Discovering Information with a Little Help from Tweet Assistant

If you noticed a spike in my Twitter posts today, there is a reason.  I have been searching #machinelearning using Tweet Assistant (more on Twitter Assistant in a future post)

These tweets, hashtags, mentions and popular posts provide a gold mine of information.  However, there is more to analyzing tweets than getting news and opinions. Within minutes, I was following some cool people and started discovering awesome tweets and visualizations.

Machine Learning – Top 10 Mentions

 

Machine Learning – Top 5 Hashtags

I started retweeting some of the posts I found but felt that there are far too many posts and a discussion of these may fill a couple of blog posts. So that is what I am going to do.

 

 

Applications of ML – Improving Depth detection, Night Sight, Video Play and Notifications

Here are a few fascinating applications of ML. I mostly track business applications of ML so I was pleasantly surprised to see how Unsupervised Learning and Reinforcement Learning (two ML techniques that do not get much coverage) was being used by two of the biggies in the AI and ML space.

The first two – Depth detection and Nightsight are posts from Google AI Blog.  The following concepts were covered in these posts.

  • PSL Positive shutter lag
  • Motion metering
  • Exposure stacking
  • Astrophotography
  • Super-resolution
  • Auto white balancing and Learning based AWB algorithm
  • Ultra long shot photography
  • Optical Image stabilization

    Facebook Horizon article covered how they were improving user experience with Reinforcement Learning.

  • Improving video playing using Reinforcement Learning (RL)
  • Improving user notifications using Reinforcement Learning

Links to articles:

  1. Under the hood of the Pixel 2: How AI is supercharging hardware – Google AI 
  2. Google AI Blog: Night Sight: Seeing in the Dark on Pixel Phones
  3. Horizon: An open-source reinforcement learning platform – Facebook Code 

Tim O’Reilly: It is up to us

Listening to this conversation with Tim O’Reilly, was one of the most rewarding experiences. In this conversation, Tim and Byron discuss several topics that make you think. I listed a few here.

  1. Fitness functions (see the quote below)
  2. On Amazon’s use of robots for business
  3. About Cognitively Augmented workers
  4. The law of conservation of attractive profits
  5. On Intelligence (human and artificial)
  6. How to pair humans with machines
  7. Step changes and their impact
  8. On anticipating  and countering the worst fears
  9. The Robustness Principle
  10. Agreement Protocols
  11. On Platforms and Eco-systems
  12. On doing meaningful work

I will pick a few of the topics (1-5) and take the liberty of quoting from the transcript. My goal is to kindle your interest enough to read the article and then the book.

On Fitness functions and how they focus companies on delivering value with technology (including AI).

If you look at Google; their fitness function on both the search and the advertising side is relevance. You look at Facebook; loosely it could be described as engagement.

On Amazon’s use of robots in their business:

an analysis of Amazon. In the same 3 years which they added 45,000 robots to their factories, they’ve added hundreds of thousands of human workers.

About Cognitively Augmented workers.

Then, Sidecar and Lyft figured out the other piece of the equation, because Uber was just black cars. They figured out that in order to have enough drivers to really fill out the marketplace, other than a small segment of well-off people, you’d get ordinary people to supply their cars. And you could do that because those drivers are cognitively augmented. It used to be that you had to be a professional driver, because [when] somebody says, “I want to go to such and such an address,” you’d need to really know the city. You [would] need to have a lot of experience to know the best routes. Well, guess what, with these apps [like] Google Maps and Waze, anybody can do it. So I started looking at that and [saw that] we have a marketplace of small businesses managed by algorithms to help them match up with customers.

Law of conservation of attractive profits:

Clay Christensen, back in 2004  talked about  “the law of conservation of attractive profits,” and that’s what helped me get from open source to web 2.0—[is] when one thing becomes a commodity, something else becomes valuable. So if self-driving cars commoditize driving, you have to ask yourself, what becomes valuable. And I think it’s going to be new kinds of augmentation for humans, new kinds of services that you’ll put on top of driving.

On Intelligence (artificial):

And so what would be something that we have today that would qualify or come close to qualifying as that in your mind?

You mean, in terms of machines?

Yes.

Nothing.

And why do you think that?

I’m with Gary Marcus on this, you know. He kind of talked about how the frontier of AI right now is deep learning, and it’s great, but you still have to train it by showing it a gazillion examples of something, and after you show it a gazillion examples, it can figure stuff out. That’s great, but it can’t figure that out without being exposed to those examples. So, we’re a long way from kind of just flicking the switch, having a machine take in its experience of the world, and basically come to conclusions about it.

Tim ends with this appeal and an inspiring message.

“Hey, we have a lot of things to worry about, we have enormous new powers, let’s put them to work, in the right, way, tackling the hard problems.”

I have been a big fan of Tim’s “Work on stuff that matters” and quoted him. It framed some of my decisions on what to spend time on.

Meta:

I have been following Tim for a couple of decades. More about that in a later post. I recently started listening to Byron’s One Minute AI podcast and the GigaOm AI podcast.