Blockchain Will Change the World in 5 Ways

“Blockchainisation” is underway and here are 5 areas that will most like

be deeply affected by the technology.

We’re sure you’ve heard a lot about Blockchain and Bitcoin (check out this insane infographic for some
cool, easy-to-digest facts).

But what makes Blockchain so special?

Here’s a quick overview:

1. Blockchain is Decentralized:

Blockchain runs on a global network of users’ computers. Each member contributes processing power
to the whole, and the network doesn’t function properly without a constant influx of new miners.

2. It’s Public:

On Blockchain, everyone can see everything all the time–it’s 100% transparent.

3. It’s Encrypted:

Blockchain uses robust encryption to maintain virtual security. Aside from a strong external defense,
there is no central database to hack in the first place.

5 ways the world will change thanks to #Blockchain technology

Throughout the day and every couple of minutes, Blockchain transactions, which are initiated by
complex math problems that computers work together to solve, are checked, erased and stored in a
block that is linked to the previous block, creating a chain, hence the name “Blockchain”.

This structure prevents any one user from modifying historical data.

If someone, for example, wants to modify data (say, to steal a Bitcoin), they’ll have to rewrite,
in real-time, the entire Blockchain record–which is virtually impossible.

This is one of the things that makes Blockchain one of the most promising networking and currency
technologies. If you consider how it can be combined with the latest advances in AI and the IoT,
we are poised to change the world.

5 Ways Blockchain Will Change the World:

1. Taxes

Taxes are collected and then delivered to local governments or the state following a complex audit system
that costs a lot of money and time to carry out.

Governments can use Blockchain technology to implement, track and receive taxes in real time.
Blockchain may help streamline and even eliminate taxes as we know them–but that’s certainly further off.

In the meantime, start-ups are developing innovative solutions toward this direction.

For instance, Chainalysis, a company specialized in analyzing the Bitcoin Blockchain, is helping some
clients, including the IRS and Europol, to identify Bitcoin tax cheaters.

2. Digital ID

United Nations stats show that globally over one 1 billion people cannot provide documented proof of
their existence, which raises many social, cultural and political challenges.

ID2020 is a global public-private initiative that seeks to remedy this situation. Microsoft and Accenture,
at the last ID2020 summit held last June in New York, presented a prototype digital identification system
based on Blockchain protocols.

Estonia issues e-residency cards for its residents, based on Blockchain technology. India also aims to use
the technology to enhance the Aadhar registry.

3. Digital Advertising

Blockchain can solve many issues now inherent to digital advertising, such as those that come along
with fraud and transparency.

The market of peer-to-peer advertising via Blockchain is taking shape.

Created by MetaX and DMA, adChain is an open protocol built on the Ethereum Blockchain.

adChain ensures that ad spending reaches where it needs to without going through a complicated ad
supply chain, without fake views, and it favors efficiency, trust, and transparency between advertisers,
publishers, and customers.

4. Transparency and Accountability

With tamperproof records, Blockchain protocols can handle more than virtual currency transactions.

Among the areas where Blockchain tech can be applied, there is the storage and exchange of documents,
certificates or the setting up of contracts.

Decentralization, autonomy, security, and transparency of accounts are what Blockchain technology can
offer to companies.

Blockchain offers a secure e-voting tool whose results are transparent and none can modify them afterward.

5. Smart Digital Assets

Investors could leverage Blockchain as a smart system to track their digital assets. Using the open ledger,
they can prove ownership of the asset and track its movement.

In case of physical assets, a digital identity of the item can be recorded in real-time. This should help with
physical transactions and shipping from getting items through foreign government customs to
shipping items remotely.

For example, Portion is a startup that allows you to easily rent out unused goods, using fraud-resistant
“smart-tags” to represent a physical item as a virtual asset and thereby authenticates ownership.

Artificial Intelligence Replace Developers

Artificial Intelligence can compose songs, paint pictures, help in cancer therapy, drive cars and play games. It’s also starting to write code.

Does it mean that the days of human developers are already numbered?

When you look at tests of personal assistants such as Siri, Google Assistant or Cortana, you will discover that probably the most frequent request they hear is:

Tell me a joke.

Is that really all that Artificial Intelligence can do for us? Not at all. Its achievements are getting more impressive every day.

So before we tackle the question of Artificial Intelligence replacing software devs, let’s explore the achievements of AI thus far.

What can AI do?

Beat humans in board games and quizzes

In 1997 in New York City, the IBM computer Deep Blue won a chess match against Garry Kasparov. It was the first time when a machine defeated the world chess champion under tournament conditions.

In 2011, another IBM computer - Watson - took part in the television quiz show “Jeopardy” against former winners. Watson had to listen to questions and give answers in natural human language.

He was not connected to the internet.

However, he learned from 200 million pages of structured and unstructured content taking up four terabytes of disk storage. Watson won the first place prize of $1 million.

In March 2016, AlphaGo - a computer programme from Google DeepMind created to play the board game Go - beat Lee Sedol, the World Champion in Go. The man and the machine played a five-game tournament in Seoul. Lee Sedol won only the fourth game. In the rest, the machine proved superior.

Lee Sedol playing against DeepMind - an Artificial Intelligence from Google

Make medical discoveries

AI has already led to breakthroughs in medical diagnostics.

In 2013, Artificial Intelligence was put to work to detect breast cancer. A neural network was trained to find signs of cancer using tens of thousands of mammographic pictures of the disease.

But the neural network has learned that it is not so important to look for the tumors themselves, but rather some other modifications of the tissue which aren’t in the immediate vicinity of the tumor cells.

This was new knowledge to humankind. Until 2013, medicine didn’t know that.

Compose songs

Magenta is a project from the Google Brain team. It tries to answer the question: “Can we use machine learning to create compelling art and music? If so, how? If not, why not?”

The team works using TensorFlow - a machine learning library from Google. Have a listen to Magenta’s first computer-generated song - composed without any human assistance.

Paint pictures

In February 2016 in San Francisco, Google sold 29 paintings on a charity auction. All of them were made by Google’s Artificial Intelligence.

The event was called “DeepDream: The art of neural networks.” The paintings (masterpieces, if you ask me) went for more than 8000$, as The Wall Street Journal reports.

Google Deep Dream painting

And that’s not all AI can do. It can also drive motorways, write poems, and much more.

What it can’t do is write code. Or can it?

AI for everyone

In December 2015, Google released the TensorFlow library to the public. Now it’s an open-source software for machine learning.

Why did Google give out this powerful piece of software for free? According to prof. Christian Bauckhage from Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme, Germany (IAIS), you can find the answer in Google’s history. About 10 years ago, Google has open sourced the Android Operating System for smartphones. 10 years later, 80% of all the smartphones in the world run on Android.

"This is what they are trying to do right now. 10 years from now, the idea is that 80% of AI will run on Google TensorFlow," prof. Bauckhage said at the CeBIT Conference last year.

What happened after Google’s release? A few weeks later Microsoft open-sourced their Computational Network Toolkit AI - a deep learning framework now called the Microsoft Cognitive Toolkit.

After another few weeks, Facebook opened-sourced their own Artificial Intelligence libraries - Caffe2.

AI is writing code

In 2015 Andrej Karpathy, now director of AI at Tesla and a Stanford Computer Science Ph.D. student, used Recurrent Neural Networks to generate code. He took a Linux repository (all the source files and headers files), combined it into one giant document (it was more than 400 MB of code) and trained the RNN with this code.

He left it running for the night. In the morning, he got this:

Sample code generated by Artificial Intelligence

Literally overnight, the AI generated code including functions and function decorations. It had parameters, variables, loops and correct indents. Brackets were opened and later closed. It even had comments.

The AI made some mistakes of course. Sometimes variables were never used. Other times, there were variables which were not declared earlier. But Karpathy was satisfied with the result.

‘The code looks really quite great overall. Of course, I don’t think it compiles but when you scroll through the generate code it feels very much like a giant C code base,’ Karpathy wrote on his blog.

The project is available on GitHub. It uses the Torch7 deep learning library. Here is the whole output file received by Karpathy.

DeepCoder

Microsoft and Cambridge University researchers have developed an Artificial Intelligence that can write code. The AI is called DeepCoder and it has the ability to learn.

DeepCoder can write working code after searching through a huge code database. It tries to make the best possible arrangement for the harvested code fragments and improves its efficiency over time.

This doesn’t mean the AI steals code, or copy-pastes it from existing software, or searches the internet for solutions. The authors of DeepCoder expect that it will be participating programming competitions in the near future .

Example programme in Domain Specific Language (DSL) created by DeepCoder

According to Marc Brockschmidt of Microsoft Research, who is a part of the project, such system could be of great utility to non-coders. They only have to describe their program idea and wait for the system to create it.

‘We might end up having such system in the next few years. But for now, DeepCoder’s capabilities are limited to programs consisting of five lines of code,’ he said.

You can find DeepCoder’s documentation is here.

Python code made by AI

Since this is a primarily Python-focused blog, we would be remiss if we didn’t give you at least one Python example.

In June 2016, a French engineer by the nickname of BenjaminTD published a blog post in which he explained how he was “teaching an AI to write Python code with Python code”.

He used Long Short Term Memory (LSTM) - one of the most popular architectures of recurrent neural networks. He fed it with lots of Python code (using libraries such as Pandas, Numpy, Scipy, Django, Scikit-Learn, PyBrain, Lasagne, Rasterio). The combined file weighed 27MB.

The AI then generated its own code. It was defining inits:

...using boolean expressions:

 ...and creating arrays:

If you look at the arrays carefully, you will find a syntax error. Benjamin’s code is far from perfect. But the engineer thinks that it’s not bad for a network that had to learn everything from reading example code.

‘Especially considering that it is only trying to guess what is coming next character by character,’ he concludes his blog post.

Will AI replace programmers?

The Hollywood fiction of AI supplanting humans hasn’t come true yet. We are far from a 2001: A Space Odyssey scenarios of a rogue AI turning against its human masters and killing off space crews.

That does not stop filmmakers from generously employing the theme of AI rebellion in their works.

But can we be so sure that real-life AI can be controlled?

In 2016, Microsoft released a Twitter bot - Tay. It was designed to mimic the language patterns of a 19-year-old American girl, and to learn from interacting with human users of Twitter. After just 16 hours following its launch, Microsoft was forced to shut Tay down because the bot began to post offensive tweets.

That’s not the only AI issue on record. In early 2017, Facebook had to shut down its bots - Bob and Alice. They were created to perform conversations between human and computer. But when the bots were directed to talk with each other, they started to communicate in a way that was impossible for people to understand.

A few months later a Chinese chatbot - Baby Q - was switched off after it started to criticize the Chinese Communist Party. Baby Q called it "a corrupt and incompetent political regime".

So, is AI a threat or an opportunity? Elon Musk is known for his scepticism towards AI. His worry is what will happen when the machine becomes smarter than the human.

"Even in the benign scenario, if AI is much smarter than a person, what do we do? What job do we have?’" he asks.

There is no doubt that computers will be much better at programming in the near future than they are now. Which brings us to a quite scary conclusion.

"It’s just a matter of time until neural networks will produce useful code. So things are looking bleak for computer scientists like me" prof. Bauckhage believes.

But is the future really that dark? According to Armando Solar-Lezama of MIT, tools like DeepCoder do have the potential to automate code development, but AI isn’t going to take away the jobs of developers. Instead, a system based on program synthesis can be used to automate the tedious parts of code development while the developers focus on complex tasks.

There are already startups using automation to build ‘smart software’. Dev9 is a custom software development company based in Seattle focused on Java and JavaScript. Dev9 assembles teams that use artificial intelligence to develop custom software, eliminating strenuous processes and drastically reducing manual overhead. Will Iverson, its Chief Technology Officer, was asked if programmers need to be worried about being replaced by AI in the near future, and replied:

Eventually, yes. But by that point, society will be very used to dealing with that kind of societal change. The millions of paid drivers replaced by self-driving cars will have long since forced our political and economic systems to figure out how to deal with these transitions. We have joked around the office that software development will be one of the last professions left.

Will Iverson, CTO at Dev9

Regardless of whether our worries are justified, the fact is that nearly a third of software developers fear that artificial intelligence will eventually take their jobs. In an Evans Data Corp.survey, 550 software programmers were asked about the most worrisome thing in their careers. The most common response (29%) was:

"I and my development efforts are replaced by artificial intelligence."

According to Janel Garvin, CEO of Evans Data, the thought of obsolescence due to A.I., "was also more threatening than becoming old without a pension, being stifled at work by bad management, or by seeing their skills and tools become irrelevant."

There is no doubt that AI technology will developing and grow smarter. Eventually it will become smarter than humans. How can we handle such a possibility? Stephen Hawking also sees a real danger that computers will develop intelligence. But he also offers advice:

"We urgently need to develop direct connections to the brain so that computers can add to human intelligence rather than be in opposition," Hawking says.

Some things that reshaping IoT for the future

1. An explosion of sensors
2. A new regulatory environment
3. Machine learning comes into its own
4. Better security & physical standards

For many Internet of Things observers, the IoT’s dizzying embracing and subsequent reshaping of modern business and cultural standards seems like it happened only yesterday. Yet the IoT is moving ever faster, developing at an astonishing rate and continuously reinventing itself. So what’s the next step in the long-line of IoT innovation, and how can eager investors cash in on tomorrow’s IoT fad today?

Those used to the ever-changing nature of the IoT will know by now that it’s constantly changing, but will also be aware that innovation in certain areas matters much more than gradual developments elsewhere. A quick review of the 4 things reshaping the IoT’s future the most shows which industries will be most vital towards its development and offers an alluring glance of what the IoT of tomorrow will look like:

1. An explosion of sensors

Few things empower the IoT as much as the sensors which make up its physical senses in the real world; already, millions of embedded sensors help companies and individuals collect useful data about the world around them, so that they may in turn shape it with better business decisions or new products. The IoT sensor market alone is expected to be worth a staggering $27 billion by 2022, and the rapid growth of an industry supplying the physical sensors and the coding that powers them will only continue in the coming decades.

[ Check out our corporate guide to addressing IoT security. ]

As billions of global consumers become the proud owners of smartphones and other handheld gadgets, the sensors which make up the IoT will not only grow in quantity, but also in financial value. As refinements to sensory technology continues, IoT-connected devices will be able to collect more data in less time at cheaper rates, and they’ll be able to better store, sort, sell, and make use of that data in the marketplace.

2. A new regulatory environment

Even strangers and newcomers to the IoT often recognize that consumer privacy and market regulation are two of the biggest concerns of the world’s largest digital phenomenon. Growing rates of corporate data breaches, such as the Equifax fiasco that jeopardized the personal data of millions of Americans alone, are making consumers and regulators alike more interested in data privacy and the general regulation of the IoT.

This will have a sizable impact on gadget producers and software developers alike; tomorrow’s IoT products and services will be created in a significantly stricter business environment, and greater cost will be shouldered by companies when it comes to things like investing in necessary IT security infrastructure and gadget security, not to mention data management.

3. Machine learning comes into its own

Anybody keeping tabs on the IoT has undoubtedly heard about machine learning and the wonders it can work in the business world, but fewer people understand that machine learning and the use of algorithms in tandem with the IoT in general are merely in their infancy. Much in the same way that today’s standard IoT devices and logo design software would look alien to someone only 10 years ago, the IoT of the next decade will be all but unrecognizable.

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Top companies like Google, Microsoft, and IBM are all pouring money into artificial intelligence and machine learning for good reasons; current applications of algorithms in the business world are only the tip of the iceberg. Tomorrow’s IoT will be shaped largely by those with control over the best data-analytics and machine learning capabilities, which will be needed to keep up with the ever-increasing deluge of data and devices that stems from the IoT’s continuous expansion.

4. Better security & physical standards

The high levels of attention being paid to IoT security in light of continuous hacks and data breaches will have a strong impact in the long term; in the IoT of tomorrow, gadgets won’t be valued based on how cheap they are to produce and program, like they are now, but rather based on their levels of security and physical endurance.

Advancements in everything from IT security infrastructure to gadget batteries and production standards will reshape current marketing trends and consumer patterns. Devices will be able to operate longer without needing a charge, and data privacy standards inside of IoT companies will themselves be much more stringent to prevent outside regulation or intrusion.

Ultimately, a public reevaluation of security standards is likely to drive the physical world of the IoT as much as it is the regulatory world that exist on paper to constrain it. The only consistent thing about the IoT is that it’s constantly evolving, surpassing modern expectations and standards and demanding new innovations and perspectives to adapt and conquer new sectors of the market. Much of the IoT of the 21st century will look completely different, but investors and consumers alike are unlikely to see more change than in these key 4 areas.

What everybody needs to know about Cognitive Computing?

1. Getting the definition right 

At present, there is no single agreed upon definition for cognitive computing. One of the best definitions I have come across is that of Bernard Marr’s. He defines Cognitive Computing “as the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.”

2. Technologies that fuel Cognitive Computing

One of the most common misconceptions among the general public is that Cognitive Computing is a standalone technology. But Cognitive Computing is a concept that is a combination of multiple technologies that helps it to mimic the human thought process. Some of the key technologies that enable Cognitive Computing are

• Machine Learning - Machine learning (ML) is a discipline where a program or system can learn from existing data and dynamically alter its behavior based on the ever-changing data. Therefore, the system has the ability to learn without being explicitly programmed. Machine Learning algorithms can be broadly categorized as classification, clustering, regression, dimensionality reduction and anomaly detection etc. The machine Learning module acts as the core computing engine, which using algorithms & techniques helps Cognitive Systems to identify patterns, perform complex tasks like prediction, estimation, forecasting and anomaly detection.
• Machine Reasoning - Machine reasoning (MR) systems generate conclusions from available knowledge by using logical techniques like deduction and induction. Machine Reasoning acts as the brain or decision engine within a Cognitive System. Machine reasoning systems are mainly employed to reason / validate the outcomes of other modules like ML, Statistical Analysis, NLP etc., Apart from validating the outcomes of other modules they can also function as a standalone module by individually solving a problem. Some of the most common types of reasoning systems include rules engine, case based reasoning, procedural reasoning systems, deductive classifiers, machine learning systems. For further reading on Machine Reasoning, I would recommend you to go through the paper titled “From Machine Learning to Machine Reasoning” by Leon Bottou 
• Natural Language Processing – Wikipedia defines Natural language processing (NLP) as a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Natural Language Understanding (NLU) and Natural Language Generation (NLG) are two of the most prominent sub fields within NLP. NLP helps cognitive systems to comprehend natural language data sources as well as present insights in the form of Natural Language. NLP is critical for applications like Search, Text Mining, Sentiment Analytics, Large Scale Content Analysis, Text Summarization, Narrative / Dialog Generation, Chatbots, Virtual Assistants.
• Speech Recognition - TechTarget defines Speech Recognition as the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable form. Speech Recognition is also commonly known as speech to text, automatic speech recognition or computer speech recognition. Common applications of speech recognitions include voice search, Home Automation (like Amazon Echo, Google Home), Virtual Assistants, Speech Analytics, Interactive Voice Response, Contact Center Analytics etc.
• Computer Vision - The British Machine Vision Association and Society for Pattern Recognition (BMVA) defines Computer vision is a field concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. Computer Vision deals with the creations of theoretical and algorithmic foundations to achieve automatic visual understanding. Some key applications of computer vision include facial recognition, medical image analysis, self-driving vehicles, asset management, industrial quality management, content based image retrieval etc.
• Human Computer Interaction - Interaction Design Foundation defines Human-Computer Interaction (HCI) as “a field of study focusing on the design of computer technology and, in particular, the interaction between humans (the users) and computers.” It encompasses multiple disciplines, such as computer science, cognitive science, and human-factors engineering. The goal of HCI is to ensure that human – computer interaction is very similar to that human – human interaction. Some popular examples of modern HCI include voice based systems, gesture controls, facial recognition systems, natural language question answering (NLQA)

3. Key Attributes of a Cognitive Computing System

Cognitive Computing Consortium mentions that any system to be qualified as a cognitive system, it should meet the following criteria

• Adaptive - The systems must have the capability to learn as information changes, and as goals and requirements evolve. The system must have the capability to overcome ambiguity and tolerate unpredictability. Also the systems should have the capability to process and analyze real time / near real time data.
• Interactive – The systems should enable users to interact with them as close to a human – human interaction by employing gestures, touch, voice and natural language. They might also need to seamlessly interact with other systems like processors, devices, and Cloud services, as well as with people.
• Iterative and Stateful – If the requirement is not clear, the systems should help in defining a problem statement by asking questions or asking more information. They must remember inputs, results from previous iterations and should be able to choose the right action applicable for a particular scenario.
• Contextual – Systems should be able to identify, and extract relevant context required such as users details, location, time, syntax etc. The system should be able to work with both structured and unstructured data sources in addition to sensory inputs (speech, visual, gesture and sensor data).

4. Key Enablers of Cognitive Computing

The following factors played a significant role in helping cognitive computing becoming mainstream from the confines of academic research 

• Big Data & Cloud Computing – Some Cognitive computing applications like computer vision, speech recognition need good storage and computing infrastructure. Enterprises now can now elastically scale their storage and processing infrastructure with Big Data Platforms like Hadoop and Cloud Computing Platforms like Azure, AWS & Google Cloud.
• Cheaper Processing Technology – Exponential decrease in processing cost is also one of the key factors enabling cognitive computing adoption. Higher processing costs in 1970s were one of the major inhibitors that prevented further research and adoption of AI. Nick Ingelbrecht from Gartner, in a Financial Review article explains that in the past eight years there has been a 10,000-fold increase in processing speeds.
• Access to Machine Learning & Deep Learning – Open source Machine Learning libraries like Mahout, Spark ML made machine learning algorithms accessible to a wider audience. Google, Microsoft, Intel and IBM played a key role in making deep learning capabilities accessible to the developer community through their Cognitive Services & APIs which could be easily embedded into other applications. 
• Innovative Start Ups – As per Bloomberg’s estimate there are around 2600+ startups in the AI & Cognitive Computing Space alone and in the last year around 200 startups raised around $1.5 Billion in equity funding. Gartner predicts that these startups will be giving the large players like IBM, Google, Microsoft a tough competition due to their niche focus and rapid pace of innovation.
• Data Availability – IDC predicts that there is around 160 ZB of data in the present digital universe. This data is available across multiple formats like machine logs, text, voice and video waiting for enterprises to exploit their potential. Data Availability is also a key factor for enterprises to embrace cognitive computing.

5. Major Benefits of Cognitive Computing

Cognitive Computing has interesting use cases catering to multiple industries and functions. Listed below are some of the major business benefits of cognitive computing

• Increased Customer Experience – In a survey conducted by IBM, 49% of the respondents mentioned that Cognitive Computing helps in improving customer engagement and service. Cognitive Computing can help enterprises to enhance customer experience by enabling them with cognitive applications like cognitive assistants, personalized recommendations, social intelligence and behavioral predictions.
• Enhanced Productivity - Since the focus of Cognitive Computing is to mimic human capabilities and tasks, it helps in enhancing employee productivity and quality of outcomes. In an article by Joshbersin, he claims that by using cognitive computing to interpret commercial loans, JPMorgan Chase & Co was able to reduce 360,000 hours of lawyer time each year. Similarly other applications that help enterprises enhance employee productivity include cognitive assistants for doctors, robo advisors for wealth management, automated data scientists etc.
• Business Growth – Based on a study by IDC, 1.7 MB of data is generated per second for each person on the planet. On the other hand, 99.5% of the world’s data is not analyzed. Cognitive Computing can help enterprises unlock business opportunities and revenues from these untapped data assets. Analyzing this dark data can help enterprises identify the right markets for expansion, new customer segments to target and new products to launch.
• Increased Operational Efficiency - Nanette Byrnes in an MIT Technology Review article mentions that General Electric is using AI & Cognitive computing technologies like computer vision to improve service on its highly engineered jet engines. Post adoption of these technologies, GE was able to effectively detect cracks and other problems in airplane engine blades. Enterprises can enhance operational efficiency by implementing cognitive applications like predictive asset maintenance, contact center bots, automated replenishment systems etc.