AI: New GPT-3 language model takes NLP to new heights

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Natural language processing is still being refined, but its popularity continues to rise. This new, better version is likely to help.

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When you speak to a computer, whether on the phone, in a chat box, or in your living room, and it understands you, that’s because of natural language processing. The computer voice can listen and respond accurately (most of the time), thanks to artificial intelligence (AI).

SEE: Hiring kit: Data Scientist (TechRepublic Premium)

Natural language processing (NLP) is the language used in AI voice questions and responses. The processing of language has improved multi-fold over the past few years, although there are still issues in creating and linking different elements of vocabulary and in understanding semantic and contextual relationships.

Despite these continued efforts to improve NLP, companies are actively using it. NLP has been a hit in automated call software and in human-staffed call centers because it can deliver both process automation and contextual assistance such as human sentiment analysis when a call center agent is working with a customer. 

NLP has also been used in HR employee recruitment to identify keywords in applications that trigger a close match between a job application or resume and the requirements of an open position.

SEE: An IT pro’s guide to robotic process automation (free PDF) (TechRepublic)

In our homes, we use NLP when we give a verbal command to Alexa to play some jazz. So there’s no surprise that NLP is on nearly every organization’s  IT road map as a technology that has the potential to add business value to a broad array of applications.

This is precisely why the recent breakthrough of a new AI natural language model known as GPT-3. is significant.

What is GPT-3?

With GPT-3, 175 billion parameters of language can now be processed, compared with predecessor GPT-2, which processes 1.5 billion parameters. This new GPT-3 natural language model was first announced in June by OpenAI, an AI development and deployment company, although the model has not yet been released for general use due to “concerns about malicious applications of the technology.” 

SEE: IBM highlights new approach to infuse knowledge into NLP models (TechRepublic)

“GPT-3 takes the natural language Transformer architecture to a new level,” said Surej Amonkar, fellow AI@scale at Fractal Analytics, an AI solutions provider. “It’s built for all of the world’s languages, and has machine translation.”

The possibilities with GPT-3 are enticing.

  • For a government or a multinational corporation, the ability to rapidly localize text and voice-based messages or translate them into virtually any world language—and to do it with automation—opens access to new customers and better support for field offices in foreign countries that are supporting company products or operations.
  • For research institutions and for medical and life sciences researchers, the ability to easily translate a paper that is written in a foreign language can be done rapidly.
  • For media, publishing, and entertainment companies, there can be a fast way to translate the spoken and written word into many different languages.

How GPT-3 can help an organization

If you’re looking at the IT strategic road map, the likelihood of using or being granted permission to use GPT-3 is well into the future unless you are a very large company or a government that has been cleared to use it, but you should still have GPT-3 on your IT road map.

There is also a strong argument that if you are the CIO of a smaller organization, that the evolution  of NLP language modeling into GPT-3 capabilities should not be ignored because natural language processing and the exponential processing capabilities that GPT-3 language modeling endows AI with are going to transform what we can do with processing and automating language translations and analytics that operate on the written and spoken word.

If you’re doing business in a global economy, as almost everyone is, that capability will be invaluable. 

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This language learning app will give your career a boost

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Do your career a favor by learning new languages with the Mondly app, which uses speech recognition and augmented reality technology.

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Mondly app

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When most people think of learning a new language, they think of mastering just enough vocabulary to make the occasional trip abroad a bit more enjoyable. But learning a new language can also have a significant positive impact on your career, and if you’re interested in landing the best and most exciting positions in an increasingly interconnected world, mastering a new language will help you get a leg up over the competition for three reasons.

1. You’ll be able to command a higher salary.

Studies have repeatedly shown that employees throughout multiple industries who speak more than one language consistently earn higher salaries than their single-language-speaking peers. And the good news is that you don’t need to torture yourself with traditional and monotonous language-learning apps in order to add a language to your resume, thanks to innovative platforms like Mondly Language Learning App, which uses state-of-the-art speech recognition technology in order to make language-learning both intuitive and enjoyable.

2. Learning a foreign language expands your business network.

It should go without saying that learning a foreign language will help you make professional connections and forge business ties overseas. Aside from the fact that your overall perception and view of the international marketplace will be broadened, learning a new language also makes it easier to build trust with international partners and clients.

3. Your brain and decision-making skills will benefit.

It’s been proven time and time again that learning a new language is one of the best things you can do in order to keep your brain active and healthy, which of course has powerful implications in business. A lifetime subscription to Mondly will help you sharpen your mind by allowing you to choose up to five languages to learn at your own pace, and you’ll receive instant feedback and positive reinforcement when you ace the pronunciation and dialogue.

Don’t wait until your next trip abroad to learn the languages you’ve always wanted to learn–see the benefits of mastering a foreign language both at work and in your personal life now. A lifetime subscription to Mondly will help you hit your goals for just $69.99 when you sign up today.

Prices are subject to change.

Natural language processing: A cheat sheet

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Learn the basics about natural language processing, a cross-discipline approach to making computers hear, process, understand, and duplicate human speech.

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It wasn’t too long ago that talking to a computer and having it not only understand, but speak back, was confined to the realm of science fiction, like that of the shipboard computers of Star Trek. The technology of the 24th century’s Starship Enterprise is reality in the 21st century thanks to natural language processing (NLP), a machine learning-driven discipline that gives computers the ability to understand, process, and respond to spoken words and written text.

Make no mistake: NLP is a complicated field that one can spend years studying. This guide contains the basics about NLP, details how it can benefit businesses, and explains where to get started with its implementation.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

What is natural language processing?

Natural language processing (NLP) is a cross-discipline approach to making computers hear, process, understand, and duplicate human language. Fields including linguistics, computer science, and machine learning are all a part of the process of NLP, the results of which can be seen in things like digital assistants, chatbots, real-time translation apps, and other language-using software.

The concept of computers learning to understand and use language isn’t a new one—it can arguably be traced all the way back to Alan Turing’s Computing Machinery and Intelligence paper published in 1950, which was where the idea of the Turing Test comes from. 

In brief, Turing attempted to determine whether machines could behave in a way indistinguishable from a human, which fundamentally requires the ability to process language and respond in a sensible way. 

SEE: All of TechRepublic’s cheat sheets and smart person’s guides

Since Turing wrote his paper, a number of approaches to natural language processing have emerged. First came rules-based systems, like ELIZA, which were limited in what they could do to a set of instructions. Systems like ELIZA were easy to distinguish from a human because of their formulaic, non-specific responses that quickly become repetitive and feel unnatural: It lacked understanding, which is a fundamental part of modern NLP.

With the advent of machine learning, which allows computers to algorithmically develop their own rules based on sample data, natural language processing exploded in ways Turing never could have predicted. 

Natural language processing has reached a state where it’s now better at understanding human speech than real humans. Even this impressive milestone still falls short of truly complete NLP, though, because the machine performing the work was simply transcribing language, not being asked to comprehend it. 

Modern NLP platforms are also capable of visually processing speech. Facebook’s Rosetta, for example, is able to “extract text in different languages from more than a billion images and video frames in real time,” TechRepublic sister site CNET said.

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What are the challenges of natural language processing?

Computers don’t need to understand human speech to speak a language–the machines operate on a kind of linguistic structure that allows them to accept input, process data, and respond to commands.

Languages like Swift, Python, JavaScript, and others all have something in common that natural language lacks: Precision.

Human speech isn’t precise by any stretch of the definition: It’s contextual, metaphorical, ambiguous, and spoken imperfectly all the time, and understanding language requires a lot of background and interpretive ability that computers lack.

Computational linguist Ekaterina Kochmar, in a talk about natural language processing, explained that words exist in a sort of imaginary semantic space. In our minds, Kochmar said, we have representations of words, and words with related or similar meanings live close together in a web of semantic understanding.

Thinking of language in that manner allows machine learning tools to be built that let computers algorithmically create their own semantic space, which lets them infer relations between words and better understand natural speech.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

That doesn’t mean challenges are overcome, though. Going from understanding simple, precise statements like those given to digital assistants to producing sensible speech on their own is still difficult for NLP programs. Candy hearts produced by artificial intelligence (AI) taught to understand romantic language are predictably absurd, and 1 the Road, a novel written entirely by an artificial neural network, is generally nonsensical with only the most occasional glimpse of semantic understanding, which could be entirely chalked up to chance.

As advanced as natural language processing is in its ability to analyze speech, turn it into data, understand it, and use an algorithm to generate an appropriate response, still generally lacks the ability to speak on its own or grasp the ambiguity and metaphor that is fundamental to natural language. 

We’ve mastered the first part: Understanding. It’s the second part, generating natural speech or human language, that we’re still a bit stuck on. And we might be stuck there for a while, if pioneering mathematician and computer scientist Ada Lovelace is correct: She posited that computers were only able to do what we told them to, and were incapable of originality. Known as Lady Lovelace’s Objection, it’s become a common part of criticism of the Turing Test and thus a criticism of natural language processing: If machines can’t have original thoughts, then is there any way to teach them to use language that isn’t ultimately repetitive?

Additional resources:

How is natural language processing used?

Natural language processing has a lot of practical applications for a variety of business uses. 

Google Duplex is perhaps the most remarkable use of natural language processing available as an example today. The digital assistant, introduced in 2018, is not only able to understand complex statements, but it also speaks on the phone in a way that’s practically indistinguishable from a human—vocal tics and all. Duplex’s goal is to carry out real-world tasks over the phone, saving Google users time spent making appointments, booking services, placing orders, and more. 

Ninety-eight percent of Fortune 500 companies are now using natural language processing software to filter candidates for job searches with products known as applicant tracking systems. These products pick through resumes to look for appropriate keywords and other linguistic elements.

SEE: Robotics in the enterprise (free PDF) (TechRepublic)

Chatbots are quickly becoming the first line of online customer service, with 68% of consumers saying they had a positive experience speaking with one. These bots use natural language processing to address basic requests and problems, while also being able to elevate requests to humans as needed.

Uses of NLP in healthcare settings are numerous: Physician dictation, processing hand-written records, compiling unstructured healthcare data into usable formats, and connecting natural language to complicated medical billing codes are all potential uses. NLP has also been used recently to screen COVID-19 patients.

NLP can be used to gauge customer attitudes in call center environments, perform “sentiment analysis” on social media posts, can be used as part of business intelligence analysis, and can supplement predictive analytics.

Natural language processing has a potentially endless variety of applications: Anything involving language can, with the right approach, be a use case for NLP, especially if it involves dealing with a large volume of data that would take a human too long to work with. 

Additional resources:

How can developers learn about natural language processing?

NLP is a complicated topic that a computer scientist could easily spend years learning the ins and outs of. If your objective is being at the cutting edge of NLP research, it’s probably best to think about attending a university known for having a good computational linguistics program.

Developers who want to learn to make use of current NLP technology don’t need to dive that far into the deep end. Text analytics firm MonkeyLearn has an excellent rundown of resources and steps to get started with natural language processing; here are a few key points from its guide.

MonkeyLearn’s guide also has a variety of links in it to articles, research, and journals that any budding NLP developer should be aware of. 

Additional resources: 

What is the best way for businesses to get started with natural language processing?

Every business uses language, so there’s a good chance you can come up with at least one or two uses for natural language processing in your organization—but how do you go from thinking about what NLP could do for you to actually doing it? There are a lot of steps to consider.

For starters, you need to know what your objectives are for NLP in your business. Do you want to use it to aggregate data as an analytics tool, or do you want to build a chatbot that can interact with customers via text on your support portal? Maybe you want to use NLP as the backbone of an e-mail filter, understand customer sentiment, or use it for real-time translation. 

No matter what you want NLP to do for your business you need to know your goal before even starting to think about achieving it.

SEE: Top cloud providers in 2020: AWS, Microsoft Azure, and Google Cloud, hybrid, SaaS players (TechRepublic)

Once you know what you want to do with natural language processing, it’s time to find the right talent to build the system you want. You may already have developers in-house who are familiar with Python and some of the NLP frameworks mentioned above. If that’s the case, get them involved in the planning stages from the very beginning. 

If you don’t have anyone in-house who can develop natural language processing software, you’re faced with a choice: Hire new people or bring in a third-party that specializes in NLP solutions.

If you choose to go about your NLP objectives in-house, you’ll need to find the right software solutions or providers for hosting your NLP platform, and there are plenty of recognizable names to choose from. 

IBM Watson has options, AWS offers Amazon Comprehend and other NLP services, Microsoft Azure has NLP services as well, as does Google Cloud. Choosing the proper platform will require input from your developers because they’re the ones who will be working with the software every day, and your NLP initiative’s success may hinge on how well they can use the platform.

Additional resources:

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How to install the Go language on Linux

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How to install the Go language on Linux

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Jul 7, 2020

Go is the go-to language for distributed and highly scalable servers. If you’re looking to start working with this language on Linux, Jack Wallen has you covered.

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How to install the Go language on Linux

how-to-install-the-go-language-on-linux

Go is the go-to language for distributed and highly scalable servers. If you’re looking to start working with this language on Linux, Jack Wallen has you covered.

Go is one programming language that’s on the rise. In fact, according to Popularity of Programming Languages, Go is at No. 14 and steadily climbing up the ranks. Go is used specifically for distributed systems and highly-scalable network servers and has replaced C++ and Java in Google’s software stack. 

Chances are, you’ll be using Go sometime soon. For those who develop on Linux, you can’t just install it from the standard repositories. So how do you install this popular programming language on the open source operating system? Fear not, I’m going to show you. 

SEE: Telephone interview cheat sheet: Software developer (TechRepublic Premium)

How to install Go on Linux

This can be done on most all Linux distributions, so log in to your favorite Linux development machine and open a terminal window. 

From that terminal window download the Go binary files with the command: 

curl -O https://storage.googleapis.com/golang/go1.13.5.linux-amd64.tar.gz

Once that file download completes, unpack it with the command:

tar -xvf go*.tar.gz

Next, move the newly-created go folder with the command:

 sudo mv go /usr/local

We now have to add the go folder to our user PATH. Issue the command:

nano ~/.profile

Scroll to the bottom of that file and add the lines:

export GOPATH=$HOME/work and export PATH=$PATH:/usr/local/go/bin:$GOPATH/bin

Save and close that file. Refresh your profile with the command: 

source ~/.profile

You can now check to make sure the go folder is in your usr PATH with the command go version. You should see the version number of the installed Go language printed out. 

And that’s all there is to installing the Go language on Linux. You are now ready to start developing with this popular language. Happy coding!

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