Capitalogix

Microsoft: Are They Where They Thought They'd Be in 2019?

 In 2009, Microsoft released a video anticipating the world in 2019. That was only 10 years ago. 

I recently showed you how much Social Media has changed in 10 years - so how close was Microsoft's guess?

Watch this video to find out: 

 

 

The answer is .... not as close as I would have thought.  Nonetheless, they just hit a Trillion Dollar market cap.  So, they must have gotten something right! 

It's interesting to think about which factors or missing innovations caused the difference between their imagined vision and reality.

They really bought into scaleable, HD, transparent, touch screen displays being not only available, but located in everything by now ... which suffice to say, isn't the case. 

The reality is ...

• A lot of these innovations actually have little use - Not every situation needs a transparent monitor - they're worse than standard monitors in almost every way. You end up using absurdly expensive screens to display a digital version of a post it note or handwriting. The desk/monitor hybrid would be covered in sheets of paper, office supplies, and your coffee. A boarding pass being a screen is highly inefficient for so many reasons - and so is a digital newspaper. We have those - they're called phones.  
• They assumed batteries would be way farther along - The thinner your monitor, the more transparent, the harder it is to create a high-performance high-fidelity battery to maintain it. Unfortunately, batteries haven't had nearly the boom like the rest of our tech (though they are getting better). 
• IoT Adoption/Security - One of the biggest problems with IoT is that the more these pieces communicate the harder it is to prevent hacks. A chain is only as strong as the weakest link - and a smart coffee maker isn't nearly secure as your computer. 
• Fingerprints (Glass) - I get this isn't a "real" concern - but every piece of technology they showed was transparent/glass. On top of being very breakable (see Samsungs new foldable phones) Could you imagine how smudged/dirty everywhere would appear? Imagine a New York subway with this technology.
• Expense v. Convenience - A lot of technologies are feasible - but aren't cost-effective. Look at the slow adoption of Solar cells as their efficiency per cost went up.

Making everything a device/screen means more opportunity for companies to serve you ads and retarget you ad infinitum. 

Ultimately, I find this perceived "modern digital office environment" very inefficient. A lot of these "innovations" are less dynamic and easy to use than their analog counterparts. Mechanical keyboards serve a purpose. 

In reality, a lot of the trends we've adopted to increase collaboration and sharing have been counterproductive. Not every office needs an open floor plan - not every team needs 15 subteams with 4 bosses - and using 20 different productivity tools actually decreases productivity. 

That being said, we've come a long way in 10 years. Think about the quality of your phone in 2009 or your desktop computer - whirring loudly as it tried to access the disk, or the internet, or anything really. 

What we have now isn't perfect - but it's leaps and bounds ahead of where we were. A lot of technology seems like science fiction - like the Babel fish from Hitchhikers Guide to the Galaxy.

Here Are Some Links For Your Weekly Reading - May 5th, 2019

 

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

Lighter Links:

• These are Amazon's 38 Rules for Success. (FastCompany)
• Amazon Used an AI to Automatically Fire Low-Productivity Workers. (Futurism)
• Cornell Scientists Create Living' Machines that Eat, Grow, and Evolve. (TheNextWeb)
• Drinking Tequila Could Help You Lose Weight. (Elle)
• Is It True that It's More Painful to Come Second Rather Than Third? New Insights from the 2016 Olympics. (Digest)

 

Trading Links:

• Making Sense of the Market in the Age of Algorithmic Trading. (Barrons)
• The next Gig Economy Will Be On-demand Knowledge.(WEForum)
• Central Banks Extraordinary Policies: What Could Go Wrong.(SeeItMarket)
• Super Rich Ditching Jet Ownership for a Portfolio of Aviation Options. (Bloomberg)
• If You Invested $1,000 in Microsoft in 2009, Here's How Much You'd Have Now. (CNBC)

 

 

Gartner's 2018 Hype Cycle for Emerging Technologies

With all the "fake news" and manufactured social buzz, it's hard to identify what technologies are reaching maturity and which ones are fads. Gartner's Hype Cycle attempts to solve that problem. 

What's a "Hype Cycle"?

As technology advances, it is human nature to get excited about the possibilities and to get disappointed when those expectations aren't met. 

At its core, the Hype Cycle tells us where in the product's timeline we are, and how long it will take the technology to hit maturity. It tells us which will survive the hype have the potential to become a part of our daily life. 

Gartner's Hype Cycle Report is a considered analysis of market excitement, maturity, and the benefit of various technologies.  It aggregates data and distills more than 2,000 technologies into a succinct, contextually understandable, snapshot of where various emerging technologies sit on the hype cycle.

Here are the five regions of Gartner's Hype Cycle framework:

1. Innovation Trigger (potential technology breakthrough kicks off),
2. Peak of Inflated Expectations (Success stories through early publicity),
3. Trough of Disillusionment (waning interest),
4. Slope of Enlightenment (2nd & 3rd generation products appear), and
5. Plateau of Productivity (Mainstream adoption starts). 

 

Understanding this hype cycle framework enables you to ask important questions like "How will these technologies impact my business?" and  "Which technologies can I trust to stay relevant in 5 years?"

What's exciting this year? 

Currently, people are probably "too" excited about IoT platforms, Deep Learning, and Virtual Assistants. The idea isn't they won't stand the test of the time, but there's an artificial influx of people inflating the adoption numbers. Once the chaff is separated from the wheat - you can expect these to succeed. Autonomous driving, connected homes, and mixed reality have matured past the hype phase. 

For comparison, here's my article from last year, and here's my article from 2015. Click the chart below to see a larger version of this year's Hype Cycle.

via Gartner

This year, Gartner organized the 17 highlighted technologies into 5 encompassing trends:

 

• Democratized AI (autonomous driving, conversational AI, deep neural nets, etc.) is one of the most disruptive classes of technology. It's becoming more readily available due to open source and cloud computing. There's a burgeoning field of "makers" inspired to push the boundaries of what's possible.  
• Digitalized Ecosystems ( blockchain, IoT) support emerging technologies by being the foundations for dynamic systems. There's a shift from discrete infrastructure systems to comprehensive ecosystems. Think of blockchains potential impact on tracking and communication.  
• Do-It-Yourself Biohacking (biotech, biochips, exoskeletons, AR) is something I've talked about before. Dave Asprey is a public face of this movement and we shot a video together last year. The future is bringing implants to extend humans past their perceived limits and increase our understanding of our bodies; biochips with the potential to detect diseases, synthetic muscles, and neural implants. 
• Transparently Immersive Experiences (connected home, smart dust, volumetric displays) represent the blending of where human stops, work starts, and things exist. Imagine a theoretical smart workspace where electronic whiteboards capture all your notes and disseminate them, sensors let you know where people are and what they're working on, and everything in your office interacts directly with your IT platform. This creates much more contextual and personalized experiences.  
• Ubiquitous Infrastructure (5G, carbon nanotubes, quantum computing) means that infrastructure is becoming a less competitive advantage. As infrastructure becomes cheaper, more ubiquitous, and faster, it becomes background noise.  

 

Looking at the overarching trends of this year, it's also fun to look at what technologies are just starting their hype cycle. 

• Artificial Tissue (Biotech) could be used to repair or replace portions of, or whole, tissues (cartilage, skin, muscle, etc.)
• Flying Autonomous Vehicles can be used as taxis, but also as transports for other things such as medical supplies, food delivery, etc. Amazon and Uber are likely excited about this development - and expect it in the next couple of years. 
• Smart Dust opens up the possibility of monitoring essentially everything by creating a vast network of minuscule sensors that can detect various inputs. 
• Artificial General Intelligence
• 4D Printing would theoretically allow us to print objects that reshape or self-assemble over time. 

 

Which technologies do you think will survive the hype?

Reinventing Hedge Funds

Two months ago, I spoke at IBM's Think 2019 Conference with iOLAP's Chris Jordan. Our talk was Gaining a Financial Edge through Fast Data and Analytics.

 

 

The crux of our presentation was that high-performance computing and advanced technology stacks are fundamental to today's trading strategies.

Earlier this week, IBM released a commercial we shot together about how IBM's Integrated Analytics System (IAS) is helping us re-invent hedge funds.  Check it out.

 

 

via IBM Analytics

Think of it this way: 

Each generation of traders finds new ways to play the game and to generate Alpha (the excess return generated by manager skill, rather than luck or excess risk). As soon as enough people adopt a strategy (or figure out a way to combat it), the edge begins to decay.

When computerized data became available, simply understanding how to download and use it generated Alpha. The same could be said for each later evolution: the adoption of complex algorithms, access to massive amounts of clean data, and the adoption of AI strategies.

Each time a new shift happens, traders pivot or fail – it's not that active trading stopped working – it's that the tools and styles necessary to play that game evolved.

We use a similar concept we call "Active Switching".  Instead of simply trying to pick stocks or markets, we use advanced technology and techniques to choose markets, techniques, risk levels, and allocation strategies. Further, we use Active Switching to make many decisions, great and small, that together allow us to build portfolios that adjust themselves to changing market conditions.

 

Playing a New Game

 

Confusion is the catalyst to most trades.  What that means is the ability to parse information ... to create a contextual map of meaning and figure out where there's signal and noise ... is the new table stakes for playing that game.   ~ Howard Getson

 

Historically, most active traders don't beat the S&P in any given year ... and even less beat it with any semblance of consistency.  But those that do – the ones that have been doing it for long enough that it's not chance ... exercise a willingness (and a skill) to adapt quickly. 

One of Charles Darwin's best-known concepts is: It is not the strongest species that survive, nor the most intelligent, but the ones most responsive to change.

While computers have made information accessible to everyone, they've also created a massive asymmetric information advantage for those who have both the access and the skill to best use the massive amounts of data now available.  This is more complicated than it seems.  You need the information, the technology, the process, and the people.  There is so much data available now that figuring out what to ignore is probably more important than what to use.  Likewise, the ability to ingest, clean, validate and curate the data is a huge hurdle that most can't clear.

 

 

Even with those skills, it's harder than ever to take advantage of inefficiencies (edges) than ever before.  The edges are smaller, more fleeting, and surrounded by more volatility and noise.  It's like finding a needle in a haystack.  That being said - finding a needle in a haystack is easy when you have a metal detector. 

That's where A.I. has come in for us. We use A.I. to develop algorithms, to analyze markets, and to create meaning where humans can't find any. 

 

 

We live in exciting times.  Onwards!

Here Are Some Links For Your Weekly Reading - April 21st, 2019

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

Lighter Links:

• What are the Most Promising Uses for Blockchain We Haven't Seen Yet? (Forbes)
• Sperm and Sensibility: The Modern World of Designer Children. (TLS)
• Thousands of Amazon Workers are Listening in on Echo Audio, Report Says. (AppleInsider)
• Strength Training for the Lungs: Why Heavy Breathing Could Be a Shortcut to Improving Your Health. (Telegraph)
• Forecasting the Future: Can the Hive Mind Let Us Predict the Future? (Futurism)

 

Trading Links:

• NVIDIA AI Research Points to an Evolution of the Chip Business. (ZDNet)
• Philip Morris International is Sticking with E-Cigs, Not Marijuana. (Fool)
• This Simple Map Shows the Biggest Employers in Each State. (Good)
• Lyft's I.P.O. is Making the Same Circle of Men Rich, Again. (New York Times)
• The Future of Electric Cars is Happening Now in China. (Quartz)

 

 

Here Are Some Links For Your Weekly Reading - April 7th, 2019

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

Lighter Links:

• Scientists Plead to Stop Creation of Killer Robots: We're at 'The Brink of a New Arms Race'. (FoxNews)
• Moratorium on Human Genome Editing: Time to Get It Right. (HastingsCenter)
• 'Those Who Obeyed the Rules Were Favored by Evolution'.(Spiegel)
• Meet the World's Oldest Confirmed Person, 116-Year-Old Kane Tanaka. (TwistedSifter)
• Why Democrats Want to Abolish the Electoral College and Republicans Want to Keep It. (Fortune)

Trading Links:

• Why Companies that Wait to Adopt AI May Never Catch Up.(HBR)
• Iranian Hackers Have Hit Hundreds of Companies in past Two Years. (Wall Street Journal)
• What is an Inverted Yield Curve and What Does It Really Mean? (TheStreet)
• S&P 500 Posts Best First Quarter in 21 Years. (FoxBusiness)
• Bill Gates is Back in the $100 Billion Club, and Not All Thanks to Microsoft. (CNW)

 

 

Here Are Some Links For Your Weekly Reading - March 31st, 2019

 

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

Lighter Links:

 

• 7 Odd Traits Geniuses, Masters and Entrepreneurs All Have in Common.(Forbes)
• Nest Founder: I Wake up in Cold Sweats Thinking, What Did We Bring to the World?. (FastCompany)
• Bill Gates: AI is like Nuclear Weapons and Nuclear Energy in Danger and Promise. (Vox)
• Artificial Intelligence and Ethics. (HarvardMagazine)
• These are All the World's Major Religions in One Map. (WEForum)

 

Trading Links:

• Just Because You're Paranoid, Doesn't Mean the Algorithms Aren't out to Get You. (TimHarford)
• Here are the Top Risks on Goldman Sachs CEO David Solomon's Radar.(Finance)
• UBS CEO Says this is One of the Worst First-quarter Environments in History. (CNBC)
• Apple Sold 35 Million AirPods in 2018, Currently Most Popular 'Hearable' Brand. (AppleInsider)
• Facebook, Google and Other Big Tech Giants are about to Face a Reckoning, State Attorneys General Warn. (Washington Post)

Billions (with a B)

Kylie Jenner was recently named the world's youngest "self-made" billionaire by Forbes. 

There was a lot of controversy over the title "self'-made" ... Regardless, her accomplishments are impressive. Jenner is an example of a different form of intelligence and hard work (and the power of the Kardashian media empire).

Clearly, Americans are fascinated with the idea of billionaires as an extension of the American dream.

Here's the world's "official" richest people. 

 

via Forbes

The actual data is interesting. The US has about doubled in Billionaires since 200.  Meanwhile, China has grown from 1 Forbes list billionaire to over 300. In that same time, the US GDP grew from approximately 10.5 trillion  USD to 21.5 trillion, and China's GDP went from about 1.3 trillion USD to over 13 trillion. 

Global GDP has increased by 2.5x since 2001.  Consequently, most people in the world are richer than they were back then. 

How many billionaires are there ... and how many in America? Forbes has an interactive chart to answer those and many more questions.,  Click the chart below to see the data. 

 

via Forbes

The issue with this chart - and any chart on this subject -  is that the data is incomplete and suspect for a variety of reasons.  For example, think about the assumptions made on the amount of money tied up in a variety of assets like private companies, art, and real estate. 

In addition, there are many billionaires who don't want to be tracked. For example, the list only shows  Saudi Arabia having 20 billionaires.  Likewise, it is estimated that India has over $500 billion in undocumented wealth (with some estimates putting it as high as  $1.5 trillion.)

Not a bad list to be on!

Here Are Some Links For Your Weekly Reading - March 24th, 2019

via KDNuggets

 

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

Lighter Links:

• Jeff Bezos Predictions from 20 Years Ago that Were Right on the Money. (BusinessInsider)
• Imagining the Smart Cities of 2050. (SingularityHub)
• Secrets of the FBI's Ten Most Wanted List. (CNN)
• How Designers Engineer Luck into Video Games. (Nautilus)
• CRISPR Gene Editing: Why We Need Slow Science. (The Conversation)

 

Trading Links:

• Why Companies like Lyft and Uber are Going Public without Having Profits. (Re/code)
• Are Animal Spirits Making a Comeback? (Bloomberg)
• This is What the Average American Spends in a Day. (Fool)
• The AI-Art Gold Rush is Here. (The Atlantic)
• Who Wants to See a Movie about High-Frequency Trading? High-Frequency Traders. (Wall Street Journal)

 

 

March Madness: The Quest For The Holy Grail

March Madness is in full swing and will have the world's attention for a few more days. As you could guess - most brackets have already busted. Louisville lost, UC Irvine won, and Duke barely weathered UCF's onslaught. 

 

via FanSided

Lots of skill, lots of adrenaline and lots of natural talents (including amazing physical talents).  Here is a picture of 7'6 Tacko Fall. Tacko still looks taller than his opponents even when kneeling. 

via UCF Basketball

The allure of March Madness is the same as gambling or trading.  These are all fertile grounds for emotion, biases, and statistics. 

The holy grail is mighty elusive in March Madness (as in most things)  ... For example, the odds of getting the perfect bracket is 1 in 9,223,372,036,854,775,808 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank).  It's easier to win back-to-back lotteries than picking a perfect bracket. Nonetheless, I bet you felt pretty good when you filled out your bracket.

Here's some more crazy March Madness Stats: 

• 60 million tournament brackets were completed
• Companies expect $4 Billion of corporate losses due to employees tracking games
• The highest paid NCAA coach, Duke’s Mike Krzyzewski, makes a salary of $8,982,325
• It's expected to create $10 Billion in gambling (twice as much as the Super Bowl)

 

via Duke University

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

 

In 2017, I highlighted 3 people that were (semi) successful at predicting March Madness. A 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world.  None of them had the same success even a year later.

Finding an edge is hard - maintaining an edge is even harder.

That's not to say there aren't edges to be found. 

Bracket-choosing mimics the way investors pick trades or allocate assets. Some people use gut feel, some base their decisions current and historical performance, and some use predictive models. You've got different inputs, weights, and miscellaneous factors influencing your decision. That makes you feel powerful. But knowing the history, their ranks, etc. can be helpful in making an educated guess and they can also lead you astray. 

The allure of March Madness is the same as gambling or trading. As sports fans, it's easy to believe we know something the layman doesn't. We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or your musical loving co-worker.

In reality, several of the highest-ranked brackets every year are guesses. 

The commonality in all decisions is that we are biased. Bias is inherent to the process because there isn't a clear cut answer. We don't have the answers to "who will win" and we don't have the answer to "what makes a perfect prediction". 

Think about it from a market efficiency standpoint. People make decisions on many factors - sometimes irrational ones - and that creates inefficiencies & complexities. It can be hard to find those inefficiencies and capitalize on them - but they're there to be found. 

In trading, that's where AI and advanced math come in - taking away our biases and identifying inefficiencies humans miss.

Can machine learning also help in March Madness?

“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting - that is, the more you should prefer simplicity” - Tom Griffiths

The data is there. Over 100,000 NCAA regular season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season. There are plenty of questions to be asked on that data that may add an extra edge. 

That being said, people have tried before with mediocre success. It's hard to overcome the intangibles of sports - hustle, the crowd, momentum - and it's hard to overcome 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems

 

Something to think about.