Posts from machine learning

What Happened In The 2010s

My friend Steve Kane suggested I take a longer view in my pair of year end posts this year:

And so I will.

Here are the big things that happened in tech, startups, business, and more in the decade that is ending today, in no particular order of importance.

1/ The emergence of the big four web/mobile monopolies; Apple, Google, Amazon, and Facebook. A decade ago, Google dominated search, Apple had a mega hit on their hand with the iPhone, Amazon was way ahead of everyone in e-commerce, and Facebook was emerging as the dominant social media platform. Today, these four companies own monopolies or duopolies in their core markets and are using the power of those market positions to extend their reach into tangential markets and beyond. Google continues to own a monopoly position in search in many parts of the world, has a duopoly position in mobile operating systems, and controls a number of other market leading assets (email, video, etc). Apple owns the other duopoly position in mobile operating systems. Amazon has amassed a dominant position in e-commerce in many parts of the world and has used that position to extend its reach into private label products, logistics, and cloud infrastructure. Facebook built and acquired its way into owning four of the most strategic social media properties in the world; Facebook, Instagram, Messenger, and WhatsApp. Most importantly, outside of China, these four companies own more data about what we do online and also control many of the important channels to reach us in the digital world. What society does about this situation stands as the most important issue in tech at the start of the 2020s.

2/ The massive experiment in using capital as a moat to build startups into sustainable businesses has now played out and we can call it a failure for the most part. Uber popularized this strategy and got very far with it, but sitting here at the end of the 2010s, Uber has not yet proven that it can build a profitable business, is struggling as a public company, and will need something more than capital to sustain its business. WeWork was a fast follower with this strategy and failed to get to the public markets and is undergoing a massive restructuring that will determine the fate of that business. Many other experiments with this model have failed or are failing right now. When I look back at the 2010s, I see a decade during which massive capital flowed into startups and much of it was wasted chasing the “capital as a moat” model.

3/ Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. Accumulating a data asset around your product and service and using sophisticated machine learning models to personalize and improve your product is not a nice to have. It is a must have. This ultimately benefits the three large cloud providers (Amazon, Google, Microsoft) who are providing much of the infrastructure to the tech industry to do this work at scale, which is how you must do it if you want to be competitive.

4/ Subscriptions became the second scaled business model for web and mobile businesses, following advertising which emerged at scale in the previous decade. Startups that developed the skills to execute a subscription business model with positive unit economics delivered fantastic returns to investors and capital flowed into this sector as a result. This was a very positive development as subscriptions better align the interests of the users and the developers of mobile and web applications and avoid many of the negative aspects of the free/ad supported business model. However, as we end the decade, a subscription overload backlash is emerging as many consumers have signed up for more subscriptions than they need and in some cases can afford.

5/ Silicon Valley’s position as mecca for tech and startups started to show signs of weakening in the 2010s, largely because of its massive successes this decade. It is incredibly expensive to live and work in the bay area and the quality of life/cost of life equation is not moving in the right direction. The physical infrastructure (transit, housing, etc) has not kept up with the needs of the region and there is no sign that it will change any time soon. This does not mean “Silicon Valley is over” but it does mean that other tech sectors will find an easier time recruiting talent to their regions and away from Silicon Valley. And talent is really the only thing that matters these days.

6/ Cryptography emerged in the 2010s as a powerful technology that can solve some of the web and mobile’s most vexing issues. Cryptography and encryption have been around for a very long time, well before the computer. Modern computer cryptography came of age in the 1970s. But the emergence of the internet, web, and mobile computing largely did not integrate many of the central ideas of cryptography natively into the protocols that these platforms were built on. The emergence of Bitcoin and decentralized money this decade has shown the way and set the stage for cryptography to be built natively into web and mobile applications and deliver control back to users. Credit to Muneeb Ali for framing this issue for me in a way that makes a lot of sense.

7/ Technology inserted itself right in the middle of society this decade. Our President wakes up and fires off dozens of tweets, possibly while still in bed. We are all hostage to our phones and the services that we rely on. Our elections are conducted using machine learning technology to segment and micro-target important voting groups. And bad actors can and do use the same technologies to interfere in our elections and our public discourse. There is no putting the genie back in the bottle in this regard, but the fact that the tech sector has such a powerful role means that it will be highly regulated by society. And there is no putting the genie back in the bottle in that regard either.

8/ The rich got richer this decade. Axios wrote in a recent email that:

“The rich in already rich countries plus an increasing number of superrich in the developing world … captured an astounding 27% of global growth.”

But the very poor also had a great decade as Axios also reported:

The rate of extreme poverty around the world was cut in half over the past decade (15.7% in 2010 to 7.7% now), and all but eradicated in China.

The losers in the 2010s were lower middle class and middle class people in the developed world whose incomes stagnated or fell.

Technology played a role in all of this. Many of the superrich obtained their wealth through technology business interests. Some of the eradication of extreme poverty is the result of technology as well. And the stagnation of earning power in the lower and middle class is absolutely the result of technology automation, a trend that will only accelerate in coming years.

9/ This a post publish addition. A huge miss in my original post is the emergence of China as a tech superpower and a global superpower. There are many areas (digital money for example) where China is light years ahead of the western world in technology and that will likely accelerate in the coming years. Being a tech superpower is a necessary condition to being a global superpower and China is already that and getting more powerful by the day.

I will end there. These are the big mega-trends I think about when I think about the 2010s. There is no doubt that I left out many important ones. You can and will add them in the comments (wordpress for now), emails to me, and on Twitter and beyond. And that is what I hope you will do.

#crypto#entrepreneurship#machine learning#policy#Politics#VC & Technology#Web/Tech

AIVC

My friend Fraser took a large number of AVC blog posts over the years and trained an AI model on them.

The result is a blog written by a machine.

You can see it here.

One one hand, it is kind of amazing that you can train a machine to write like someone.

On the other hand, I don’t think I will be out of a job anytime soon.

#machine learning

The Self Driving Bus

If you are in or around the Brooklyn Navy Yard and want to get to the East River Ferry, you can have a self driving car take you there.

I did that yesterday:

It’s sort of like a van. There are six passenger seats in the vehicle rand I saw four of them lined up waiting for passengers next to the main gate off Flushing and Cumberland.

There is a driver in the front seat but the van drives itself. That takes some of the excitement factor down a notch. But it increases the comfort factor. I assume the driver can take control of the vehicle and drive it manually if necessary.

It makes a ton of sense that autonomous vehicles would start out in places like the Navy Yard where there is not a lot of vehicle traffic and the map is fairly simple.

If you want a taste of the future go over to the Navy Yard and get a ride. It’s free.

And while you are there check out the amazing new Dock 72 building next to the East River Ferry stop. They are leasing it up now and there are some great office spaces still available there. They have smaller offices for startups. Link here.

#machine learning#NYC#robots and drones#Uncategorized

Facial Recognition

I would like to start this post with a disclosure. USV portfolio company Clarifai has one of the best facial recognition models on the market and is very active in the facial recognition market. Now that I have disclosed that, we can move on.

Facial recognition has come of age. Machines can figure out who we are and more.

One of the most popular booths with students at The Annual CS Fair this year was the Microsoft booth where they were showing some of their facial recognition technology.

The delight and amazement on the students’ faces was infectious.

But of course, not everyone is excited about facial recognition technology being deployed in the market.

I particularly like that question in the embedded image in that tweet:

How does Jet Blue know what I look like?

The answer turns out that there are many ways to know what we look like and you can start with the federal government and go from there.

Like all technologies, facial recognition can be used for good and bad. And it will be.

I like what my partner Albert wrote on this topic recently:

And then some things are incredibly hard. Such as face and object recognition. There are tons of amazing positive applications for such technology. And yet they could also be used to bring about a dystopian future of autonomous killer weapons chasing citizens in the streets. Does that mean we should not develop these capabilities? Should we restrict who has access to them? Is it OK for corporations to have them but not the military? What about the police? What about citizens themselves? Those are hard questions and anyone who thinks they have obvious answers I submit hasn’t thought long enough about them.
So what is to be done? A good start is personal responsibility. 

We used to have to stop at toll booths and wait in long lines to get across bridges and tunnels. Now we drive past the tolls at 60mph and the machines detect our license plates and debit our accounts.

The same is going to happen with our faces and that will be great for many things. But, of course, it will also freak us out on a regular basis and add to the “technology is turning everything into a surveillance state” narrative that has more truth than we would like to admit.

So what is my point? Well for one, the technology is here and we had better get used to finding it deployed in the wild. And second, that it will bring a lot of good. So we should not over react. But we should be mindful of the downsides and those of us who are working on this technology, those of us who are financing the development of it, and those of us who are deploying it, need to take great care with it.

#machine learning

Video Of The Week: AI and Society

Earlier this week, Kara Swisher interviewed Kate Crawford and Meredith Whittaker, who run NYU’s AI Now Institute.

It is an interesting and thought provoking discussion. I don’t personally love Kate and Meredith’s answers on how society should be thinking about these issues. They feel very “20th century” to me.

But regardless of what you think about their particular take on the issues, I do think we all ought to be paying a lot of attention to AI and its impact on and role in our society. It is important.

#machine learning#policy

Video Of The Week: Elon Musk on Joe Rogan Experience

I have never been as obsessed with Elon Musk as many are in the tech sector. We own two Tesla cars. We pre-ordered Tesla’s solar roof tiles several years ago but have not yet received delivery of them. I appreciate his ingenuity and creativity and we like the Tesla products we own. We are not and have never been shareholders of Tesla or SpaceX.

With all of that disclosure, I want to share the video of Elon’s appearance on Joe Rogan Experience as the video of this week. Much has been made of Elon’s decision to take a puff on a tobacco/weed joint on the show. I don’t make too much of that. I’ve been around people smoking pot since I was a teenager and I think it is a lot like alcohol. I believe it is fine if it is done responsibly and appropriately and I am pleased that it is becoming legal in many states around the country.

What is more interesting to me in this video is how introspective and thoughtful Elon is in this interview, particularly about the role of AI in our society and the likely impact of AI on our world in the coming years. It is a lengthy conversation, but worth watching if you have some time this weekend.

#machine learning

Explainability

Last week I started getting lots of stories about Kendrick Lamar and SZA in my Google Now news feed on my phone.  I thought to myself “why all of sudden does Google think I’m interested in Kendrick Lamar and SZA?”

Then I recalled sending a text message to my son about the new Kendrick/SZA song from the Black Panther film and thought “Google saw that text message and added Kendrick to my interests.” I don’t know if that is in fact the case, but the fact that I thought it is really all that I am talking about right now.

That whole “why did I get this recommendation” line of thinking is what the machine learning industry calls Explainability. It’s a very human emotion and I bet that all of us have it, maybe as often as multiple times a day now.

I like this bit I saw on a blog post on the topic today:

Explainability is about trust. It’s important to know why our self-driving car decided to slam on the breaks, or maybe in the future why the IRS auto-audit bots decide it’s your turn. Good or bad decision, it’s important to have visibility into how they were made, so that we can bring the human expectation more in line with how the algorithm actually behaves. 

What I want on my phone, on my computer, in Alexa, and everywhere that machine learning touches me, is a “why” button I can push (or speak) to know why I got that recommendation. I want to know what source data was used to make the recommendation, and I’d also like to know what algorithms were used to produce confidence in it.

This is coming. I have no doubt about it. And the companies that offer it to us will build the trust that will be critical to remaining relevant in the age of machine learning.

#machine learning

What Happened In 2017

As has become my practice, I celebrate the end of a year and the start of a new one here at AVC with back to back posts focusing on what happened and then thinking about what might happen.

Today, we focus on what happened in 2017.

Crypto:

I went back and looked at my predictions for 2017 and I completely whiffed on the breakout year for crypto. I did not even mention it in my post on New Year’s Day 2017.

Maybe I got tired of predicting a breakout year for crypto as I had mentioned it in my 2015 and 2016 predictions, but whatever the cause, I completely missed the biggest story of the year in tech.

If you look at the Carlota Perez technology surge cycle chart, which is a framework I like to use when thinking about new technologies, you will see that a frenzy develops when a new technology enters the material phase of the installation period. The frenzy funds the installation of the technology.

2017 is the year when crypto/blockchain entered the frenzy phase. Over $3.7bn was raised by various crypto teams/projects to build out the infrastructure of Internet 3.0 (the decentralized Internet). To put that number into context, that is about equal to the total seed/angel investment in the US in 2017. Clearly, not all of that money will be used well, maybe very little of it will be used well. But, like the late 90s frenzy in Internet 1.0 (the dialup Internet) provided the capital to build out the broadband infrastructure that was necessary for Internet 2.0 (the broadband/mobile Internet), the frenzy in the crypto/blockchain sector will provide the capital to build out the infrastructure for the decentralized Internet.

And we need that infrastructure badly. Transaction clearing times on public, open, scaled blockchains (BTC and ETH, for example) remind me of the 14.4 dialup period of the Internet. You can get a taste of what things will be like, but you can’t really use the technology yet. It just doesn’t work at scale. But it will and the money that is getting invested via the frenzy we are in is going to make that happen.

This is the biggest story in tech in 2017 because transitions from Internet 1.0 to Internet 2.0 to Internet 3.0 cause tremendous opportunity and tremendous disruption. Not all of the big companies of the dialup phase (Yahoo, AOL, Amazon, eBay) made a healthy transition into the mobile/broadband phase. And not all of the big companies of the broadband/mobile phase (Apple, Google, Facebook, Amazon) will make a healthy transition into the decentralized phase. Some will, some won’t.

In the venture business, you wait for these moments to come because they are where the big opportunities are. And the next big one is coming. That is incredibly exciting and is why we have these ridiculous valuations on technologies that barely/don’t work.

The Beginning Of The End Of White Male Dominance:

The big story of 2017 in the US was the beginning of the end of white male dominance. This is not a tech story, per se, but the tech sector was impacted by it. We saw numerous top VCs and tech CEOs leave their firms and companies over behavior that was finally outed and deemed unacceptable.

I think the trigger for this was the election of Donald Trump as President of the US in late 2016. He is the epitome of white male dominance. An unapologetic (actually braggart) groper in chief. I think it took something as horrible as the election of such an awful human being to shock the US into deciding that we could not allow this behavior any more. Courageous women such as Susan Fowler, Ellen Pao, and many others came forward and talked publicly about their struggles with behavior that we now deem unacceptable. I am not suggesting that Trump’s election caused Fowler, Pao, or any other woman to come forward, they did so out of their own courage and outrage. But I am suggesting that Trump’s election was the turning point on this issue from which there is no going back. It took Nixon to go to China and it took Trump to end white male dominance.

The big change in the US is that women now feel empowered, maybe even obligated, to come forward and tell their stories. And they are telling them. And bad behavior is being outed and long overdue changes are happening.

Women and minorities are also signing up in droves to do public service, to run for office, to start companies, to start VC firms, to lead our society. And they will.

Like the frenzy in crypto, this frenzy in outing bad behavior, is seeding fundamental changes in our society. I am certain that we will see more equity in positions of power for all women and minorities in the coming years.

The Tech Backlash:

Although I did not get much right in my 2017 predictions, I got this one right. It was easy. You could see it coming from miles away. Tech is the new Wall Street, full of ultra rich out of touch people who have too much power and not enough empathy. Erin Griffith nailed it in her Wired piece from a few weeks ago.

Add to that context the fact that the big tech platforms, Facebook, Google, and Twitter, were used to hack the 2016 election, and you get the backlash. I think we are seeing the start of something that has a lot of legs. Human beings don’t want to be controlled by machines. And we are increasingly being controlled by machines. We are addicted to our phones, fed information by algorithms we don’t understand, at risk of losing our jobs to robots. This is likely to be the narrative of the next thirty years.

How do we cope with this? My platform would be:

  1. Computer literacy for everyone. That means making sure that everyone is able to go into GitHub and read the code that increasingly controls our lives and understand what it does and how it works.
  2. Open source vs closed source software so we can see how the algorithms that control our lives work.
  3. Personal data sovereignty so that we control our data and provision it via API keys, etc to the digital services we use.
  4. A social safety net that includes health care for everyone that allows for a peaceful radical transformation of what work is in the 21st century.

2017 brought us many other interesting things, but these three stories dominated the macro environment in tech this year. And they are related to each other in the sense that each is a reaction to power structures that are increasingly unsustainable.

I will talk tomorrow about the future, a future that is equally fraught with fear and hope. We are in the midst of massive societal change and how we manage this change will determine how easily and safely we make this transition into an information driven existence.

#blockchain#bots#crypto#Current Affairs#economics#employment#machine learning#mobile#policy#Politics#VC & Technology#Web/Tech