Philosophical and practialy considerations of working with artificial intelligence

An ongoing philosophical discussion should be renewed as tool supported by artificial intelligence slowly appear on the market, namely by whom was a certain product / art / service created. The original discussion if for instance a certain painting was painted by the painter or by the brush seems to be a little bit too hypothetical for the first run, however considering paintings that are painted by artificial intelligence algorithm, like by DeepDream, the question seems to be less theoretical. Supposing that I am a painter creating paintings with the help of DeepDream or sculptures with the help of DeepMind, who is the creator of the art ? Me ? The AI algorithm or somehow both of us ? 

The question can be much less philosophical if we consider for instance products that were designed and created with the help of AI algorithms. Who can we call as creator, who should have actually the rights for that product ? Similarly if an online service is provided almost 100% by an AI algorithms, then it is an interesting question who should be responsible for the service quality ? The AI algorithm ? The one who hosts the algorithm ? The one who trained the algorithm ? I think these questions will provide a lot of legal and society discussions on a long run. 

Notes on Turing Test

Turing test :"The Turing test, developed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human" - Wikipedia.

But how about on sub domains where the Machine intelligence actually beats the human ones, like playing chess ? Does the definition make sense ? Supposing that the machine intelligence is better, is there actually a chance to evaluate from humans? As the definition was perfectly working 50 years ago, it is getting to seem to be a little paradox.

Different measures of blockchain Anaytics

Considering a complex Blockchain ecosystem there are many measures, reports and KPI-s that can be taken into consideration. We can categorize measures of a Blockchain ecosystem as follows: 

Technical measures for the network:

- Size and distribution of the Network, like the number of  mining validation or light weight nodes or geographical distribution. 

- Measures for Blocks and transactions, like Blocks minded, number of Transaction validated in a certain time frame, time between mined blocks, 

- Crypto parameters, like Hash rate, Hash difficulty, 

Economic measures for the network:

- General economic values: supposing that the Blockchain includes an internal Cryptocurreny we can define general capital market measures like market price, trade volume, market capitalization, and market share either compared with all other cryptocurrencies or with general capital market measures.   

- Micro economic values: like cost or average cost of a transaction, transaction fess or revenue of a miner, 

Application specific measures:

These measures are really specific to the application that run on the top of Blockchain. Probably the most general one at considering a cryptosystem is UTXO (Unspent transaction Output), or parameters related to cash-flow or crypto-flow, like the amount of money that was transferred between two wallets, or the amount of money that came in or left certain group of wallets. 

Comparing Blockcian with a classical relational database

Despite of the hype phase of a Blockchain, perhaps it makes sense to investigate the architecture from a little bit classical, computer engineer perspective and comparing with a classical relational database. 

- data storage: classical relational database stores the data in tables, as a Blockchain based system stores the data in an immutable ledger.

- data amount: classical relational databases are optimized to store a large amount of data, as Blockchain system are not performing very well on large data. For this reason there is usual to provide a hybrid infrastructure if a large number of data should be stored, like storing the data on off-chain storage and integrate for instance and make an integration with Merkle roots or hash values with the Blockchain. 

- data availability: data is replicated and kept synchronous on all of the nodes at Blockchain providing a high available robust architecture by design that can always be scaled up very easily simply by attaching further nodes to the network, At classical database technology data replication and availability are always additional issues to deal with.

- tamper resistance: Blockchain is tamper resistant by design, it is both computationally and photographically difficult to change existing elements of a chain, even if a couple of nodes have been hacked. As opposed if a relational database is hacked, like an administrator password is leaked, than any data of the database can be easily modified. 

- hacking resistance: We can say that Blockchain systems are much more hacking resistant than traditional databases as a certain transaction is validated by all of the nodes. 

- performance: from a performance perspective a traditional database technology is much more efficient then a Blockchain one. At a Blockchain both the validation of the transaction and the consensus mechanism take time, like considering the Bitcoin network the throughput at the moment is about 7 transactions per second. 

From a system architecture point of view, it makes sense to combine benefits of the classical relational database technology with the Blockchain building up software systems where the Trusted Computing Base is realized this way.  

Difference between the supply curve of the IT good versus mass production of physical goods

There a fundamentally difference between the supply curve of an IT good and  the mass production of a material good. At both cases there is a certain prize on which it is practicable to consider something as a mass production. On the physical goods it means practically building up something like conveyor belts to for producing efficiently. However, despite of conveyor belt the variable cost of a product will be bigger than zero meaning that producing more and more is manifested in more number of or more efficient conveyor belts. On the other hand, reproducing an IT good or service if the platform already given requires practical zero additional cost, meaning that as soon  a profitability point is reached to build up automated platforms for mass production, the supply will be practically infinite.  

Figure 1. Supply curve of IT goods versus material mass production.

Supply demand analysis on the robo-advisor market

Analysis of the robo-advisor market from the supply-demand perspective can be seen on Figure 1. Robo advisor market is characterized by the demand characteristic of a standard IT good, meaning that as soon as it is profitable to roll out a a robo-advisor there is a possibility to produce as many additional replicas or copies as needed practically for a zero additional price. 

Let we consider S1 as a standard supply curve without robo-advisors, S as the supply curve with robo advisors. Market equilibrium will be pushed off from the original {P1, Q2} point to the  {P2, Q1} new equilibrium manifesting in a P1 - P2 price reduction and Q3 - Q2 general quantity increase on the market. It is important to note however that Q3 - Q1 quantity is not produces by humans anymore, meaning that comparing to the Q3 - Q2 jobs have been automated comparing with the original market equilibrium.

Certainly the model is ideal, it considers only the characteristic of a market segment on which the robo-advisors can produce a high quality service. Certainly segments might remain where the human competence and experience is still needed and the segment can not be served by automated robo-advisor services. 

Figure 1. Supply demand analysis of the robo-advisor market

On the economy of a goldrush

It is interesting to analyse the economy of a gold rush considering the two fundamental different roles who were taking part in the rush: The gold hunters and the shovel sellers.

The gold hunter: is motivated to find gold, to find the big business and for that takes a big risk and works (or realizes a negative cash flow) on a long run. If the gold is found or the business is succeeded then the return can be really big. On the other hand relative few of the people really found a big amount of gold.   

The shovel seller: is selling simply shovel for the people who are hunting the gold. It produces a stable low income on a long run, without the real possibility to get a big business very fast. Certainly the shovel seller is motivated that the gold rush is 'going on', as more people think there is a possibility to find gold, the more shovel can be sold. In this sense the shovel sellers market does not depend on the actual gold market but on the expected gold market. 

Figure 1. Cash Flow model of the gold hunter and shovel seller

Figure 2. Risk - Return diagram of the gold hunter and shovel seller

Emerging trend: Reporting under encryption, KPI under encryption

Reporting and big-data analysis has been having a hype phase in the last couple of years, however considering a system architecture point of view a reporting systems has a pretty big security hole it is because it aggregates different kind of a data from probably the whole company or from several companies. The data is processed and presented in a way for some end-users, however in most cases there is no detail analysis about the fact who can see what. It is usually not such a huge problem as we speak of one company (although the security hole is certainly given), however it will be pretty big cross company discussion if the data is coming from several companies.

As a solution there might be possible to use one of the emerging encryption technology that aggregates the data without actually encrypting them, like with the help of zero knowledge proofs, homomorphic encryption or secure multiparty protocols. In this way not all of the data is leaked only some kind of a specific result is presented.  Let we called the field as Reporting under Encryption or KPI under encryption.

Another solutions might be to heavily use information labeling and filtering identifying who should actually be able to see that kind of data or information. In this sense, the field should be rather called secure reporting and secure KPI-s.

Notes on Blockchain analytics

Analyzing data based on the Blockchain seems conceptional more difficult than simply analyzing unstructured data. The problem is that for an efficient analysis not only the pure data should be considered, but basically the structure of the network or the structure of the transactions as well. So, from a theoretical point of view, not only classical data analysis techniques should be used but elements of advanced network and graph theory as well. Similar theoretical elements can be found for example in different analytics tools for social physics. 

Notes on Collaboration 3.0 and corporate efficiency 3.0

The next version of collaboration and corporate efficiency will be much less identified by the nowadays way. Collaboration and cooperation with machine intelligence agents and algorithms will play a much bigger role. From a practical point of view, it is probably true that everything that is produced on a task basis on a measured way will be done by algorithms. As a consequence, human efficiency will be rather represented how one can collaborate with machine intelligence and produce something more creative, something more innovative than just the certain set of steps.

Probably the old saying becoming slowly true:

"Efficiency is for robots"