by MYLE Team | Mar 23, 2016 | Assistive Technology, Healthcare Tech, Other, Technologies, Wearable Tech |
Do you remember the 2014 FIFA World Cup in Brazil, when the first kick was delivered by a man in an elaborate exoskeleton suit? This man was Juliano Pinto – a fully paralyzed person. Remarkably, but that exoskeleton suit enabled him to kick a soccer ball with his foot using – only his thoughts!
The progress hasn’t been frozen since the World Cup. Many companies have actively started developing these technologies and today we can see the first results of their hard work.
Wearable robotics are a logical extension of the modern wearable technologies, namely smartwatches, smart clothing, and many others. Noteworthy, the popularization of many wearable devices made it possible to reduce size of the computers, so that people could comfortably wear them on their bodies.
Dmitry Grishin, an entrepreneur and investor who serves as chief executive of Mail.ru Group and founder of Grishin Robotics, says that the revolution in technology and smartphones made many components – cameras, sensors, batteries, processors and others – incredible cheap. This factor boosted development of the modern exoskeletons.
According to recent market research by WinterGreen Research, the market for rehabilitation robots, active prostheses and exoskeletons is already worth $43 million with projected reach of $1.8 billion by 2020.
However, most people think of exosuits as standalone military machines that give superhuman abilities to anyone who wear it. For instance, Iron Man is the most obvious example of a person wearing exosuit.
However, coming back to reality, the United States Department of Defense works on the innovative project Tactical Light Operator Suit (TALOS) that aims to develop a soft, low-powered exosuit that will augment the physical capabilities of soldiers. This suit will allow the soldiers carry 100-plus pounds of equipment or 17 times more weight than a regular soldier without risking the joint and back injuries. These technologies will exceed the physical abilities of all troops.
TALOS project is a collaborative work involving 56 corporations, 16 government agencies, 13 universities, and 10 national laboratories. Below you can watch a video describing TALOS project.
Albeit, this statement (exosuits are the standalone military machines) is a half-truth, application of this technology is not limited to the only military.
Many companies like Panasonic, Kene Wearable Bionics or Cyberdyne strive to develop wearable robotic suits that will help people move again, or reduce strain on workers who are involved in physical labour for prolonged hours.
For instance, Cyberdyne created the Hybrid Assistive Limb (HAL) – the world’s first cyborg-type robot that enables person move again or reduce common strain on joints and back.
When a person wants to move a leg or an arm, then the brain sends signals through the spinal cord and the nerves that surround it. So, when the person is paralyzed, these spinal nerve structures are damaged, and the signals are too weak to reach the leg or the arm.
HAL functions by picking up these weakened brain signals through sensors that are attached to a person’s skin.
HAL could be applied to many fields such as welfare, medical, industrial or disaster sites. You can watch a video below describing HAL at work.
Another applicability of wearable robotic suits could be in the industrial sector. Panasonic is one of the companies that aim to integrate exosuits into every aspect of our lives.
In their video, they highlighted two exosuits – the Assist Suit and the Power Loader—that according to their vision would improve productivity and change the entire industry. You can watch their video below and share if you’d like to.
Apparently, such technology encounters lots of challenges. For instance, the first generation of the HAL suit weighed around 30 kilograms and required two persons to set up the suit while the last generation weighs only 10 kilos.
You may have guessed that weight and mobility of the exosuits are the crucial factors to consider because they’re supposed to give you wings to ease your strain on joints.
However, in order to make the exosuits lighter, the engineers should first reduce a size of the suit’s parts.
The biggest challenge in reducing size and weight of the parts is the battery life. Analogically to all modern devices, the battery is the biggest headache for all engineers because it can only last for a couple of hours.
Therefore, the engineers should find a way to invent a limitless power source, or, at least, a miniature battery that will last for a prolonged time.
Even though we’re far away from seeing the Iron Man suits on the streets – we should consider this fact that wearable robotics are the fast growing industry that’s already finding its use in medicine, manufacturing and the military.
by MYLE Team | Mar 4, 2016 | MYLE, Other, Technologies, Wearable Tech |
Evernote is an app for your smartphone, tablet or computer. It’s been created to boost your productivity, ameliorate your life, and make it easier to keep organized your notes, ideas, thoughts and to-dos.
However, how can you make the most effective productivity system out there more productive? Well, why not to think about MYLE wearable personal assistant?
MYLE is not a substitute to Evernote or any other apps. Rather, through instantly capturing your voice notes in the simplest and most natural way – by voice – MYLE simplifies and streamlines saving of your thoughts and notes into your existing systems that you use and already accustomed to. Like Evernote. Moreover, you don’t need to have your mobile devices in hand all the time.
Here are the three ways you can master your life with Evernote and MYLE.
Record Notes.
You love the smell of real books and regularly taking down your favourite quotes and unfamiliar words into your Evernote account. You have specified a folder for quotes, thoughts, or ideas from the books, and appropriate tags for non-fiction, fiction, classics, sci-fi books, or other genres.
However, how distractive is this process becoming when you should continuously reaching for the phone, unlocking it, logging into your Evernote account, typing quotes and tags to send them to the folders.
Apparently, when you read a lovely novel or scientific research, this distraction takes away your attention and sense of the story or information, so instead of reading the book you’re spending half of the time on transferring the highlights into your account.
MYLE cuts this distractive process to five seconds. You only need to say your quote and this intelligent device will do the rest for you. It will convert your words into text and audio notes (if you prefer audio notes over text), understand whether a word is a tag or not, categorize them by groups, and then send to the proper folder.
There is no more need to type everything manually. This feature is especially handy for those who love taking hundreds of notes either to share them on their favourite social media accounts, or to use it while doing research for a university project.
Remind yourself of your forgetfulness.
Friedrich Nietzsche, a German philosopher, once said,
“The advantage of a bad memory is that one enjoys several times the same good things for the first time,” Friedrich Nietzsche.
There’s no doubt it’s a great feeling to enjoy the same moment several times in a row.
However, forgetfulness can also lead to bad outcomes too. For instance, you can forget about important project deadlines, the massive to-do list that is due by the end of the week, client meetings and other important things you’re trying to keep in mind.
Unfortunately, humans tend to forget more than half of the information they receive due to lack of attention, poor memory, or external distractions. Therefore, Evernote may become an excellent solution for someone who’s looking to keep their lives more organized.
Whilst, using MYLE with your Evernote account you can quickly set reminders for the weeks ahead. It will automatically send reminders to your account, set a type of the reminder whether it’s email, in-app notification or a badge on the app title.
Meal Planning.
Another excellent way to master your life with Evernote and MYLE is to have a meal planning. For those of you who’re not familiar with this cash-time-saving tip, meal planning is whatever way you’re going to cook a particular meal on a specific day of a week or even a month.
This helps you organize a list of necessary ingredients and recipes, a budget for the coming week and a shopping list to spend time more efficiently in the grocery store.
It’s especially handy when you’re out doing the grocery shopping. You don’t feel anxious and panicky in the store because your shopping list is ready and you already know how much you’re going to spend, and more importantly why you need those products.
So, Evernote can be an excellent storage for all your meal ideas, shopping lists, and recipes. MYLE in integration with Evernote, can automatically save your recipes in your account, tag the notes with, let’s say “#mygrocerylist,” add meals to the calendar and sort everything for quick access. The only thing you need to do with MYLE is to tap and say what’s your meal on that particular day.
MYLE has one more distinctive feature. MYLE learns your behaviour to improve its suggestions to you.
For example, through analyzing your previous purchase history, such as your most visited grocery stores, the amount of money you regularly spend, your previous and current shopping lists, your weekly grocery budget and other relevant metrics, MYLE can suggest you products and special deals to stick to your weekly budget.
This makes your life more productive and efficient.
by Pavel Bondarev | Feb 10, 2016 | Other, Technologies, Uncategorized, Wearable Tech |
As you probably remember, not so long time ago I wrote about Pebble – why I believe it is the best smartwatch on the market, and why its simplicity and intuitivity, as well as lack of over-complicated gizmos, are what users really need.
So now I’d like to reiterate my belief and to thank Pebble team for their amazing product and the service they provide.
It looks like that pebbloids (or how the Pebble guys call themselves) keep a close eye on the market and search for the ways to deliver better product and improve services for their customers. I know it for sure, because they somehow found my post where I briefly mentioned the troubles with my Pebble, found and connected with me, and exchanged my old watch for a new one.
Just… Wow!
The entire process took a couple of days to communicate and work out the details, and a few days later I got a parcel with my new Pebble Classic. And you know, I liked the simplicity of the product replacement service – all I had to do was to take a photo of the Pebble watch back side with a handwritten number I received from their support. That’s it! The picture was sent to their Support team, and the next day I found a confirmation of the new watch shipping.
As you know, the crisis affected almost all the markets, including the hi-tech industry, and made companies pay close attention to the quality of services provided. Until quite recently, Zappos were traditionally on the top in terms of the service quality, and now the newcomers overtake it and grab the leading positions. Based on what I saw Pebble is one of such new leaders. This is really great – the more competition and the better services for us customers, the happier we are! 😉
I was lucky to meet Eric Migicovsky at CES 2016, and used the opportunity to express my personal gratitude to him for that amazing devices he made.
Hope that Pebble will avoid the transformation when a cool young and energetic startup turns into a small dinosaur with all the problems common for big corporations. I believe in the guys and think they will continue developing cool products and deliver their amazing services for us. We’re looking forward folks to see your new offerings!
by MYLE Team | Feb 8, 2016 | Artificial Intelligence, Other, Technologies |
The team from Massachusetts Institute of Technology (MIT) has built an energy-friendly chip that performs powerful artificial intelligence (AI) tasks, which is specially designed to implement neural networks.
According to MIT researchers this chip is 10 times as efficient as a mobile GPU (Graphics Processing Unit), that is, it could enable mobile devices to run powerful AI algorithms locally, rather than uploading data to the Internet for processing.
Vivienne Sze, the Emanuel E. Landsman Career Development Assistant Professor in MIT’s Department of Electrical Engineering and Computer Science whose group developed the new chip, says that currently most of the networks are pretty complicated due to their high-power GPUs.
Sze states the reason why it’s better for devices to operate locally rather than via the Internet: your cell phone still powerfully operates even if you don’t have a WI-Fi connection nearby required to process large amount of information. You have much better privacy of your information, and another reason is the avoidance of any transmission latency, so that you can react much faster to certain applications.
MIT researches have presented the new chip at the “International Solid State Circuits Conference” in San Francisco.
The new chip is called “Eyeriss,” and according to MIT – “its key to efficiency is to minimize the frequency with which cores need to exchange data with distant memory banks, an operation that consumes a good deal of time and energy.”
Moreover, many of the cores in a GPU share a single, large memory bank while each of the Eyeris’s cores has its own memory. Thus, the chip is capable of compressing data before sending it to individual cores.
The Eyeriss chip has 168 cores, which could communicate directly with each other, so that if they need to share data between each other, they don’t have to route it through main memory.
“This is essential in a convolutional neural network, in which so many nodes are processing the same data.”
Neural nets, also known under the name “deep learning,” were widely studied in the early days of AI, however, by the 1970s, the researchers had thrown this field out of favour.
Sze says the deep learning is useful for many applications, such as speech and object recognition, and face detection.
by MYLE Team | Jan 20, 2016 | MYLE, Other, Wearable Tech |
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.
by MYLE Team | Jan 14, 2016 | Artificial Intelligence, MYLE, Other, Technologies |
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:
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Representation: how to represent knowledge.
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Evaluation: the way to evaluate candidate programs (hypotheses).
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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.
