Capitalogix

Which Industries are Prime for Disruption?

Last year, I talked with the CEO of CB Insights about how Fintech was disrupting the value chain of financial services. 

Since then, it's become even easier to see how disruptors are changing every industry, and becoming leaders in their fields.

 

via McKinsey

 

Companies like Lyft, Tesla, and Netflix changed paradigms and are now leading the pack, but what industries are next?

Looking at a report by McKinsey&Company, we find that the 5 industries with the least digitization account for 34% of GDP and 42% of all employees. 

 

1. Agriculture and Hunting
2. Construction
3. Hospitality
4. Healthcare
5. Government

via Quora

But, being less digitized than competitors, alone, isn't enough to indicate it's ready for disruption: 

 

How Regulated Is The Industry?

Heavily regulated industries mean that any company that can find a way around current regulations and tax structures can become an effective competitor fast ... i.e Lyft and Tesla

Is There Too Much Competition Already?

How many competitors are in the space? Is there spare capacity, or does the current model waste resources? Think taxis waiting for customers or empty seats on airplanes. 

Can it Be Automated?

Lot's of industries and tasks are ready and waiting to be automated - and only haven't been due to the cost of switching to new technology. 

 

To put it another way - are you solving a customer headache while lowering costs for you and the customer? 

The only constant is change, and digitization and automation are only increasing the rate of change. The type, quality, and the number of jobs being offered are changing rapidly. 

As digitization increases so does adoption of AI-related technologies. 

McKinsey Institute via Vox

AI is coming, disruption is coming.

It's no longer possible, it's not even just probable, it's inevitable

This interactive graph tells you if your job is automation proof, and what percentage of your job can be automated.

What industry do you think is next?

Here Are Some Links For Your Weekly Reading - January 21st, 2018

It's day 2 of the government shutdown; but what does that actually mean?

In short: 

• "Essential" government services will still run even if employees aren't getting paid - USPS, TSA, Social Security
• Other "non-essential" government employees will be "furloughed" and told to stay at home. 
• Military will be required to work, but won't be paid until Congress comes to an agreement
• Employees will be paid retroactively once an agreement is met. 

 Efficiency at its finest ... or a force function to get the children to play nicer with each other?

 

 

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• Want to Get Wisdom? Be Your Own Crowd. (Bloomberg)
• NASA Just Proved It Can Navigate Space Using Pulsars. Where to Now? (Wired)
• It's So Cold in Russia that Thermometers are Breaking. (BGR)
• What Robot Strippers Say about Sexism, Tech and the Future. (AP News)
• Benjamin Franklin Built His Character around 13 Virtues and Following His Weekly Plan Could Change Your Life. (BusinessInsider)

Trading Links:

• How Donald Trump Has Remade the Rules for Business. (Wall Street Journal)
• Fed Paid $29.3 Billion to Banks 'Not' to Lend in 2017; Gave $80 Billion to Treasury. (ZeroHedge)
• One Person is Probably Responsible for Almost 600 Percent of Bitcoin's Price Rise. (Mashable)
• Kodak's Pivot to Crypto Seems to Be Working out Quite Nicely. (FastCompany)
• These are the Biggest Risks for Businesses in 2018, According to the World Economic Forum. (Fortune)

Algorithms are Watching: How Machines Learn

Machine Learning algorithms are everywhere.

They're helping you pick out your next gift on Amazon, controlling what you find on Google, they're suggesting new music for you on Spotify, and they're doing their best to keep you on their website.

When trying to explain how machines learn we tend to try and describe it in human terms. Unfortunately (or fortunately) machine learning isn't based on how we teach humans.

The video is a bit simple in its explanations, but it describes some important concepts. So, watch how machines learn.

 

CGP Grey via Youtube

The video focuses on Genetic Algorithms, which is one type of machine learning, and neglects some of the other more complicated approaches.

As machine learning gets more complicated and evolved, it gets harder for a human to understand what makes it good ... and that's okay. 

It's human nature to feel safer when we understand something. It's human nature to envision machines as making human-like decisions, just faster.

Of course, just because it suits human nature to believe something, that doesn't make it true. 

Part of what makes machine learning exciting is that it can do a lot of things well that humans are really bad at. 

In reality, it doesn't matter why a bot is making a decision, or what inputs the bot is making the decision on. What matters is the performance and level of decision-making in relation to itself and to other options.

One of the lessons I teach when I speak publically is that in trading, caring about the markets is a weakness.

It's a distraction. 

I don't care how markets are doing.

I care how my systems are doing. I care how my portfolio is doing.

There's too much data for me to try and care about anything else. 

It's a brave new world, and not only is big brother watching, but algorithms are too.

Here Are Some Links For Your Weekly Reading - January 14th, 2018

 

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• Tesla Model 3: the First Serious Review. (The Drive)
• How Religion Made a Global Comeback in 2017. (The Atlantic)
• New Year's Fitness Resolutions: the Smartest Way to Keep Them. (WSJ)
• How Kaspersky's Software Fell under Suspicion of Spying on America. (Wall Street Journal)
• Could Your Apple Watch Save Your Life?: How Smartwatch Sensors Could Help Tackle a Dangerous Heart Condition. (ZDNet)

Trading Links:

• Kodak Announces Blockchain Plan, Share Price Soars. (FinExtra)
• Here's Why Spotify is Bypassing the Normal IPO Process and Why More Companies Don't Do It. (BI)
• As Dow Tops 25000, Individual Investors Sit It Out. (Wall Street Journal)
• Amazon Has a Master Plan to Turn Alexa into a Full-fledged Economy All on Its Own. (Insider)
• A Crypto Expert Explains the Difference between the Two Largest Cryptocurrencies in the World: Bitcoin and Ethereum. (BusinessInsider)

Ultra Spiritual Wisdom on Bitcoin

Seemingly everywhere I go, someone asks me about Bitcoin.

Clearly, there are speculators and day traders banking coin. 

I am a little surprised by how fast it captured the cultural zeitgeist.

Here's a clever spoof ... worth watching.

  

 AwakenWithJP via Youtube

I'm sure we'll talk more about Bitcoin ... but, more importantly, Blockchain (which is the technology that Bitcoin and other cryptocurrencies were built upon).

I am currently investigating and planning how we'll use Blockchain.

Onwards.

Related articles

The technology behind bitcoin is only halfway through its evolution

Blockchain and Cryptocurrencies: ScaleVP Comments From the Sidelines

Blockchain Learning hub - Stories of UNICEF Innovation

97% of all bitcoins are held by 4% of addresses

Bitcoin Ban: Merrill Lynch Prevents Clients, Advisors From Trading Cryptocurrency

Bitcoin conference stops accepting bitcoin for tickets, oh the irony

The Time Value of Time

In many senses time is relative. 

You don't have to be a rocket scientist to understand this quote from Einstein.

"When you are courting a nice girl, an hour seems like a second.  When you sit on a red-hot cinder, a second seems like an hour.  That's relativity."

It is about more than perception. 

Here is something that highlights the relative value of time. 

 

The Value Of Time:

• To understand the value of a year, talk to a student who has failed an important exam.
• To understand the value of a month, talk to a mother who has given birth to a baby a month prematurely.
• To understand the value of a week, talk to the publisher of a weekly newspaper.
• To understand the value of an hour, talk to a couple in love who are separated and want only to be together again.
• To understand the value of a minute, talk to someone who has just missed their train or plane flight.
• To understand the value of a second, talk to someone who has lost a loved one in an accident.
• And to understand the value of a millisecond, talk to someone who won the silver medal at the Olympic Games.

Time waits for no one.  So it is important to remember to make the best use of the time you have.

 

That Doesn't Mean Time Is Scarce Or Has To Be A Constraint:

Time is often thought of as a constraint or a scarce resource. There are lots of phrases that highlight this type of thinking.  For example: I don't have enough time; I'm running late; I'm up against a deadline; There are only 24-hours in a day; or, I’m going as fast as I can. As you might guess, that list goes on further.  Yet, time does not have to be that way ... it can be a tool instead.

So, I started to think about how I used time. 

Was I making the most of it ... or taking it for granted?  It didn’t take much introspection to notice a few of the ruts I fell into.  I'm going to talk about one of them, here, because a small shift can have a massive impact. 

To start, let's talk about pace.

 

A Change of Pace:

When I jog, the beginning and the end are the hardest for me. Yet, after I find that initial pace and I settle into a comfortable rhythm, the majority of the run is relatively painless. My mind and body switch to a nearly automatic mode and I have time to think about many things.

Work is similar in many respects. Once a team gets into a rhythm, work and progress are somewhat automatic. Breaking inertia is a challenge; but, people recognize that it's a challenge. The more insidious problem is to fail to recognize that the work rhythm that's comfortable, and which produces progress, is still a rut. It doesn't stretch and challenge the team to strive for more. Yet, this stretching is what drives innovation. It's the thought we haven't had yet … and a new perspective that changes everything.

Changing your pace can be an incredible catalyst to make that happen for you. For example, imagine that we put together a new portfolio in two weeks, on a wholly new tech platform, with new markets, and using new techniques. Then we tested, re-balanced and rebuilt that portfolio in one week. What we did, or the time in which we did it, wasn’t important. The important part is that it caused the team to work at a radically different pace than before. It was a sprint.

Moreover, this sprint caused us to re-think what we do, and more importantly, how we do it.  Many of the innovations and new distinctions that we discovered through this process will work their way into other areas of our work (and will act as a catalyst for us to re-evaluate the way we do things).

 

A Challenge For You:

I challenge you to consciously change the pace of something that you are already comfortable doing a certain way. The pace can be faster, or the pace can be slower ... it doesn't matter.  Then notice what comes up for you, and what new opportunities and possibilities you discover.

Time is a valuable resource. Take this opportunity to re-examine how you can best view and use time to make the most of it.

Here Are Some Links For Your Weekly Reading - December 22nd, 2017

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• How One Connection Could Change Your Life (In Less Than 90 Days). (Forbes)
• Why Facts Don't Change Our Minds. (NewYorker)
• Long Island Iced Tea Soars after Changing Its Name to Long Blockchain. (Bloomberg)
• How Quantum Computing Will Change the World. (NewStatesman)
• Cracking the World Famous Enigma Machine with Artificial Intelligence in Just 13 Minutes. (7WData)

Trading Links:

• Here's How the Tax Bill Will Affect the Richest 1% in All 50 States. (CNBC)
• Amazon CEO Jeff Bezos Just Sold $1.1 Billion Worth of His Stock. (Money)
• This Animation Shows the Explosion in ICO Funding over the Last Four Years. (Insider)
• The First $100 Billion Dollar Blockchain Company Could Be the next SAP. (Forbes)
• Worldwide Cognitive Systems and Artificial Intelligence Revenues Forecast to Surge past $47 Billion in 2020, According to New IDC Spending Guide. (IDC)

Here Are Some Links For Your Weekly Reading - December 17th, 2017

I just saw the new Star Wars with my family.

It's a bit crazy to think about what percentage of my life occurred since the original came out

I can still remember watching the first one. 

 

BTW, I'm not 60 yet ... but, I am pushing double nickels.

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• LeBron James: Keeping a Billion-dollar Body in Shape. (CNN)
• Success Strategies from a Self-made Billionaire. (TonyRobbins)
• When Robots Dream: AI Systems May Learn Better and Faster If They Can Sleep'. (Siliconangle)
• Machine Ethics and Artificial Moral Agents. (KDNuggets)
• The CIA is Telling Us How the Agency Uses Americans' Data. (Mashable)

Trading Links:

• FCC Revokes the Net Neutrality Rules. (NordVPN)
• Janet Yellen Calls Bitcoin 'Highly Speculative'. (CNN)
• Driverless Cars Will Change Auto Insurance. Here's How Insurers Can Adapt. (HBR)
• This Investor Says Millennials are 'Starting to Buy Homes in a Big Way'. (CNBC)
• Google Leads in the Race to Dominate Artificial Intelligence. (Economist)

 

Creating An Artificial Intelligence Methodology: AI's Hierarchy of Needs

We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:

How do we use AI effectively?

Many people misuse AI.  They think they can simply plug in a dataset, press a button, and poof!  Magically, an edge appears.

Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations.

That doesn't even touch machine learning or deep learning (where you have to understand math and statistics to make sure you use the right tools for the right jobs).

Even though this is the golden age of AI ... we are just at the beginning.  Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.

Do you remember Maslow's Hierarchy of Needs?  Ultimately, self-actualization is the goal ... but before you can focus on that, you need food, water, shelter, etc.

In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive. 

Artificial Intelligence and Data Science follow a similar model. Here it is:

  Monica Rogati via hackernoon

First, there's data collection. Do you have the right dataset? Is it complete?

Then, data flow. How is the data going to move through your systems? 

Once your data is accessible and manageable you can begin to explore and transform it. 

Exploring and transforming is a crucial stage that's often neglected.

One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.

The data stream from exchanges isn't perfect.

Consequently, using real-time market data as an input for AI is challenging.  We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible.  Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).

Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things ... and then begin to train your data. 

Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it. 

That's the foundation to allow you to start model creation and optimization.

The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).

If you put garbage into a system, most likely you'll get garbage out. 

Slower sometimes means faster.

Onwards.

What's Next ... Robot Rockstars?

Artificial Intelligence has already been used to compose various pieces of music, but they're not replacing real "stars" anytime soon. 

A New Zealand musician, Nigel Stanford, released a video with an imagined collaboration between himself and a team of crane-armed robots.

He spent about a month programming the robots. 

Check it out.

 

DailyDot via Youtube