At the PAIR symposium. Maya Gupta runs Glass Box at Google, which looks at black box issues. She is talking about how we can control machine learning to do what we want

The core idea of machine learning are its role models, i.e., its training data. That’s the best way to control machine learning. She’s going to address by looking at the goals of controlling machine learning.

A simple example of monotinicity. Let’s say we’re tring to recommend nearby coffee shops. So we use data about the happiness of customers and distance from the shop. We can fit the model ot a linear model. Or we can fit it to a curve, which works better for nearby shops but goes wrong for distant shops. That’s fine for Tokyo but terrible for Montana because it’ll be sending people many miles away. A montonic example says we don’t want to do that. This controls ML to make it more useful. Conclusion: the best ML has the right examples and the right kinds of flexibility. [Hard to blog this without her graphics. Sorry.] See “Deep Lattice Networks for Learning Partial Monotonic Models,” NIPS 2017; it will soon by on the TensorFlow site.

“The best way to do things for practitioners is to work next to them”The best way to do things for practitioners is to work next to them.

A fairness goal: e.g., we want to make sure that accuracy in India is the same as accuracy in the US. So, add a constraint that says what accuracy levels we want. Math lets us do that.

Another fairness goal: the rate of positive classifications should be the same in India as in the US, e.g., rate of students being accepted to a college. In one example, there is an accuracy trade-off in order to get fairness. Her attitude: Just tell us what you want and we’ll do it

Fairness isn’t always relative. E.g., E.g., minimize classification errors differently for different regions. You can’t always get what you want, but you sometimes can or can get close. [paraphrase!] See fatml.org

It can be hard to state what we want, but we can look at examples. E.g., someone hand-labels 100 examples. That’s not enough as training date, but we can train the system so that it classifies those 100 at something like 95% accuracy.

Sometimes you want to improve an existing ML system. You don’t want to retrain because you like the old results. So, you can add in a constraint such as keep the differences from the original classifications to less than 2%.

You can put all of the above together. See “Satisfying Real-World Goals with Dataset Constraints,” NIPS, 2016. Look for tools coming to TensorFlow.

Some caveats about this approach.

First, to get results that are the same for men and women, the data needs to come with labels. But sometimes there are privacy issues about that. “Can we make these fairness goals work without labels? ”Can we make these fairness goals work without labels? Research so far says the answer is messy. E.g., if we make ML more fair for gender (because you have gender labels), it may also make it fairer for race.

Second, this approach relies on categories, but individuals don’t always fit into categories. But, usually if you get things right on categories, it usually works out well in the blended examples.

Maya is an optimist about ML. “But we need more work on the steering wheel.” We’re not always sure we want to go with this technology. And we need more human-usable controls.

Be the first to comment »

At the PAIR conference, Hae Won Park of the MIT Media Lab is talking abiut personal social robots for home.

Home robots no longer look like R2D2. She shows a 2014 Jibo video.

What sold people is the social dynamic: Jibo is engaged with family members.

She wants to talk about the effect of AI on people’s lives at home.

For example, Google Home changed her morning routine, how she purchases goods, and how she controls her home environment. She shows a 2008 robot called Autom, a weight management coach.

A studied showed that the robot kept people at it longer than using paper or a computer program, and people had the strongest “working alliance” with the robot. They also had emotional engagement with it, personalizing it, giving them names, etc. These users understand it’s just a machine. Why?

She shows a video of Leonardo, a social robot that exhibits bodily cues of emotion. We seem to share a mental model.

They studied how children tell stories and listen to each other. Jin Joo Lee developed a model in which the robot appears to be very attentive as the child tells a story. It notes cues about whether the speaker is engaged. The children were indeed engaged by this reactive behavior.

Researchers have found that social robots activate social thinking, lighting up the social thinking part of the brain. Social modeling occurs between humans and robots too.

Working with children aged 4-6, they studied “growth mindset”: the belief that you can get better if you try hard. Parents and teachers have been shown to affect this. They created a growth mindset robot that plays a game with the child. The robot encourages the child at times determined by a “Boltzmann Machine”en.wikipedia.org/wiki/Boltzmann_machine”>Boltzmann Machine. [Over my head.]

Their researc showed that playing puzzles with a growth-mindset robot fosters that mindset in children. For example, the children tried harder over time.

They also studied early literacy education using personalized robot tutors. In multiple studies of about 120 children. The robot, among other things, encourages the child to tell stories. Over four weeks, they found children more effectively learn vocabulary, and when the robot provided more expressive story telling (rather than speaking in an affect-less TTY voice) the children retained more and would mimic that expressiveness.

Now they’re studying fully automonmous storytelling robots. The robot uses the child’s responses to further engage the child. The children respond more, tell longer stories, and stayed engaged over longer periods across sessions.

We are headed toward a time when robots are more human-centered rather than task focused. So we need to think about making AI not just human-like but humanistic. We hope to make AI that make us better people.

Be the first to comment »

At the Google PAIR conference, Karrie Karahalios is going to talk about how people make sense of their world and lives online. (This is an information-rich talk, and Karrie talks quickly, so this post is extra special unreliable. Sorry. But she’s great. Google her work.)

Today, she says, people want to understand how the information they see comes to them. Why does it vary? “Why do you get different answers depending on your wifi network? ”Why do you get different answers depending on your wifi network? These algorithms also affect our personal feeds, e.g., Instagram and Twitter; Twitter articulates it, but doesn’t tell you how it decides what you will see

In 2012, Christian Sandvig and [missed first name] Holbrook were wondering why they were getting odd personalized ads in their feeds. Most people were unaware that their feeds are curated: only 38% were aware of this in 2012. Thsoe who were aware became aware through “folk theories”: non-authoritative explanations that let them make sense of their feed. Four theories:

1. Personal engagement theory: If you like and click on someone, the more of that person you’ll see in your feed. Some people were liking their friends’ baby photos, but got tired of it.

2. Global population theory: If lots of people like, it will show up on more people’s feeds.

3. Narcissist: You’ll see more from people who are like you.

4. Format theory: Some types of things get shared more, e.g., photos or movies. But people didn’t get

Kempton studied thermostats in the 1980s. People either thought of it as a switch or feedback, or as a valve. He looked at their usage patterns. Regardless of which theory, they made it work for them.

She shows an Orbitz page that spits out flights. You see nothing under the hood. But someone found out that if you use a Mac, your prices were higher. People started using designs that shows the seams. So, Karrie’s group created a view that showed the feed and all the content from their network, which was three times bigger than what they saw. For many, this was like awakening from the Matrix. More important, they realized that their friends weren’t “liking” or commenting because the algorithm had kept their friends from seeing what they posted.

Another tool shows who you are seeing posts from and who you are not. This was upsetting for many people.

After going through this process people came up with new folk theories. E.g., they thought it must be FB’s wisdom in stripping out material that’s uninteresting one way or another. [paraphrasing].

They let them configure who they saw, which led many people to say that FB’s algorithm is actually pretty good; there was little to change.

Are these folk theories useful? Only two: personal engagement and control panel, because these let you do something. But there are poor tweaking tools.

How to embrace folk theories: 1. Algorithm probes, to poke and prod. “It would be great, Karrie says, to have open APIs so people could create tools”(It would be great to have open APIs so people could create tools. FB deprecated it.) 2. Seamful interfaces to geneate actionable folk theories. Tuning to revert of borrow?

Another control panel UI, built by Eric Gilbert, uses design to expose the algorithms.

She ends with a wuote form Richard Dyer: “All technolgoies are at once technical and also always social…”

Be the first to comment »

I’m at the PAIR conference at Google. Jess Holbrook is UX lead for AI. He’s talking about human-centered machine learning.

“We want to put AI into the maker toolkit, to help you solve real problems.” One of the goals of this: “How do we democratize AI and change what it means to be an expert in this space?” He refers to a blog post he did with Josh Lovejoy about human-centered ML. He emphasizes that we are right at the beginning of figuring this stuff out.

Today, someone finds a data set, and finds a problem that that set could solve. You train a model and look at its performance, and decided if it’s good enough. And then you launch “The world’s first smart X. Next step: profit.” But what if you could do this in a human-centered way?

Human-centered design means: 1. Staying proximate. Know your users. 2. Inclusive divergence: reach out and bring in the right people. 3. Shared definition of success: what does it mean to be done? 4. Make early and often: lots of prototyping. 5. Iterate, test, throw it away.

So, what would a human-centered approach to ML look like? He gives some examples.

Instead of trying to find an application for data, human-centered ML finds a problem and then finds a data set appropriate for that problem. E.g., diagnosis plant diseases. Assemble tagged photos of plants. Or, use ML to personalize a “balancing spoon” for people with Parkinsons.

Today, we find bias in data sets after a problem is discoered. E.g., ProPublica’s article exposing the bias in ML recidivism predictions. Instead, proactively inspect for bias, as per JG’s prior talk.

Today, models personalize experiences, e.g., keyboards that adapt to you. With human-centered ML, people can personalize their models. E.g., someone here created a raccoon detector that uses images he himself took and uploaded, personalized to his particular pet raccoon.

Today, we have to centralize data to get results. “With human-centered ML we’d also have decentralized, federated learning”With human-centered ML we’d also have decentralized, federated learning, getting the benefits while maintaining privacy.

Today there’s a small group of ML experts. [The photo he shows are all white men, pointedly.] With human-centered ML, you get experts who have non-ML domain expertise, which leads to more makers. You can create more diverse, inclusive data sets.

Today, we have narrow training and testing. With human-centered ML, we’ll judge instead by how systems change people’s lives. E.g., ML for the blind to help them recognize things in their environment. Or real-time translation of signs.

Today, we do ML once. E.g., PicDescBot tweets out amusing misfires of image recognition. With human-centered ML we’ll combine ML and teaching. E.g., a human draws an example, and the neural net generates alternatives. In another example, ML improved on landscapes taken by StreetView, where it learned what is an improvement from a data set of professional photos. Google auto-suggest ML also learns from human input. He also shows a video of Simone Giertz, “Queen of the Shitty Robots.”

He references Amanda Case: “Expanding people’s definion of normal” is almost always a gradual process.

[The photo of his team is awesomely diverse.]

Be the first to comment »