#i do have some.. Opinions on newer software that is able to use AI. including synth v and vocaloid 6

[animemusicbrackets]

for the bracket, are songs using Synthesizer V vocals allowed? i know you said that utau was alright, but i just wanted to make sure before i put my submission in

yes synthesizer v is fine to submit

[cryptonewsworldwide]

They feed supercomputers MASSIVE amounts of data on the crypto markets, then ask artificial intelligence "what will happen next?”

When it comes to gathering, and analyzing the information we need to make wise trades - there's some problems with the current standard methods. The worst of the worst - downright bold and manipulative tactics. A perfect example of this would be organized 'pump and dump' groups, and while the SEC has taken some action targeting them, there's still plenty out there. These groups usually work by telling members to meet up online through Telegram or Discord. Then, the group leaders announce which coin every member should buy immediately. Let's imagine they choose a coin that's worth around $10, the ring leaders get it at that price.  Then regular members of the group are told to buy it, they may raise it up to $13 if it's a larger group with hundreds of members. Now that everyone in the group has their coins, they begin the next phase - publicly spreading fake news and rumors all across social media. They'll tweet things like 'just heard that this token is about to announce a major new partnership' and 'the coin is probably going to hit $20 by the end of the day'. To those unfamiliar with what they're looking at, things may appear legitimate.  They search twitter and see multiple people saying the same thing. Then they check the charts and see - this coin is on it's way up! For many, this is enough to trigger an impulse buy. In reality, they're buying coins the scammers bought an hour earlier - because now the scammers are selling (dumping) them at a profit. That's the worst of the worst, on the other end of the spectrum are those who share their thoughts and analysis of the market with the absolute best intentions. Genuinely good people who honestly aim to help fellow investors. The problem here just can't be fixed - subconscious bias. Everyone, even the expert seasoned traders have their own personal favorite coins & projects. When there's valid criticism or bad news they may acknowledge it, but often it's downplayed or followed by an explanation of why that coin will 'bounce back.' I'm absolutely not saying to stop listening to the opinions of other traders, there's some great minds in the cryptocurrency world and to ignore them would be a mistake. The honest ones would be the first to agree with me - that's why they say "but do your own research" right after they share theirs. Point being - there's no such thing as someone giving unbiased advice. Doing your own research... Clearly, it's essential to have information that comes from a source immune to human manipulation. Those seeking this kind of data are typically led to Technical Analysis (TA for short).   For those who may be newer to all this, you've probably seen it, it's those charts people post that continue on into dates in the future and they've then drawn in what they think will happen next. To be clear, I'm not taking a view against it - but be aware of its shortcomings. While current daily volume levels can be taken into account, TA relies very heavily on historical market data. Many argue that there simply isn't enough historical data, period.  Bitcoin turned 1 decade old just last week, but these methods come from stock traders who developed them with plenty of historical data to take into account. An even larger point often ignored is how drastically the crypocurrency market changed in 2017. Late 2017 specifically, so barely over a year ago.  The year ended with millions of people first discovering cryptocurrency even existed, and a market of around a dozen coins became thousands of coins competing with each other.   I would argue that 'historical' data before this is virtually useless - it's historical data on a fundamentally different market. With that in mind we're now left with 1 year of data. I'm not trying to be a downer here, but 2018 was spent correcting 2017, so I'd question even using that. Point being - TA predictions should be taken with a grain of salt, and i'd strongly discourage making any trades based on TA alone. The solution: We need to be able to look at the information that’s important, and come up with an honest prediction of what will come next. Sounds simple, it's not. Since the human element must be removed to guarantee there is no bias, what we're really talking about here is giving data to a computer and saying 'here's everything we know up to today, tell us what will happen tomorrow'. While computers haven't been turned into digital crystal balls that perfectly predict future events - we have reached a point where those with access to their answers are given a clear indisputable advantage. I first heard of these methods using complex algorithms, machine learning, and artificial intelligence a few years ago. At the time, it was only in reference to the stock market. Since then developers have made some significant technical advances, and more importantly - it's now being applied to the cryptocurrency markets. Wanting to try these tools in my own hands, I found US based Quantamize. (not an affiliate link, I don't get anything if you join). There are others saying they use similar technology, but the ones I came across were either very expensive with no way to try it first, one was an ICO recently and you need to first acquire their token as they only accept that for payment, and the last one I looked at sells software that you need to keep running on your own computer. Quantamize, however, gives you 3 free weeks. Their business model isn't 'hope they forget to cancel the trial' they didn't even ask for a credit card number, which seemed like a good sign. They also seemed to cover the most ground and include 30 coins, it's web based so no need to run software on your computer - they handle all that, then you get the results on their site. How it works, and why it makes sense: Imagine you're not just an investor - you're starting a cryptocurrency trading fund. First thing to do is hire your research team. You hire someone whose only job is to analyze daily trading volume, another person to focus only on price movements, another to analyze using all historical data, another to scan social media and gauge public sentiment, and another who reads every news article about cryptocurrency every day. At the end of the day, you call a team meeting. You go through the list of coins your fund invests in,  and each team member answers based on the data they looked at - will the coin go up or down in price?  If most of the team says a coin is going up, it probably will. Now - replace all of these people with artificial intelligence and machine learning algorithms running non-stop on a network of high powered computers, endlessly consuming and analyzing new data as it's available - this is Quantamize! Called a multi-factor approach, for each market fundamental (daily volume, sentiment, etc) Quantamize develops a proprietary version of their artificial intelligence that only looks at the 1 cryptocurrency it was built for – there are unique AI algorithms for every cryptocurrency that Quantamize analyzes. Let's circle back to the introduction where we discussed pump and dump scammers colluding to create false excitement around a specific coin to artificially 'pump' the price up. First, Quantamize's AI dedicated to scanning social media posts will detect that currently there is a large increase in positive things being said about this coin.  But - the AI's analyzing market fundamentals may see this coin has been on the decline for some time, daily trading volume has been low, and for months the long term outlook has been negative. The end result would be a 'do not own' signal - because tricks that rely on using human emotion to make someone buy or sell simply won't fool the algorithms. Using the data: You still do all your own trades, on whatever exchanges you currently use.  You don't put your cryptocurrency in the control of artificial intelligence or group of traders, you simply see what Quantamize is suggesting and decide if you want to act upon it or not. Quantamize presents the data to be used in 3 different ways. ● Signals - All this research summed up and shared as a simple list of the 30 coins, with a green "buy" or red "do not own" (sell) next to it. You don’t need to be a day trader to use these, the term 'signals' is often associated with high frequency traders who watch for signals changing minute by minute - Quantamize’s signals  represent a 3 day outlook. I consider myself a trader who manages his own portfolio, most cryptocurrency investors are. So for myself and most of you reading this, Quantamize's signals are a resource to check before you press that buy/sell button - make sure Quantamize isn't seeing something you may have missed. ● Portfolios - Quantamize uses their research to suggest full portfolios for those who may want to build one from scratch based on Quantamize’s research.  There’s multiple options depending on if you who want to play it safe, or willing to take larger risks for potentially larger rewards. If you're looking for a complete strategy where all you need to do is follow suggestions - this is for you. ● Research reports - in depth look at the news affecting the market, and in depth reports on specific coins and projects.  If you've been hearing about a coin and want to really dive in and learn more, this is the place you would find the information. Take a look, try adding this data to your decision making process, and see if it leads to better, more profitable trading. For 3 weeks of full access for free visit: https://www.quantamize.com

-------  Author: Ross Davis E-Mail: Ross@GlobalCryptoPress.com Twitter:@RossFM San Francisco News Desk //<![CDATA[ (adsbygoogle = window.adsbygoogle || []).push({}); //]]>

from Global Cryptocurrency Press - The latest in bitcoin and cryptocurrency. http://bit.ly/2MfKBcD Read More! http://bit.ly/2CpW3hr

[babbleuk]

Five Questions For… Seong Park at MongoDB

MongoDB came onto the scene alongside a number of data management technologies, all of which emerged on the basis of: “You don’t need to use a relational database for that.” Back in the day, SQL-based approaches became the only game in town first due to the way they handled storage challenges, and then a bunch of open source developers came along and wrecked everything. So we are told.

Having firmly established itself in the market and proved that it can deliver scale (Fortnite is a flagship customer), the company is nonetheless needing to move with the times. Having spoken to Seong Park, VP of Product Marketing & Developer Advocacy, several times over the past 6 weeks, I thought it was worth capturing the essence of our conversations.

  Q1: How do you engage with developers that is the same, or different to data-oriented engineers? Traditionally these have been two separate groups to be treated separately, is this how you see things?

MongoDB began as the solution to a problem that was increasingly slowing down both developers and engineers: the old relational database simply wasn’t cutting the mustard anymore. And that’s hardly surprising, since the design is more than 40 years old.

MongoDB’s entire approach is about driving developer productivity, and we take an object-focused approach to databases. You don’t think of data stored across tables, you think of storing info that’s associated, and you keep it together. That’s how our database works.

We want to make sure that developers can build applications. That’s why we focus on offering uncompromising user experiences. Our solution should be as easy, seamless, simple, effective and productive as possible. We are all about enabling developers to spend time on the things they care about: developing, coding and working with data in a fast, natural way.

When it comes to DevOps, a core tenet of the model is to create multi-disciplinary teams that can collectively work in small squads, to develop and iterate quickly on apps and microservices. Increasingly, data engineers are a part of that team, along with developers, operations staff, security, product managers, and business owners.

We have built capabilities and tools to address all of those groups. For data engineers, we have in-database features such as the aggregation pipeline that can transform data before processing. We also have connectors that integrate MongoDB with other parts of the data estate – for example, from BI to advanced analytics and machine learning.

  Q2: Database structures such as MongoDB are an enabler of DevOps practices; at the same time, data governance can be a hindrance to speed and agility. How do you ensure you help speed things up, and not slow them down?

Unlike other non-relational databases, MongoDB gives you a completely tunable schema – the skeleton representing the structure of the entire database. The benefit here is that the development phase is supported by a flexible and dynamic data model, and when the app goes into production, you can enforce schema governance to lock things down.

The governance itself is also completely tunable, so you can set up your database to support your needs, rather than being constrained by structure. This is an important differentiator for MongoDB.

Another major factor which reduces speed and agility is scale. Over the last two to three years, we have been building mature tooling that enterprises and operators alike will care about, because they make it easy to manage and operate MongoDB, and because they make it easy to apply upgrades, patches and security fixes, even when you’re talking about hundreds of thousands of clusters.

One of the key reasons why we have seen such acceleration in the adoption of MongoDB, not only in the enterprise but also by startups and smaller businesses, is that we make it so easy to get started with MongoDB. We want to make it easy to get to market very quickly, while we’re also focusing on driving down cost and boosting productivity. Our approach is to remove as much friction in the system as possible, and that’s why we align so well with DevOps practices.

In terms of legacy modernization, we are running a major initiative enabling customers to apply the latest innovations in development methodologies, architectural patterns, and technologies to refresh their portfolio of legacy applications. This is much more than just “lift and shift”. Moving existing apps and databases to faster hardware, or on to the cloud might get you slightly higher performance and marginally reduced cost, but you will fail to realize the transformational business agility, scale, or deployment freedom that true legacy modernization brings.

In our experience, by modernizing with MongoDB organizations can build new business functionality 3-5x faster, scale to millions of users wherever they are on the planet, and cut costs by 70 percent and more, all by unshackling from legacy systems.

  Q3: Traditionally you’re either a developer or a database person … does this do away with database engineers? Do we need database engineers or can developers do everything?

Developers are now the kingmakers; they are the hardest group of talent to retain. The biggest challenge most enterprises see is about finding and keeping developer talent.

If you are looking for the best experience in working with data, MongoDB is the answer in our opinion! It is not just about the persistence and the database …MongoDB Stitch is a serverless platform, drives integration with third-party cloud services, and enables event-based programming through Stitch triggers.

Ultimately, it comes down to a data platform that any number of roles can use, in their “swim lanes”. With the advent of cloud, it’s so easy for customers not to have to worry about things they did before, since they consume a pay-as-you-go service. Maybe you don’t need a DBA for a project any more: it’s important to allow our users to consume MongoDB in the easiest way possible.

But the bottom line is that we’re not doing away with database engineers, but shifting their role to focus on making a higher-value impact. For engineers we have capabilities and features like the aggregation pipeline, allowing us to transform data before processing.

  Q4: IoT-related question … in retail, you want to put AI into the supermarket environment, it could be video surveillance or inventory management. It’s not about distributing across crowd but into the Edge and “fog” computing…

At our recent MongoDB Europe event in London, we announced the general availability of MongoDB Mobile as well as the beta for Stitch Mobile Sync. Since we already have a lot of customers on the network edge (you’ll find MongoDB on oil rigs, across the IoT, used by airlines, and for the management of fleets of cars and trucks), a lot of these elements are already there.

The advantage is how easy we make it to work with that edge data. We’re thinking about the experience we provide in terms of working with data – and giving people access to what they care about – tooling, integration, and to look at what MongoDB can provide natively on a data platform.

  Q5: I’m interested to know what proportion of your customer base, and/or data/transaction base, are ‘cloud native’ versus more traditional enterprises. Indeed, is this how you segment your customers, and how do you engage with different groups that you do target?

We’d argue that every business should become cloud native – and many traditional enterprises are on that journey.

Around 70 percent of all MongoDB deployments are on a private or public cloud platform, and from a product portfolio perspective, we work to cover the complete market – from startup programs to self-service cloud services, to corporate and enterprise sales teams. As a result, we can meet customers wherever they are, and whatever their size.

  My take: better ways exist, but how to preach to the non-converted?

Much that we see around us in technology is shaped as a result of the constraints of its time. Relational databases enabled a step up from the monolithic data structures of the 1970s (though of course, some of the latter are still running, quite successfully), in no small part by enabling more flexible data structures to exist. MongoDB took the same idea one step further, doing away with the schema completely.

Is the MongoDB model the right answer for everything? No, and that would never be the point – nor are relational models, nor any other data management structures (including the newer capabilities in MongoDB’s stable). Given that data management vendors will continue to innovate, more important is choosing the right tool for the job, or indeed, being able to move from one model to another if need be.

This is more about mindset, therefore. Traditional views of IT have been to use the same technologies and techniques, because they always worked before. Not only does this risk trying to put square pegs in round holes, but also it can mean missed opportunities if the definition of what is possible is constrained by what is understood.

I would love to think none of this needs to be said, but in my experience large organisations still look backward more than they look forward, to their loss. We often talk about skills in data science, the shortage of developers and so on, but perhaps the greater gap is in senior executives that get the need for an engineering-first mindset. If we are all software companies now, we need to start acting accordingly.

from Gigaom https://gigaom.com/2018/12/12/five-questions-for-seong-park-at-mongodb/

[clarenceomoore]

Five Questions For… Seong Park at MongoDB

MongoDB came onto the scene alongside a number of data management technologies, all of which emerged on the basis of: “You don’t need to use a relational database for that.” Back in the day, SQL-based approaches became the only game in town first due to the way they handled storage challenges, and then a bunch of open source developers came along and wrecked everything. So we are told.

Having firmly established itself in the market and proved that it can deliver scale (Fortnite is a flagship customer), the company is nonetheless needing to move with the times. Having spoken to Seong Park, VP of Product Marketing & Developer Advocacy, several times over the past 6 weeks, I thought it was worth capturing the essence of our conversations.

  Q1: How do you engage with developers that is the same, or different to data-oriented engineers? Traditionally these have been two separate groups to be treated separately, is this how you see things?

MongoDB began as the solution to a problem that was increasingly slowing down both developers and engineers: the old relational database simply wasn’t cutting the mustard anymore. And that’s hardly surprising, since the design is more than 40 years old.

MongoDB’s entire approach is about driving developer productivity, and we take an object-focused approach to databases. You don’t think of data stored across tables, you think of storing info that’s associated, and you keep it together. That’s how our database works.

We want to make sure that developers can build applications. That’s why we focus on offering uncompromising user experiences. Our solution should be as easy, seamless, simple, effective and productive as possible. We are all about enabling developers to spend time on the things they care about: developing, coding and working with data in a fast, natural way.

When it comes to DevOps, a core tenet of the model is to create multi-disciplinary teams that can collectively work in small squads, to develop and iterate quickly on apps and microservices. Increasingly, data engineers are a part of that team, along with developers, operations staff, security, product managers, and business owners.

We have built capabilities and tools to address all of those groups. For data engineers, we have in-database features such as the aggregation pipeline that can transform data before processing. We also have connectors that integrate MongoDB with other parts of the data estate – for example, from BI to advanced analytics and machine learning.

  Q2: Database structures such as MongoDB are an enabler of DevOps practices; at the same time, data governance can be a hindrance to speed and agility. How do you ensure you help speed things up, and not slow them down?

Unlike other non-relational databases, MongoDB gives you a completely tunable schema – the skeleton representing the structure of the entire database. The benefit here is that the development phase is supported by a flexible and dynamic data model, and when the app goes into production, you can enforce schema governance to lock things down.

The governance itself is also completely tunable, so you can set up your database to support your needs, rather than being constrained by structure. This is an important differentiator for MongoDB.

Another major factor which reduces speed and agility is scale. Over the last two to three years, we have been building mature tooling that enterprises and operators alike will care about, because they make it easy to manage and operate MongoDB, and because they make it easy to apply upgrades, patches and security fixes, even when you’re talking about hundreds of thousands of clusters.

One of the key reasons why we have seen such acceleration in the adoption of MongoDB, not only in the enterprise but also by startups and smaller businesses, is that we make it so easy to get started with MongoDB. We want to make it easy to get to market very quickly, while we’re also focusing on driving down cost and boosting productivity. Our approach is to remove as much friction in the system as possible, and that’s why we align so well with DevOps practices.

In terms of legacy modernization, we are running a major initiative enabling customers to apply the latest innovations in development methodologies, architectural patterns, and technologies to refresh their portfolio of legacy applications. This is much more than just “lift and shift”. Moving existing apps and databases to faster hardware, or on to the cloud might get you slightly higher performance and marginally reduced cost, but you will fail to realize the transformational business agility, scale, or deployment freedom that true legacy modernization brings.

In our experience, by modernizing with MongoDB organizations can build new business functionality 3-5x faster, scale to millions of users wherever they are on the planet, and cut costs by 70 percent and more, all by unshackling from legacy systems.

  Q3: Traditionally you’re either a developer or a database person … does this do away with database engineers? Do we need database engineers or can developers do everything?

Developers are now the kingmakers; they are the hardest group of talent to retain. The biggest challenge most enterprises see is about finding and keeping developer talent.

If you are looking for the best experience in working with data, MongoDB is the answer in our opinion! It is not just about the persistence and the database …MongoDB Stitch is a serverless platform, drives integration with third-party cloud services, and enables event-based programming through Stitch triggers.

Ultimately, it comes down to a data platform that any number of roles can use, in their “swim lanes”. With the advent of cloud, it’s so easy for customers not to have to worry about things they did before, since they consume a pay-as-you-go service. Maybe you don’t need a DBA for a project any more: it’s important to allow our users to consume MongoDB in the easiest way possible.

But the bottom line is that we’re not doing away with database engineers, but shifting their role to focus on making a higher-value impact. For engineers we have capabilities and features like the aggregation pipeline, allowing us to transform data before processing.

  Q4: IoT-related question … in retail, you want to put AI into the supermarket environment, it could be video surveillance or inventory management. It’s not about distributing across crowd but into the Edge and “fog” computing…

At our recent MongoDB Europe event in London, we announced the general availability of MongoDB Mobile as well as the beta for Stitch Mobile Sync. Since we already have a lot of customers on the network edge (you’ll find MongoDB on oil rigs, across the IoT, used by airlines, and for the management of fleets of cars and trucks), a lot of these elements are already there.

The advantage is how easy we make it to work with that edge data. We’re thinking about the experience we provide in terms of working with data – and giving people access to what they care about – tooling, integration, and to look at what MongoDB can provide natively on a data platform.

  Q5: I’m interested to know what proportion of your customer base, and/or data/transaction base, are ‘cloud native’ versus more traditional enterprises. Indeed, is this how you segment your customers, and how do you engage with different groups that you do target?

We’d argue that every business should become cloud native – and many traditional enterprises are on that journey.

Around 70 percent of all MongoDB deployments are on a private or public cloud platform, and from a product portfolio perspective, we work to cover the complete market – from startup programs to self-service cloud services, to corporate and enterprise sales teams. As a result, we can meet customers wherever they are, and whatever their size.

  My take: better ways exist, but how to preach to the non-converted?

Much that we see around us in technology is shaped as a result of the constraints of its time. Relational databases enabled a step up from the monolithic data structures of the 1970s (though of course, some of the latter are still running, quite successfully), in no small part by enabling more flexible data structures to exist. MongoDB took the same idea one step further, doing away with the schema completely.

Is the MongoDB model the right answer for everything? No, and that would never be the point – nor are relational models, nor any other data management structures (including the newer capabilities in MongoDB’s stable). Given that data management vendors will continue to innovate, more important is choosing the right tool for the job, or indeed, being able to move from one model to another if need be.

This is more about mindset, therefore. Traditional views of IT have been to use the same technologies and techniques, because they always worked before. Not only does this risk trying to put square pegs in round holes, but also it can mean missed opportunities if the definition of what is possible is constrained by what is understood.

I would love to think none of this needs to be said, but in my experience large organisations still look backward more than they look forward, to their loss. We often talk about skills in data science, the shortage of developers and so on, but perhaps the greater gap is in senior executives that get the need for an engineering-first mindset. If we are all software companies now, we need to start acting accordingly.

[cryptonewsworldwide]

When it comes to gathering, and analyzing the information we need to make wise trades - there's some problems with the current standard methods. The worst of the worst - downright bold and manipulative tactics. A perfect example of this would be organized 'pump and dump' groups, and while the SEC has taken some action targeting them, there's still plenty out there. These groups usually work by telling members to meet up online through Telegram or Discord. Then, the group leaders announce which coin every member should buy immediately. Let's imagine they choose a coin that's worth around $10, the ring leaders get it at that price.  Then regular members of the group are told to buy it, they may raise it up to $13 if it's a larger group with hundreds of members. Now that everyone in the group has their coins, they begin the next phase - publicly spreading fake news and rumors all across social media. They'll tweet things like 'just heard that this token is about to announce a major new partnership' and 'the coin is probably going to hit $20 by the end of the day'. To those unfamiliar with what they're looking at, things may appear legitimate.  They search twitter and see multiple people saying the same thing. Then they check the charts and see - this coin is on it's way up! For many, this is enough to trigger an impulse buy. In reality, they're buying coins the scammers bought an hour earlier - because now the scammers are selling (dumping) them at a profit. That's the worst of the worst, on the other end of the spectrum are those who share their thoughts and analysis of the market with the absolute best intentions. Genuinely good people who honestly aim to help fellow investors. The problem here just can't be fixed - subconscious bias. Everyone, even the expert seasoned traders have their own personal favorite coins & projects. When there's valid criticism or bad news they may acknowledge it, but often it's downplayed or followed by an explanation of why that coin will 'bounce back.' I'm absolutely not saying to stop listening to the opinions of other traders, there's some great minds in the cryptocurrency world and to ignore them would be a mistake. The honest ones would be the first to agree with me - that's why they say "but do your own research" right after they share theirs. Point being - there's no such thing as someone giving unbiased advice. Doing your own research... Clearly, it's essential to have information that comes from a source immune to human manipulation. Those seeking this kind of data are typically led to Technical Analysis (TA for short).   For those who may be newer to all this, you've probably seen it, it's those charts people post that continue on into dates in the future and they've then drawn in what they think will happen next. To be clear, I'm not taking a view against it - but be aware of its shortcomings. While current daily volume levels can be taken into account, TA relies very heavily on historical market data. Many argue that there simply isn't enough historical data, period.  Bitcoin turned 1 decade old just last week, but these methods come from stock traders who developed them with plenty of historical data to take into account. An even larger point often ignored is how drastically the crypocurrency market changed in 2017. Late 2017 specifically, so barely over a year ago.  The year ended with millions of people first discovering cryptocurrency even existed, and a market of around a dozen coins became thousands of coins competing with each other.   I would argue that 'historical' data before this is virtually useless - it's historical data on a fundamentally different market. With that in mind we're now left with 1 year of data. I'm not trying to be a downer here, but 2018 was spent correcting 2017, so I'd question even using that. Point being - TA predictions should be taken with a grain of salt, and i'd strongly discourage making any trades based on TA alone. The solution: We need to be able to look at the information that’s important, and come up with an honest prediction of what will come next. Sounds simple, it's not. Since the human element must be removed to guarantee there is no bias, what we're really talking about here is giving data to a computer and saying 'here's everything we know up to today, tell us what will happen tomorrow'. While computers haven't been turned into digital crystal balls that perfectly predict future events - we have reached a point where those with access to their answers are given a clear indisputable advantage. I first heard of these methods using complex algorithms, machine learning, and artificial intelligence a few years ago. At the time, it was only in reference to the stock market. Since then developers have made some significant technical advances, and more importantly - it's now being applied to the cryptocurrency markets. Wanting to try these tools in my own hands, I found US based Quantamize. (not an affiliate link, I don't get anything if you join). There are others saying they use similar technology, but the ones I came across were either very expensive with no way to try it first, one was an ICO recently and you need to first acquire their token as they only accept that for payment, and the last one I looked at sells software that you need to keep running on your own computer. Quantamize, however, gives you 3 free weeks. Their business model isn't 'hope they forget to cancel the trial' they didn't even ask for a credit card number, which seemed like a good sign. They also seemed to cover the most ground and include 30 coins, it's web based so no need to run software on your computer - they handle all that, then you get the results on their site. How it works, and why it makes sense: Imagine you're not just an investor - you're starting a cryptocurrency trading fund. First thing to do is hire your research team. You hire someone whose only job is to analyze daily trading volume, another person to focus only on price movements, another to analyze using all historical data, another to scan social media and gauge public sentiment, and another who reads every news article about cryptocurrency every day. At the end of the day, you call a team meeting. You go through the list of coins your fund invests in,  and each team member answers based on the data they looked at - will the coin go up or down in price?  If most of the team says a coin is going up, it probably will. Now - replace all of these people with artificial intelligence and machine learning algorithms running non-stop on a network of high powered computers, endlessly consuming and analyzing new data as it's available - this is Quantamize! Called a multi-factor approach, for each market fundamental (daily volume, sentiment, etc) Quantamize develops a proprietary version of their artificial intelligence that only looks at the 1 cryptocurrency it was built for – there are unique AI algorithms for every cryptocurrency that Quantamize analyzes. Let's circle back to the introduction where we discussed pump and dump scammers colluding to create false excitement around a specific coin to artificially 'pump' the price up. First, Quantamize's AI dedicated to scanning social media posts will detect that currently there is a large increase in positive things being said about this coin.  But - the AI's analyzing market fundamentals may see this coin has been on the decline for some time, daily trading volume has been low, and for months the long term outlook has been negative. The end result would be a 'do not own' signal - because tricks that rely on using human emotion to make someone buy or sell simply won't fool the algorithms. Using the data: You still do all your own trades, on whatever exchanges you currently use.  You don't put your cryptocurrency in the control of artificial intelligence or group of traders, you simply see what Quantamize is suggesting and decide if you want to act upon it or not. Quantamize presents the data to be used in 3 different ways. ● Signals - All this research summed up and shared as a simple list of the 30 coins, with a green "buy" or red "do not own" (sell) next to it. You don’t need to be a day trader to use these, the term 'signals' is often associated with high frequency traders who watch for signals changing minute by minute - Quantamize’s signals  represent a 3 day outlook. I consider myself a trader who manages his own portfolio, most cryptocurrency investors are. So for myself and most of you reading this, Quantamize's signals are a resource to check before you press that buy/sell button - make sure Quantamize isn't seeing something you may have missed. ● Portfolios - Quantamize uses their research to suggest full portfolios for those who may want to build one from scratch based on Quantamize’s research.  There’s multiple options depending on if you who want to play it safe, or willing to take larger risks for potentially larger rewards. If you're looking for a complete strategy where all you need to do is follow suggestions - this is for you. ● Research reports - in depth look at the news affecting the market, and in depth reports on specific coins and projects.  If you've been hearing about a coin and want to really dive in and learn more, this is the place you would find the information. Take a look, try adding this data to your decision making process, and see if it leads to better, more profitable trading. For 3 weeks of full access for free visit: https://www.quantamize.com -------  Author: Ross Davis E-Mail: Ross@GlobalCryptoPress.com Twitter:@RossFM San Francisco News Desk // from Global Cryptocurrency Press - The latest in bitcoin and cryptocurrency. http://bit.ly/2MfKBcD

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