What Top Tech Skills Should You Learn for 2025?

This AI Recognises Bangla Speech And Text

Nowadays, while we tend to use Google Translate to quickly turn everyday English into Bangla and call it a day, the accuracy of the translation might be far from perfect. Native speakers tend to find notable flaws in AI-generated translations, and many would even claim that AI lacks the fluency and touch of conversational language, especially Bangla - a language filled with many different regional dialects that are quite different from the more standardised written form that we are used to.

So is it simply not possible to teach AI conversational, everyday Bangla in spoken and written form? While we may not have reached a definitive answer to that question yet, there is a certain language project going on that tackles this exact conundrum. 

The Bangla speech recognition campaign

Founded in December 2017 by a group of BUET, KUET and BRACU graduates, Bengali.AI is a voluntary research initiative that aims to democratise AI research in Bangla by teaching AI the Bangla we use every day. By looking into how computers read, speak and understand language, this non-profit initiative creates large-scale machine learning datasets to teach AI the complex yet beautiful Bangla language. Their datasets, collected from various Bangla-speaking demographics, are made available to researchers for free to help train artificially intelligent systems.

Bengali.AI's currently ongoing project on Bangla speech recognition has produced the largest publicly available Bangla speech corpus. As diversity is a key part of building a conversational Bangla dataset, Bengali.AI is aiming to collect voice data from as many people as possible. Right now, they are running a special campaign on social media called 'Bok bok', where Bangla speakers from anywhere in the world can contribute their "voice data" for the public domain research dataset. 

How to participate in 'Bok bok'

To contribute, simply login to the contribution platform and start reading the sentences that appear on the screen, loudly and clearly. As a token of your contribution to this constant development of the Bangla database, Bengali.AI awards an honorarium to anyone who contributes. If you want to participate, simply follow the procedures in this link: https://commonvoice.Mozilla.Org/bn/speak

According to the Coordinator of Bengali.AI, Asif Sushmit, since February 21, 2022, the Bangla speech recognition campaign has gathered around 2,000 hours of data from over 22,000 people - the standard benchmark for training industry-grade speech recognition models. They aim to achieve a higher landmark of 10,000 hours to include more nuances and diversities of the language so that researchers can train their own AI better.

Bengali.AI plans to use this voice data to fine-tune the conversational aspect of the language AI and build a publicly available automated speech recognition system. In theory, this AI will be able to understand conversational Bangla similar to Alexa and Siri, with the added benefit of being tweaked by anyone, anytime, free of cost. 

Other projects

Bengali.AI's journey began in 2018 with NumtaDB, a dataset containing over 85,000 images of hand-written Bangla digits. The dataset was compiled to build Bangla digit recognition algorithms that are free of geographical, gender and age-based biases.

In 2020, the Bengali.AI team published the first-ever dataset of over 500,000 handwritten Bangla graphemes, and launched an international Kaggle competition in collaboration with Google. This competition had over 2,000 teams from all over the world, regardless of their native tongue. These teams consisted of some of the biggest names in AI like NVIDIA and H20.AI - all joining forces to solve optical character recognition for Bangla.

By 2021, Bengali.AI launched many side projects, all catered towards the advancement of machine learning and natural language processing of the language. Some of these publicly available projects include: a Bangla dictionary that can automatically analyse speech annotation, a transcriber aimed towards transliterated or "Murad Takla" Bangla text, and Bethik - the opensource Bangla spell checker that can spot and fix high-degree Bangla typing errors. Made by a team of 40 students from SUST and BRACU, as well as voluntary researchers from many different fields, Bethik will be made publicly available within the next month. 

Currently, Bengali.AI is a community of over 6,000 international researchers who are all working towards achieving what many deem impossible: democratising Bangla language-related technology. By making everything open source, publicly accessible and 'rectifiable' - an important part of the recipe, the lack of which has led to the downfall of similar projects in the past - Bengali.AI wishes to help make technology accessible to anyone 'Bangla'. With their ever-expanding database of Bangla derived from thousands of native speakers, we may be on the path towards an AI we can speak everyday Bangla with.

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.

Top 10 Programming Languages for AI and Natural Language Processing

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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.

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