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You Need a Password Manager

Another day, another security breach.

It wasn’t even hackers this time, just the gang at Facebook who can’t shoot straight.

Update: 4/4/2019. It just keeps getting worse.

You Need a Password Manager

Facebook exposed your FB and IG passwords to its 20,000 employees. You need to change both, now.

But this is just one example of a much larger problem. Both consumers and providers are subject to hacks and leaks, and they happen all the time.

It’s likely that at least one of your accounts has been compromised at some point. Don’t believe me? Check here.

The problem isn’t going away, so you need to take responsibility for holding up your end. Unfortunately, you probably suck at security.

How many of the following are you guilty of?

  • Reusing passwords.
  • Using simple passwords – yourfavoritenoun76, qwerty14, my$t3r10u$.
  • Writing down passwords, or storing them in a text file.
  • Not using multi-factor authentication (MFA) where available.

Passwords are both an annoyance and prone to hacking. But despite advances in and wider availability of various biometric protocols, we’re stuck with passwords for the foreseeable future and we need to do better.

 


What you can do

Use MFA/2FA where you can. It’s not perfect, but you’d be a fool to have any meaningful data behind a login without it.

Use complex passwords, 16+ characters where you can. Complex phrases are at least as good as a long string of random charters.

And since you probably have 100+ accounts, you need a password manager (PM).

(Chrome now does a nice job of recommending and saving passwords, but you still need a password manager.)

 

If you know how PMs work and aren’t using one…shame.

If you don’t know how they work, let’s review the basics.

A password manager helps you to generate, store, and retrieve passwords.

Thus, your accounts are more secure, you’re less likely to be hacked, your information is protected, and your life is better.

 


Which password manager?

Doesn’t matter.

Why not?

Because adopting any of the decent ones and using it the right way will be massively better than whatever else you’re doing today.

 

Personally, I’ve been using 1Password for years and it’s been great. But I chose it at a time when I could “own it” for about $30. Naturally, it’s offered on a subscription basis today.

If I were starting into a new one, I’d give LastPass a go. There’s a free tier and it offers a 30-day test of the premium version.

 

Secure your accounts. Start today.

Stay safe out there.

Anomaly Detection in Video & Image Classification

We’re seeing and doing all sorts of interesting work in the image domain. Recent blog posts, white papers, and roundtables capture some of this work, such as image segmentation and classification to video highlights. But an Image area of broad interest that, to this point, we’ve but scratched the surface of is Video-based Anomaly Detection. It’s a challenging data science problem, in part due to the velocity of data streams and missing data, but has wide-ranging solution applicability.

In-store monitoring of customer movements and behavior.

Motion sensing, the antecedent to Video-based Anomaly Detection, isn’t new and there is a multitude of commercial solutions in that area. Anomaly Detection is something different and it opens the door to new, more advanced applications and more robust deployments. Part of the distinction between the two stems from “sensing” what’s usual behavior and what’s different.

Anomaly Detection

Walkers in the park look “normal”. The bicyclist is the anomaly. 

 

Anomaly detection requires the ability to understand a motion “baseline” and to trigger notifications based on deviations from that baseline. Having this ability offers the opportunity to deploy AI-monitored cameras in many more real-world situations across a wide range of security use cases, smart city monitoring, and more, wherein movements and behaviors can be tracked and measured with higher accuracy and at a much larger scale than ever before.

With 500 million video cameras in the world tracking these movements, a new approach is required to deal with this mountain of data. For this reason, Deep Learning and advances in edge computing are enabling a paradigm shift from video recording and human watchers toward AI monitoring. Many systems will have humans “in the loop,” with people being alerted to anomalies. But others won’t. For example, in the near future, smart cities will automatically respond to heavy traffic conditions with adjustments to the timing of stoplights, and they’ll do so routinely without human intervention.

Human in the Loop

Human in the loop.

As on many AI fronts, this is an exciting time and the opportunities are numerous. Stay tuned for more from Doctrina.ai, and let’s talk about your ideas on Video-based Anomaly Detection or AI more broadly.

Programmatic Video Highlights

AI Video Classification

For many years, and with rapidly accelerating levels of targeting sophistication, marketers have been tailoring their messaging to our tastes. Leveraging our data and capitalizing upon our shopping behaviors, they have successfully delivered finely-tuned, personalized messaging.

Consumers are curating their media ever more by the day. We’re buying smaller cable bundles, cutting cords, and buying OTT services a la carte. At the same time, we’re watching more and more short-form video. Video media is tilting toward snack-size bites and, of course, on demand.

Cable has been in decline for years and the effects are now hitting ESPN, once the mainstay of a cable package. Even live sports programming, long considered must see and even bulletproof by media executives, has seen declining viewership.

 

So what’s to be done?

To thrive, and perhaps merely to survive, content owners must adapt. Leagues and networks have come a long way toward embracing a “TV Everywhere” distribution model despite the obnoxious gates at every turn. But that’s not enough and the sports leagues know it.

While there are many reasons for declining viewership and low engagement among younger audiences, length of games and broadcasts are a significant factor. The leagues recognize that games are too long. The NBA has made some changes that will speed up the action and the NFL is also considering shortening games to avoid losing viewership. MLB has long been tinkering in the same vein. These changes are small, incremental, and of little consequence to the declining number of viewers.

Most sporting events are characterized by long stretches of calm, less interesting play that is occasionally accented by higher intensity action. Consider for a moment how much actual action there is in a typical football or baseball game. Intuitively, most sports fans know that the bulk of the three-hour event is consumed by the time between plays and pitches. Still, it’s shocking to see the numbers from the Wall Street Journal, which point out that there are only 11 minutes of action in a typical football game and a mere 18 minutes in a typical baseball game.

 

A transformational opportunity

There is so much more they can do. Recent advances in neural network technology have enabled an array of features to be extracted from streaming video. The applications are broad and the impacts significant. In this sports media context, the opportunity is nothing short of transformational.

Computers can now be trained to programmatically classify the action in the underlying video. With intelligence around what happens where in the game video, the productization opportunities are endless. Fans could catch all of the action, or whatever plays and players are most important to them, in just a few minutes. With a large indexed database of sports media content, the leagues could present near unlimited content personalization to fans.

Want to see David Ortiz’s last ten home runs? Done.

Want to see Tom Brady’s last ten TD passes? You’re welcome.

Robust features like these will drive engagement and revenue. With this level of control, fans are more likely to subscribe to premium offerings, offering predictable recurring revenue that will outpace advertising in the long run.

Computer-driven, personalized content is going to happen. It’s going to be amazing, and we are one step closer to getting there.

Voice Ordering != Voice Shopping

Voice Ordering is Here

Voice shopping is coming, and it’s far more interesting.

Siri has been with us for years, but it’s in the last few months and largely due to Amazon that voice assistants have won rapid adoption and heightened awareness.

Over the last few months, we’ve been shown the power of a new interaction paradigm. I have an Echo Dot and I love it. Controlling media and some lights are the most useful applications so far. The Rock, Paper, Scissors skill… yeah, that one’s probably not going to see as much use.

But let’s not forget that this slick device is brought to us by the most dominant ecommerce business in the known universe. So it’s great for voice shopping, right? No. Not at all. It doesn’t do “shopping” at all.

But I heard the story about the six-year-old who ordered herself a dollhouse? So did I, and it reinforces my point. Let me explain.

The current state of commerce via Alexa is almost like a broad set of voice-operated Dash Buttons. For quick reorders of things you buy regularly and when you’re not interested in price comparisons, it’s fine. What it’s not — voice shopping.

Shopping is an exercise in exploration, research, and comparison. That experience requires a friendly and intelligent guide. As such, voice shopping isn’t supported by the ubiquitous directive-driven (do X, response, end) voice assistants.

Shopping is about feature and price comparison, consideration of reviews, suggestions from smart recommendation engines, and more. Voice shopping is enabled by a conversational voice experience, one that understands history and context, and delivers a far richer experience than is widely available today.


The Mobile Impact

Mobile commerce isn’t new and is still growing fast. But despite consumers spending far more time on mobile devices than on desktops (broadly defined, including laptops), small screen ecommerce spending still lags far behind.

So why can’t merchants close on mobile? The small screen presents numerous challenges.

Small screens make promotion difficult and negatively impact upselling and cross-selling. Another major factor, one you’ve probably experienced, is the often terrible mobile checkout process. Odds are, you’ve abandoned a mobile purchase path after fiddling with some poorly designed forms. I have. Maybe you went back via your laptop. Maybe you didn’t. Either way, that’s terrible user experience.

Through voice, retailers can now bring a human commerce experience to the small screen. It’s a new, unparalleled engagement opportunity; a chance to converse with your customer, capture real intelligence about their needs, and offer just the right thing. It’s an intelligent personal shopper in the hands of every customer.


Come reimagine voice shopping with us. Imagine product discovery and comparison, driven by voice. Imagine being offered just what you were looking for, based on a natural language description of what you need. Imagine adjusting your cart with your voice. Imagine entering your payment and shipping info quickly and seamlessly, via voice. It’s all possible and it’s coming soon.

 

Watson’s Reckoning

Watson’s Reckoning

To most in the know, IBM’s Watson has long been considered more hype and marketing than technical reality. Presented as infinitely capable, bleeding edge technology, you might think the well-known Watson brand would be delivering explosive growth to IBM.

Reality is far different. IBM’s stock is down in a roaring market. The company is, in effect, laying off thousands of workers by ending its work-from-home policy. More than $60M has perhaps been wasted by MD Anderson on a failed Watson project. All of this is happening against the backdrop of a rapidly expanding market for Machine Learning solutions.

But why? I saw Watson dominate on Jeopardy.

And dominate it did, soundly beating Ken Jennings and Brad Reuter. So think for a moment about what Watson was built to do. Watson, as was proven then, is a strong Q&A engine. It does a fine job in this realm and was truly state of the art…in 2011. In this rapidly-expanding corner of the tech universe, that’s an eternity ago. The world has changed exponentially, and Watson hasn’t kept pace.

So what’s wrong with Watson?

  • It’s not the all-encompassing answer to all businesses. It offers some core competencies in Natural Language and other domains, but Watson, like any Machine Learning tech, and perhaps more than most, requires a high degree of customization to do anything useful. As such, it’s a brand around which Big Blue sells services. Expensive services.
  • The tech is now old. The bleeding edge of Machine Learning is Deep Learning, leveraging architectures Watson isn’t built to support.
  • The best talent is going elsewhere. With the next generation of tech leaders competing for talent, IBM is now outgunned.
  • …and much more discussed here.

The Machine Learning market is strong and growing. IBM has been lapped by Google, Facebook, and other big-name companies, and these leaders are open sourcing much of their work.

Will Watson survive? Time will tell.