Category: Uncategorized

Introduction
I often think of AI as something separate from traditional computer programming, something transcendent. However, most of the advances in modern AI are not the result of revolutionary new concepts or fields of study but rather the application of previously developed algorithms to significantly more powerful hardware and massive datasets.

Hannah Fry’s take on the world of AI covers topics ranging from justice to autonomous vehicles, crime, art and even to medicine. While the author is an expert in the field, she does a great job distilling the topics down to a level understandable by a layperson, but also keeps it interesting for someone with more background in programming and AI.

My favourite quote from the first part of the book comes on page 8, where Hannah succinctly describes the essence of what an algorithm is in only one sentence:

An algorithm is simply a series of logical instructions that show, from start to finish, how to accomplish a task.

Fry, Hannah. Hello World: Being Human in the Age of Algorithms (p. 8). W. W. Norton & Company. Kindle Edition

Once you read it, it seems obvious, but trying to describe to a first-year computer science student what an algorithm is can be a challenging task. The author manages this well. Despite the complexity and depth of the subject matter, Fry is able to bring context and relevance to a broad array of topics. The remainder of my review will speak to some of the book’s many sections and how someone with a business-facing view into the topics sees them.

Data
This section covers some of the unknown giants in data-science including Peter Thiel’s Palantir. The section also touches on some very public examples where analytics has played a negative role – Cambridge Analytica’s use of private user data during the 2016 Presidential Elections.

The story here is about data brokers. Data brokers are companies who buy and collect user data and personal information and then resell it or share it for profit. A surprising fact is that some of these databases contain records of everything that you’ve ever done from religious affiliations to credit-card usage. These companies seem to know everything about just about everyone. It turns out that it is relatively simple to make inferences about a person based on their online habits.

The chapter converges to one of the major stories of 2018, the Cambridge Analytica scandal. But it begins by discussing the five personality traits that psychologists have used to quantify individuals’ personalities since the 1980s: openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. By pulling data from users’ Facebook feeds, Cambridge Analytica was able to create detailed personality profiles to deliver emotionally charged and effective political messages.

Perhaps the most interesting fact though, is how small of an impact this type of manipulation actually has. The largest change reported was from 11 clicks in 1000 to 16 clicks in 1000 (less than 1 percent). But even this small effect, spread over a population of millions can cause dramatic changes to the outcome of, say, an election.

That’s the end of part 1 of this review. In Part 2, I’ll touch on some of the other sections of the book including Criminal Justice and Medicine.

Introduction
I often think of AI as something separate from traditional computer programming, something transcendent. However, most of the advances in modern AI are not the result of revolutionary new concepts or fields of study but rather the application of previously developed algorithms to significantly more powerful hardware and massive datasets.

Hannah Fry’s take on the world of AI covers topics ranging from justice to autonomous vehicles, crime, art and even to medicine. While the author is an expert in the field, she does a great job distilling the topics down to a level understandable by a layperson, but also keeps it interesting for someone with more background in programming and AI.

My favourite quote from the first part of the book comes on page 8, where Hannah succinctly describes the essence of what an algorithm is in only one sentence:

An algorithm is simply a series of logical instructions that show, from start to finish, how to accomplish a task.

Fry, Hannah. Hello World: Being Human in the Age of Algorithms (p. 8). W. W. Norton & Company. Kindle Edition

Once you read it, it seems obvious, but trying to describe to a first-year computer science student what an algorithm is can be a challenging task. The author manages this well. Despite the complexity and depth of the subject matter, Fry is able to bring context and relevance to a broad array of topics. The remainder of my review will speak to some of the book’s many sections and how someone with a business-facing view into the topics sees them.

Data
This section covers some of the unknown giants in data-science including Peter Thiel’s Palantir. The section also touches on some very public examples where analytics has played a negative role – Cambridge Analytica’s use of private user data during the 2016 Presidential Elections.

The story here is about data brokers. Data brokers are companies who buy and collect user data and personal information and then resell it or share it for profit. A surprising fact is that some of these databases contain records of everything that you’ve ever done from religious affiliations to credit-card usage. These companies seem to know everything about just about everyone. It turns out that it is relatively simple to make inferences about a person based on their online habits.

The chapter converges to one of the major stories of 2018, the Cambridge Analytica scandal. But it begins by discussing the five personality traits that psychologists have used to quantify individuals’ personalities since the 1980s: openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. By pulling data from users’ Facebook feeds, Cambridge Analytica was able to create detailed personality profiles to deliver emotionally charged and effective political messages.

Perhaps the most interesting fact though, is how small of an impact this type of manipulation actually has. The largest change reported was from 11 clicks in 1000 to 16 clicks in 1000 (less than 1 percent). But even this small effect, spread over a population of millions can cause dramatic changes to the outcome of, say, an election.

That’s the end of part 1 of this review. In Part 2, I’ll touch on some of the other sections of the book including Criminal Justice and Medicine.

These days it seems like businesses are trying to use AI to do everything. At least for startups, that isn’t far off. Anywhere there is a dataset remotely large enough and an answer that is vaguely definable, companies are putting together a business model to use machine learning to solve the problem. With some incredible successes in areas like image classification and defeating humans at video games, its hard not to be impressed.

One of the best channels for following recent breakthroughs in AI is the 2 Minute Papers YouTube Channel, started by Károly Zsolnai-Fehér, a professor at the Vienna University of Technology in Austria. Károly’s videos combine interesting clips of the programs in action with well-delivered summaries of recent papers illustrating advances in artificial intelligence.

In one of his latest videos, he covers an AI that not only can copy the most successful actions that humans take in video games but can actually improve on those actions to be better than the best human players. So does that mean that AI will be displacing office workers once it learns how to do their jobs better than them? Probably, yes. But maybe not quite how you think it might.

As much of a ‘black-box‘ as AI has been in the past, modern systems are becoming better and better at explaining how they arrived at an answer. This gives human operators predictive capabilities that we didn’t have with systems of the past that could spit out an answer but gave us no indication of how that answer was formulated.

This Forbes article on Human-Centric AI provides some examples of how modern AI systems can be implemented to train employees to do their jobs better and even enjoy their jobs more while doing it! If that doesn’t sound incredible to you, you may be a machine who is only reading this page to improve your search algorithm.

So what does this all mean? A lot of research is showing that AI is actually creating many more jobs than it destroys. So, as long as you’re willing to try and understand the systems that will one day be our overlords, you should be able to upgrade your career and stay employed.

Whether you still want the job that remains is another question entirely.

These days it seems like businesses are trying to use AI to do everything. At least for startups, that isn’t far off. Anywhere there is a dataset remotely large enough and an answer that is vaguely definable, companies are putting together a business model to use machine learning to solve the problem. With some incredible successes in areas like image classification and defeating humans at video games, its hard not to be impressed.

One of the best channels for following recent breakthroughs in AI is the 2 Minute Papers YouTube Channel, started by Károly Zsolnai-Fehér, a professor at the Vienna University of Technology in Austria. Károly’s videos combine interesting clips of the programs in action with well-delivered summaries of recent papers illustrating advances in artificial intelligence.

In one of his latest videos, he covers an AI that not only can copy the most successful actions that humans take in video games but can actually improve on those actions to be better than the best human players. So does that mean that AI will be displacing office workers once it learns how to do their jobs better than them? Probably, yes. But maybe not quite how you think it might.

As much of a ‘black-box‘ as AI has been in the past, modern systems are becoming better and better at explaining how they arrived at an answer. This gives human operators predictive capabilities that we didn’t have with systems of the past that could spit out an answer but gave us no indication of how that answer was formulated.

This Forbes article on Human-Centric AI provides some examples of how modern AI systems can be implemented to train employees to do their jobs better and even enjoy their jobs more while doing it! If that doesn’t sound incredible to you, you may be a machine who is only reading this page to improve your search algorithm.

So what does this all mean? A lot of research is showing that AI is actually creating many more jobs than it destroys. So, as long as you’re willing to try and understand the systems that will one day be our overlords, you should be able to upgrade your career and stay employed.

Whether you still want the job that remains is another question entirely.

Transfer learning. It’s a branch of AI that allows for the style transfer from one image to another. It seems like a straightforward concept: take my selfie and make it look like a Michelangelo painting. However, it is a fairly recent innovation in Deep Neural Networks that has allowed us to separate the content of an image from its style. And in doing so, to combine multiple images in ways that were previously impossible. For example, taking a long-dead artist’s style and applying it to your weekend selfie.

Just to prove that this is pretty cool, I’m going to take my newly built style transfer algorithm and apply it to a ‘selfie’ of my good dog, Lawrence. Here’s the original:

And here’s the image that I’m going to apply the style of:

That’s right, it’s Davinci’s Mona Lisa, one of the most iconic paintings of all time. I’m going to use machine learning to apply Davinci’s characteristic style to my iPhone X photo of my, admittedly very handsome, pupper.

If you’re interested, here’s a link to the original paper describing how to use Convolutional Neural Networks or CNNs to accomplish image style transfer. It’s written in relatively understandable language for such a technical paper so I do recommend you check it out, given you’re already reading a fairly technical blog.

So what is image content and style and how can we separate out the two? Well, neural networks are built in many layers, and the way it works out, some of the layers end up being responsible for detecting shapes and lines, as well as the arrangement of objects. These layers are responsible for understanding the ‘content’ of an image. Other layers, further down in the network are responsible for the style, colors and textures

Pretty striking, if I do say so myself.

Using a pre-trained Neural Network called VGG19 and a few lines of my own code to pull the figures and what’s called a Gram Matrix I choose my style weights (how much I want each layer to apply). Then using a simple loss function to push us in the right direction we apply the usual gradient descent algorithm and poof. Lawrence is forever immortalized as a Davinci masterpiece.

Impressed? Not Impressed? Let me know in the comments below. If you have anything to add, or you think I could do better please chime in! This is a learning process for me and I’m just excited to share my newfound knowledge.

Here’s a link to my code in a Google Colab Notebook if you want to try it out for yourself!

A friend sent me a video today. It started off rather innocuously, with a program called EarWorm, designed to search for Copyrighted content and erase it from memory online. As many of these stories do, it escalated quickly. Within three days of being activated by some careless engineers with no backzground in AI ethics, it had wiped out all memory of the last 100 years. Not only digitally, but even in the brains of the people who remembered it. Its programmers had instructed it to do so with as little disruption to human lives as possible, so it kept everyone alive. It might have been easier to just wipe humanity off the map. Problem solved. No more Copyrighted content being shared, anywhere. At least that didn’t happen. Right?

This story is set in the year 2028, only ten years from now. These engineers and programmers had created the world’s first Artificial General Intelligence (AGI) and it rapidly became smarter than all of humanity, with the computing power and storage capacity surpassing what had been available previously though all of human history. Assigned a singular mission, the newly formed AGI sets out to complete its task with remorseless efficiency. It quickly invents and enlists an army of nanoscopic robots that can alter human minds and wipe computer memory. By creating a mesh network of these bots that can self-replicate, the AI quickly spreads its influence around the world. It knows that humans will be determined to stop it from accomplishing its mission, so it uses the nanobots to slightly alter the personalities of anyone intelligent enough to pose a threat to its mission. Within days it accomplishes its task. It manipulates the brains of its targets just enough to achieve the task while minimizing disruption. It does this by simply reducing the desire of the world’s best minds in AI to act. It creates apathy for the takeover that is happening right in front of them. By pacifying those among us intelligent enough to act against it, its mission can proceed, unencumbered by pesky humans.

Because it was instructed to accomplish its task with ‘as little disruption as possible’ the outcome isn’t the total destruction of humanity and all life in the universe, as is commonly the case in these sorts of AI doomsday scenarios. Instead, EarWorm did as it was programmed to do, minimizing disruption and keeping humans alive, but simultaneously robbing us of our ability to defend ourselves by altering our minds so that we posed no threat to its mission. In a matter of days, AI drops from one of the most researched and invested-in fields to being completely forgotten by all of humanity.

This story paints a chilling picture (though not as chilling as many ‘grey-goo’ scenarios, which see self-replicating, AI-powered nanobots turning the earth, and eventually the entire universe, into an amorphous cloud of grey goo). It is a terrifying prospect that a simple program built by some engineers in a basement could suddenly develop general intelligence and wipe an entire century of knowledge and information from existence without a whimper from humanity.

How likely is it? Do we need to worry about it? and What can we do about it? are some of the questions that sprang to mind as I watched the well-produced six-minute clip.  It is a scenario much more terrifying and unfortunately, more plausible than those of popular TV and films like Terminator and even Westworld. There are a lot of smart people out there today who warn that AI, unchecked, could be the greatest existential threat faced by humanity. It’s a sobering thought to realize that this could happen to us and we wouldn’t even see it coming or know it ever happened.

Then, the real question that the video was posing dawned on me: Has this already happened?

We could already be living in a world where AI has already removed our ability to understand it or to act against it in any way…

I hope not, because that means we’ve already lost.

Here’s the video if you’re interested

My company, InOrbis Intercity, is doing a capital raise. We’ve been operating profitably for nearly two years now and we’re growing at a steady 10-20% per month. It’s exciting, to say the least, but this is the first time I’ve raised outside money. I have read book after book on Venture Capital, Angel Investment, Private Equity, and even raising money via an ICO (Initial Coin Offering). I have pitched our company several times, but the interest has been limited in this neck of the woods (Alberta, Canada). I think it’s time to start investigating alternative funding sources.

I know this business can be a billion dollar company, but we need to build scalable software to allow us to operate in more markets. We have developers, but we need more, and we need to hire some of the best in the business to work on our team. We’re building something great, applying AI to make intercity travel a better experience for millions of people. 

Raising money is a battle, only about 0.01% of companies actually manage to raise the funds for a billion-dollar valuation. There are hundreds of unreported examples where companies raise multiple-millions, the company grows and is sold for tens of millions but the founders are left with nothing.

It is a monumental risk, to be sure, but at this point, it seems like the best option we have available to grow the business.

If you’re interested in being part of something special, the future of autonomous, sustainable, intercity transportation, and you are a passionate, intelligent developer, or software engineer, willing to risk it all on a moonshot (I know, there must be at least one or two out there), send me a message and we’ll meet for coffee.

On the flip side, if you’re an Angel or VC and you’d like to learn more, drop us a line. I’d be happy to pull out our pitch deck and wow you with this vision. We will make it happen, but your help could make it a reality that much sooner.

Cheers,

Rosario

My company, InOrbis Intercity, is doing a capital raise. We’ve been operating profitably for nearly two years now and we’re growing at a steady 10-20% per month. It’s exciting, to say the least, but this is the first time I’ve raised outside money. I have read book after book on Venture Capital, Angel Investment, Private Equity, and even raising money via an ICO (Initial Coin Offering). I have pitched our company several times, but the interest has been limited in this neck of the woods (Alberta, Canada). I think it’s time to start investigating alternative funding sources.

I know this business can be a billion dollar company, but we need to build scalable software to allow us to operate in more markets. We have developers, but we need more, and we need to hire some of the best in the business to work on our team. We’re building something great, applying AI to make intercity travel a better experience for millions of people. 

Raising money is a battle, only about 0.01% of companies actually manage to raise the funds for a billion-dollar valuation. There are hundreds of unreported examples where companies raise multiple-millions, the company grows and is sold for tens of millions but the founders are left with nothing.

It is a monumental risk, to be sure, but at this point, it seems like the best option we have available to grow the business.

If you’re interested in being part of something special, the future of autonomous, sustainable, intercity transportation, and you are a passionate, intelligent developer, or software engineer, willing to risk it all on a moonshot (I know, there must be at least one or two out there), send me a message and we’ll meet for coffee.

On the flip side, if you’re an Angel or VC and you’d like to learn more, drop us a line. I’d be happy to pull out our pitch deck and wow you with this vision. We will make it happen, but your help could make it a reality that much sooner.

Cheers,

Rosario

Index funds are the most highly traded equity investment vehicles, with some funds like ones created by Vanguard Group cumulatively being valued at over $4 Trillion USD. Index funds have democratized investing by allowing access to passive investments for millions of people. But what are they?

An index fund is a market-capitalization weighted basket of securities. Index funds allow retail investors to invest in a portfolio made up of companies representative of the entire market without having to create that portfolio themselves. Compared to actively managed funds like mutual funds and hedge funds, index funds tend to have much lower fees because the only balancing that happens occurs based on an algorithm to keep the securities in the fund proportional to their market cap (market capitalization, or market cap, is the number of shares that a company has on the market multiplied by the share price).

Starting in the 1970s, the first ‘index funds’ were created by companies that tried to create equally weighted portfolios of stocks. This early form of the index fund was abandoned after a few months. It quickly became apparent that it would be an operational nightmare to be constantly rebalancing these portfolios to keep them equally weighted. Soon companies settled on the market capitalization weighting because a portfolio weighted by market cap will remain that way without constant rebalancing.

With the incredible advancement of AI and extraordinarily powerful computers, shouldn’t it be possible to create new types of ‘passively managed’ funds that rely on an algorithm to trade? What that could mean is that index funds might not have to be market cap weighted any longer. This push is actually happening right now and the first non-market cap weighted index funds to appear in over 40 years could be available to retail investors soon.

But this means that we need to redefine the index fund. The new definition has three criteria that must be met for a fund to meet:

1. It must be transparent – Anyone should be able to know exactly how it is constructed and be able to replicate it themselves by buying on the open market.
2. It must be investable – If you put a certain amount of money in the fund, you will get EXACTLY the return that the investment shows in the newspapers (or more likely your iPhone’s Stocks app).
3. It must be systematic – The vehicle must be entirely algorithmic, meaning it doesn’t require any human intervention to rebalance or create.

So, what can we do with this new type of index fund?

We can think of investing like a spectrum, with actively managed funds like hedge funds on one side and passively managed index funds on the other and all the different parameters like alpha, risk control and liquidity as sliders on a ‘mixing board’ like the one in the image above. Currently, if we wanted to control this board, we would have to invest in expensive actively managed funds and we wouldn’t be able to get much granular control over each factor. With an AI-powered index fund, the possibilities of how the board could be arranged are endless. Retail investors could engage in all sorts of investment opportunities in the middle, instead of being forced into one category or another.

The implications of a full-spectrum investment fund are incredible. Personalized medicine is a concept that is taking the industry by surprise and could change the way that doctors interact with patients. Companies like Apple are taking advantage of this trend by incorporating new medical devices into consumer products, like with the EKG embedded into the new Apple Watch Series 4.

Personalized investing could be just as powerful. Automated portfolios could take into account factors like age, income level, expenses, and even lifestyle to create a portfolio that is specifically tailored to the individual investor’s circumstances.

So why can’t you go out and purchase one of these new AI managed, customizable index funds?

Well, unfortunately, the algorithms do not exist, yet. The hardware and software exists today to do this but we’re still missing the ability to accurately model actual human behaviour. Economists still rely on some pretty terrible assumptions about people that they then use to build the foundations of entire economic theories. One of these weak assumptions is that humans act rationally. Now, there is a lot of evidence to suggest that many people act in the way that we are programmed to by evolution. The problem is, a lot of what allowed us to evolve over the last 4 billion years of life on earth, is pretty useless for success in 2018-era financial planning and investment.

All hope is not lost, however. New research into the concept of bounded rationality, the idea that rational decision making is limited by the extent of human knowledge and capabilities, could help move this idea forward. One of the founding fathers of artificial intelligence, Herbert Simon,  postulated that AI could be used to help us understand human cognition and better predict the kinds of human behaviours that helped keep us alive 8,000 years ago, but are detrimental for wealth accumulation today. 

By creating heuristic algorithms that can capture these behaviours and learning from big data to understand what actions are occurring, we may soon be able to create software that is able to accentuate the best human behaviours and help us deal with the worst ones. Perhaps the algorithm that describes humanity has already been discovered.