls in your yard and it was healthy, you’re responsible.

If your neighbor was negligent, then their insurance could be responsible. (eg if their tree was rotting months ago, it should have been taken down, but they refused to do so)

Your pesky neighbor’s tree hit your house?
You’re covered. Phew. Many awkward summer barbeques in the backyard trying to avoid eye contact avoided. In some cases, your insurance company may have your neighbor’s homeowner’s insurance pay for any damages/removal through subrogation. And if that happens, you could recover your deductible.

How Much Coverage Will My Policy Provide?

Again, this varies from policy to policy. Generally speaking, the limit per tree is $500 for removal. Some policies limit the amount you can claim per event. For example, if your policy limits your claim total to $1,000 for tree removal, and it cost $500 to remove each tree, and 4 of them were knocked down, then you’d have to pay the extra $1,000 out of pocket.

Will My Insurer Buy New Trees?

Trees and other shrubs add value to your home. In some cases, it can add between 5% – 10% more value. That’s $15,000 – $30,000 on a $300,000 home. Wanting to replace them makes sense. Many policies have limits on trees and shrubs while some offer no coverage at all. Read your policy documents to see what coverage you have.

Keep in mind that if you sell plants as a business, then you would need to consult your business insurance for filing claims and getting replacements, not your personal homeowners policy.

Why Your Deductible Matters

Most common deductibles on a homeowners insurance policy are between $500 and $1,000. The average cost of tree removal can vary between $150 – $1,500 per tree. If it only costs $350 to remove the tree, then filing a claim would not be in your best interest.  Some home insurance policies have a $0 deductible specifically for the removal of fallen trees, so you may be in luck. Read your policy.

At your service,
Young Alfred

Drawing credit: McCulloch and Pitts 1943.

Some organizations chose to leave the DS process alone and looked for paradigm analogies, like using demo as the DS-analogous operator to replace code review of SWE. They combined this with changing the way they think about problem-solving. However, the inherent issues of applying Agile SWE rigor to data science have been skirted.  We know that the major argument for leaving DS as a waterfall approach is that it’s inherently a holistic process which can’t, or shouldn’t be broken down. Several authors take the pragmatic approach of breaking up the process into research and engineering and applying Agile to the latter while leaving the former as a waterfall. Here we see the first references towards making the process scalable, i.e. allow more people to be added to a task to speed it up, which is a great goal. Specifically, these recommend a heterogeneous team consisting of data scientists, data engineers, and software engineers.  The argument, again pragmatic, is that it is nearly impossible to find engineers capable of both performing in both research and SWE roles. Pragmatism is always the right way to go for a startup, but a chain is only as strong its weakest link.  Having a process by which product engineers need to wait on a waterfall DS task to complete before we can start incorporating results into the product leaves us feeling uneasy at TWOSENSE.AI.

AGILE PROCESSES FROM DATA SCIENCE

There are some great pieces shared from the trenches of early attempts.  One of the first pieces of learning is to keep individual DS tasks focused on a scope by the type of work that’s being done, e.g. literature review, data exploration, algorithm development, result analysis, review, and deployment phases for each sprint or story [RECOMMENDED READ]. This is a great piece of insight because it shows us their approach to actually breaking down a big thing into smaller steps and tasks as it relates to DS. We also see the concept of skipping or reassessing the remaining tickets based on what we learn during execution.  This concept is so exciting because it addresses one of the largest concerns of Agile ML critics: you don’t know what you don’t know. The big takeaway here is that you need to be continuously adjusting throughout the sprint based on what you’re learning.  This goes against mainstream Agile SWE wisdom since it makes projects more difficult to plan.  However, it’s a pragmatic approach to planning yet incorporating the intrinsic component of not knowing what you don’t know. Trying to enforce strict adherence to the plan in the face of new understandings feels to me like it would subtract from focus on the sprint goals, like dogma instead of pragmatism.

With this dynamic process, it’s important to stay on focus and make each ticket or task something with a tangible benefit. A great piece [RECOMMENDED READ] articulates the iterative nature of this process, starting off with a baseline and iteratively improving it with each task or ticket.   With a dynamic process that includes many unknowns and iterative improvements, it is also dangerous to be overly focused on story points [RECOMMENDED READ]. Doing so here can encourage gamification of the process where engineers optimize for the human performance metrics at the cost of ML performance. It’s important to evaluate the benefit of a small iteration, and the context switching cost of picking this back up later at another iteration. The same could easily be said about Agile SWE as well, however, the difference here is that DS/ML tasks are about answering a question far more often. One of the ways to address this is to acknowledge that there are some jobs that just don’t have deliverable code results [RECOMMEND READ] that should still be reviewed but to have these be shared as show-and-tell or demos.

FROM AGILE DATA SCIENCE TO AGILE MACHINE LEARNING

So far, we’ve looked at Agile DS and gotten as much as possible from the literature that seems relevant to MLE as well.  One thing to keep in mind is that the vast majority of what we’ve been speaking about thus far was designed for very long timelines.  The article above was speaking about sprints on a timeline of about 1 year (!!), so while many of the lessons learned are still relevant, there may be quite a bit further yet to go to get things in line with a 2-week sprint.  Furthermore, they’re looking at DS, which is not the same as MLE. DS is about finding questions the data can answer and answers to those questions where the consumer is most often a human. A DS sprint is therefore focused on answering a question.   MLE is about starting with a problem independent of the data and building an ML solution to it, where the consumer is usually a piece of software or an actuator of some kind. 

AGILE FOR MACHINE LEARNING ENGINEERING

When we dive into what’s been published on Agile for MLE, the literature is much thinner. A company called YellowRoad (I’m not sure what they do exactly, but I’m sure they do it really well!) has put out some great content.  They propose starting off with quick solutions over optimal ones [RECOMMENDED READ] as a way to make things iterative yet shippable. Google famously “blur the line between research and engineering activities” and say just about the same thing. <CONJECTURE> There is a vast chasm between knowing what the best solution is and implementing that in a product.  Bridging that chasm is the hardest part, not just in DS/ML, but in startups, art, everything: it’s called “execution.” Trying to find a process that closes that gap and unifies research and product dev is really hard, but you only have to invest that effort once. It’s what we’re trying to accomplish with all of this here. Having to bridge that gap every quarter, every sprint, or even every ticket feels like an ongoing cost that will be fatal for a startup’s execution. I think that this is also what Google discovered, and why they’ve made the decisions that they have.</CONJECTURE>

Google also says [RECOMMENDED READ] (Rule #4) that it’s better to start with a bare-bones model (we call it a skeleton) and focus on building the infrastructure first, then building out the ML iteratively once that’s in place. They even go a step further (Rule #1) and say try and start off with an expert system as your baseline to focus on the infrastructure alone before you even get into ML at all.  Those rules are fantastic, I recommend at least scanning all 37. On top of that, they have a great paper on ML technical debt [RECOMMENDED READ] that while slightly off-topic here, will greatly help anyone on the same journey with us.  It also concludes by saying that even if your team isn’t made up of full-stack ML engineers, these processes and the technical debt affects both SWEs and DSs, and there needs to be buy-in from both sides and tight integration between them.

In a recent conversation with Sven Strohband of Khosla, Sven stated that every ML/AI company he’s worked with that was able to execute successfully followed a “Challenger Champion” approach to MLE.  This is a concept that stems from the early days of financial modeling (as far as I can tell), where an incumbent system (the Champion) is A/B tested against a proposed improvement.  I love this concept because when combined with rigorous evaluation metrics, it creates a form of end-to-end testing where the metrics are the test.  It also creates a Kaizen culture, which is one of my favorite concepts, where small changes that improve the whole are adopted over time.

CONCLUSION

Before designing our process, we read everything we could find on the topic.  Here’s what we took away.

The TL;DR is this. Agile is a way of thinking more than anything else as it applies to Data Science and Machine Learning. It’s about creating a process that breaks big things down into small tasks, continually readjusts those tasks for changing requirements, and ships as often as the product does. Make it scalable starting off with the bare minimum to touch all components, and iterating from there on the different pieces. Try to beat the performance of the incumbent system with your “challenger” improvement by measuring against performance metrics that are relevant to you. Watch out for overly focusing on story points along the way, and use demos to show non-shippable results.  Finally, if you want to work in an Agile way, you’re going to need a process that combines Research and Software Engineering efforts into Agile Machine Learning Engineering.