Blockchain new paperless currency

These are my thoughts & data collected from various news like BBC, TOI & Wikipedia plus a few other websites. This is for learning purpose only still a nascent stage of the Blockchain technology so could not promise a confirm architecture yet, I will say evolving than a concrete shape acquired platform for paperless currency.  

Wikipedia states "Blockchain is a distributed database that maintains a continuously-growing list of data records secured from tampering and revision. It consists of blocks, holding batches of individual transactions.[6] Each block contains a timestamp and a link to a previous block."

Blockchain is a method of recording data - a digital ledger of transactions, agreements, contracts - anything that needs to be independently recorded and verified as having happened.  The big difference is that this ledger isn't stored in one place, it's distributed across several, hundreds or even thousands of computers around the world. This is next step from bitcoin.

And everyone in the network can have access to an up-to-date version of the ledger, so it's very transparent. Digital records are lumped together into "blocks" then bound together cryptographically and chronologically into a "chain" using complex mathematical algorithms. 

This encryption process, known as "hashing" is carried out by lots of different computers. If they all agree on the answer, each block receives a unique digital signature.

Banks think it could be the future of financial transactions, while diamond miners hope it will help end the trade in conflict diamonds. And this week the UK's chief scientific adviser encouraged the British government to adopt the technology.

The blockchain is the main technical innovation of bitcoin, where it serves as the public ledger for bitcoin transactions. Every user is allowed to connect to the network, send new transactions to it, verify transactions, and create new blocks, making it permission less. The bitcoin/blockchain design has been the inspiration for other applications. 

In the bitcoin context, a blockchain is a digital ledger that records every bitcoin transaction that has ever occurred. It is protected by cryptography so powerful that breaking it is typically dismissed as "impossible". More importantly, the blockchain resides across a network of computers. Whenever new transactions occur, the blockchain is authenticated across this distributed network, before the transaction can be included as the next block on the chain.

"You don't store details of the transaction, just the fact that it happened and the hash of the transaction," explains Adrian Nish, head of threat intelligence at BAE Systems.

Once updated, the ledger cannot be altered or tampered with, only added to, and it is updated for everyone in the network at the same time. Well, the distributed nature of a blockchain database means that it's harder for hackers to attack it - they would have to get access to every copy of the database simultaneously to be successful.

It also keeps data secure and private because the hash cannot be converted back into the original data - it's a one-way process. So if the original document or transaction were subsequently altered, it would produce a different digital signature, alerting the network to the mismatch.

In theory then, the blockchain method makes fraud and error less likely and easier to spot. The idea has been around for a couple of decades, but came to prominence in 2008 with the invention of Bitcoin, the digital currency.  Bitcoins are created by computers solving complex mathematical puzzles and this requires lots of computing power and electricity. Blockchain is the technology underpinning it.

Current Players 

Big player already started building the platform all by their thought process so this is shaping up. There isn't just one program - lots of companies, from Ethereum to Microsoft, are developing their own blockchain services. Some are open to all ("unpermissioned", in the jargon), others restrict access to a select group ("permissioned").

"Banks do very similar things to each other, even though they compete," says Simon Taylor, vice-president of blockchain research and development at Barclays.

"They basically keep our money safe and a big computer keeps track of who has what. But getting these computers to talk to each other is remarkably complex and expensive - the tech is getting a little old," he says. If banks started sharing data using a tailor-made version of blockchain it could remove the need for middlemen, a lot of manual processing, and speed up transactions, says Mr Taylor, thereby reducing costs.

 

Having access to an open, transparent ledger of bank transactions would also be useful for regulators, he adds. And it could help governments tackle tax fraud.

Tech company R3 CEV has persuaded more than 40 banks around the world, including Barclays, UBS and Wells Fargo, to join a consortium exploring distributed ledger technology.

Just this week, R3 announced that 11 global financial institutions had taken part in an experiment involving the exchange of tokens across a global private network without the need for a central third party verifying the transactions.

If banks and other financial institutions are able to speed up transactions and take costs out of the system, it should mean cheaper, more efficient services for us. For example, sending money abroad could become almost instantaneous.

Last year, investment bank Goldman Sachs and Chinese investment firm IDG Capital Partners invested $50m (£35m) in Circle Internet Financial, a start-up aiming to exploit blockchain technology to improve consumer money transfers.

Circle, co-founded by entrepreneur Jeremy Allaire, has created a digital wallet for bitcoins, but users can decide whether they send or receive money in dollars as well. The idea is to make cross-border payments as easy as sending a text or email.

It's not all about banking. Tech company Everledger is using blockchain to develop a system of warranties that enable mining companies to verify that their rough-cut diamonds are not being used by militias to fund conflicts, and that they comply with the Kimberley Process - a government and community-backed certification scheme for diamonds.

The ownership history and value of each diamond is available to anyone who wants it, and you can be confident that the information has not been tampered with or corrupted.

Current shape of technology

A blockchain implementation consists of two kinds of records: transactions and blocks. Transactions are the content to be stored in the blockchain. Transactions are created by users who wish to record information in the blockchain. In the case of cryptocurrencies, a transaction is created any time a cryptocurrency owner sends cryptocurrency to another user.

Transactions are passed from node to node on a best-effort basis. The system implementing the blockchain defines a valid transaction. In cryptocurrency applications, a valid transaction must be digitally signed, spend one or more unspent outputs of previous transactions, and ensure that the sum of transaction outputs not exceed the sum of inputs.

Blocks record one or more transactions. A transaction's presence in a block confirms when and in what sequence it occurred. Blocks are created by users known as "miners" who use specialized software or equipment designed specifically to create blocks. Miners compete with each other to see who can first complete the next block and therefore earn the reward(s) for doing so.

In a cryptocurrency system, miners collect two types of rewards: a pre-defined per-block award, and fees offered within the transactions themselves, payable to any miner who confirms the transaction.

Every node in a decentralized system has a copy of the blockchain. No centralized "official" copy exists and no user is "trusted" more than any other.Transactions are broadcast to the network using software applications. Mining nodes validate transactions, add them to the block they're creating and then broadcast the completed block to other nodes. Blockchains use various timestamping schemes, such as proof-of-work to serialize changes. 

Blockchain technology may be permissionless—"open for anyone to use"—or private: "closed off and accessible only to chosen parties". Blockchains are a technology that may be integrated into multiple areas. Examples include a payments system and store of value, facilitating crowdsales, or implementing prediction markets and generic governance tools.

The advantages

•   The ability for independent nodes to converge on a consensus of the latest version of a large data set such as a ledger, even when the nodes are run anonymously, have poor interconnectivity and have operators who are dishonest or malicious (see Sybil attack).
•  The ability for any well-connected node to determine, with reasonable certainty, whether a transaction does or does not exist in the data set (see consistency).
• The ability for any node that creates a transaction to, after a confirmation period, determine with a reasonable level of certainty whether the transaction is valid, able to take place and become final (i.e., that no conflicting transactions were confirmed into the blockchain elsewhere that would invalidate the transaction, such as the same currency units "double-spent" somewhere else).
• A prohibitively high cost to attempt to rewrite or alter transaction history.
• Automated conflict resolution that ensures that conflicting transactions (such as two or more attempts to spend the same balance in different places) never become part of the confirmed data set.

Evolution or Teething troubles

Wait everything is not green with blockchain, An ongoing debate disputes whether a private system with verifiers tasked and authorized (permissioned) by a central authority, should still be considered a blockchain.

Proponents of permissioned or private chains argue that the term "blockchain" may be applied to any data structure which batches data into blocks which are timestamped and that these blockchains serve as a distributed version of multiversion concurrency control (MVCC) in databases. Just as MVCC prevents two transactions from concurrently modifying a single object in a database, blockchains prevent two transactions from spending the same single output in a block chain.

The opponents say that the permissioned systems look like traditional corporate databases, not supporting decentralized verification of the data, and that such systems are not hardened against tampering and revision by their operators. The Harvard Business Review defines blockchain as a distributed ledger or database open to anyone.

In the era of big data and the internet of things, being able to assign a digital signature to each bit of data is also useful. So building the traciability with a text based DB crunching helping to track every transaction & with time stamp plus IP trace so this concludes who, what & when part.

And verifying and recording each stage in the development of a software program or product will help improve quality and reliability, he maintains.

Feel free to contact me at ravindrapande@gmail.com. I would like to research for India retail markets going on Blockchain. As the market is huge , data and analytic started gathering the pace and technology platform still maturing along.

Machine Learning & IoT

The idea of an intelligent, independently learning machine has fascinated humans for decades. I remember how the concept became reality for me after I purchased my first computer.

When I demonstrated the computer for my grandfather, he began by saying, "Could you ask that machine ... ?"

He was clearly ahead of his time. Not to downplay the capability of the Assembled PC 8080, with B&W TV as monitor but at the time, it would have been premature to discuss, for example, neurorobotics as a part of everyday life in Nagpur India, where I grew up.

Businesses expect employees not only to be smart and capable, but flexible and adaptable. This expectation is no different in the way we use technology. Our devices—and our data—are becoming more flexible in their potential uses and how they’re relevant to our everyday lives.

Machine learning has experienced a boost in popularity among industrial companies thanks to the hype surrounding the Internet of Things (IoT). Many companies are already designating IoT as a strategically significant area, while others have kicked off pilot projects to map the potential of IoT in business operations. As a result, nearly every IT vendor is suddenly announcing IoT platforms and consulting services.

If you’re a business owner or enterprise beginning to leverage the Internet of Things (IoT), chances are you’ve started connecting your operational technologies – including machinery, building HVAC, and other assets – to your current software systems. As a result, you’re likely collecting a ton of new data and think see the potential transformational value in there…somewhere. One of the most difficult questions to answer when starting out with IoT is how to take vast amounts of raw information and create real business intelligence from it.

The combination of IoT and data and analytics may seem like a “chicken vs. egg” dilemma, but it doesn’t have to be. Companies who have adopted IoT with the ultimate goal of optimizing physical processes or providing predictive analytics solutions still have an opportunity to use data and analytics to advance their business, even if an implementation has stalled after the technology is in place. It’s a more common issue than you might think, as businesses new to IoT often lack the necessary expertise to move to the final step of figuring out how to work with the data they’re collecting from their Internet of Things (IoT) initiatives.

The Internet of Things (IoT) has received massive coverage and widespread adoption. What few people have stopped to consider thus far is where these connections will take us. As chatbots become more popular, we’re bearing witness to a move toward further machine learning. As the natural progression from smart objects to learning objects occurs, this new wave will encompass the globe.

Witness The IoT Ripple Effect :At the heart of IoT is a desire to connect items we already own into one cohesive network. These objects are useful for an increasing number of purposes. The variety and value of the data these devices collect is constantly growing. Though this is a solid first step, it certainly isn’t the last down this pathway. While IoT adds value to the products we already own and the services we already use, the data extracted from IoT is meant to tell marketers what we’ll want to own and what services we’ll use in the future.

Data analysis is the second phase. Analytic systems collect, analyze, organize, and feed data to the most relevant users. Though this is useful, it presents several issues. The first is the sheer amount of data collected. Processing this vast amount of data effectively to produce accurate, overarching reports is difficult. This causes a further push toward automation and cloud computing. The second issue is that IoT can’t learn from the information it generates.

Businesses expect employees not only to be smart and capable, but flexible and adaptable. This expectation is no different in the way we use technology. Our devices—and our data—are becoming more flexible in their potential uses and how they’re relevant to our everyday lives.

Watch The Rise Of The Chatbot Tide : Chatbots have received some attention recently, as several large companies have announced progress in their development. The ultimate goal is for these to replace all other platforms across devices—covering laptops, tablets, smartphones, and everything else in IoT. Rather than opening a browser, searching for “Italian food” by area, and then clicking through websites, one would simply verbally request the nearest location with the highest ratings. The chatbot would do all of the work and produce an answer. This kind of interaction and immediate response places much more power in the hands of the consumer than ever before.

Though some may read this and assume Siri has it covered, she’s a long way from the true potential of this arena. An individual’s work, personal projects, social contacts, and family calendars could all be connected and accessible through a chatbot. This could revolutionize the way people function in relation to their devices.

In my opinion, these systems will pave the way for true learning platforms. IoT will become the internet of learning objects. With this in mind, many design initiatives are transitioning from functionality to adaptability.

Anticipate The AI Wave : The billions of data points IoT produces must be organized. By paring them down to what’s important and analyzing this data, the public and private sectors benefit. This addresses everything from running a business, to military logistics, to ordering groceries. Patterns, problems, and correlations will be easier to address. Intelligent automation will make huge strides—leading to a revolution in predictive analytics—and proactive intervention will be truly possible. Enter Artificial Intelligence, or AI.

Machine learning may start with chatbots, but AI is the true potential of IoT. The processing of this data (and likely the interpretation and learning of it) will happen in the edge-computing realm. This will be fast and uninhibited. I firmly believe more companies will allocate money to AI development in the coming months and years. Once relegated to the realm of Asimov and science fiction, these innovations will be borne of IoT and cover the globe.

Big Data to Machine Learning

Lets start with basic. Big data is a blanket term for any collection of data sets so large or complex that it becomes difficult to process them using traditional data management techniques such as, for example, the RDBMS (relational database management systems). The widely adopted RDBMS has long been regarded as a one-size-fits-all solution, but the demands of handling big data have shown otherwise. Data science involves using methods to analyze massive amounts of data and extract the knowledge it contains. You can think of the relationship between big data and data science as being likethe relationship between crude oil and an oil refinery. Data science and big data evolved from statistics and traditional data management but are now considered to be distinct disciplines.

The characteristics of big data are often referred to as the three Vs:
Volume—How much data is there?
Variety—How diverse are different types of data?
Velocity—At what speed is new data generated?
Often these characteristics are complemented with a fourth V, veracity: How accurate is the data? These four properties make big data different from the data found in traditional data management tools. Consequently, the challenges they bring can be felt in almost every aspect: data capture, correction, storage, search, sharing, transfer, and visualization. In addition, big data calls for specialized techniques to extract the insights.

Data science is an evolutionary extension of statistics capable of dealing with the massive amounts of data produced today. It adds methods from computer science to the repertoire of statistics. In a research note from Laney and Kart, Emerging Role of the Data Scientist and the Art of Data Science, the authors sifted through hundreds of job descriptions for data scientist, statistician, and BI (Business Intelligence) analyst to detect the differences between those titles. The main things that set a data scientist apart from a statistician are the ability to work with big data and experience in machine learning, computing, and algorithm building. Their tools tend to differ too, with data scientist job descriptions more frequently mentioning the ability to use Hadoop, Pig, Spark, R, Python, and Java, among others. Don’t worry if you feel intimidated by this list; most of these will be gradually introduced here,  though we’ll focus on Python. Python is a great language for data science because it has many data science libraries available, and it’s widely supported by specialized software. For instance, almost every popular NoSQL database has a Python-specific API. Because of these features and the ability to prototype quickly with Python while keeping acceptable performance, its influence is steadily growing in the data science world. As the amount of data continues to grow and the need to leverage it becomes more important, every data scientist will come across big data projects throughout their career.

Data science and big data are used almost everywhere in both commercial and noncommercial settings. The number of use cases is vast, and the examples we’ll provide throughout this book only scratch the surface of the possibilities. Commercial companies in almost every industry use data science and big data to gain insights into their customers, processes, staff, completion, and products. Many companies use data science to offer customers a better user experience, as well as to cross-sell, up-sell, and personalize their offerings. A good example of this is Google AdSense, which collects data from internet users so relevant commercial messages can be matched to the person browsing the internet. MaxPoint (http://maxpoint.com/us) is another example of real-time personalized advertising. Human resource professionals use people analytics and text mining to screen candidates, monitor the mood of employees, and study informal networks among coworkers. People analytics is the central theme in the book Moneyball: The Art of Winning an Unfair Game. In the book (and movie) we saw that the traditional scouting process for American baseball was random, and replacing it with correlated signals changed everything. Relying on statistics allowed them to hire the right players and pit them against the opponents where they would have the biggest advantage.

Financial institutions use data science to predict stock markets, determine the risk of lending money, and learn how to attract new clients for their services. At the time of writing this book, at least 50% of trades worldwide are performed automatically by machines based on algorithms developed by quants, as data scientists who work on trading algorithms are often called, with the help of big data and data science techniques.

Governmental organizations are also aware of data’s value. Many governmental organizations not only rely on internal data scientists to discover valuable information, but also share their data with the public. You can use this data to gain insights or build data-driven applications. Data.gov is but one example; it’s the home of the US Government’s open data. A data scientist in a governmental organization gets to work on diverse projects such as detecting fraud and other criminal activity or optimizing project funding. A well-known example was provided by Edward Snowden, who leaked internal documents of the American National Security Agency and the British Government Communications Headquarters that show clearly how they used data science and big data to monitor millions of individuals. Those organizations collected 5 billion data records from widespread applications such as Google Maps, Angry Birds, email, and text messages, among many other data sources. Then they applied data science techniques to distill information.

Nongovernmental organizations (NGOs) are also no strangers to using data. They use it to raise money and defend their causes. The World Wildlife Fund (WWF), for instance, employs data scientists to increase the effectiveness of their fundraising efforts. Many data scientists devote part of their time to helping NGOs, because NGOs often lack the resources to collect data and employ data scientists. DataKind is one such data scientist group that devotes its time to the benefit of mankind.
Universities use data science in their research but also to enhance the study experience of their students.

The rise of massive open online courses (MOOC) produces a lot of data, which allows universities to study how this type of learning can complement traditional classes. MOOCs are an invaluable asset if you want to become a data scientist and big data professional, so definitely look at a few of the better-known ones: Coursera, Udacity, and edX. The big data and data science landscape changes quickly and MOOCs allow you to stay up to date by following courses from top universities. If you aren’t acquainted with them yet, take time to do so now; you’ll come to love them as we have.

In data science and big data you’ll come across many different types of data, and each of them tends to require different tools and techniques. The main categories of data are these:

• Structured
• Unstructured
• Natural language
• Machine-generated
• Graph-based
• Audio, video, and images
• Streaming

The data science process typically consists of six steps, as you can see in the mind map. We will introduce them briefly here and handle them in more detail Later.

1. Setting the research goal
2. Collect Data or Retrieving data
3. Data preparation, Validation
4. Data exploration Analysis
5. Data modeling or model building
6. Presentation and automation

This is AN ITERATIVE PROCESS The previous description of the data science process gives you the impression that you walk through this process in a linear way, but in reality you often have to step back and rework certain findings. For instance, you might find outliers in the data exploration phase that point to data import errors. As part of the data science process you gain incremental insights, which may lead to new questions. To prevent rework, make sure that you scope the business question clearly and thoroughly at the start.





Now in nest blog we will understand "The big data ecosystem and data science". Feel free to share your suggestion at RavindraPande@gmail.com.

AI Machine Learning

I have been thinking to write on this for few months. This is very fast evolving science. Just puting down collected thoughts on this topic.

AI is a computer program that does something smart. It can be a pile of if-then statements or a complex statistical model. Usually, when a computer program designed by AI researchers actually succeeds at something -- like winning at chess -- many people say it's "not really intelligent", because the algorithms internals are well understood. So you could say that true AI is whatever computers can't do yet. ;)

Machine learning, as others here have said, is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. For example, symbolic logic (rules engines, expert systems and knowledge graphs) as well as evolutionary algorithms and Baysian statistics could all be described as AI, and none of them are machine learning.

The "learning" part of machine learning means that ML algorithms attempt to optimize along a certain dimension; i.e. they usually try to minimize error or maximize the likelihood of their predictions being true. How does one minimize error? Well, one way is to build a framework that multiplies inputs in order to make guesses as to the inputs' nature. Different outputs/guesses are the product of the inputs and the algorithm. Usually, the initial guesses are quite wrong, and if you are lucky enough to have ground-truth labels pertaining to the input, you can measure how wrong your guesses are by contrasting them with the truth, and then use that error to modify your algorithm. That's what neural networks do. They keep on measuring the error and modifying their parameters until they can't achieve any less error.

They are, in short, an optimization algorithm. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is a technology within the sphere of 'Artificial Intelligence'.

Artificial intelligence is a complex topic, but a beautiful one. Its basis are very profound since it asks What is intelligence? The question have bothered many disciplines in science for decades. But I think that artificial intelligence research is a very good candidate answering it. It is not that AI tries to mimic human or animal intelligence; it goes deeper. It tries to formulate a theory of intelligence, in order to build intelligent machines.

Machine learning is just an umbrella term of different algorithms and technologies what is used to automate learning from new information. It is part of theory of intelligence research because every intelligent system needs a way to learn the rules of its environment. We don't know how humans or animals really do it. Machine learning models some concepts from these worlds but it also implements statistical analysis methods from mathematics. Many today's artificial intelligence implementations are holistic, so they are relying heavily on machine learning to learn patterns from vast data sets. This makes them kind of mimics that mimic human or other behavior described in data sets.

The pioneering technology within Machine Learning is the neural network (NN), which mimics (to a very rudimentary level) the pattern recognition abilities of the human brain by processing thousands or even millions of data points. Pattern recognition is pivotal in terms of intelligence.

It is worth keeping in mind that a lot of people assume that through Machine Learning we are developing general AI rather than applied AI

Machine learning is a science that involves development of self-learning algorithms. These algorithms are more generic in nature that it can be applied to various domain related problems. Machine learning uses statistics (mostly inferential statistics) to develop self learning algorithms.

Artificial Intelligence is a science to develop a system or software to mimic human to respond and behave in a circumstance. As field with extremely broad scope, AI has defined its goal into multiple chunks. Later each chuck has become a separate field of study to solve its problem.

Here is a major list of AI goals

1. Reasoning
2. Knowledge representation
3. Automated planning and scheduling
4. Machine learning
5. Natural language processing
6. Computer vision
7. Robotics
8. General intelligence, or strong AI

As mentioned in the list Machine learning is field emerged from one the AI goal to help machine or software to learn on it own to solve problems it’s can come across.

On a bird’s eye view, Machine Learning is the only kind of AI there is.

AI is changing. We are now recognizing that most things called "AI" in the past are nothing more than advanced programming tricks. As long as the programmer is the one supplying all the intelligence to the system by programming it in as a World Model, the system is not really an Artificial Intelligence. It's "just a program".

Don't model the World; Model the Mind.

When you Model the Mind you can create systems capable of Learning everything about the world. It is a much smaller task, since the world is very large and changes behind your back, which means World Models will become obsolete the moment they are made. The only hope to create intelligent systems is to have the system itself create and maintain its own World Models. Continuously, in response to sensory input.

Following this line of reasoning, Machine Learning is NOT a subset of AI. It really is the ONLY kind of AI there is.

And this is now proving to be true, and in a big way. Since 2012, a specific Machine Learning technique called Deep Learning is taking the AI world by storm. Researchers are abandoning the classical "Programming Tricks" style of AI in droves and switching to Deep Learning... based mainly on the fact that it actually works. We've made more progress in three years since 2012 than we've done in the preceding 25 years on several key AI problems, including Image Understanding (a really hard one), Signal Processing, Voice Understanding, and Text Understanding.

Another clue that we are now on the right track: Old style AI projects like CYC ran to millions of propositions or millions of lines of code. Systems that (successfully) Model the Mind can be as small as 600 lines of code; several recent Deep Learning projects clock in somewhere in that range. And these programs can move from one problem domain to another with very few changes to the core; this means these methods are GENERAL intelligences, not specific to any one problem domain. This is why it is called Artificial General Intelligence. And we've never had any AI programs that could do this in the past. As an example, the language understanding programs we are creating using DL will work equally well in any language, not just English. It just takes a re-training to switch to Japanese... another indication that Deep Learning is closer to true intelligence than traditional NLP systems.

Google is currently using Machine Learning a lot - in my estimate, over a hundred places in their systems have been replaced by Deep Learning and other ML techniques in the past few years. even their patented "PageRank" algorithm which was the initial key to their success is being replaced, even as I write this, with a new algorithm called "RankBrain" which is based on Deep Learning. In the shareholder's call last week, the CEO of Google said that they were looking at using ML (probably Deep Learning) *everywhere* in all their products. More generally, I expect a deluge of apps and systems that understand languages and images in the next several years, all based on Deep Learning.

I really shouldn't confuse things but strictly speaking, Deep Learning is not AI either. We are currently using Supervised Deep Learning, which is another (but less critical) programmer's cheat since the "supervision" is a kind of World Model. Real AI requires Unsupervised Deep Learning. Many people including myself are working on this; it is possibly thousands of times more difficult that Supervised Learning. But this is where we have to go.

Deep Learning isn't AI but it's the only thing we have that's on the path to True AI.

In books sense Machine Learning is basically a subset of Artificial Intelligence. Since machine learning is a subset, I will first talk about artificial intelligence. There are many definitions for what an AI is. John McCarthy's (person who coined this term) defines it "As the Science and Engineering of making Intelligent Machines." The ultimate goal is to create software and hardware that is capable of intelligent behavior.

Traditional algorithms generate unique answers (not considering the NP classes) for particular set of inputs. One already knows what output will be generated, or at least of what kind. They don't show non deterministic kind of behavior. AI on the other hand would be more like humans. We take decisions that could be unpredictable. We often deal with the same kind of problem in different ways. The whole fuss to provide this kind of capability to machines.

Learning by experience, reasoning, planning, natural language processing are different aspects of this field. Since this field is so fast and diverse, it has been divided into several fields. One of them is Machine learning.

Origin of machine learning can be traced back to the study of pattern recognition and computational learning theory. This field is more centralized towards data (Data Mining and Statistics). Main focus is to form algorithms that can process chunks of data provided to them and provide us with predictions accordingly. Instead of following particular set of instructions (like traditional algorithms do), it builds model from example inputs provided in order to make predictions or take decisions.

Today machine learning is being used in many websites, like Youtube, Amazon, etc to provide options to their viewers according to their previous searches.

Cyber security Next steps

Cyber security matters. Products are hacked, in order to misuse, abuse and confuse. Unlike other technologies which are mastered by specific teams and functions, security is a base technology which belongs to the body of knowledge of each single software developer. I have try to ponder on  / about some best practices for security engineering in this blog. This is also a thought of continuous improvement & evolving process for a given enterprise.

Sophisticated functionality and ever-increasing perfection of embedded and distributed IT systems have been made possible through an increasing amount of interconnected components. Open interfaces, standardized platforms, and a variety of heterogeneous networks drive complexity and security risks. For any given system it is only a question of time before the resulting security vulnerabilities are systematically identified and exploited at the harm and expense of users and manufacturers. 

Security is a quality attribute which interacts heavily with other such attributes, including availability, safety, and robustness. It is the sum of all of the attributes of an information system or product which contributes towards ensuring that processing, storing, and communicating of information sufficiently protects confidentiality, integrity, and authenticity. Cyber security implies that it is not possible to do anything with the processed or managed information which is not explicitly intended by the specification of the embedded system.

Currently used security engineering concepts, such as proprietary subsystems, the protection of components, firewalls between components, and the validation of specific features are necessary basics but insufficient to ensure end-to-end security at the systems-level. Intelligent attack scenarios evolve from different directions, such as attacks on unprotected networks, introduction of dangerous code segments through open interfaces, changes to configurations, and prove that security has to become a topic throughout the entire organization and with high management attention.

Cyber security needs evolve fast with the advent of the Internet of Things (IoT). Let us look to modern automotive systems as an example of connectivity and IoT. Distributed networks such as inside cars and from car to roadside are an essential part for our today’s modern infrastructures with their needs for safety and comfort. Besides the further development of innovative sensors like radar and camera systems and the analysis of the signals in highly complex systems, the connected cars will be a driving factor for tomorrow‘s innovation. Internet connections will not only provide the need for information to the passenger - functions like eCall, communication between cars, and car to infrastructure (vehicle2x) shows high potential for revolutionizing the individual traffic. The advantages are obvious, such as improvement of the traffic flow controlled by intelligent traffic lights, warnings from roadside stations, or brake indication of adjacent cars towards enhanced driver assistant systems and automated driving. But the connection to the outer world also bears the risk for attacks to the car.

Based on our experiences with clients worldwide, we show which security engineering activities are required to create secure systems and how these activities can be performed efficiently in the automotive domain. Key points in the development of protected systems are the proper identification of security requirements, the systematic realization of security functions, and a security validation to demonstrate that security requirements have been met. Here some obvious items from the cyber security checklist:

·         Standardized process models for a systematic approach which is anchored in the complete development process. This starts in the requirements analysis phase, and continues through the design and development to the test and integration of components and the network.

·         Quick software updates to close vulnerabilities in the deployed and operational software.

·         Reliable protocols that are state-of-the-art and meet long-term security demands. Related to security, this is often combined with cryptographic keys. So a key management over the lifecycle of the vehicle must be maintained.

·         In-vehicle networks and a system architecture that provide flexibility and scalability and are designed with consideration of security aspects.

Dependability requirements are a good starting point to identify relevant security requirements and to guide elicitation of further functional requirements that will mitigate security risks. The same technique as outlined here can be applied for other scenarios – always starting with attacker motivation or functional risks due to the system architecture. Our guidance: Do not limit exposure to known incidents and defects as some textbooks suggest. Security analysis is not a checklist approach. It has to consider attack motivations of persons thinking differently than the usual engineer. However, utilizing an engineering approach, we can more easily identify vulnerabilities in our architectures.

The results of security risk and hazard analysis starting with asset identification to misuse, abuse and confuse cases and the entire security protection scheme should be well-documented. It is of utmost interest to understand the approach specifically when modifications are made at a later point. Form a legal perspective complete and maintained documentation is necessary for governance and compliance reasons. Security threats and resulting damages impact the safety of products and the integrity of private data, and are thus directly endangering the financial health of a company. Our guidance: Document the security case similarly to the safety case by means of a ALM/ PLM environment. Maintain the related documentation and enhance it with regression test scenarios for future updates.

Security requires an end-to-end perspective. Security engineering must start with a clear focus on security requirements and related critical quality requirements, such as safety, footprint, or performance and how they map to functional requirements. Software component suppliers and integrators first define the key functional requirements. These requirements are then analyzed for security risks and impacts. Security requirements are expanded into further functional requirements or additional security guidelines and validation steps. Security concepts are subsequently and consistently (i.e. traceable) implemented throughout the development process. Finally, security is validated on the basis of previously defined security requirements and test cases.

Today, cyber security by design is in the foreground due to safety, legislative and intellectual property concerns. We recommend a life-cycle perspective which takes a systems engineering perspective and drives security starting with security requirements and the related test cases, while stepwise and comprehensively building the security case in line with the impacted functional requirements and quality requirements. After all it does not help much if transactions are piecemeal encrypted and thus slow down performance.

Many security attacks are the result of poorly managed software updates and uncontrolled complexity growth. Architectures, systems, and protocols must be developed with security in mind (i.e., design for security). Competences have to be developed around security engineering, and employees have to be trained how to design, verify, and sustain security throughout the product’s life-cycle. Only with continuous measurements on their effectiveness the value of security measures improves.

Contact me at ravindrapande@gmail.com for more information or to discuss these trends.

Office 2016 Review

At IndiaTrainigServices.in we got a good look at the US editions of the Office 2016 Developer edition.
Office 2016 is a major upgrade, but not in the way you’d first suppose. Just as Windows 10 ties notebooks, desktops, phones and tablets together, and adds a layer of intelligence, Office 2016 wants to connect you and your coworkers together, using some baked-in smarts to help you along.
We have tested the client-facing portion of Office 2016. Microsoft released the trial version of Office 2016 in March as a developer preview with a focus on administrative features (data loss protection, multi-factor authentication and more) that we didn’t test.

Office 2013 users can rest easy about one thing: Office 2016’s applications are almost indistinguishable from their previous versions in look and feature set. To the basic Office apps, Microsoft has added its Sway app for light content creation, and the enterprise information aggregator, Delve. 

Collaboration in the cloud is the real difference with Office 2016. Office now encourages you to share documents online, in a collaborative workspace. Printing out a document and marking it up with a pen? Medieval. Even emailing copies back and forth is now tacitly discouraged.
Microsoft says its new collaborative workflow reflects how people do things now, from study groups to community centers on up to enterprise sales forces. But Microsoft’s brave new world runs best on Office 365, Microsoft’s subscription service, where everybody has the latest software that automatically updates over time. And to use all of the advanced features of Office, you must own some sort of Windows PC.

You could still buy Office 2016 as a standalone product: It costs Rs. 6,000 for Office 2016 Home & Student (Word, Excel, PowerPoint and OneNote ) and Rs. 18,500 for Office Home & Business, which adds Outlook 2016. Office 365 is Rs. 330 per month for a Personal plan (with one device installation) and Rs. 450 per month for a Home Plan, where Office can be installed on five devices and five phones.

If you subscribe to Office 365, it’s a moot point; those bits will stream down to your PC shortly. Windows 10 users already have access to Microsoft’s own baked-in, totally free version of Office, the Office Mobile apps. It’s those people who fall somewhere in the middle—unwilling to commit to Office 365, but still wavering whether or not to buy Office—who must decide.

Our advice to an individual, family, or small business owner: Wait. If you’ve never owned Office, the free Office Mobile apps that can be downloaded from the Windows Store onto iOS, Android, and Windows Phones are very good—and include some of the intelligence and sharing capabilities built into Office 2016. Microsoft’s Office Web apps do the same.

There’s no question that Office 2016 tops Google Apps, and I haven’t seen anything from the free, alternative office suites that should compel you to look elsewhere. But Microsoft still struggles to answer the most basic question: Why should I upgrade? That’s a question that I think Microsoft could answer easily—and I’ll tell you how it can, at the end.

Before that, here’s what works, and what doesn’t, in Office 2016. With PowerPoint, however, most of that goes out the window. You can ask coworkers to collaborate, and you can still send them links by which they can edit your shared presentations. You can still comment, and coworkers can still make changes to the text as they wish. But you can’t really manage their changes, or restrict what they can or can’t do. (You can compare and reconcile versions of the same document that a coworker has worked upon separately, however, which is vaguely similar.)

But—and this is a big but—any revisions to a document show up only if you click a teeny-tiny Save icon, way down at the bottom of the screen, that serves as a sort of CB-radio-style ‘Over’ command. It’s almost impossible to find unless you know what you’re looking for. Click it, and changes made by others show up. When your colleague makes another change, you have to click it again. It’s a pain.
Granted, collaborative editing wasn’t in the Office 2016 preview Microsoft released earlier this year. And, given that there’s an enormous blank space in the ribbon header to the right half of the screen, you have to imagine that more managed sharing is heading to PowerPoint.