Developer Nation Blog

The Rise of the Chat Bots?

Post author By Mark Wilcox
Post date March 7, 2016
No Comments on The Rise of the Chat Bots?

Developers struggling to get noticed on the app stores, or hoping to capitalise on the growth of enterprise messaging, are looking to a new way to reach their users – via a chat interface. The logic of reaching users where they spend the most time seems sound, and that is clearly in messaging apps. Does the typical consumer or business user really want to be taken back to the days of the command line interface though? Is this the next big market, or will it just be a trendy niche? [tweetable]Natural Language Processing (NLP) technology seems to be the key to mass market adoption[/tweetable]. Can that interface scale across thousands or millions of apps, or will it be dominated by a few key players and use cases?

An old idea

[tweetable]Chat bots – apps that communicate via a textual, conversational interface are nothing new[/tweetable]. A convincing conversational computer program has been a goal of artificial intelligence research since Alan Turing proposed his famous test in 1950. As long as there have been chat-rooms, including the bulletin board systems that dominated the Internet before the invention and adoption of the web, there have been chat bots. Even commercial scale and conversational access to internet services are far from recent developments. The SmarterChild bot on AOL Instant Messenger and MSN Messenger (now Windows Live Messenger) had 10 million active users and processed 1 billion messages per day. It provided access to news, weather, sports, a personal assistant, calculator etc.

New scale & changing habits

Why the renewed excitement in chat bots? Two reasons – mobile messaging scale and enterprise messaging growth. Third party mobile messaging apps are rapidly heading towards 2 billion active users globally. These apps are typically the most used on any mobile device. Following the model of Asian messaging apps WeChat and LINE, the owners of other messaging apps want to turn them into platforms. At the same time, Slack is attempting to create a platform out of their enterprise messaging product. [tweetable]The impressive growth of Slack in the enterprise, where people are actually happy to pay for software[/tweetable], means a lot of entrepreneurs would like to ride their coattails to success. There are two separate markets here, with a common interface and some common technology.

Better technology for smarter chat bots

It’s the improvement in that common technology, particularly for NLP, that leads many people to think it might be different this time. Conversational interfaces might finally be able to deliver a great experience. However, there are some tradeoffs here for developers. One appealing aspect of a textual interface is that it can be much less effort to develop than a mobile app UI. Unfortunately NLP research has been disproportionately focused on English so far – the technology isn’t as good in other languages, so these interfaces automatically have a more limited audience. The more sophisticated the NLP, the more work involved in developing the interface. Using a 3rd party NLP service can significantly reduce this effort but also removes a key source of differentiation if your product is only a chat bot. Without NLP a bot is either focused on a very specific task or only for power users – mass market consumers aren’t going to want to memorise a lot of specific command syntax. At the other end of the NLP sophistication spectrum, is it going to be viable trying to compete with the likes of Google and Facebook as the best way to access mass market services?

Will Facebook own the consumer market?

Telegram’s bot platform might seem interesting as it reduces the need for NLP (and typing) with dynamic custom keyboards but their reach is a tiny fraction of the likes of Facebook Messenger or WhatsApp. Unfortunately [tweetable]Facebook doesn’t have a good record as a developer platform provider[/tweetable], which they managed to prove again recently, if such a reminder were needed, by announcing they’re shutting down Parse after promising developers their backends were in safe hands. Indeed chat bots on the big consumer messaging platforms may have some success at first but it’s likely to be the platform owners that take the lion’s share of the revenue in the end. These platforms will probably be great for businesses to reduce marketing and customer support costs with chat bots and they’ll be paying Facebook (and possibly others) for the privilege of talking to their customers in this way. There are probably also good opportunities for smaller developers to help companies build these bots.

An opportunity to differentiate enterprise services

The enterprise opportunity is different. As a growing number of companies reduce their email usage and build workflows around chat in apps like Slack and HipChat, the most natural way to access some premium services will be through chat (at least for some use cases). For example, when discussing past sales, or future sales projections, it would be much more natural to ask an accounting, or forecasting SaaS tool to give you a chart for the relevant period than to switch away to another application to look that up. However, as with most of the obvious examples you could imagine, it’s unlikely that these services would operate entirely through a chat interface. It doesn’t make much sense to enter all of your accounting data via chat or build your sales forecasting model that way. As such, chat bots become just one of many interfaces to a cloud service. There are opportunities for new services to get discovered, or existing services to gain market share, by being early to support chat interfaces but 100% chat-based is unlikely to be a giant new market.

Evolution not revolution

In most cases, consumer or enterprise, conversational interfaces are just another channel. Much like mobile apps are just another channel for many services. They can be a channel that increases convenience or reduces friction for some key use cases. This also follows mobile but their increased convenience and usability lead to a massive increase in total use. The change messaging platforms will bring is nowhere near the same scale as the shift to mobile. The things that were really interesting on mobile were the ones that couldn’t be done, or were too inconvenient to bother with on desktop platforms. What new possibilities do conversational interfaces bring on mobile platforms? What mass market use cases haven’t already been tried (and widely under-used) by Apple with Siri? The most interesting areas are probably those that involve some element of discovery – when you don’t know which app to go to – or embrace the asynchronous nature of messaging. That said, how do you discover a new chat bot to help if you don’t know what you’re looking for? What business models would work for a purely conversational interface? The chat bots may be on the rise, but it’s likely this is more of an evolutionary step than a revolutionary one.

Tags chatbots

Languages Platforms

The Significance of AlphaGo: Has a golden age for artificial intelligence just dawned?

Post author By Mark Wilcox
Post date February 17, 2016
No Comments on The Significance of AlphaGo: Has a golden age for artificial intelligence just dawned?

In recent years artificial intelligence (AI) has returned to the forefront of technological debate. That debate has moved on from when, and even whether, computers will ever display intelligent behaviour to how smart they will get, how quickly, and what the implications are for society. Although there are multiple approaches to creating AIs, the ones that involve machine learning from large datasets are generally outperforming all others. The results from such systems are often so impressive that large companies are rushing to hire data scientists, collect more data, and apply the latest machine learning techniques to inform their management decision making. Google’s DeepMind team recently demonstrated that without any human in the loop they can build a system that makes complex strategic decisions better than a human expert. Their approach suggests a way forward for building such systems in many diverse fields.

The game computers couldn’t beat

The announcement from the DeepMind team that their AlphaGo program had defeated the European champion at the game of Go was a highly significant landmark in AI. Not only did they accomplish a long-standing ‘grand challenge’ in AI and surpass rival Facebook’s efforts by an enormous distance, but the way the system works is in many ways very human-like. At first glance it’s easy to dismiss game-playing AI systems as not immediately applicable to real-world problems. The ‘world’ the AI operates in is incredibly simple compared to our physical world – in the case of Go, a 19×19 board where a black or white stone can be placed on each intersection. However, [tweetable]advances in AI from the pursuit of better Go playing programs are already being used[/tweetable] in real-world applications elsewhere. Also, the ‘deep convolutional neural networks’ that AlphaGo uses to ‘perceive’ the board are similar to those currently being employed to push forward the state-of-the-art in image and speech recognition, as well as natural language processing.

It’s different this time

Back in 1997, IBM’s Deep Blue beat the world Chess champion, Garry Kasparov. How is this different? First, Go is significantly more complex than Chess. There are nearly an order of magnitude more moves possible from every position and each move can have a bigger impact on the strength of a player’s position. Second, Deep Blue used a supercomputer and some hand-crafted heuristics to effectively do a brute force search of all reasonable future move combinations to pick the best move to make next. This was nothing like the way a human would play Chess and also not generalisable to other problems.

In contrast to Deep Blue, AlphaGo combines two deep convolutional neural networks with a Monte Carlo Tree Search algorithm to select moves in a way that’s quite similar to the way a human would play. The first neural network, called the policy network, picks a few promising positions for the next move. A human player doesn’t systematically evaluate all possible moves, rather through experience they develop an intuition for moves that should make their position stronger. They would struggle to explain why they selected a specific move over others in many cases. This suggests they’ve developed a model for how to play that exists below their conscious awareness. AlphaGo’s policy network is trained to predict the moves that expert players would make using a dataset of 30 million different positions from real games. The second neural network, called the value network, estimates how strong any given position is. It was trained, simplifying slightly, using the results of the policy network alone playing against itself from 30 million distinct positions. The Monte Carlo Tree Search is then used to look ahead from each move selected by the policy network at the opponent’s likely responses and AlphaGo’s subsequent moves. However, rather than search all the way to the end of the game, the value network is used to evaluate the end position after a sequence of moves. This is also similar to human play, looking a handful of moves ahead to assess the probability of gaining an advantage with each possible move. The lookahead searches are shallow (constrained by the processing power and time allowed for a move) and yet the results are better than existing leading systems that look much further ahead but with much less sophisticated move candidate selection and position evaluation capabilities.

Widely applicable artificial intelligence

This might all still sound a long way from a truly human style of thinking but if we abstract and generalise it slightly then it becomes more familiar. For any goal-oriented behaviour in a complex or changing environment we can assess our current situation versus our goal and generate some options for moving towards the goal. We can then simulate or predict the results of taking those actions and evaluate the new situations we could get into. We choose the option that moves us closest to our goal, or has the highest probability of moving us closer to that goal. This is just a description of iterative planning.

AlphaGo has shown that we can train a machine-learning system to emulate the options a human would select in a relatively-complex environment. If we simulate the immediate results of those selections we then just need to evaluate where we get to with each option. Again: machine learning comes to the rescue. If we can acquire or generate enough data we can train another machine learning system to perform the evaluation. None of this is really a new idea but now it has been demonstrated to be good enough to beat a professional at Go, it’s a fair bet it can be made to work for a huge range of other problems too. This is possibly why it’s such a landmark for AI research. It’s a challenge that until very recently was thought to require a completely new breakthrough in AI and probably another decade of research (and Moore’s Law) to get us there. It turns out the techniques we’ve already invented, when suitably combined, can achieve very intelligent behaviour.

Dawn of a golden age?

There’s an outside chance Go just happened to be a lot easier than we thought, or just unusually suited to these ‘deep learning’ techniques. However, given the progress that’s being made with deep learning on other longstanding AI problems it seems more probable that [tweetable]we’re about to enter a golden age for AI[/tweetable]. In this context it’s interesting to note that AlphaGo beat the European champion a month before Google opened the source code for their TensorFlow deep learning framework (which prompted Microsoft to follow suit with theirs). These open source moves can be seen as part of a land grab for talent and mindshare. The techniques are the subject of published research and efficient implementations are valuable but nowhere near as much as the data required for training and the talent to utilise it. Then of course, Google, Microsoft, Amazon and a bunch of startups will all offer managed solutions for training and running these kinds of framework on their clouds. As the significance of DeepMind’s accomplishment sinks in, more researchers and developers will rush to jump on the machine learning bandwagon and there will be no shortage of tech giants waiting to welcome them aboard.

Tags ai, alpha go, artificial intelligence

Business Tips

Best Practices for a successful IoT Developer Program

Post author By Stijn Schuermans
Post date February 15, 2016
No Comments on Best Practices for a successful IoT Developer Program

Events and training programs are a main component in many IoT developer programs. But just how effective are they?

This infographic sheds some light into the effectiveness of training and events. Insights are based on our Best Practices for IoT Developer Programs report.

Best Practices for an IoT Developer Program — Infographic

Tags best practises, iot developer programs

Business

Developer stories: Mobile Development Runs Deep

Post author By Jeff Hadfield
Post date February 4, 2016
No Comments on Developer stories: Mobile Development Runs Deep

At VisionMobile, we believe in the people behind the numbers. While it’s important to understand numbers, trends and segments, it’s equally important to understand the people we’re trying to reach with our research. This developer profile is one in a series designed to help us get to know some of the people behind the statistics.

Developer Profile:
Levent Gurses

Company, job title and duties:
Movel: CEO, developer entrepreneur. Movel is an enterprise development company, and Levent says, “I code on a daily basis because other people are doing the less interesting stuff.”

Country/Area:
Virginia, US

Development Focus:
Movel focuses on mobile, but that’s too easy of an answer. They work on both native and cross-platform apps (iOS, Android, Windows Mobile, Phonegap, and more.) Levent emphasizes that mobile development doesn’t mean just the front-end app. The back end, he points out, is the biggest part of a mobile project, particularly one with multiple front ends. The back end includes key capabilities such as RESTful, secure APIs and identity and transaction management.

Levent explains that when building enterprise apps, most of a mobile app’s back end applies to a Web app/client’s back end as well. As he builds enterprise solutions, he’s able to build a “21st century back end.” Everything that applies to a mobile app backend applies to a Web app as well, bringing Web apps up to speed for 2016.

Languages used:
While some developers have a specific language they prefer, Levent and Movel use a mix of languages and clients. AngularJS, however, is a favorite. He’s also fond of Google’s Polymer framework and feels it’ll be a standard in 6-12 months … if Google doesn’t shut it down.

While there’s something to be said for using established frameworks and languages, when a new, promising technology comes along, he takes a pragmatic approach. If a new technology provides sufficient promise, he discusses the potential risks and rewards with the client, along with the backup plan. If the client agrees, they’ll try those less established technologies on a new product.

Favorite devices:
As with some people, Levent is frustrated with the Apple ecosystem, and frustrated by Apple. And like many of us, he says, “I can’t live without my IPhone 6 Plus.” But he uses a variety of devices for testing and exploring. He contrasts iOS devices to Android devices like this: “If I want to use a small computer, I’ll use Android. If I want to use a phone, I’ll use my iPhone.”

He also has an Internet of Things (IoT) shop with about 20 Raspberry PIs running office projects like presentations, dashboards, and temperature sensors. The Raspberry PI is his favorite of this genre for “general, light computing power.” In fact, he says, these eliminate the need for desktops in many applications with the open source Raspbian OS. Arduinos, by comparison, are more limited, but useful for training.

They don’t need any Windows computers, he says, since their MacBooks and Macs run Parallels.

Favorite project built recently:
His favorite recent project stands out to him not because of the technology used but because of the solution. Built for an educational nonprofit, the solution streamlines the application process for college. US high school students use the app to more easily apply to multiple colleges.

Favorite tools:
“Nothing can touch the power of the Chrome developer tools,” even simulating slow connections, network traffic and simulated security issues.

But Movel doesn’t lock its developers into a particular toolset. “That’s what’s beautiful about development nowadays … it’s all open unless it’s Adobe (PDF).” Some prefer no IDE at all, others love heavier IDEs. “Use whatever makes you more efficient.”

They do, however, enforce backend standards like Ansible and Docker.

Best developer-related advice ever heard
“Premature optimization is the root of all evil,” commonly attributed to Donald Knuth. He explains, we need to understand the art of creating the minimum viable product that addresses the business problem.

Best developer-related advice ever given
“Get involved in local meetups and hackathons.” He continues, “Here’s what happened to a lot of developers with the Internet. Everything became impersonal, even resumes. But it takes away the joy. Get out to talk with like-minded people to discuss tools and techniques. You’ll learn stuff you wouldn’t come across in your usual stream, be that Twitter, GitHub or other social media. Everyone you follow is likely like-minded. This takes away the coincidences – local encounters, conferences, meetups, hackathons – all give you a chance to get out of your comfort zone.”

This advice applies to not just new but to experienced developers. He counsels to not get too comfortable: instead, build a trusted network of real people you meet and spend time with. “We grow based on our environment and who we know. When we increase the caliber of people we know then we can grow in leaps and bounds.”

Tags app business, developer interview, mobile developer

Platforms

Facebook Messenger: All your numbers are belong to us

Post author By Bill Ray
Post date January 25, 2016
No Comments on Facebook Messenger: All your numbers are belong to us

Facebook started 2016 with the bold claim that it intends to eradicate phone numbers and replace web browsing, but the Social Network has a mountain to climb before Facebook Messenger becomes the centre of our online world.

That’s the stated intention of the Zuckerberg empire – to replace all our myriad internet communication systems with one interface.

Facebook claims that its Messenger app has been installed 800 million times, but at VisionMobile our latest research shows that those installations are very much concentrated into the lower end of the market.

If Facebook is going to recruit the shops, taxi companies and airlines it needs to make Messenger a one-stop internet shop it will need to get the app installed across the demographics before Microsoft (with Skype) steps in to take the cream.

[tweetable]Facebook has long known that the days of pokes and personal walls are fast disappearing[/tweetable], and has quite a history in struggling to adapt to whatever the future might bring. Facebook Gifts/Credits/Deals/Questions/Beacon haven’t lit up the future, so now the company is betting on messaging, and value-added messaging platforms.

Such platforms are proliferating in business. The bots that proliferate across Slack and Yahoo Messenger have turned those platforms into much more than messaging, but taking that functionality into the consumer sphere is much harder.

The medium is the Messenger

With that in mind, Facebook Messenger was forked from the main Facebook mobile app back in 2011, but messaging remained possible in the main app until 2014. These days, the Facebook app will notify you that a message has been received, but if you want to read that message then you’ll have to download and install Facebook’s new Trojan Horse.

That analogy isn’t perfect: the horse of Troy was disguised while Facebook has made no secret of its plan to migrate key internet functionality into the Messaging client. If Facebook can’t own the interface to your phone (it tried that), then it will own the interface to the internet, which the company believes will be Facebook Messenger.

The inspiration behind this idea isn’t hard to see. In China, where Facebook/Google/Twitter fears to tread, the competitive market created in their absence has driven huge innovation as companies strive to differentiate themselves with new features and functionality. Every month, 600 million Chinese are using Weixen, Tencent’s WeChat client, to book taxis, check into flights, play games, buy cinema tickets, make doctors’ appointments, and even manage bank accounts, all without touching the web browser.

[tweetable]In China, messaging has become the platform of choice for accessing a wide variety of services[/tweetable], and Facebook plans to replicate that model in the rest of the world – with it owning the messaging platform, obviously.

This process has already started with Facebook integrating Uber into its messaging platform. It’s worth noting that Uber isn’t integrated into the Facebook website, or the mobile client, but into the Facebook Messenger app.

And Uber is just the beginning. As David Marcus, Facebook’s vice president of messaging products, makes abundantly clear: “We can help you interact with businesses or services to buy items (and then buy more again), order rides, purchase airline tickets, and talk to customer service in truly frictionless and delightful ways” – and that’s before Facebook becomes your personal assistant, Facebook M.

“Facebook M” starts listening in to all your conversations to suggest ways it can make your life more, as they say in such circles, “delightful.”

The Facebook wall will be supplanted by the Custom Conversation, providing a personalised interface (colour, style, emojis) for every chat thread. The visual equivalent of a ring-back tone, customised for every caller, will enable you to decide how both sides of the conversation see their interface, unless the other side has other ideas.

Walled garden of Zuck

In Facebook’s brave new world, everything is done through Facebook Messenger, and Facebook takes control of the delivery channel, removing that irritating “Open in Web Browser” which takes so much control away from the Social Network.

But that brave new world is predicated on the idea that people will install Facebook Messenger, rather than relying on the website, and email notifications, to stay in touch. Our research, in partnership with Celltick, looked at the top 10 applications installed on different handsets, and shows that while many low-end handsets do have Facebook Messenger installed, the application is almost invisible in handsets costing more than $200.

In high-end phones, Skype consistently rates top – well above the main Facebook application – and Facebook Messenger isn’t even in the top 10. In handsets costing less than $200, Facebook Messenger rates around four or five – a couple of positions below the main Facebook application, and very close to Skype.

What this means is that those who can’t, or won’t, invest more than $200 in a handset are happily installing Facebook Messenger, while those with a bit more disposable income are refusing to commit.

What it makes abundantly clear is the opportunity this presents to Microsoft. If messaging really is the future of mobile interaction, as Facebook seems to think, then Skype is perfectly positioned to grab the most important demographic.

If Microsoft were half as willing as Facebook to launch into value-added messaging, then it could make Skype into the messaging platform of the future, if indeed users really want such a platform at all.

You can read more in our free report, here (email address required.) ®

Article first published on the Register

Tags android, android trends, celltick

Business

The Internet of Things is about to reshape e-commerce

Post author By Andreas Constantinou
Post date December 11, 2015
No Comments on The Internet of Things is about to reshape e-commerce

E-commerce as we know it is about to be fundamentally reshaped, as every connected object in the future becomes a potential commerce channel.

Internet of Things – from smart home devices to connected cars – will transform e-commerce and allow it to stretch across the breadth of the customer journey – from awareness, to intent, to purchase. Billions of “things” will double as e-commerce points of sale (PoS), unbundling and extending PoS for e-commerce outside the web, app and product silos controlled by e-commerce players. But how will we get there?

E-commerce is forecast to continue to grow fast, and m-commerce twice as fast for that matter, the latter poised to reach a value of $600 billion by 2018. The Internet of Things (IoT) is at last leaving the hype phase and is becoming a revenue-creating reality. By 2020, there could be as many as five connected objects per every smartphone user. And by then, the IoT market is set to reach a value of $1.7 trillion.

IoT and e-commerce have until now evolved in parallel. They are now embarking on a common journey where every connected object becomes a potential e-commerce real estate. With IoT, washing machines can now not just deliver detergent just in time by knowing when your supplies run out, they can also recommend the right detergent, based on your usage or type of clothes, on demand. Car makers can recommend where you buy your gas, by understanding your drive journey, availability of gas stations, pricing on-demand discounts, and gas station commission – in fact Google’s Waze does this already. Watchmakers can command a commission from health insurers, as they can monitor your heart rate, temperature, fitness habits and determine what risk zone you are in. Moreover, makers of connected devices can now afford a negative BOM (bill of materials) “à la Dell”, by subsidizing the cost of hardware with the revenues from bundled e-commerce services.

E-commerce is already the biggest revenue generator among mobile developers, yet only a small minority have acknowledged it and few have seized it with both hands. Mobile developers using e-commerce (for physical or digital goods) have median monthly revenues of $1,000-$2,000 compared to a measly $200-$350 median monthly revenue for mobile developers across all revenue models.

Yet, only a small share of mobile developers, 9%, have chosen to work with e-commerce, based on our 9th Developer Economics survey wave of May 2015, of more than 13,000 software developers globally.

We expect however that this number will grow as off-the-shelf fulfilment and payment platforms ease the pain of managing inventory, customers and transactions. Scaling up will become easier and therefore e-commerce a less daunting and more appealing option for an increasing number of developers.

Services such as Dash Replenishment Service (DRS) and Pinterest’s buyable buttons are all early, and telling, examples of the commerce things to come. They show how e-commerce is evolving towards letting customers make purchasing choices based on impulse and context instead of having to browse and select among a myriad of items. They also show how a purchasing decision is vastly simplified when discovery and payment friction has been removed.

The e-commerce of things journey has only started but it will have far-reaching consequences for e-commerce, IoT, and overall how goods and services are consumed in the future. For an in-depth analysis of how developers and IoT are shaping e-commerce, download the free VisionMobile report on the Commerce of Things.

Tools

The state of UI and Interaction Prototyping tools in 2015

Post author By Filippos Protogeridis
Post date December 3, 2015
No Comments on The state of UI and Interaction Prototyping tools in 2015

The UI design process has changed radically over the past few years. With the addition of innumerous tools for wireframing and prototyping, designers are spoilt for choice. Which is the best tool to use?

One thing is for sure, static designs simply won’t cut it any more. A designer ought to employ animation and interactive elements to stand-out from the crowd, now more than ever.

Why Prototype?

A prototype is an early sample, model, or release of a product built to test a concept or process or to act as a thing to be replicated or learned from.

The most obvious reason to incorporate prototyping in your design process is the ability to evaluate the interaction pattern before moving to development phase. This is extremely important especially for mobile applications, where implementing an advanced animation or interaction usually takes a significant amount of time compared to desktop websites. Prototyping will solve your design problems before they even arise.

The other major reason is to enhance intercommunication between designers and developers. While an animation might be very clear in the designer’s mind, a developer will often struggle to actually visualise and implement it. That’s where prototyping comes into play. Instead of trying to verbally explain how the UI design should look or feel like, the designer can use a visual prototyping tool to communicate to the development team exactly what they have in mind.

Flow Prototyping

The most basic form of prototyping that every designer needs to know is flow prototyping, which can be used to create an interactive and functional screen-to-screen prototype. Flow prototyping can help showcase a product and thus act as the basis of communication between designers and developers.

What you should look for in a proper flow prototyping tool is ease-of-use, speed, collaboration features (e.g. user/team management, comments), and version history.

Invision & Marvel

For basic screen-to-screen prototyping, the two most popular choices are InvisionApp and MarvelApp. Both apps allow designers and product teams to quickly come up with working prototypes for their web or mobile applications by simply uploading screens (or working designs), adding hotspots and transitions from one page to another, and forming solid prototypes that can be used for collaboration and/or developer handoffs.

A large amount of transitions between screens is provided out-of-the-box, therefore allowing users to create high-fidelity prototypes with an extremely easy-to-use interface that requires little to no learning time. It’s worth noting however, that none of these apps provide a way to transition or animate individual elements of the UI (with the exception of the overlay feature of Invision that can mimic such behaviors up to a point).

In terms of collaboration, Invision is a clear winner because of its sharing options, user and team management, project management tools, and moodboards. However, the Invision free plan allows for one project only, as opposed to Marvel which has an unlimited projects free plan and is perfect for designers who are just looking for a tool to get the job done without a price.

UI Design: Advanced Interaction Prototyping

When it comes to advanced screen transitions or in-screen animations and interactions, Invision and Marvel are not enough. Τools like Framer, Principle, Quartz Composer and Form make advanced interaction prototyping easy for designers and developers.

Principle

Principle is probably one of the easiest-to-use tools out there, giving designers the ability to create high-fidelity prototypes in a very short period of time, with the great addition of a timeline viewer for better animation handling. If you’ve ever used Keynote for prototyping, you’ll find yourself at home, with an out-of-the-box “Magic Move” transition as well as a variety of tools for advanced transition handling.

Integration with Sketch is excellent, allowing direct copy-paste of layers and groups, which saves time from importing/exporting particular elements – a time-consuming, repetitive task most designers prefer to avoid.

Framer

Framer is one of the most advanced prototyping tools out there, mainly because it’s based on code. Unlike Principle, there is no fancy WYSIWYG editor and Drag & Drop interface.

All the interactions and animations are done through CoffeeScript/JavaScript. This naturally results in a steeper learning curve, but on the bright side it means designers can prototype pretty much anything they like.

http://share.framerjs.com/a0aaba2lyu5l/

Framer has a very strong community and an unending collection of examples and tutorials to learn from, which makes it one of the most solid tools for prototyping for people who aren’t afraid of code. It features integrations for both Photoshop and Sketch, allowing for easy asset importing straight from the designs. Prototypes can be viewed on an actual device as well, like all of the other popular tools out there.

Quartz Composer, Origami, Form

Besides purely code-based tools like Framer, and completely WYSIWYG tools like Principle, there is another set of tools that utilize the so-called Visual Programming or Node based Programming. Tools like Quartz Composer or RelativeWave Form (now acquired by Google) let users prototype through building blocks (called patches) that are connected through noodle-like arrows, in order to describe interaction and transitions between states. Although these tools have a much steeper learning curve than Principle, they also allow for more advanced animations and interactions between states – making them an alternative for code-based tools like Framer.

Summing it up

Product	Company	Cost	Advanced animation features	Requires coding skills	Overall
Invision	Invision	Free for 1 project, up to 25$/mo for unlimited	No	No	For quick flow prototypes and simple animations. Good collaboration features.
Marvel	Marvel	Free with limited features, up to 15$/mo	No	No	For quick flow prototypes and simple animations.
Framer	Framer	99$ one-off	Yes	Yes	Very detailed prototypes. Steep learning curve. Based on code (Javascript/Coffeescript)
Principle	Principle	99$ one-off	Yes	No	Easy to create prototypes and export video. Uses a Visual/WYSIWYG Editor.
Quartz Composer	Apple	Free (included in the Xcode Development enviroment)	Yes	No	Very detailed prototypes. Steep learning curve. Based on visual programming (nodes).
Form	RelativeWave, acquired by Google	Free	Yes	No	Very detailed prototypes. Steep learning curve. Based on visual programming (nodes).

For the majority of designers who want basic UI flow prototyping and team collaboration, Invision or Marvel will cover most of their needs, while requiring little to no time to pick up and enabling them to start prototyping right away. When it comes to more advanced transitions and animations, Principle is an excellent choice, taking into account the exceptional ease-of-use and time needed to come up with a solid result. For even more advanced interactions designers can use either Framer if they’re into coding or a Node-based tool like Quartz/Origami or Form if that type of visual programming appeals to them.

One thing is for sure, modern designers should master prototyping and interaction design as soon as possible. Tools might change, but incorporating rapid prototyping and motion design in their workflow will change their perspective and potential and undoubtedly help them create outstanding experiences.

Tags animated prototype, prototype, prototyping tools

Languages

So… is HTML really a programming language?

Post author By Bill Ray
Post date November 26, 2015
No Comments on So… is HTML really a programming language?

Earlier this year we polled more than 13,000 developers during our biannual Developer Economics survey (updating now), and 11% of those developers told us that HTML is their primary development language – that’s Hypertext Markup Language to the uninitiated. This response immediately begs the question: can HTML really be considered a “programming language” at all, or if we should consign to being a tool for the layout of JavaScript functions?

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Developers answering our survey were asked to pick from a list of languages, HTML5 was on that list, along with JavaScript and more-traditional languages including C and Java. Most programmers work in more than one language, so perhaps those who selected HTML5 as their primary language really meant that they were JavaScript programmers who used a lot of HTML? It’s a nice thought, but the idea breaks down when look at those additional languages and see that only 13% of those who said their primary language was HTML admitted to also using JavaScript, so how are these people creating applications?

14% said they also use ActionScript, which can also come wrapped in HTML, but 12% of those primarily using HTML said they also program in C/C++, which is a combination we’re unlikely to see in the same project.

HTML was never designed as a programming language – the original 18 tags permitted the most-basic of layout options. The only interesting tag was the hyperlink itself; the revolutionary concept that created the web as we now know it, the rest are trivial. HTML was based, loosely, on SGML, which is another bastard offspring of XML – the eXtensible Markup Language – but the key word across these is “markup”: all three are intended to provide syntactical data to accompany textual information*, not applications as we know them.

But HTML has come a very long way since then, and has capabilities we would normally associate with a development language. Drag and Drop, Geolocation, and Local Storage, are blurring the line between applications and web sites, allowing cross-platform development where the only way to spot the difference is the title bar at the top of the window (and sometimes not even then), and there are a host of applications which bear testament to the fact that HTML can be used to create real applications.

Zero Lines JS is a fine example. A graphical game, requiring the player to navigate their ship between approaching enemies at increasing speed to a suitably-irritating soundtrack. It might not be the next Watch Dogs, or even the next Candy Crush Saga, but it would be hard to deny that it is a real application and one which (as the name infers) is written entirely in HTML with a few Cascading Style Sheets (CSS).

Less gaudy is the aptly named “You Don’t Need JavaScript for That”, which demonstrates various techniques to accomplish programmatical tasks without recourse to programming languages. Examples include a tabbed panel (bringing content to the front based on the selected tab) and an image slideshow, all done entirely in HTML5.

Purists will moan, of course, that these examples don’t make it a “real” programming language, that HTML is nothing more than a markup language made to enrich documents, and there was a time when that was true. Developers aren’t as hierarchal as they used to be, but those closer to the metal still look down on those who’ve traded an intimate knowledge of the hardware for speed of development. C programmers consider objects to be unnecessary fluff, but concur with users of C++ that anything which isn’t run through a compiler is just improper (and that includes Java with its bytecode nonsense). Java programmers consider anything without proper encapsulation to be faking its object orientation, while JavaScript developers see no reason for strong typing, and consider HTML to be a layout tool.

Meanwhile those versed in Assembler look down from their ivory towers, stroke their beards, and concur that when performance really matters they will always get the gig.

But despite being at the bottom of the heap we can see that HTML5 is being used to create applications, and it must therefore be considered a programming language. We might argue whether validating a filled-out form constitutes an application, but when you can crash a spaceship into an oncoming armada then there’s little room for discussion.

At Vision Mobile we’re currently updating our survey, asking developers what language and tools they’re now using, including those who choose to program in HTML. It will be interesting to see if an increasing number think that the layout tool has evolved, or if a momentary fad is passing. Take a look at the survey, and use the feedback from at the end to let us know how you feel about HTML being included in the list of languages, and what you think might end up on that list next time.

* To be accurate, XML is intended to be a framework from which one can derive markup languages, but that’s not really pertinent here.

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Tags html, HTML5, javascript

Platforms

Self-driving cars are about platforms, not about cars

Post author By Michael Vakulenko
Post date November 16, 2015
No Comments on Self-driving cars are about platforms, not about cars

There is growing consensus that fully autonomous cars will become a reality by 2020. Google self-driving cars have driven over 1.2 million miles. Elon Musk, Tesla CEO, predicted in September 2015 that Tesla cars will have fully autonomous capability in 3 years. Zvi Aviram, CEO of MobileEye, a supplier of self-driving systems to many car makers, expects their technology will support fully autonomous driving by 2019.

Most traditional car makers still see autonomous driving as a feature of the car, rather than a market shift that will open the path to the creation of a completely new winner-takes-all industry. It’s just like PC makers focusing on adding connectivity to their products and missing the transition to the Internet platforms (Google Search, Amazon, Facebook). Or telecom operators focusing on adding always-on fast data connectivity to their networks and missing the transition to the mobile platforms (Google Android, Apple iOS).

Is the same about to happen in the car industry? Are car makers about to miss the transition to transportation platforms in the same way as PC makers missed the transition to Internet platforms and telecom operators missed the transition to mobile platforms?

The future transportation value stack will be very different from the existing automotive industry. It quite remarkable that only two companies, Google and Uber, are present in all layers of the stack that are necessary for creating a dominant transportation-as-a-service platform.

The car hardware (the body, the power train, the wheels) increasingly becomes a commodity. Modern cars are good-enough for typical everyday use offering little opportunity for differentiation. Car commoditisation will only accelerate with the transition to electric vehicles. Electric vehicles are much simpler mechanically and easier to make, which opens the gates for new players, including such electronics and Internet services players like Apple, Google, LeTV and even Acer. It’s also notable that Tesla ‘open-sourced” their electric vehicle patents in 2014 pledging not initiate patent lawsuits against anyone who, in good faith, uses Tesla’s technology.

Autonomous driving is about guiding the car along the road, following the rules while avoiding obstacles and crashes. It involves lots of sensors, computing power and sophisticated software, but the most important part here is the ‘data’. Self-driving systems are machine learning systems that are trained to evaluate the environment and make fast decisions on how to react.

The ‘data’ represents all the collective experience learned by multiple cars driving in test and real-world conditions. The more cars you have on the road and the more miles these cars have driven in all possible conditions, the more experienced, safe and precise the self-driving system becomes. Google is undisputed leader here having its fleet of test cars driven over 1 million miles. Tesla’s Autopilot feature introduced in October 2015 on Model S cars will allow Tesla to start training its self-driving system in real-life conditions on tens of thousands of cars.

Uber seem to be behind in terms of putting real self-driving cars on the roads. The company poached 40 researchers and engineers from the Carnegie Mellon’s robotics lab in March 2015 and partnered with University of Arizona on optics research for self-driving cars.

Navigation is about figuring out which roads and streets the car should drive on in order to get from point A to point B. Google is again is a clear leader here with Google Maps and Waze. A consortium of German carmakers (Audi, BMW and Daimler) is trying to uphold an alternative acquiring the Here Maps business from Nokia in August 2015 for $3.1 Billion. Uber also works to create a proprietary mapping platform winning independence from Google and Here Maps. The company acquired San Jose-based deCarta in March 2015, absorbed part of Microsoft Bing mapping assets in June 2015 and has partnered with TomTom in November 2015 to use its mapping and traffic data. (Is Microsoft about to miss the huge opportunity in the future automotive and transportation markets?)

Fleet routing this is where it gets much more interesting. Self-driving cars combined with Uber-style on-demand services make individual car ownership less and less attractive. Some people even claim that hardware-as-a-service is the end game for Tesla. The shared usage models will turn car market into something that looks like a public transport platform, where operators will match in real-time the demand for transportation with the location and the capacity of self-driving vehicles. In other words, fleet guidance is about deciding in real-time where every car needs to go. Which car needs go to a specific pick up point? Shall the car drive to where the demand is expected in the coming 15 minutes? What is the optimal time to recharge or refuel? When and where to go to do the service and maintenance? Where to park, and more.

This is a very complex computational problem to solve at the scale required to support fleets of thousands of self-driving cars. Bill Gurley, one of Uber’s early investors, gives a glimpse into how difficult it is in his blog explaining why UberPool is the new Uber’s “Big Hairy Audacious Goal.” (BHAG). UberPool helps the company to build capabilities that will be directly relevant for the optimal routing of large autonomous fleets.

I’m sure Google is not standing still here as well. Being a machine learning company, it has the scale and the technical depth to become the leader in this space. Add to that real-time bidding capabilities with extremely complex optimisations that Google has mastered for its online ad business. One can even argue that building such transportation platform is the reason for Google’s interest in self-driving cars.

It’s very difficult to see how traditional car makers will be able to compete with software-centric companies in this space.

Finally, the transportation platform is the most intriguing part of the value stack. Moving people around Uber-style is not the only use for self-driving cars. What else can we do with the fully autonomous fleet of robotic vehicles, given that they don’t not have to look as Uber or Google cars of today? These robotic vehicles can be specialized delivery vehicles (see this Domino’s Pizza car as a hint for how they may look like), small delivery drones like Transwheel or StarShip or even autonomous motorbikes, like Motobot by Yamaha.

The number of possibilities and applications for autonomous transportation is mind boggling. No single company, even as nimble and well-funded as Google or Uber, will be able to address all possible needs and use cases by themselves. The recipe for addressing these yet to be known needs and use cases is in plain sight. It is a platform connecting vehicle manufacturers, vehicle operators, service providers and application developers with users (much like Google did with Android).

The platform will harvest permissionless innovation by startups and developers to discover and deploy new services and applications we cannot even imagine today – in the same way that no one could predict Instagram, Snapchat or WeChat on smartphones. Uber already works with developers extending its service into a platform. Google also has a long history of relying on permissionless innovation by developers to win its competitive battles, from Google Maps to Android. It’s only natural that Google will use the same approach to dominate self-driving cars.

It’s still too early in the game to say which companies will dominate the future transportation market. One thing is a safe bet: The future transportation ecosystem will look very different from the existing automotive industry. It will resemble modern technology ecosystems with their platform business models, permissionless innovation by developers, and domination of software-centric companies.

Tags cars, Google, self-driving cars, tesla, uber

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