Our CEO talks at TedX about our AI

By on 13 January, 2017 in News

Do you want to know how artificial intelligence will make us collectively creative? If you do this is the perfect opportunity to listen to our CEO, Jakob Lindvall, telling our story at TedX.

We have a clear goal with our entrepreneurship, we will help large organizations become collectively innovative at scale by applying artificial intelligence to the collaborative innovation process. There are two problems that arise: too much data and no time to manually process it. To fulfill this and make it possible we initiated a joint research project together with Swedish KTH (Royal Institute of Technology) during 2015-2016. During this project we developed artificial intelligence, powerful deep learning algorithms, to make it possible to use data from insights and ideas provided by users and combine that with data from e.g. Office 365 and Workplace by Facebook. This makes it possible to automatically matchmaking ideas with the individuals who are best able to contribute to further development.

Further on, we also solved an important challenge, that is the need of an automatically clustering of ideas by matchmaking existing ideas with other ideas that could be supplementary and merge the ideas into cluster of related ideas.

You can say that we robotize the innovation process.

Now we’re launching the Wide Engine API

By on 15 September, 2016 in News

Our strategic intention has always been to deliver Wide Ideas as a SaaS (Software as a Service) using a subscription-driven software licensing mechanism. Both we as vendors and our customers take advantage of the economies of scale associated with this model. But we also see an increasing demand for customization according to user interface and process orientation, as well as need for integration with intranets and 3:rd party data sources.

With the aim to add more flexibility for our customers, we have developed the Wide Engine API. It is a comprehensive set of web services that makes it possible to extend the functionality of Wide Ideas to the web, mobile apps or other applications. It is even possible to build your own fully functional custom made Idea management system based on Wide Engine API.

We have put a lot of effort in this development and I will give a short description of three main areas that we consider as crucial for an API with enterprise grade.

Authentication and authorization API
We decided to use OAuth2 is an authorization framework that enables external applications to obtain limited access to Wide Ideas. Our implementation works by delegating user authentication to the service that hosts the user account, and authorizing third-party applications to access the user account. Authorization of external applications that need access to Wide Engine, e.g. web and desktop applications, and mobile devices is also handled according to OAuth2 standards.

Operational data API
No enterprise application supporting an important business process exists in a vacuum. Neither do Wide Ideas. Thus we opened up an API to the Operational data store. The aim is to make it trivial for third-party developers to integrate. Some basic operations covered are e.g.:

  • Post idea
  • Categorize idea
  • Tag idea
  • Comment on idea
  • Vote on idea
  • Feed of events
  • Notifications

It’s worth noting that we make our machine learning algorithms accessable via API. First in line is ”Score”. This algorithm analyse a huge amount of ideas simultaneasly and rank them based on predictions of the probability that they will be selected for implementation. This feature save time and resources in the evaluation process and ensure that every idea gets evaluated.

Usage metrics collection and logging API
Usage metrics is crucial in determining how to support the users, drive the process and extend reach witin your community. The Wide Engine API let you access the data you need to access statistics and move the process forward.

Now what?
If you are a customer looking for an Idea Management platform for your business vertical, you probably will need to customize and extend it. The Wide Engine API is what lets you do that. For more information, please have a look at our API documentation or ask us for code examples and best practices and we will help you out.

What we currrently work on

By on 13 June, 2016 in Blogg

We are always keen on listening to customer thoughts and reflections on Wide Ideas. And we have some great ideas our selves as well.

It’s a strong trend among all types of organizations to introduce one (or several) Enterprise Social Networks too boost individual connectivity and productivity. We see many customers who now make serious efforts to introduce Yammer or Slack, and we see customers considering Facebook at Work as a suitable option when its available for mass market.

Idea Management in Enterprise Social Networks

One aspect of the underlying need for using enterprise social networks is to become more agile when it comes to capturing and refining new ideas. Knowing that the next great idea could come from anywhere in the organization. From a product developer who got a sudden stroke of new insight, an employee on the shop floor that always wanted a smarter shortcut, or a marketer that shares a daily mistake they’d like to avoid in the future. One can never know — the next big idea could come from an accountant a Sunday morning as she walks her dog.

And anything goes in terms of ideas. It’s good to encourage them even if they’re ultimately impossible or impractical, because even those can lead to other ideas that work. But there are new challenges coming up on the table when companies communicate internally in social networks.

Basically there are two problems that arise: Too much data and no time to manually process it. And we have the solution.

Find value from too much data

Successful implementation of social communication give people the power to continuously generate a very large amount of ideas. Thus the primary problem is that ideas will be difficult to find and extract in the often overwhelming flow of information that exists in those networks. Thus ideas don’t get properly shared and discussed and doesn’t reach their full potential. And the more people that are involved, the more ideas needs to be evaluated and being acted upon, which makes the evaluation and decision-making process virtually impossible to conduct accurately and efficiently using traditional manual methods.

Our solution to that problem is a technology that make it possible to extract ideas discussed in the network and algorithmically process data related to ideas, insights and people.

Save time by letting our AI make the selection

We have already shown that we can use artificial intelligence to rank ideas and find the best ones and we are now working on a broader family of algorithms. Algorithms will help users collaborate and create better ideas by automatically matchmaking ideas with the individuals who are best able to contribute to further development and support clustering of ideas by matchmaking existing ideas with other ideas that could be supplementary and merge the ideas into cluster of related ideas. Further on we continuously develop our existing algorithms that give automated support to the evaluation and selection process if ideas, which makes it possible to handle very large amount of ideas efficiently and accurate.

Our technology will thus make it possible to involve extremely large numbers of people in various social networks to co-create ideas and we are eager to find more customers who are in the forefront in the intersection of innovation and internal social collaboration. We are confident in our belief that this is the area where companies can leapfrog and create a truly innovative organization. Stay tuned!

Jakob Lindvall, CEO Wide Ideas.

Future way working

By on 30 May, 2016 in Blogg

There is no doubt that the world of work as we know it is undergoing a rapid change. Albeit at different speeds in different industries, it affects all types of work and everyone involved, regardless of previous experiences or age.

In this age of rapid change, we see a few strong trends which together are the driving force for the future of work in terms of development of idea management and innovation. As we see it, these trends are a direct consequence of the increased pressure on organisations to continuously develop and innovate, together with the employees’ increasing demands for participation and flexible working conditions.

Future way of working, in brief:

  • Your corporate culture must encourage a constant flow of ideas.
  • Everyone in the organization should have the possibility to participate in idea development.
  • Supporting applications must be available for the user anytime and anywhere, with the user experience in center.

We have written a white paper on this subject – as it feels to complex to present only in a blog post: Future way of working (pdf).

Are you ready for the future?

Key competence from Spotify and Plejmo appointed to the Board of Directors for Idea2Innovation Sweden AB

By on 3 May, 2016 in News

The CEO of Plejmo, Ulrika Viklund, is now a part of Idea2Innovation Board of Directors. The company flagship product is Wide Ideas – a cloud-based application for Idea Management.

Ulrika_Viklund

Ulrika Viklund is currently Managing Director at Plejmo, a Nordic TV On Demand platform, and did previously work as Director of International Growth at the music streaming service Spotify. She is also a board member of myAcademy.

To get Ulrika in our team feels fantastic. Her knowledge and background from digital business and rapid global growth will benefit us tremendously as we now enter the international market with our application, says Jakob Lindvall, CEO of Idea2Innovation.

It feels great to join the board and support the Wide Ideas team, especially in the current phase of company development. Far from all companies use smart tools and processes to drive innovation, despite having innovation at the top of their agenda. Wide Ideas and their use of machine learning to enhance innovation is a unique solution. I have extensive working experience regarding digital transformation and there are many similarities between the streaming media industry for movie and music and the rapidly evolving industry for cloud applications where Wide Ideas operates. Especially when it comes to product packaging and processes for scalability and growth. This will be an exciting journey, says Ulrika Viklund.

In addition to Ulrika Viklund, the founder Åse Angland-Lindvall and Chairman Per Skyttvall is on the Board of Directors of Idea2Innovation Sweden AB.

What I learned about Big Data and Machine Learning from trying to predict football matches.

By on 16 March, 2016 in Blogg

The past few weeks we’ve talked a lot about the brand new algorithm that we have designed for Wide Ideas. The story behind Score, which is the name of the new functionality, is a bit interesting. Actually Score is a result from my hobbie to predicting fotball matches…

Two years ago I asked myself if it in any way would be possible to use Machine Learning techniques to predict the outcome of football matches.

Data mining
To describe the process briefly I started by collecting as much data as I could get hold of. I mined data about old games from every different source and API I could find. Some of the more important ones were Football-data, Everysport and Betfair. I then took all the data for from the old matches, with its corresponding results, quantified it and put it in a database. Finally I used the data to train a Machine Learning model, using it to predict upcoming games.

How to measure how well a model performs
Now, the nature of a football game is, of course, that it is unpredictable. I guess that is why we love the game. But I still was a bit obsessed by the naive idea that I with a Machine Learning approach was going to be able to predict games better than I would using my own mind. I knew from the past that I, as most humans do, base predictions on emotions, rather than facts and that I am somewhat biased. I know for myself that I quite often placed bets in the past based on a “gut feeling”.

The first question I now had to answer was how to measure if my Machine Learning model was successful or not. I quite quickly came to realize that measuring the actual percentage of correctly guessed games didn’t say that much if I didn’t put it in relation to something else. And the best thing I could come up with relating the model to was what other people were thinking. The easiest way to assess that would be to look at market regulated odds. So I started comparing how my model would perform if betting on Betfair because their odds are regulated on people betting against each other, making the odds a reflection of what the “market” predicts.

The result

theresult

So now, two years has passed. Has the model made me rich? No, not at all. Quite soon I realized that the predictions my model made for most part was aligned with the market. Since I use a regression based model I’m able to predict the strength of the probability of a certain outcome of a game. And at the strongest grades of probability my model gives, it predicts roughly 70% of the games correctly. Now the problem is that the market more or less performs just as well making it hard to actually make money out of my model. But, to be honest, I never really thought that I would create a money machine. Instead I have come to several insights about the possibilities and limitations of Big Data and Machine Learning.

How much does a model learn over time?

modelOne of the first things I started looking at was that, since the nature of Machine Learning would be that it in theory gets better over time – as the amount of data the model has to learn from grows, the outcome of the predictions would improve. This is something I haven’t seen at all. Two years ago I started with having about 2000 games in my database with quite a limited dataset attached to them. Now I have almost 30000 games, complete with lots of data covering everything from weather and distances between the teams home grounds to shots and corners for and against. So, given all this data and the fact that the model has been able to “learn” over time still hasn’t improved the predictions. This has taught me that machine learning only takes you so far in trying to predict the unpredictable.

Generalization
Another important lesson I have learned is the power of machine learning still in many ways lies in its power to make unbiased generalizations. Over the past two years I was very curious to see if my model could predict when winning or losing streaks were to be broken. If it for instance could predict when Barcelona would finally lose after winning 10 straight games. If the model could find small signs that would indicate some kind of anomaly. Well, it has shown to not be that good at that.

What I found instead was that it was really good at, over time bet against over valued teams. Last season I for instance saw how my model quite often predicted against Borussia Dortmund while the market made another prediction. Dortmund ended up having a bad season making my model really successful here in relation to the market. This season I have seen the same when it comes to teams like Liverpool and Chelsea. So the lesson learned is that some people tend to make decisions based on emotions. Liverpool and Dortmund are teams liked by lots of people and at times you make predictions with your heart instead of your brain. My Machine Learning model does not.

Easy gains
Last but not least I guess I learned that making better predictions than the market is hard. Still, when I started looking at what I actually had achieved instead of looking at what I hadn’t I realized some quite amazing things. From a simple Python program and less than 10000 rows of code I still had made something that performed just as well as the market. How many man-hours aren’t behind bookies odds models and predictions? My model also is able to, on a weekly basis pick out interesting bets, just as any newspaper or expert does, but theirs with lots of manual labour behind. So the main insight is that by making generalizations you might not be able to find the one bet that makes you rich but it may save lots of time placed in the correct context.

Wide Ideas
With these insights I started to look at another project I’ve been involved in for the last 5 years. The idea platform Wide Ideas. What I wanted to do was to look at the ideas companies gather from their employees and try to predict whether the idea would be implemented or not.

We started by looking at the ideas just as if it was a football match. We quantified the data but instead of shots and weather we looked at how many who had interacted with an idea and in what way. Due to discretion I won’t go into details but the outcome was quite similar to the football model previously described. We now can make a quite good prediction on whether an idea will be implemented or not given the data the idea contains. This is a way of generalizing the ideas, answering the question, in general, what are the factors behind a good idea?

However – can we find a good idea that doesn’t follow the general patterns of a successful idea? No, not really – not yet at least. Still, for the product, and given that you look at an organisation that creates say 10000 ideas per year, finding any good idea is really hard and time consuming. So just by going from 10000 ideas to 100 probably good ideas and visualizing the result saves a lot of time. And this is where Machine Learning has given us the most gain.

predictingPredicting the unpredictable
To sum my thoughts up. We see companies gathering lots of data promising that they might be able to predict anything from finding cancer to making self driving cars. And they might. Especially where generalization saves time. The medical implementations I think is a good example of this. Looking at pictures of birthmarks a Machine Learning model can pick the most likely ones to be cancer from a large set of pictures saving doctors important time and money.

But a lot of the things companies may try to predict has an unpredictable nature. Human behaviour is one. In what way is human behaviour predictable? How far can we come in predicting the human behaviour if it essentially is unpredictable? We will be able to generalize, placing people into different categories based on what you like to eat, watch or do, but honestly, who likes to be generalized?

What the past two years has taught me is that we in some way right now may be seeing a Big-Data-bubble. Will Big Data really find the anomalies or will it just be really good at making generalizations? I often believe that many of the promises made by companies tend to be that they will be able to find the needle in the haystack but that the results most often are generalizations. I guess that one of the reasons they do this is because their values as companies right now often are based on the amount of data they possess and not what they do with it. And if they were honest with the fact that the make generalizations, good ones but still generalizations, their value would decrease. I hope that we can see a future where companies values are based on what they do with the data rather than how much data they have. This will require transparency and honesty, just as I’ve been with my football model.

So, until someone proves me wrong I’m not convinced in the power of Big Data in general. I only believe in it where the cases are clear and one of the most obvious and best ones are examples within healthcare. The risk otherwise is that you end up with as much data that the sheer amount suffocates every possibility to make sense out of it in any other way than vast generalizations.

Accurate basis for decision-making through a unique use of artificial intelligence for idea management

By on 9 March, 2016 in News

Involving a large number of people in creative idea processes and development work is frequently very rewarding and valuable, but it can also be complex – particularly when it comes to selecting and prioritising among many ideas. On this basis, Idea2Innovation, a company from Åre, Sweden, and researchers from KTH Royal Institute of Technology, have designed unique support for selection and decision-making in Wide Ideas, their cloud-based application for shared idea development and innovation.

kampanjbild-forslag-01-hires

Increasing numbers of businesses are starting to use social applications in order to involve their employees and customers in the development of new ideas. The new tools make it possible to involve large numbers of people in developing and improving products and working methods; an efficient way of benefitting from the collective creativity found in people both inside and outside your organisation. However, many organisations who start to use this new way of working with ideas may face a dilemma, because the number of ideas that is submitted often vastly exceeds expectations. Providing feedback and selecting which ideas should be developed risks becoming a demanding and challenging task.

­“Wide Ideas’ newly developed decision-making support is based on cutting edge technology. It is worth pointing out that decision-making in these types of creative idea processes can only be done by people; computers with mathematical algorithms will never be able to replace a human’s ‘gut feeling’. We have therefore combined two different technologies to make decision-making more efficient – algorithms that help highlight promising ideas and visual tools that facilitate the individual or shared evaluation of ideas,” says Jakob Lindvall, CEO, Idea2Innovation.

Development has been conducted in partnership with innovation researchers at KTH and a number of businesses that already work systematically with idea development.

Together, we have now demonstrated that it is possible to create self-learning systems that can help people find the best ideas. Companies that succeed in involving many people in idea development may well receive thousands of ideas, and this technology allows a radical reduction in the time needed to select the ideas for investment. It is also considerably easier to provide rapid feedback to all those involved, which is important for motivation and thus for the idea work’s sustainability,” says Mats Magnusson, Professor of Innovation at KTH Royal Institute of Technology.

A beta version of the new decision-making support is supplied as a module for Wide Ideas and is now available to a selection of existing and new customers.

FACTS
Wide Ideas’ new algorithms
The new and unique algorithms are self-learning and based on a technology called deep learning. They analyse the ideas’ content and context, such as taking account of the ideas’ description, comments and particularly whether the communication surrounding the idea is positive or negative – a sentiment analysis. It also reads which people have been involved in the idea and their histories in idea creation.

Wide Ideas’ new visual tools
The new visual tools are based on interactive information visualisation. They are classic evaluation tools that have been digitally produced and allow ideas to be compared and evaluated using a range of optional parameters. This can be done individually or in groups. Additionally, all the available data related to the organisation’s own idea development process is visualised – it is now visible, available and quantifiable.

Wide Ideas selected for Dustin Sweden’s product portfolio

By on 5 November, 2015 in News

The Wide Ideas digital platform for shared creative idea development will now be available as an exclusive product in Dustin Sweden’s portfolio, as Dustin and the world’s biggest technology distributor, Ingram Micro, are making a shared investment in apps for business support.

The Idea2Innovation IT company, from the northern Sweden ski resort of Åre, captured the companies’ interest when they were looking for a partner for idea development. Their Wide Ideas digital platform is now being presented as one of ten new products in Dustin’s portfolio.

The platform supports the shared creative development of ideas and, using advanced artificial intelligence, Wide Ideas can see patterns among ideas, interaction and participants. Wide Ideas does what we humans can’t; sorting through thousands of ideas and finding both the gems and the valuable patterns.

There is great pressure on organisations to work with renewal and development. We have looked for applications that can help companies with that within their niches. Wide Ideas is a product that we believe will transform businesses into innovative organisations and support our customers in their idea work,” says Marcus Lindqvist, Country Manager for Dustin Sweden.

This is an important partnership for us,” says Jakob Lindvall, Managing Director at Idea2Innovation. “We love what good ideas do for people and businesses and we hope to reach even more people through this partnership.”