Ólafur Andri Ragnarsson

Winner of the TechCrunch Disrupt 2015 Startup Battlefield that took place in London in December, was a small startup from England called Jukedeck. They presented a solution that can help you create music for videos. The create part is up to an algorithm that will automatically generate the music according to your preferences. You can specify type and spirit of the music as well as length. The real news here is that people now have access to a machine learning algorithms can generate music. Forget the music videos part: algorithms are now composing music. This is just one of the many examples of the new generation of machine learning algorithms we have seen emerge in the last few years. We are in the early stages of a new era of machine learning.

Creating an algorithm to generate music is not new. It turns out that Prof. David Cope at the University of California at Santa Cruz experimented with computer music at the end of the 20th century. He specialised in classical music, in particular Johan Sebastian Bach. By 1997 he had perfected his algorithm so it could generate sufficiently good composition to be able to fool the general listener. Maybe not a masterpiece but sufficiently good.

In 2012, Google announced  their research into finding cats in videos. It may seems like a stupid problem to solve but it is actually a breakthrough in computer science. Ever since the introduction of the first computers – ironically called “electronic brains”, people have been fascinated by their capacity to act like a brain. However, computers are far from anytime like the brain. If you use a computer to multiply 1,000 three digit number, even an old 1960s computer would be by far better then a human. It turns out that what is hard for us humans is easy for computers, and what we find easy is hard problem for computers. If you ask a two year old kid to point to a cat on a picture, it would be an easy task. But give that problem to a computer, it’s remarkably difficult to solve.

Despite the fact that problems like image recognition and creative task such as composition are hard, it has not stopped people from working on machine learning or Artificial Intelligence (AI) in general. In the 1980s there was a promising new wave of AI technology called Neural Networks. Those are networks that in some ways try to act similar to the brain, with nodes and connections between them. The the idea was to train the network to get better at specific tasks. Although promising at the time, these networks did not deliver much and became a disappointment. Yet another AI winter followed, not so common in the quest for intelligent machines over the years.

Neural Networks may not have worked in the 1980, but since that time we have seen exponential growth in compute power, storage and bandwidth. Now we have cloud computing and big data. Furthermore we have video games and for video games we need Graphical Processing Units or GPUs and this means we can build really powerful supercomputers, relatively cheap. This is the adjacent possible for a new era of AI. It turns out that the basic idea of neural networks was not wrong, but the capacity to make it work was not available in 1980. Really good example of adjacent possible.

Prof. Cope’a algorithm was a programmatic way to generate specific type of musical composition. The new type of algorithms we see today, like the Junkbox service, works in totally different way. Machine learning algorithms are trained, not programmed. Part of this is deep learning algorithms that are fed with huge amounts of data and use layers of nodes to try different combinations, strengthening those that work and repeat. One class is Recurrent Neural Networks which seem to be able solve particular types of programs like speech recognition, understanding handwriting and, surprisingly, composing music.

Even with all the knowledge on machine learning available, creating a machine learning software is really hard and requires huge infrastructure. The technology is very much academic but is starting to produce practical solution that will open up new levels of possibilities. Big technology vendors are democratising machine learning and offering relatively easy and affordable access to machines learning software. Google has their Prediction API  and Amazon has their Machine Learning Services. Access to machine learning systems is now  as simple as signing up for subscription on the web.

So what does this mean? This means that apps we use will get smarter and work better for us. They will be able to predict out preferences and help with many problem only capable of humans. Services like speech understanding,  pattern recognition, personal recommendations, document and image categorising, fraud detection, and all sorts of creative task will be done by software. It will mean a shift in jobs as software can increasingly replace some tasks, previously only capable of humans. At first we will find this scary, but then, as usual get used to it, and expect some smartness of all things, including everyday objects like cars, TVs and coffee machines. We expect to be able to talk to these things and they talk back.

We are still in early days of this machine learning renaissance and we have a lot to understand what this means for business and people’s jobs, in particular white collar jobs. With the access to enormous cloud computing services, more and more solutions will appear that try to predict and analyse our behaviour. More and more tasks will become software task and this will change the job market. Companies that want to stay relevant, even non-IT companies, need to think about how software can help them.

Economist Events hosted an event in Madrid 4-5th of November 2015 called Digital Transformations. The topic was how digital technologies are disrupting businesses and changing our lives. In fact, I believe that we are experiencing the last years of the world as we know it. There are new technologies coming for example in robotics, artificial intelligence, predictive intelligence and virtual reality to name few, that will have huge impact and shape the 21st century just technologies have in the past. Here are some thoughts about the discussion.

Technology moves fast but the diffusion of technology into our everyday live is slow in comparison. Many of the technologies that will disrupt businesses in the future emerged in the 1990s and 2000s. Just think about the Internet becoming mainstream and the rise of the smartphones. The iPhone is already eight years old. These technologies have changed our lives, sure, but most businesses are still working as they did in the 20th century more or less. Banks, insurance companies, hospitals, schools, and government to name a few. The reason is that technology change is very much a human issue and it takes time to change the way we work and behave.

In my lectures, I have always talked about the digital decade as the first decade of the 21st century. This is the time when everything analogue became digital. The second decade is the transformation decade where the intangible things change. Things like business models, shopping behaviour, ownership, life-style and so on. And this is the decade of confusion and collisions between the old way and new way. Think about the content owner’s war with privacy  and how controversial Uber causing riots and strikes.

However there seems to be a general disconnect between general knowledge of the digital transformation and the few that “get it”. Companies like Google, Facebook and Amazon operate on a scale we have not seen before. At the same time many traditional business are ignorant of the possibilities of digital technologies and face getting disrupted. The conversation is simply not taking place. An important theme echoed at the event was the need to educate leaders on the possibilities of emerging technologies.

Some companies seem to be aware of the trends though. European carmakers like Daimler are shifting their attention to digital opportunities. The odds however are stacked against them. Incumbent companies have huge luggage to carry. They may understand Clayton Christiansen’s disruptive innovation theory, but he actually had another theory which explains why incumbent companies fail. It is called the The Resource, Processes and Values Theory – or RPV theory which states that company’s resources – the people, the processes that state how work is done, and the values of a company define how the company functions. This gets optimised over time and if there is a new opportunity in the market it is difficult for the company to take advantage if it. And if is a cheaper and less profitable version of their existing product, there is strong resistance to change.

Kodak was mentioned as an example. Sure, Kodak enjoyed good success all through the 20th century. By 1975 they had 90% of film sales and 85% of camera sales in US. That would be classified as owning the industry. Kodak had experimented with digital cameras in the 1970s. They had actually built the first electronic camera. However, the technology was crude, expensive and with low quality. And this is always the reaction of incumbents companies when faced with threat: It’s expensive, it’s low quality and nobody wants it. It maybe the case at any given time, but the exponential growth distorts our view. By the time the threat is real it is usually too late.

However, transforming companies in disruptive times is very much possible. There was another company in the same business as Kodak that was not mentioned, and that is Fuji. When they realised the change from film to digital they totally reorganised the company laying off thousands of people. They squeezed as much as they could out the film industry while it lasted by putting cheap cameras on the market. They diversified their chemical operations into cosmetics and sold that unit. While Kodak is an example of a Mammoth that went extinct, Fuji is an example of a company that survived.

Another discussion at the event was the observation of real-time. We have moved to a business that is real-time. If there is a moment, you have to respond to it. Many businesses find this hard. If a customer complains on Twitter, you cannot call a meeting and discuss the reaction. You have to respond now.

Privacy was also a big topic. We are transforming from a very private world into a sharing world. Any move to limit peoples privacy is not taken so lightly. If not done right that is. People seem to be willing to give some of their privacy away if they receive value by doing so. Probably the most private – you health, is not excluded. If you can use some services that will benefit your health, people will accept that. In general, the discussion was that privacy is something we haven’t figured out yet.

Big data also got is share of the discussion. While the definition not so clear it is obvious that data is growing exponentially. Just with the Internet of Things we can expect floods of data. But what is important is the value we get from the data. This is the challenge many business are facing. There are also technical issues as the traditional enterprise systems simply cannot cope with this scale. Not surprisingly, with the internet there was a rise of new solutions, for example databases, collectively called NoSQL database to meet this demand. Hadoop is an example of new solutions to tackle these problems.

Another great discussion was about machine learning. This is about getting machines better by experience. One section of machine learning is deep neural network which have shown remarkable progress in the last few years. Advances in the field of machine learning are likely to have huge impact on the labor market. However it is not clear how this will play out. Will people be replaced? Are new jobs coming instead? How will we retrain all the people? What will the unions do? These are some of the hard questions that people are asking. One theme on the event was that technology is all about augmenting the human. Making people more productive.

Finally, I want to mention Blockchain. It was a reoccurring theme that came up. Understanding blockchain is not so easy as it is not product but rather a byproduct of Bitcoin, a protocol layer, a distributed trust mechanism. Someone defined it as a living organism. At least, Blockchain has the potential to have significant impact, maybe on par with the Internet.

Events like this forward the evolution of technology. It is a about an intellectual conversion about technology trends and their impact. The world will face new challenges due to technology in future. At least some people got together and talked about it.

Picture from Sol, Madrid:

The internet as we know it can trace it roots beck to the late 60s in America, but it was not until the mid 90s that this simple and primitive network started to become popular.  This was primarily due to the RFC (Request for Comments) 1945 labelled Hypertext Transfer Protocol — HTTP/1.0. In other words, the World Wide Web.

Today there are over 3 billion people connected to this single network of computers, smartphones and devices. This has of course had huge impact on most societies. People use Internet banking, email, social media, browsing and so on. So you might be wondering what innovation could possible follow. With the Internet, we a practically done, all that can be invented is already here. However, that view is simply wrong.

I believe we are still in early phases of this revolution. There are still many disruptions that are yet to come. And to understand the potential of the Internet you really need to understand its characteristics and what that means. So here are my six points about the Internet.

1. Everywhere – Ubiquitous Connection

Basically, today the Internet always there. People are always connected, in their workplace, home, hotels, restaurants or with their mobile where ever they go. This means that all the online services are available all the time where ever your are.

Most people have smartphones and thus access to all these services 24/7. Today, the smartphone is probably the one tool made by man that they get attached to very deeply. At least you never go far without it and people are constantly looking at their screens.

This is a very human trend and thus global and goes across cultures and countries. This picture shows the growth of the Internet and in comparison, the smartphone:

Picture is from Benedict Evans: http://ben-evans.com/benedictevans/2014/10/28/presentation-mobile-is-eating-the-world

2. Untangleble – Digital

The first decade of the 21st century is called the Digital Decade. This is the time when all consumer products with content moved from analog to digital. Be it pictures with digital cameras, music with MP3 players, TV shows or films you played on your computer and books you read with e-readers. This is the greatest shift in technology since Thomas Edison invented the phonograph. You can say that the content lost it form. A song on an LP or CD became file on a disk drive – untangleble and thus very copiable. In fact, the Internet is a giant copying machines.

It is not just entertainment, this also includes invoices, information, educational material, airplane tickets, bookkeeping just to name few examples. All this has huge impact on consumer products, how we consume them and how we buy them. As an example, digital books can be bought in just few seconds, downloaded and ready to be consumed in few seconds. This has changed consumer behaviour and that has impact on businesses.

3. Data Floating in the Sky – Cloud Computing

More and more users find it convenient to store their data in the cloud. Most apps do that automatically anyways, and the user might not even be aware of the fact. This means that data is not fixed at a particular location or device. Since they are available from the network, they are available anywhere where and when there is connectivity. There are several general solutions for storing documents like Google Drive, Dropbox and Microsoft OneDrive.

With data and documents in the cloud, new possibilities emerge. You can share documents with others without making multiple copies. Others get access to the same document and this creates the possibilities of collaboration. It turned out the people started to prefer the then primitive Google Docs instead of sophisticated Microsoft Office because of the sharing and collaboration capacities.

4. Infinite computing – Utility computing

Its not only data in the cloud, its also computing power. Computing is like a utility that you tap into. Software today runs in data centres where the computing power is seems is unlimited and the power you need can be adjusted according to the load. Companies can save huge amount of capital investment in computing power that sits idle most of the time except for peek loads. Cost of maintenance and upgrades such as due to security is much lower. This also saves real estate since companies do not need to have a room full of computers.

The cost of creating a new startup that can offer and sell software to millions of consumers has fallen dramatically. Today, startups are born in coffee house by few people with their laptops and lattes.

5. The Invisible Middleman – Rise of Platforms

For centuries, the coordination between consumers and producers, buyers and sellers have been according to hierarchical structure. If you wanted to book a hotel room in a foreign city, you would talk to a travel agent. The travel agent would talk to an other travel agent that would talk with the hotel owner and thus book a room. For each transaction several people needed to be involved. This is called coordination cost. For most of the 20th century this cost was high and the process slow.

Now replace this coordination with software and the cost drops to as good as zero. And anything common that goes from expensive to nothing will be a revolution. We see the examples: Airbnb for booking rooms and Uber for rides. But we can also look at banking. With internet banking or bank apps, paying the bills can be done during breakfast in few seconds, instead of going to the bank and have a bank teller do the same thing using the enterprise software of the bank. Just put the software in the hands of the consumer and cut out the middleman.

6. Machine Communications – Rise of the API

Over the last few years we have seen huge increase in access to APIs (Application Programming Interface). With APIs, one software program can call the next software program over the Internet. Everything is in the cloud anyways.

Thus you can with an smartphone app get the temperature since the app you are using calls the Met Office for the information. This gives us frictionless connections of devices and the end of closed systems as devices can communication. This is the Internet of things.

But so what?

What does this mean? It means that despite enormous impact of the internet so far, there is still a huge untapped potential. It’s really when technology “goes away” and becomes invisible and taken for granted that it begins to be relevant and make impact. In all of those examples above, its not about the technology but the uses of technology.

We are now entering an era of wearables, drones, Internet of things, deep learning networks and virtual reality. All of these technologies will use the internet as a building block enabling access to those technologies.