Why net neutrality is important now

Since its inception in the 1980’s, the Internet has offered unrestricted access to one and all. Irrespective of the content, subject matter, and information, individuals have been able to visit websites easily. It’s right here that a crucial question pops up. When it comes to using the internet and exploring the web, would you like to face restrictions?

The answer is simple, predictable, and obvious. It’s a big ‘NO.’

Content supervision and restrictions are like fatal blows on your freedom. You don’t have the liberty to browse through websites of your choice, and nothing can get worse than that. Net neutrality is important and highly crucial. While Millennials are quite vocal about the issue, traditional users are also putting up logical protests. Here’s all that you should know and understand about Net Neutrality!

Comprehending the term

The term ‘net neutrality’ has been doing the rounds for quite some time now. However, the concept isn’t quite clear to all. The concept of Net Neutrality refers to a principle that ensures complete freedom for internet users. It prohibits internet service providers from slowing down, restricting, and blocking contents. That’s not all; restrictions on certain applications can be implemented too.

If we take a look at the situation abroad, 2015 proved to be a historic year for internet users. Millions of users and numerous activists urged the ‘Federal Communications Commission’ to take a strong stand against internet restrictions. The outcome was impressive as the internet offered open and free access to all. Information sharing and access was also unrestricted and could take place without any interference.

Understanding the implications of net neutrality

Going by the concept and sensitivity of net neutrality, it’s highly imperative to understand its implications for internet users. Net neutrality is a concept that encompasses free will and openness of mass media. Quite naturally, the entire situation is critical and we should be aware of the global implications.

Here’s a quick look at what will happen without net neutrality:

• Restricted access

You won’t have control over the contents or websites. Internet service providers will gain the opportunity to decide the fate of a website. It’s them who get to decide which website wins and which doesn’t.

• Closed-down network

Internet without neutrality will resemble a closed-down network. It’s the internet service providers and telephone companies that will call the shots.

• Discrimination

Marginalized communities and those belonging to LGBT groups and indigenous races will feel neglected. The internet happens to be a weapon as well as an effective platform to voice their opinions. They won’t get the opportunity to organize, assemble, and gather targeted resources.

• Implications on the business arena

Restricted internet access will have devastating impacts on businesses too. They will suffer to a great extent, as startups won’t have the opportunity to establish their presence on the web.

The current situation

Although Net Neutrality continues to prevail, its future doesn’t seem to be secure. It’s living in a vulnerable time where equations can change at the drop of a hat. We have to wait and see what future has in store!

Deep Learning in Marketing and Sales

The buzz and enthusiasm about deep learning has significantly increased over the past few years. With numerous business ventures embracing this technology for good, deep learning is earning huge popularity now. If you are aware of Machine Learning and its implications, understanding Deep Learning won’t be that tough.

Here’s a quick look at what is Deep Learning and its serious applications in the business arena.

What is Deep Learning?

DL or Deep Learning happens to be a significant part of ML. It can be referred to as a subset or subdivision of Machine Learning that maps artificial neural networks. The mapping takes place to recreate or replicate processes performed by human brain.

That’s not all. Deep Learning also plays a crucial part in the identification of speech patterns, algorithms, images, and data analytics.

In spite of this simple and easy-to-understand introduction, there’s no denying the complexities involved in Deep Learning. Implementing DL strategies and incorporating them into existing business processes isn’t an easy affair. It becomes imperative to keep numerous aspects in mind thus devising effective DL strategies.

Transforming the business world with Deep Learning

Deep Learning can be revolutionary. If implemented in the right way, this particular technology can transform business processes to a great extent. Deep Learning helps to decode complex unstructured data and derive consumer insights that are crucial for creating sales and marketing strategies. These insights can significantly enhance the effectiveness of digital marketing services.

From retail and transport to healthcare and manufacturing, DL has started making a mark in various sectors. Let’s take a look at how it’s transforming sales and marketing for businesses:

1. Automating end-to-end customer journey

As mentioned earlier, deep learning will allow marketers to access insights from unstructured data sets such as image, video analytics, speech recognition, facial recognition, text analysis tools and much more. In short, deep learning becomes a way to accurately understand the voice of a customer.

Customer feedback and expectations can be gauged on a real-time basis and business organizations can get information to upgrade their products and services. Based on these premium insights from deep leaning, Brands can articulate the right messaging to the right customer.

2. Understanding analytics from IoT products 

Home automation is creating profitable avenues for organizations across the globe. Deep learning can help businesses understand the analytics for IoT products. It helps to capture data from the machines in different scenarios and monitor them in an easy and cost-effective way.

Through these analytics, deep learning can help to understand the interactions between machines and customers better and based on the data, the performance of IoT products can be enhanced from time to time.

3. Using Chat Bots to enhance CX

The presence of chat bots has revolutionized business marketing to a great extent. Chat bots leverage data mining, artificial intelligence, and natural language processing, thus creating new ways to interact with the end user.

Through chat bots, customers have the opportunity to engage in personalized communications. Apart from ensuring unmatched consumer experience, Chat Bots help an organization to have timely conversations with users and give them product recommendations or suggestions. You can create your marketing strategies with consumer preferences in mind thus offering them targeted products.

4. On-going predictive analysis

Deep Learning plays a highly significant role in the data analysis process. Whether it’s a small or big organization, entrepreneurs will have the chance to perform successful data analytics. Predictive analysis becomes easier, and you can develop crystal clear ideas of customer preferences.

Signing off

If market reports and figures are anything to go by, Google is running 1000 deep learning projects as of now. The number of projects was only two in 2012, and this will succinctly explain the massive importance of DL in business marketing.

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

Designing for Artificial Intelligence

A new age design manifesto needs to grow beyond its traditional scope. As an outcome, it needs to factor in the growing space and time ecosystem enabled by new age technologies like Artificial intelligence (AI) and robotics. Hence the design principles of the past have evolved significantly to include new policies and principles.

Interested to explore what these new age design principles for AI are? Then read on..

1. It solves a real life human problem

The modern day design principles around AI should focus on solving a specific human problem. Going beyond the buzz and hype, a well-designed AI system has to concentrate on resolving a human problem (for e.g. delivering true value in service or product). The intent has to come out clearly when looking at using design to increase the value proposition of the AI system.

An example is the supportive body suit from SuperFlex. It mimics the natural body and muscle movement and helps out elderly people who have muscle or bone issues preventing them from carrying out routine tasks like moving hands or standing up.

2. Design for AI need not follow historical context

As mentioned earlier and as depicted by the emerging design trends, design need not conform to historical context. With new technologies it is obvious that design too needs to move beyond what we have experienced in the past and open our eyes to something totally new. This is essential when designers are working on truly ‘smart’ objects and should not be limited to just AI-based robots.

3. Design needs to understand the utility value of AI

AI was never designed or promoted to replace humans. It was instead designed to add value to human lives and make it more efficient/productive. If designers keep this basic difference in mind the resultant AI system would have better success potential in the market. When you are brainstorming for design ideas you need to ask yourself “Can AI  complement human lives rather than replicate it?”

4. Good AI Design needs to help everyone

A smart AI based product needs to be embraced equally by the tech lover and the senior persons of the family. Typically it is seen that one person who brings the system inside the house loves it while others aren’t easily swayed by its prowess. Designers need to figure out how they can have the entire household to get to use the product and derive benefits from it.

5. Good AI design doesn’t get in your way

A designer needs to understand that the AI product has to be subtle and discreet in its functioning so that it delivers a stellar experience without getting in your way. Such a well-designed AI system needs to generate subtle signals about the action being performed without disturbing the activity that you are doing.

August Smart Lock is one good example here.  It allows the user to unlock the door automatically when he/ she is nearby. You need not stop to take out keys from the bag or retrieve the smartphone from your pocket to unlock the door.

To conclude

With these principles in place, designers for AI systems will be in a truly remarkable place in the near future. This will be important as design will definitely be playing an increasingly influential role in building complex Artificial Intelligence solutions and systems.

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!