Bitcoin, Ethereum are most profitable investments of the decade

As the decade draws to a close, it’s time to look at the investments that were the most successful. And, unsurprisingly, cryptocurrencies top the list of …

As the decade draws to a close, it’s time to look at the investments that were the most successful. And, unsurprisingly, cryptocurrencies top the list of the most profitable investments of the decade.

Up first, is Bitcoin. The first cryptocurrency, built by an anonymous programmer known as Satoshi Nakamoto, it led to the creation of many Bitcoin forks—alternative versions running on similar code—and thousands of altcoins, either using the same code or trying out new features. But, if you got in early, you had the chance to make a quick buck.

Since the first bitcoin was available for trading, its price has accelerated 62,500 percent. Outshining many traditional stocks, it even spawned an entire culture built around prices “mooning” and the promise of lovingly labelled “lambos.” Due to the extreme rise, many critics have called it a Ponzi Scheme and say that its price pumps are bubbles that keep popping. But despite the criticism, an entire industry has been built around Bitcoin and other cryptocurrencies, leading many countries around the world to start adoption blockchain technology.

Much of the promise of blockchain technology can be seen with Ethereum. It offers features known as smart contracts, which allow for the creation of decentralized apps. These have interesting applications, particularly in the world of finance.

The price of Ethereum has shot up too. Even though the price has dropped heavily since its all-time high in January 2018, the price of Ethereum is still up by 17,900 percent. One ETH is currently worth $132.

However, some traditional stocks have not been far off. Netflix had a strong performance this decade, rising 4,280 percent. It’s not too surprising given how ubiquitous it now is. Even new films are now launching on Netflix instead of heading to the cinema. But it’s epic rise has led to an increase in the number of competitor video streaming companies. Will it be able to fend off the competition going into 2020?

Along with the rise of Netflix, and watching TV at home in general, another company did particularly well. Domino’s Pizza saw an increase in share price of 3,000 percent. Who knew pizza and TV were a winning combination?

In line with the trend of not needing to go outside, Amazon grew considerably in the last decade, rising 1,250 percent. It’s worth noting that not only does Amazon ship products to your door but it also offers a TV streaming service. What’s next, Amazon pizza?

Those doing yoga, trying to work off the 1,000 calorie pizzas, helped to boost the price of Lululemon shares, a retailer known for creating activewear and clothes for “most other sweaty pursuits.” They rose by 1,300 percent.

On a different track, healthcare company Abiomed saw a 2,000 percent rise in the last decade. It creates medical devices, such as artificial hearts.

Shotly behind Amazon is NVIDIA, known for creating computer chips. Interestingly, it pulled in $1.95 billion in revenue from its crypto mining business. But it wasn’t without controversy. In September, critics accused it of surreptitiously influencing the development of an upgrade to the Ethereum network. But nothing was ever proved.

Other profitable investments of the decade were payments processors, including Mastercard and VISA, up 1,100 percent and 760 percent respectively. Google shares rose by 350 percent and Apple shares went up by 840 percent.

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Technology And Society: Can Marketing Save The World?

Few administrations and a handful of companies are charting the road of post-quantum encryption. The U.S. is one of those. The National Institute of …

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In 1991, Stuart Haber and Scott Stornetta worked to develop uncrackable encrypted stacks of blocks, creating a database nobody could tamper with. At that time, they likely could not have imagined this technology would become the foundation of blockchain. Blockchain was born after Satoshi Nakamoto’s paper in 2008 about cryptocurrency that unraveled the many more applications this technology could have.

The foremost practical benefit of blockchain, in any application, is that of taking away reliability from humans and putting it into machines. It is the ultimate automated trust it generates through an uncrackable system of collaborating computers that creates encrypted blocks that guarantee the security and authenticity of any transaction or interaction, avoiding data bridges and human intermediation

In the last 10 years, we’ve seen the birth of several initiatives and organizations that are attempting to make the most out of this technology. It seems we are on the verge of a revolution that will change our lives in much the same way personal computers did throughout the last 30 years.

While it seems clear the value this technology may bring, we tend to forget that most technologies used today are data-driven, running over binary systems. Blockchain, artificial intelligence (AI), the internet of things (IoT), industry 4.0, autonomous vehicles and most of the amazing achievements of the last 50 years are based on this type of computing. What would happen if these types of binary systems became obsolete?

Change Is The Only Constant

With the technology we have today, cracking current encryptions that guarantee cryptocurrency security through blockchain is not an easy feat. That is what makes blockchain a safe place to authenticate transactions. But what if a new type of computer could do it in just minutes? What’s known as a quantum computer is already used by companies like Google and IBM.

Suddenly, blockchain, the technology that was supposed to change the future, becomes obsolete, and with it, most attempts to be its early adopters. Few administrations and a handful of companies are charting the road of post-quantum encryption. The U.S. is one of those. The National Institute of Standards and Technology (NIST) has already identified 26 algorithms that could become the standard to protect information today and tomorrow.

But there is no reason for panic. As Ian Kahn mentions in his acclaimed “Blockchain City” documentary, “Tomorrow is not here yet,” and it seems, as he also reminds, that our tomorrow is made of the only constant there is: change. Through constant change, evolution is happening at an accelerating pace, giving us little time to adapt and transforming governments, organizations, companies and consumers all into forced early adopters.

While quantum computers may seem a giant bridge, it is no different than all the other technologies we are benefiting from and do not realize we are using. As consumers, we do not understand internet protocols, and yet, we buy online every day. With quantum technology, it will happen the same: We may not understand it, but we will still run applications that will reap the benefits of this giant disruption that will boost innovation in a way we cannot even imagine.

I believe quantum computers are the new giant leap by humankind that will boost our capacity to understand, learn and build. With them, we will be able to open the doors to unimaginable discoveries and possibilities that will likely make us look like aliens on our own planet. This is the power that is being unleashed for which we will have to work on defining a purpose beyond profits and power, securing its use for the benefit of all. Dreamers will no longer exist the way we know them today.

Innovation Must Have A Greater Purpose

After many years doing marketing for companies of all sorts and sizes on three different continents, I came to the conclusion that focusing on technological innovation only could be a fatal — or at least dangerous — mistake. Marketing is one of the industries that has embraced and adapted to these new technologies at a really fast pace. However, having the power unleashed through technology is not enough if you don’t have a clear aim, and that aim cannot be only profits.

Technology, in most cases, increases efficiency. In essence, we achieve the same results, but faster, safer, in a cleaner way, with fewer resources. Take marketing, for instance: Social media, digital environments and IoT are all techniques marketing is using to the benefit of businesses’ profit and loss. Yet, these technological innovations are obtaining the very same results, though more efficiently, than our old, traditional, nondigital media: reach and segmentation.

I believe society is clamoring for a different impact. Innovation in technology is not enough. We need to innovate in management models that can guarantee, through the use and development of new technologies, that the impacts we generate are different. We need a broader base of prosperity that generates larger social equity and improves our environment.

Richard Branson has stated, “The brands that will thrive in the coming years are the ones that have a purpose beyond profit.” The future is now, and companies need to use technologies, products and services that allow them to go beyond, but never forgetting, profits.

Looking To Marketing As A Model To Follow

Marketing is the leverage that can serve as a bridge between corporations and society at large, launching profitable projects that also have social and environmental impacts. Marketing can also make consumers understand that they have the collective power, fostered by individual behavior, to demand those kinds of projects while accepting that companies make money along the way. It’s not bad to make money while helping others and the environment, and it is necessary to make those improvements sustainable.

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Exhaustive Study on Big Data IT Spending in Financial Market to Grow with an Impressive CAGR

The report also offers qualitative and quantitative investigation to deliver an entire and extensive analysis of the big data it spending in financial market …

New York City, NY: December 30, 2019, Published via (Wired Release) According to the latest report by Market.us Research, The Report study is collected on the basis of the latest and upcoming innovations, opportunities and trends. Global Big Data IT Spending in Financial Market predicts that the overall demand growth will remain moderate all over the forecast period (2020 – 2029). The report also offers qualitative and quantitative investigation to deliver an entire and extensive analysis of the big data it spending in financial market Competition, Insights market. It is a detailed report concentrating on primary and secondary drivers, market share, leading sections, and geographical analysis. It is a collection of analytical research based on past records, current, and forthcoming statistics and expected developments. The research on various sectors including opportunities, volume, growth, technology, demand, and trend of high leading players has been examined.

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The Aim of Big Data IT Spending in Financial Market report is to present a complete assessment of the market and contains thoughtful insights, facts, actual data, industry-validated market data and predictions with a proper set of hypotheses and methodology. The report also analyses global businesses including growth trends, industry opportunities, investment strategies, and expert conclusions.

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Know More About Big Data IT Spending in Financial Report

Global Big Data IT Spending in Financial Market report explains market segment such as product type, application, end-users, and region are presented in the report.Big Data IT Spending in Financial the market report concentrates on several key regions: North America, South America, Europe, Asia-Pacific, Middle East, and Africa, and the rest of the world.

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Segment 3 Big Data IT Spending in Financial Market Size by Manufacturers

Segment 4 Production by Regions

Segment 5 Consumption by Regions

Segment 6 Big Data IT Spending in Financial Market Size by Type

Segment 7 Big Data IT Spending in Financial Market Size by Application

Segment 8 Manufacturers Profiles

Segment 9 Production Forecasts

Segment 10 Consumption Forecast

Segment 11 Upstream, Industry Chain and Downstream Customers Analysis

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Monero Strength Is That It Cannot Be Analyzed or Traced

The overall market capitalization trends somewhere around $820.07 million. Monero cannot be purchased directly; instead, buy Bitcoin or Ethereum …

Monero is known as the king of privacy coins, and it is still under the control of the bears. Being an obfuscated decentralized public ledger, anyone can broadcast and send transactions; however, outside observers will not be able to tell the source, destination of the money sent. Among all the privacy-focused coins, Monero is the best.

Monero traded 3.4% versus the USD. The overall market capitalization trends somewhere around $820.07 million. Monero cannot be purchased directly; instead, buy Bitcoin or Ethereum with fiat, and later the cryptocurrencies can be converted to XMR.

Monero Transactions Are Untraceable

Privacy coins are considered a more significant threat than Bitcoin and Ethereum. An official from the European Cybercrime Center (EC3) stated that Monero transactions are not traceable. The EC3 was not able to trace down the details of neither the IP address nor the XMR movements.

While speaking at a conference at Blockchain Alliance webinar on privacy coins, Jerek Jakubcek of EC3 noted that Monero transactions could not be analyzed or traced.

Jakubcek stated that “Since the suspect used a combination of Tor and [Monero], we could not trace the funds. We could not trace the IP-addresses. Which means, we hit the end of the road. Whatever happened on the Bitcoin blockchain was visible, and that’s why we were able to get reasonably far.”

Monero Hides Traces of the Currency Transactions

Tor, on the one hand, hides only the IP address. Monero permits hiding the traces of the currency transactions; this is the strength of Monero.

The Europol makes use of Chain Analysis simulator to help with law enforcement. This testimonial from Europol vouches for the privacy of the public blockchain. The sooner the funds were transferred, the Europol stated that their investigation ended there.

“But with Monero blockchain, that was the point where the investigation has ended. Thus providing a classic example of one of several cases we had where suspect decided to move funds from Bitcoin or Ethereum to Monero.”

Trading privacy coins happens in only 32% of the top 120 exchanges. And, about 63% of them have KYC provisions which are not adequate, thus making the identification of the user difficult.

Some governments are putting privacy coins under fire, and many of them have passed laws to remove these privacy coins. The reason mainly being to prevent terrorism and money laundering.

The XMR prices are currently trading below the simple moving averages. The prices are trending downwards, and it is to be so until it touches upon critical support.

Explainable AI: The Rising Role Of Knowledge Scientists

Artificial intelligence is expected to create trillions of dollars of value across the economy. But as the technology becomes a part of our daily lives, …

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Artificial intelligence is expected to create trillions of dollars of value across the economy. But as the technology becomes a part of our daily lives, many people are still skeptical. Their main concern is that many AI solutions work like black boxes and seem to magically generate insights without explanation.

At the same time, knowledge graphs have been recognized by many industries as an efficient approach to data governance, metadata management and data enrichment and are increasingly being used as data integration technology. But knowledge graphs are also more and more identified as the building blocks of an AI strategy that enables explainable AI through the design principle called human-in-the-loop (HITL).

Why does artificial intelligence often work like a black box?

The promise of the AI, which is based on algorithms of machine learning such as deep learning, is to automatically extract patterns and rules from large datasets. This works very well for specific problems and, in many cases, helps automate classification tasks. Why exactly things are classified in one way or another cannot be explained. Because machine learning cannot extract causalities, it cannot reflect on why certain rules are extracted.

Machine learning algorithms learn from historical data, but they cannot derive new insights from it. In an increasingly dynamic environment, this is causing skepticism because the whole approach of deep learning is based on the assumption that there will always be enough data to learn from. In many industries, such as finance and healthcare, it is becoming increasingly important to implement AI systems that make their decisions explainable and transparent, incorporating new conditions and regulatory frameworks quickly. See, for example, the EU’s guidelines on ethics in artificial intelligence.

Can we build AI applications that can be trusted?

There is no trust without explainability. Explainability means that there are other trustworthy agents in the system who can understand and explain decisions made by the AI agent. Eventually, this will be regulated by authorities, but for the time being, there is no other option than making decisions made by AI more transparent. Unfortunately, it’s in the nature of some of the most popular machine learning algorithms that the basis of their calculated rules cannot be explained; they are just “a matter of fact.”

The only way out of this dilemma is a fundamental reengineering of the underlying architecture involved, which includes knowledge graphs as a prerequisite to calculate not only rules, but also corresponding explanations.

What is semantic AI, and what makes it different?

Semantic AI fuses symbolic and statistical AI. It combines methods from machine learning, knowledge modeling, natural language processing, text mining and the semantic web. It combines the advantages of both AI strategies, mainly semantic reasoning and neural networks. In short, semantic AI is not an alternative, but an extension of what is currently mainly used to build AI-based systems. This brings not only strategic options, but also an immediate advantage: faster learning from less training data, for example to overcome the so-called cold-start problem when developing chatbots.

What is a knowledge scientist?

Semantic AI introduces a fundamentally different methodology and, thus, additional stakeholders with complementary skills. While traditional machine learning is mainly done by data scientists, knowledge scientists are the ones who are involved in semantic AI or explainable AI. What is the difference?

At the core of the problem, data scientists spend more than half of their time collecting and processing uncontrolled digital data before it can be explored for useful nuggets. Many of these efforts focus on building flat files with unrelated data. Once the features are generated, they begin to lose their relationship to the real world.

An alternative approach is to develop tools for analysts to directly access an enterprise knowledge graph to extract a subset of data that can be quickly transformed into structures for analysis. The results of the analyses themselves can then be reused to enrich the knowledge graph.

The semantic AI approach thus creates a continuous cycle of which both machine learning and knowledge scientists are an integral part. Knowledge graphs serve as an interface in between, providing high-quality linked and normalized data.

Does this new AI approach lead to better results?

Apart from its potential to generate trustworthy and broadly accepted explainable AI based on knowledge graphs, the use of knowledge graphs together with semantically enriched and linked data to train machine learning algorithms has many other advantages.

This approach leads to results with sufficient accuracy even with sparse training data, which is especially helpful in the cold-start phase, when the algorithm cannot yet draw inferences from the data because it has not yet gathered enough information (see also: zero-shot learning). It also leads to better reusability of training datasets, which helps to save costs during data preparation. In addition, it complements existing training data with background knowledge that can quickly lead to richer training data through automated reasoning and can also help avoid the extraction of fundamentally wrong rules in a particular domain.

Developing An Interest In Semantic AI

If you are a data scientist or data manager — or if you manage someone in such a position — it’s important to start digging into this research and developing the skills to work with semantic AI.

Semantically enriched data serves as a basis for better data quality and offers more opportunities for feature extraction. This leads to higher accuracy in prediction and classification, calculated by machine learning algorithms. Furthermore, semantic AI should create an infrastructure to overcome information asymmetries between AI system developers and other stakeholders, including consumers and policymakers. Semantic AI ultimately leads to AI governance that works on three levels: technical, ethical and legal.

Most ML algorithms work well with either text or structured data. Semantic data models can close this gap. Relationships between business and data objects can be made available for further analysis. This allows you to provide data objects as training datasets composed of information from structured data and text.

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