Artificial Intelligence in Manufacturing Market Size, Share, Trends and Growth Analysis 2033
Top 10 Programming Languages For AI And Natural Language Processing - Yahoo Finance
In this article, we'll discuss the top 10 programming languages for AI and Natural Language Processing. You can skip our detailed analysis of global market trends for NLP and AI development and trending programming languages for AI development and go directly to the Top 5 Programming Languages for AI and Natural Language Processing.
We have seen a recent boom in the fields of Artificial Intelligence (AI) and Natural Language Processing (NLP). Revolutionary tools such as ChatGPT and DALL-E 2 have set new standards for NLP capabilities. These tools are harnessing the power of language processing to store information and provide detailed responses to inputs.
In fact, according to research by Fortune Business Insights, the global market size for Natural Language Processing (NLP) is expected to witness significant growth. The market is projected to expand from $24.10 billion in 2023 to $112.28 billion by 2030, exhibiting a robust compound annual growth rate (CAGR) of 24.6%. This indicates a promising outlook for the NLP market, driven by the increasing demand for advanced language processing solutions across various industries.
With the presence of major industry players, North America is anticipated to dominate the market share of natural language processing. In 2021, the market in North America already accounted for a significant value of USD 7.82 billion, and it is poised to capture a substantial portion of the global market share in the forthcoming years. The region's strong position further reinforces its leadership in driving advancements and adoption of natural language processing technologies.
As the demand for AI and NLP continues to soar, the question arises: which programming languages are best suited for AI development? When it comes to AI programming languages, Python emerges as the go-to choice for both beginners and seasoned developers. Python's simplicity, readability, and extensive libraries make it the perfect tool for building AI applications. In addition, Python allows easy scaling of large machine learning models. Python, along with Lisp, Java, C++, and R, remains among the most popular programming languages in the AI development landscape.
The dominance of Python is further reinforced by the job market, where employers increasingly seek Python language skills. According to TIOBE Programming Community index, Python, SQL, and Java top the list of in-demand programming skills, with Python securing the first spot. With its versatility and ease of use, Python finds applications in various domains, including app and website development, as well as business process automation.
While the utilization of NLP and AI has become imperative for businesses across industries, some companies such as Microsoft Corporation (NASDAQ:MSFT), Amazon.Com, Inc. (NASDAQ:AMZN), and Alphabet Inc. (NASDAQ:GOOG) have played a crucial role in driving recent advancements in these technologies.
Notably, Microsoft Corporation (NASDAQ:MSFT)'s significant investment of $10 billion in OpenAI, the startup behind ChatGPT and DALL-E 2, has made waves in the AI and NLP landscape. These tools have not only transformed the technological landscape but have also brought AI and NLP innovations to the general public in exciting new ways.
Also, Microsoft Corporation (NASDAQ:MSFT)'s Azure, as the exclusive cloud provider for ChatGPT, offers a wide range of services related to NLP. These include sentiment analysis, text classification, text summarization, and entailment services.
The significance of AI and NLP is palpable at Amazon.Com, Inc. (NASDAQ:AMZN) as well. The widely recognized Alexa device, capable of playing your favorite song or providing product recommendations, exemplifies AI and NLP in action. Additionally, Amazon.Com, Inc. (NASDAQ:AMZN)'s Amazon Web Services (AWS) provides cloud storage solutions, enabling businesses to complete their digital transformations.
The impact of AI and the recent surge in generative AI extends beyond Google's homegrown products, as parent company Alphabet Inc. (NASDAQ:GOOG) is actively investing in startups. Alphabet Inc. (NASDAQ:GOOG)'s venture capital arm, CapitalG, recently led a $100 million investment in corporate data firm AlphaSense, valuing the company at $1.8 billion.
So, if you are curious to discover the top programming languages for AI and NLPs, keep reading and delve into the realm of these exciting technologies.
Top 10 Programming Languages for AI and Natural Language ProcessingZapp2Photo/Shutterstock.Com
To rank the top 10 programming languages for deep learning and NLPs, we conducted extensive research to identify commonly used languages in these fields, considering factors such as community support, performance, libraries, ease of use, scalability, and industry adoption. We collected relevant data and evaluated each language on these criteria, assigning scores on a scale of 1 to 5. Higher scores were given to languages demonstrating more robust performance and broader usage in AI and NLP development. We sorted the list in ascending order of the best programming languages for machine learning applications.
Here is the list of the top 10 programming languages for AI and Natural Language Processing.
Performance Level: 3.5
Rust, known for its high performance, speed, and a strong focus on security, has emerged as a preferred language for AI and NLP development. Offering memory safety and avoiding the need for garbage collection, Rust has garnered popularity among developers seeking to create efficient and secure software. With a syntax comparable to C++, Rust provides a powerful and expressive programming environment. Notably, renowned systems including Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, and Discord rely on Rust as their backend programming language. Due to its memory safety, speed, and ease of expression, Rust is considered an ideal choice for developing AI and leveraging it in scientific computing applications.
Performance Level: 3.7
Prolog is a logic programming language. It is mainly used to develop logic-based artificial intelligence applications. Prolog's declarative nature and emphasis on logic make it particularly well-suited for tasks that involve knowledge representation, reasoning, and rule-based systems. Its ability to efficiently handle symbolic computations and pattern matching sets it apart in the AI and NLP domains. Prolog's built-in backtracking mechanism allows for elegant problem-solving approaches. With Prolog, developers can focus on specifying the problem's logic rather than the algorithmic details. These characteristics make Prolog an appealing choice for AI applications that involve complex inference, knowledge-based systems, and natural language processing tasks.
Performance Level: 3.8
Wolfram programming language is known for its fast and powerful processing capabilities. In the realm of AI and NLP, Wolfram offers extensive capabilities including 6,000 built-in functions for symbolic computation, functional programming, and rule-based programming. It also excels at handling complex mathematical operations and lengthy natural language processing tasks. Moreover, Wolfram seamlessly integrates with arbitrary data and structures, further enhancing its utility in AI and NLP applications. Developers rely on Wolfram for its robust computational abilities and its aptitude for executing sophisticated mathematical operations and language processing functions.
Performance Level: 4
Haskell prioritizes safety and speed which makes it well-suited for machine learning applications. While Haskell has gained traction in academia for its support of embedded, domain-specific languages crucial to AI research, tech giants like Microsoft Corporation (NASDAQ:MSFT) and Meta Platforms, Inc. (NASDAQ:META) have also utilized Haskell for creating frameworks to manage structured data and combat malware.
Haskell's HLearn library offers deep learning support through its Tensorflow binding and algorithmic implementations for machine learning. Haskell shines in projects involving abstract mathematics and probabilistic programming, empowering users to design highly expressive algorithms without compromising efficiency. Haskell's versatility and fault-tolerant capabilities make it a secure programming language for AI applications, ensuring robustness in the face of failures.
Performance Level: 4.3
Lisp, one of the pioneering programming languages for AI, has a long-standing history and remains relevant today. Developed in 1958, Lisp derived its name from 'List Processing,' reflecting its initial application. By 1962, Lisp had evolved to address artificial intelligence challenges, solidifying its position in the field. While Lisp is still capable of producing high-quality software, its complex syntax and costly libraries have made it less favored among developers. However, Lisp remains valuable for specific AI projects, including rapid prototyping, dynamic object creation, and the ability to execute data structures as programs.
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Disclosure: None. Top 10 Programming Languages for AI and Natural Language Processing is originally published on Insider Monkey.
What Is Natural Language Processing? - EWeek
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Natural language processing (NLP) is a branch of artificial intelligence (AI) that focuses on computers incorporating speech and text in a manner similar to humans understanding. This area of computer science relies on computational linguistics—typically based on statistical and mathematical methods—that model human language use.
NLP plays an increasingly prominent role in computing—and in the everyday lives of humans. Smart assistants such as Apple's Siri, Amazon's Alexa and Microsoft's Cortana are examples of systems that use NLP.
In addition, various other tools rely on natural language processing. Among them: navigation systems in automobiles; speech-to-text transcription systems such as Otter and Rev; chatbots; and voice recognition systems used for customer support. In fact, NLP appears in a rapidly expanding universe of applications, tools, systems and technologies.
In every instance, the goal is to simplify the interface between humans and machines. In many cases, the ability to speak to a system or have it recognize written input is the simplest and most straightforward way to accomplish a task.
While computers cannot "understand" language the same way humans do, natural language technologies are increasingly adept at recognizing the context and meaning of phrases and words and transforming them into appropriate responses—and actions.
Also see: Top Natural Language Processing Companies
Natural Language Processing: A Brief HistoryThe idea of machines understanding human speech extends back to early science fiction novels. However, the field of natural language processing began to take shape in the 1950s, after computing pioneer Alan Turing published an article titled "Computing Machinery and Intelligence." It introduced the Turing Test, which provided a basic way to gauge a computer's natural language abilities.
During the ensuing decade, researchers experimented with computers translating novels and other documents across spoken languages, though the process was extremely slow and prone to errors. In the 1960s, MIT professor Joseph Weizenbaum developed ELIZA, which mimicked human speech patterns remarkably well. Over the next quarter century, the field continued to evolve. As computing systems became more powerful in the 1990s, researchers began to achieve notable advances using statistical modeling methods.
Dictation and language translation software began to mature in the 1990s. However, early systems required training, they were slow, cumbersome to use and prone to errors. It wasn't until the introduction of supervised and unsupervised machine learning in the early 2000s, and then the introduction of neural nets around 2010, that the field began to advance in a significant way.
With these developments, deep learning systems were able to digest massive volumes of text and other data and process it using far more advanced language modeling methods. The resulting algorithms had become far more accurate and utilitarian.
Also see: Top AI Software
How Does Natural Language Processing Work?Early NLP systems relied on hard coded rules, dictionary lookups and statistical methods to do their work. They frequently supported basic decision-tree models. Eventually, machine learning automated tasks while improving results.
Today's natural language processing frameworks use far more advanced—and precise—language modeling techniques. Most of these methods rely on convolutional neural networks (CNNs) to study language patterns and develop probability-based outcomes.
For example, a method called word vectors applies complex mathematical models to weight and relate words, phrases and constructs. Another method called Recognizing Textual Entailment (RTE), classifies relationships of words and sentences through the lens of entailment, contradiction, or neutrality. For instance, the premise "a dog has paws" entails that "dogs have legs" but contradicts "dogs have wings" while remaining neutral to "all dogs are happy."
A key part of NLP is word embedding. It refers to establishing numerical weightings for words in specific context. The process is necessary because many words and phrases can mean different things in different meanings or contexts (go to a club, belong to a club or swing a club). Words can also be pronounced the same way but mean different things (through, threw or witch, which). There's also a need to understand idiomatic phrases that do not make sense literally, such as "You are the apple of my eye" or "it doesn't cut the mustard."
Today's models are trained on enormous volumes of language data—in some cases several hundred gigabytes of books, magazines articles, websites, technical manuals, emails, song lyrics, stage plays, scripts and publicly available sources such as Wikipedia. As the deep learning system parse through millions or even billions of combinations—relying on hundreds of thousands of CPU or GPU cores—they analyze patterns, connect the dots and learn semantic properties of words and phrases.
It's also often necessary to refine natural language processing systems for specific tasks, such as a chatbot or a smart speaker. But even after this takes place, a natural language processing system may not always work as billed. Even the best NLPs make errors. They can encounter problems when people misspell or mispronounce words and they sometimes misunderstand intent and translate phrases incorrectly. In some cases, these errors can be glaring—or even catastrophic.
Today, prominent natural language models are available under licensing models. These include the OpenAI codex, LaMDA by Google, IBM Watson and software development tools such as CodeWhisperer and CoPilot. In addition, some organizations build their own proprietary models.
How is Natural Language Processing Used?There are a growing array of uses for natural language processing. These include:
Conversational AI. The ability of computers to recognize words introduces a variety of applications and tools. Personal assistants like Siri, Alexa and Microsoft Cortana are prominent examples of conversational AI. They allow humans to make a call from a mobile phone while driving or switch lights on or off in a smart home. Increasingly, these systems understand intent and act accordingly. For example, chatbots can respond to human voice or text input with responses that seem as if they came from another person. What's more, these systems use machine learning to constantly improve.
Machine translation. There's a growing use of NLP for machine translation tasks. These include language translations that replace words in one language for another (English to Spanish or French to Japanese, for example). Google Translate and DeepL are examples of this technology. But machine translation can also take other forms. For example, NLP can convert spoken words—either in the form of a recording or live dictation—into subtitles on a TV show or a transcript from a Zoom or Microsoft Teams meeting. Yet while these systems are increasingly accurate and valuable, they continue to generate some errors.
Sentiment analysis. NLP has the ability to parse through unstructured data—social media analysis is a prime example—extract common word and phrasing patterns and transform this data into a guidepost for how social media and online conversations are trending. This capability is also valuable for understanding product reviews, the effectiveness of advertising campaigns, how people are reacting to news and other events, and various other purposes. Sentiment analysis finds things that might otherwise evade human detection.
Content analysis. Another use case for NLP is making sense of complex systems. For example, the technology can digest huge volumes of text data and research databases and create summaries or abstracts that relate to the most pertinent and salient content. Similarly, content analysis can be used for cybersecurity, including spam detection. These systems can reduce or eliminate the need for manual human involvement.
Text and image generation. A rapidly emerging part of natural language processing focuses on text, image and even music generation. Already, some news organizations produce short articles using natural language processing. Meanwhile, OpenAI has developed a tool that generates text and computer code through a natural language interface. Another OpenAI tool, dubbed Dall-E-2, creates high quality images through an NLP interface. Type the words "black cat under a stairway" and an image appears. GitHub Copilot and Amazon CodeWhisperer can auto-complete and auto-generate computer code through natural language.
Also see: Top Data Visualization Tools
NLP Business Use CasesThe use of NLP is increasingly common in the business world. Among the top use cases:
Chatbots and voice interaction systems. Retailers, health care providers and others increasingly rely on chatbots to interact with customers, answer basic questions and route customers to other online resources. These systems can also connect a customer to a live agent, when necessary. Voice systems allow customers to verbally say what they need rather than push buttons on the phone.
Transcription. As organizations shift to virtual meetings on Zoom and Microsoft Teams, there's often a need for a transcript of the conversation. Services such as Otter and Rev deliver highly accurate transcripts—and they're often able to understand foreign accents better than humans. In addition, journalists, attorneys, medical professionals and others require transcripts of audio recordings. NLP can deliver results from dictation and recordings within seconds or minutes.
International translation. NLP has revolutionized interactions between businesses in different countries. While the need for translators hasn't disappeared, it's now easy to convert documents from one language to another. This has simplified interactions and business processes for global companies while simplifying global trade.
Scoring systems. Natural language is used by financial institutions, insurance companies and others to extract elements and analyze documents, data, claims and other text-based resources. The same technology can also aid in fraud detection, financial auditing, resume evaluations and spam detection. In fact, the latter represents a type of supervised machine learning that connects to NLP.
Market intelligence and sentiment analysis. Marketers and others increasingly rely on NLP to deliver market intelligence and sentiment trends. Semantic engines scrape content from blogs, news sites, social media sources and other sites in order to detect trends, attitudes and actual behaviors. Similarly, NLP can help organizations understand website behavior, such as search terms that identify common problems and how people use an e-commerce site. This data can lead to design and usability changes.
Software development. A growing trend is the use of natural language for software coding. Low-code and no-code environments can transform spoken and written requests into actual lines of software code. Systems such as Amazon's CodeWhisperer and GitHub's CoPilot include predictive capabilities that autofill code in much the same way that Google Mail predicts what a person will type next. They also can pull information from an integrated development environment (IDE) and produce several lines of code at a time.
Text and image generation. The OpenAI codex can generate entire documents, based a basic request. This makes it possible to generate poems, articles and other text. Open AI's DALL-E 2 generates photorealistic images and art through natural language input. This can aid designers, artists and others.
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What Ethical Concerns Exist for NLP?Concerns about natural language processing are heavily centered on the accuracy of models and ensuring that bias doesn't occur. Many of these deep learning algorithms are so-called "black boxes," meaning that there's no way to understand how the underlying model works and whether it is free of biases that could affect critical decisions about lending, healthcare and more.
There is also debate about whether these systems are "sentient." The question of whether AI can actually think and feel like a human has been expressed in films such as 2001: A Space Odyssey and Star Wars. It also reappeared in 2022, when former Google data scientist Blake Lemoine published human-to-machine discussions with LaMDA. Lemoine claimed that the system had gained sentience. However, numerous linguistics experts and computer scientists countered that a silicon-based system cannot think and feel the way humans do. It merely parrots language in a highly convincing way.
In fact, researchers who have experimented with NLP systems have been able to generate egregious and obvious errors by inputting certain words and phrases. Getting to 100% accuracy in NLP is nearly impossible because of the nearly infinite number of word and conceptual combinations in any given language.
Another issue is ownership of content—especially when copyrighted material is fed into the deep learning model. Because many of these systems are built from publicly available sources scraped from the Internet, questions can arise about who actually owns the model or material, or whether contributors should be compensated. This has so far resulted in a handful of lawsuits along with broader ethical questions about how models should be developed and trained.
Also see: AI vs. ML: Artificial Intelligence and Machine Learning
What Role Will NLP Play in the Future?There's no question that natural language processing will play a prominent role in future business and personal interactions. Personal assistants, chatbots and other tools will continue to advance. This will likely translate into systems that understand more complex language patterns and deliver automated but accurate technical support or instructions for assembling or repairing a product.
NLP will also lead to more advanced analysis of medical data. For example, a doctor might input patient symptoms and a database using NLP would cross-check them with the latest medical literature. Or a consumer might visit a travel site and say where she wants to go on vacation and what she wants to do. The site would then deliver highly customized suggestions and recommendations, based on data from past trips and saved preferences.
For now, business leaders should follow the natural language processing space—and continue to explore how the technology can improve products, tools, systems and services. The ability for humans to interact with machines on their own terms simplifies many tasks. It also adds value to business relationships.
Also see: The Future of Artificial Intelligence
8 Top Natural Language Processing (NLP) Companies Today - EWeek
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As more and more companies adopt artificial intelligence (AI) in a variety of sectors, these AI are inevitably put in positions where they have to interact with human beings. From customer support chatbots to virtual assistants like Amazon's Alexa, these use cases necessitate teaching an AI how to listen, learn, and understand what humans are saying to it and how to respond.
One method for teaching AI how to communicate with humans is natural language processing (NLP). Sitting at the intersection at AI, computer science, and linguistics, natural language processing's goal is to create or train a computer capable of not just understanding the literal words humans say but also the contextual implications and nuances found in their language.
As the AI industry has grown in prominence, so too has the NLP industry. A report from Allied Market Research valued the global NLP market at $11.1 billion in 2020, and it is expected to grow to $341.5 billion by 2030. Within that valuation lies a myriad of both promising startups and experienced tech veterans pushing the science further and further.
History of Natural Language ProcessingNatural language processing has been part of AI research since the field's infancy. Alan Turing's landmark paper Computer Machinery and Intelligence, in which the famous Turing Test was introduced, includes a task requiring the automated interpretation of natural language.
From the 1950s to the 1990s, NLP research largely focused on the symbolic NLP, which attempts to teach computers language contexts through associative logic. Essentially, the AI is given a human-generated knowledge base designed to include the conceptual components of a language and how those components relate to one another.
Using this knowledge base, the AI can then understand the meanings of words in context through IF-THEN logic. An example of this would be similes. If you said, "He's as fast as a cheetah," the AI would understand that the person you are talking about would not be a literal cheetah.
Thanks to increases in computing power starting in the 1990s, machine learning algorithms were introduced into natural language processing. This is when machine translation programs started gaining prominence. Examples you might use would be Google Translate or DeepL.
As the internet grew in popularity through the 2000s, NLP machines gained access to even more raw data to sift through and understand. As such, researchers began focusing on developing unsupervised and semi-supervised learning algorithms. These algorithms were less accurate than supervised learning algorithms, but the sheer quantity of data they processed can offset these inaccuracies.
Today, many natural language processing AIs use representational learning and deep neural network-style machine learning techniques to develop more accurate language modeling and parsing capabilities.
Read More At: What Is Artificial Intelligence?
Benefits of Natural Language ProcessingUsing natural language processing in business has a number of benefits. For instance, NLP programs used in customer support roles can be active 24/7 and can be cheaper to implement and maintain than a human employee. This makes NLP a potential cost-saving measure.
NLP can also be used to nurture leads and develop targeted advertising, ensuring that an organization's products are being put in front of the eyes of the people most likely to buy them. This can help boost the effectiveness of human marketing teams and drive revenue up without necessarily needing to spend money on more widespread advertising campaigns.
Natural language processing can also be used to boost search engine optimization (SEO) and help make sure a business stays as high in the rankings as possible. NLP can analyze search queries, suggest related keywords, and help save time on SEO research, giving businesses more time to optimize their content quality.
Top Natural Language Processing Companies GoogleOne of the biggest names in AI and tech, Google naturally has a long history of utilizing NLP in its products and services. Just this year, one of its researchers asserted that one of the company's Language Model for Dialogue Applications (LamDA) was sentient, thanks in large part to its responses to the researcher via text chat. Google even began public testing of LamDA in late August 2022.
In terms of product offerings, it has a Natural Language API which allows users to derive new insights from unstructured text. Its AutoML provides custom machine learning models to better analyze, categorize, and assess documents. The Dialogflow development suite can be deployed in a variety of different settings to create conversational user interfaces such as chatbots on websites, mobile apps, and other platforms.
Finally, Google Cloud's Document AI solution lets customers automate data capture at scale, allowing them to extract more data from documents without boosting costs.
Read More At: The Future of Artificial Intelligence
WordsmithAutomated Insights' Wordsmith platform is touted as the world's first publicly-available natural language generation (NLG) engine. By inputting information into the engine, users can create clear, understandable content
Being one of the first of its kind, the platform has a number of interesting clients. Notably, the Associated Press has partnered with Automated Insights to power over 50,000 AI-generated news articles, according to Automated Insight's website.
Wordsmith's interface is one of the easiest to use on the market with a high degree of customizability. However, initial setup can take longer than expected. Those looking for quick-deployment options might need to look elsewhere. The content output will also likely need some touching up by in-house staff before publication.
Overall, Wordsmith is a solid choice for companies looking for a way to convert large volumes of data into readable, formatted content.
Indata LabsBased out of Cyprus, Indata Labs leverages its employees' experience in big data analytics, AI, and NLP to help client companies get the most out of their data. Organizations in industries like healthcare, e-commerce, fintech, and security have made use of Indata Labs' expertise to generate new insights from their data.
The firm offers a wide range of services and solutions, from data engineering to image recognition to predictive analytics. In the NLP space, the firm offers customer experience consulting, consumer sentiment analysis, and text analysis to ensure clients generate as much value from their datasets as possible.
Indata Labs also maintains its own in-house AI R&D (research and development) Center and works with some of the best computer vision and NLP companies in the world to develop new solutions and push the fields of business intelligence, AI, and natural language processing forward.
IBMAnother tech titan, IBM's suite of Watson AI products are some of the best on the market. Naturally, Watson's wide array of services features a number of NLP solutions. Watson Discovery is an intelligent search and text analysis platform which enterprises can use to help find information potentially hidden in their vast stores of data.
Watson Assistant is a customer support platform which collects data from client conversations. Through this, Watson Assistant chatbots can better learn how to make the customer support process less stressful and time-consuming for customers.
Finally, Watson Natural Language Understanding uses deep learning to identify linguistic concepts and keywords, perform sentiment analysis, and extract meaning from unstructured data.
Read More At: The Benefits of Artificial Intelligence
SynthesiaSynthesia is a web-based AI video generation platform. Through its library of video templates, AI voices, and avatars, users can craft videos at-scale to meet whatever needs they might have. Synthesia's tech has been used by over 10,000 companies, including Nike, Google, the BBC, and Reuters, to create videos in over 60 languages, according to its website.
Other features on the platform include a screen recorder, custom AI avatar crafting, closed captioning, and access to a library of royalty-free background music. If an organization has access to its own library of media assets, they can easily upload and then use these assets in Synthesia.
IntelA major tech name like Intel is bound to have a whole host of NLP-related services. There is, of course, Intel's wide array of AI products, from development tools to deployment solutions.
For organizations interested in leveling up their NLP knowledge, Intel offers an extensive natural language processing developer course where students can learn the ins and outs of actually utilizing NLP in AI training.
There is also the Natural Language Processing Architect, a Python library developed by the Intel AI Labs. A Python library is, in essence, a collection of premade collections of code which can be repeatedly implemented in different programs in scenarios. The NLP Architect specifically is meant to help make developing custom NLP-trained AI easier.
MindMeldMindMeld offers a conversational AI platform through which companies can develop conversational interfaces designed to best suit their apps, algorithms, and platforms.
Through MindMeld, companies have developed and deployed interfaces for food ordering, home assistance, banking assistance, and video discovery. It provides training at each step of the NLP hierarchy, ensuring each level of logic in the process is accounted for.
It's thanks to this innovative platform that Entrepreneur Magazine placed MindMeld in its 100 Brilliant Companies list in 2015. Companies using MindMeld include Cisco, Appspace, Davra, and Altus.
MicrosoftMicrosoft's reach expands across the entire tech landscape. It's no surprise that AI, and by extension natural language processing, is one area of interest to the Washington-based tech giant. In fact, Microsoft's Research Lab in Redmond, Washington, has a group dedicated specifically to NLP research.
Through Microsoft's Azure cloud computing service, customers can train and deploy customized natural language processing frameworks. The company even offers documentation on how to do so. To utilize NLP in Azure, Microsoft recommends Apache Spark, an open-source unified analytics engine built for large-scale data processing.
Notable features of these customized NLP frameworks for Azure include sentiment analysis, text classification, text summarization, and embedding. Additionally, Microsoft's Azure AI can support a multilingual training model, allowing organizations to train NLP AI to perform in multiple different languages without retraining.
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