Machine assisted business models

Similarly to machine assisted human intelligence, there can be a brainstorming about machine assisted business models, especially if we regard the current trends and improvements of the machine intelligence and communications technologies. In broader sense machine assisted business model that would not exist without some core IT or communication technologies. As a classical name, we usually call these companies as born on the web. Perhaps it is better to focus however on the business models, so perhaps the most exciting cases are companies that business model is based on some core communication technologies but the product or service that the company offers is independent from this technology. 

From a smaller perspective, machine assisted business models are companies where the core business model is based on machine intelligence. It is again more interesting if we speak really about the business model or the value chain and not about the product or the service of a company. Perhaps one example might be financial institutes that are having robo-advisor services as well. In these examples part of the value chain, that is customer interaction, service is based on machine intelligence. A little bit less machine intelligence example is The DAO, where general company management was considered to be replaces by smart-contracts and Blockchain analytics systems. 

There might be a general consideration based on the value-chain model from Porter which activity of a company can be supported by machine intelligence. If we consider general reporting and big-data as well as part of AI then we have already a lot of needs on the management level for such a technologies, however they are not necessarily part of the business model itself. I think we should clearly distinguish the two directions "supported by machine intelligence" versus "based on machine intelligence" and we should consider our efforts to the second one.  

Figure 1. Porter's value chain model.

As Porter's value chain model might be a good starting point for the analysis however it is based on the old-fashioned model of a company. It is an exciting question if the old model can be disrupted considering the new technologies and if we can imagine companies that work somehow absolutely differently. As an example let we imagine something as a fully autonomous cash-flow system that works absolutely without any human involvement. I would certainly consider that as a company though it would be pretty difficult to put into the value chain model from Porter.  

Notes on machine assisted software services

PowerBI, it is awesome,

PowerApps, it is awesome,

Microsoft Teams, it is awesome,

However, actually the next releases will be much better. The next releases will probably have the possibility to create BI reports, Apps and communication best practices in a fully automated way based on artificial intelligence. 

And the idea is basicaly simple, put relative simple quasi freemium service onto the market, train your AI on the data (yes you can do it without actually seeing the data, without having problem with any data protecton regulations) and in two years you can launch the next release of your software fully automated based on machine intelligence trained on a huge amount of industry best practice data.

Welcome to the dawn of the machine assisted report generation and machines assisted software applications.

 

An attempt to define the field: Machine Assisted Human Intelligence

At the beginning of the second machine age, life has become increasingly complicated especially if someone works usually online. The reason for that is simple, the amount of knowledge and information that can be easily accessed online has been being boomed in the last couple of years on the one hand because of more and more people getting online and sharing directly or indirectly information. On the other hand, we are at the age of an IoT revolution as well, where not only people, but billions of intelligent devices are getting online, implying a huge wave of device generated data. 

From an evolutionary and biologically perspective this situation is something that is pretty new. We have been aware of the limit our physical body and human perception for thousands of years and we have been making contiguously attempts to overcome these limitations. Examples ranging from the tools of the prehistoric age via the steam engine to the industrial revolution. As an example, let we consider the capability of the human body to be able to lift at most around 100 kg. That is known to be too little already several thousands of years, implying inventions to overcome the limit from different lifting devices to different crane tools. Another well known example is the limitation of the human perception not to see in the dark. Attempts were made to overcome this limit from simply lighting a fire in the prehistory age to the nowadays night-vision systems. However information overload is something that is from an evolutionary point of view totally new. The amount of information throughout the world was something that one could overview even hundred years ago. Nowadays, the existing information is being doubled in each three years, everything is online, everything is real time and in certain fields you need pretty much of it to work or to make decisions. The human brain is simply not capable to work with such a huge amount information and well as the requirements are pretty new, we are simply not sure where exactly are our limits.     

Good examples are for instance many IT fields, like cloud computing. The field did not exist 10 years ago, despite nowadays there are thousands of books of information about and it is being continuously increased. Working in a field like this requires huge amount of time to simply keep up with the information without doing anything productive. There are not many companies and business models that can support such a thing. As a consequence the need for extending information processing limits  of the brain is an always increasing and demanding field.  

On the other hand current trends of artificial intelligence and machine learning provide many intelligent tools that could be efficiently used extend the limits of the humans' recognition and information collecting process. These fields have rather the focus of creating something that is thinking - working - acting similarly as a human. However with the little refocusing the tools might be used not to copy something that already exist and slowly will not be sufficient for the modern world requirements but rather to extend the human's thinking capabilities. Certainly it should be analysed which AI tools and methodologies can be really used in this field as well: rather methods that produce at the end symbolic results in a human understandable way. With other worlds, let machines support our cognitive capabilities by gathering, analyzing, structuring and storing data.  

Fields of machine assisted human intelligence might be the followings:

- Integrated man-machine interfaces
- Integrated man-machine knowledge discovery
- Integrated man-machine learning and knowledge integration:
- Integrated man-machine memory or storage.
- Integrated man-machine decision making.
- Integrated man-machine collaboration

In this sense the field is located somewhere between cognitive science and machine intelligence, having perhaps more general elements from computer science (Figure 1).

Figure 1. The field of machine assisted human intelligence.

An example for such an ecosystem which already can be found practically in each car is the navigation system. The navigation system extends the human memory simply by storing all of the required maps. It helps with the integrated decision making by collecting online data about traffic collecting both from individuals and from automatic sensors as well, practically realizing an integrated knowledge discovery environment as well. Advanced navigation systems take into account the experience and preferences of the driver as well, providing at the everything on a human readable interface (which I would say can be further developed in the future).

Another innovative field that is emerging at the moment and can be regarded as machine assisted human intelligence is the robo-advisory trend of the fintech industry. 

The need for machine assisted human intelligence

As more and more information getting available on the internet, sometimes in a pretty well organised way, the natural question rises: how can one cope with dealing with this always increasing amount of knowledge and information. Several educational platform like Coursera already exist there is already a way for getting this knowledge in a very well structured and high quality way. Despite the amount of information is simply huge. As we hear practically every day the achievements of the artificial intelligence, the questions raise pretty much naturally: is there a way to support the systematic learning process with machine intelligence ? Is there a way that I get access for the knowledge somehow in a machine assisted modular way, that I do not have to learn everything by myself ?  And well basically the answer should not be just having some data mining algorithm to offer the next course if I have already finished one, but something much more efficient, like getting the whole knowledge instead of 2 weeks in 2 hours. 

The next generation of Business Productivity == Machine Assisted Human Intelligence

Artificial intelligence research has been producing a couple of surprising results nowadays. As the target of most artificial intelligence research is to copy or reproduce somehow the human thinking the question is exciting but somehow much less studied from the other direction: what are the limits of human thinking; where are the limits of the human cognitive capabilities, what are the limits of the human communication or collaboration and how can be these limit overcome with the help of computers, algorithms or generally by IT technology. 

Figure 1. positioning of machine assisted human intelligence.

I think the field is to be found in the intersection of three different areas:

1. Cognitive Science or, generally psychology and sociology should provide input regarding the limits of human communication and thinking.

2. Artificial Intelligence provides probably the best toolkit for algorithmic and IT support of the whole area.

3. Last but not least, Business Productivity is the market. The area in which the results of the field should be positioned, like thinking faster, making better and faster business decisions, communicating better, or just generally achieving more result with less effort. 

Considering current trends in cloud computing and the fact that most providers like Amazon or Microsoft offers Artificial Intelligence as cloud solutions, it is not impossible that in a couple of years productive work will mean that you plug in into a cloud service of machine assisted human intelligence platform.   

Brainware Plugins or Brainware as a Service

Artificial intelligence has got a long history of trying to achieve a computer program that can match with humans in thinking, like passing Turing test, beating humans in games like chess or go. As it is certainly an ambitious research direction, there is another direction that is much more practical and probably business oriented as well. As opposed to create thinking machines it would be similarly exciting systematically analyse the limits of the human thinking and focusing on extending it with different kind of IT support, like brain computer interface, extended memory, additional external knowledge for unknown domains. As there are already some achievements in this area they seem to be rather island solutions, there is not seem to be a general platform for that. It would be more existing somehow mimic the  mobile app platforms and provide a basic brainware computer interface and provide the possibility to write custom Brainware apps or custom Brainware plugins on top. Considering that the platform is probably supported by the cloud, we might as call as Brainware as a Services.

Artificial Intelligence as a Service - Cognitive Science as a Service

Recent trends in cloud computing shows the direction of integrating several artificial intelligence and machine learning tools into a cloud platform. From the Microsoft side tools like Cognitive Science, Azure machine learning, or Cortana Analytics provide machine learning and artificial intelligence in the cloud. Similarly tools can be found from AWS, like Amazon Web Services Machine Learning. In this sense, it make sense to identify the whole area as Artificial Intelligence as a Service, or rather Cognitive Science as a Service or just simply Machine Learning as a Service. 

On the other hand applications can be found as well, that use intelligent cloud services to achieve certain domain specific tasks, like intelligent thread analytics from Microsoft.