Blockchain Technology - is this the new internet euphoria?

What is Block Chain?

A blockchain, originally block chain, is a continuously growing list of records, called blocks, which are linked and secured using cryptography. ... It is "an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way".

All of a sudden, Blockchain is everywhere.    The technology that started to power the Bitcoin, is now used for almost everything – from copyrights, data analysis, security and protection of applications and environments

How Does Blockchain Work?

To start, here’s the simplest explanation with no metaphors or hyperbole. In the language of cryptocurrency, a block is a record of new transactions (that could mean the location of cryptocurrency, or medical data, or even voting records). Once each block is completed it’s added to the chain, creating a chain of blocks: a blockchain.

Because cryptocurrencies are encrypted, processing any transactions means solving complicated math problems (and these problems become more difficult over time as the blockchain grows). People who solve these equations are rewarded with cryptocurrency in a process called “mining.”

If you own any cryptocurrency, what you really have is the private key (basically just a long password) to its address on the blockchain. With this key you can withdraw currency to spend, but if you lose the key there’s no way to get your money back. Each account also has a public key, which lets other people send cryptocurrency to your account.

Information on the blockchain is also publicly available. It’s decentralized, meaning it doesn’t rely on a single computer or server to function. So any transactions are instantly visible to everyone. That brings us to our first metaphor: the public ledger.

Blockchain Is Like a Public Ledger

If you send Bitcoin (or some other cryptocurrency) to a friend, or sell it, that information is publicly available on the blockchain. Other people may not know your identity, but they know exactly how much value has been transferred from one person to another.

Many people see blockchain as an alternative to traditional banking. Instead of needing a bank or some other institution to verify the transfer of money, you can use blockchain and eliminate the middle man.

“The Internet of Value”

Building off the idea of a public ledger, another popular way to describe blockchain is as the internet of value. The idea is pretty simple: the internet made it possible to freely distribute data online, blockchain does the same thing for money.

Instead of relying on newspapers, television and radio (which are mainly controlled by big corporations), the internet gives everyone a voice—for better or worse. Blockchain and cryptocurrency make it just as easy to transfer money across the world by bypassing traditional middlemen like banks and even governments.

Blockchain Is Like Google Docs

Here’s a clever metaphor for blockchain from William Mougayar, the author of The Business Blockchain: blockchain is like Google Docs.

Before Google Docs, if you wanted to collaborate on a piece of writing with someone online you had to create a Microsoft Word document, send it to them, and then ask them to edit it. Then you had to wait until they made those changes, saved the document, and sent it back to you. 

Google Docs fixed that by making it possible for multiple people to view and edit a document at the same time. However, most databases today still work like Microsoft Word: only one person can make changes at a time, locking everyone else out until their done. Blockchain fixes that by instantly updating any changes for everyone to see.

For banking, that means that any money transfers are simultaneously verified on both ends. Blockchain could also be used in the legal business or architecture planning— really any business where people need to collaborate on documents.

Blockchain Is Like a Row of Safes

Here’s another useful explanation from online forum Bitcoin Talk. This one does a really good job of explaining how public and private keys work:

Imagine there are a bunch of safes lined up in a giant room somewhere. Each safe has a number on it identifying it, and each safe has a slot that allows people to drop money into it. The safes are all made of bulletproof glass, so anybody can see how much is in any given safe, and anybody can put money in any safe. When you open a bitcoin account, you are given an empty safe and the key to that safe. You take note of which number is on your safe, and when somebody wants to send you money, you tell them which safe is yours, and they can go drop money in the slot.

Blockchain Is Like DNA

Blockchain is a record of transactions, spreading across the internet as more people use cryptocurrencies. Similarly, DNA is a record of genetic transactions and mutations that spread as life expanded across the earth. Both become more complicated over time as our DNA evolves and new blocks are added to the blockchain.

Each blockchain (Bitcoin, Ether, Ripple) is like as a distinct species (human, chimpanzee, etc.). A blockchain can also be forked (like with Bitcoin Cash) to create a competing currency in the same way that two distinct species can share common ancestor.

Of course, changes to DNA don’t happen easily—scientists believe it takes about a million years for a genetic mutation to catch on—and building a blockchain isn’t easy, either. The process of evolution and natural selection is a little bit like mining, a complicated series of steps that creates something incredible.

Source: 

www.lifehacker.com

https://en.wikipedia.org/wiki/Blockchain

https://drive.google.com/file/d/1wveE9EgrTmXsFl9udXbAiz440cKCxezS/view

http://bit.ly/mCbCPP http://bit.ly/BcGand http://bit.ly/ChIDpp Additional collateral is available at http://bit.ly/CMbcDB

From the Blockchain Guru IBM Fellow Prof C Mohan

 

Artificial Intelligence - A Way Forward

Artificial intelligence can be applied to disparate solution spaces to deliver disruptive technologies.

AI is positioned to be a game-changer.   

 

Artificial intelligence is a technology that is already impacting how users interact with, and are affected by the Internet. In the near future, its impact is likely to only continue to grow. AI has the potential to vastly change the way that humans interact, not only with the digital world, but also with each other, through their work and through other socioeconomic institutions – for better or for worse.

Artificial intelligence (AI) traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language.

Artificial intelligence (AI) has received increased attention in recent years. Innovation, made possible through the Internet, has brought AI closer to our everyday lives. These advances, alongside interest in the technology’s potential socio-economic and ethical impacts, brings AI to the forefront of many contemporary debates. Industry investments in AI are rapidly increasing, and governments are trying to understand what the technology could mean for their citizens. 

The collection of “Big Data” and the expansion of the Internet of Things (IoT), has made a perfect environment for new AI applications and services to grow. Applications based on AI are already visible in healthcare diagnostics, targeted treatment, transportation, public safety, service robots, education and entertainment, but will be applied in more fields in the coming years. Together with the Internet, AI changes the way we experience the world and has the potential to be a new engine for economic growth.

Few Uses of AI

Although artificial intelligence evokes thoughts of science fiction, artificial intelligence already has many uses today, for example

•       Email filtering: Email services use artificial intelligence to filter incoming emails. Users can train their spam filters by marking emails as “spam”.

•       Personalization: Online services use artificial intelligence to personalize your experience. Services, like Amazon or Netflix, “learn” from your previous purchases and the purchases of other users in order to recommend relevant content for you.

•       Fraud detection: Banks use artificial intelligence to determine if there is strange activity on your account. Unexpected activity, such as foreign transactions, could be flagged by the algorithm.

•       Speech recognition: Applications use artificial intelligence to optimize speech recognition functions. Examples include intelligent personal assistants, e.g. Amazon’s “Alexa” or Apple’s “Siri”.

Artificial intelligence is further defined as “narrow AI” or “general AI”. Narrow AI, which we interact with today, is designed to perform specific tasks within a domain (e.g. language translation). General AI is hypothetical and not domain specific, but can learn and perform tasks anywhere. This is outside the scope of this paper. This paper focuses on advances in narrow AI, particularly on the development of new algorithms and models in a field of computer science referred to as machine learning.

Challenges

Decision-making: transparency and “interpretability”. With artificial intelligence performing tasks ranging from self-driving cars to managing insurance payouts, it’s critical we understand decisions made by an AI agent. But transparency around algorithmic decisions is sometimes limited by things like corporate or state secrecy or technical literacy. Machine learning further complicates this since the internal decision logic of the model is not always understandable, even for the programmer

Data Quality and Bias. In machine learning, the model’s algorithm will only be as good as the data it trains on – commonly described as “garbage in, garbage out”. This means biased data will result in biased decisions. For example, algorithms performing “risk assessments” are in use by some legal jurisdictions in the United States to determine an offenders risk of committing a crime in the future. If these algorithms are trained on racially biased data, they may assign greater risk to individuals of a certain race over others. Reliable data is critical, but greater demand for training data encourages data collection. This, combined with AI’s ability to identify new patterns or re-identify anonymized information, may pose a risk to users’ fundamental rights as it makes it possible for new types of advanced profiling, possibly discriminating against particular individuals or groups.

Safety and Security. As the AI agent learns and interacts with its environment, there are many challenges related to its safe deployment. They can stem from unpredictable and harmful behavior, including indifference to the impact of its actions. One example is the risk of “reward hacking” where the AI agent finds a way of doing something that might make it easier to reach the goal, but does not correspond with the designer’s intent, such as a cleaning robot sweeping dirt under a carpet. 

Social and Economic Impact. It is predicted that AI technologies will bring economic changes through increases in productivity. This includes machines being able to perform new tasks, such as self-driving cars, advanced robots or smart assistants to support people in their daily lives. Yet how the benefits from the technology are distributed, along with the actions taken by stakeholders, will create vastly different outcomes for labor markets and society as a whole.

Governance. The institutions, processes and organizations involved in the governance of AI are still in the early stages. To a great extent, the ecosystem overlaps with subjects related to Internet governance and policy. Privacy and data laws are one example.

Guiding Principles and Recommendations:

• Adopt ethical standards: Adherence to the principles and standards of ethical considerations in the design of artificial intelligence, should guide researchers and industry going forward.
• Promote ethical considerations in innovation policies: Innovation policies should require adherence to ethical standards as a pre-requisite for things like funding.
• Ensure Human Interpretability of Algorithmic Decisions: AI systems must be designed with the minimum requirement that the designer can account for an AI agent’s behaviors. Some systems with potentially severe implications for public safety should also have the functionality to provide information in the event of an accident.
• Empower Users: Providers of services that utilize AI need to incorporate the ability for the user to request and receive basic explanations as to why a decision was made.
• “Algorithmic Literacy” must be a basic skill: Whether it is the curating of information in social media platforms or self-driving cars, users need to be aware and have a basic understanding of the role of algorithms and autonomous decision-making. Such skills will also be important in shaping societal norms around the use of the technology. For example, identifying decisions that may not be suitable to delegate to an AI.
• Provide the public with information: While full transparency around a service’s machine learning techniques and training data is generally not advisable due to the security risk, the public should be provided with enough information to make it possible for people to question its outcomes.
• Humans must be in control: Any autonomous system must allow for a human to interrupt an activity or shutdown the system (an “off-switch”). There may also be a need to incorporate human checks on new decision-making strategies in AI system design, especially where the risk to human life and safety is great.
• Make safety a priority: Any deployment of an autonomous system should be extensively tested beforehand to ensure the AI agent’s safe interaction with its environment (digital or physical) and that it functions as intended. Autonomous systems should be monitored while in operation, and updated or corrected as needed.
• Privacy is key: AI systems must be data responsible. They should use only what they need and delete it when it is no longer needed (“data minimization”). They should encrypt data in transit and at rest, and restrict access to authorized persons (“access control”). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices.
• Think before you act: Careful thought should be given to the instructions and data provided to AI systems. AI systems should not be trained with data that is biased, inaccurate, incomplete or misleading.
• If they are connected, they must be secured: AI systems that are connected to the Internet should be secured not only for their protection, but also to protect the Internet from malfunctioning or malware-infected AI systems that could become the next-generation of botnets. High standards of device, system and network security should be applied.
• Responsible disclosure: Security researchers acting in good faith should be able to responsibly test the security of AI systems without fear of prosecution or other legal action. At the same time, researchers and others who discover security vulnerabilities or other design flaws should responsibly disclose their findings to those who are in the best position to fix the problem.
• Ensure legal certainty: Governments should ensure legal certainty on how existing laws and policies apply to algorithmic decision-making and the use of autonomous systems to ensure a predictable legal environment. This includes working with experts from all disciplines to identify potential gaps and run legal scenarios. Similarly, those designing and using AI should be in compliance with existing legal frameworks.
• Put users first: Policymakers need to ensure that any laws applicable to AI systems and their use put users’ interests at the center. This must include the ability for users to challenge autonomous decisions that adversely affect their interests.
• Assign liability up-front: Governments working with all stakeholders need to make some difficult decisions now about who will be liable in the event that something goes wrong with an AI system, and how any harm suffered will be remedied.
• Social and Economic Impacts: All stakeholders should engage in an ongoing dialogue to determine the strategies needed to seize upon artificial intelligence’s vast socio-economic opportunities for all, while mitigating its potential negative impacts. A dialogue could address related issues such as educational reform, universal income, and a review of social services.
• Promote Multistakeholder Governance: Organizations, institutions and processes related to the governance of AI need to adopt an open, transparent and inclusive approach. It should be based on four key attributes: Inclusiveness and transparency; Collective responsibility; Effective decision making and implementation and Collaboration through distributed and interoperable governance 

Thanks to the Source with References

Notes

https://www.internetsociety.org/resources/doc/2017/artificial-intelligence-and-machine-learning-policy-paper/?gclid=Cj0KCQjwhoLWBRD9ARIsADIRaxSW35i1Jr27SJ2jh7v-fdLBMNc27bfq50GbD-h4KdAh2g-5_fxNxXUaAsSZEALw_wcB

 Images Sources :  Google, Internet