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Can machine learning improve customer service?

How To Apply Deep Learning To Improve Customer Service

How much can we expect a machine to learn from a human?

Is it realistic to believe a machine is capable of adopting human communication, actions, and responses to the point of replicating human behavior?

It’s not crazy to think entire businesses may eventually be automated and able to adapt to market changes based on how well machines can learn from their human instructors.

How To Apply Deep LearningThink about the last time you called your cell phone carrier or Internet service provider to talk about your service. You probably had to use an automated voice answering system before you eventually spoke to a human. Many automated answering systems aren’t very intuitive. In fact, you probably have more than a few memories of yelling into the phone or speaking like a robot before you either finally reached the person you wanted to or hung up in frustration.

A recent Forrester Research study shows almost 80 percent of U.S. adults choose “valuing a customer’s time” as the most important sign of good customer service. It’s hard to feel like your time is being valued if you’re becoming more frustrated with every attempt. For every misroute or repeated voice command the customer experiences, each instance can erode a caller’s trust in your company’s service.

What is Machine Learning, or Deep Learning?

So, what exactly is machine learning? Global management and consulting firm McKinsey & Company has an excellent definition of machine learning:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

Instead of being programmed to do a certain task, computers can be fed a series of information. The systems will detect whatever data is entered and will match the new data with the data set already in its memory. For instance, rather than teaching a computer how to draw a dog, programmers can load millions of pictures of dogs into the computer’s database so it will recognize any dog or sketch that resembles a dog.

So, why is this important?

Machine learning builds mathematical and logical algorithms inside a computer’s processing system. This allows the computer to learn, adapt, and respond based on the information it receives.

You may wonder, “Is machine learning really that important?” It is if you can use machines to enhance your client’s care and provide admin support for more mundane tasks, like resetting passwords, collecting contact information, such as email addresses and phone numbers, and even provide sales support. Having a reliable machine-learning system in place frees up live customer service agents to handle more complex conversations with customers or revenue-generating activities, like closing a sale. With 75 percent of people preferring to solve a tech problem on their own, the value of having an automated system that learns what people truly need in the moment is priceless.

This is only made possible if the machine-learning experience is efficient, nimble, and most importantly, hyper-accurate. A Harvard Business Review study article states that businesses incorporating machine-learning processes and systems into their everyday operations are experiencing both top line and bottom line performance increases. Study author H. James Wilson wrote, “Powerful machine-learning algorithms that adapt through experience and evolve in intelligence with exposure to data are driving changes in businesses that would have been impossible to imagine just five years ago.”

Machine Learning Helps Businesses Handle High Volumes of Clients

Machine learning allows businesses to engage with a high volume of clients on a daily basis. Each interaction creates its own set of data specific to both the client and the customer service process itself. Machine-learning systems can curate the data collected from each interaction and adjust the algorithms for even better accuracy. Now, does continual training of a computer lead to ongoing costs? Yes, but think of how much time is already being invested in a customer service personnel base that experiences high turnover, low expectations, and a stigma of being one of the least fulfilling jobs in almost every industry.

Imagine if a customer service machine-learning process can also use predictive analytics to suggest a repeated issue that the caller may be calling about again. Imagine if a customer starts a phone call with Tech Support and receives a personalized response based on what the system already knows about their previous interactions. How do you think the customer will feel about the company? It’s a faster, more accurate, and friendlier process that no longer feels cold and uninformed. Integrating a Natural Language Processing (NLP) system may even make the customer service experience one of the easiest ways for a customer to feel appreciated and heard by your company.

As with many tech sectors, machine-learning startups are the ones with the freedom to explore some of the newer frontiers. Google spends billions of dollars acquiring fledgling startups, some of which turn into headline-snatching mistakes, like acquiring Nest’s smart home technology valued at $3.2 billion but riddled with bugs. Of course, many of Google’s acquisitions are massive successes. Is acquiring scores of wild-card startups expensive and unpredictable? Yes, but in the case of Google, money is never the issue and when they hit paydirt, the windfall may be historic. One of the biggest winning acquisitions made by Google is their $500 million purchase of DeepMind, a London-based artificial intelligence startup whose acquisition signaled the beginning of AI as a major tech sector.

DeepMind has already helped Google with energy efficiency. Google operates data centers around the world and put DeepMind’s data and machine learning capabilities to use improving power usage efficiency, or PUE as it’s also called, in all its data centers. DeepMind adjusted the computer usage patterns and reduced Google’s power usage by several percentage points. That may not sound like a lot, but think about this: Google used 4,402,836 MWh of electricity in 2014, roughly the same amount of energy used by 366,903 U.S. family homes in one year. Reducing power usage by one percent every year will translate to hundreds of millions of dollars saved in energy costs… and all because a machine learned how to use data to make changes. Using DeepMind to save energy costs will pay for the acquisition of DeepMind, but that’s just the tip of the iceberg.

In fact, Google had probably some of the most efficient structures in place for its data centers, and DeepMind still improved on their design. In spite of excellent over-all efficiency, Google had inefficient cooling systems in place before integrating DeepMind’s AI learning. Imagine how much energy, time, and most importantly, money could be recouped or saved by implementing machine learning for everyday processes. Retail giant Amazon is also experimenting with machine learning for fraud detection, website marketplace optimization, and even packaging and shipping processes. Simply restructuring a conveyer belt by a matter of feet could save Amazon tens of thousands of dollars every year in wasted energy.

Microsoft is rumored to be building a machine learning tool suite called Open Mind Studio. The intent is to integrate Microsoft’s machine-learning technology across its product spectrum, including Outlook, Skype, HoloLens, and search engine Bing. At the core of Microsoft’s machine-learning efforts is its Cortana Intelligence Suite combined with its Azure Machine Learning Studio that can be used by businesses and developers for creating, testing, and integrating predictive analytics with user data. The more tech leaders expand the reach of machine learning, the greater the potential for businesses to enjoy the possibilities.

Over a dozen European banks have replaced their outdated statistical-modeling systems with machine learning. The results are eye-opening in some instances:

  • 10 percent increase in new product sales.
  • 20 percent saving in capital expenditures.
  • 20 percent increase in cash collections.
  • And, 20 percent decline in churn.

Part of the machine learning systems initiated by the banks are microtargeted models that can accurately predict which patrons will default on bank loans or cancel service with a built-in intervention process. Machines are now helping save humans from their own spending habits by predicting failure and intervening before it becomes a reality.

This article is an excerpt from NEO founder Jesse Morris’ new book Data and the World of Today: The Reality of Today that will Impact your Business Tomorrow. Purchase your copy via Amazon.com.

Machine Learning and Google: Match Made In Heaven

How Will Machine Learning Affect Business?

What were you doing in November 2016? If I were to make a guess, you were probably getting ready for the holidays. It was around that same time Google announced one of the most under-the-radar announcements regarding machine learning.

Google Translation & Machine Learning

On November 22nd, 2016 Google published an articled entitled Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System. Just two months earlier, Google announced Google Translate’s transition from phrase-based translation to a new system called Google Neural Machine Translation (GNMT).

How Will Machine Learning Affect BusinessYou might wonder what’s wrong with phrase-based translation. It’s the same approach you used in freshman Beginner Spanish memorizing phrases in Spanish to hold broken conversations with your classmates. You also probably used phrase-based translation during your spring break trip to the Alps while you awkwardly fumbled through your new English-French dictionary to hit on that one girl at the Louvre.

There’s one problem: phrase-based translation is effective, but clumsy and stilted to use. It doesn’t provide the nuance of language any of us incorporate throughout our everyday conversations. Phrase-based translation doesn’t capture the natural flow of communication used in every language around the world today.

Massive Upgrades In Online Translation!

The GNMT platform serves as a complete end-to-end learning framework. The challenge facing Google is that Google Translate currently translates over 140 billion words every day in 103 languages. With its previous phrase-based translation platform, Google Translate required a suite of network systems to accurately translate between any two languages. This necessity cost Google a significant amount of computation and financial resources. By connecting Google Translate to Google Neural Machine Translation (GNMT), it effectively received massive upgrade in translation horsepower.

Google believed the GNMT system would allow Google Translate to improve translation quality. In initial testing it was obvious the GNMT system’s ability to provide more accurate translation results was significant. This raised another dilemma: how do you scale a neural network as dynamic as GNMT to accommodate 103 unique languages worldwide?

Google elected to extend its previous GNMT system into a single system for language translation. There was no specific change in the existing GNMT system with one exception. Google included an extra “token” connected with the input sentence that allowed the system to select the desired output language for translation. Google also enabled “Zero-Shot Translation,” which occurs when language pairs with no previous inter-translation history in the GNMT system were triggered for translation between the two languages.

As you can see in the graph below, a multilingual GNMT system expands on the previous single GNMT system. In this example, the different language pairs share the same system for translation. This allows the system to transfer “translation knowledge” between language pairs to leverage the full potential of the multilingual model.

Photo Creds: https://1.bp.blogspot.com/-jwgtcgkgG2o/WDSBrwu9jeI/AAAAAAAABbM/2Eobq-N9_nYeAdeH-sB_NZGbhyoSWgReACLcB/s1600/image01.gif

Google was hopeful this zero-shot translation opportunity would be realized. Would the GNMT system be able to translate two languages never before encountered by the system in a translation pair? Google was amazed to learn the GNMT system created reasonable translation results between language pairs in a zero-shot translation scenario.

Google Learning On Its Own

How did the GNMT system know how to translate between languages with no previous experience in its system? It learned. In essence, the GNMT system appeared to created a new language to help translate more effectively… without human influence. The artificial intelligence capabilities inside the neural network of the GNMT system developed an interlingua of sorts to aid in translation accuracy.

Translation, pun intended, the GNMT system seemed to invent an internal language to increase translation efficacy without anyone telling it to in just a few short weeks.

GNMT Google Translate team member Mike Schuster (Google Brain Team), Melvin Johnson (Google Translate), and Nikhil Thorat (Google Brain Team) shared their perspective:

“Within a single group, we see a sentence with the same meaning but from three different languages. This means the network must be encoding something about the semantics of the sentence rather than simply memorizing phrase-to-phrase translations. We interpret this as a sign of existence of an interlingua in the network.” (Google Research Blog)

As of Google’s article published date, the multilingual GNMT system is enabled for all Google Translate users for ten of the 16 launched language pairs. This new approach is leading to better translation results with a simplified framework for helping people around the world understand each other better.

What the success of Google Neural Machine Translation (GNMT) means for neural networks

Does the GNMT system success signal a shift in neural networks? Technology experts have long believed in the power of neural networks to realize the potential of artificial intelligence. Neural networks are built in similar infrastructure of organic, biological brains. Their capacity to reason, deduct, implement, and even react is what drives technology leaders to explore new challenges to solve.

Neural networks, also known as artificial neural networks (ANN), thrive on recognizing patterns in data. IBM supercomputer Watson is already recognizing patterns in medical data, weather forecasts, legal matters, and education systems. I believe neural networks have the power to change every industry through artificial intelligence. The ability to think and respond in a human-like manner at an exceptionally high level of computation is opening new frontiers and solving countless problems.

What Does This Mean For Everyday Life?

In our immediate context, what does the Google Neural Machine Translation system’s success mean for your everyday life? It means our ever-diversifying world will better accommodate the melting pot of languages in every major city. I see a future where language groups can communicate more easily than ever. AI headsets connected wirelessly with Google Translate will allow us to automatically hear and understand any foreign language in our native language. Businesses will span language barriers and conduct business more efficiently than ever because neural networks will make translation easier than any other time.

The way the world communicates is changing because of Google Translate. Will we need to learn new languages? Maybe not. The potential to overcome millennia of miscommunication and communication barriers cannot be overstated. With the right technology, Google is committed to making sure this opportunity isn’t lost in translation.

 

Learn more from NEO about how machine learning, natural language processing, and other emerging technologies impact business on our main website.

7 Simple Steps to Tracking Business Data

How Can Small Businesses Use Data?

What sets ‘Ma and Pop’ grocery stores apart from nationwide grocery chain stores?

How Can Small Businesses Use DataIt’s the feeling you get when you walk in and the produce manager yells a greeting to you by name. It’s the familiarity of having your cashier ask about your Golden Retriever and when they might see you at the dog park again. It’s that level of connection that keeps you coming back every other Saturday morning for groceries, even though you drive past two regional grocery chain stores with cheaper prices and more convenient access.

One of the biggest value differentiators for small businesses is being able to truly know their customers. Data is now allowing global businesses the same opportunity to deliver a personalized experience. Small businesses are facing stiff competition in this arena and you don’t have the margins to compete on price.

Location-Based Data

Location-based data specific to consumers is arguably the treasure trove of tomorrow’s business. The ability to track, document, and analyze a person’s driving habits, commuter routes, demographics, consumer locales, and time frames for all of these recorded activities is priceless to businesses. GPS-tracking is being used regularly in every smartphone around the world.  Geo-tracking is the ability to track any device in real time, such as Pokémon. We have expanded geo-tracking into wearables, browsers, kiosks, and other user-friendly technology.

Imagine a world where you’re standing on a street corner and a city bus passes in front of you with a personalized ad for a piece of clothing that fits your style and the coming season. This experience is possible because your phone is sharing your geolocation, social media profile, and other data in real time. This is the reality of today.

Business-Generated Data

It’s virtually impossible for any of us to stay current with all the potential data that’s generated for our businesses, let alone our personal lives. An estimated 90 percent of all data generated is unstructured, including photos on your smartphone, social media interactions, customer transaction trends, and more. Data overload is also a side effect of our current reality. While this is a challenge, it can be solved and turned into great profit.

Become a Client Whisperer…

Data allows you to be a client whisperer. The right data points help you see client needs before your client ever realizes the need is there. If you’re a financial advisor, knowing that a client is due with their first baby in October means you may want to track when the delivery is so you can send a congratulations card, a piggy bank, and information about setting up a college fund. This is turning data into real-life, profit-driving action.

Track The Effect Of Any Changes

The second part of making data-driven changes is tracking the effect of your changes on your business success. What is the impact of eliminating a specific time frame for client appointments? Were the select few clients who were available during that time frame also available at another time frame? Of the potentially affected clients, what is the value and social influence of each of those clients on the rest of your client base?

Make the changes, analyze the impact of those changes, and then make additional changes or confirmation from your analyses. This is a fluid process focused on optimal output based on your goals and objectives.

Tracking how your business operates down to the minutest detail creates market separation. Maintaining as much actionable data as possible on every one of your clients helps personalize your client interactions. Knowing what effect data-driven changes are having on your everyday operations and bottom-line revenue is what keeps businesses like yours ahead of global competitors.

How to start tracking business data

We know the importance of data and how it is shaping tomorrow… now what?

Step 1: Decide what data you would like to gather. I live by the concept that the more data, the better. I like to gather data on the following information at the core: contact information, personal information, social media information, web usage data, communication preferences, demographic data, hobbies and interests, family makeup, business data, consumer buying data, and any other data that would be helpful for your business. For example, a law firm needs to know the names of all of your beneficiaries and contact information.

Step 2: Collect as much data as possible. When I say collect, I don’t mean collecting data in your head. Gather data using a central system, like a CRM, that can integrate with the rest of your technologies.

Step 3: Engage in data cleanup and organization. While this might seem like a basic task, nothing could be further from the truth. If you talk to Data Scientists or Data Geeks, they will tell you the most time spent in the data world is on cleanup and organization. In fact, many say that 80% of their time is spent working with data on cleanup.

Step 4: Ask questions. What questions do you need or want to answer? What decision are you in the midst of needing to make? Do you need to hire more staff? Build out more space? Develop a new product? Change the hours you are open? Get rid of one product so you can focus on another product? Those are just a few of the questions that data can help you answer, providing you the clarity and peace of mind you need. You need to know you have all the information possible to understand the impact of any decision you make.

Step 5: Summarize and visualize the data. We are summarizing our data and reporting on it in this step. In other words, if we want to know how much revenue we did last year in September versus this year in September, we should have a report that clearly shows us that information.

Step 6: Identify actionable insights. Once we have summarized the data, we now use the data to provide actionable insights. We did some consulting for a dental practice that was considering if it was time to expand their space. After looking at the summary data, we found that while they were close to capacity, the type of patients they were serving wasn’t in line with their ideal patient. So, instead of building out more space, they first needed to focus their efforts on finding more of their ideal patients. We were able to provide clarity using data to make actionable insights.

Step 7: Implement advanced analytics and modeling. Being a data geek, this is one of my favorite parts of this whole process. While summary data and actionable insights are extremely helpful in providing us clarity and peace of mind, advanced analytics and modeling is what can propel us into innovation. In the previous story of the dental practice looking for more ideal patients, we developed a model that would help schedule appointments based on an algorithm we developed.

The purpose here is to not only provide actionable insights but also make it as easy as possible for the company to focus its efforts in the most direct way possible. This occurs while gaining valuable insights and improving automatically along the way. Automatically?… How is that possible? We can make baseline assumptions through machine learning and modeling and then let the data tell us how accurate we are and suggest any modifications into our models. This process is fascinating to say the least.

This article is an excerpt from NEO founder Jesse Morris’ new book Data and the World of Today: The Reality of Today that will Impact your Business Tomorrow. Purchase your copy via Amazon.com.

Artificial Intelligence Is Literally Changing Lives

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Artificial Intelligence: I know I mention this as a topic and your thought immediately goes to the movie Terminator: droids that are built to look, act, and sound like humans who are intent on taking over the world. Don’t worry, droids taking over the world probably won’t happen any time soon.

What is closer than we might realize is the integration of Artificial Intelligence (AI) into our everyday world.

How Artificial Intelligence Will Change The FutureThe concept has been growing for years, but it’s never been closer to reality than today. Artificial Intelligence is finally living up to its potential. It’d be helpful to know what we’re talking about when it comes to Artificial Intelligence.

As silly as it may sound, ‘artificial’ may not be as clearly understood as it seems, and also, ‘intelligence’ may have different levels of expectation from each of us. The human brain as the home of natural intelligence is, of course, the birthplace of artificial intelligence. The human brain experiences a cacophony of electrical activity that translates into motor movement, spoken and nonverbal communication, situational processing, and at the very basic level, our ‘fight or flight’ response.

A Story of a Monkey and AI Changing Lives

Dr. Miguel Nicolelis, Professor of Neuroscience at Duke University (Durham, NC), became fascinated with ‘brain storms’: electrical activity caused by neurons firing between the more than 100 million cells in the human brain. Dr. Nicolelis’ research recorded this neural activity and decoded the neurons as a sort of alphabet to communicate with extensions and devices outside the body.

Dr. Nicolelis and his team developed a Brain Machine Interface from 2000 to 2001 with a multi-channel sensor system to receive the electrical communication from the brain. The receptor system then processed the electrical signals as part of real-time analysis of brain activity. The sensors are designed to specifically look for any signals that are connected to motor movement: raising an arm, shifting a foot, flexing fingers, or even standing up from a seated position.

Any motor movement information was then sent through a telemetry processor to a 3D artificial limb, such as a robotic arm. But the question remained, how well did the translation of the motor movement work, from the brain’s electrical impulse to the robotic arm? Dr. Nicolelis’ team started experimenting with a rhesus monkey named Aurora in early 2003. The research team monitored and recorded Aurora’s brain activity while playing a simple computer game with a joystick. If Aurora completed a basic challenge in the game, she received an automated drip of Brazilian orange juice as her reward.

Aurora’s ‘brain storms’ were uploaded to a robotic arm using the Brain Machine Interface so the computer and a robotic arm could begin learning Aurora’s impulse and motions to play the same game. When Dr. Nicolelis’ research team switched to the Brain Machine Interface after thirty days, Aurora was able to simply think of the direction to move the sensor in the computer game and the robotic arm responded based on her brain activity.

Two Monkeys Changing The World

In a similar situation, researchers from the University of Pittsburgh and Carnegie Mellon University experimented with two macaques monkeys in using robotic arms to eventually establish brain control over an artificial appendage. The scientists identified 100 motor neurons that a computer analyzed in their electrical activity and translated the neural activity into an electronic command to move the robotic arm. The arms were mounted flush with the macaques’ left shoulders and the computer initially helped the monkeys move the robotic arm to help establish motion control.

As the monkeys learned to adopt the movements, the research team noticed an adaptation of movement that could not be anticipated in virtual environments. The testing results show the brain’s amazing ability to adopt, adjust, and use a prosthetic appendage based solely on the brain’s motor activity fired in a specific area of the cortex. Dr. John F. Kalaska, neuroscientist at the University of Montreal, after seeing the macaques’ progress, noted that, “[Brain-activated prosthetic limb adoption] would allow patients with severe motor deficits to interact and communicate with the world not only by the moment-to-moment control of the motion of robotic devices, but also in a more natural and intuitive manner that reflects their overall goals, needs and preferences.”

So, if the brain is capable of creating the right type of data to control a prosthetic limb, could the brain control more than one prosthetic limb at a time? Dr. Nicolelis and his research team posed that same question while continuing to study Aurora’s ‘brain storms’. The difference is that Aurora was controlling a single robotic arm over 7,000 miles away at Kyoto University in the Kyoto Prefecture of Japan. The control signal between Aurora’s brain and the robotic arm at Kyoto University was registered at 20 milliseconds faster than the brain signal between her brain and other muscles in her body.

The Duke University research team added a second monkey to their experiment… and a second robotic arm for both monkeys. Implants in the monkeys’ brains tracked and translated between 374 to 497 motor-controlling neurons to send the appropriate signal to the robotic arms. The two rhesus monkeys have successfully controlled both arms at the same time using a new and improved bimanual brain-interface machine. The results are promising because, of course, the ultimate goal isn’t just to allow perfectly functional monkeys to control robotic arms. The hope is to empower paraplegics and amputees with the brain-controlled capabilities to enjoy life without limits.

What Does This Mean For Humans?

To put this simply, this research proves that our brain has the ability to form new pathways. How does this translate into our daily lives? It means that while you are working there is the potential for your brain to be controlling a robot at home cleaning your house. It takes the concept of multitasking to a whole new level.

Think about the possible implications!

But, how can artificial intelligence be applied for quadriplegics? What if no neural activity is registering any motor control inside a human body? The same advances in brain-machine interface technology are now allowing monkeys to control a robotic wheelchair simply by thinking. Dr. Nicolelis and his team monitored the brain activity of two rhesus monkeys that were trained to maneuver a wheelchair just by watching it move. The monkeys transitioned to using their brains’ neuron signals to navigate a two-meter path across the room to retrieve grapes from a dispenser. The experiment required careful insertion of intracranial implants to register the monkeys’ neural activity for far superior motor control of the wheelchair.

The data received from monitoring the two monkeys’ brain activity while telematically controlling the wheelchair is the same type of data that may be used in the future to improve the livelihood of severely disabled people. People suffering from Amyotrophic Lateral Sclerosis (ALS), Parkinson’s, or any number of motor neuron diseases, now have hope of controlling their livelihood. Dr. Nicolelis and his team have now started implementing the discoveries and data tracking capabilities of their brain-machine interfaces into human experimentation.

This article is an excerpt from NEO founder Jesse Morris’ new book Data and the World of Today: The Reality of Today that will Impact your Business Tomorrow. Purchase your copy via Amazon.com.

 

How Data Is Transforming Business & Game Shows!

Ken Jennings holds the record for the longest winning streak on the TV game show Jeopardy! with 74 straight victories in 2004. Jennings’ historic string of wins resulted in total cash winnings of $2,520,700. His breadth of knowledge (and in some cases, timely wagers) made him one of the best, if not the best, contestants to ever play Jeopardy! His success may only be second to Brad Rutter, winner of 19 regular season and tournament games of Jeopardy!

On February 14th, 2011, both Rutter and Jennings were introduced to their greatest trivia opponent: Watson, an IBM supercomputer equipped with cognitive computing capability. Watson can analyze astronomical volumes of data with a more human-like cognition, including natural language processing, hypotheses generation, and intuitive machine learning. At the very base level, Watson is a textbook example of a computer powerful enough to sort and learn from the 2.5 quintillion bytes of data being created every day.

Watson more than held its own against Rutter and Jennings winning the $1,000,000 cash prize. IBM donated Watson’s total winnings to charity, but the value of Watson’s win goes far beyond any cash prizes. It showed the world the power of Natural Language Processing (NLP).

What is Watson’s Purpose?

It wasn’t long after Watson’s win over Ken Jennings and Brad Rutter on Jeopardy! that IBM elected to transition Watson’s purpose. To simply call IBM’s solution ‘Watson’ misses one important detail: there are actually two distinct units from IBM. Watson is home to cloud-based cognitive computing technologies while Watson Health is dedicated to aiding physicians, researchers, and insurance providers in personal health data and analysis.

IBM announced in February 2013 that Watson’s software system would be initially applied to utilization management situations involving lung cancer patients at Memorial Sloan Kettering Cancer Center. Their efforts teamed IBM’s Watson with the work of health insurance carrier WellPoint. This led to an amazing transformation in patient care with approximately 90 percent of field nurses relying on Watson’s insight.

The grim reality is that IBM needed Watson to wow and win the world as much as any other product in their illustrious history. With innovative technologies pulling more of IBM’s traditional IT business into the cloud, IBM recognized the need to re-create its relevancy. Since Watson’s roaring success at Jeopardy!, IBM has integrated a suite of services with Watson’s name recognition.

Watson Rivaling Humans

What gives Watson influence on the world around us is how capable and efficient it is at processing real-life problems. It would be easy to assume that Watson’s responses would be grounded in data alone. The amazing reality is that Watson’s natural language processing (NLP) and data learning algorithms create an interactive experience that probably rivals the communication and interaction levels of many humans.

Artificial intelligence is helping reprogram amputees’ brains to adopt a robotic arm. Machines are learning like humans with greater cognition capabilities. Exoskeletons are giving paralyzed children the ability to walk again. A voice-activated intelligence system is now capable of bringing your car to wherever you are. A supercomputer is helping diagnose cases of cancer at up to four times more accurately than the average medical professional.

How Data Is Transforming Business & The World

This may all sound like science fiction. It’s not. It’s how data is transforming the world of today. As more businesses understand the immense value of data, they will need more advanced systems to collect and analyze structured and unstructured data for real-life implementation.

How much easier would your life be if you could have any conversation at any time and the other person understood you perfectly every time?

How much confidence would you have knowing that your words were received exactly the way you meant them?

Now, imagine if that other person was actually a computer. No more shouting the same business via Bluetooth. No more repeating yourself slowly and painstakingly so Siri can do a simple search through Google. Natural Language Processing is about giving computers the information (data) they need to understand humans and communicate their responses like a human would.

Natural Language Processing Is Changing Business

Talk-to-text technology has unlimited applications in today’s world, but how well can your phone, tablet, or computer align with your spoken communication? The ability to communicate with a computer as if it were human is no longer science fiction. The question is how realistic and accurate the experience may be.

Natural Language Processing (NLP) believes that computers have the end-capacity to communicate in human language with the same user experience. To put it simply, NLP is meant to replace the need to edit and double-check a computer’s work. It means that your virtual assistant could truly become an AI assistant that you don’t ever have to check up on.  Natural Language Processing is meant to use the data of language communication in the most human way possible to better impact user experiences in search, mobile, apps, marketing, and translations, among other applications. The goal of Natural Language Processing is for humans to be able to communicate with computers as if they were human, arguably the pinnacle of Artificial Intelligence (AI).

Currently, there is a company called x.ai that is doing just that. They have created a virtual AI assistant called Amy Ingram (AI) that will schedule appointments for you. “She” can communicate just like a human can. You wouldn’t even know you were communicating with an AI system while talking with Amy.

Global enterprises Google, Facebook, Stanford University, Cambridge University, Microsoft, and the Mind Project are in a virtual arms race to develop the most conversational Natural Language Processing platform based on data intelligence. Google’s Natural Language Processing research incorporates syntax to structure language data for further development,

“Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.

On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.

Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level.”

Microsoft established a Natural Language Processing group at their Redmond (WA) campus in June 2016.  In their own words, the Natural Language Processing (NLP) group exists:

“…to design and build software that will analyze, understand, and generate languages that humans use naturally, so that eventually you will be able to address your computer as though you were addressing another person.”

Natural Language Processing incorporates two fundamental components: Natural Language Understanding (NLU), and Natural Language Generation (NLG). Natural Language Understanding involves cataloging and disseminating language into its appropriate representations. It also incorporates the analysis of any given input into understood communications. Natural Language Generation produces intelligent communication responses based on data.

If a restaurant has an automated NLP-based menu-ordering kiosk, the ability of the data processing computer to accurately understand a patron’s spoken order is crucial to the patron’s dinner arriving in the exact detail that they requested. Think of how many times you’ve tried to talk with Siri or Cortana only to have them provide inaccurate search queries or map directions. Natural Language Processing helps clients experience the same level of service from entry-level human servers with the convenience of leveraging data and time for greater profit.

This article is an excerpt from NEO founder Jesse Morris’ new book Data and the World of Today: The Reality of Today that will Impact your Business Tomorrow. Purchase your copy via Amazon.com.

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Press Release Denver Dental Analytics Company Leads The Way In Data Analytics For The Dental Industry

Data Analytics Company Creates Dental Analytics Tools

Dental Analytics Denver ColoradoNever Ever Ordinary is a data analytics company, Denver, who is out to change the way dentists approach their business.  Founded by a financial expert who worked closely with dentists and dental specialists for many years in the finance and accounting space, NEO works closely to bring a comprehensive perspective to how you run a dental practice.  The financial piece is only just a piece. The insurance, the clinical stats, the staffing, the marketing, are all just pieces, but they all impact each other and impact the practice’s bottom line.  Jesse Morris saw the desperate need for dentists to embrace data to help them look at all of the pieces together and use data analytics in a smart way to grow their business.

Data Analytics For Dentists

Never Ever Ordinary has created data analytics targeted specifically for the dental industry.  These analytics help dentists use and organize much of the data that they are already gathering via their practice management softwares, financials, and other data.  The reports organize the information in digestible charts and tables in a dashboard that updates real-time.  Dentists and doctors can have a current understanding of what is going on in their practice.  Reports can be customized to answer specific questions the dentist has about the practice and business.  The dashboard also collects data in a meaningful way, giving the practice hard and fast data to benchmark themselves against year after year.

Take the Guesswork Out Of Managing a Practice

Up until now, most practices have made their decisions based on postulation or guesses.  Now, most of the time, these guesses are highly educated looking at some KPI’s or getting insights from practice consultants or specialists.  However, if you’re not using the data your practice has in an intelligent, and analytical way, you’re still just making educated decisions that you hope will work out.  Using business intelligence to combine this massive amount of information together allows you to see the impact of your decision in a numerical, mathematical way.  You can KNOW what you need to do, rather than have a hunch of what to do.

Changing The Game For Dentists

Practice owners and dentists often hire a myriad of specialists and consultants to help them run the business and clinical sides of their practice.  While these roles are still very important, the NEO Analyzer dashboard for dentists pulls together all of these factors and data from all aspects of the business to help the dentist see the big picture.  This is a new thing for many dentists to grasp, and many simply aren’t ready for it.  However, those that don’t employ the use of data for their practice in an efficient and intelligent way, WILL be left in the dust by practices that do, or by corporate dentistry operations that have the resources to use data in a big way.

The reality is that data is a big deal in business, and that includes the private practice business model as well.  While data hasn’t always been easy to employ in a small business, Never Ever Ordinary has broken that barrier to bring data analytics and business intelligence to dentists, orthodontists, and other dental specialists.

Never Ever Ordinary is a business intelligence and data analytics company, Denver, CO.  They work primarily with dental practices, and other business to provide consultation on data gathering, real-time dashboards showing trends and data analytics, and customized reports that put data in a easy-to-understand format.  Never Ever Ordinary has developed many proprietary solutions to help businesses gather and use data in an effective way.  NEO serves clients locally in Denver, Colorado near their headquarters, but also services clients all over the country.

To learn more about these innovative reports, contact Jesse Morris at the contact information provided below.

Never Ever Ordinary

Jesse Morris

info@nevereverordinary.com

www.nevereverordinary.com

Twitter:  @neverordinaryBI