Bet on machine learning

Companies like Google and Microsoft offer impressive machine learning capabilities in their public cloud products. This means artificial intelligence is significantly more accessible to any business than before.

How does it work?
At a high level, machine learning is a data analysis method which uses historical data, examples and experience to devise a model to automatically predict future outcomes (instead of hard-coded rules). The key is the "learning" part: the algorithm continues to evolve to make the predictions more accurate over time.

The traditional ways of machine learning involved more manual methods of developing models and algorithms. IBM's DeepBlue, for example, was programmed to learn to play chess in the 1990's (and beat the world champion). However, chess has a relatively small and finite set of moves per position (about 20) -- fairly easy to program a computer to learn through brute force.

Fast forward to 2016 and Google's DeepMind project AlphaGo. It utilizes sophisticated neural network algorithms, and was used to defeat the Go world champion. Go has about 200 moves per position, with more possible board configurations than there are atoms in the universe! This demonstrates the power of the neural network algorithm. Most importantly, it shows that general-purpose artificial intelligence can exist.

Neural networks mimic the learning process of the human brain. The AI from DeepMind uses a technique called Deep Reinforcement Learning. It learns from experience, using raw pixels as data input. AlphaGo was shown hundreds of thousands of Go games so it could learn from human players. Then Google had the AI play against itself 30 million times. Over time, it got better; to the point where one of the algorithms had an almost 90% win-rate against the other. That was the one selected.

Naturally, a human could never play 30 million Go games in their lifetime. The machine does not get tired, nor make emotional mistakes. The AI's experience becomes super-human, despite the fact it originally learned from humans.

Watch what happens when Google used the same algorithm to train the machine on the famous Atari game Breakout. The goal given to the machine was to maximize the score it could achieve in the shortest amount of time. At first, the AI is pretty terrible at the game. However, after about 2 hours of playing, it is very good. After 6 hours it does something amazing: it becomes super human.

Swiftkey, the makers of a keyboard app for mobile devices, nicely demonstrate how a neural network helps improve their word predictions.

Using ML in your organization

The ability to plug directly into some of Google's (and others' like Microsoft and Amazon) algorithms in the cloud make ML much more accessible. I am more familiar with Google's offerings, so will highlight a few:

Google's Cloud Vision API is image recognition in the cloud. It can detect what is occurring in images (including sentiment analysis of humans). A city in Canada trained Google's AI using thousands of school bus stop sign videos. The goal was to have the machine watch the videos and identify if a vehicle went passed the bus' stop sign illegally. The algorithm was trained to identify when the sign was out and active, and when vehicles had passed through it. It turned out to be 99% effective, while humans were only 83%. This resulted in increased revenue through traffic violation tickets.

Disney used Cloud Vision for their marketing campaign for the movie Pete's Dragon. The site set children on a hunt in their homes for common objects (like chair, door, tree, clouds, etc.). Once detected by the algorithm, Elliot the dragon would magically appear on the screen.

Google's natural language processing API is something which could be leveraged in the example given in my earlier blog post on data. By analyzing millions of public social media posts for certain sentiments and cues, the sales team can potentially land deeper leads.

Google also has a translation, audio-to-text, and even a new job search API.

Lastly, Google's open-source TensorFlow is a machine learning library for numerical computation using data flow graphs. Developers can use this to build models with very little code and eventually translate them into products in Google's cloud.

The future: humans + machines

I believe the businesses which adopt and master machine learning the best will be the most successful in the future, regardless of industry. (Of course, it helps to have a lot of data to train your model.)

While ML may eliminate some jobs, I feel it will be the successful partnership of humans and machines which will bring the most fruitful benefits. Take a radiologist, for example: she may leverage ML to assess her readings faster, but also provide additional oversight for deeper analysis. 

Ultimately, where the AI takes us is hard to predict, but the positive impact and advancements made will most certainly be exponential.

Data, data, data

"Data, data, data" is the new "location, location, location."

Uber owns no taxis, yet is the largest taxi company in the world. AirBNB owns no real estate, yet has the most accommodations in the world. These companies run their businesses on data, and lots of it.

Data is king, and it is only becoming more important. Proper analysis and utilization of data helps to uncover the what, the why, and even predict the future. As a result, data must be a core component of your digital strategy.

Hindsight
At the most basic level, data gives us hindsight. A simple example is how grocery stores utilize loyalty cards. Customers sign up for them with some basic personal information, and in return the store gives the customers discounts when they use their card. Data collected from these cards helps the grocer identify individual purchasing habits -- it gives them hindsight.

This is why online retailers encourage customers to create accounts. The data collected (which products are being viewed, which terms are being searched for, etc.) all help track what is happening in their store.

Insight
Understanding the "what" is just the basics when it comes to data analytics. Having the view into the "why" provides insight.

Why do certain customers buy one product over another? Why do certain products sell better at certain times of the year? These are the types of questions the data can help provide insight into.

Foresight
Being able to predict behavior is the next step; this is where the most positive transformation can occur for an organization.

Again using grocers as an example, stores can use big data to predict and suggest the price points of certain products at certain times to ensure the right amount is in stock and fresh. If the price of strawberries, for example, is too high grocers risk having too many in stock and the strawberries going bad. If they accurately predict the right price point, they can keep the right amount moving off the shelves at a pace that ensures each package is still fresh.

Lastly, there are some scenarios where proper data analysis can actually help to prescribe some actions. In other words, using data can help make things happen.

Let us use the car company Fiat as a fictitious example for this. If Fiat mined the publicly available social media posts specifically looking for terms which suggest a propensity to buy their car, they may be able to help drive more sales. The scenario could go something like this: John Smith posts to Twitter, "Thinking about buying the new Fiat. Can't decide between that or the Toyota Prius." That post will get picked up in Fiat's social media scanning algorithm, and alert the salesperson in John's region to contact him directly. That contact may help to influence John in purchasing a Fiat.

Making it happen in your organization
To leverage data effectively, naturally you need data. Determine the sources, and if none exist start setting up your data collection processes.

Once you have the data, it needs to be usable. Having it in 25 disparate systems will make life tough. Rather centralizing it and "cleansing" it for use (i.e. ensuring accuracy, removing duplicates, etc.) is key.

Additionally, data can help create a source of revenue. Identify any data which may be unique to your organization which others externally may pay to access. Ensure proper usage controls and governance are in place.

Also keep in mind potential external integrations or partnerships.

Ultimately, there are endless possibilities to how you can utilize data. Start small, take an MVP approach, and build from there as you learn what works for your organization.

The future: Our fluid connectivity

I enjoy making predictions about the future. It is fun to see how accurate the predictions are as time goes by.

I believe the technologies of the future are a continuous building upon the present. At times inventions may appear to be huge leaps, but in reality they are logical progressions of existing ideas or novel combinations of both existing and new.

The iPhone, for example, can seem extremely ahead of its time (it was, of course, as compared to the competitors), but even that device was a combination of existing tech. As Steve Jobs introduced the iPhone in 2007, he said it was a:

1. Widescreen iPod with touch controls,
2. Revolutionary mobile phone, and
3. Breakthrough internet communicator

MP3 players, mobile phones, and the networks required to communicate over the internet all existed before the iPhone. But the iPhone brought them together in novel ways.

The "problem"

Many innovations aim to solve problems or enhance our way of life. My prediction is no different. Here are some "problems" which I envision can be enhanced:

1. We are dependent on physical screens to access our digital world. TV's, computer monitors, and smartphones all have screens. They are a fixed size, and TV's and PC monitors generally are fixed to a single location. Even with laptops and smartphones, moving around while looking at the screen requires coordination and one or both hands.
2. With these many devices comes different data stores and modes of authentication. At your PC you may store documents, photos, etc. While your Xbox is used for gaming, and your smartphone for apps. Each one is registered to you in some way, usually authenticated by some form of a password. 

The problems listed above may not seem like problems to you, but to me it seems inefficient to depend on so much physical hardware (especially screens and monitors).

My present-day connectivity is intermittent, bouncing between all devices. Here is a typical weekday:

• I wake up and check for any important messages on my smartphone.
• I arrive at work and utilize that PC for work in my office.
• I come home and use my laptop for browsing the web and other personal work. Maybe watch something on TV as well.
• While throughout the day I may have several brief interactions with my smartphone.

Each bullet above requires actively seeking out, authenticating, and differentiating the devices for specific tasks.

My prediction: hyper fluidity
Like the iPhone, my prediction builds on and combines many existing technologies:

1. Wireless hard drives. These devices allow you to connect your data to any device over Wi-Fi. This is useful as it removes the need for wires or complicated connections requiring software, drivers, etc.
2. Smartphone to laptop. This invention allows you to turn your smartphone into a laptop (kind of). Plugging the phone into a special laptop gives you essentially a full laptop experience running off the phone's software.
3. Authentication apps and tech. These allow you to authenticate with other services using a two-factor approach or your fingerprint, eyes, etc.
4. Voice and gesture control. Think Amazon Echo and Xbox Kinect.
5. The cloud. Software as a service through cloud providers is key as it reduces the need for both hardware and software at every endpoint.
6. Drones (of course). Drones are getting quite sophisticated. They can stick to walls and ceilings, and can even coordinate among themselves.

My vision is one of hyper-fluid connectivity. Where your data, files, etc. are always within "reach," but you do not use your hands. Where authentication is seamless. Where moving from business to personal is instant. And where you are not tied to a specific location.

Imagine having a cup of coffee at your kitchen in the morning, and wanting to see the news. Why lug your laptop over? Why pull out your smartphone? Why not be hands-free and have your super-quiet smart drones do it? And they'll do it in a way which ensures proper posture by putting a display at your exact eye level.

Here's a concept sketch -- note that the screen in this is not a typical monitor simply being held up by the drone, rather it could be a projection or other type of light-weight and re-sizable display medium.

Illustration by Empty Bee Artwork & Photography

The Smartphone to Laptop idea listed above demonstrates the desire for people to need just one device for everything. My prediction is that one device will turn into something which can help authenticate and connect to a fleet of drones. It will be the bots who know who you are and which apps, files, etc. you have access to. There be significantly less need for traditional physical screens or monitors. A display will be able to appear anywhere the necessary amount of drones can go.

Our phones may get smaller, and used simply for as a secondary authenticator. Allowing us to connect to drones and other bots as needed. The drones will learn our preferences, styles, and eventually be able to predict what we want. It may not just be displaying the news while drinking coffee, but also making the cup of coffee. 

Will I buy a new TV soon? Yes, probably. But I am hoping soon after I will just need to buy 4K-capable projection drones with internet connectivity, authentication, and swarm capabilities to coordinate between my other ultra-smart drones.

UPDATE (10/24/16): I feel like we are one step closer to the vision above based on this amazing research done by the Ishikawa Watanabe Laboratory in Tokyo. Their image projection technology can keep an image stable even with a moving target object as the screen. Here is their video: