I’ve been writing a fun column on machine learning and cheap hardware for O’Reilly. One of the articles was How to build an autonomous, voice-controlled, face-recognizing drone for $200. It was a really fun project, but the coolest part is that a Microsoft employee, Mark Torr took the project and improved it and then wrote up a super thorough guide for how to do it. I’m putting it here to make it easier to find his great work:
Pretty cool! Mark’s writing is way more thorough and much easier to follow along. Seeing his work reminds me the joy of someone taking one of my open source projects and running with it. I would love to see open source apply to more than code.
Technology makes some types of jobs obsolete and creates other types of jobs — that’s been true since the stone age. While in the past, machines have replaced people in jobs that require physical labor, we’re increasingly seeing traditionally white collar jobs augmented by machines: financial analysts, online marketers, and financial reporters, just to name a few. Of course, these advances also create new jobs. The electronic computers that we know today, for example, replaced human beings performing the actual calculations, but in the process created all kinds of new types of work.
Artificial intelligence seems like it might work the same way, creating jobs for artificial intelligence researchers and slowly displacing all other kinds of knowledge work. And while this might be where we end up a century from now, the path to get there won’t quite look the way people think. We can see where we’re going from AI design patterns used at Google, Facebook and other companies investing heavily in artificial intelligence. In the most common design patterns, AI can actually increase demand for exactly the kind of work that it is automating.
Design Pattern 1: Training Data
Byfar the most common kind of artificial intelligence used in the business world is called supervised machine learning. The “supervised” part is important: it means that an algorithm is learning from training data. Algorithms still don’t learn anywhere near as efficiently as humans, but they can make up for it by processing far, far more data.
The quantity and quality of training data is actually the most important factor for ensuring a machine learning algorithm works well and the best companies take this training data collection process very, very seriously.Many people don’t realize that Google pays for tens of millions of man-hours collecting and labeling data that they feed into their machine learning algorithms.
Collecting training data is a never-ending process.Every time Twitter invents a new word or emoji, machine learning algorithms have no way of understanding it until they see many examples of its usage. Every time a company wants to expand into a new language or even a new market with slightly different patterns, they need to collect a new set of training data or their machine learning algorithms are working under dubious circumstances.
As machine learning becomes more well understood and high quality algorithms become something you can buy off the shelf, training data collection has become the most labor intensive part of launching a new machine learning algorithm.
Design Pattern 2: Human-in-the-loop
Ofcourse, some problems (like spreadsheet math) are incredibly easy for computers and some problems (like walking on two feet) are incredibly hard. It’s the same with machine learning. In every domain where machine learning works there are situations the algorithms figure out right away and situations that are maddeningly difficult to get them to perform well.This is why machine learning algorithms are famously easy to get to 80% accuracy and really, really tough to get to 99% accuracy.
Luckily, good machine learning algorithms can tell the cases where they are likely to do well and likely to struggle.Machine models have no ego, so they’re happy to tell you when their confidence is low. This is why the “human-in-the-loop” design pattern has become very widespread: humans get passed the processes and decisions that a machine can’t confidently make.
For years people have dreamed of a robot personal assistant, and products like Facebook M and Clara Labs are making this a reality. But they don’t automate everything. Instead they have algorithms handle emails and scheduling issues where the intent is clear to them and hand more complicated messages and requests to human being.
This design pattern has taken off far faster than anyone expected. Self driving cars don’t immediately replace human drivers; they take over in certain situations (like parallel parking) and hand back control to the human driver when things get complicated (such as on a busy street with construction). ATMs don’t automatically read every check you deposit, only the ones where the handwriting is clear. In both instances, machines handle a sizeable percentage of the work but when they’re unsure if they can perform well, human input is needed.
Instead of machine learning replacing one job function at a time, machine learning actually replaces pieces of every job function. This makes the person doing the job increasingly more efficient. In some cases, this can lead to fewer jobs, but in others, this can create new markets and create more jobs for the same type of work. If one personal assistant can now handle twenty customers at once, personal assistants become much more inexpensive and maybe one hundred times as many people will work with one.
Design Pattern 3: Active Learning
Active learning is a design pattern that combines the first two patterns. The training data collected by the “Human in the Loop” can be fed back into the algorithm to make it better. Algorithms learn like people — novel, complicated situations help them learn much faster. So the examples that the algorithm can’t do that get labeled by a human are the perfect examples to help the algorithm improve.
In the future, as we do our jobs, we may be simultaneously teaching the same system that is slowly replacing us. On the other hand, we could see it as getting more and more leverage out of our work. It’s really a matter of your point of view.
It’s coming sooner and faster than you think
Most knowledge work has been spared from the effects of artificial intelligence because the upfront costs of building a machine learning algorithm have historically been so high.Unlike software, every machine learning model has to be custom-built for every individual application. So the only business applications that machine learning automated were massively profitable or cost-saving undertakings, like predicting energy usage or targeting ads.
But all that is changing. Two trends have been rapidly bringing the cost of machine learning down. For one, computing power is getting cheaper, as it always does. For the second, machine learning algorithms are becoming productized. In 2015 alone, Alibaba, Microsoft, Amazon and IBM all launched general-purpose cloud machine learning platforms.Companies no longer need Google-like R&D budgets to use machine learning internally.
What this means is that many smaller scale business functions are about to feel the effects of machine learning. When it costs a million dollars to build an algorithm, only the largest companies apply machine learning to classifying their support tickets, organizing their sales database, or handling collections. But when it costs twenty dollars a month, everyone will do it. And with all of the machine learning platforms launched in the last year that moment might have just happened.
AI has been making a lot of progress lately by almost any standard. It has quietly become part of our world, powering markets, websites, factories, business processes and soon our houses, our cars and everything around us. But the biggest recent successes have also come with surprising failures. Tesla impressed the world by launching a self driving car, but then crashed in cases a human would have easily handled. AlphaGo beat the human champion Go player years before most experts possible, but completely collapsed after its opponent played an unusual move.
These failures might seem baffling if we follow our intuition and think of artificial intelligence the same way we think about human intelligence. AI competes with the world’s best and then fails in seemingly simple situations. But the state of the art in artificial intelligence is different from human intelligence, and its different in a way that really matters as we start deploying in the real world. How?: machine learning doesn’t generalize as well as humans.
The two recent Tesla crashes and the AlphaGo loss highlight how this plays out in real life. Each of the Tesla crashes happened in a very unusual situation — a car stopped on the left side of a highway, a truck with a high clearance perpendicular to the highway, and a wooden stake in an unpainted highway. In the game AlphaGo lost, it fell apart when the Go champion Lee Sedol played a highly unusual move that no expert would have considered.
Why is it that AI can look so brilliant and so stupid at the same time? Well, for starters, it knows less about what’s going on then you think. Let’s look at a simple example to explain. AI can get spectacularly good at distinguishing between the use of the word “cabinet” to refer to a wooden cabinet and to refer to the president’s cabinet. Our intuition, based on our understanding of human intelligence, is that a machine would have to “understand” these two cabinet concept to make this distinction so consistently. The human approach is understand two different concepts by learning about politics and woodworking. Machine learning doesn’t need to do this — it can look at 1,000 sentences containing the word cabinet, each labeled (by a human) as corresponding to one or the other meaning, It learns how frequently words like “wood” or “storage” or “secretary” occur nearby in each case. So it knows that when the word “wood” is present, chances are extremely high that we’re referring to a storage cabinet. But If Obama starts talking about how he’s getting into woodworking, the AI may fail completely.
Artificial intelligence can work as well as it does without “knowing’ the way humans “know” for a simple reason: machines can process far more training data than a human. Peter Norvig, Google’s head of research, most famously first highlighted this idea in a paper and talk called, “The Unreasonable Effectiveness of Data”. This is how modern machine learning works in general — it pours over massive datasets and learns to generalize in smart ways, but not in the same smart way that humans generalize. As a result, it can be brilliant and also get very confused.
So how should we we take all of this into account when we manage artificial intelligence in the real world?
1) Play to AI’s strengths: Collect more training data
Why does Facebook have such amazing facial recognition software? They have fantastic researchers, but the core reason is that they have billions of selfies. Why did Google build a better translation system than the CIA as a side project? They scraped more websites than anyone else, so they had more examples of translated documents.
Real breakthroughs in machine learning always come when there are new data sets. Deep learning isn’t much better than other algorithms on small amounts of data but it continues to improve on larger and larger data sets better than any other method.
2) Cover for AI’s weaknesses: Use human-in-the-loop
Artificial intelligence has a second advantage over human intelligence: it knows where it is having trouble. In the latest Tesla crash, the autopilot knew it was in an unusual situation and told the human repeatedly to take the wheel. Your bank does the same thing when it reads the numbers off a check. As of a few years ago, AI reads numbers off of almost all deposited checks, but checks with particularly bad handwriting still get handed off to a human for review. And more than fifteen years after Deep Blue beat Kasparov, there are still situations where humans can outplay computers at chess.
When done well, keeping a human-in-the-loop can give the best of both worlds: the power and cost savings of automation, without the sometimes unreliability of machine learning. A combined system has the power to be more reliable, since humans and computers make very different kinds of mistakes. The key to success is handing off between humans and computers in smart ways that may very well require new types of interfaces to effectively take advantage of relative strengths and weaknesses. After all, what good is a near perfect self driving car AI that hands off control to a human it has let fall asleep?
I love workflowy. I’ve used every day for years. I think if everyone used it, the world would be a more productive, happier place.
If you haven’t tried it, it’s basically Gmail for your to do lists.
Remember when you had to put your emails in folders so you could find them? I really tried to keep organized folders because it because it was so painful when I had to search for an email. Some people seem to take great joy in organizing things, but I am not one of them. Foldering emails was my least favorite thing so I did it only in spastic fits of frustration. I would name the folders awful things like “MSFT – Misc” or “Legal BS” that made sense to me in the moment, but never made sense again.
Then gmail came along with essentially unlimited storage and awesome search and I never had to worry about categorizing emails again. It was so powerful that if I wanted to remember something I would email it to myself and add a bunch of keyword tags to help me find it in the future. This made my life so much better.
When I started my company, I tried organizational tools and processes in the same haphazard way that I organized my email. I would keep track of performance reviews in google docs or word docs in my dropbox. I would try to track engineering todos in Jira. I tried a million tools to keep myself focused and none of them worked. I reverted to using a physical notebook.
But workflowy is like a notebook that’s always with you and more importantly, that you can search. This is so powerful because even when you change your processes you can still find everything you wrote down. In my 1:1s with employees sometimes we talk about urgent things and sometimes we talk about their career goals. Sometimes we talk about their comp and sometimes we talk about their organizational concerns. There’s no single good way to organize everything, because often I don’t know in advance what my employees are going to care about. I write everything down in my workflowy in the haphazard, disorganized way that it comes at me. I rarely refactor my notes and yet I can still find everything that someone has said to me. With my longer tenured employees we can reflect on what their goals were in 2012 and how they’ve evolved. I can pull up every conversation we’ve had about compensation when I do a comp review. Most importantly, I don’t have to ask people the same questions multiple times.
I scribble notes in my workflowy about ideas I have for my blog and ideas I have for our conference and things I want to accomplish. I write down things I want to say to customers the next time I see them and things I want to say to my mom. Then I purge these thoughts from my mind until I see this person. It’s amazing how freeing this feels.
I love workflowy because it doesn’t tell me how to organize things or do things but it’s made me so much more organized and effective.