Facial recognition is used to nag you when you're not paying attention to your studies

Amor Too, TechCrunch, reports on software for online courses that uses artificial intelligence and facial analysis to determine whether students are paying attention in class:

The software uses students’ webcams to analyze eye movements and facial expressions and determine whether students are paying attention to a video lecture. It then formulates quizzes based on the content covered during moments of inattentiveness. Professors would also be able to identify moments when students’ attention waned, which could help to improve their teaching, Saucet says.

Even though this is on The Verge, I'm still not sure if this is a joke or not.

Rose Luckin, a professor at the University College London Knowledge Lab, had this to say, “A much more pedagogically sound approach would be to show the student when they are focused, and how that relates to their performance, So that you're offering information back to the student that helps them to structure their work time more effectively and helps them to become a more effective learner.”

Translation: this is a dumb idea.

Blockchain is the new Linux

Jon Evans, TechCrunch, shares a compelling argument:

...blockchains today aren’t like the Internet in 1996; they’re more like Linux in 1996. That is in no way a dig — but, if true, it’s something of a death knell for those who hope to profit from mainstream usage of blockchain apps and protocols.

Decentralized blockchain solutions are vastly more democratic, and more technically compelling, than the hermetically-sealed, walled-garden, Stack-ruled Internet of today. Similarly, open-source Linux was vastly more democratic, and more technically compelling, than the Microsoft and Apple OSes which ruled computing at the time. But nobody used it except a tiny coterie of hackers. It was too clunky; too complicated; too counterintuitive; required jumping through too many hoops — and Linux’s dirty secret was that the mainstream solutions were, in fact, actually fine, for most people.

We're going to see fantastic creations because of blockchain; and perhaps blockchain will fundamentally change the financial industry. But blockchain isn't the revolutionary change the the internet was (and continues to be). The Linux analogy is a better fit.

At present, there is no practical means for testing the safety of AVs prior to widespread use...

According to the Bureau of Transportation Statistics, there were about 35,000 fatalities and over 2.4m injuries on American roads in 2015. While these numbers sound high, given that Americans drive three trillion miles a year, the accident rates are remarkably low—1.12 deaths and 76 injuries per 100m miles. Because accidents are so rare (compared with miles traveled), autonomous vehicles “would have to driven hundreds of millions of miles, and sometimes hundreds of billions of miles, to demonstrate their reliability in terms of fatalities and injuries,” says Nidhi Kalra of RAND Corporation, a think tank in California. At present, there is no practical means for testing the safety of AVs prior to widespread use. For many, that is a scary thought.

That's a lot of miles. Also, the article has good examples for each of the different levels (Level 1-5) characterizing the capabilities of autonomous vehicles.

Via The Economist

Mossberg out

We’ve all had a hell of a ride for last few decades, no matter when you got on the roller coaster. It’s been exciting, enriching, transformative. But it’s also been about objects and processes. Soon, after a brief slowdown, the roller coaster will be accelerating faster than ever, only this time it’ll be about actual experiences, with much less emphasis on the way those experiences get made.

As a gadget-lover, this makes me a little sad. But, as a tech believer, it’s tremendously exciting. I won’t be reviewing all the new stuff anymore, but you can bet I’ll be closely watching this next turn of the wheel.

Thanks for reading. Mossberg out.

The world will be less, absent Walt's weekly.

Are flying cars really going to be a thing?

Flying cars seem to have lots of problems: energy-intensive, loud, and they can fall out of the sky onto other people. Even still, smart people are interested and actively investing in this space. 

Sebastian Thrun, CEO of Kitty Hawk (and co-founder Udacity), believes he can solve - or at least mitigate - these problems. Here's an excerpt of an interview between him and Stephen Levy (writing for Backchannel):

Is there any other cranky question I forgot ask?

You could ask about regulators.

Good point. Won’t any level-headed regulator just nix this whole idea?

We are working very actively with the FAA and other regulators, because at the core we share the same concern, which is safety. Especially as you innovate in something that has the potential to put bodily harm or even death to people. It is really important that this is done ethically and safely. As a result we see our friends from the FAA very, very frequently. And we’ve experienced really great collaboration. I am a technologist, so I can invent the technology, but it is the society that has to accept the technology. The more everyone can work together, the better for everyone involved.

Assuming Sebastian's right, then flying cars have at least two big advantages over their land-based analogs: obviously there's more space to fly and thus less congestion, but more importantly, self-flying cars are easier to get right than self-driving cars (fewer obstacles, they don't have to fit with existing infrastructure, and can be instrumented to communicate with other flying vehicles).


AI services for developers

I wanted to better understand what Artificial Intelligence APIs are available for developers. What follows are the services available today via the big providers (Amazon, Google, IBM, and Microsoft), organized into three layers:

  • Applications - pre-trained deep learning models for a specific purpose such as: vision, speech, and speech recognition.
  • Platforms - fully-provisioned services ready to be trained on big data; often pulled from a warehouse, file system, or database.
  • Frameworks - the technologies underpinning the platforms and applications such as TensorFlow on Hadoop or Caffe on Spark.

Applications

  • Amazon Lex - provides automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text.
  • Amazon Polly - converts text into lifelike speech including dozens of lifelike voices across a variety of languages.
  • Amazon Rekognition - detects objects, scenes, faces; searches and compares faces; and identifies inappropriate content in images.
  • Google Cloud Speech API - convert audio to text; recognizes over 80 languages and variants.
  • Google Cloud Vision API - classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images.
  • IBM Watson Conversation - allows you to quickly build, test and deploy a bot or virtual agent.
  • IBM Watson Speech to Text - converts audio voice into written text.
  • IBM Watson Text to Speech - converts written text into natural sounding audio in a variety of languages and voices.
  • IBM Watson Visual Recognition - understands and tags visual concepts recognizes food, finds human faces, approximates age and gender, and finds similar images in a collection. 
  • Microsoft Computer Vision API - extracts rich information from images to categorize and process visual data; and machine-assisted moderation of images to help curate your services.
  • Microsoft Bing Speech API - converts audio to text, understands intent, and converts text back to speech for natural responsiveness.
  • Microsoft Recommendations API - recommends items your customers want
    by learning from previous transactions.

Platforms

Frameworks


Notes:

  • Microsoft has an extensive (and growing) list of special-purpose AI APIs. I've just listed a few of the most generally useful.
  • IBM does offer more services than listed above, though most deal with natural language processing - likely as part of it's AlchemyAPI acquisition.
  • Google, IBM, and Microsoft all offer language translation services (not listed here).
  • Google and Microsoft both offer services for working with video (Microsoft appears to offer a real-time video analysis offering).

A look at the daily routines of nine workers

I really enjoy getting a glimpse into how other people live their lives - like wandering history museums reading the placards or visiting people's homes - so this piece by The Outline really resonated with me.

Here's an excerpt from non-fiction writer, Deborah Baker:

What are your typical hours?
I get up at 6:15 a.m., swim at the Y if I'm in Brooklyn, or at the public pool if I'm in India. I'm generally at my desk by 8 a.m. and don't leave it again until about 6 p.m., except for a break for lunch or to reheat my coffee. Regular office hours.
What do you spend most of your workday doing?
These days it is fine tuning sentences or figuring out if I need to cut some details or re-arrange the order of sentences in which a paragraph unfolds. A month ago it was reframing entire chapters, moving stuff around, paying attention to the pace and asking myself whether, in this bit or that bit, I was expecting too much from the reader. Most of my job takes place in my head. To call this labor makes it sound grander than it is.
What do you find to be hardest part of your job?
If I was pressed to answer I would say that the hardest part for me of being a writer is finding a subject that will sustain my interest for the three or so years it takes to get from initial idea to complete manuscript, but also one which I feel I can convince other people (editors, readers) to be as engaged with as I am.

Authors: Rawiya Kameir, Khalila Douze, Ann-Derrick Gaillot

Ben Edwards on the benefits of self-driving cars

Ben Edwards, of Alt Text, shares a terrific overview of the many benefits we will gain with autonomous cars: lives saved, the end of traffic, a better use of our time, improved health, a cleaner earth, and much more. But I'm still worried that it's going to be awhile before we get there - not because the technology won't be ready, but because people won't be ready.

Here's Ben's take:

There will be some significant hurdles to get over with regards to public adoption, as many recent polls suggest the U.S. population is not yet ready to give up the wheel.
Three out of four U.S. drivers said they would feel “afraid” to ride in self-driving cars, according to the AAA survey released on 1 Mar 2016. Just one in five said they would actually trust a driverless vehicle to drive itself with them inside.
There was likely a time when people didn’t trust calculators to add and subtract for them either. I’m sure we’ll soon be in the “break-in” period, where people get used to the numerous semi-automated features already in place on many vehicles, as a way to build trust. But as I like to say, autonomous cars don’t need to be 100% perfect in order for them to be a vast improvement over human drivers.

The world will be a better place when humans are no longer driving.

Google Lens brings search to the physical world

Here is Sandar Pachai, on Google Lens, at Google's I/O keynote yesterday (text from Stratechery):

We are clearly at an inflection point with vision, and so today, we are announcing a new initiative called Google Lens. Google Lens is a set of vision-based computing capabilities that can understand what you’re looking at and help you take action based on that information. We’ll ship it first in Google Assistant and Photos, and then other products.

How does it work? If you run into something and you want to know what it is, say a flower, you can invoke Google Lens, point your phone at it and we can tell you what flower it is…Or if you’re walking on a street downtown and you see a set of restaurants across you, you can point your phone, because we know where you are, and we have our Knowledge Graph, and we know what you’re looking at, we can give you the right information in a meaningful way.

As you can see, we are beginning to understand images and videos. All of google was built because we started understanding text and web pages, so the fact that computers can understand images and videos has profound implications for our core mission.

And Ben Thompson's reaction::

The profundity cannot be overstated: by bringing the power of search into the physical world, Google is effectively increasing the addressable market of searchable data by a massive amount, and all of that data gets added back into that virtuous cycle. The potential upside is about more than data though: being the point of interaction with the physical world opens the door to many more applications, from things like QR codes to payments.

Ben's excitement is contagious: AI provides a transport for information between the digital and physical worlds. We're on the cusp of amazing change.