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.
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!
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.
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.
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.