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Google's Play Store has more than 2 million apps users can pick from.

With that many to search and choose, discovery is a major challenge for users. And that is a concern to both Google and also app developers.

While searching for famous and popular app won't be that difficult, many others will stay down below the surface. Here is where artificial intelligence (AI) comes to work. By having neural network to aid the human touch, Google can figure out better which apps belong in which categories.

According to Google's AI researchers on November 8th, 2016:

AI with the technology called neural network, works by loosely mimicking how the human brains work. On the Play Store, Google processes the app names and their description, to then try to figure out which ones to show in the search result.

This is because AI can be designed to do a better job in sorting apps according to categories, especially when there are many of them to choose from. With the attempt, Google is focusing to balance new apps with popular standbys, take out malicious apps, and help developer to get their own apps to show up high in search results.

In order to make this happen, Google trains the system by creating very rough approximation of language-centric learning. The team trained the neural network to learn how language was used to describe apps. By using a Skip-gram model, the team can make the neural network predict the words around a given word. After that, the neural network encodes its knowledge as embeddings where they can be then used to train another model called classifiers to distinguish which topics apply to which apps.

While the architecture produced good results, it would sometimes overgeneralized. As the team explained, it might associate Facebook with "dating" or "educational games."

To produce more precise classifiers, the team needed a lot more data to train the system. Here is where humans take their role. The data that is funneled can be evaluated by human raters to get the classifier output, to then feed it back again to the system. The process allowed the team to bootstrap the existing system, giving them a way to improve classifier performance.

And because the AI solutions learn more about users' habits and preferences and make better recommendations than they currently do, Google allows people to assess how well the categorization worked and thus steer it toward even better results.

Redesigned Play Store

Besides putting AI into a good use by helping how the Play Store works, Google is also making some visual changes to the Play Store. So here changes behind the scene follows minor redesigns at the front.

The changes aren't drastic and are more cosmetics than an overhaul. The focus here is to make Play Store less cluttered.

At first, the green background has been given a darker shade, and each app's page has been given a larger install button and the app ratings are also made a lot more distinguishable. This should make call-to-action more prominent, putting engagement first before information.

Additional changes include the removal of the box which listed the appropriate age category for the app. Badge is also removed.

The new UI still shows the average stars received by the app by users, along with an approximate number of downloads. While these stats are no longer posted inside a badge, they remain displayed under the larger install button. Other changes include larger screenshots of the app, and a new position for the "Read More" link that expands the description of the app.

App carousel on the main Play Store page has been made a bit smaller, and the "More Information" page that was previously located at app description has been relocated above the developer's information.

The new UI was first spotted on Nexus devices running Android 7.1.1. At the moment, Google seems to be settled with the Play Store's current design. Adopting Material Design with colorful visuals, the minor adjustments are more based on user testing than its own willingness.

In addition to the above, Google also wants to expand Play Store's support to more payment methods, introducing numerous new tools aimed to help developers monetize their creations and expansion to Daydream, Android Wear 2.0, and more Chromebooks.

With all the above, Google is planning to help developers to cope with increased competition by expanding the Play Store ecosystem as a whole.

Google and Apple have different taste in design. While Apple opts for "dull" but useful design interactions, the former likes splashy colors and attractive visual cues.

To push more of its design language forward into Apple, Google revamps its Gmail in what it calls "the biggest overhaul of the app in nearly four years."

"When you get the next Gmail iOS app update, you’ll notice some exciting changes," said Google on its November 7th, 2016 blog post.

The first up is the new looks. Gmail for iOS now looks a lot more like its Android counterpart: Material Design with a red-colored bar up on its display. By having more of its Material Design-oriented products running on Apple, Google is further pushing its brand influence to the faithful competitor.

While most of the changes are just plain aesthetic, Google also introduces some new features.

And as for the new features, the first is the Undo Send. This feature should come in handy when users realized that they've made a mistake soon after sending an email. Almost immediately to a few seconds after sending a message, users can tap "Undo" at the bottom of the screen. With this, users can prevent embarrassing moments when sending wrong emails to the wrong person.

In the meantime, Gmail for Android has yet to have this feature while Google' own Inbox app already does.

The next is the speed. Google said that it sped up its search with 'instant' results, and also provides suggestion while the user type. And for last, users can also swipe to the side to see archive and deleted emails.

According to Google, these can make the overall user interactions with the app a lot faster.

And not just Gmail. Google also makes a few update to its Calendar app based on users' feedback.

1. Users can now see month and week view in landscape mode.
2. The ability to set up alternate calendars (non-Gregorian calendars - like Lunar, Islamic or Hindu).
3. Users can now search for things like events, reminders and goals, straight within Apple' Spotlight Search.

Material Design is a design language created by Google. It has been the design foundations of many Google products, as well as third-party apps as well as websites.

According to Google, "Material is a metaphor, a system for uniting style, branding, interaction, and motion under a consistent set of principles. With Material we believe product teams can realize their greatest design potential."

With its popularity rising, Google is still expecting more people to use Material Design. As a strategy, it releases a new set of designing tools. This is partially its way to expand its design language by making it friendlier and easier to implement

Material Design was introduced in 2014 alongside Android Lollipop. Initially, it was meant to make people rethink how software should be designed on touchscreens. But now, Google wants the design to be a unified system that combines theory, resources, and tools for crafting digital experiences.

So it's much more about interaction than how it looks.

Previously, the search giant has released several tools to help designers and developers to build products using it, now the company wants more than that.

Design As A Continuous Effort

Design is not just something to please the eyes. It's also about interactions and how can something be represented in a way that appeals in user-experience. Because design is an art of continuous problem solving, Google kept investigating and validating needs by crafting and developing new ideas to create new solutions.

As a "metaphor" to rationalize design and implementation, Material design is a shared language to help designers and developers to create a style, brand, interaction and motion under a cohesive set or principles.

And because the new suite of tools are open-source projects, Google is expecting their expansions to be fast.

Gallery

Gallery is where users can organize their design workflow in one place. This way, everyone in a team can have a clear view and keep up with any progress and process. Here, team members can give feedback on everything.

The set of tools work similarly like a GitHub for designers. It makes things easier for people to upload, share, and comment on designs. With version control, users can also add different interactions. And because it also has a presentation layer, users can give out simple web links when they want to share the design to others.

Stage

Stage is brought forward by the teams behind Pixate and Form. Stage changes the way designers and developers build interactive experiences. It enables a more dynamic intentional and systematic workflow for crafting digital experience, apposed to static design.

This way, users can speed up their prototyping process and enables them to test out and demo movement in their apps at much earlier stages.

Remixer

Remixer is a real-time, on-device, connected UI tweaker. Designers and developers can create prototypes of apps that they can interact with directly, and with it, they can also make adjustments on-the-fly to Android, iOS and web interfaces.

App makers can also change values across multiple devices.

Putting The Tools Into A New Home

To make things easier for designers and developers, Google also launched Material.io. In its quest to make all of Material Design accessible, the company put existing works together into one place.

The website and its tools are not exclusively meant for people trying to work with Material Design; they're also for those that are inspired by the same philosophy. The idea of integrating the motion and feel of apps early in the design process is essential to Material Design. So in the website, Google establishes a new visual identity to those that have interest in the the design's initiatives.

People can get support on the latest visual guidance, as well as releases from tools and reusable components.

The goal of Material.io is to focus on different aspects of design process, and to streamline the works. And besides the listed tools above, Google also included a Resizer as an interactive viewer, and Devices that is essentially a list of device sizes, resolutions and more.

With the set of tools, Google hopes that ti will get more designers and developers to adopt Material Design in their work.

Google has a lot in mind, and many of the things involves Artificial Intelligence (AI). Meant to make computers smarter, AI is a way humans want computers to represent them. This time, Google is putting it into a good test, and the subject is cryptography.

A team of researchers at Google Brain, a division that focuses on deep learning, built a game with three entities powered by deep neural networks: Alice, Bob, and Eve.

They have discovered that if AI, when given a task properly, could create an inhuman (odd) sort of cryptographic schemes. And because AI made it, they're also able to encrypt and decrypt the information.

What the researchers did was simple: Alice was designed to send an encrypted message of 16 zeroes and ones to Bob, which was designed to decrypt the message. The two bots shared the same key, the foundation for the message's encryption. Then they put another neural network into the equation. The name was Eve. Here, Eve was tasked to intercept the communication and designed to try to decrypt the message, without the key.

The project works on one condition: a "loss function" for each party. Bob should read Alice's encrypted message, and so should Eve. But the encrypted message had to be as close to the original message as possible.

To avoid Eve figuring out the encryption, Alice started encrypting the message in different ways. Bob then adapted to the shift in order to keep up. The researchers measured Eve's success by how close it got to the correct message.

Researchers Martın Abadi and David G. Andersen said:

The three AI were designed as generative adversarial networks. What this means is that they weren't previously taught about encryption or shown examples on encrypted and decrypted messages. They were just designed to outsmart each other.

In the image below, Bob (red) quickly adapted to the learning encryption because it had the shared key, while Eve (green) was unable to keep up.

For the first 7,000 messages, Alice and Bob started out simply and Bob figured out Alice's messages quite easily. Meanwhile, Eve had to guess and that wasn't easy for the AI. But after the next 6,000 messages, Bob and Alice became very good at encrypting and decrypting, with Bob able to decrypt messages with no errors at all. Eve that has learnt its way around the subject, did get some correct with seven or eight of the 16 characters missing.

What happened was, over time and eventually, Bob and Alice were able to communicate using the shared key. They got really good at sharing encrypted information and many of their techniques were quite odd and unexpected (uncommon in human-generated encryption).

This is because the kind of learning is done over thousands of iterations by a mosaic of mathematic weights being altered by algorithms.

Without time-consuming analysis, even the researchers themselves can't understand how the encryption were built.

What this research has concluded is that AI can be good in creating their own solid encryption as long as they value security. Eve has had a hard time in decrypting encrypted message communications because it had no key. This is similar to humans. What this means is that computers can be made talk to each other in a way that even humans or other robots won't be able to crack.

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