Notes on Business Productivity and DAO

Business Productivity means somehow working more efficient, in other words producing as much output for a given time as possible. Considering however from a pure financial perspective human resource is basicaly just a resource that is actually pretty much expensive and risky. We see the tendece for decades that human labour has been actively replaced by software and hardware compontents and noone has ever complaind from the inverstor side.

However if it is really a tendency and a need producing as much output for as little input as possible than we might as well ask the question from the other side, similarly as the question was asked by Ethereum and DAO. 

- Do we actually need humans as work force for value creation ?

- Which activities and Organisation structures can be 100% replaced by algorithms ?

- Which companies and company structures can be fully automated ?    

- How is it possible to invest in a fully automated company ?

Notes on crowd buying

As crowdfunding is an interesting area which is just getting more and more popular, a similar direction gets less attention and that is crowd-buying or social-buying. Crowd buying means practically allocating several buying requests into a thread having a much better position to negotiate and probably getting much better price and delivery conditions. 

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.   

summoning demons in IT == decentralized deep learning

summoning demons in IT == decentralized deep learning

Notes on social as a service

Current trends of social platforms, like Facebook, LinkedIn, WhatsUp try to extend in some way the original human social communication. However, they seem to be pretty much ad-hoc for the first sight.  What is missing somehow the systematical analysis: these are the drawbacks and awkward of the human communication and social interaction, these are the points that can be done better with different IT systems and as a result these are systems that are available.

As at the end of day all of these systems are extensions of the human interaction, we might as well call the whole field as Social as a Services.   

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.  

How not to invest in Cryptocurrency

Well investing into Concurrency is pretty much risky, it is not a bad idea to know at least something about concurrency before you invest. For that topic I would propose the following perhaps rather funny flowchart. 

Notes on Business Productivity

From a clear economic point of view Business Productivity is simple: human resource is simply too expensive and too risky. So Business Productivity software solutions make to increase the speed of carrying out a task or increase the general availability of a human resource. Examples are efficient collaboration and project systems, offline and home office availability, automated business processes and so on.  

  

However if we stay with the economic point of view: the cheaper and more effective solution is to fully replace the human resource with artificial intelligence software. In this sense in Business Productivity, the major question is not how a certain activity can be delivered with more efficient Business Productivity software solutions, but the question is if a certain activity still needs humans or is it possible to replace fully with artificial intelligence. 

It may sound shocking for the first sight, however the same thing has been being happened with the human physical labor in the last hundred years: everything that could be automated were automated.

Welcome to the second machine age.   

Blockchain and the technology limits

Considering the Blockchain Hype that will be being evolved in the next couple of years, one of the most important questions, where are the limits of the Blockchain technology. In other words, for which Business scenarios does the technology make sense and which are the tipcial examples where rather traditional client server models should be used.

- Decentralized database: blockchain is actually a database that is stored with all the past changes in all of the full nodes of the network. In this sense it is critical that the size of the data that is actually stored in the blockchain is limited. Perhaps there will be in the future for efficient mixed blockchain - off-chain storage possibilities, however until that point blobkchain should be regarded as an extreme expensive storage, in which only a limited amount of highly sensitive data should be stored.  

- Transactions: The state of the database is modified or even read out by several transactions by different actors.

- Trust: Central use case of the blockchain is to guarantee a trust of several different agents, so that normally these agents would not trust each other. From the system perspective both trust of the state of the central database and the validity and order of the transactions should be guaranteed.

As a conclusion, in examples where a lot of data have to be stored, there are no many actors that are cooperating, there is trust outside the system as well or transactions do not really play  a relevant  role, rather classical client server models should be evaluated.