Personal Digital Assistants (PDAs) Are Making Our Lives Simpler

Personal Digital Assistants (PDAs) Are Making Our Lives Simpler

Back in the 1990s the IT world came with an idea of the personal digital assistants (PDAs). At that time this concept start quickly becoming popular because most of the PDAs combined many features of cell phones, and they were also called “the first smartphones,” yet PDAs were handy at managing personal and business lives.

IBM was the first company to introduce the world’s first PDA with full cell phone functionality. They called their innovation the IBM Simon, which is also considered the first smartphone. Simon featured many applications such as calendar, appointment scheduler, world time clock, notepad and other apps that aimed to keep users’ lives organized.

Then in 1996, the mobile giant Nokia, produced the world’s best-selling PDA, the 9000 Communicator, with full cell phone functionality. Later the same year, Palm Computing presented the first generation of their PDAs, which were a great solution for someone who wanted to keep organized their busy lives.

Fifteen years later, Apple launched the Siri personal assistant, which made impossible things real. It allowed people speak their search queries into a phone, and Siri would understand and perform their queries for them. IPhone users started to feel some sort of emotional attachment to their devices.

After this enormous success, Apple’s main rival – Google – launched the Google Now feature in 2012, and then Microsoft developed Cortana personal assistant. In 2015, Facebook launched M assistant. So in spring 2016, MYLE will reveal its first intelligent personal assistant, which has an ability to outpace any other solutions on the market.

Many companies are interested in the PDAs market, and as according to recent research, nearly 39% of smartphone owners use some sort of PDAs such as Google Now or Siri. This trend will be rising over the next five years.

Most of the PDAs are in favour for many entrepreneurs because they can practically organize their day: suggesting routes for travel, advising on current weather conditions, finding the best restaurants based on the previous history, conducting faster web searches and setting up meetings. They also help by suggesting places for business meetings, remembering things like important thoughts and crucial business ideas.

Majority of the contemporary PDAs could be controlled by voice or through manual data input.

However, those features above are only one drop in the ocean compared to what they will do for its users in the next couple of years. Other key features of the PDAs are their integration into different systems and apps, such as Slack, Wunderlist, Evernote or Salesforce.

For instance, in order to save notes, ideas, to-dos and other tasks to Evernote, a MYLE user should only say those commands to the device. It will automatically recognize and understand the words, convert speech-to-text and categorize all users’ commands into assigned applications.

However, MYLE device is not limited to these functions. Moreover, its smart AI algorithm will enhance itself to better understand users’ behaviour using the latest breakthroughs in machine learning.
MYLE sets a purpose for its PDA, namely, to take over routines of many entrepreneurs to free their tight schedule for leisure time, family and other activities.

PDAs are powered by artificial intelligence and they have already become an essential feature for many businesses and consumers.

Dealing With Information Overload – the MYLE Way

Dealing With Information Overload – the MYLE Way

Busyness – crazy busyness – due to information overload is the definite challenge of the modern age.

But once you think about it, don’t you have that funny sense that we have a bit too much of this good thing?  Public polling results seem to support this suspicion.

An absolute majority of workers and everyday people of the developed world complain about Information overload, stating amount of data they have to deal with on a daily basis has gone so much up that it is becoming or has already become unbearable and plain stressful. Just to give you an example – 91% of US workers say they sometimes delete or discard work information without fully reading it. While 65.2% of their UK counterparts stated that their work was negatively affected by the amount of data they had to process.

The IT industry seems to recognize the challenge, flooding the market with all sorts of activity trackers, organizers, schedulers and other endless apps.  In doing so though, they create a problem of its own.

Now there are too many applications that you have to spend your time and attention, learning and working with them. Again, the proof is in the numbers: 72% of US workers admit that they would be more productive if they didn’t have to switch back and forth between apps to get their work done.

Science has its own proof too. A Temple University study found that as you give people more and more data, they reach “cognitive and information overload.” Activity falls off in the part of the brain responsible for decision making and control of emotions and the quality of their decisions suffer, with the number of errors going up drastically.

So MYLE to the rescue by addressing at the mighty 2/3 of the problem – data input and processing. Forget those tiny buttons on your smartphone. Or filling of endless forms and timesheets at work.

With MYLE everything can be done with the most natural way of data input – by using your voice and simply talking to your device. Thanks to its infinitely customizable, self-learning and very smart analytical platform that powers MYLE, all you have to do is tap the device and start speaking.

All the downstream work of processing the data and assigning it to appropriate destinations – be it fillable forms, schedulers, activity or expense trackers or pretty much any mobile application or a industrial software you can think of  – happens automatically.

Additionally, all your voice notes are saved in your MYLE account not just as text, but as an audio file too. And even if you have some peculiar pronunciation,  MYLE is capable of learning them and adjusting the input accordingly.

Once MYLE becomes an integral part of your daily life, you will suddenly discover that you are not drowning in the data anymore – and still have some extra spare time on your hands!

Reining in the Information Deluge

Inforgraphic design by Visually.

Design vector designed by Freepik

The Secret To Good Time Management

The Secret To Good Time Management

“How did it get so late so soon? Its night before its afternoon. December is here before its June. My goodness, how the time has flown. How did it get so late so soon?” – Dr. Seuss.

The passage of time goes unnoticed most of the, well, time. Yet once we realize its transient nature, there is no hiding from this bitter realization – time flies by and does it too quickly for comfort. Because for us, humans, time is the only absolutely unredeemable asset. Yet it keeps pouring through our hands like water leaving us with the thought of how much time was lost – on what, exactly?!

The modern life presents us with hundreds if not thousands of opportunities to lose time. Each task or a thought bites its little chunk off our time. So if you want to do more in less time – effective time management will be the key.

Modern technologies did wonders to help us with that, tracking amount of time we spend on each particular task, making our hectic and busy living more organized, orderly and thus simpler and easier for us.

However, even though the purpose of all these numerous applications and gadgets is to save us time, they make us spending more – just accessing and using them. Just try to note how long it takes to use an app or a gadget. With a phone app it goes like this: reach for the phone, unlock, start the app, do your thing by typing a command or a note, close the app, switch off the phone and put it back where it was. Sounds pretty fast and nothing to worry about, doesn’t it? Now multiply it by the number of times you repeat the procedure during the day, it starts to look rather worrisome.

Yet once you do it with MYLE, using our most natural way of data input – our voice – the whole routine is cut to 5 seconds. Tap, say the note and get back to what you were doing, allowing MYLE to complete all tasks in the background – automatically. Additional boon is that all can be done with a minimal distraction from your main activity, be it work or play.

“Time,’ spent two hours on emails, 30 minutes talking with the boss, 10 minutes on coffee break”.

“Schedule, meeting with Jim, this Friday 10 am, remind 10 minutes prior”.

“Shop list. Buy milk today on way home”.

MYLE will automatically save your note as an audio file, convert it into text file and push it to an appropriate app based on the key words that you teach MYLE to know. The built-in activity-tracking feature will analyze your active time and how you spend it using its powerful algorithms to make the personalized suggestions based on your past records.

It will learn your behavior and adjust the predictive analytics to tell you what you could do to maximize your productivity and efficiency. And it will not bite any time off your busy day to achieve all that.

Use Technology to Spend Less Time Working

Use Technology to Spend Less Time Working

Everyone has been in this situation; you look at the watch without a clue as how could the time pass so quickly.

It would seem that you’ve recently arrived at your job desk and about to start working, however, it’s already 5 p.m., and you’re not even done half of your tasks.

You’re thinking, “where is that lost time? What could be the reason for such a poor productivity?”

The reason could be hundreds of the emails you were supposed to reply on from your laptop; or dozens of messages from your financial department that your cell phone was receiving; or notifications and phone calls from the CEO and clients that you’ve forgotten to schedule a meeting with all of them.

Meanwhile, you’ve got a brilliant million dollar idea on how to make a winning marketing campaign, but it happened so that this routine caused you to forget your fabulous idea.

These tasks of constantly switching from your laptop to cell phone to communicate with different departments simultaneously writing down your ideas and notes, are driving your attention and productivity away leaving you in a situation of total madness and uncontrolled processes.

However, your tasks are not over yet, because you have a report that’s due tomorrow morning for the meeting with a CEO. Then you start realizing that 5 minutes required to finish your routine tasks, apparently have taken the entire day. You forget about your leisure time and staying up until midnight at your office desk.

It’s a reality of the modern person. We’re all stuck in this routines funnel where escape is only possible through the use of contemporary technologies.

“When you kill time, remember that it has no resurrection.”
― A.W. Tozer

It’s true that we cannot redeem the time and opportunities we wasted on useless things. Remember, perhaps you thought about it at least once, “Only if I had had more time, then I would’ve changed everything to better. »

We tend to spend senselessly our time doing what we make ourselves believe in – doing important things – when in reality we are disconnecting from our lives.

Today’s reality – we have the full spectrum of opportunities to influence the time with a maximum outcome.

Technologies are called to save our time.

Today’s market has produced thousands of devices and apps that aim to make people’s lives simpler through automating their daily communication and other processes.

Look, for instance, at Slack, their slogan is to simplify the working lives by making them more pleasant and productive. Slack eases the entire communication process within large teams in the most time efficient way. However, Slack is one of the thousands of different tools that strive to simplify people’s lives.

MYLE is another intelligent assistive technology tool to manage a busy business life. It’s already been in the development stage for the past several years, and 2016 will be the year when people get to see this new technology.

The team of developers has set a goal to minimize your time required to execute a particular task and maximize your productivity. Moreover, applying the latest breakthroughs in speech recognition and predictive analytics, this miniature device can save you, at least, one hour daily.

Savings come from all the time spent daily reaching for the smartphone, unlocking, opening apps, putting the phone back. On top of that, MYLE will save much more time via automating routines and accomplishing tasks that previously were completed manually. Moreover, you’ll remember everything.

All day on the run, endless meetings, endless list of things to remember, an avalanche of ideas. So, simply not enough time for everything. A single finger tap will effectively capture and memorize any inquiry or a million dollar idea, and will track and optimize time.

When technology is used wisely, it can provide us with more time to enjoy leisure activities and pursue our passions.

Intelligent Virtual Assistant Market Is Expected to Reach $3.07B by 2020.

Intelligent Virtual Assistant Market Is Expected to Reach $3.07B by 2020.

The global market of assistive technologies is expected the growth of USD 3.07 billion by 2020, according to a new study by San Francisco-based market research and consulting company Grand View Research, Inc.

In 2014, this perspective market was valued at USD 572.2 million. Currently, many large enterprises and SMEs are looking for solutions to reduce its overall expenses, increase productivity, improve customer-related services and process the enormous amount of data – that’s expected to rise over the next four years.

According to this research, VAs can be used across many industries, such as insurance, healthcare, finance, travel & hospitality, retail and utility sectors and safety regulations.

Noteworthy, the commercial sector is highly interested in VAs due to their desire to increase productivity of their employees and to eliminate the human workforce.

VAs are estimated to replace a significant percentage of workers in business operations.

Advancements in speech recognition and powerful analytics that are integrated into the VAs will make this technology more desirable for the commercial sector.

Think of the powerful device that’s able to collect automatically, process, describe and later use data to predict the working efficiency, financial stability and even possible recommendations for a business or an individual worker.

Personalized analytics and applications created for the specific needs of each business will add more accurate analytics allowing the VAs minimize the time required to perform a particular task, and maximize the company’s growth.

If you’ve become interested in this technology and its limitless potential, then follow these pioneers: MYLE Electronics Inc., Creative Virtual Ltd., Next IT Corporation, Artificial Solutions, eGain Communications, and Nuance Communications, IntelliResponse, Google and Apple.

MYLE is a touch-activated wearable personal assistant that helps you do more in less time without even having to touch your smartphone. Just tap MYLE and say your thought, idea or instruction – MYLE will instantly memorize everything you said and automatically take care of the rest.

An Introduction to Machine Learning (ML).

An Introduction to Machine Learning (ML).

We’re starting this series of articles about the discipline of Machine Learning with an opening question from Tom Mitchell:

“How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

According to Mitchell, this question covers a broad range of learning tasks, and it really does. Try to think, How to develop a system that will automatically be able to learn its environment in the settings, which the humans encounter with on a daily basis?

Which algorithms should the computer science engineers write so that the system is building rationale predictions and conclusions, herewith in the case of an error the system could identify and remember this problem so avoiding it in the future.

For example, can we develop an autonomous system that’s being able to build the accurate predictions to determine which medications will best impact the patient’s treatment based on the gained observations and data?

We can consider such question in this meaning, as of Mitchell states:

“A machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E. Depending on how we specify T, P, and E, the learning task might also be called by names such as data mining, autonomous discovery, database updating, programming by example, etc.”

Many specialists say that the machines should learn in the way as humans do – through some observations, data checking, use cases, experiences or direct instructions.

So basically, the principles of ML are to learn the processes to perform them subsequently better and more accurate in the future. This is what MYLE team aims to build – an assistant powered with the algorithm that learns your speech manner, financial behaviour, your communication habits and other important aspects of your life to make accurate predictions that will enhance your life.

Also, ML refers to a core subarea of artificial intelligence (AI). Apparently you cannot build an intelligent system if it doesn’t automatically operate without human intervention or assistance.

Thus, ML is a core element of making the AI because AI must, for example, process language just as humans do. Humans sometimes communicate using extremely complex sentence structures with hidden meaning. Moreover, still yet it has been challenging to develop a computer system that would be capable of understanding those phrase patterns.

This is where a truly intelligent system begins – with the ability to understand and use gained knowledge to simplify and enhance existing processes. Hence, we cannot consider a system to be truly intelligent if it’s unable to learn since learning is a universal process of gaining knowledge or skills by studying, practicing or experiencing something.

Since the last decade, engineers have written thousands of ML algorithms, and all of them consisted of these three essential components – important to mention that these three components are the framework of any ML algorithm:

  • Representation: how to represent knowledge.
  • Evaluation: the way to evaluate candidate programs (hypotheses).
  • Optimization: the way candidate programs are generated known as the search process.

However, each ML algorithm could be taught by thousands of different methods. Albeit, only four methods are commonly used today.

Supervised learning is a learning model where a training process requiring to make predictions and is corrected when those predictions are wrong.

The training process continues until the model achieves the desired level of accuracy on the training data. The goal of this model is to get the system to learn a classification system that we have created and presented to the system. One of the most typical examples of the supervised learning is the digit recognition.

This learning model is especially useful when it’s easy to determine the problem. However, if a machine is unable to find a common pattern in the given problem, then it’s more likely not to identify the problem. Hence, this learning method is not practical for the systems in constantly changing environments

Unsupervised learning is a learning model where input data is not labelled and without a known result. This method seems more intriguing and harder because the main purpose – to make a system learn a process that we don’t tell it how to do.

This learning model has two approaches to teaching the machine how to do certain tasks.

The first approach is to teach the system by not giving straightforward classifications, but instead using some sort of reward approach to indicate its success. It should be mentioned that this approach fits into the decision problem framework because the primary objective is not to produce classification rather make correct decisions to maximize rewards.

It’s interesting that this approach closely simulates the real-world environment, where the system has a motivation to be rewarded and is also afraid of being punished for not doing the certain task or doing it wrong.

The second approach of this learning model is called clustering. That is, the system’s primary objective is to find similarities in data. This approach could be well used when there is sufficient data; for instance, social information filtering algorithms, such as those that use Amazon in its books recommendations.

Their algorithms are based on classifying and finding those clusters of the similar groups of people that read the same books or fit into the specific category of readers.

Semi-supervised learning is the model that combines both labelled and unlabelled methods.

In the case of semi-supervised learning, the system is given a desired prediction for the certain problem. However, it has to do self-learning to be able to organize the data and make predictions.

Reinforcement learning is the learning model that allows the system to determine automatically the ideal behaviour within a specific task to maximize its performance. That is, the system is not told which actions to take, but instead it must decide which actions generate the most reward.

This model is using the Markov Decision Methods because in some cases actions may affect not only the immediate reward but also the following situation and through that all subsequent rewards.

One of the good examples to understand reinforcement learning could be this (retrieved form University of Alberta):

A mobile robot decides whether it should enter a new room in search of more trash to collect or start trying to find its way back to its battery recharging station. It makes its decision based on how quickly and easily it has been able to find the recharger in the past.

In our next article we’re going to look at some key elements, history and challenges related to Machine Learning.

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