Blockchain and AI – Convergence Foundational Technologies
“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year time frame. 10 years at most.”
—Elon Musk wrote in a comment on Edge.org
“We must address, individually and collectively, moral and ethical issues raised by cutting-edge research in artificial intelligence and biotechnology, which will enable significant life extension, designer babies, and memory extraction.”
I call the blockchain ‘the Internet of value’ and ‘the Internet of trust.’ Because everything becomes trustless. It’s a big distributed ledger. Think of it like an Excel file that’s being maintained and updated and managed by millions of computers around the world.
Blockchain-AI convergence is a key factor for the present and future of technology as both these emerging and foundation technologies deal with data, value storage and creation. On one hand, Blockchain builds a foundational encryption architecture that enables secure digital storage and sharing, rewarding with data or anything of value. AI, on the other hand, creates a foundational direction to gather, analyze, generate and predict insights from data which generates, predicts and manages value. These two (out of many) areas of blockchain and AI have to be combined to create solutions, scaled technology and business infrastructure. However, to properly manage and use data, there is the need of a third holistic technology, Big data.
In order to understand blockchain and AI, one needs to look at the basic intelligence and assets of our time, data. And Big Data actually is the biggest dimension field of our times. Big data is a series of interconnected technologies that looks at massive amounts of data and analyzes it. Big data systematically extracts information from, or otherwise deals with, data sets that are increasingly large and complex as we increase digital transformation. This is particularly important as we look at digitising all the areas of society with digital transformation tools and creating increasing new areas that require new ways outside of the traditional data-processing application software to manage. This as the first iterations of artificial intelligence increases its ways to manage big data.
When we talk about AI we need to highlight also
Blockchains will drop search costs, causing a kind of decomposition that allows you to have markets of entities that are horizontally segregated and vertically segregated.
When we talk about Blockchain we need to highlight:
Blockchain 1.0 – Public chain 1st iteration with Bitcoin – copycat coins, cryptocurrencies – with inception of ICOs and all the first implementations in finance and digital transactions payments;
Blockchain 2.0 – with the advent of Ethereum – Hyperledger and Public chain Alliances and emergence of Private chains plus the first iteration of decentralised apps – Dapps
Blockchain 3.0 – the present and future new evolution comprehended advanced Super smart AI contracts with decentralised computing capacities and blockchain as a service and the creation of a 360 degree Internet ID of value, with integration multi-chain convergence possibilities.
Four main considerations to look when thinking about Blockchain-AI convergence. These two foundational technologies will:
A) Redesign and create a new distributed artificial intelligence – making sure that AI evolves in an integraated distributed open source open way where different sources intercat with each other and can learn and grow in a gloable scalled and secure way;
B) Together will enable and create advanced digital trust and transparency – that will create a new internet of value and digital transformed society enabled and managed by trust;
C) Will allow better interconnectivity between marketplaces, ecommerce and services, goods when it comes to digital track record infrastructure and data;
D) Optimise and secure identity – ID and Increase the overall more secure and clear User Journey.
This infographic highlights and visualises the multiple areas of convergence of AI and Blockchain: evolution, areas of development and focus.
There are four characteristics that make data “big data” :
1. Data Scale and Volume: The scale and volume of data being created daily is increasing and critical to manage and digest. Most companies and governments have at least 100 Terabytes of data stored and this will increase to levels never seen before.
2. Data Management Velocity: How fast the data comes in is critical for accuracy and management. Examples are news, insights sentiment in Twitter or the New York Stock Exchange, that captures 1 Terabyte of trade information during each trading session.
3. Data Supply Chain Variety: The different and varied forms of data – structured and unstructured are critical for the quality of data. Organisations and companies store multiple varieties of data such as emails, text messages, video, audio files, IoT data, and other formats. This will increase as we store financial, psychological wellness and healthcare data.
4. Data Transparency / Veracity: Uncertainty and the questions around quality of transactions, the data, are the most important things when it comes to big data and its relationships with AI and Blockchain. Governments are struggling to look at data transparency / veracity and studies show that one out of three business leaders don’t trust the quality or veracity of the information / data they use to make decisions.
Big data technologies are critical to look at the convergence of blockchain and AI foundational technologies. They are a more efficient way to manage the complexity of our societies and have the capacity to transform societies and our business enterprise backends for many companies as they replace the data warehouses with “data lakes” running big data software. A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. A data warehouse holds structured data and is expensive for large data volumes while a “data lake” is designed for low cost storage. The importance of big data lies not only in how much data a company collects, but how it utilizes the data to change the way its business processes it for: • Cost savings • Time reductions • Customer experience improvements • New product development
AI systems help to concentrate power in the hands of the few organizations who can source and process large amounts of data. Blockchain technology helps individuals secure their personal information while allowing agents to generate and exchange economic value at smaller operational scales.
Blockchain is decentralized, distributed and a secured ledger to store and to access the data, whereas AI is the engine that will do decision making by making use of the collected data. With this, they get mutual benefits from each other.
Blockchain and artificial intelligence (AI) have evolved into leading technologies that power innovation across almost every industry. Blockchain is a decentralized network of computers that records and stores data to display a chronological series of events on a transparent and immutable ledger system.
“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”
— Stephen Hawking told the BBC
“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”
Artificial Intelligence is the biggest thing in our world of the so-called Fourth Industrial Revolution solutions. There are mostly 3 types of artificial intelligence (AI):
1. narrow or weak AI,
2. general or strong AI,
3. artificial general superintelligence – AGI – with the advent of Singularity.
Currently, we have only achieved the first stage of a narrow AI and we are moving into the first stage of General AI.
AI is slowly being deployed within all our daily interactions on social media and the internet, from the sensors on our IoT driven smartphones to our financial, wellness and healthcare data. AI is now being integrated on the top of blockchains solutions too, especially in the financial industry and supply chain transactions.
This will be increasingly intertwined with Machine Learning solutions, capability and even opening up new ways of creating new social media, wellness, and financial products. We can protect people’s data with technology that integrates the convergence of Blockchain and AI. This is the way forward and inevitable in the short to mid run, because this convergence deals with both secure data management and value storage. On one hand Blockchain enables the secure storage and sharing of data or anything of value. On the other hand AI offers new ways to analyze and generate better solutions to manage insights from data and enable the generation of value.
The best practices that are required to build and to protect people’s data solutions are offered by Blockchain tech. Blockchain makes use of advanced cryptography and smart contracts to ensure that data, transactions, and identities can be secured by having:
1. Ways to protect the record of ID, data and transactions, incorruptibly, securely and irreversibly. A way to look at verification as being trustworthy whilst remaining private — participants can verify the veracity of data without needing to look at the data, and only see what they are authorized to see
2. Ways to easily share P2P digital actions / services / operations so that everyone in a tech blockchain driven network ecosystem has an identical copy of the entire ledger, including updates as they occur
3. Reward of transactions, actions and operations based on performance, loyalty and integrating the best gamification solutions.
While there are many benefits with an effective AI implementation, there are also increasingly organisational business challenges that needs to be considered when building an organisational structure and business case to present to leadership and to their operations:
1. AI is complex and expensive: this means that all areas of preparation, purchase and installation/ integration tools and systems set up and respective prices can be high, along with ongoing costs of managing and keeping up with all areas of set up, updating, licensing, support, and maintenance that need to be considered when it comes to machine learning basic areas of AI.
2. AI has infrastructure security and privacy foundational concerns: Because AI systems require massive amounts of infrastructure around policies and definition of data, cybersecurity is a main focus and concern. Privacy issues are also a concern given the collection of user information.
3. AI creates disruption, biases and requires holistic organisational focus, especially between leadership, employees and users: The biggest challenge now is how we can create strong policy making and work around tackling the challenges of AI to manage a solid robust way to use AI for good and scale AI. In parallel, some governments, organisations and businesses who make use of the convergence of Big Data, Artificial Intelligence, and Blockchain for Competitive Advantage will be able to focus on higher-level strategic thinking. This will allow for repetitive manual tasks to be automated, while other positions will be reduced or eliminated.
“What the internet did for communications, blockchain will do for trusted transactions.” – Ginni Rometty
“The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?”
Blockchain is poised to be the next major disruptive technology for virtually all industries. It is a peer-to peer technology that protects the integrity of a digital piece of information. Bitcoin was built on blockchain technology, so it is easy to see why most companies think its relevance, if any, as a payments technology. But this technology can be used for much more, such as online signature services, voting systems, and many other applications. Blockchain technology is distributed, permitted, and secured.
Blockchain, Artificial intelligence and AI models are now being used together to analyze, classify, create a safety of supply chain and transactions and make better management and predictions from data. AI models improve and learn over time since they are continuously being fed with new data. The table above shows that as we move into the digital world there are 10 areas that need to be addressed to ensure the effective and safe use of these digital technologies.
Machine Learning, which is a subset of artificial intelligence, is now being the main area of research to glean insights from data, and the opportunities to improve and learn with this data are increasing. Larger datasets help create better intelligence, insights and their very own machine learning models. Because at this point what is more important is the quality of the data — and all the datasets that are necessary to be updated with recent and relevant data so that these models can remain effective.
Data is the central DNA for AI and blockchain effectiveness. Together with the correct usage of blockchain and AI we can create models that are based on the best practices of collaborative and secure data sharing. Blockchain can therefore ensure the right trustworthiness of our data and can enable and protect (whilst creating identity and protection with smart contracts) the growing sources of our daily data and share it securely before AI extracts insights from it. So in order to create protection for users, we need a set of solutions based on blockchain and a decentralised open source AI tech that is based on a technique called differential privacy, designed to protect individuals’ identity even when data allegedly has been anonymized.
For Blockchain architecture, infrastructure decentralised databases are key. Databases summarize recent developments and innovations in these particular areas. Additionally it allows us to reward users for the use of their data, when using their wallets, doing e commerce activities, for their loyalty, for being ambassadors or for engaging in their business or financial transactions.
Behind the scenes, machine learning based algorithms assess the profile of the users under anonymity and minimise the risk of leaking information about individuals. Machine Learning solutions also help manage and inject random noise into the data to neutralize that risk and protect privacy and data.
An increasing and broader spectrum of so called blockchain applications have shown and are still promising advanced solutions for problems in areas ranging from risk management, financial services, supply chain, healthcare to cryptocurrencies, digital assets and from the Internet of Things (IoT) to public, government and social services and things. Furthermore, the convergence of Artificial Intelligence (AI) and blockchain technologies are revolutionizing the infrastructure of nations and specialize the new elements of smart city network architecture to build advanced and more sustainable ecosystems.
However, these advancements in technologies bring both opportunities and challenges when it comes to achieving the goals of creating sustainable smart cities.
Using advanced and ethically driven, differential privacy data compliance neutral software, with blockchain to make sure that you have ID integrity but at the same time ownership of your own data. These solutions offer new cutting edge ways to create a new economic and community model, that is P2P and ownership driven.
Blockchain makes AI more coherent and understandable, and we can trace and determine why decisions are made in machine learning. Blockchain and its ledger can record all data and variables that go through a decision made under machine learning.
When Artificial Intelligence (AI) and blockchain converge, the latter benefit from AI’s ability to accelerate the analysis of an enormous amount of data, specvial when we relate with IOT and the sensor infrastructure we are building around cities and countries. In fact, putting the two together can potentially create a totally new paradigm and enable smart cities and smarter governance and nations.
To conclude, Blockchain and AI are the present critical foundational technologies for humanity. These technologies together with the other technologies of the Fourth Industry Revolution, sspecial IOT and Finrech are and will increasingly transform cities, countries and enterprise data strategies. It is therefore imperative for a competitive intelligence organisation or professional to not only understand each of the technologies, including the role big data plays, but also to be a pragmatic futurist thinker and leader in their company to help guide decision makers on how to leverage them.
“Compassionate Artificial Superintelligence or “AI 5.0”, empowers humanity and machine with super-intelligence, super-creativity and super-compassion, which will help humanity and machine to reach new levels of evolution of consciousness.”
― Amit Ray, Compassionate Artificial Superintelligence AI 5.0 – AI with Blockchain, BMI, Drone, IOT, and Biometric Technologies
To summarise and conclude we need to tackle when it comes to the convergence of Blockchain and AI the following three areas:
1. Blockchain and AI require big data open and share: While blockchain relies on open data APIs, special integration and sharing, AI applications can benefit from big data multiple ways to sharing as well, with the idea that more the data, better the algorithm will be.
2. Blockchain and AI require advanced ethics and security: Both technologies introduce new advanced infrastructure for security, transparency and ownership or data and intelligence. Decentralized ledgers and smart contracts in blockchain and automated analysis, predict elements and decision making in AI.
3. Blockchain and AI require trust, ethics and governance: Blockchain and AI systems must be regulated, compliant, auditable, and adhere to high levels of compliance and regulation policymaking, and uphold the highest humanitarian levels of values and transparency and secure identity and 360 level of access.