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. 

Blockchain 2 Business, Blockchain 2 Customers

Considering Blockhain applications it should be considered that the classical market segmentation look differently. We can not really speak about B2B (Business to Business) or B2C (Business to Customer) solutions as the Blockchain itself is usually not a company and it is not run or operated  by a company. Instead they are rather community solutions, developed by community, operated by individuals and can be used practically by everyone. As a consequence, these old terms should be newly interpreted: 

- B2C (Blockchain to Customer): Blockchain services for end-users.

- B2B (Blockchain to Business): Blockchain services for other companies.    

Notes on Blockchain privacy and private Blockchains

Blockchain applications like Bitcoin or Ethereum are highly secure by design, despite some of the data that is stored in the Blockchain are actually far from being private. Even in the Bitcoin Blockchain practically every pieces of transaction and account are visible even with a simple browser, like Bitcoin Block explorer. That is certainly not a desired functionality, professional Blockchain 2 Customer or Blockchain 2 Business services would require extended information privacy. Let we just summarize some of the possibilities.

- decentralized public ledger: that is the most basic model, all accounts and all transactions are publicly available, all mining and validating nodes of the network run public as well.  

- private identity: all transactions and accounts are visible in the blockchain however identity behind an account is practically impossible to identify. There are attempts for such a mechanism in Bitcoin protocol with the always newly generated addresses. 

- private transactions: well it is pretty difficult question. On the one hand all transactions have to be validate by all nodes, on the other hand it is a normal customer requirement that certain transactions should be fully analysed only from theirs owner and not from other third party users. If the two requirements technically satisfiable in the same time is questionable.  

  

- private network nodes: processing and mining of information is not available for everyone, but only a certain group has got the privilege to do. 

- private blockchain: the whole blockchain is not available for everyone and does not contain every transactions but only transactions of a certain application and party of member companies are recorded and certainly the information is only for this group visible. 

The major question is certainly if it makes sense to create such a private blockchain applications or is it better to use for such a scenarios classical client server models.