Tech Skills You Need to Nail That Presentation

We’ve all been there.  Standing in front of some really important people, trying to make a great first impression when:

  1. You can’t get the projector working
  2. The people in the back can’t read your PDF and they ask if you can zoom in
  3. You accidentally move forward too many slides and don’t know how to go back

Any presentation that includes a projector can be riddled with technical challenges.  You may not be in IT but that’s no excuse for not having a basic knowledge of computer presentation skills.  It doesn’t matter how prepared you are, and it doesn’t matter how comfortable you are speaking in front of a crowd.  Stopping the presentation to wait for the tech guy to come to show you how easy it is to fix your problem will derail even the best presentation.  Don’t get stuck in this rut – learn the basic technical skills you need to nail that presentation.



If you’re bringing your laptop with the expectation of presenting from it, it’s important to know what video ports you have and make sure you’re equipped for maximum compatibility.  There used to be a time where 1 display port ruled them all, and asking “can I hook up my laptop” was the only question to ask.  Now, it’s important to know what ports you can use – “does your projector have an HDMI hookup?”.  Different laptops have different ports, regardless of age or make.  Refer to the chart below to determine what port(s) you have.  Also, if you plan to present often from this laptop, it might be worth investing in an adapter to ensure you never end up having to project from someone else’s computer.


There are two types of adapters I want to talk about.  One will convert from one port to another, and the second will give you multiple ports via USB.

For the first adapter, let’s day you have a super thin ultra book, so the manufacturer used a mini display port.  Nobody has a mini display port cable and projector just hanging around, so you’re going to want to get a mini display port to HDMI or VGA adapter.

The second type of adapter doesn’t use your current display port, but instead it creates a new one through USB.  I don’t recall seeing a laptop made since the year 2000 that didn’t have a usb port.  You can buy adapters that will turn USB ports into VGA or HDMI, which should pretty much ensure your compatibility and only required carrying around 1 adapter instead of multiple.


Most laptops will automatically mirror your display when you plug a projector in.  If it doesn’t you’re going to want to press and hold the Windows Key on your keyboard and use the “P” key to set the display type.

Changing inputs

Some projectors are set to auto input, most are not.  This means that once you have your computer sending a signal to the projector, you still need to set the projector to show your computer.  Grab the controller for the projector and look for a source or input button.  Often times you will have to click this repeatedly until you find the right input.  Inputs are often labelled by the port type you are using, so being familiar with your ports (see above) will help you find your input source on the projector.

Controlling Content

Navigating slides

Most people simply use a left mouse click to progress their PowerPoint slides forward.  This is fine until they advance too far and need to go back.  Then you get into a fiasco of right clicking and selecting “Previous Slide”.  If you’re this person, there’s an easier way.  The two preferred ways of navigating slides is by using the arrows keys on the keyboard or the scroll wheel on the mouse.  Using these methods (or at least knowing about them), makes moving back and forth a breeze.

Creating awesome slides

Think less is more.  Take the content of what you’re saying into consideration.  People don’t want to read what they are hearing.  Your slideshow should highlight key points in what you are saying, or provide illustration and visuals to help visual learners better understand.  Slides full of text – so small that nobody in the back can read – will put an audience to sleep because their brains won’t know what to focus on, your voice or their inner voice reading your slides.

Stick to a theme.  You don’t need fancy backgrounds with flashy fonts, but once you chose some basic colors and fonts – stick with them.  There should be some consistency to your presentation.

Visuals are key.  The reason you have a slideshow is to cater to visual learners.  Remember that people won’t be able to read small numbers or text, and that high contrast is important.  Grey font on a white background will wash out once you are a few feet away from the screen.

Animations are a nightmare.  You should only use animations under very specific circumstances (ie on one slide you ask a question and then want to advance to show the answer).  Timed drop-ins, fly-ins and rotating images will seriously mess up the flow of your presentation and are never well received by an audience.

Zoom at will

Remember what I said about the people in the back?  If you find yourself showing websites, PDFs or any other content and you get complaints that it’s too small, the solution is simple.  For almost any program (the only exception I can think of is Excel), zooming in and out is as simple as holding the Ctrl button on your keyboard and either using the scroll wheel on your mouse or the + and – buttons on your keyboard.  No longer will you have to look for the tiny magnifying glass that zooms in at glacial speeds.  People at the back can’t read the table? Ctrl and +.  Now the picture is too big to fit on the screen?  Ctrl and scroll down.


Stop projecting when not in use – people will automatically watch the screen if it’s there.  Turn it off (most projectors have a Blank button as well to blank out the screen) so that the focus shifts to you, and so that people aren’t reading your email notifications coming in.  This is important while looking for things on your PC or just setting up.  If you aren’t showing the audience anything, stop projecting.

Turn off applications with notifications – Outlook, Gmail, Skype…all of these applications have popups that will present themselves at inopportune times.  Your best bet is to close down any unused apps before the big presentation so that you don’t get an email notification that lets the audience know you’ve been shopping for underwear.

Don’t count on audio – Some people like to jazz up presentations with sound effects or video.  Not all meeting rooms are equipped with speakers, and your little laptop speakers are nowhere loud enough to fill a meeting room.  If you need audio, bring your own speakers – mini Bluetooth speakers are cheap and usually have a headphone jack so you can easily connect it to your laptop

Don’t count on screen size – More and more meeting rooms are going for 50 inch TVs rather than huge projectors.  This means that you’re going to want to stick with large fonts and big pictures.

Don’t count on internet – Again I’m going to put a damper on your fancy embedded YouTube video by saying that internet connections in meeting rooms are spotty.  Most of the time you’ll be projecting over WiFi and if you’re in a large meeting, there’s a good chance that an extra 50 people just hopped on the company’s already strapped WiFi with their phones, tablets and laptops.  You don’t want to end up waiting 8 minutes for your video to buffer, or have key information that you can’t show because the web page simply won’t load.

Have a backup plan – Always bring a thumb drive.  Easy peasy.  I’ll also usually email a copy of the presentation to myself as well.  Can never have too many backups

Show up early and setup the tech – Nothing is more frustrating for the local IT guy or gal than being called into a full meeting room, 5 minutes after you were supposed to start, to troubleshoot the projector.  Do everyone a favor and as soon as you arrive at your meeting, get your tech setup

Proper computer maintenance prevents problems – Windows updates force restarts, Java asks every 3 minutes if it can update.  Do yourself a huge favor by keeping your machine up to date and run anti-virus scans regularly.  Stop ignoring that flashing window on the bottom right hand side of your screen, because it’ll be when 50 people are staring at your screen that your machine will magically decide to reboot, and to install Windows updates while doing so.  20 minutes later and you’ll be right back where you were, except now you’ll be terribly embarrassed and only have 5 minutes left to present.

Insurance in the Age of AI, Block Chain and an Overabundance of Data


The traditional insurance model has had a pretty good run. It has been slowly evolving over the past few hundred years to include new coverages, multiple distribution channels (broker, agent, online), and create more complex actuarial models.  The financial industry has been known to be relatively slow adopters of new technology, mostly because companies simply cannot take undue risk – and those working in insurance are experts in minimizing risk.


This article is going to outline the current state of insurance, and the current progress technology has made, and will argue that the industry may be on the verge of significant disruption – the likes of which has the potential to render most policies, companies, employees and value added services obsolete.


Two things to keep in mind as we move through this piece.

  1. Nobody likes insurance. They need insurance, therefore they don’t like it.  It’s a necessary evil, and the perceived value isn’t there for those who do not have claims and have limited risk
  2. Technology has increased the rate of change to the point where the time it takes science fiction to become a reality can be reduced to months. The telegraph was the only mode of long distance communication for over 50 years before the telephone was invented.  And even then, it took an additional 60 years before they were mainstream.   Twitter started off with 400,000 per quarter and grew to 100,000,000 per quarter after one year.  Now, there are 6,000 tweets per second and 200 billion (with a b) per year.


Artificial Intelligence


There are a number of terms used when describing AI.  Neural networks, machine learning, deep learning, the list goes on.  This article will not provide a rich overview of how artificial intelligence works, there are a number of great resources that do that already.  But let me give you the Cole’s Notes version.  Traditional computer programs and algorithms have been exceptionally good at performing computational tasks.  Give it a problem to solve, or some code to run, and it can do so significantly faster than a human ever could.  The bottleneck with that approach is that it needed a human to give it clear and specific instructions on exactly what to do.  Furthermore, the computer could only process data that it could understand, which has typically been something coded into a language a computer can translate (programming language/machine code) or broken down into pure numbers (computers are great at math).  What traditional computers haven’t been able to do, in clear contrast to AI, is come to it’s own conclusions and uncover for itself the best solution given a specific rule set.  Furthermore, computers are getting really good at understanding non-traditional sources of information.  Image recognition, speech recognition, unsorted datasets.  When given access to a large number of YouTube videos, DeepMind’s AI taught itself what a cat looked like, and became very good at identifying cats against other images of animals.  Nobody told it what a cat looked like, it learned that on it’s own.




Everyone’s favorite subject.  It’s the technology that is going to change the world, and none of us really know what it is.  Again, I’m just going to provide a nutshell description of the technology, and it should become clearer as we go through the thought experiment below.  Blockchain technology provides a single ledger of transactions that is secured by cryptography and the fact that everyone works off the same books, at the same time.  You cannot change the record maliciously because your update will be in conflict with what everyone else’s records hold as the truth, and therefore it will spot the lie.  Blockchain effectively reduces the need for traditional institutions because it increases the self service capability without the risk of fraud.  It means that the gatekeeper mentality is no longer necessary because once the rules are established, everyone gets forced into playing nicely together, tracking whatever transactions are implemented.


The real cause of disruption for insurance – data


We’ve heard the stories about AI for awhile, but it’s nothing really new for the insurance world.  Analytics has been a growing part of the industry for years, and it’s obvious that with increased computing power we would inevitably evolve the rating algorithms and risk assessment tools to use the latest available tech.  The problem is that the AI boom is only made possible by the explosion of available data.  AI needs huge datasets to learn from, and these datasets are collected and aggregated by some of the largest companies in the world.  Google and Amazon are fighting toe to toe in a race to develop the smartest most capable AI, and others are joining that fight constantly.


Let’s step through the traditional method of getting insurance.  First it starts with someone seeking out insurance, physically deciding that they want/need, and then shopping for it.  Then they go through the drudgery of providing all of their information, which is tedious because they don’t readily know 90% of it, and because they also know that everything they say from here on in will impact the price of their insurance.  It’s kind of like going to a new doctor every time without any patient records, and relying on the patient to provide their entire medical history from memory – guaranteed they will some things wrong, or leave some things out.  And the more complicated their medical history is, the higher the likelihood of them forgetting something, and the greater chance that whatever they forgot was very important.


Now let’s contrast this to Google.  A quick look through my Google Account and Privacy Settings shows a detailed history of everything that Google knows about me.  They have my banking institution, my spending habits, obviously my address, where I work, how I get to work, what music I’m into, what food I eat, my hobbies and interests.  I also have a Nest thermostat and smoke alarm, so they have real time monitoring of my house’s temperature.  But I also upload all of my photos to Google Photos.  So they have pictures of the inside and outside of my house. They also have instant access to my photos if, let’s say, I get into a car accident and my first response is to snap pictures for proof.  Your first response might be “you should do more to protect your privacy” and my answer is that I already do.  I am relatively conservative in my approach to letting “Big Brother” track me.  I’m an informed user with a tech background and I do monthly audits of my privacy settings and sift through my personal data to weed out anything sensitive.  My question to you is “when is the last time you reviewed your privacy settings on Google, Facebook, Twitter, Instagram…?”.


Customer acquisition over profits


In the data driven age, profits are not your first priority, what you’re looking for is customers and data.  If you acquire enough users, you will be able to collect enough data, and many companies rely on that data to bring in the money. Companies will either sell that data, use the data to teach their algorithms, or analyze the data to determine how to become profitable.  More data equals higher levels of certainty, and you move from guessing to knowing when making decisions. It took Amazon 14 years to become profitable.  How many companies sat idly by saying “Amazon’s business model isn’t sustainable, they aren’t even profitable yet”?  Tesla still isn’t profitable, having started manufacturing electric cars in 2003 and being valued at $51 billion.  Just because a company isn’t profitable today does not mean it won’t become a world leader tomorrow.


The Future


This is the story of a fictitious insurance company named Acme Insurance.  The board of directors at Acme decided that the most important thing for their company to do is acquire as much data as possible and start using AI to rate policies, check claims for fraud, and to determine the best risks by segmenting the market into micro markets and focusing all their efforts on acquiring the right risks.


Their approach was simple, direct sales to consumers, 100% online, a beautiful user interface that was intuitive and easy to use. They undercut the market by minimizing their expenses.  Investment was small because they ran on Amazon Web Services, therefore only using the computing power they needed.

To identify their initial target markets, they hired contract programmers who had experience scraping the web.  They scoured the internet for every quick quote tool, price comparison website, rate manual, info sheets etc. that were publicly available. They then ran scenarios against these data sources to determine which risks received the best rates.  This let them use the knowledge and experience of the established industry to determine how to rate their clients.  They also used the loss ratios and experience from each insurance company to weight their algorithms, so Acme’s rating would more closely reflect higher performing companies.


Now that they knew how the industry priced risk, they hired the top digital marketing firms to go after the most attractive clients.  Marketing online is significantly cheaper and easier than traditional marketing.  They mapped out their demographics based on what social media platforms cater to each group.  They used information from Google search to figure out where these people lived, and what they searched for online.  They used sentiment analysis AI to read and understand all the reviews found online for their competitors, from Google to Glassdoor to Facebook.  Using all of this information, they determined the pain points for their key target markets, exactly what language they use to describe their frustrations, and exactly where these people spend their time online.


Acme is growing at an astronomical rate, increasing it’s book by about 40% month over month.  About 6 months in, they start to see the claims that go with that growth.  Luckily enough, the company is growing so fast that it artificially keeps their loss ratio to a controllable 200%.  The company is losing money, but with every claim they are gaining equity.  The way they are doing that is by scrutinizing every claim to teach an AI to better predict fraudulent claims and better understand risk factors.  Every minute detail of a claim is tracked and analyzed.  The system collects all like policies, determines the exposure of risks with similar geography, coverage, customer demographic, construction type, build year… The AI then incrementally adjusts the rates, limits, deductibles and wordings of those coverages and details to fine tune it’s algorithms to cover future losses.  But it doesn’t just have one set of rates and rules. The system maintains 10+ different rates and rules in order to test them against their policies to determine which action made sense.  Some policies will see an increase in premium, some will see a drop in limits, some will have increased deductibles, some will have all 3, some will see no change.  By doing this, Acme can perform an A/B test on it’s book to determine which actions properly mitigate risk, but also which actions are acceptable by their user base (by tracking number of cancellations).


After 5 years of rapid growth and losing money, Acme insurance seems to be a failure.  They grew too big too fast.  They tried breaking into too many different markets, servicing different countries, and stretching their region too thin.  They weren’t advertising using traditional methods, so most people hadn’t even heard of them before.  As a private company that only employs roughly 100 staff, they didn’t seem to pose a threat.  Furthermore, their CEO had been active on social media, and was quite transparent about the unsustainable losses they experienced.  With over $400 million in venture funding, everyone from big banks to housing developers, even governments had jumped on the bandwagon.  The company appeared to be a bust.  But that was from the outside looking in.


Year 5 marked a turning point for Acme. Their client base reached 50 million users world wide, writing in just about every developed country.  Their learning algorithm used it’s ability to constantly retrain and learn with every new policy, claim and customer complaint to successfully rate any building or automobile, anywhere in the world.  They were also able to successfully eliminate the individual from the rating equation, making decisions purely based on the physical item that was insured. By partnering with a number of companies developing smart home technologies, the vast majority of their houses were outfitted with appliances and sensors to speak with their AI and the insured in real time.  This provided constant reminders to minimize the risk of a claim – a hot spot reader will alert the insured that a candle is still on when they head for bed, the scheduler will notify the insured that it’s been 2 years since they cleaned their wood fireplace, and the smart stove automatically shuts off if it detects too much smoke in the range hood. Acme also had partnerships with every major automobile manufacturer, and created a data sharing pool which allowed them to gain insights into driving patterns, and in return Acme was able to significantly discount insurance rates on new cars – effectively reducing the cost of ownership and acting as a discount.  All policies were rating month to month, and were based on the previous month’s usage and learnings.


Acme’s final move was to leverage governments to allow it’s product to be sold in conjunction with mortgages, car loans, property taxes, and in some cases they just rolled the predicted cost into the purchase price.  Acme’s algorithm was so successful that it could confidently assess the 5-10 year premium for certain makes and models, and since most people own new cars for roughly 10 years, they simple added it onto the initial sale of the car.  Acme also began leveraging it’s capital to hire more and more climate scientists, and began modelling weather patterns in-house.  The company became synonymous with environmentalism and safety.  Acme rebuilt damaged homes using state of the art building materials to prevent future risks, such as breathable concrete, fire suppression systems, and hot spot detection devices.  Even houses located in flood plains received giant inflatable balloons that would circle the entire house to protect it from floods.


History would look fondly on the impact that Acme had, not just as a business and AI developer, but because it saved lives and changed the world.  Acme’s largest financial risk was climate change, and so the company leveraged it’s relationships with governments and it’s positive relationship with it’s insured to make drastic changes towards sustainability.  Acme led the charge towards fighting climate change, developing self driving cars that reduced traffic accidents to zero, and eliminated the need to shop for the best price for insurance.


Did you like this article? Want to learn about how Tesla and other car manufacturers will disrupt the insurance industry? Check out my other post here.

My Road to Data Science and Machine Learning

Time Lapse Road

The great thing about learning anything technology related is that the internet is overrun with resources.  The downside of too many options is the paradox of choice.  After scratching at the surface of researching A.I., I decided to get smart and make sure that I strategically step through the different requirements of becoming a data scientist.  I finished by bachelors degree 10 years ago, and while pursuing a Master’s Degree seems attractive, the headache of applying for competitive programs and then balancing structured course work and working full time just doesn’t interest me right now.

I’ve come up with my own road map to data science, and wanted to document my plan, and the execution of that plan here – updating and tweaking it along the way.  Here it is:

  1. Brush up on my Python programming.  I know enough Python to copy/paste, debug and hack my way through.  Machine learning seems complicated enough – struggling with the coding behind it will make it too hard.  So I’m going to make sure I’m super comfortable with the language
  2. Math refresher – Linear Algebra, Calculus, Probability and Stats.  These are the fundamentals laid out in the very useful video here. I’ll check out the MIT OpenCourseWare and most likely follow along with the corresponding courses there.
  3. Udemy Data Science A-Z – Next I think I’ll need a solid understanding of what data science is, and some practical uses of it.  That’s why I bought this course on sale, which comes highly recommended by the community.
  4. YouTube tutorials – Now it will be time to put my knowledge to work.  I’ve watched a number of videos by Siraj Raval and his YouTube Channel. He has a lot of practical examples that I’d like to not only implement, but build off of.
  5. Lastly, I will foray into playing with Google’s DialogFlow to build some real world tools that I can launch to Google Assistant and other services online. This will let me build a conversational AI without needed to program the back and forth dialog and focus my machine learning skills toward analyzing and processing data.

30 reasons why Tesla will inevitably insure its own cars

Tesla Model S Interior


Let’s start off with a non-exhaustive list of what driver/car information Tesla collects or has access to:

  • Location
  • Telematics
    • Acceleration
    • Deceleration
    • Driving Speed
    • Odometer
    • System health
    • Short videos from 8 cameras
    • Data from 12 ultrasonic sensors
  • Credit information from application
    • Credit score
    • Credit history
    • Employer location and job details
    • Date of birth
    • SIN/Social Security
  •  Remote analysis
    • Contacts
    • Browsing history
    • Music history
    • Navigation history
  • Charging station
    • Charge rate
    • What kind of charger was used
    • Supercharger/Destination charger history


That’s the data, but what can Tesla actually learn or understand about the risk of the driver?  Well let’s use our imagination here

  • Does the driver
    • Frequently accelerate rapidly?
    • Follow too closely behind other cars?
    • Speed?
    • Drive on major highways to get to work?
    • Drive home from bars on weekend nights?
    • Go on long road trips often?
    • Service their vehicle regularly and in compliance with the owner’s manual?
    • Drive out of country/province/state often?
    • Listen to loud music while driving?
    • Talk on the phone while driving?


To drive home exactly how much Tesla knows and understands its drivers, I’d like to take you through two actual scenarios (features) that I have witnessed


  • It’s a cold winter’s day and I’m getting into my car, ready for the bitterly cold steering wheel and waiting for the car to warm up.  Typically it takes a while for an electric car to warm up in -20 temperatures because the batteries don’t operate efficiently when too cold.  To my surprise, the heat was already on, along with the seat warmer, and the battery heater.  The car is already drivable, and quite comfortable.  You might be saying “who cares, most cars have remote starters now” to which I would answer that I did not start any of this.  Tesla knows when I typically begin my commute, and takes care of warming the car up automatically for me.
  • Getting into my car after leaving work a little later than usual, the screen turns on and notifies me that it has found a faster way home from work.  It knows I’m at work, it knows I’m going home, it knows my typical route home, it knows that traffic is heavier than usual, and it knows a better route home.  I didn’t tell my car any of this, it assumed everything from previous history, and merges that with real time traffic data.


Why Tesla Will Need to Insure its Own Cars


AAA has already come out stating that Teslas require higher premium to cover higher claim frequency and severity.

“AAA is raising rates on Tesla vehicles based on data showing that the Model S and Model X had abnormally high claim frequencies and high costs of insurance claims compared with other cars in the same classes.” –

If the frequency and severity of claims for a specific make and model are higher than the rest of your book, you increase rates to cover the risk.  This only makes sense.  What’s troubling is that Tesla believes that this study is highly flawed, and does not represent the reality of their cars on a whole.

This isn’t a new topic for Tesla, in fact they already have partnered with Aviva in Canada, Direct Line in the UK and Liberty Mutual in the US.

The problem is that actuarial models may not account for the unknowns of such a new and innovative approaches to automobile safety.  Teslas are notoriously expensive to repair.  Parts are often scarce as Tesla has a hard enough time producing parts for its new fleet, let alone replacement parts.  Furthermore, the high tech design of Teslas, including the sensors, cameras etc. make repair more difficult, and specialized.  Lastly, the company has made a number of design decisions around safety first, rather than ease of repair.  This means that simple fender benders can result in a total loss if the node happens to get damages.  In this link you will find an actual driver’s description of a simple dent in his bumper resulting in a write off of his $100,000 car.  For a little more information, click here to see the actual patent Tesla owns for its sub-frame design.

Elon Musk, Tesla’s CEO, has already said that they will get into the insurance business if the need arises.

“Not to the exclusion of insurance providers but if we find that insurance providers are not matching the insurance proportionate to the risk of the car, then if we need to, we will in-source it. I think we will find that insurance providers do adjust the insurance cost proportionate to the risk of a Tesla.” –

The insurance industry is risk averse, relies on proven actuarial data and underwriting/claims experience to make decisions.  The introduction of self-driving cars alone will cause a mighty stir in the industry, as it tries to react to a new technology that doesn’t fit within the well designed box of an auto policy.  Tie that in with the (perceived) increase of frequency and severity with these cars, and we will see raising rates.  If Tesla decides to take on insuring its own vehicles, with access to a vast amount of data that a traditional insurer could only dream of, we will see the rating of an auto policy move from an art to a science.  Once this happens, and Tesla proves profitable, other manufacturers will follow suit.  In fact, because Tesla is a new entrant already into a mature auto industry that is traditional and tired, we will see other car manufacturers clamoring for additional sources of revenue to make up for their loss in market share.

The insurance industry has become complacent, seeing many previous new entrants, like big banks, fail.  This is because those previous entrants tried to do what insurance companies have been doing, just a little bit better. I would argue that if Tesla approaches insurance the way that it approaches auto manufacturing, product design, charging network development, and marketing, the insurance industry is in for a rude awakening.  And for those that think that this is just one car company, what can they possibly do?  Many are predicting that Tesla will do to the car market what Apple did to the cell phone market. Rapid acquisition of a large proportion of the market.

In my next post, I will dig a little deeper into how Tesla could use this data, along with its experience in artificial intelligence, to hone in on the science of risk assessment and eventually eliminate the need for car insurance altogether. Stay tuned.