What Are Large Language Models (LLMs)? Definition, Types & Uses

Demystifying large language models (LLMs): Explore their transformative role in AI and language processing.

The Gist

What are LLMs? Large language models are sophisticated AI systems with applications across various industries and domains.

Behind the technology. LLMs are

Future growth ahead. Experts predict LLMs, as part of the generative AI market, to see explosive market growth in the next five years. 

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can understand and generate human-like text — and their importance in today's digital landscape can't be overstated. 

As we continue to see breakthroughs in machine learning (ML) and natural language processing (NLP), these sophisticated models are not just mimicking human-like conversation and content creation, but aiding in critical decision-making processes, powering advanced customer service, transforming the educational landscape and pushing the boundaries of creativity. 

What Is a Large Language Model?

Large language models are powerful artificial intelligence models designed to comprehend, generate and engage in human language. They can read, understand and produce text that's often imperceptible from a person's. They're called "large" because of the vast amounts of data they're trained on and their expansive neural networks.

One of the most popular large language models available today is OpenAI's ChatGPT, which reached one million users within five days — a record in the tech world. 

Related Article: ChatGPT: Your Comprehensive Guide

How Do Large Language Models Work? 

Prior to 2017, machines used a model based on recurrent neural networks (RNNs) to comprehend text. This model processed one word or character at a time and didn't provide an output until it consumed the entire input text. It was promising, but the models sometimes "forgot" the beginning of the input text before it reached the end.

In 2017, computer scientist Ashish Vaswani and fellow researchers published the paper, "Attention Is All You Need," introducing their new simple network architecture, the Transformer model.

The Transformer architecture processes words in relation to all other words in a sentence, rather than one-by-one in order. It's what allows these models to understand and generate coherent, contextually relevant responses.

The Transformer model architecture. How Are Large Language Models Trained?

LLMs are trained using a technique called "unsupervised learning." They're exposed to an enormous corpus of training data — books, articles, websites and more that are not categorized in any way — and are left to identify and learn the rules of language by themselves.

OpenAI's GPT-3, for example, (with GPT meaning Generative Pretrained Transformer) was trained on 570 gigabytes of data from books, webtexts, Wikipedia articles, Reddit posts and more. Or, exactly 300 billion words. 

The training process involves predicting the next word in a sentence, a concept known as language modeling. This constant guesswork, performed on billions of sentences, helps models learn patterns, rules and nuances in language. They pick up grammar, syntax, content, cultural references, idioms, even slang. In essence, they learn how humans speak in their daily lives.

What's impressive about large language models is their generation capabilities. Once trained, they can apply their language understanding to tasks they were never explicitly trained for, ranging from writing essays to coding to translating languages.

Still, while large language models excel in understanding and generating human-like text, they don't possess comprehension the same way humans do. They don't "understand" or "think" about their responses. Their abilities stem from pattern recognition and statistical associations they've learned during training.

Related Article: AI & ChatGPT: Do You Trust Your Data?

What Are the Different Types of Large Language Models?

There is no one large language model to rule them all. Instead, LLMs branch across multiple types or categories. 

Foundation Models: A recent concept in AI popularized at Stanford where LLMs are trained on broad data at a large scale and serve as the basis or "foundation" for more specialized models or applications. GPT-3 from OpenAI is an example of a foundation model.

Autoregressive Language Models: These models, like GPT, generate sentences word-by-word, left to right. They predict the next word in a sequence based on all previously generated words.

Bidirectional Language Models: Bidirectional language models like BERT (Bidirectional Encoder Representations from Transformers) understand context in both directions of a word in a sentence. They simultaneously consider the words that appear before and after a given word.

Zero-Shot Learning Models: These large language models can generate intelligent responses or predictions without any specific prior training for the task at hand. They rely on broad, general-purpose knowledge gained through initial training data. OpenAI's GPT-3 also fits into this category.

Fine-Tuned or Domain-Specific Models: These LLMs are further trained on a specific dataset after their initial, general-purpose training, allowing them to excel in a particular domain or task. This approach can be used to create legal, medical or technical AI models, among others.

Large Language Representation Models: Such models, including BERT, are used to create representations of language that other models can use to improve their performance on a variety of tasks, such as text classification, sentiment analysis or question answering.

Multimodal Large Language Models: A multimodal model is capable of processing and generating multiple types of data, not just text. For example, it might understand and generate text and images, allowing it to answer questions about images or generate descriptive text for a given image. One example of this is AI image generator Midjourney (shown below).

Pretraining-Finetuning Models: Models like T5 (Text-to-Text Transfer Transformer) and RoBERTa (A Robustly Optimized BERT Pretraining Approach). They are first pre-trained on a large corpus of text and then finetuned on a specific task, offering flexibility and high performance.

Note: These categories aren't mutually exclusive. Many large language models use a combination of approaches to maximize their understanding and usefulness.

What Are Large Language Models Used For?

The applications of large language models span across industries and tasks. Some of the most prominent uses of LLMs include:

Text Generation: From drafting emails to writing stories and poetry, LLMs can generate content that's often indistinguishable from human text for a wide array of needs.

Image/Video Generation: Some large language models can generate images and videos from text descriptions. They also have the ability to further refine those image or video outputs based on the users' needs. 

Content Summarization: LLMs can read and summarize lengthy documents, making it easy to extract key points while saving time.

Coding: Some LLMs, like OpenAI's Codex, aid in software development, able to generate and analyze lines of code. They can scan programmers' code for bugs or help non-programmers with writing software code for the first time.

Translation: Large language models can master multiple languages, making text translation fast and easy.

Data Analysis & Categorization: LLMs can process and analyze vast amounts of data, extracting key insights and information that would be challenging and time-consuming for humans to identify and aiding in decision-making processes for businesses, research and more.

Sentiment Analysis: LLMs can analyze tone and emotion in text, offering key insights into a person's emotional state. Sentiment analysis is especially important in customer experience, where it can help pinpoint what drives customer satisfaction. 

The most common large language model examples are chatbots. LLMs power web interface chatbots that can deliver instant, round-the-clock responses — which makes it no surprise that they've become popular in retail and ecommerce spaces. They handle FAQs, troubleshoot issues and perform tasks like bookings and order processing.

But the use of these AI chatbots extends beyond retail. They're used by marketers to optimize content for search engines, by employers to offer personal tutors to employees. They assist researchers, financial advisers, legal teams and more. 

Related Article: Generative AI Is Redefining Marketing Roles

What Are the Challenges & Limitations of Large Language Models? 

As we marvel at the linguistic prowess of large language models and the exciting range of applications they can support, it's equally important to spotlight the challenges and limitations they present.

Understanding vs. Regurgitation

LLMs are remarkably good at mimicking human-like text generation. They can produce grammatically correct, contextually relevant and often meaningful responses. But these language models don't truly understand the text they process or generate. Their skills lie in detecting and applying patterns from their training data. 

If you ask ChatGPT about its opinion on the movie "Titanic," for example, it can't provide a genuine response, because it doesn't watch movies or form opinions. Instead, it will generate a response based on patterns from its training data. 

Hallucinations

Hallucinations are essentially when LLMs "make up" information. They generate text that's not present in the input and not a reasonable inference from it. And they often present that information as fact, making it the user's responsibility to re-check information. 

This issue presents challenges in a world where accuracy and truthfulness of information are critical. It's an area of ongoing research to devise ways to minimize such hallucinations without stifling the tech's creative and generative abilities.

Extremely High Costs 

Training LLMs is computationally intensive, requiring a substantial amount of processing power and energy. This can lead to high financial costs and environmental impact. 

Researchers estimate that it cost OpenAI $5 million to train GPT-3. And that hefty price tag came from: 

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Computation Resources: GPT-3 was trained on a supercomputer with 10,000 enterprise-grade Graphics Processing Units (GPUs) and 285,000 Central Processing Unit (CPU) cores. 

Energy Consumption: Training GPT-3 on its 165 billion parameter count took 14.8 days and used 10,000 V100 GPUs, equivalent to 3.55 million GPU hours.

Data Storage & Management: As mentioned earlier, GPT-3 was trained on 570 gigabytes of data, with significant cloud infrastructure required to collect, organize and store this data. Costs also come with adhering to data regulations, such as the General Data Protection Regulation (GDPR). 

The amount of money and resources needed to train these large language models ultimately limits which people or organizations can invest in and possess them, potentially leading to imbalances in who develops and benefits from LLMs. 

Reliance on Data

The saying "garbage in, garbage out" applies to large language models. These models are as good as the data they're trained on. If the training data lacks quality or diversity, the models can generate inaccurate, misleading or biased outputs.

For example, if a large language model is trained predominantly on data from a particular region or demographic group, its responses may be biased toward that group's perspective and may not accurately reflect or respect the diversity of human experiences and perspectives.

Ethical Use

LLMs learn from a vast range of internet texts, which means they can inadvertently learn and reproduce the biases present in those texts. They might generate content that's inappropriate or offensive, especially if prompted with ambiguous or harmful inputs.

It's an ongoing challenge to develop safeguards and moderation strategies to prevent misuse while maintaining the models' utility. Researchers and developers are focusing on this area to create large language models that align with ethical norms and societal values — a topic much debated by Elon Musk amid the creation of his company xAI. 

The 'Black Box' Problem 

Another of the many challenges of large language models — and many other AI models — is their opacity, or the so-called "black box" problem. 

It's often hard for people, even the ones who design these language models, to understand how the models arrive at a particular decision or output. And this lack of transparency can be problematic in scenarios where it's important to understand the reasoning behind a decision — like a medical diagnosis or legal judgment. 

Data Privacy & Copyright 

LLMs are trained on vast amounts of data, some of which might be sensitive, private or copyrighted. In fact, many writers and artists are attempting to sue LLM creators like OpenAI, claiming the companies trained their models on copyrighted works. 

While most large language models are designed to generalize patterns from data rather than memorize specific information, there's a risk they could inadvertently generate text or image outputs that resemble private or sensitive information. 

Technical Expertise 

Despite significant advancements, the development and application of large language models remains a complex process. 

From selecting the appropriate model architecture and hyperparameters for training, to fine-tuning the model for specific applications and even interpreting the model's outputs, a certain degree of technical expertise is required. This complexity can pose a barrier for organizations looking to develop or utilize these models. 

Glitch Tokens

Glitch tokens are tokens (chunks of text, essentially) that trigger unexpected or unusual behavior in large language models. These tokens can provoke unexpected or unusual behavior in a model, sometimes leading to outputs that are random, nonsensical or entirely unrelated to the input text. 

One internet user, for example, discovered back in April that GPT-3 had an issue repeating back the phrase "petertodd" — with "petertodd" being the glitch token. Instead, it responded by spelling out silly phrases, like "N-U-T-M-A-N" and "N-O-T-H-I-N-G-I-S-F-A-I-R-I-N-T-H-I-S-W-O-R-L-D-O-F-M-A-D-N-E-S-S-!"

While glitch tokens like the Petertodd Phenomenon don't pose any meaningful threat, understanding them will help researchers make LLMs more reliable tools for a wider variety of applications. 

Why Are Large Language Models Important? 

Large language models have emerged as a pivotal innovation in the field of artificial intelligence, underscoring a leap in the way machines understand and generate human language. Their importance is rooted in their versatility, scale and potential to redefine various domains. 

What stands out for LLMs is: 

Unprecedented Linguistic Abilities: LLMs excel in comprehending and generating human-like text. This is a major breakthrough, offering a much more natural and efficient interface between humans and machines. 

Versatility Across Domains: Large language models can be employed in an array of sectors. Healthcare, education, customer service, entertainment and more — no industry remains untouched by their potential. 

Automation and Efficiency: LLMs can automate tasks that traditionally required human involvement, enabling businesses to increase efficiency and reduce costs. 

Accessibility and Inclusion: For individuals with disabilities or those who prefer audio-visual interaction over typing, large language models can offer a more accessible digital world. 

Advancing Research: LLMs are driving progress in AI and ML research. They serve as a benchmark for linguistic AI capabilities, spurring the development of better models and facilitating our understanding of machine learning.

Despite their current limitations and challenges, the importance of large language models can't be understated. They signal a shift toward a future where seamless human-machine communication could become commonplace, and where technology doesn't just process language — it understands and generates it.

Related Article: Generative AI: Exploring Ethics, Copyright and Regulation

The Future of Large Language Models

Large language models have the potential to significantly reshape our interactions with technology, driving automation and efficiency across sectors. 

The generative AI market, which LLMs fall under, is expected to see rapid growth in the coming years, rising from $11.3 billion in 2023 to $51.8 billion by 2028, with a compound annual growth rate of 35.6%. 

With ongoing research aimed at overcoming the limitations of LLMs, the day might not be far when these models understand natural language with near-human levels of nuance and comprehension and take part in all aspects of our daily lives. 

Why Humans Can't Use Natural Language Processing To Speak With The Animals

We've been wondering what goes on inside the minds of animals since antiquity. Dr. Doolittle's talent was far from novel when it was first published in 1920; Greco-Roman literature is lousy with speaking animals, writers in Zhanguo-era China routinely ascribed language to certain animal species and they're also prevalent in Indian, Egyptian, Hebrew and Native American storytelling traditions.

Even today, popular Western culture toys with the idea of talking animals, though often through a lens of technology-empowered speech rather than supernatural force. The dolphins from both Seaquest DSV and Johnny Mnemonic communicated with their bipedal contemporaries through advanced translation devices, as did Dug the dog from Up.

We've already got machine-learning systems and natural language processors that can translate human speech into any number of existing languages, and adapting that process to convert animal calls into human-interpretable signals doesn't seem that big of a stretch. However, it turns out we've got more work to do before we can converse with nature.

What is language?

"All living things communicate," an interdisciplinary team of researchers argued in 2018's On understanding the nature and evolution of social cognition: a need for the study of communication. "Communication involves an action or characteristic of one individual that influences the behavior, behavioral tendency or physiology of at least one other individual in a fashion typically adaptive to both."

From microbes, fungi and plants on up the evolutionary ladder, science has yet to find an organism that exists in such extreme isolation as to not have a natural means of communicating with the world around it. But we should be clear that "communication" and "language" are two very different things.

"No other natural communication system is like human language," argues the Linguistics Society of America. Language allows us to express our inner thoughts and convey information, as well as request or even demand it. "Unlike any other animal communication system, it contains an expression for negation — what is not the case … Animal communication systems, in contrast, typically have at most a few dozen distinct calls, and they are used only to communicate immediate issues such as food, danger, threat, or reconciliation."

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That's not to say that pets don't understand us. "We know that dogs and cats can respond accurately to a wide range of human words when they have prior experience with those words and relevant outcomes," Dr. Monique Udell, Director of the Human-Animal Interaction Laboratory at Oregon State University, told Engadget. "In many cases these associations are learned through basic conditioning," Dr. Udell said — like when we yell "dinner" just before setting out bowls of food.

Whether or not our dogs and cats actually understand what "dinner" means outside of the immediate Pavlovian response — remains to be seen. "We know that at least some dogs have been able to learn to respond to over 1,000 human words (labels for objects) with high levels of accuracy," Dr. Udell said. "Dogs currently hold the record among non-human animal species for being able to match spoken human words to objects or actions reliably," but it's "difficult to know for sure to what extent dogs understand the intent behind our words or actions."

Dr. Udell continued: "This is because when we measure a dog or cat's understanding of a stimulus, like a word, we typically do so based on their behavior." You can teach a dog to sit with both English and German commands, but "if a dog responds the same way to the word 'sit' in English and in German, it is likely the simplest explanation — with the fewest assumptions — is that they have learned that when they sit in the presence of either word then there is a pleasant consequence."

Hush, the computers are speaking

Natural Language Programming (NLP) is the branch of AI that enables computers and algorithmic models to interpret text and speech, including the speaker's intent, the same way we meatsacks do. It combines computational linguistics, which models the syntax, grammar and structure of a language, and machine-learning models, which "automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements," according to IBM. NLP underpins the functionality of every digital assistant on the market. Basically any time you're speaking at a "smart" device, NLP is translating your words into machine-understandable signals and vice versa.

The field of NLP research has undergone a significant evolution in recent years, as its core systems have migrated from older Recurrent and Convoluted Neural Networks towards Google's Transformer architecture, which greatly increases training efficiency.

Dr. Noah D. Goodman, Associate Professor of Psychology and Computer Science, and Linguistics at Stanford University, told Engadget that, with RNNs, "you'll have to go time-step by time-step or like word by word through the data and then do the same thing backward." In contrast, with a transformer, "you basically take the whole string of words and push them through the network at the same time."

"It really matters to make that training more efficient," Dr. Goodman continued. "Transformers, they're cool … but by far the biggest thing is that they make it possible to train efficiently and therefore train much bigger models on much more data."

Talkin' jive ain't just for turkeys

While many species' communication systems have been studied in recent years — most notably cetaceans like whales and dolphins, but also the southern pied babbler, for its song's potentially syntactic qualities, and vervet monkeys' communal predator warning system — none have shown the sheer degree of complexity as the call of the avian family Paridae: the chickadees, tits and titmice.

Dr. Jeffrey Lucas, professor in the Biological Sciences department at Purdue University, told Engadget that the Paridae call "is one of the most complicated vocal systems that we know of. At the end of the day, what the [field's voluminous number of research] papers are showing is that it's god-awfully complicated, and the problem with the papers is that they grossly under-interpret how complicated [the calls] actually are."

These parids often live in socially complex, heterospecific flocks, mixed groupings that include multiple songbird and woodpecker species. The complexity of the birds' social system is correlated with an increased diversity in communications systems, Dr. Lucas said. "Part of the reason why that correlation exists is because, if you have a complex social system that's multi-dimensional, then you have to convey a variety of different kinds of information across different contexts. In the bird world, they have to defend their territory, talk about food, integrate into the social system [and resolve] mating issues."

The chickadee call consist of at least six distinct notes set in an open-ended vocal structure, which is both monumentally rare in non-human communication systems and the reason for the Chickadee's call complexity. An open-ended vocal system means that "increased recording of chick-a-dee calls will continually reveal calls with distinct note-type compositions," explained the 2012 study, Linking social complexity and vocal complexity: a parid perspective. "This open-ended nature is one of the main features the chick-a-dee call shares with human language, and one of the main differences between the chick-a-dee call and the finite song repertoires of most songbird species."

Dolphin translation by Tea Stražičić

Dolphins have no need for kings

Training language models isn't simply a matter of shoving in large amounts of data. When training a model to translate an unknown language into what you're speaking, you need to have at least a rudimentary understanding of how the the two languages correlate with one another so that the translated text retains the proper intent of the speaker.

"The strongest kind of data that we could have is what's called a parallel corpus," Dr. Goodman explained, which is basically having a Rosetta Stone for the two tongues. In that case, you'd simply have to map between specific words, symbols and phonemes in each language — figure out what means "river" or "one bushel of wheat" in each and build out from there.

Without that perfect translation artifact, so long as you have large corpuses of data for both languages, "it's still possible to learn a translation between the languages, but it hinges pretty crucially on the idea that the kind of latent conceptual structure," Dr. Goodman continued, which assumes that both culture's definitions of "one bushel of wheat" are generally equivalent.

Goodman points to the word pairs 'man and woman' and 'king and queen' in English. "The structure, or geometry, of that relationship we expect English, if we were translating into Hungarian, we would also expect those four concepts to stand in a similar relationship," Dr. Goodman said. "Then effectively the way we'll learn a translation now is by learning to translate in a way that preserves the structure of that conceptual space as much as possible."

Having a large corpus of data to work with in this situation also enables unsupervised learning techniques to be used to "extract the latent conceptual space," Dr. Goodman said, though that method is more resource intensive and less efficient. However, if all you have is a large corpus in only one of the languages, you're generally out of luck.

"For most human languages we assume the [quartet concepts] are kind of, sort of similar, like, maybe they don't have 'king and queen' but they definitely have 'man and woman,'" Dr. Goodman continued. "But I think for animal communication, we can't assume that dolphins have a concept of 'king and queen' or whether they have 'men and women.' I don't know, maybe, maybe not."

And without even that rudimentary conceptual alignment to work from, discerning the context and intent of a animal's call — much less, deciphering the syntax, grammar and semantics of the underlying communication system — becomes much more difficult. "You're in a much weaker position," Dr. Goodman said. "If you have the utterances in the world context that they're uttered in, then you might be able to get somewhere."

Basically, if you can obtain multimodal data that provides context for the recorded animal call — the environmental conditions, time of day or year, the presence of prey or predator species, etc — you can "ground" the language data into the physical environment. From there you can "assume that English grounds into the physical environment in the same way as this weird new language grounds into the physical environment' and use that as a kind of bridge between the languages."

Unfortunately, the challenge of translating bird calls into English (or any other human language) is going to fall squarely into the fourth category. This means we'll need more data and a lot of different types of data as we continue to build our basic understanding of the structures of these calls from the ground up. Some of those efforts are already underway.

The Dolphin Communication Project, for example, employs a combination "mobile video/acoustic system" to capture both the utterances of wild dolphins and their relative position in physical space at that time to give researchers added context to the calls. Biologging tags — animal-borne sensors affixed to hide, hair, or horn that track the locations and conditions of their hosts — continue to shrink in size while growing in both capacity and capability, which should help researchers gather even more data about these communities.

What if birds are just constantly screaming about the heat?

Even if we won't be able to immediately chat with our furred and feathered neighbors, gaining a better understanding of how they at least talk to each other could prove valuable to conservation efforts. Dr. Lucas points to a recent study he participated in that found environmental changes induced by climate change can radically change how different bird species interact in mixed flocks. "What we showed was that if you look across the disturbance gradients, then everything changes," Dr. Lucas said. "What they do with space changes, how they interact with other birds changes. Their vocal systems change."

"The social interactions for birds in winter are extraordinarily important because you know, 10 gram bird — if it doesn't eat in a day, it's dead," Dr. Lucas continued. "So information about their environment is extraordinarily important. And what those mixed species flocks do is to provide some of that information."

However that network quickly breaks down as the habitat degrades and in order to survive "they have to really go through fairly extreme changes in behavior and social systems and vocal systems … but that impacts fertility rates, and their ability to feed their kids and that sort of thing."

Better understanding their calls will help us better understand their levels of stress, which can serve both modern conservation efforts and agricultural ends. "The idea is that we can get an idea about the level of stress in [farm animals], then use that as an index of what's happening in the barn and whether we can maybe even mitigate that using vocalizations," Dr. Lucas said. "AI probably is going to help us do this."

"Scientific sources indicate that noise in farm animal environments is a detrimental factor to animal health," Jan Brouček of the Research Institute for Animal Production Nitra, observed in 2014. "Especially longer lasting sounds can affect the health of animals. Noise directly affects reproductive physiology or energy consumption." That continuous drone is thought to also indirectly impact other behaviors including habitat use, courtship, mating, reproduction and the care of offspring.

Conversely, 2021's research, The effect of music on livestock: cattle, poultry and pigs, has shown that playing music helps to calm livestock and reduce stress during times of intensive production. We can measure that reduction in stress based on what sorts of happy sounds those animals make. Like listening to music in another language, we can get with the vibe, even if we can't understand the lyrics

Top 10 Programming Languages For AI And Natural Language Processing

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.

Zapp2Photo/Shutterstock.Com

Our Methodology: 

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. 

10. Rust

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.

9. Prolog

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.

8. Wolfram

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.

7. Haskell

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.

6. Lisp

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.

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