Designing an intuitive dashboard for IoT Products

Smart homes are soon becoming a more acceptable norm of society now with devices such as Amazon Echo, Google home, Alexa etc. But it is not just the technology that makes these products a massive hit among customers. A lot of thought has gone into designing such user-friendly IoT products.

As a product engineer or a UX designer who is working on an IoT project, your main focus is centered on creating and designing a product that brings immense value addition to your client.

One of the most crucial designs in IoT products involves the look and feel of an IoT dashboard which serves as the touch point for the user to interact with the device.

In this blog post, we are going to talk about the 4 steps involved in designing an intuitive dashboard for IoT products.

1. Break down the User Journey:

Take this a thumb rule – before you even get to the stage of picking up your pencil and sketch pad, spend some time to research and understand what kind of IoT product are you designing for?

It could be anything ranging from a smart system that controls the lighting of the house to a smart refrigerator that sends updates on the grocery list or a super efficient home locking system.

For better understanding, let us take the example of designing an IoT dashboard for a smart refrigerator that allows the user to track the details of grocery items and to set up reminders in the user’s mobile to stock up items.

Now, before we get down to designing this dashboard, as a product designer you first need to understand the underlying technology that will be used by the refrigerator such as, what kind of sensors would be triggered, what kind of data will be recorded, how will the data be analyzed etc.

Your job is to represent this underlying data in an intuitive manner. This is where your creative mind kicks in and is expected to think about the user journey and different use case scenarios. Also, while designing it is very necessary to be mindful about the target audience who is bound to use the product.

In the above example, the smart fridge is going to be used by a household which would include:

• a family of 4-5 members
• the users could be aged between 24 to 55 years
• the user could be either male or female
• pet-friendly house

Now based on the above user scenarios, you will be able to sketch out different user journeys.

2. Create the Skeleton with Wireframes

This is the fun part because here is where you actually visualize each and every action of the user. Some people call the wireframe stage as setting up the blueprint of the design and functionality of the product.

Now, when you are designing the dashboard of an IoT product, it is necessary that you visualize each and every step of the user and jot them down into squares, circles, and triangles. The key here is attention to detail. Step into the shoes of the user and think of the ways he/she will use the product on a real-time basis. Document scenarios that are not so obvious but have a probability to pop up.

In this stage, you also get to logically test the flow of user actions. Yes, ‘Logic’ is the word here. Don’t just go by your gut or instinct, you need to think if the steps in your wireframe make logical sense in the bigger picture. Sketch out as many possible scenarios here. Do not restrict your mind to – ‘this is it’! Explore different options and also be conscious to capture different user emotions while sketching the wireframes.

Don’t just put a button because it has to be there. Think of how you expect the user to navigate to a particular page such as – to check the grocery stock, to get alerts on food items that are running low on storage, setting timers for bakery products etc.

Once you have a gamut of ideas, sit down and evaluate the best possible user flows to create an unparalleled user experience (UX). This is the most important part of the whole designing process and so it is imperative that you get it right. The UX is the make or break deal for the success of your IoT product.

3. Bring your Product to life with Visual Designs

For all those creative artists out there, this is the stage where you need to find the Picasso inside you and splash those colors into the wireframes. But don’t just add any color – keep in mind factors such as color psychology and branding guidelines (if any) while choosing the colors.

Let us go back to our example of the refrigerator dashboard and see the kind of colors we could use.

It would be advisable to use a lot of white space in the design to give it a neat and easy user interface (UI). Also, depending on the USP of the overall IoT product, either blue or green could be a great choice of color for a dashboard.

If the highlight is more on the technology, then blue would be an ideal choice as it showcases intelligence and is a color that is most commonly used with electronics. But let’s say if the USP of the IoT product is to highlight health or any environmental benefits, then green would be an excellent choice to complement the messaging. Also, colors such as red, yellow and green can be used to indicate any functionality features to the user.

So, in a nutshell, while you are creating the visual designs keep the end user in mind and choose colors that are able to communicate instantly with the user.

4. Test your Designs – Time to Prototype:

This is the last and final stage of the design process where your designs are put to the ultimate test. With the help of many online tools such as Adobe XD or Invision, you can test your designs in real time and showcase the functionality to your clients.

This real-time feedback helps you to iterate designs on-the-go and incorporate all feedback at one point. It is important that all designers prototype their designs since quick and responsive designs translate into happy clients.

Thus, designing for IoT products can be slightly different from creating mobile apps and websites as User Experience is the key here. But with the above-guided approach, you can be assured of designing a vibrant and intuitive dashboard for IoT products

Artificial Intelligence and Machine Learning in the Retail Industry

While the world is busy talking about Artificial Intelligence powered technologies such as self-driven cars, machines challenging human intelligence at a game of chess, and AI technologies in recruitment, there still remains an untapped potential for AI in the retail industry.

With most retailers now focusing towards providing an omnichannel experience for their shoppers, AI can play a crucial role in disrupting the retail industry.

Creating Smart Shops with Artificial Intelligence

While AI assistants such as Siri and Google Home help us maintain our day-to-day groceries list and change our shopping experience, an ideal situation would be to walk into a smart store without any shop attendees or long check-out queues.

Imagine a shopping experience where you can just enter a shop, pick up the stuff you want, a bunch of facial recognition algorithms process your purchase, and automatically money gets deducted from your digital wallet when you leave the store! Suddenly, shopping seems to become more of technology experience.

Amazon has been marginally successful in replicating this experience for customers with its Amazon Go grocery store. In order for shoppers to use this, they need to install the Amazon Go app. There are digital sensors on the shelves that detect when purchases are made and the money gets deducted from the customer’s account when he or she exits the shop.

Amazing isn’t it? This is what a future-ready AI store should ideally look like!

Predicting online customer behavior with Machine Learning

We live in a digital world today where every minute truckload of data about customer behavior is recorded and stored. But most companies struggle in extrapolating this data into actionable steps that can increase their ROI by 10X times.

You may have luxurious marketing and advertising budgets to target your customers, to get awesome click-through rates, but how are you making your customers tip over the fence and make a sale?

With Machine Learning tools, you can create an intelligent and automated marketing system that helps you with:

• customer segmentation
• predicting customer value and
• designing product recommendations.

But it doesn’t end with just that. Machine learning also helps us optimize our Ad budgets and invest them in the right customers.

In a world where resources and time are always limited, we are forced to make quick and smart decisions. But machine learning algorithms are programmed to analyze this huge pool of customer data within a fraction of seconds and identify customers who are 65% more likely to make a purchase. Thus, in this way, ML helps to justify and set optimum ad bids to target customers.

Product recommendation is another favorite pick for top marketers. ML helps to analyze online customer behavior in depth, such as the products they are interested in, the blogs they read, the price ranges they operate at etc. Based on all these interactions, a business can invest in curating content that makes the shopping experience as “personal” as possible.

Artificial Intelligence and Cognitive Computing in the Retail industry:

AI and cognitive computing are adding the “innovation” to the retail industry. Creating an omnichannel experience for customers has become the top most priority for retail brands.

Out of the many retail sections, in this blog post we are going to concentrate mainly on two AI integrated retail categories:

1) Product Recommendations:

IBM’s Watson, powered with its cognitive computing abilities is an excellent choice for brands who are looking to provide both in-store and online product recommendations.

• AI-powered retail store

In this example, we see how a New York based wine company called Wine4Me decided to choose the AI technology of IBM Watson to make in-store wine recommendations for its customers. The goal was to make wine shopping an easy and personalized experience.

But the first step was to provide Watson with ample data from wine tasters and also teach Watson how people ask and shop for wine. Based on the occasion, price, sweetness,  brand, and age of the wine, Watson should be able to bring up a list of wine recommendations that would suit the customer’s demand.

Now, this whole AI-powered shopping technology works as a win-win for both the retailer and the customer. Customers, on one hand, enjoy a hassle-free and engaging shopping experience while retailers can make better inventory decisions by tracking customer preferences.

• AI-powered digital store

In this example, we will talk about how the San-Francisco based premium brand The North Face integrated Watson on its digital platform to create an unparalleled shopping experience for its customers. The North Face is a premium outdoor product company specializing in outerwear, coats, backpacks etc

Though SEO filters and keyword phrases help in attracting customers to the website, most brands forget to communicate with their customers to help them find what they want. This is where AI can help you establish that user connect.

Let us assume that a user wants to buy a jacket online, then the AI platform asks the user a series of questions such as the purpose of the jacket, the time period during which he will use it, which place will be visiting, color preferences, material preferences and so on. The user is free to answer all these questions in his own style and type in any response.

With every response, the AI will trigger a series of product recommendations that closely match the user’s requirement. Now, this sounds fancy, doesn’t it?

But it all boils down to data and also literally teaching the AI what would the possible use case scenarios be that users would identify themselves with. Even the most non-obvious statements and taken for granted business scenarios need to be keyed in so that the AI helps to generate the right response.

This next-generation online shopping experience is sure to make both customers and retailers conscious of their expectations. Thus, if used in the right way, AI has great potential to provide product recommendations that can increase revenue from sales by 10x.

2. In-store Sales:

AI is all set to change the traditional shopping experience when a customer walks into the store. With e-commerce booming, you find lesser customers who are willing to enter a store and interact with a store assistant.

So, how can AI help in increasing the customer foot-fall at a brick and mortar retail store? The answer is simple – by using AI-powered robots.

Pepper,  a humanoid robot from Softbank’s robotics company has been in the news for enriching the customer shopping experiences at retail stores and serving as a retail concierge. Stores across the US, who have adopted Pepper as their smart shop assistant, have recorded almost 70% increase in customer walk-ins and sales.

Now that’s huge, isn’t it?

But what does this humanoid Pepper do differently from a salesman?

Firstly, Pepper acts as a brand ambassador for the store that drives in customers. Based on sheer curiosity, customers are more likely to stop by your store to experience an interaction with a robot who could help them with their shopping decisions. It almost feels like having a friendly celebrity with whom you can take a tour of the store.

Secondly, the humanoid robot is programmed to sense movement, emotion, offer shopping advice, product information, etc. Thus, it helps the user make an informed purchase. It has a tablet positioned on its chest where the user can key in his preferences and based on that Pepper will offer suggestions.

This definitely boosts user engagement and conversations inside a store. And the best part is that Pepper is always connected to the internet and all the data thus collected is stored in the cloud. So, next time when the same customer enters the shop, it becomes easy to showcase products based on his/her interests.

Thus, Pepper helps to automate all the obvious and mundane tasks at a retail store and transform in-store shopping into an engaging and joyful experience for customers.

shopping experience while retailers can make better inventory decisions by tracking customer preferences.
add: The rise of retail AI solutions is further transforming the industry by enabling real-time analytics, personalized recommendations, and demand forecasting, leading to increased efficiency and customer satisfaction

The future of Artificial Intelligence in Retail

While we are positive and hopeful that the future of retail is likely to be dictated by AI, we also notice that there is still time until this technology gets widely accepted in stores around the world.

One of the major constraints involves high budgets, thus making it easy only for the bigger players such as Amazon, Walmart, Target etc to become early adopters of such technologies.

But having said that, it is time that retailers identify the potential that AI and Machine Learning can make to their business and take steps to make the shift soon.

If you would like to build an AI-powered solution for your retail business, then drop us a short message with your requirements

The Architecture Of Data

Data is a critical aspect of every single business. Handling it becomes even more critical. Unless you have set protocols to handle and assimilate your data to be utilized wisely, your business can suffer in the long run. A stringent architecture of your data platform can save you a lot of future hassles.

Today, we try to understand the basic setup of such data platforms.

The main components of a data management platform are as below:

The Data Collection Layer

The data collection layer is divided into 2 parts:

Client-side – the part is responsible for collecting the data and sending it to the server-side data collector. There are a number of ways this could be done, for example with a JavaScript tracker, an SDK, or other libraries.

A JavaScript tracker and impression pixel may also set off piggyback pixels to sync cookies with third-party platforms.

Server-side – provides the endpoints responsible for:

• Receiving the data from the client-side libraries – typically, very lightweight and just used for logging the data or pushing them to the queue(s) for the next layer to process.
• Syncing cookies with third-party platforms and building cookie matching tables that are used later during the audience export stage (see below).

The Data Normalization and Enrichment Layer

Once the data has been captured from the data collection endpoint, the DMP normalizes and enriches the data.

The data normalization and enrichment process can include a number of the following actions:

• Deleting redundant or useless data.
• Transforming the source’s data schema to the DMP’s data schema.
• Enriching the data with additional data points, such as geolocation and OS/browser attributes.

The Data Storage, Merging, and Profile Building Layer

The next step is to store and merge the newly collected data with existing data and create user profiles.

Profile building is an essential part of the whole data-collection process, as it is responsible for transforming the collected data into events and profiles, which are the cornerstones of audience segmentation (the next stage).

A user profile could contain several identifiers, such as cookies or device identifiers, as well as persistent identifiers that are pseudo-anonymized – e.g. hashed usernames or email addresses.

Another important part of the profile-building stage is the matching of data sets using common identifiers — e.g. matching an email address from a CRM system with an email address from a marketing-automation platform.

A profile consists of user attributes (e.g. home location, age group, gender, etc.) as well as events (e.g. page view, form filled in, transaction, etc.). The latter is typically a separate collection or table in the database.

The Data Analysis and Segmentation Layer

The core functionality of a DMP is analyzing the data and creating segments (e.g. audiences).

An audience segment is useful to advertisers and marketers (and publishers) because it allows them to cut through the mass of data available to them and break it down into digestible pieces of information about potential customers, site visitors or app users.

With good audience segmentation, advertisers can buy display ads targeted at a group of Internet users and publishers can analyze site visitors and then sell inventory at a higher price to media buyers whose target segments match the publisher’s.

Audience Export

Audience export is a component that periodically exports segments to third-party platforms, for example demand-side platforms (DSPs), in order to allow advertisers to use them in campaign targeting.

User Interface

This is pretty self-explanatory – you need to give the users a UI to create segments, configure data sources, analyze and visualize the data, as well as provide them with the ability to configure the audience exports to third-party platforms.

Application Programming Interfaces (APIs)

APIs can be divided into the following categories:

• Platform API used to create, modify, and delete objects such as users, segments etc. – basically for whatever task the user is able to do via the UI in the platform.
• Reporting API used to run reports on the data. Due to the sheer amount of data, some of the reports may need to be scheduled for offline processing and made available for download once generated.
• Audience API that allows client libraries to query in real-time whether a given visitor belongs to the audience or not.
• Data ingestion API used for importing the segments or other data from third-party platforms. Again, as the data volume may be large, this can happen through an Amazon S3 bucket or file upload that is queued by your DMP for offline processing.

This, of course this a simplified example and the actual components and architecture may get more complex as you add additional features and integrations.

Is It Really Smart To Live In A Small Home

Always thought of building a smart home for yourself?

Always thought of living like a technological king?

Is this the right time or is it really smart to live in a smart home?

Ask yourself these 5 questions to know better.

Question 1: Do you even need a Smart Home?

Technically, no one needs a Smart Home any more than anyone needs a Casper mattress. But enough of your inner circle has talked about it that you’ve developed an itch. That itch, my friend, is the want for something ,  not a need. If you’re okay with admitting this nuance, then follow along.

Question 2: What problems are you trying to solve?

As quoted by a user on Mashable:

“When I started thinking about what I wanted a Smart Home for, I had some very specific pain points:

• My daughter couldn’t reach the light switch in the hallway that led to her bedroom.
• When my daughter was in her bedroom, she couldn’t reach the light switches there, either.
• I didn’t have an alarm system but having one would make me feel more comfortable; preferably one that didn’t require a subscription or phone line.
• Anytime we hired a doggy sitter, there was this dance of “How do we get you the key” and “How do we get the key back.” Ideally, no key is necessary — I have a smart lock that has personalized codes, or at the very least, I can control the lock remotely.

There are many other things I wanted, but those are the top 4. Lights turning off automatically, speakers announcing that a door has opened — those were just niceties that came expanding the system. “

Question 3: Do you have the money?

If you don’t have disposable income, stay away, because you really don’t need a Smart Home.

But for this post, let’s pretend that you do, but you’re still budget conscientious.

Consider this:

If you wanted a basic system that would turn on lights based on motion, you’d need:

• A motion sensor that doesn’t require a hub($40+)
• A bulb that doesn’t require a hub ( $30+)

= That’s $70 (again, this assumes you can use WiFi and some service like IFTTT that can get the 2 products to communicate with each other)

But then you realize that your WiFi isn’t sufficient/reliable, so now you have to purchase a Hub.

• A Hub (~$50+)

That makes it $120

And if that’s not enough, you just remember that there are 4 light bulbs in that room/hall that need to be Smart, not just 1. You’re clever though — you realize a switch is the cost effecting thing to do here.

Well, do you need an electrician or can you install it yourself? Are there multiple panels that turn the lights on and off? If so, you may need multiple smart switches. Oh, you want the bulbs to be able to change colors? Well, back to the drawing board!

Question 4: Are you in your forever home?

Things to consider:

Compatibility

You may not want a system that only “Works with Apple HomeKit”. If you land a buyer that is an Android user, the smart home becomes less…smart.

Switches over Bulbs

Bulbs will eventually burn out and the buyer may not want to be stuck purchasing these over and over again. A smart switch may be your best bet here. Just make sure that it’s using something that’s open like Z-Wave or Zigbee (Note: one technology is more open than the other.)

Smart Locks

When a person moves in, they’re likely going to want to change the lock. So, consider if you want a smart lock that actually supports keys (some don’t). And if you do, see how easy it is for the lock itself to invalidate your key and support new ones.

Risk

All the technologies above will eventually become outdated. Either because the technologies themselves have continued to improve (Z-Wave vs Z-Wave Plus vs Z-Wave v3), or in the worst case scenario, the technology itself has become obsolete.

Question 5: Do you care for your roommates?

We’re telling you now. Whether your roommate is a friend, a dog, or a spouse, you’re going to do something that’s going to annoy them. Whether it be the WiFi going down as you’re tuning frequencies, or the light is waking people up that just want to cross the hall to pee.

Artificial Recruitment

In its most basic form, Artificial Intelligence is a computer system designed to learn, make decisions and carry out tasks that would normally require human intervention.

Speech recognition software, self-driving cars, chatbots that talk to the public and manufacturing robots all rely on Artificial Intelligence.

In the future, perhaps we can look forward to robot butlers that can cook and clean, police that patrol the streets 24–7, robotic friends and if you believe Tesla boss Elon Musk, an eventual Terminator-style apocalypse where the machines become self-aware and decide to wipe us out…

You’ve also probably read the headlines about artificial intelligence and how “robots are going to take all of our jobs” one day. Administrators, production line workers, customer service reps, professional drivers and perhaps even surgeons are all set to be casualties of the machine learning age.

You need that gut instinct and judgement to know whether someone will actually fit into the team.

And we all know hard skills aren’t everything. We also need to assess cultural fit, personality and soft skills like confidence and emotional intelligence. Machines can’t do that.

Plus, if you were a job-seeker, you’d not  like to get interviewed and hired by a machine…

What AI can and will do, however, is help us track down great candidates, faster!

We’re not quite there yet, but that is the future of AI-powered recruitment.

Here’s what AI will be able to do.

• Scour the internet to find great candidates.
• Make contact with them.
• Conduct first stage interviews. (Automated video interviewing already exists.)
• Help eliminate bias from the process.
• Standardise interviews and CV assessment.

Obviously, you will still have to make a final decision on the right hire for you… but if artificial intelligence can manage the process up until that point, imagine how much time you could save?!

The good news is that AI is already here in its most basic form.

The systems are clunky, but these are simply beta versions. The AI systems will learn fast, so don’t judge them too harshly right now!

Here are three example.

1. Beamery

Beamery is a candidate relationship management system that uses machine learning to enable proactive recruitment, “build talent pools, power collaboration and drive better decisions with predictive analytics.”

The start-up works with Facebook, among others, and analyses interactions between candidates and employers to identify candidates you should target and helps recruiters to build relationships with them.

2. Mya

You can encourage the right candidates to apply in the first place with the help of Mya, which parent company FirstJob claims will automate approximately 75% of the recruitment process.

It’s a combination of a chatbot at the front end, which effectively answers queries and gives feedback through messenger apps like Facebook, and an AI-powered search at the backend.

That search tool can eliminate irrelevant resumes and help find the needle in the haystack that is the perfect candidate.

If there’s information missing, the chatbot can get in touch and ask the right questions, thanks to Natural Language Processing, and plug the gaps in a resume that you might have rejected previously.

If Mya gets stuck, it refers the question to your HR department, but it can save you a huge amount of time and potentially rescue an application that simply did not cover all the bases.

It also learns, based on its past conversations, so the system will get better with time.

3. ThisWay Global

ThisWay “does in seconds, what it takes an HR professional 40 hours to do” apparently.

It’ll track down the most skilled candidates for your business, whilst removing any unconscious (or conscious) bias from the process, altogether.

And it also takes into consideration personality, culture, goals and motivations.

Must Use Swift UI Libraries

Developed by Apple Inc, Swift is currently the most popular programming language on Github and it has one of the most active communities that kindly contribute their open source projects.

Open source libraries can be sweet and they can make your life dramatically easier in building your iOS apps. For those iOS folks spending hours and days hunting for good libraries, you may find this post useful.

1. Spring: A library to simplify iOS animations in Swift. [9164 stars on Github].

1. Material: An animation and graphics framework that is used to create beautiful applications [6120 stars on Github].

3. RazzleDazzle: A simple keyframe-based animation framework for iOS, written in Swift. Perfect for scrolling app intros [2291 stars on Github].

4. Stellar: A fantastic Physical animation library for swift [1881 stars on Github].

5. Macaw: Powerful and easy-to-use vector graphics Swift library with SVG support [594 stars on Github].

6. PagingMenuController: Paging view controller with customizable menu in Swift [1305 stars on Github].

7. PreviewTransition: A simple preview gallery controller [1025 stars on Github].

8. YouTube Transition: Watch a video on the right corner like Youtube iOS app, written in Swift 3. [786 stars on Github].

9. Twicket Segmented Control: Custom UISegmentedControl replacement for iOS, written in Swift [680 stars on Github].

10. SCLAlertView-Swift: Beautiful animated Alert View written in Swift [3056 stars on Github].

11. SwiftMessages: Very flexible alert messages written in Swift. [1356 stars on Github].

12. XLActionController: Fully customizable and extensible action sheet controller written in Swift 3 [1346 stars on Github].

13. Popover: Balloon pop up library like Facebook app, written in pure swift. [852 stars on Github].

14. Presentr: Wrapper for custom ViewController presentations [635 stars on Github].

15. FoldingCell: An expanding content cell inspired by folding paper material [4285 stars on Github].
16. ExpandingCollection: A card peek/pop controller [2425 stars on Github].

17. DGElasticPullToRefresh: Elastic pull to refresh component written in Swift [2308 stars on Github].

18. DGElasticPullToRefresh: Elastic pull to refresh component written in Swift [2308 stars on Github].

19. IGListKit: A data-driven UICollectionView framework for building fast and flexible lists — Instagram Engineering. [2443 stars on Github].

20. PullToMakeSoup: Custom animated pull-to-refresh that can be easily added to UIScrollView [1301 stars on Github].

21. DZNEmptyDataSet: Empty State UI Library [6552 stars on Github].

22. Instructions: Create walkthroughs and guided tours in Swift. [2256 stars on Github].

23. Presentation: Make tutorials, release notes and animated pages [1680 stars on Github].

24. Chameleon: Flat Color Framework for Swift Developers [7071 stars on Github].

25. DynamicColor: Extension to manipulate colors easily in Swift [1310 stars on Github].

The Depth Of Deep Learning

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now.

The term “AI” is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don’t understand what AI is.

Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

The first step towards understanding how Deep Learning works is to grasp the differences between important terms.

Artificial Intelligence vs Machine Learning 

Artificial Intelligence is the replication of human intelligence in computers.

When AI research first started, researchers were trying to replicate human intelligence for specific tasks — like playing a game.

They introduced a vast number of rules that the computer needed to respect. The computer had a specific list of possible actions, and made decisions based on those rules.

Machine Learning refers to the ability of a machine to learn using large data sets instead of hard coded rules.

ML allows computers to learn by themselves. This type of learning takes advantage of the processing power of modern computers, which can easily process large data sets.

Supervised learning vs unsupervised learning

Supervised Learning involves using labelled data sets that have inputs and expected outputs.

When you train an AI using supervised learning, you give it an input and tell it the expected output.

If the output generated by the AI is wrong, it will readjust its calculations. This process is done iteratively over the data set, until the AI makes no more mistakes.

An example of supervised learning is a weather-predicting AI. It learns to predict weather using historical data. That training data has inputs (pressure, humidity, wind speed) and outputs (temperature).

Unsupervised Learning is the task of machine learning using data sets with no specified structure.

When you train an AI using unsupervised learning, you let the AI make logical classifications of the data.

An example of unsupervised learning is a behavior-predicting AI for an e-commerce website. It won’t learn by using a labelled data set of inputs and outputs.

Instead, it will create its own classification of the input data. It will tell you which kind of users are most likely to buy different products.

You’re now prepared to understand what Deep Learning is, and how it works.

Deep Learning is a machine learning method. It allows us to train an AI to predict outputs, given a set of inputs. Both supervised and unsupervised learning can be used to train the AI.

We will learn how deep learning works by building an hypothetical airplane ticket price estimation service. We will train it using a supervised learning method.

How Deep Learning can build an AI  to estimate Airplane ticket prices

We want our airplane ticket price estimator to predict the price using the following inputs (we are excluding return tickets for simplicity):

• Origin Airport
• Destination Airport
• Departure Date
• Airline

Like animals, our estimator AI’s brain has neurons. They are represented by circles. These neurons are interconnected.

The neurons are grouped into three different types of layers:

1. Input Layer
2. Hidden Layer(s)
3. Output Layer

The input layer receives input data. In our case, we have four neurons in the input layer: Origin Airport, Destination Airport, Departure Date, and Airline. The input layer passes the inputs to the first hidden layer.

The hidden layers perform mathematical computations on our inputs. One of the challenges in creating neural networks is deciding the number of hidden layers, as well as the number of neurons for each layer.

The “Deep” in Deep Learning refers to having more than one hidden layer.

The output layer returns the output data. In our case, it gives us the price prediction.

So how does it compute the price prediction?

This is where the magic of Deep Learning begins.

Each connection between neurons is associated with a weight. This weight indicates the importance of the input value. The initial weights are set randomly.

When predicting the price of an airplane ticket, the departure date is one of the heavier factors. Hence, the departure date neuron connections will have a big weight.

Each neuron has an Activation Function. These functions are hard to understand without mathematical reasoning.

Simply put, one of its purposes is to “standardize” the output from the neuron.

Once a set of input data has passed through all the layers of the neural network, it returns the output data through the output layer.

Nothing complicated, right?

Training the Neural Network

Training the AI is the hardest part of Deep Learning. Why?

1. You need a large data set.
2. You need a large amount of computational power.

For our airplane ticket price estimator, we need to find historical data of ticket prices. And due to the large amount of possible airports and departure date combinations, we need a very large list of ticket prices.

To train the AI, we need to give it the inputs from our data set, and compare its outputs with the outputs from the data set. Since the AI is still untrained, its outputs will be wrong.

Once we go through the whole data set, we can create a function that shows us how wrong the AI’s outputs were from the real outputs. This function is called the Cost Function.

Ideally, we want our cost function to be zero. That’s when our AI’s outputs are the same as the data set outputs. 

Thus at this juncture, with the help of deep learning we have almost trained the AI to project an output that is in line with the input data. This ladies and gentleman to be honest is just the basics, but deep learning along with precision is used to train the AI for supervised learning.

We hope that at this juncture you have some idea of how the different elements of Machine Learning, Deep Learning and Neuron networks all come together to create Artificial Intelligence.

Stay tuned for more interesting facts on AI and Machine Learning!

The Best Of AR Gaming

Augmented Reality has taken over the world already and in the coming days is slated to be even bigger and better. Be it gaming, eCommerce or real estate, AR is going to be the big game changer.

We believe we should start experiencing it from now itself and hence we chose to bring you the best 4 Augmented Reality games in the market now.

Go ahead download them and start playing.

1. Temple Treasure Hunt

This is a  geolocation based augmented reality game for mystery and myth lovers. You can play outdoors or in the room. While playing this game you can choose a role: treasure protector or treasure hunter. As a treasure protector, you have to create treasure trails and as a treasure hunter, you’ll have to discover the treasure. Indian mythological characters come up as treasure guardians. The game uses the real map of the location.

2. WallaMe

This augmented reality game combines geolocation features of AR technology with fun social quests where people can leave hidden messages to one another. Users take a picture of a real physical place (a street, a wall, a shor, etc.) and then add texts, pics or hand-drawn sketches over it. After that they can share it with friends or anyone so that friends may come by and discover those hidden messages.

What’s also great is that there’s no ads or in-app purchases. It is totally free to enjoy! Messages can be private or public. On the other hand, only users of the AR app can view the messages. Although on the whole, a nice and fun game to try out.

3. Pokemon Go

This is the legendary project by Niantic which brought augmented reality games into the crowds. The nostalgy of the players enabled Pokemon GO to hold several Guinness records and gain incredible earnings. It also got to the list of top earning games of the last year.

Pokemon GO is a geolocation game, which places the battlefield on the real surroundings. You catch and train Pokemon, and then fight other players and their “pets”. Although we are quite sure you already know the gameplay and can play this AR game with closed eyes.

4. Ingress

Another hit by Niantic studio released way back in 2012 and still popular among gamers. The credit goes to captivating plot. Scientists have discovered the dark energy, which can influence the way we think. There are two factions. The Enlightened want to use this energy to control humanity, the Resistance aims to protect the mankind. You should choose the side, discover and capture the energy sources, which are located in your city. Which faction wins? It depends on you!

5. Parallel mafia

Have you ever dreamed of being at the heart of the world of criminal rules? If your answer is Yes, Parallel Mafia by PerBlue offers you this opportunity. With this augmented reality game, you can become a real boss of your proper criminal clan. Parallel mafia also has a big choice of entertainment at your disposal. You can create your business, build fronts or earn the reputation. In any case, you’ll be surprised!

And Along Came Android

Android is a software system package and UNIX operating system based mostly OS for mobile devices like pill computers and smartphones.

The goal of the project is to form a self-made real-world product that improves the mobile expertise for users.

What is Open Handset Alliance (OHA)?

It’s a consortium of 84 companies such as google, samsung, AKM, synaptics, KDDI, Garmin, Teleca, Ebay, Intel etc.

It was established on 5th November, 2007, led by Google. It is committed to advance open standards, provide services and deploy handsets using the Android Platform.

Why Is Android  So Popular?

• It is open-source.
• Anyone can customize the Android Platform.
• There are a lot of mobile applications that can be chosen by the consumer.
• It provides many interesting features like weather details, opening screen, live RSS (Really Simple Syndication) feeds etc.
• It provides support for messaging services(SMS and MMS), web browser, storage (SQLite), connectivity (GSM, CDMA, BlueTooth, Wi-Fi etc.), media, handset layout etc.

Now that we know and understand a bit about Android, we would love to showcase a few amazing Android features you might have not probably tried.

1. Use your smartphone camera’s ability to detect infrared light to determine if your remote control’s batteries are dead.

“Our eyes can’t see it, but digital cameras surely can. A smartphone’s camera is indeed sensitive to IR radiation, and if you want to try it for yourself, just use a common IR remote control. The infrared beam emitted when a button is pressed will show as white or purple light in the viewfinder of your camera app. You can use this trick to check if a remote control’s batteries are dead when it stops working.”

2. Field Mode: *3001#12345#*

The USSD protocol allows you to access hidden features you didn’t know about right from your smartphone’s dialer. But there is some trickiness you’ll need to know about.

Type *3001#12345#* into your phone’s dialer and then press the green call button to access “Field Mode,” which can give you access to info about local networks and cell towers.

You’ll probably never ever have to know about your local cell tower’s “Measured RSSi,” but it’s fun to look around for a bit.

3. Force reboot

All of a sudden android devices freeze on few occasions. In case your android phone is frozen, you can reboot it instead of trying out other things and getting irritated by doing so. Just press Power Button+ Home Key + Volume up button simultaneously.

4. Get detailed information about phone status

We can get the detailed statistics related to our device like phone information, battery information, usage statistics and WiFi information by just dialing *#*#4636#*#* This is a handy USSD to get the details about battery usage, DNS check, Ping, Application time, usage time and so on.

5. Android Launchers

We don’t know how many of us actually use different launchers available in the play store. Generally people stick to the launcher provided by the manufacturer, and never realise that this can be modified. The launchers available these days can totally modify the look of the home screen and provide additional functionality to the phone. Just search for launchers in the play store.

Python – The Coder’s Marathon Tool

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido van Rossum during 1985- 1990. Like Perl, Python source code is also available under the GNU General Public License (GPL).

Today, we bring to you 10 advantages that you might have not known about Python.

1. Easy Syntax

Python’s syntax is easy to learn, so both non-programmers and programmers can start programming right away.

2. Readability

Python’s syntax is very clear, so it is easy to understand program code. (Python is often referred to as “executable pseudo-code” because its syntax mostly follows the conventions used by programmers to outline their ideas without the formal verbosity of code in most programming languages; in other words syntax of Python is almost identical to the simplified “pseudo-code” used by many programmers to prototype and describe their solution to other programmers. Thus Python can be used to prototype and test code which is later to be implemented in other programming languages).

3. High-Level Language

Python looks more like a readable, human language than like a low-level language. This gives you the ability to program at a faster rate than a low-level language will allow you.

4. Object oriented programming

Object-oriented programming allows you to create data structures that can be re-used, which reduces the amount of repetitive work that you’ll need to do. Programming languages usually define objects with namespaces, like class or def, and objects can edit themselves by using keyword, like this or self. Most modern programming languages are object-oriented (such as Java, C++, and C#) or have support for OOP features (such as Perl version 5 and later). Additionally object-oriented techniques can be used in the design of almost any non-trivial software and implemented in almost any programming or scripting language. (For example a number of Linux kernel features are “objects” which implement their own encapsulation of behavior and data structive via pointers, specifically pointers to functions, in the C programming language).

Python’s support for object-oriented programming is one of its greatest benefits to new programmers because they will be encountering the same concepts and terminology in their work environment. If you ever decide to switch languages, or use any other for that fact, you’ll have a significant chance that you’ll be working with object-oriented programming.

5. It’s Free

Python is both free and open-source. The Python Software Foundation distributes pre-made binaries that are freely available for use on all major operating systems called CPython. You can get CPython’s source-code, too. Plus, you can modify the source code and distribute as allowed by CPython’s license.

6. Cross-platform

Python runs on all major operating systems like Microsoft Windows, Linux, and Mac OS X.

7. Widely Supported

Python has an active support community with many web sites, mailing lists, and USENET “netnews” groups that attract a large number of knowledgeable and helpful contributes.

8. It’s Safe

Python doesn’t have pointers like other C-based languages, making it much more reliable. Along with that, errors never pass silently unless they’re explicitly silenced. This allows you to see and read why the program crashed and where to correct your error.

9. Batteries Included

Python is famous for being the “batteries are included” language.

There are over 300 standard library modules which contain modules and classes for a wide variety of programming tasks.

10. Extensible

In addition to the standard libraries there are extensive collections of freely available add-on modules, libraries, frameworks, and tool-kits. These generally conform to similar standards and conventions; for example almost all of the database adapters (to talk to almost any client-server RDBMS engine such as MySQL, Postgres, Oracle, etc) conform to the Python DBAPI and thus can mostly be accessed using the same code. So it’s usually easy to modify a Python program to support any database engine.