The Future of Machine Learning

The Future of Machine Learning

Team Polyrific

You probably already know that machine learning is an incredibly powerful technology that has the ability to solve difficult problems in a surprisingly effective manner.  What you may not have realized, however, is that since machine learning algorithmically builds it's "gray matter" by learning from previous patterns, trends and data models, we are at present witnessing only the very early stages of what machine learning can do for us.

Recently the science behind machine learning hit a significant milestone in fields that hadn't really moved the needle for some time like speech recognition and image understanding. With the recent proliferation of sufficiently capable computing hardware, we witnessing a BIG BANG in machine learning technology that represents a major step forward in how computers can learn and perform.

For several years now, the use of machine learning has been used as a form of automation for low-value tasks that are easy to do but time-consuming when carried out by human hands. As we move into the near future, expect to see an explosion of applied machine learning as the necessary computing power and software implementation proliferate around you but it won't all be easy; machine learning algorithms tend to have errors, and it is very interesting to know how we humans-in-the-loop in "coach" of those errors out of the result sets through training and deep-instruction in neural networks.

With that said, machine learning will have a great impact on all areas of business. One of the important things for enterprises to bear in mind is that they need to look beyond the AI hype for practical ways to incorporate machine learning into their operations. Expect too much too fast and we will find ourselves in another "AI Winter": a season we have witnessed before during which confidence in machine learning plummets and investment stops. Machine learning algorithms should be regarded as a child in need of time and instruction to become truly effective. Goldcorp – a mining company that uses immense vehicles to haul tailings and other debris away from mining sites - is taking this step-by-step approach with great results by iterating a machine learning algorithm over time that now predicts with over 90 % accuracy, when will their machines need maintenance. Since a vehicle breakdown can cost Goldcorp over $2 million per day, it's hard to argue the economy of this kind of applied machine learning; however, had Goldcorp expected for machine learning to first be able to make all of their monster vehicles self-driving, it is very possible that the program would have failed and the more simple, but extremely useful, algorithms would have never been implemented.

Short Term Predictions

More enterprises will begin their machine learning journey over the next 18 months than any other time in history. The smarter ones will create competitive separation for their enterprise by getting started with machine learning now while still learning from other's mistakes. Resisting the urge to expect too much too fast will pay-off handsomely - as was the case with Goldcorp - while machine learning quietly takes hold beneath a cacophony of AI marketing speak.

Employment

Some of the gloomier predictions will end on a higher note: machine learning will automate some human jobs out of the equation, but those jobs will be replaced with higher-value, more stimulating work. Retail and sales jobs are primed for machine learning implementation and automation. We will see robots in hospitals delivering medicines, materials, and meals. Self-driving cars will rule the highways in the next few years. In fact, we will see autonomous trucks, tractors, taxis, forklifts, and cargo handlers etc.

Automation of such large parts of our workforce is going to require that our governments come together in a very bipartisan way to avoid economic strive, but on the positive side, the world will see a tidal wave of creativity and innovation like never before due to the freeing of creative thought afforded by machine learning-based automation.

Culture Shift

Machine Learning will become so powerful in the future that it will shape culture by driving us to make better decisions and providing us a more profound vision for the pursuit of happiness and showing you the outcomes, explanations or evidence that you might be missing in topics both big and small. And it will not only show you those missing elements but will also support you in weighing and making sense of them.

Machine learning will also bring about revolutionary personalization in the services and products based on your tastes, historical choices, location, even your DNA. This of course changes the way products are made, consumed, and marketed.

In conclusion, machine learning is changing everything quietly at the moment with the volume increasing dramatically over the next two years. Ignoring the technology is not an option, but it is important to measure your expectations an have a long-game for machine learning in order to reap the highest rewards. 

It's impossible to predict exactly where this phenomena will lead us, but in the words of Peter Thiel, 

"Not being able to get the future exactly right doesn’t mean you don’t have to think about it". 

We are here to help you and think about your future and how machine learning can become a part of it as soon as possible. Please contact us to get started.


Team Polyrific | Feb 14, 2018

You may have heard the term "continuous integration" or "continuous deployment" or even "continuous delivery" tossed about in the your department as a catch-all phrase for "we need to ship code quickly and constantly". It's true that a well honed continuous integration (CI) program can result in rapid, hyper-agile delivery of software but in order to reap the philosophy's rewards you have to establish and adhere to a disciplined protocol that is based on a true understanding of what CI actually is.


In order to understand CI, let's look at the way software used to be shipped in the years leading up to the golden age of Agile, say, the aughts (2000 to 2010-ish). During these years, even the smallest feature change to an in-place application was a major undertaking. Budgets had to be approved, designs made, code written and tested, bugs fixed, user acceptance granted, and then a big monolithic chunk of code was released as a new version of the software. Because making a release was such a big undertaking, the needs of most stakeholders from the business fell to the wayside; there simply wasn't enough time or budget to cater to all of their needs. 


Things began to change, however, as we entered the teens (2013 to present).  Web and native apps intended for consumption on smartphones exploded filling more and more nich needs and the typical business stakeholder became ever savvier in all things software as they grew accustomed to having myriad software features to solve problems in their personal life. This created a demand that spilled over into the workplace and became common in just about every conference room around the world:  "Amazon sends me updates about the location of my order every step of the way! Why can't we do that with our replacement part orders??" or "Searching for information on Google is so intuitive--it should be the same when we search our inventory" or how about, "We should make a mini-game like Angry Birds to promote this new ad campaign". Overnight, software delivery professionals--from developers to quality assurance to analysts--were overwhelmed with requests and outnumbered by throngs of stakeholders with wishlists a mile long. The age of carefully planned, waterfall-like software release delivery schedules and the age of "I want it all and I want it now" Agile methodology had begun.


In the years since that critical inflection point in the art of software delivery Agile, a stream-of-consciousness approach to software delivery, has proliferated in response to stakeholder demand / impatience and this has in turn given rise to CI which is the process of streamlining and automating the business of requirement specification, development, quality assurance, testing, user acceptance and, finally, production deployment. 


In a CI world, a stakeholder may express a desire for a new feature in the company intranet during the Monday morning meeting. By lunchtime, the business analyst has gathered detailed requirements and placed the requirements into a ticketing system such as Visual Studio Team Services or Jira. This alerts the dev team automatically so that they can step away from the foosball table and get back to their workstations. By Monday afternoon, the developer(s) has accepted the ticket and used it's automatic integration with the source control repository to create a new "branch" of the code. The developer's job is done within the hour and her code is checked in which triggers an automatic execution of unit and end-to-end tests and then an automatic build to the QA environment and Slack notification to the QA testers. Once the QA staff has approved the build, the CI pipeline takes over once more and automatically handles the placement of the new branch into the production environment while maintaining the ability to easily roll back to the previous build if necessary.  By Tuesday morning, the stakeholder is happily using the feature he requested during the previous day's morning meeting. This would never be possible without an established CI program in the organization.

A well-developed CI program isn't just for the benefit of the stakeholders; it has plenty of deep technical advantages as well. For example, most projects have multiple developers working in isolation. Adherence to a CI protocol forces a degree of work atomization which limits the ability for discrete tasks to become too large. This means more frequent code check-ins and integration with the production environment which means fewer nasty merge conflicts and bugs.

By now you probably get what CI does, but you may be asking yourself what exactly it is. Is it a tool? A platform? A philosophy? In reality, it's a little bit of everything. DevOps professionals create "build definitions" using popular build engines like Visual Studio Team Services, Team City by JetBrains, Jenkins, or Octopus. You can think of these definitions as scripts that have hooks into both the source control repository where your application's code resides as well as into the environments (servers) that run the working code. In a sense, these build definitions are a collection of IF THIS THEN THAT statements: "If a new ticket is added then create a new branch and email the dev team", "If a developer checks in their code, then run unit tests", and then, "If all unit tests pass, then deploy to the QA environment and email the testers". 

Different build engines have different strengths and it is possible that your organization use more than one of them. In fact, we developed our NoOps/digital developer product called Catapult to help abstract and alleviate the stress of managing multiple build servers and other resources in order to further streamline the continuous integration process.

The other aspect of a solid CI program is to enforce a protocol to be followed by all team members. This is very important because if the team does not use the correct toolchain, the CI program won't work and it's benefits are lost. For example, if the stakeholder from the Monday morning meeting were to have simply emailed his request directly to the developer, that developer--eager to please--may have coded the feature and then checked it directly into the source control repository without following the proper branching protocol. This may have caused merge conflicts that then require a manual review and possibly fail to trigger automatic tests which would then result in the very real possibility that bugs slip through to production environments and create the need for site downtime.  The good news is, there are some pretty great tools out there to make adherence to a CI protocol pretty easy. We are, of course, partial to Catapult but regular ol' VSTS or Jira are pretty good as well.

If you are interested in instituting a CI program at your enterprise but don't know where to start, please feel free to contact us for help. We are experts in the field of CI and we can either help you design and roll out a custom CI program or implement a licensed instance of Catapult to make CI (and devOps in general) feel like magic.



Team Polyrific | Feb 06, 2018

If you haven't already heard the term "NoOps" as it pertains to enterprise software development and delivery you probably will soon. NoOps is an emerging movement that seeks to relieve a bottleneck created by traditional IT operations and on-premise application hosting by utilizing solutions rooted in automation and cloud-based infrastructure. At Polyrific, we have developed an outstanding NoOps solution called Catapult and we offer this article in hopes that it helps you better understand why Catapult is such a big deal.

From DevOps to NoOps

Perhaps the best way to begin understanding the NoOps movement is to first understand the DevOps movement. The term "DevOps" is an amalgamation of "Development" and "Operations" and refers to the interplay between software developers and IT operations during the process of deploying applications to the world. In every enterprise, it is necessary for these two departments to stay close to one another in order to best serve the needs of the business.

At most enterprises, responsibilities for developers generally include the following:

  • Work with stakeholders to understand the needs of the business
  • Distill those needs into requirements and specifications
  • Develop applications that fulfill said requirements

By contrast, IT operations are generally responsible for interfacing with network hardware:

  • Allocation & management of server resources
  • Fault planning & monitoring
  • Security & compliance
  • Device management

Obviously, applications that are developed to suit the needs of the business have to be deployed somewhere so that they can be consumed, and this is where the interplay between the developers and IT operations managers comes in: they must work together to take the developer's work and deploy it to the world on their enterprises resources. This makes perfect sense if the picture were so simple but, as we will see in the next section, reality is a bit more complicated.


Agile & Continuous Deployment

In the early days of enterprise software solutions, very few enterprises created custom software solutions or applications of their own. However, as workplace environments have become more dynamic and reliant on smart hardware and software solutions, the demand for rapid release of custom software applications has grown dramatically. The Agile movement was largely a response to this exponential growth in application demand and it is founded on principles inspired by the Silicon Valley "fail fast & fail early" philosophy.  Gone are the days of months of planning, tedious software architecture design, and software release schedules following a waterfall schedule into a deployment phase that is given equal weight by the IT operations team. Today's software development teams are expected to respond immediately to a seemingly never-ending stream of features and demands requested by the business.

Often, projects are started as bare-bones applications that are immediately thrust into production environments where they will be constantly updated and expanded upon as the business requirements evolve. This sounds great, but it presents a few challenges to software development and IT operations teams, especially with regards to quality of the end-user experience and application uptime. To counter this, the development and ops teams employ a set of automation tools and checkpoints, collectively referred to as "Continuous Integration" or "Continuous Deployment" that smooth out the problems caused by rapid iterations in the software development life cycle. For example, when properly configured, and CI pipeline can trigger a series of automated tests whenever a developer checks in new code to ensure that the new code does not break anything or cause "regression" bugs.


The (Traditional) IT Bottleneck

It operations experts are fantastic but, in our view, their role is best executed when the evidence of their work is everywhere, but their presence is not so apparent. A good server at a restaurant will keep your glass full and your food coming without you noticing them much at all and it should be the same with IT operations managers but sometimes--often through no fault of their own--this is not the case. Without considerable depth of automation in your software development life cycle (SDLC), it becomes necessary for the development team to spend significantly more time with the IT operations team in order to coordinate downtime, deployments, rollbacks, and so forth. This is especially true in the case of on-premise deployments. This close coupling between IT ops folk and the developers is bad for at least three reasons:

  1. It takes the developer's focus away from understanding the needs of the business stakeholders
  2. It cuts into development time
  3. It can influence the engineering and delivery schedule of the application

Given the above, you can probably start to see where this is headed: interaction between development and IT operations should be automated so that the software engineers can remain focused on what they do best: delivering application-based solutions that serve the immediate needs of the business.


NoOps Produces Better Outcomes

So in order to respond to ever-changing demands of the business, development teams must be capable of quickly organizing the stakeholder's needs into business requirements and then parlaying those requirements into working code that is tested, quality assured, accepted by the end user and deployed into production environment on a frequent and recurring basis without being slowed down or distracted by hardware and deployment challenges on the IT ops side of things. Does this mean that IT operations professionals must be removed from the SDLC? Of course not. What it does mean is that IT operations personnel should join forces with the developers to implement game-changing solutions that help to automate the business of getting the developer's changes into production with very little interfacing required between development and operations.

In a NoOps world, developers don't check with IT operations before deploying code or to schedule downtime. In fact, they don't deploy code at all--they simply check their changes into source control and the rest happens automatically, behind-the-scenes, just like the server who always keeps your drink full without your noticing they were there at all. Similarly, developers do not need to request allocation of new resources from the IT department. They can, in theory, "spin up" a new ecosystem of server and database environments for a special purpose app while they sit with the stakeholder during a requirements gathering session.


The Catapult Digital Developer & NoOps Solution

As previously mentioned, we have developed a software solution called Catapult that takes automation of enterprise software delivery to the extreme. Using Catapult, even non-technical stakeholders can create new application projects on a meta-level that immediately spin up server resources using popular cloud platforms such as Azure and AWS. Catapult then allows entry of high-level data models in order to populate databases (or it can connect to existing ones) and generate, and deploy comprehensive codebases all without the user knowing how to write the simplest of SQL queries.

Like the restaurant server that deftly keeps your needs satisfied without making his or her presence known, Catapult allocates hardware resources, creates code bases, sets up source control repositories, allows stakeholders to manage content and seed test data, manages branching strategies, communicates with engineering team members to let them know of code changes, and pretty much anything else a competent developer and IT operations professional on your team would do. That is why we refer to Catapult as the "enterprise digital developer".


If you'd like to learn more about Catapult or any of our other software development solutions, please contact us or call us at 833-POLYRIFIC.






Team Polyrific | Jan 24, 2018

CES 2018 is now over. The parties have ended, the eye-popping demos have ceased, and the amazing booths (if you can even call them booths) have been taken down. As usual there were some amazing gadgets (HyperVSN by Kino Mo especially), but overall we are left with five major trends that defined CES 2018 and, by extension, our new year in tech. Here is a run-down from Polyrific's perspective:

5G


As we have previously written, 5G New Radio (NR) cellular data transfer and millimeter wave technology was a much buzzed-about technology and important trend towards an infrastructure that can may be able to quench our growing thirst for a connected world. Broadly, "5G" describes the next generation of post-4G mobile networks. It will be always-on and have almost imperceptible latency.

5G is like nothing we have seen before. Whereas 4G can transfer data at 100 Mbps, by 2020 5G will transfer data at a searing 10 Gbps. Some experts even speculate that 20 Gbps are possible. To put this into perspective, 20 Gbps data rates will allow you to download a two hour long high definition movie to your smart device in just over one second

The importance of 5G speed isn't in the fact that we can download more media in less time--5G is important because of the industries it will enable such as streaming 8K video for digital medicine, data streaming for self-driving cars, mega-encryption for the Internet of Things, and smart cities.


Smart Cities


Smart Cities were a major theme at CES 2018 and served as a superset of constituent technologies such as AI, self-driving vehicles, and the Internet of Things. Around the world populations are booming and cities are scheduled to represent 60% of the worldwide population by 2030. In order to operate efficiently and cost-effectively, cities will have to implement smart infrastructures that help to better manage traffic, reduce accidents, improve waste management, analyze terabytes of video for a variety of public concerns, aid in water sanitation and wastewater management, and provide thorough public safety services for our changing threat landscape.


Augmented Reality


Virtual and Augmented Reality were everywhere at CES 2018 and while the VR side of things provides a very immersive and interesting entertainment experience, we haven't been as convinced about AR (augmented reality) until now. Most of the firms pitching AR technologies at CES had some sort of goggles or glass that the user needs to wear which we think should be reserved for very special use cases. What piqued our interest, however, were the growing number of companies creating useful AR apps that can be used in a familiar handheld way by anyone who owns a smartphone.  A very simple use case for this technology is waypoint finding in shopping malls, casinos, amusement parks, museums, and airports. 


Virtual Assistants


Although virtual assistants have been making waves for a few years now, the intensity was ratcheted up even more during CES 2018. Siri, Cortana, Bixby were a bit scarce at the show--perhaps they were hiding in the shadows of Alexa and "Hey Google" (notice a syllable missing from the previous "OK Google") which are fast becoming the undisputed titans of the space. It's easy to understand why this is: Where as Siri, Cortana, and Bixby are confined to their respective devices, Amazon and Google have worked hard to open their VA platform so that it can be used across multiple devices to span the life and clock of its user base. Expect to see a common virtual assistant experience that knows your individual context and follows you from home, into your car, to the office, and back home again very soon. Alexa and Hey Google enabled devices were everywhere at CES. 


Virtual Medicine


With healthcare becoming more complicated and less affordable (at least in the US) than ever, we see the new wave of digital medicine as a major force in the healthcare market. Enabled by other emerging technologies such as 5G, 8K video resolution, and IoT, patients will soon be able to consult with affordable health care providers directly from their homes for the most common non-emergency situations such as flu symptoms and sinus infections.  We look forward to watching this technology and hoping for a positive impact on our society.

As we see it, these are the five trends that mattered at CES 2018 and are worth keeping a close eye on as we progress throughout the year. Sure, there are other notable technologies such as self-driving cars, but we think those already get enough air time even though they aren't quite ready for prime time. 

Feel free to contact us if you need any help in understanding or developing software for use in these areas.



Team Polyrific | Jan 24, 2018

The 2018 Consumer Electronic Show is now underway in Las Vegas, Nevada. Each year CES brings forth emerging technologies to the world stage that will soon power the way we live, work, and play. Here are the buzz-worthy technology trends at CES this year:

5G

Of notable buzz is the expansion of 5G New Radio (NR) cellular data transfer and millimeter wave technology. Five years ago, the upgrade to 4G felt like a big deal, but 5G is like nothing we have seen before. Whereas 4G can transfer data at 100 Mbps, by 2020 5G will transfer data at a searing 10 Gbps.  To put this into perspective, 10 Gbps data rates will allow you to download a two hour long high definition movie to your smart device in about three seconds

Such high data transfer rates should catch the US up to other areas of the world that have newer (and therefore faster) data infrastructure like South Korea, Japan, and Singapore. The importance of 5G speed isn't in the fact that we can download more media in less time--5G is important because of the industries it will enable such as streaming 8K video for digital medicine, data streaming for self-driving cars, mega-encryption for the Internet of Things, and so forth.

AI (again)

Be prepared to hear more about AI now and for the next several CES conferences. Specific intelligence, that is intelligence trained for a very specific purpose, is now a mature technology and one that you most likely already use on a daily basis. There is a heavy focus this year on the application of AI to building better and more conversational digital assistants like Alexa, Siri, Cortana, and "Hey Google" (seems Google dropped the additional syllable in "OK Google"). 

As AI goes from specific to general (a process that will take many more years), conversational interfaces become more, well, conversational. For example, instead of "Hey Google, find Italian restaurants", we would have, "Hey Google, I want to go out tonight. The weather is going to be bad so I don't want to travel far from home. Just go ahead and make a reservation somewhere close--you know I love Italian food but Mexican is fine as well".

Robotics

AI and Robotics are the peanut butter and jelly of the tech world. You can't have efficacious robots without strong AI. AI has come a long way in the last few years and this is giving rise to a whole new family of robotics here at CES this year.  There have already been unveiling events for several humanoid robots which, like there predecessors, have been clunky and prone to errors; however, the more purpose-built robots geared towards specific industrial or practical purposes are faring much better. Among such technologies are "smart baggage" and self-driving vehicles. Check back for more detailed articles on such robotics in the future.

Virtual & Augmented Reality

Virtual reality is still limping it's way to mass adoption with Sony announcing that just under 3 million Playstation VR units have been released as of the Holiday 2017 season. Many of the big names such as Oculus and HTC have announced lower-cost and self-contained VR units in a move to catch up with Sony who currently dominates the space. In our view, VR seems to still be a ways off in terms of mass commercial adoption; however, there are interesting applications such as therapy for post traumatic stress disorder that we believe will be useful in the near term. 

By contrast, augmented reality technology is just beginning to sprint towards mass commercial adoption. When you think of augmented reality, think about viewing the world through the window that is your smartphone rather than through special glasses (though both are happening). What we are seeing here at CES are several applications wherein ordinary smart phone owners can use the phone to overlay useful information onto the real world like where the nearest restroom is. We will be adding more articles about augmented reality in the coming weeks.

Digital Therapeutics

Digital therapy is another big topic at CES 2018. The term "digital therapeutics" encompasses all types of sensor-based diagnostics that enable virtual medicine. At Polyrific, we view emerging technologies in digital therapeutics and virtual medicine as essential for the well-being of US citizens in our changing healthcare landscape. We will be publishing articles on digital therapy in the future, but essentially this topic involves the gathering of personal health data from a variety of sensors in our smart devices and checking that information against oceans of data to indicate trends and even perhaps make a diagnosis. Additionally, with your permission digital therapy enables doctors from across the world to review your medical history and deliver a consultation which, depending on your healthcare situation, might be critical to your well being.

Internet of Things (IoT)

The Internet of things is nothing new to CES and is prevalent once again this year as it continues to expand and serve as the world's digital nervous system. Of particular focus this year are the IoT implementations that drive smart cities and energy conservation.

Various Improvements to Consumer Electronics

As you might imagine, there are many fun updates to consumer technology being announced at CES 2018. We won't go too deep into these areas but a few highlights include 8k video, thinner, lighter, and more powerful laptops, hand-held mini-camcorders with built-in stabilization gimbals, and new ways to enjoy sports in virtual reality.

So these are the primary trends driving CES 2018! Stay tuned throughout the week and follow @Polyrific on twitter for more CES coverage.


Team Polyrific | Jan 09, 2018

Impresarios of hip hop have long spawned businesses that capitalize on their personal brand. Jay-Z publishes music under his Roc Nation brand, Sean "Brother Love" Combs peddles vodka under the Cîroc label, Lil Wayne joins the fun with Bogey Cigars, his Cigar company.

So it isn't too surprising that will.i.am created a tech company called i.am+ which began as a purveyor of wearable tech. What is surprising is the fact that he is now leading the company into the enterprise B2B space with Omega for Enterprise, a virtual assistant for the office:



So will Omega carry the same moxy in the office as Alexa, Siri, and OK Google do in the home? It's too early to tell really. Hip hop and enterprise IT seem to be strange bedfellows but who knows--maybe Omega will get it started in the office, finally.



Team Polyrific | Jul 31, 2017

Twenty years ago, widespread adoption of the world Wide Web Web transformed nearly every aspect of how we conduct business. New marketing channels emerged overnight. Information silos came crashing down. New communication modalities emerged. Even our vocabulary changed. 

In the last few years of the 20th century, amid the scramble to mitigate the dreaded Y2K doomsday event, enterprises were rapidly integrating the Web into their processes, both internal and external. Early digital marketing initiatives manifested in the form of simple HTML web pages and basic email campaigns while early cloud-based CRMs and ERPs began to firmly root themselves into enterprise business culture. These events represent the first epoch in the history of the Internet.

Enterprise marketing departments had only a brief chance to catch their collective breath before the second epoch of the Internet came crashing onto the scene: social media. A decade-long scramble to integrate with the major social media channels like Facebook and Twitter was a particular challenge because of how quickly these outlets were changing the way their own software worked as they reacted to a shockingly fast rate of adoption by hundreds of millions (and now billions) of users. Social media changed the business landscape profoundly because it introduced a new communication modality that was unlike anything that came before it. At least emails were similar in concept to actual mailed letters, but the notion of social media chats, likes, shares, and trends were like nothing marketeers, or humans in general, had seen before.

It has now been about two years since we could safely begin to say that most enterprises have a presence on the major social media channels and now we are due for another major epoch in Internet history: the rise of the digital assistant. Once again the scramble for adoption and integration will be like nothing we have seen before. The stakes will be higher, the variety of outlets more profound, and the output of actionable data profoundly more rich.

During the first Internet epoch, mass adoption of the World Wide Web, the few new modes of communication that surfaced were natural replacements for their predecessors: email replaced real mail and early search engines (although not particularly effective) replaced the Yellow Pages. These were straight-forward changes that were relatively easy to implement on the enterprise side. During the social media epoch, posts began to replace emails as a means for marketing a product and providing customer service. The new epoch that is upon us, however, represents an explosion of modalities that are very personal in nature and  as diverse as they are disruptive.

Consider that the past few years alone has seen the rise of personal digital assistants and the rapid fading of traditional industries like newspapers and taxi services in lieu of delivery methods that are made more personal by making full use of technological advancements to meet customer demands. Two years ago, very few of us were barking orders into our smart phones to do things like send a text, schedule an appointment, or get the weather forecast. Today, most of us do this multiple times a day except we aren't just barking orders at a phone any longer, we are also interacting with smart watches, smart speakers, smart media consoles, and even smart refrigerators! 




Some may define this epoch as the IoT (Internet of Things) epoch, and that is fair, but we think it goes farther: it isn't just about making the devices around you smarter and more connected, it's about making the smart and connected devices around you create a more personal experience tuned to your precise needs.

So what has all of this to do with enterprises? To us the answer to that question is clear: customers have adopted the digitization and personalization of everything as rapidly, if not more so, than they did social media and are already demanding that their needs are met in a personal way using the modalities that they prefer whether that be via an Amazon Echo device, a personal assistant like Siri, Samsung Voice, or OK Google, or any number of emerging smart, connected appliances such as televisions and refrigerators. 

Connected devices offer skills that their owner can request on demand with the simple use of their voice. For example, Domino's Pizza has released a skill on Alexa--the personal assistant behind Amazon's Echo devices--that allows customers to order a pizza. There are many skills that can be created to give your customers new ways to engage with your brand such as online ordering, product recommendations, order status checking, and so forth. For example, a Vineyard that published a skill allowing customers to ask for the best wine to pair with the dish they are cooking. A local urgent care facility could allow customers to set an appointment as they gather their keys and put their feverish toddler into the car so that their wait is limited when they arrive to the facility. Enabling skills of this type of personal nature tip the scale when customers are deliberating with whom to do business.  

The digitization of everything does not only represent a change in how customers engage with brands, it also represents a change in the way operations can be carried out in an enterprise environment. For example, enterprises are beginning to embrace the concept of an office digital assistant in order to make both knowledge and trade workers more efficient. Each day is brings new mash-ups between these digital assistants and common enterprise platforms like Salesforce, Microsoft Office, or even Slack. Building a skill to accomplish a particular enterprise task on these digital assistants does not necessitate the need for typical user interfaces which results in a much lower development cost and more rapid delivery schedule. According to Cisco, 60% of 2000 surveyed organizations indicated that they are seeing a positive ROI on their investments in personalization technologies.

As we enter this third, and very transformational, epoch of Internet history it will be critical that enterprises adapt and adopt digital assistants quickly to avoid being left behind by customers and employees who wish to engage with your brand through the use of them.

At Polyrific, we have experience in developing digital assistant skills and tying those skills to established enterprise libraries that integrate back end systems like Salesforce and Oracle. Don't be left behind in this revolution,  please contact us to learn more.


Team Polyrific | Jul 05, 2017

The story of Polyrific began back in 2011 when company founder Matt Cashatt was thinking of a name for a polymorphic database concept and landed on the portmanteau "Polyrific" as a great way to describe a product that could make many different facets of enterprise data management faster and easier. It didn't take long for Matt to decide that the name, and the concept behind it, was bigger than any single product: so many different facets of enterprise software creation and management need to be made faster and easier. And with that, a brand was born.

Since those early days we have grown into an enterprise-focused technology company that specializes in software development, machine learning and devOps. Our original vision is woven in everything we do: we constantly streamline and perfect the way custom software is designed and delivered so that the process becomes faster, easier, and more economical with each project. Our imperative is to stay close to our clients and understand their needs clearly while continuing to develop the game-changing technologies that delight them.  

This latest website of ours was designed to give our clients, colleagues, and friends insight into contemporary technology topics that today's enterprises must embrace if they hope to stay relevant in the marketplace as well as to stimulate ideas related to these technologies. Here you will find engaging articles intended to quickly get you up-to-speed on such topics, as well as the ways in which Polyrific can help guide your enterprise into territory that, for many, may be unfamiliar. We have also created high-level pages to help our new guests understand the types of services that Polyrific can offer them such as custom software development, general technology consulting, and on-premise devOps automation.

Perhaps our most important corporate value is that "we go farther together”.  This value is meant for not only our internal team members, but for our clients and friends as well. We hope to be a catalyst for positive and impactful change that helps your enterprise soar to new heights by aggressively growing our expertise and offerings in machine learning, data science, bots, personal assistants, and new form factors such as the Amazon Echo Show, which we believe will have far-reaching uses in the enterprise environment. We'll bring to the table the knowledge, expertise, and even some good ideas. You bring the desire, imagination, and vision for an incredible future.

We are glad you are here, and hope to see you back often. We would like to hear your feedback about our new website and hope you will share your thoughts and suggestions about any section you find interesting.




Team Polyrific | Apr 11, 2017

According to a recent Gartner survey, only 37% of CEOs and other senior managers rate the efficacy of machine learning as being transformational to their enterprise. This isn't too surprising, really. We commonly hear about tech giants such as Facebook, Google, and Netflix who are applying machine learning solutions to problems faced by hundreds of millions of people and so the notion somehow makes more sense when considered in that mass-market context.

As with most cutting-edge technologies, however, adoption tends to go from a trickle to a roar. Consider, for example, the automobile, the personal computer, and the cellular telephone. These were all technologies that seemed over-the-top at the time of their creation with few thinking that these things would be useful to a business, let alone a household. But of course, history has told us a different story. It turns out that the problem isn't with the technology, the problem is with our ability to envision the use of the technology which leads to the point of this article: How is machine learning actually useful to most enterprises? In this article, we will answer that question, but it is helpful to start with an analogy.

Machine learning is great at detecting patterns in unstructured data and then formulating conclusions. This is the kind of thing that humans are generally good at doing. For example, if you go to a car dealership and notice that there is only one car remaining from the previous model year, and it's the end of the month, you somehow realize that you can probably negotiate a great purchase price for this vehicle. But how did you know that? You knew it from prior articles you have read and advice your friends have given you with regards to getting a great deal on the purchase of a vehicle. You might have also picked up on visual cues at the dealership such as a "Must Sell!" sticker on the car.

This is what machine learning does except that instead of drawing only on individual experience, it can draw on the experience of hundreds or thousands of people who have been through similar scenarios in order to formulate positive insights and conclusions.

There are troves of data containers collecting dust at every enterprise stored by your CRM, ERP, email servers, and documents as well as external data sources such as news stories, weather events, commodity prices, etc. Implementing machine learning algorithms across this data can help you to develop insights about shortening your sales cycles, the communication cost around certain internal processes, employee and customer sentiment, customer buying habits, new product recommendations, and so forth.

Consider a machine learning algorithm that draws upon this information to help prompt you with tips on when to close a deal (and at what price). Or consider a machine learning algorithm that can help you spot communication gaps that are leading to quality defects on the production line. These things may seem like uniquely human problems, but they aren't. Machine learning is excellent at detecting patterns, optimal timing for events, and even emotions. How would it impact your enterprise if you could predict the optimal time to launch a product, close a deal, raise your prices, discontinue a product, or even liquidate bill-of-materials stock?

Areas in which machine learning can help your enterprise

Here are a few other areas where machine learning can be readily applied to the enterprise:

Fraud Detection

Fraud detection via machine learning is already widely adopted for credit card companies and insurance companies, but what about putting that same pattern recognition technology to use in your enterprise to help resolve and steer customer service and return matters or perhaps where sales or productivity is being over or under reported by employees?

Document Classification

Machine learning is excellent at classifying documents. You already use machine learning based classification if you have a spam filter on your email client. This is no more than an algorithm that has learned to detect patterns in email text that has a high probability of being spam. What if you applied the same logic to help your employees classify and organize emails, comments, documents, Slack posts, etc by project, or recommend that they should not copy people to whom the information would be unnecessary while copying others who may have the ability to answer questions in their emails.

Machine learning can also make short work of crawling hundreds of thousands of historical documents, parsing their content, and storing them in a database for further analysis. This is not only true for word processing documents and spreadsheets, but it's also true for images, video, and audio as well.

Automation

Machine learning can help to automate many processes within the enterprise by consuming unstructured data, such of scanned bills-of-lading, and taking the appropriate action to update your systems. What's more, machine learning can even help you to identify which processes can easily be automated in the first place.

Diagnosis

The medical community is using machine learning for diagnosis more and more frequently but what about diagnosing equipment failures, detecting conflicts in a set of building design documents, and finding manufacturing processes that are the root cause of defects? Machine learning handles and solves this kind of diagnostic problem very well.

Machine Vision & Shape Detection

Machine vision is a trait enabled by machine learning wherein streaming video or images can be processed and certain objects within them can be recognized. This can help you sort through thousands of hours of footage when looking for a particular event, recognize installed quantities on a construction site, keep tabs on deliveries, and even let you know when the lawn should be tended to for a rental property.

Product Recommendations

Product recommendations are sort of old hat at this point, but is your enterprise using a good recommendation engine? You should be--targeted recommendations to your customers based on conclusions drawn by machine learning algorithms can increase sales by 50%-60%. Even if you don't run an ecommerce business, you can use these algorithms to tip you off as to when to send regular mailers to certain customers.

There are some who believe that the next wave of enterprise software will be powered by machine learning and here at Polyrific, we agree. If you are interested in exploring the possibilities offered by integrating machine learning into your enterprise, we are here to help. We can be both a creative force driving your team towards greater heights with this ground-breaking technology and we can also handle the implementation. Working with us doesn't have to mean that we are building software for you, though we'd like to; we can simply be a source of ideas and direction. 

Please contact us today to learn more.


Team Polyrific | Apr 10, 2017

Use of bots, or "chatbots", are an immensely impactful, rapidly-growing trend in enterprise operations and customer interaction that change the way we work. 

What is a bot?

Bots are a light-weight form of machine learning that basically convert unstructured human language into structured data that serves as instructions for the software that consumes it. For example, the following statements all initiate the same software action of adding an appointment to the user's calendar:

  • "Put a meeting on my calendar for tomorrow at 10am"
  • "Add appointment called 'Meet with Bob' tomorrow on my calendar"
  • "Invite Bob to a meeting tomorrow at 10am"

In each case, the bot parses out the user's intent as well as the parameters that complete the request. In the above examples, the bot understands that the intent is to create a meeting. The parameters are the date ("tomorrow"), the time ("10am"), and a meeting participant to invite ("Bob"). If there are any required parameters missing from the command, the bot will follow up with the user by requesting that information: "What is the subject for this meeting?".

 This interaction is fundamentally the same as filling out a form that, upon validation, alerts you that you missed a required field. In fact, the code that consumes the structured data from the bot is processing your meeting request in exactly the same way. 

It's a big deal

It may not seem to be very earth-shattering at first glance, but bots are quite monumental in the evolution of human-computer interaction (HCI). Think about it--bots represent a total inversion of control wherein we, the users, command the computer in a way that's natural for us rather than needing to conform to a series of steps that the software dictates. This can save untold amounts of time and frustration by not having to learn the detailed work-flows of a given application or website and instead get right to what you need by simply "talking" to the program.

Enterprise bots

Bots have become pretty common in our personal lives. We use them for anything from scheduling doctor's appointments, shopping for the latest fashion styles, playing online games, to sending money to friends and so forth. But bots hold a tremendous value proposition for the enterprise as well which is why heavy-hitters like Microsoft and Google's GSuite are becoming major players in the space.

People often associate bots with customer service when thinking about them within the enterprise context. While it's true that bots can be tremendously helpful in directing your customers to the resources they need without incurring the cost of human assistants, there is tremendous value to be had in regular enterprise operations as well. For example, an IT department can utilize bots to provision new user accounts, automate devOps tasks, and request security scan reports. Executives can use bots to request sales reports and financial forecasts. Field staff can use bots to conduct inspections, request supplies and materials, update stock levels, and report progress.

Implementing a custom bot specifically for your enterprise will make your team more efficient, increase data accuracy, and refocus your human resources on higher-value work. Bots get "smarter" over time and as they do, more obstacles are removed between us users and the outcomes we seek. As you use any application today, think about the steps you take to get the outcome you need and ask yourself whether a bot could have improved the process by allowing you to get straight to what you needed with a single command stated in your own way.

Bots and your customers

Regardless of whether you are in healthcare, retail, travel, hospitality, or any other industry you can’t afford to ignore the bot concept. You have to "meet the customer where they are" by ensuring that your applications work in a way similar to what they have become accustomed to in their personal lives. Whether the medium is a messenger app, SMS, or your own application interface, you need to provide your customer with a way to simply "tell" the application what they need.

Facebook CEO Mark Zuckerberg agrees with this notion. Facebook is investing heavily into bot integration for their Messenger product. Says Zuckerberg, “You should just be able to message a business the same way that you message a friend, you should get a quick response and it shouldn’t take your full attention like phone calling and you shouldn’t install a new app”.

The time is now

 Whether your enterprise is prepared or not, bots have arrived and are being further interwoven into our cultural fabric each day. It is critical that you act now to implement bots of your own before you lose customers to a competitor who offers a better experience or employees to a workplace that allows them to do their job with less frustration. At Polyrific, we have a special affinity for bots and machine learning in general and we have the experience necessary to successfully implement a family of bots throughout your enterprises application ecosystem. 

Please contact us today if you are ready to join the bot revolution and take your enterprise operations and sales to new heights!

 

 


Team Polyrific | Apr 10, 2017

Machine learning is a system of algorithms aimed at detecting patterns in big data and then learning from those patterns without being explicitly programmed by a human operator. These algorithms take a probabilistic, rather than a deterministic, approach to accomplishing goals. Let's take a quick look at what that means by way of example:

Deterministic Approach

A human programs software in no uncertain terms to remind him to bring his umbrella to work if the chance of precipitation in his area is greater than 40%:

//Get the weather forecast
var chanceOfRain = myWeatherForecast().precipitationChance;

//Send the message
if(chanceOfRain > 40%){
    sendEmail("Bring your umbrella to work!");
}

This approach is deterministic because the outcome is predetermined by the the author of the code in no uncertain terms.

Probabilistic (Machine Learning) Approach

An algorithm crawls big datasets such as tweets about the weather, weather news, forecasts, umbrella sales, and supervised feedback (we'll get to that later) such as, "did you bring an umbrella to work today?":


machine learning


As you can now see, Machine Learning is about statistics and probability. Whereas humans typically write algorithms with a predetermined output for a given set of a few inputs, machine learning algorithms consume a vast amount of inputs and surface the most likely output. Over time, machine learning algorithms get better by adjusting their own internal methods to predict outcomes based on a growing body of data or knowledge. Eventually, a machine learning algorithm can become better at predicting outcomes than can its human counterparts because human beings, and deterministic algorithms written by humans, can not factor the massive quantities of inputs than machine learning algorithms easily digest.

Machine learning is nothing new

Believe it or not, the early concepts of machine learning and statistical analysis were proposed by British mathematician Alan Turing more than sixty years ago. So why is machine learning just now becoming so popular? There are several factors for this but chiefly it is because processors are more powerful, data storage is incredibly cheap, and thanks to the Internet, everything is now connected. In other words, it is now common for the phone in your pocket to display to you a message such as "bring your umbrella" as it detects you are leaving your home for the day as a result of a server to which it is connected crunching potentially millions of connected data points. Even more impressive, this happens in less than a second and it is all thanks to machine learning.

You use it every day

You might not have realized it, but you already benefit from machine learning every day. Here are a few of the common areas in which machine learning is making our daily lives better:

Search Results

Think that Google and Bing get your search results simply by how many times your search term is displayed on a given web page? Think again. Such a deterministic approach would never help you wade through the universe of irrelevant information to get to what you actually need. Search engines use complicated machine learning algorithms that factor information such as your current location, the time of day, your recent searches, the page you last visited, your recent purchases, and so on. This probabilistic approach usually surfaces what you were probably after within the top ten results on your page. A deterministic approach would never succeed in doing the same.

Spam Filters

Every time you classify an email as "spam" you are training a machine learning algorithm to recognize similar emails as having a high-degree of probability of being spam.

Recommendation Engines

Netflix, Amazon, and even Spotify use your searches and feedback as fodder for machine learning engines that in turn offer you better recommendations.

News Feeds

You may thank that your Facebook feed is already full of posts that don't hold your interest, but this would be even worse without machine learning which is at least promoting to the top of your feed people and topics you seem to care about the most.

Translation

You may have already heard of Google Translate which helps you translate text from one language to another but it gets even better: Microsoft recently capitalized on machine learning algorithms to translate spoken Skype conversations in real time. This means you can literally talk to someone in english while they hear Mandarin and vice versa.

Fraud Detection

Ever had your credit card declined when you are travelling or making a large purchase? Machine learning algorithms help credit card companies detect events that seem out of character for you and decline the card to prevent fraudulent use.

Will machine learning replace humans in the workplace?

Probably, but but it should be considered as good news because it will promote productivity and creativity. In some cases, machine learning will augment workforces wherein there are currently shortages of skilled workers. 

Consider the example of a general practice physician. Currently, you will need to schedule an appointment with your GP when you have a medical issue and, once you get in to see the doctor, you have to carefully explain your symptoms and answer the doctor's questions. Drawing on her previous experience the doctor may be able to make a diagnosis, but if she happens to not have prior experience or knowledge about your particular situation, then she may refer you to a specialist or send you to a lab for tests. 

Now consider a digital doctor which is trained by vast quantities of shared data and experience. At any given time, this digital doctor may be processing--and learning from--millions of cases. This doctor becomes more experienced and better at making correct diagnoses by the second. So when this doctor interviews you and makes a diagnosis, it is drawing on millions upon millions of past cases, symptoms, and outcomes in order to let you know what is ailing you in a matter of seconds. There is not likely a need to go for further tests or a need to go see a specialist.

Human doctors can use such a digital doctor to augment, rather than replace, their care of you. Now they can receive a report and know immediately whether your appointment should be expedited, or whether they can simply write a prescription for you to come pick up.

Get started!

Machine learning is a productivity multiplier. You can think of it as a way to unload cognitive tasks to the computer so you can free your staff's minds to focus on higher value work. Machine learning is no longer the property of big tech companies alone, we can all now capitalize on the amazing things it can accomplish. Contact us today to learn more about how machine learning can benefit your business.