Reflections on the demise of Yahoo!

By now we've all heard that Yahoo!'s web assets were bought by Verizon. According to the Wall Street Journal, Verizon paid $4.83 billion in cash for the assets. Yahoo itself will continue to hold the remaining assets but will eventually change its name and become an investment company. In total, the company was rumoured to be worth $6 billion.

For us Gen Xers this is an interesting day: we witnessed the end of a company we saw as innovative and fresh just a "few" (i.e. read ~20) years ago.

I was recently explaining to a young lad in his early 20s about life before the Internet: you had to find books at the library and it was almost impossible to connect socially with people beyond your classmates. So to use Yahoo or other search engines to access information or people was a completely new and mind-blowing concept.

As I noted in this post commemorating Google's 17th anniversary:

"It's especially memorable for those of us who were in university in the late 90s because we had access to high speed internet on campus unlike the painfully slow dial-up at home. 

I remember my first job as a coop student at the UW Federation of Students (I can't believe this quote is still hanging around from that time!) when a co-worker was explaining to me how OpenText was the best search engine (of course using my NetScape Browser). Of course back then there was a number of search engines including, Yahoo, Lyco, Alta Vista, etc. However, I stuck to OpenText for a while then eventually switched, along with everyone else, to Google...Well Lycos, OpenText (as a search engine) and AltaVista may be long gone, but it looks like plaid is back!"

So now we can add Yahoo! to the pile of "has beens" search engine.

Beyond nostalgia, I had the following reflections on the Verizon of Yahoo based on the WSJ article above:

• Verizon is no longer just pipes: Verizon has a strategy to move beyond just serving mobile and broadband services. Verizon is adding Yahoo to its existing portfolio of content plays, such as AOL. For Verizon, it's an overall strategy to make billions through content and advertising. Net neutrality can potentially limit their ability to use this vertical integration to undermine competition, but regardless it shows how being a "pipes-only" company is not enough. Of course it is a bit ironic that former rivals, Yahoo and AOL, are now sitting in the same tent.  
• Big Data is monetized at the expense of privacy: The ability of Verizon to combine the data plays between its various content plays is a great illustration of a point that I have noted before: for big data achieve value it must water down privacy. Since there are synergistic values (i.e. instead of just being additive) of combining the data, it could be argued that it's something that a user should explicitly consent because a user may simply not want Verizon to use their Yahoo data this way.  
• Remember the Internet Bubble? Yahoo! had a market capitalization of "more than $125 billion at the height of the dot-com boom in early 2000", which is quite a steep decline to $6 billion. I wonder if it ever produced the cash flows to justify that valuation. 
• Algorithms win over people: WSJ today published a good read comparing the algorithmic approach of Google, in contrast manual effort required to index the Internet. This is similar to Amazon's who found that the algorithms to better than humans in getting people to buy things: "Amabot replaced the personable, handcrafted sections of the site with automatically generated recommendations in a standardized layout," according to The Everything Store, a new book exploring the history of Amazon. "The system handily won a series of tests and demonstrated it could sell as many products as the human editors."
• Innovation and exponential thinking: On a separate note, but related note Yahoo could have bought Google for $3B in 2002 but it didn't. It's a great example of how Google embraced leading-edge technology to deal with the exponential growth of the Internet and Yahoo's inability to recognize Google's approach as the winning approach led to its demise.

Yahoo! is now literally a shell of its former self - both in structure and the assets it holds. However, it's a good case study of how failing to identify exponential trends - and acting on them - can ultimately lead to disaster.

Hacking reading: Is there a better way?

Came across Google's latest use of machine-learning: making "e-comic books" more readable.

One of the challenges of reading such fine literature on a mobile device is the small print that is within the bubbles.

Google's solution? Bubble Zoom.

As per Ars Technica:

"Google is tackling this problem the way it seems to be tackling every problem lately: with machine learning. Google has taught its army of computers to detect the speech bubbles in comic books, allowing you to zoom in on them with just a tap. The bubbles lift off the page and get bigger without affecting the underlying image. This lets you see the entire page while still reading the text. Google calls the feature "Bubble Zoom.""

Here are a couple of screenshots that show how it works:

For those that want to try this out on their Android device, you can download some free preview titles on the Google Play store.

Of course the obvious point, as mentioned by Ars Technica above, is that machine learning is being by Google and others to solve such interesting problems. The entire DC and Marvel comic book library has the Bubble Zoom feature enabled, which shows the power of machine learning to essentially reconfigure a massive amount of content.

The other point worth noting is how this technology fundamentally alters the way we consume text.

We have different channels, video, podcasts, and audio-books and can access books digitally but plain old reading has not changed that much. Zoom Bubble attempts to do that by building interactivity into the traditionally static medium of comic books.

To be honest I was surprised when I polled my IT Audit and Innovation class in January 2016 to see really none of them had shifted to e-books. They still rather have the physical copy, highlight and take notes.

That being said, a lot of credit should be given to Amazon for trying to go a long way to make it comfortable to read and enable you to access the content from multiple devices.

I’ve been experimenting with e-reading the Kindle, Samsung Note 4, iPad and iPhone.

The reader of choice depends on how you absorb information. If you want to savor your book and slowly digest, then Kindle is the easiest on the eyes

However, for us reading-for-productivity, i.e. if you are the type of person that needs to highlight and then extract notes, for the purposes presenting, researching, or blogging, then I think the Note 4 or the iPad is best. 

With the Kindle ecosystem, when you highlight the text (regardless of the device) its captured and stored on the cloud and then you can always access your notes there. For example, I highlighted the text below on my mobile device and it appears in the cloud (i.e. by logging into https://kindle.amazon.com/your_highlights): 

“ although GitHub is currently optimized for developers, similar platforms will eventually emerge for lawyers, doctors, publicists and other professionals. The platform has already been extended into enterprise software development with a successful paid business model, and can or soon will be used by governments, non-profits and educational institutions. GitHub charges users a monthly subscription—ranging from $7 to $200—to store programming source code. Andreessen Horowitz, one of the world’s leading venture capital firms, recently invested $100 million in GitHub. It was the VC firm’s largest investment round ever.”

In terms of iPad/iPhone versus Note 4, the Note 4 you can use its stylus to highlight text but you have to take an extra step to select the colour you want (you have 3 colors to choose from). In contrast, with iPad/iPhone you can just pick the colour right from the menu that pop-ups when you select a piece of text. The iPad’s larger form factor is also good for scan-reading. Of course the advantage for me on the Note 4/iPhone is that it’s my mobile device so it eliminates the need to carry around extra device.

One way to improve the readability is to change the background colour to Sepia from white. I have found it to be easier on the eyes.

The ability to move through multiple devices shows the brilliance of Amazon harnessing the power of open, mobile, cloud and seamless connectivity across platforms.

They could have gone the closed approach, i.e. you have to read their e-books off of their device. But by being open it enables the consumer to consume content in a manner that works for us. Microsoft has gone down this road as well with Office. I originally thought this was a bad idea but later recanted.

On a more critical note, as I have blogged before Amazon offers to US customers ONLY the ability to sync their audiobooks (Audible is owned by Amazon)  to their kindle ebooks for certain titles. It would be nice if this feature was also available out of the US.

What I've found to be a productivity hack, is to listen to the audio book on my Audible app at 2-3X speeds while driving around. I've self-diagnosed myself as an audiolearner it does help to learn things and get a good grasp of the topic. Such an approach can also help get the overall context of the material being presented. The Audible app enables you to bookmark, so that is a good way to track what you have to read up later.

Then I go through the Kindle e-book and highlight the parts I want to extract off the cloud. You can do this on the commute in or just waiting in line. The trick here is not to re-read the book but just extract those pieces of texts you wanted to focus on while listening to the audiobook. Moving the bookmarks from the audiobook to the e-books acts like a secondary review ensuring you've extracted all the content that's relevant to your presentation, research, blog post, etc. Alternatively, moving from the audiobook to the e-book may be the way you actually digest the content if you are more of a visual/text oriented learner. I personally need to do this with numbers and dates.

Finally, if you want to move the highlighted text off the cloud, try this to move the content to Evernote.

Although I think there are better ways out there to hack reading, I think the Amazon ecosystem goes a long way to get us there. One day, I hope, they will bring Immersion Reading to the world :)

Passwords: How's that still a thing?

Passwords.

How is this topic still a thing? 

In two words: Mark Zuckerberg. 

In June 2016, Mark Zuckerberg got hacked and his secret password was revealed for all to see. Did it meet all those wonderful rules we learn in information security school? Was it ISO27001/2 compliant? 

Well his password was "dadada" - so I'll let you decide. 

The Wall Street Journal's Nathan Olivarez-Giles had a great article on hacking/passwords. 

The article refers to a site where you can check to see if you've been hacked https://haveibeenpwned.com/ - definitely worth checking out. 

Of course the next step is to then change the password on the 7 million devices you own, but who says hackers make your life boring? 

Passwords are the best illustration of trade-off between convenience and security: you don't want the bad guys getting but at the same time you want to make it easy to use your email and the other services that you use.

One possible antidote to this unending saga of deal with hackings - managing the convenience versus security divide - is the use of password manager services. 

WSJ's Geoffrey Fowler had an article which reviewed "1Password, Dashlane, LastPass and PasswordBox"; giving the win to Dashlane.

Of course two factor authentication, as Oliveraz-Giles points out, is a key control that we all need to implement in our lives - especially since many popular services are making it easier two use such a feature. 

The fact passwords continue to be an issue reminds us that the most challenging aspect of a system is not the technology, but the people that use them.

Blockchaincanada.org: The inaugural meetup

Just finished attending a meet up sponsored by www.blockchaincanada.org. The room was filled to capacity - illustrating the excitement around the disruptive technology right here in Toronto. 

Alan Wunsche, co-founder of the organization, walked through the road map of the non- profit organization, which looks at multiple initiatives to raise the profile of blockchain in Canada and prevent the departure of luminaries in the field, such as Vitalik Buterin (Alan wasn't so specific, but I decided to read between the lines).

The organization is driven by community, and the thoughtware to be produced by the group will rely on the volunteers. For example, I volunteered for the accounting working group to explore Canadian initiatives around triple entry bookkeeping and alternative accounting models.

For those interested, in a deeper dive into blockchain checkout their blockchain hackathon this weekend.

Big Data and Predictive Policing: Can algorithms become racists?

Interesting article on Forbes by Thomas Davenport on Big Data. The articles discusses how various government, including Canadian Public Safety Operations Organization (CanOps), have used big data tools for "situational awareness". These systems draw on myriad sources of data to give users (e.g. law enforcement) the information they need to deal with a particular situation.

Here are a few points that I thought were worth noting:

Government is making strides in big data: We often think of Amazon, Google and other tech-giants as key users of this data. However, as the Davenport points out that the government is using this technology to assist with decision making. However, whether this is something that should be celebrated remains to be seen (see predictive policing below)

Privacy versus Value trade-off: He talks about how CanOps use of MASAS, the Multi-Agency Situational Awareness System, is limited by the filtering of sensitive information: "breadth of MASAS is noble, but it seems to limit its value. For example, as the CanOps website notes, because agencies are reticent to share sensitive information with other agencies, all the information shared was non-sensitive (i.e. not terribly useful)." It seems that this continues to be a theme that we had noted in back a couple years when discussing a similar trade-off the companies face when dealing with big data. As I noted in this post:

"privacy policies require the user to consent to a specific uses of data at the time they sign up for the service. This means future big data analytics are essentially limited by what uses the user agreed upon sign-up. However, corporations in their drive to maximize profits will ultimately make privacy policies so loose (i.e. to cover secondary uses) that the user essentially has to give up all their privacy in order to use the service."

Consequently, there still needs to be a solution as to how privacy can be respected but organizations can use the data they have collected to make better decisions.

Predictive Policing is an emerging reality: The sci-fi movie, Minority Report, paints a future where law enforcement arrests people before they commit crimes.


That future seems to be well on its.  Davenport mentions how "predictive policing" was introduced in 2014 to the NYPD.  He also mentions how much data is being collected by the police:

"It collects and analyzes data from sensors—including 9,000 closed circuit TV cameras, 500 license plate readers with over 2 billion plate reads, 600 fixed and mobile radiation and chemical sensors, and a network of ShotSpotter audio gunshot detectors covering 24 square miles—as well as 54 million 911 calls from citizens. The system also can draw from NYPD crime records, including 100 million summonses."

The idea of predictive policing was also raised in the book,  Big Data: A Revolution That Will Transform How We Live, Work, and Think, which I had explored in a multi-blog post series (click here for the first installment).

Andrew Guthrie Ferguson, Law professor UDC David A. Clarke School of Law, wrote an article on how that predictive policing is something that has not be really sorted in out in terms of legality. He notes:

"The open question is whether this big-data information combined with predictive technologies will create “predictive reasonable suspicion“ undermining Fourth Amendment protections in ways quite similar to the stop-and-frisk practices challenged in federal court.

In two law review articles I have detailed the distorting effects of predictive policing and big data on the Fourth Amendment and have come to the conclusion that insufficient attention has been given at the front end to these constitutional questions. New York has the chance now to address these issues before the adoption of the technology and should be encouraged by the same civil libertarians and ordinary citizens who challenged the stop and frisk policies."

His commentary highlights another limitation: big data predictions are biased based on how the data is collected. The stop and frisk policies he refers to disproportionately targeted minorities. Furthermore, policing is more focused on poor, black/hispanic neighbourhoods. Michelle Alexander documents in her book, The New Jim Crow, how this happens:

"Alexander explains how the criminal justice system functions as a new system of racial control by targeting black men through the “War on Drugs.” The Anti-Drug Abuse Act of 1986, for example, included far more severe punishment for distribution of crack (associated with blacks) than powder cocaine (associated with whites). Civil penalties, such as not being able to live in public housing and not being able to get student loans, have been added to the already harsh prison sentences."

Consequently, if the data by law enforcement is used to predict crime that essentially the targeting of minorities will continue to target such groups given that it is based on biased data. 

Technology often is seen to be a silver bullet for problems. However, we need to keep in mind that it is vulnerable to the human element that makes it. Given Microsoft's recent faux pas of accidentally allowing an AI avatar to become a Nazi, it is something that should actively be considered in the systems that are built to police and govern. 

Telsa Autopilot Fatality: Let's not blame the robots...yet.

By now most have heard of the fiery crash involving a Tesla roadster. This episode from the Young Turks does a good job at examining incident:

For more sensationalist coverage of the incident, watch the following:


The gentlemen at the end of the video notes how he would never trust a computer to drive him and his family.

So are such fears of computers warranted? 

If you look at the original press release from Tesla, it notes:

"What we know is that the vehicle was on a divided highway with Autopilot engaged when a tractor trailer drove across the highway perpendicular to the Model S. Neither Autopilot nor the driver noticed the white side of the tractor trailer against a brightly lit sky, so the brake was not applied. The high ride height of the trailer combined with its positioning across the road and the extremely rare circumstances of the impact caused the Model S to pass under the trailer, with the bottom of the trailer impacting the windshield of the Model S. Had the Model S impacted the front or rear of the trailer, even at high speed, its advanced crash safety system would likely have prevented serious injury as it has in numerous other similar incidents."

So analyzing the incident both the auto-pilot system and the driver didn't recognize the truck in the distance. With the fear of robots, it's easy to fan the flames of "robotophobia" and quickly blame the robots. For example, some could claim the driver would have been vigilant had he not had such an auto-pilot system. But this is mere speculation and hard to prove. 

A couple other things should be noted when evaluating this incident.

• Robot record is superior to the human only record: As Tesla has noted, that the car has driven safely 130 million miles, contrasting this to a fatality every 94 million miles driven in the US and 60 million world wide. In other words, the robot record is superior to the human only record. 
• What about the times the robot has saved people from crashes? The other problem is how do you balance this bad news with the good news that never gets reported. This refers to the time the autopilot acted to save the human beings from crashes. It similar to investments in information security that save the company from countless malware incidents. However, because nothing happens no one really notices the value of technology. Similarly, we're not able to balance the "fear, uncertainty, doubt" associated with this incident with all the times the auto-pilot system actually avoided a crash. 

The incident does point out, however, a bigger looming issue of how human beings and machines work together. Despite the caveats, people are already eager to let the auto-pilot drive them around. And really why not? Commuting is giant waste of time and we could be more productive while letting the computer drive us around. 

Nicholas Carr explores this issue in his latest book The Glass Cage. In the book, he explore how the more reliant we are on a technology, the less connected we are to the world. For example, by moving from manual to automatic transmission, in his opinion, driving is less fun. He also points out how airplane pilots are really just babysitting the computer that actually flies the plane. The trouble occurs when there is a crisis situation where the pilots are unable to handle the situation because they have lost their ability to actually fly planes. 

To be fair, this was not the issue in the Tesla crash - it's way too soon to say that the individual driving the car was overly dependent on the car. However, it is plausible to see how this will occur quite quickly if someone like Google were to offer driverless cars to the massed (as I noted in this  post). However, the government didn't made it mandatory to learn to ride a horse - just in case all the cars stopped worked. So I doubt they will force us to learn to drive cars, just in case the autonomous cars stop driving.