Let’s assume that we’re able to give a super intelligent AI orders and it follows those orders; it may just take the quickest and easiest route to solve them. Just because we make a super intelligent AI, that doesn’t mean that it’s going to be wise.
There’s a difference between intelligence and wisdom; Intelligence is more about making mistakes and acquiring knowledge and being able to solve problems through that. Wisdom, on the other hand, is about applying the correct knowledge in the most efficient way. Wisdom reflects on being able to see beyond the intelligence gained and being able to apply that to other things, hopefully, in a productive way. If we give AI an order to solve world hunger, the easiest way to solve world hunger is just to kill all life on the planet and then nothing would ever be hungry again.
The solution of such a problem comes down to Data and how we crunch the right Data to feed Artificial Intelligence concepts.
Nowadays Data and the way it’s distributed is one of the key fundamentals on which new solutions are built. Blockchain is clearly the buzzword of our age, and yet, few actually easily understood the principles behind it; especially how it differentiates itself from a traditional distributed database.
There is much confusion as to what a blockchain is and its dichotomy with a database. A blockchain is actually a database because it is a digital ledger that stores information in data structures called blocks. A database likewise stores information in data structures called tables. However, while a blockchain is a database, a database is not a blockchain. They are not interchangeable in a sense that though they both store information, they differ in design. There is also a difference in purpose between the two, which is perhaps what is not clear to those who want to understand why blockchains are needed and why databases are better suited for storing certain data.
A traditional database is a data structure used for storing information. This includes data that can be queried to gather insights for structured reporting used by entities to support business, financial and management decisions. Government also make use of databases to store large sets of data which scale to millions of records.
Distributed databases have the goal of maintaining a consistent copy of a particular dataset across a number of nodes.
A blockchain (referring to the original Bitcoin whitepaper published by Satoshi Nakamoto) stores information in uniform sized blocks. Each block contains the hashed information from the previous block to provide cryptographic security.
Blockchain uses a distributed network of nodes that is decentralized. Decentralization means that all nodes on the network store a copy of the blockchain. The nodes either store a full copy (full nodes) of the blockchain or perform mining operations or they can do both. There is no administrator to validate a block of transactions.
Once the block has been added to the blockchain, the information is immutable and transparent to all. Blockchain transactions are non-recursive, meaning they cannot be repeated once validated in a block. A blockchain is highly fault tolerant since if one or more nodes are down, there will always be other nodes available that will run the blockchain. Another advantage of decentralization is that it can be permission-less and trustless, allowing people who don’t know or trust each other to transact. What the blockchain does is provide that trust through transparency by recording the transaction and providing a cryptographically secure way to exchange value.
There are many types of blockchain solutions already in production mode, offering different attributes and variables, but will not go that deep.
The difference between Blockchain and Distributed Databases
These terms are often used carelessly, and, more often than not, incorrectly. Both blockchains and distributed databases have a similar goal of maintaining a consistent copy of a particular dataset across a number of nodes. Maintaining consensus on the data that is stored, as well as keeping redundant copies of this dataset, are the major similarities between the technologies.
On the surface, their fundamental technology is quite similar, but that’s as deep as it goes.
The core value of blockchain technology is not to provide rudimentary data services (like the decentralized database), but to build a new ecosystem of digitized data assets and automated trust services. The global blockchain updates its state autonomously, and data is traceable to its source.
On the other hand, the core value of distributed database is to provide data storage and access services to business systems. The database is designed to provide operational-support, mainly for business products and development projects, with the data being stored with a focus on supporting analysis and retrieval.
Public blockchains are a collaborative creation, with their ultimate goal being to create a world that is completely decentralized, and where the ownership of digital assets is protected and transferable at all times. On the other hand, distributed databases are centrally managed by a service provider. Their goal is to create a logical center, that can provide efficient, low cost services with great scalability.
Both technologies face technical trilemmas, which is referring to the difficulty of optimizing a technology while balancing tradeoffs. For example, the blockchain trilemma is concurrently achieving high security, decentralization and scalability.
Let’s talk about Artificial Intelligence (AI)
We’re overwhelmed with information, articles and opinions on AI and Blockchain. Experts and non-experts alike, are attempting to envision a future driven by the rise of this exponential technology. Because of the constant flow of information on AI, it’s becoming increasingly difficult to pinpoint what exactly AI is. Few of us are able to actually define Artificial Intelligence. Many of us make the mistake of using it synonymously with other buzzwords, like “robots”.
AI is not a single technology, but a diverse set of methods and tools continuously evolving in tandem with advances in Data Science, Chip Design, Cloud Services and End-User Adoption. The most common examples of AI methods and tools include Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), Computer Vision, Conversational Intelligence and Neural Networks.
You can think of Deep Learning (DL), Machine Learning (ML) and Artificial Intelligence (AI) as a set of Russian dolls nested within each other, beginning with the smallest and working out. DL is a subset of ML, and ML is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all ML is AI, but not all AI is ML, and so forth.
Data as an Asset
Over the past decade, almost all aspects of how we work and how we live – from retail to manufacturing to healthcare – have become increasingly digitized. The internet and mobile technologies drove the first wave of digital, known as the Internet of People. However, analysis carried out by PwC’s AI specialists anticipates that the data generated from the Internet of Things (IoT) will outstrip the data generated by the Internet of People many times over.
7 of the 10 most valuable public companies in the world are using Deep Learning and AI at the heart of their operations. Most of them are in the process of reimagining every aspect of their operations, their business, their products, their services to deepen customer relationships, to grow new capabilities, or design better products.
And nothing can help make a product or a service better, than data. That allows a company to attract more customers, more users, and better outcomes; Of course, that results in more data, and the cycle just repeats. Now companies are starting to use AI, ML, IoT, Neural Networks, Quantum Computing to crunch all that data more effectively.
So bottom line, if AI is our rocket ship, Data is the fuel for this rocket. The more data we have, the more accurate AI, better learning, inference and better outcomes.
When we get this right, it turns into what we call, Data Capital, and it becomes one of the most valuable assets.
In this case, blockchain it’s just the right vehicle to drive Data Capital.
Data Architectures and Frameworks
AI & Data is emerging as one of the most potentially disruptive themes in the digital world. As the world’s data grows exponentially, AI capabilities are tracking close behind, the far-reaching implications of which are becoming clearer every day.
AI powered by the current Data Architectures, will lead to a strong increase in labor productivity (by up to 40 %) due to innovative technologies enabling more efficient workforce-related time management. Secondly, AI will create a new virtual workforce – described as ‘intelligent automation’ in the report – capable of solving problems and self-learning. Third, the economy will also benefit from the diffusion of innovation, which will affect different sectors and create new revenue streams (EPRS, 2019).
In the near-term, the biggest economic potential uplift from AI is likely to come from improved productivity. This includes automation of routine tasks, augmenting employees’ capabilities and freeing them up, to focus on more stimulating and higher value-adding work. Capital-intensive sectors such as manufacturing and transport are likely to see the largest productivity gains from AI, given that many of their operational processes are highly susceptible to automation.
AI technologies differ significantly on the opportunities and risks they create, and therefore it’s important that organizations consider what type of AI is appropriate for their particular use case.
Before starting an AI project, organizations should ensure that the following four conditions have been considered and met to the degree required for their specific use case:
Ethics – The AI system needs to comply with ethical and social norms, including corporate values. This includes the human behavior in designing, developing and operating AI, as well as the behavior of AI as a virtual agent. This condition, more than any other, introduces considerations that have historically not been mainstream for traditional technology, including: moral behavior, respect, fairness, bias and transparency.
Social Responsibility – The potential societal impact of the AI system should be carefully considered, including its impact on the financial, physical and mental well-being of humans and our natural environment. For example, potential impacts might include workforce disruption, skills retraining, discrimination and environmental effects.
Accountability and Explainability – The AI system should have a clear line of accountability to an individual; Also, the AI operator should be able to explain the AI system’s decision framework and how it works. This is about demonstrating a clear grasp of how AI uses and interprets data, how it makes decisions, how it evolves as it learns and the consistency of its decisions across sub-groups.
Reliability – The AI system should be reliable and perform as intended, this involves testing the functionality and decision-framework of the AI system to detect unintended outcomes, system degradation or operational shifts – not just during the initial training or modelling but also throughout its ongoing operation.
Trusted AI frameworks emphasizes four attributes necessary to sustain trust:
Bias: Inherent biases arising from data, the development team composition and training methods are identified, and addressed through the AI design. The AI system is designed with consideration for the need of all impacted and to promote a positive societal impact.
Transparency: When interacting with an AI algorithm, an end user is given appropriate notification and an opportunity to select their level of interaction. User consent is obtained, as required for data captured and used.
Resiliency: The data used by the AI system components and the algorithm itself is secured from unauthorized access, corruption and adversarial attack.
Governance: Track emergent issues across social, regulatory, reputational and ethical domains to inform processes that govern data sourcing and management, the integrity of a system, its uses, architecture and embedded components, model training, and monitoring.
Amazon, Google, Apple and Facebook all used very different business strategies to gain their current market dominance and global influence, but their common success is arguably their foresight in understanding the value of data and positioning themselves early. They worked from the inside out, placing continuous emphasis on capability building, alongside developing, testing and deploying the top technologies internally. They have opted for a freeware model for most of their services, for which we all pay in return with all our data. The value of our data is hard to be monetize within a personal business model, but we hope that it’s used in such a way to fuel cognitive technologies to deliver trust and future advantages for our society.
Creating trust in AI will require both technical and cultural solutions. To be accepted by users, AI must be understandable, meaning its decision framework can be explained and validated. It must also perform as expected and be incorruptible and secure.
We have endless amounts of data to compute and power the most creative minds and with an AI presence it sounds like an absolutely glorious future, but is it?
With every promise of a breakthrough technology also comes the looming threat of widespread unemployment and job loss. In the past you could hedge against this, you could go to school, get a master’s degree, PhD, you could also specialize by getting skills learn a trade, get a certification, but this time it’s bit different.
Algorithms and their computers can process thousands of images and hours millions of rows, of text, and minutes, and hundreds of millions of lines of data in seconds, so in a world already out read, out processed, out memorized and out analyzed by computers and their algorithms, and their chips, how do we differentiate ourselves from our silicon counterparts?
As we have the historical data and we are constantly creating and updating it, we should focus on harnessing the power of AI and all its subsets to help people. On this note, the value of data that we own is priceless, and the main idea isn’t to replace people with machines, but to supplement human capabilities with the unmatched ability of AI to analyze these huge amounts of data and find patterns that would otherwise be impossible to detect.
Using Blockchain to drive Data Capital and secure the right Data, is one of the right means to harvest best outcomes for AI solutions.
Co-Founder of COSS.IO & SCX Holdings and Seasoned Blockchain Tech Start-Ups Investor/Advisor.
Vision-driven entrepreneur with career-long record of business growth and innovation.
Andrei helped in building great companies in Singapore, Austria, Italy and Romania with extraordinary people, while seeking continuous intellectual stimulation through a broad set of experiences in dynamic, challenging, and high-intensity environments. He has been engaged on helping organizations that evolve in challenging markets to break down the barriers that prevent them from reaching their potential, which operate in Scandinavia, Eastern Europe, China and Asia-Pacific.
Andrei is an active participant in the FinTech sphere, Crypto & Alternative Assets Class Ecosystems and the Blockchain/DLT communities in Southeast Asia, Europe and the USA; who attends meetups regularly, speaks at the conferences and advises on Blockchain/DLT/IoT/AI/Alternative Investments & FinTech Projects.
As an Alternative Asset Class management, Andrei is bridging new FinTech instruments, with traditional world’s best corporate and institutional investors, where alternative FinTech assets are still in an early, immature, evolving stage of their existence.
Andrei believes that the introduction of Alternative FinTech Assets into the financial services sector, will not only stabilize the investment landscape, but will radically disrupt old-school ways of the financial world.
Thanks to the increasing offer of FinTech solutions, the segment of alternative investments is opening up the trading sphere to assets that, until now, could not be traded quickly and easily, also will enable us to create faster and more cost-effective financial instruments.
His focus and interest are on Strategic Investments in Disruptive Technologies & Innovations, FinTech, Financial Innovations, Blockchain, DLT, Crypto Asset Trading Systems, Digital Markets & Trading Platforms, Data Science, Data Capital, Artificial Intelligence (ANI, AGI, ASI, ML, DL) Applications, AI based Automated Trading Strategies, Business Strategy Implementations via AI systems.