This is a talk my partner Albert gave at an AI conference in Berlin last month. Although he gave it to an AI conference, it is really about where we are in our society and what we need to do about it. It is about 20mins long.
Posts from machine learning
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.
woooooow pic.twitter.com/qWvGQeSjLb— Laurie Charles (@TheStuffOfMemes) April 20, 2019
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.
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.
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.
My friend Gordon texted me this podcast and said “AI, blockchain, and homomorphic data. Trifecta!”
I gave it a listen and indeed some very interesting concepts are discussed in this one.
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.
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.
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:
- 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.
- Open source vs closed source software so we can see how the algorithms that control our lives work.
- Personal data sovereignty so that we control our data and provision it via API keys, etc to the digital services we use.
- 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.
It is a mobile SDK that any developer can put into their mobile app and it will allow for machine learning on the device:
Machine learning (the process by which computers can get smarter through data examples instead of explicit programming) requires massive computational power, the kind usually found in clusters of computer servers in massive datacenters (ooooh, the cloud). This means that machine learning technology is usually only available to those who can connect to the cloud.
Not anymore! Clarifai’s Mobile SDK gives users the power to train and use AI in the palms of their hands by installing machine learning capability directly on their devices, bypassing the traditional requirement of internet connectivity and massive computing power. After all, these days we have tiny supercomputers in our pockets – our mobile phones. Starting with an iOS SDK, Clarifai is on a mission to make user experiences uniquely personalized on any device from your cellphone to your toaster, anywhere in the world.
Here’s a slideshow that explains how this works: