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Buy Cryptocurrency with Credit Card or Bank Transfer now …

Over 1M investors from over 190 countries already use Coinmama for making cryptocurrency investments, thanks largely to our 24/7 customer service.

Do you wish to buy cryptocurrency instantly? Coinmama has created. Simplified procedure for you! You can now buy cryptocurrency by doing the following:

Sign up with Coinmama and create your account. It takes just a few minutes! A confirmation link will be sent to your email once you are done.

You need to verify your account before buying cryptocurrency. This involves the uploading of your passport, national ID or other documents based on your preferred level of verification. Once processed, approved and cleared, you can buy cryptocurrency for up to 15,000 USD with your credit or debit card.

Log in to the Coinmama account you created earlier, enter the desired wallet address, fill out the form and buy cryptocurrency.

Dont have a credit card at your disposal? You can also buy cryptocurrency with debit card! Coinmama accepts payments via Visa and Mastercard. American Express, Discover and PayPal are currently not accepted. Regardless of your preferred method of payment, just make sure that the card belongs to you.

To learn more about how to buy cryptocurrency, visit our Knowledge Base.

You can now buy cryptocurrency with bank transfer! We currently accept orders of up to 12,000 USD per business day, offering higher spending limits and lower fees.You can now buy cryptocurrency with bank transfer! We currently accept orders of up to 12,000 USD per business day, offering higher spending limits and lower fees.

Please note that this method is available through SEPA bank transfer in Europe and through SWIFT transfer in the rest of the world.

Cryptocurrency is a general name referring to all encrypted decentralized digital currencies such as Bitcoin, Litecoin, Ethereum and Ethereum Classic. They use cryptography to create coins and secure transactions. Typically, cryptocurrencies are open source and the transactions are based on blockchain technology.

Cryptocurrencies are traded via wallets, which are used to store, send, and receive digital currency. Most coins have an official wallet or a few officially recommended third-party wallets. You cannot invest in any cryptocurrency without using a dedicated cryptocurrency wallet.

With Coinmama, you can skip complex processes like mining and just buy cryptocurrency securely with your credit or debit card. Lets get started!

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Best 10 Cryptocurrency Brokers – Updated for 2019 (Safe …

Daniel Harris Major

I have tested 40+ crypto brokers. You can find the best cryptocurrency brokers below. This is by far the easiest way to get started with cryptocurrencies.

Cryptocurrency trading has become really popular in the past years. Its growing more ever year.

Many big trading brokers have already added some of these cryptocurrencies. Everybody can sign up and start trading Bitcoin or other crypto coins. This is the easiest and probably the simplest way to invest in cryptocurrency.

After trying out most brokers, here are the best cryptocurrency brokers:

Rated #1 out of 40+ tested brokers.

Plus500 is one of the most known names in the trading industry. They have a really low minimum deposit requirement and a great trading site.

Buy/Sell Bitcoin on Plus500 in addition to buying stocks, commodities, forex etc. Plus500 is the most complete trading platform.

Plus500 Pros:

Plus500 Cons:

80.6% of retail CFD accounts lose money. Plus500CY Ltd authorized & regulated by CySEC (#250/14).

Rated #2 out of 40+ tested brokers.

eToro is a really popular trading site. They have been around since 2006. eToro is regulated by CYSEC, FCA and ASIC.

Buy/Sell Bitcoin on eToro in addition to buying stocks, commodities, forex etc. eToro is the most complete trading platform.

eToro Pros:

eToro Cons:

eToro Disclaimer: 75% of retail investor accounts lose money when trading CFDs with this provider.

Rated #3 out of 40+ tested brokers.

If you dont want to make a huge initial investment, then IQ Option is the best broker for you. The minimum deposit requirement is only $10. This is by far the lowest in the industry.

IQ Option Pros:

IQ Option Cons:

Rated #4 out of 40+ tested brokers.

Due to regulation HighLow stopped accepting traders from the EU and UK.

Highlow is an Australian trading broker (AFSL No.364264). What I like most about this broker is the intuitive platform. It is so easy to use. Especially new traders will like the clean layout.

Ive been using highlow for a few years now and I never had a problem. Highlow publishes their number of trades on the homepage. There are millions of trades on this broker each month.

I use the main platform which is web based. This means, that you dont have to download any software. This platform is stable and safe (they use SSL).

I have also tried the iOS app and the android app. They are great, but I just dont like trading on smartphones in general.

The payouts on Highlow are amazing (up to 200%, which is more than on other brokers).

Highlow is a great broker for cryptocurrencies (but only if you are not from EU,UK, US). They have a good reputation, great support team and awesome promotions for new traders.

Highlow Pros:

Highlow Cons:

Here are the most popular cryptocurrencies offered by cryptocurrency brokers:

These are the best brokers with low minimum deposit requirements:

This is really important because the crypto market itself is unregulated. Depositing on a regulated broker means that your money is safe.

Weve also looked at the support team and the payment methods. You can deposit easily using multiple payment methods on the brokers below.

While we are all familiar with the broad definition of a broker, is there something more to it when it comes to cryptocurrencies?

Simply put, a cryptocurrency broker refers to a website that traders will visit to trade cryptocurrencies at a set price. In many ways, they are similar to forex brokers whose services are a lot more familiar to the general public.





Plus500 Disclaimer: 80.6% of retail CFD accounts lose money.eToro Disclaimer: 75% of retail investor accounts lose money when trading CFDs with this provider.

You still cant decide which broker is best for you?

Is Leverage Really Necessary? Cryptocurrency markets are volatile in nature with prices fluctuating immensely thereby generating high profits even in the absence of leverage. But for some traders, the desire for more earnings justifies the use of leverage.

Before choosing your broker, settle yourself on whether the already immense profits that come from a cryptocurrency market are enough for you or you would like to use leverage to enhance them even further, bearing in mind, of course, that will also significantly increase the risk factor to your funds.

Negative Balance Protection: It is always advisable to trade with a broker that affords the negative balance protection facility. That way, you will never be at risk of losing more than what you invested in case you sustain very heavy losses.

How Suitable Is The Required Capital For You? This is another subjective part to your decision. Do you prefer trading with a small capital or bigger one to help you zero in on bigger returns? Find out what size trades your broker is offering before you open an account.

Narrow Spreads for the Win: Let us be honest, it is only fair that your broker gets a cut from the money you generated having provided you with the essential facilities for it and all. But let us be honest, you definitely want every last dime for yourself.

A spread refers to the difference between the buying price and selling price of a trade and it varies with each broker. The spread is what counts as fees for your broker so the smaller it is, the lower the cost will be on your part.

Lets go into what aspects you as a trader must consider when choosing a broker to trade cryptocurrencies.

Make sure you have decided on which cryptocurrencies you want to trade in before signing up with your broker.

If your choice is a prominent cryptocurrency like Bitcoin (official site), Litecoin (official site), or Ethereum(official site), there will be a relatively broad availability of brokers that have trading options for those. However, less prevalent examples like Monero, IOTA, or Zcash may be a little harder to come by.

So study the cryptocurrencies being offered by a broker before you sign up for an account.











Plus500 Disclaimer: 80.6% of retail CFD accounts lose money.eToro Disclaimer: 75% of retail investor accounts lose money when trading CFDs with this provider.

No broker is perfect.

Here are some features that these broker have or dont:

Here are some of the more understated details about your cryptocurrency broker that could make your trading experiences so much more efficient and successful.

1. What Are The Weekend Hours Like?

This is a key detail that distinguishes cryptocurrencies from other prominent trading markets like forex, futures, and equity. Cryptocurrency brokers operate during the weekends as well.

There is virtually no difference in purchasing bitcoin at the end of the week as opposed to the middle of the week during peak hours.

But there is a catch. While digital currency exchanges are available during the weekend, your broker may not (Why is the stock market closed on the weekend?). So in effect, if there is considerable movement within your relevant cryptocurrency market during a weekend when your broker is not operating, you may not have the ability to respond in any way.

2. How Is It Hedging?

Your broker is probably not keen on revealing if it is hedging cryptocurrency traders but it is important information for you to know.

Why is it so important for you to know?

Because, as a trader, you want to be absolutely in the clear regarding the policies for risk management being followed by your broker. Let us not forget that cryptocurrency markets are extremely volatile and an unhedged broker is more easily prone to major losses incurred by its clients. Naturally, those costs will warrant compensation via fatter spreads and additional costs for traders. It will be best to sign up with a broker that will not withhold the relevant information from you.

Commissions and Trading On Margin An important thing to note while trading cryptocurrencies is that their prices tend to be more similar to equities than they are to real currencies. So your broker could be charging you commissions in addition to a wide spread on each trade. That, of course, raises the cost probably a bit much for your liking.

Another similarity with equities is that the margin conditions with cryptocurrencies are significantly more than they are with forex or CFDs. Consequently, leverage is usually up to 10x. How considerable the margin rate will be for traders is down to what trading strategies and risk management they subscribe to.

Authenticity of Market Data Aside from merely evaluating how it influences spreads, market data for cryptocurrency CFD prices can be a good indicator of how the quality of the product will be in the future.

One of the more recent examples of this is BTC-e which was an immensely popular platform for trading bitcoin before it was shut down by Feds. BTC-e was the first bitcoin exchange to incorporate forex trades and so made their exchange accessible via MT4 and supplied cryptocurrency liquidity for brokers.

But once it was shut down, every broker that depended squarely upon BTC-e was left with no hedging options or market data to put a price on its crypto CFDs.

Are Short Sales Available? For many traders, shorting is a crucial strategy option, the lack of which can be a deal breaker with a potential broker. Many brokers tend to offer long only since there are only a few hedging solutions when opening short trades.

As you see, investing in cryptocurrencies can be a truly lucrative venture for you but only if you get it right.

And a lot of that is down to which broker you decide to trade with.

Trade with the cryptocurrency brokers listed above to make sure that your money is safe, you pick a broker with a stellar reputation and you keep the fees low. Excel at trading bitcoin by creating your own trading strategy.

These cryptocurrency brokers make it really easy to trade bitcoin and other coins. You dont have to be a trading expert. It helps if you know what a blockchain is or how the ledger and transactions work, but this is all optional.

Sign up now and see for yourself how easy it is to get started.

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Best 10 Cryptocurrency Brokers - Updated for 2019 (Safe ...

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Best cloud storage service – Consumer Reports

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Last updated: April 2014

Cloud storage gives you 24/7 access to your documents, photos, music, and videosand you can get all of it wherever you are and on any compatible device, as long as you have an Internet connection. Cloud storage also makes sharing easy. And not least, it's an excellent way to back up all your digital content.

If you're trying to decide which cloud-storage service is best for your needs, check our rundown of 12 popular services, which covers pricing, security, special features, and other details you'll need to choose. And take a look at "Public cloud vs. personal cloud: Which is right for you?" for more storage options.

Price: Files: 5GB, free; 20GB, $10/year; 50GB, $25/year; 100GB, $50/year; 200GB, $100/year; 500GB, $250/year. Music: 250 imported songs, free; 250,000 imported songs, $25/year.

Supported OSes and devices: Android, iOS, OS X, Windows, Kindle Fire (Web UI requires (FlashPlayer).

Collaboration: No (only the subscriber can work on cloud-based files).

Security: Data is sent to and from the companys servers using 28-bit RC4 encryption, but files are not encrypted on company servers.

Customer support: Free 24/7 phone calls (you can either click the Call Me button for a call back from a live person or get an automated customer-service system by calling 1-866-395-6761), e-mail, and chat.

Special features: Dedicated photo apps for iOS and Android devices, Kindle integration via Photos tab on Kindle devices, and video playback on Kindle and Android devices.

Price: 5GB, free; 15GB, $20/year; 25GB, $40/year; 50GB, $100/year.

Supported OSes and devices: iOS, OS X, Windows with iCloud Control Panel, Apple TV (photos, iTunes in the Cloud).

Collaboration: No.

Security: Data is sent and stored using a minimum of 128-bit AES encryption and uses secure tokens for authentication. Encryption keys are never provided to third parties.

Customer support: An online knowledgebase.

Special features: iCloud Photo Sharing lets you share photos and videos with others, and you can invite them to add their own.


Price: 20GB, free; 1TB, $10/month or $99/year; 5TB, $49/month or $499/year; infinite, $99/month or $999/year.

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: No.

Security: Enterprise-grade 256-bit encryption on the client side, when sent, and on company servers.

Customer support: Help Center, community forums, and Twitter support feed for free-service users; paid users get all of the above plus e-mail and chat.

Special features: Centralized storage makes even large video files available instantaneously on all devices. Folder mirroring enables users to interrupt a large backup and resume later, say when a network connection is available.


Price: 10GB, free; 100GB, $5/month.

Supported OSes and devices: Android, iOS, OS X, Windows, Blackberry.

Collaboration: Yes.

Security: Provides 256-bit encryption, both in transit and on the companys servers.

Customer support: US-based, 24/7 phone support (1-877-729-4269), e-mail, and self-service Web support.

Special features: Includes a website optimized for mobile browsers; document creation, previewing, and editing capabilities with support for more than 75 file types; and a collection of 700 apps, such as education tools for students and teachers, and general productivity.


Price: Unlimited, $60/year (1 computer only).

Supported OSes and devices: Android, iOS, OS X, Windows, Blackberry.

Collaboration: No.

Security: Data is stored using 128-bit Blowfish encryption and sent using SSL technology.

Customer support: U.S.-based phone (877-222-5488), e-mail, and chat.

Special features: none (for individual users)


Price: 2GB, free (500MB more per referral, up to 18GB); add 100GB, $99/year.

Supported OSes and devices: Android, iOS, OS X, Windows, Blackberry, Kindle Fire.

Collaboration: Yes

Security: Data is stored using 256-bit AES encryption and two-step verification.

Customer support: FAQs, e-mail, and community forums.

Special features: Others can upload their own files to your Dropbox.


Price: 15GB, free; 100GB, $5/month.

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: Yes.

Security: Supports 2-factor authentication and uses HTTPS and TLS for transmissions to and from your device. You can also encrypt files before adding them to Google Drive.

Customer support: Phone, e-mail, knowledge base, and community.

Special features: Includes tools to create presentations, documents, spreadsheets, and drawings.


Price: 7GB, free; add 20GB, $10/year; add 50GB, $25/year; add 100GB, $50/year

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: Yes.

Security: Password-protected data is SSL-encrypted during uploads and downloads.

Customer support: Phone (800-642-7676, during business hours), self-service Web, Facebook, and Twitter.

Special features: Includes free browser-based versions of Word, Excel, PowerPoint, and OneNote.


Price: 2GB, free; 50GB, $6/month (1 computer); 125GB, $10/month (3 computers).

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: No.

Security: Data is sent using 256-bit AES encrytion; data is stored using 448-bit Blowfish encryption, or users can use their own 256-bit encryption key.

Customer support: Chat (with paid service), video tutorials, knowledge base, and community.

Special features: None


Price: 100GB, $10/month, 150GB, $16/month; 250GB, $20/month.

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: Yes, with Filelocker Pro (free)

Security: Data is stored on both the customer's computer and at the data center using 256-bit encryption. It is transferred using SSL.

Customer support: Chat, e-mail, and knowledgebase.

Special features: Each past version of every file is kept, but only the largest version is ever "counted" toward your storage.


Price: 60GB, $7.50/month; 100GB, $10/month; 250GB, $25/month.

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: No.

Security: Files are sent using Transport Layer Security and are stored using 256-bit AES encryption.

Customer service: Chat, knowledgebase, community, and e-mail (phone support, $100/year).

Special features: Remote wipe and automatic photo backup for mobile devices.


Price: 2TB, $150; 3TB, $180; 4TB, $250. No monthly fee.

Supported OSes and devices: Android, iOS, OS X, Windows.

Collaboration: Yes.

Security: 256-bit encryption.

Customer service: Phone (800-275-4932) and e-mail.

Special features: This product differs from the others in that you buy a personal server, called the MyCloud, and keep your content on it; its accessible from anywhere, as content on other cloud services would be. So it doubles as a traditional backup drive, and data is not stored on public servers.


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Best cloud storage service - Consumer Reports

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Cloud Server – what is and how does it work? | Probrand

A cloud server is a virtual server (rather than a physical server) running in a cloud computing environment. It is built, hosted and delivered via a cloud computing platform via the internet, and can be accessed remotely. They are also known as virtual servers. Cloud servers have all the software they require to run and can function as independent units.

The cloud is commonly used to refer to several servers connected to the internet that can be leased as part of a software or application service. Cloud-based services can include web hosting, data hosting and sharing, and software or application use.

The cloud can also refer to cloud computing, where several servers are linked together to share the load. This means that instead of using one single powerful machine, complex processes can be distributed across multiple smaller computers.

One of the advantages of cloud storage is that there are many distributed resources acting as one often called federated storage clouds. This makes the cloud very tolerant of faults, due to the distribution of data. Use of the cloud tends to reduce the creation of different versions of files, due to shared access to documents, files and data.

Cost-effective solutions that will save your business money when compared with a physical server.

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Cloud Server - what is and how does it work? | Probrand

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Start Here with Machine Learning

These are the Step-by-Step Guides that YouveBeen Looking For!What do you want help with?

How Do I Get Started?

The most common question Im asked is: how do I get started?

My best advice for getting started in machine learning is broken down into a 5-step process:

For more on this top-down approach, see:

Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists.

Applied Machine Learning Process

The benefit of machine learning are the predictions and the models that make predictions.

To have skill at applied machine learning means knowing how to consistently and reliably deliver high-quality predictions on problemafter problem. You need to follow a systematic process.

Below is a 5-step process that you can follow to consistently achieve above average results on predictive modeling problems:

For a good summary of this process, see the posts:

Linear Algebra

Linear algebra is an important foundation area of mathematics required for achieving a deeper understanding of machine learning algorithms.

Below is the 3 step process that you can use to get up-to-speed with linear algebra for machine learning, fast.

You can see all linear algebra posts here. Below is a selection of some of the most popular tutorials.

Statistical Methods

Statistical Methods an important foundation area of mathematics required for achieving a deeper understanding of the behavior of machine learning algorithms.

Below is the 3 step process that you can use to get up-to-speed with statistical methods for machine learning, fast.

You can see all of the statistical methods posts here.Below is a selection of some of the most popular tutorials.

Understand Machine Learning Algorithms

Machine learning is about machine learning algorithms.

You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them.

Heres how to get started withmachine learning algorithms:

You can see all machine learning algorithm posts here. Below is a selection of some of the most popular tutorials.

Weka Machine Learning (no code)

Weka is a platform that you can use to get started in applied machine learning.

It has a graphical user interface meaning that no programming is required and it offers a suite of state of the art algorithms.

Heres how you can get started with Weka:

You can see all Weka machine learning posts here. Below is a selection of some of the most popular tutorials.

Python Machine Learning (scikit-learn)

Python is one of the fastest growing platforms for applied machine learning.

You can use the same tools like pandas andscikit-learn in the development and operational deployment of your model.

Below are the steps that you can use to get started with Python machine learning:

You can see all Python machine learning posts here. Below is a selection of some of the most popular tutorials.

R Machine Learning (caret)

R is a platform for statistical computing and is the most popular platform among professional data scientists.

Its popular because of the large number oftechniques available, and because of excellent interfaces to these methods such as the powerful caret package.

Heres how to get started with R machine learning:

You can see all R machine learning posts here. Below is a selection of some of the most popular tutorials.

Code Algorithm from Scratch (Python)

You can learn a lot about machine learning algorithms by coding them from scratch.

Learning via coding is the preferred learning style for many developers and engineers.

Heres how to get started with machine learning by coding everything from scratch.

You can see all of the Code Algorithms from Scratch posts here.Below is a selection of some of the most popular tutorials.

Introduction to Time Series Forecasting (Python)

Time series forecasting is an important topic in business applications.

Many datasets contain a time component, but the topic of time series is rarely covered in much depth from a machine learning perspective.

Heres how to get started with Time Series Forecasting:

You can see all Time Series Forecasting posts here. Below is a selection of some of the most popular tutorials.

XGBoost in Python (Stochastic Gradient Boosting)

XGBoost is a highly optimized implementation ofgradient boosted decision trees.

It is popularbecause it is being usedby some of the best data scientists in the world to win machine learning competitions.

Heres how to get started with XGBoost:

You can see all XGBoosts posts here. Below is a selection of some of the most popular tutorials.

Deep Learning (Keras)

Deep learning is afascinating and powerful field.

State-of-the-art results are coming from the field of deep learning and it is asub-field of machine learning that cannot be ignored.

Heres how to get started with deep learning:

You can see all deep learning posts here. Below is a selection of some of the most popular tutorials.

Better Deep Learning

Although it is easy to define and fit a deep learning neural network model, it can be challenging to get good performance on a specific predictive modeling problem.

There are standard techniques that you can use to improve the learning, reduce overfitting, and make better predictions with your deep learning model.

Heres how to get started with getting better deep learning performance:

You can see all better deep learning posts here. Below is a selection of some of the most popular tutorials.

Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) Recurrent Neural Networks are designed for sequence prediction problems and are astate-of-the-art deep learning technique for challenging prediction problems.

Heres how to get started with LSTMs in Python:

You can see all LSTMposts here. Below is a selection of some of the most popular tutorials using LSTMs in Python with the Keras deep learning library.

Deep Learning for Natural Language Processing (NLP)

Working with text data is hard because of the messy nature of natural language.

Text is not solved but to get state-of-the-art results on challenging NLP problems, you need to adopt deep learning methods

Heres how to get started with deep learning for natural language processing:

You can see all deep learning for NLP posts here. Below is a selection of some of the most popular tutorials.

Deep Learning for Computer Vision

Working with image data is hard because of the gulf between raw pixels and the meaning in the images.

Computer vision is not solved, but to get state-of-the-art results on challenging computer vision tasks like object detection and face recognition, you need deep learning methods.

Heres how to get started with deep learning for computer vision:

You can see all deep learning for Computer Vision posts here. Below is a selection of some of the most popular tutorials.

Deep Learning for Time Series Forecasting

Deep learning neural networks are able to automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs.

Methods such as MLPs, CNNs, and LSTMs offer a lot of promise for time series forecasting.

Heres how to get started with deep learning for time series forecasting:

You can see all deep learning for time series forecasting posts here.Below is a selection of some of the most popular tutorials.

Generative Adversarial Networks

Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks.

GANs are an exciting and rapidly changing field, delivering on the promise of generative models in their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks.

Heres how to get started with deep learning for Generative Adversarial Networks:

You can see all Generative Adversarial Networktutorials listed here. Below is a selection of some of the most popular tutorials.

Need More Help?

Im here to help you become awesome at applied machine learning.

If youstill have questions and need help, you have some options:

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Start Here with Machine Learning

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VPN Publishes Forbes Internet Security Guide for Law Firms

ATLANTA, LONDON and TORONTO, Sept. 17, 2019 (GLOBE NEWSWIRE) -- VPN recently published a guide to internet privacy for law firms on The article, Protecting Attorney-Client Privilege With A VPN, was created to help law firms understand the dangers unsecure internet connections pose to their firm reputation, employees and clients.

The judicial branch of our government is the crown jewel of justice in the United States. It is a great opportunity to present your case before a judge in this country. Many places around the world have no such environment for trials of justice. Protecting the communication and sensitive details of these cases and clients is critical, stated Michael Gargiulo, CEO at VPN.

Many large law firms have established internet access policies. This defines how employees communicate internally and externally. Policies like this work to curb the abuse and cyberattacks large law firms typically attract.

Smaller firms or solo practices are often less fortunate when it comes to how they protect their attorney-client privilege and communication. Unfortunately, if that information is compromised by an adversary or publication it can completely erode the reputation of the firm, the attorney-client privilege and the clients case. In fact, Terry Myers, who operates one of the largest VPNs for businesses,, agrees without a VPN, your identity and communication are completely exposed, said Gargiulo.

IBM reports the average breach is now costing companies upwards of $4 million. Law firms and attorneys around the world are realizing the reality of growing threats against their internet connection and client-base. With the right internet access policies, business VPN connections and security protocols a firm can be much more prepared and secure in their digital operation.

To protect your firm or organization with a VPN, please visit: https://encrypt.meTo learn more about Protecting Attorney-Client Privilege With A VPN please visit:

To learn more about how helps families, entrepreneurs and organizations secure their internet activity, please visit:

To learn more about how VPN helps brands and visionaries secure premium domain names, please visit:

To learn more about premium dedicated hosting for your firm or enterprise, please visit: our #1 host of VPN providers in 2019 or email Jason Brozena at

To learn more news about VPN, please visit:

To learn more about Michael Gargiulo on Forbes, please visit:

To view VPNs Inc. Verified Profile, please visit:

See More: The WAR on VPN & FREE SPEECH in Hong Kong, China Explained!

See More: VPN Remembers 9/11 by Donating to Heros Family

See More: Can You Build Your Own VPN?

See LinkedIn Post: Sells to Digital Trends

See LinkedIn Post: Protecting-Attorney Client Privilege with a VPN

More Information: is a worldwide leader in VPN research and statistics. The company has collected 188,000 data points across 900 VPN providers, giving people the only way to quickly review and compare hundreds of VPN providers at once. Through transparent research, extensive testing and a drive to protect the world, will help 100 million consumers and businesses find the best privacy and security services by 2022.

In addition to VPNs, helps countless entrepreneurs and brands the best domain name. has bought and sold millions of dollars of domains and has helped tens of millions of people protect their privacy online. Whether you need to buy or sell, VPN help you transact your premium domain today at:

For media and interview inquiries, please email:pr@vpn.comor visit:

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VPN Publishes Forbes Internet Security Guide for Law Firms

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Artificial Intelligence | GE Research

At GE, Artificial Intelligence (AI) development is primarily focused on connecting minds and industrial machines to enable intelligent and user-friendly products and services that move, cure and power the world. GE Research spearheads this charter via the invention and deployment of AI solutions that can execute on industrial devices, at the edge or in the cloud.

AIresearch is practiced as a multidisciplinary exercise at GE, where insights from data-driven machine learning is fused with domain-specific knowledge drawn from areas such as materials, physics, biology and design engineering, to amplify the quality as well as causal-veracity of the predictions derivedwhat we call hybrid AI. We are creating state-of-the-art perception and reasoning capabilities for our AI technology to observe and understand contextual meaning, to improve the performance and life of our assets, industrial systems and human health. We are developing continuous learning systems that teach or learn from other assets or agents and learn from real and virtual experiences to understand and improve behavior.

Some key challenges we tackle include a lack of sufficient labels needed for traditional supervised learning approaches, the need to ingest and link multiple data modalities, and the need to build AI solutions that are interpretable due to safety-related regulatory requirements.

State-of-the-art capabilities in computer vision, machine learning, knowledge representation, reasoning and human system interactions are used to robustly monitor, assess and predict the performance and health of assetsinformation that, when coupled with uncertainty quantification and assurance, provides the information needed to multi-objectively optimize customer-specific metrics.

Examples of customer outcomes enhanced by AI products include reduced downtime on assets through AI-driven proactive intervention (for e.g., airline delays and cancellation), increased throughput (for e.g., optimal control of wind turbine settings to maximize farm output), or reduced costs (for e.g., optimal power plant operation to minimize fuel costs). GE Research is developing and integrating artificial intelligence in healthcare by working to incorporate the technologyinto every aspect of the patient journey (for e.g., improved disease diagnosis, augmenting doctors and clinicians by increasing workflow efficiencies to save precious time).In addition to asset-awareness and management, active AI research areas include Computer Vision, automation, autonomy, User Experience, Augmented Reality and Robotics.

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Artificial Intelligence & the Pharma Industry: What’s Next …

Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.

Given the growing importance of Artificial Intelligence for the pharma industry, we wanted to create a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.

Last year, Verdict AI asked businesses how vital artificial intelligence will be in their respective industries and over 70% of them thought it would be very important. From the same group, only 11% of businesses have not considered investing in AI technology.

Furthermore, according to Narrative Science, 61% of companies investing in innovative strategies are using AI to identify opportunities that they would have otherwise missed. For pharmaceutical businesses that thrive on innovation, this is an important statistic to understand.

This article aims to help business executives learn what to expect from artificial intelligence in pharma. It will cover:

Artificial intelligence and pharma can help save more lives than ever before.

A study published by the Massachusetts Institute of Technology (MIT) has found that only 13.8% of drugs successfully pass clinical trials. Furthermore, a company can expect to pay between $161 million to $2 billion for any drug to complete the entire clinical trials process and get FDA approval.

With this in mind, pharma businesses are using AI to increase the success rates of new drugs while decreasing operational costs at the same time.

Novartisis embracing advancements in AI technology to create new and improved treatments and find ways to get people access to treatment quickly.

Novartis is currently using machine learning to classify digital images of cells, each treated with different experimental compounds. The machine learning algorithms collect and group compounds that have similar effects together, before passing on the clean data to researchers who can decide how to leverage these insights in their work.

Drug discovery often takes a long time to test compounds against samples of diseased cells. Finding compounds that are biologically active and are worth investigating further requires even more analysis.

To speed up this screening process, Novartis research teams use images from machine learning algorithms to predict which untested compounds might be worth exploring in more details.

As computers are far quicker compared to traditional human analysis and laboratory experiments in uncovering new data sets, new and effective drugs can be made available sooner, while also reducing the operational costs associated with the manual investigation of each compound.

But theres another reason why Novartis is at the top of our list. CEO,Vas Narasimhan is one of the forward-looking digital leaders in healthcare who is constantly advocating for the role AI, predictive analytics and big data can play in Pharma.David Shaywitz, in an excellent Forbes articlesummarizes all the challenges Novartis is facing in adopting AI but also how the company is still pursuing AI with some notable results in clinical trials and finance.

Verge Genomics develops drugs by automating their discovery process. They use automated data gathering and analysis to create solutions to some of the most complex diseases known today, including ALS and Alzheimers.

Cost aside, one of the reasons why drug discoveries fail is because they only target one disease gene at a time.

Using the same technologies that power Googles search engines, Verge has discovered ways to map out the hundreds of genes responsible for causing disease and then finding drugs that target them all at once.

Their platform is specifically designed for neurological diseases and can predict the effect of new treatments, while also reducing the cost of drug development.

Bayer and Merck & Co were granted the Breakthrough Device Designation from the FDA for artificial intelligence software that aims to support clinical decision making of chronic thromboembolic pulmonary hypertension (CTEPH).

This form of pulmonary hypertension affects around five people per million, per year around the world. Its symptoms are similar to conditions like asthma and COPD, meaning it can be tricky to accurately diagnose.

The aim of the software is to help radiologists detect certain patterns faster, who are often on the frontline for identifying CTEPH patients. The AI would analyze image findings from cardiac, lung perfusion, and pulmonary vessels in combination with a patients clinical history and then pass the insights to the radiologists leveraging this technology.

Both Bayer and Merck note that the development of their CTEPH Pattern Recognition Artificial Intelligence Software remains complex due to the nature of the disease they are attempting to better diagnose.

However, should it prove successful, the tool will eventually be able to assist in diagnosing patients earlier and more reliably, leading to earlier treatment and better patient outcomes.

Cyclica is a biotechnology company that combines biophysics and AI to discover drugs faster, safer, and cheaper. They have partnered with Bayer to create an AI-augmented integrated network of cloud-based technologies, known as the Ligand Express.

The Ligand Express screens small-molecule drugs against repositories of structurally-characterized proteins to determine polypharmacological profiles. From here, the company identifies significant protein targets and then they use artificial intelligence to determine the drugs effect on these targets. Finally, the AI produces a visual output of how the drug and proteins interact.

By understanding how small-molecule drugs interact with all proteins in the body, Ligand Express can produce the best solution, understand potential side effects, and determine new uses for existing drugs.

AI in pharmacology can also be used to find cures for known diseases such as Parkinsons and Alzheimers, as well as rare diseases. This is great news considering the fact that 95% of rare diseases do not have a single FDA approved treatment, according to Global Genes.

Traditionally, pharmaceutical companies dont focus their efforts on treatments for rare diseases because the return on investment doesnt warrant the time and cost it takes to produce the drugs.

However, with advancements in AI technology, there has been a renewed interest in rare disease treatments.

Tencent Holdings has partnered with UK-based Medopad to build artificial intelligence algorithms capable of remotely monitoring patients with Parkinsons disease and reducing how long it takes to conduct a motor function assessment from over 30 minutes to less than three minutes.

The AI will leverage smartphone apps that monitor how a patient opens and closes their hands. The smartphones camera captures a patients movement to determine the severity of their symptoms. The frequency and amplitude score the patient receives can determine the severity of their Parkinsons.

This will allow doctors to remotely monitor patients and set new drug doses. If a patients treatment program needs changing, the AI will raise an alert to notify their doctor and arrange a checkup if required.

The technology will also reduce the patients costs of traveling back and forth to the clinic.

Mission Therapeutics, a drug creation company known for its chemistry and proprietary enzyme platform, and AbbVie, a pharmaceutical business known for its strong neurodegenerative disease research, have partnered to develop Deubiquitinase (DUB) inhibitors in the fight against Parkinsons and Alzheimers.

Both Alzheimers and Parkinsons patients have an abnormal accumulation of misfolded, toxic proteins, resulting in impaired brain functionality and the death of nerve cells.This is where DUBs comes in. They regulate the degradation of these proteins to maintain their health and stability.

By modulating specific DUBs within the brain, Mission Therapeutics is aiming to find potential treatments which will enable the degradation of these toxic proteins and prevent their accumulation.

Healx is a promising startup focused on accelerating treatments for rare diseases and artificial intelligence is at the center of their operations. Their AI platform HealNet enables scientists to increase production in disease drug discovery while simultaneously reducing time, cost and risk.

The company isnt directly focused on creating new drugs to cure these conditions. Instead, they use AI technology to examine existing drugs and repurpose them for curing rare diseases.

HealNet uses machine learning techniques to access data from a range of sources, including scientific literature, patents, clinical trials, disease symptoms, drug targets, multiomics data and chemical structures.

Drug adherence is huge for pharma. In simple terms, to prove the success rate of a drug, a pharma company uses voluntary participants in clinical studies. If these patients dont follow the trial rules, they are eitherremoved from the trial or they poison the drug results. As a result, having amazing drug adherence is crucial to any pharma company out there.

Another critical component for a successful drug trial is that participants take the necessary dosage of a particular drug at all times. For example, its been reported that machine learning algorithms can cut incorrect drug dosage intake by as much as 50% for glioblastoma patients.

Traditional methods to measure drug adherence require patients to submit the data themselves without any evidence of them taking a pill or other type of treatment. They are also subject to tampering, such as deceptively removing pills to feign higher adherence.

AiCure, a New York-based mobile SaaS platform, has developed an image recognition algorithm that removes these issues. Using a mobile phone, AiCure tracks drug adherence by videoing the patient swallowing a pill. The facial recognition system then confirms that the right person took the right pill.

In 2016, they published findings from their study that confirms that the use of their AI platform significantly increases adherence in patients with schizophrenia, as measured by drug concentration levels.The results showed that cumulative adherence was at 89.7% for those using the AiCure platform compared to 71.9% for subjects using modified Directly Observed Therapy (mDOT).

Even with the obvious advantage that this brings, AI will also decrease costs and accelerate drug development for clinical research and practices.

A research team led by the National University of Singapore (NUS) has used an AI platform called CURATE.AI to successfully treat a patient with advanced cancer and completely halting disease progression.

In this clinical study, a patient with metastatic castration-resistant prostate cancer (MCRPC) was given a novel drug combination consisting of an investigational drug, namely ZEN-3694, and an already-approved prostate cancer drug, enzalutamide.

CURATE.AI was used by the research team to continuously identify the optimal doses of each drug to result in a durable response, giving each individual patient the ability to live a free and healthy life.

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The Impact of Artificial Intelligence – Widespread Job Losses

Advances in Artificial Intelligence (AI) and Automation will transform our world. The current debate centers not on whether these changes will take place but on how, when, and where the impact of artificial intelligence will hit hardest. In this post, Ill be exploring both optimistic and pessimistic views of artificial intelligence, automation, job loss, and the future.

Questions around the impact of artificial intelligence and automation are critical for us to consider. While technology isnt inherently good or evil, in the hands of humans, technology has a great capacity for both. Id certainly prefer the good over the evil, and that will be dependent on the choices that we make today.

Technology-driven societal changes, like what were experiencing with AI and automation, always engender concern and fearand for good reason. A two-year study from McKinsey Global Institute suggests that by 2030, intelligent agents and robots could replace as much as 30 percent of the worlds current human labor. McKinsey suggests that, in terms of scale, the automation revolution could rival the move away from agricultural labor during the 1900s in the United States and Europe, and more recently, the explosion of the Chinese labor economy.

McKinsey reckons that, depending upon various adoption scenarios,automation will displace between 400 and 800 million jobs by 2030, requiring as many as 375 million people to switch job categories entirely. How could such a shift not cause fear and concern, especially for the worlds vulnerable countries and populations?

The Brookings Institution suggests that even if automation only reaches the 38 percent mean of most forecasts, some Western democracies are likely to resort to authoritarian policies to stave off civil chaos, much like they did during the Great Depression. Brookings writes, The United States would look like Syria or Iraq, with armed bands of young men with few employment prospects other than war, violence, or theft. With frightening yet authoritative predictions like those, its no wonder AI and automation keeps many of us up at night.

The Luddites were textiles workers who protested against automation, eventually attacking and burning factories because, they feared that unskilled machine operators were robbing them of their livelihood. The Luddite movement occurred all the way back in 1811, so concerns about job losses or job displacements due to automation are far from new.

When fear or concern is raised about the potential impact of artificial intelligence and automation on our workforce, a typical response is thus to point to the past; the same concerns are raised time and again and prove unfounded.

In 1961, President Kennedy said, the major challenge of the sixties is to maintain full employment at a time when automation is replacing men. In the 1980s, the advent of personal computers spurred computerphobia with many fearing computers would replace them.

So what happened?

Despite these fears and concerns, every technological shift has ended up creating more jobs than were destroyed. When particular tasks are automated, becoming cheaper and faster, you need more human workers to do the other functions in the process that havent been automated.

During the Industrial Revolution more and more tasks in the weaving process were automated, prompting workers to focus on the things machines could not do, such as operating a machine, and then tending multiple machines to keep them running smoothly. This caused output to grow explosively. In America during the 19th century the amount of coarse cloth a single weaver could produce in an hour increased by a factor of 50, and the amount of labour required per yard of cloth fell by 98%. This made cloth cheaper and increased demand for it, which in turn created more jobs for weavers: their numbers quadrupled between 1830 and 1900. In other words, technology gradually changed the nature of the weavers job, and the skills required to do it, rather than replacing it altogether. The Economist, Automation and Anxiety

Looking back on history, it seems reasonable to conclude that fears and concerns regarding AI and automation are understandable but ultimately unwarranted. Technological change may eliminate specific jobs, but it has always created more in the process.

Beyond net job creation, there are other reasons to be optimistic about the impact of artificial intelligence and automation.

Simply put, jobs that robots can replace are not good jobs in the first place. As humans, we climb up the rungs of drudgery physically tasking or mind-numbing jobs to jobs that use what got us to the top of the food chain, our brains. The Wall Street Journal, The Robots Are Coming. Welcome Them.

By eliminating the tedium, AI and automation can free us to pursue careers that give us a greater sense of meaning and well-being. Careers that challenge us, instill a sense of progress, provide us with autonomy, and make us feel like we belong; all research-backed attributes of a satisfying job.

And at a higher level, AI and automation will also help to eliminate disease and world poverty. Already, AI is driving great advances in medicine and healthcare with better disease prevention, higher accuracy diagnosis, and more effective treatment and cures. When it comes to eliminating world poverty, one of the biggest barriers is identifying where help is needed most. By applying AI analysis to data from satellite images, this barrier can be surmounted, focusing aid most effectively.

I am all for optimism. But as much as Id like to believe all of the above, this bright outlook on the future relies on seemingly shaky premises. Namely:

As explored earlier, a common response to fears and concerns over the impact of artificial intelligence and automation is to point to the past. However, this approach only works if the future behaves similarly. There are many things that are different now than in the past, and these factors give us good reason to believe that the future will play out differently.

In the past, technological disruption of one industry didnt necessarily mean the disruption of another. Lets take car manufacturing as an example; a robot in automobile manufacturing can drive big gains in productivity and efficiency, but that same robot would be useless trying to manufacture anything other than a car. The underlying technology of the robot might be adapted, but at best that still only addresses manufacturing

AI is different because it can be applied to virtually any industry. When you develop AI that can understand language, recognize patterns, and problem solve, disruption isnt contained. Imagine creating an AI that can diagnose disease and handle medications, address lawsuits, and write articles like this one. No need to imagine:AI is already doing those exact things.

Another important distinction between now and the past is the speed of technological progress. Technological progress doesnt advance linearly, it advances exponentially. Consider Moores Law: the number of transistors on an integrated circuit doubles roughly every two years.

In the words of University of Colorado physics professor Albert Allen Bartlett, The greatest shortcoming of the human race is our inability to understand the exponential function. We drastically underestimate what happens when a value keeps doubling.

What do you get when technological progress is accelerating and AI can do jobs across a range of industries? An accelerating pace of job destruction.

Theres no economic law that says You will always create enough jobs or the balance will always be even, its possible for a technology to dramatically favour one group and to hurt another group, and the net of that might be that you have fewer jobs Erik Brynjolfsson, Director of the MIT Initiative on the Digital Economy

In the past, yes, more jobs were created than were destroyed by technology. Workers were able to reskill and move laterally into other industries instead. But the past isnt always an accurate predictor of the future. We cant complacently sit back and think that everything is going to be ok.

Which brings us to another critical issue

Lets pretend for a second that the past actually will be a good predictor of the future; jobs will be eliminated but more jobs will be created to replace them. This brings up an absolutely critical question, what kinds of jobs are being created and what kinds of jobs are being destroyed?

Low- and high-skilled jobs have so far been less vulnerable to automation. The low-skilled jobs categories that are considered to have the best prospects over the next decade including food service, janitorial work, gardening, home health, childcare, and security are generally physical jobs, and require face-to-face interaction. At some point robots will be able to fulfill these roles, but theres little incentive to roboticize these tasks at the moment, as theres a large supply of humans who are willing to do them for low wages. Slate, Will robots steal your job?

Blue collar and white collar jobs will be eliminatedbasically, anything that requires middle-skills (meaning that it requires some training, but not much). This leaves low-skill jobs, as described above, and high-skill jobs which require high levels of training and education.

There will assuredly be an increasing number of jobs related to programming, robotics, engineering, etc.. After all, these skills will be needed to improve and maintain the AI and automation being used around us.

But will the people who lost their middle-skilled jobs be able to move into these high-skill roles instead? Certainly not without significant training and education. What about moving into low-skill jobs? Well, the number of these jobs is unlikely to increase, particularly because the middle-class loses jobs and stops spending money on food service, gardening, home health, etc.

The transition could be very painful. Its no secret that rising unemployment has a negative impact on society; less volunteerism, higher crime, and drug abuse are all correlated. A period of high unemployment, in which tens of millions of people are incapable of getting a job because they simply dont have the necessary skills, will be our reality if we dont adequately prepare.

So how do we prepare? At the minimum, by overhauling our entire education system and providing means for people to re-skill.

To transition from 90% of the American population farming to just 2% during the first industrial revolution, it took the mass introduction of primary education to equip people with the necessary skills to work. The problem is that were still using an education system that is geared for the industrial age. The three Rs (reading, writing, arithmetic) were once the important skills to learn to succeed in the workforce. Now, those are the skills quickly being overtaken by AI.

For a fascinating look at our current education system and its faults, check out this video from Sir Ken Robinson:

In addition to transforming our whole education system, we should also accept that learning doesnt end with formal schooling. The exponential acceleration ofdigital transformation means that learning must be a lifelong pursuit, constantly re-skilling to meet an ever-changing world.

Making huge changes to our education system, providing means for people to re-skill, and encouraging lifelong learning can help mitigate the pain of the transition, but is that enough?

When I originally wrote this article a couple years ago, I believed firmly that 99% of all jobs would be eliminated. Now, Im not so sure. Here was my argument at the time:

[The claim that 99% of all jobs will be eliminated] may seem bold, and yet its all but certain. All you need are two premises:

The first premise shouldnt be at all controversial. The only reason to think that we would permanently stop progress, of any kind, is some extinction-level event that wipes out humanity, in which case this debate is irrelevant. Excluding such a disaster, technological progress will continue on an exponential curve. And it doesnt matter how fast that progress is; all that matters is that it will continue.The incentives for people, companies, and governments are too great to think otherwise.

The second premise will be controversial, but notice that I said human intelligence. I didnt say consciousness or what it means to be human. That human intelligence arises from physical processes seems easy to demonstrate: if we affect the physical processes of the brain we can observe clear changes in intelligence. Though a gloomy example, its clear that poking holes in a persons brain results in changes to their intelligence. A well-placed poke in someones Brocas area and voilthat person cant process speech.

With these two premises in hand, we can conclude the following: we will build machines that have human-level intelligence and higher. Its inevitable.

We already know that machines are better than humans at physical tasks, they can move faster, more precisely, and lift greater loads. When these machines are also as intelligent as us, there will be almost nothing they cant door cant learn to do quickly. Therefore, 99% of jobs will eventually be eliminated.

But that doesnt mean well be redundant. Well still need leaders (unless we give ourselves over to robot overlords) and our arts, music, etc., may remain solely human pursuits too. As for just about everything else? Machines will do itand do it better.

But whos going to maintain the machines? The machines.But whos going to improve the machines? The machines.

Assuming they could eventually learn 99% of what we do, surely theyll be capable of maintaining and improving themselves more precisely and efficiently than we ever could.

The above argument is sound, but the conclusion that 99% of all jobs will be eliminated I believe over-focused on our current conception of a job. As I pointed out above, theres no guarantee that the future will play out like the past. After continuing to reflect and learn over the past few years, I now think theres good reason to believe that while 99% of all current jobs might be eliminated, there will still be plenty for humans to do (which is really what we care about, isnt it?).

The one thing that humans can do that robots cant (at least for a long while) is to decide what it is that humans want to do. This is not a trivial semantic trick; our desires are inspired by our previous inventions, making this a circular question. The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, by Kevin Kelly

Perhaps another way of looking at the above quote is this: a few years ago I read the book Emotional Intelligence, and was shocked to discover just how essential emotions are to decision making. Not just important, essential. People who had experienced brain damage to the emotional centers of their brains were absolutely incapable of making even the smallest decisions. This is because, when faced with a number of choices, they could think of logical reasons for doing or not doing any of them but had no emotional push/pull to choose.

So while AI and automation may eliminate the need for humans to do any of thedoing, we will still need humans to determine what to do. And because everything that we do and everything that we build sparks new desires and shows us new possibilities, this job will never be eliminated.

If you had predicted in the early 19th century that almost all jobs would be eliminated, and you defined jobs as agricultural work, you would have been right. In the same way, I believe that what we think of as jobs today will almost certainly be eliminated too. But this does not mean that there will be no jobs at all, the job will instead shift to determining, what do we want to do? And then working with our AI and machines to make our desires a reality.

Is this overly optimistic? I dont think so. I still think that the transition might be a painful one and that its critical that we invest in the education and infrastructure needed to support people as many current jobs are eliminated and we transition to this new future.

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What’s the Difference Between Robotics and Artificial …

Is robotics part of AI? Is AI part of robotics? What is the difference between the two terms? We answer this fundamental question.

Robotics and artificial intelligence serve very different purposes. However, people often get them mixed up. A lot of people wonder if robotics is a subset of artificial intelligence or if they are the same thing.

Let's put things straight.

The first thing to clarify is that robotics and artificial intelligence are not the same thing at all. In fact, the two fields are almost entirely separate.

A Venn diagram of the two would look like this:

I guess that people sometimes confuse the two because of the overlap between them: Artificially Intelligent Robots.

To understand how these three terms relate to each other, let's look at each of them individually.

Robotics is a branch of technology which deals with robots. Robots are programmable machines which are usually able to carry out a series of actions autonomously, or semi-autonomously.

In my opinion, there are three important factors which constitute a robot:

I say that robots are "usually" autonomous because some robots aren't. Telerobots, for example, are entirely controlled by a human operator but telerobotics is still classed as a branch of robotics. This is one example where the definition of robotics is not very clear.

It is surprisingly difficult to get experts to agree exactly what constitutes a "robot." Some people say that a robot must be able to "think" and make decisions. However, there is no standard definition of "robot thinking." Requiring a robot to "think" suggests that it has some level of artificial intelligence.

However you choose to define a robot, robotics involves designing, building and programming physical robots. Only a small part of it involves artificial intelligence.

Artificial intelligence (AI) is a branch of computer science. It involves developing computer programs to complete tasks which would otherwise require human intelligence. AI algorithms can tackle learning, perception, problem-solving, language-understanding and/or logical reasoning.

AI is used in many ways within the modern world. For example, AI algorithms are used in Google searches, Amazon's recommendation engine and SatNav route finders. Most AI programs are not used to control robots.

Even when AI is used to control robots, the AI algorithms are only part of the larger robotic system, which also includes sensors, actuators and non-AI programming.

Often but not always AI involves some level of machine learning, where an algorithm is "trained" to respond to a particular input in a certain way by using known inputs and outputs. We discuss machine learning in our article Robot Vision vs Computer Vision: What's the Difference?

The key aspect that differentiates AI from more conventional programming is the word "intelligence." Non-AI programs simply carry out a defined sequence of instructions. AI programs mimic some level of human intelligence.

Artificially intelligent robots are the bridge between robotics and AI. These are robots which are controlled by AI programs.

Many robots are not artificially intelligent. Up until quite recently, all industrial robots could only be programmed to carry out a repetitive series of movements. As we have discussed, repetitive movements do not require artificial intelligence.

Non-intelligent robots are quite limited in their functionality. AI algorithms are often necessary to allow the robot to perform more complex tasks.

Let's look at some examples.

A simple collaborative robot (cobot) is a perfect example of a non-intelligent robot.

For example, you can easily program a cobot to pick up an object and place it elsewhere. The cobot will then continue to pick and place objects in exactly the same way until you turn it off. This is an autonomous function because the robot does not require any human input after it has been programmed. However, the task does not require any intelligence.

You could extend the capabilities of the cobot by using AI.

Imagine you wanted to add a camera to your cobot. Robot vision comes under the category of "perception" and usually requires AI algorithms.

For example, say you wanted the cobot to detect the object it was picking up and place it in a different location depending on the type of object. This would involve training a specialized vision program to recognize the different types of object. One way to do this is using an AI algorithm called Template Matching, which we discuss in our article How Template Matching Works in Robot Vision.

As you can see, robotics and artificial intelligence are really two separate things. Robotics involves building robots whereas AI involves programming intelligence.

However, I leave you with one slight confusion: software robots.

"Software robot" is the term given to a type of computer program which autonomously operates to complete a virtual task. They are not physical robots, as they only exist within a computer. The classic example is a search engine webcrawler which roams the internet, scanning websites and categorizing them for search. Some advanced software robots may even include AI algorithms. However, software robots are not part of robotics.

Do you have any fundamental robotics questions you would like answered? Tell us in the comments below or join the discussion on LinkedIn, Twitter, Facebook or the DoF professional robotics community.

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