Natural Language Processing

Waters Wrap: Fintech funding—follow the money

As funding for startups and young companies dries up due to Inflation and rising interest rates, Anthony looks at some of the vendors that have received monetary infusions this year to see if there are any patterns to be gleaned.

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.

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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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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.

Taking Back The Meaning Of "Inflation"

Words matter and definitions often become imprecise and "slippery." There is a natural evolution of language wherein words gradually change over time, but often a key meaning gets lost and there no longer remains a single word to describe a concept. This has been the case with the common word "inflation."

Over the last few years, I have kept a list of quotes about the accurate definition of inflation (and I am always looking for more quotes). What inspired this list was the recognition that what most people mean and understand by the word "inflation" is price inflation—increasing consumer prices. But this is not inflation; it is a consequence of inflation. In fact, a former coworker and friend asked me during the time of massive covid spending by the government, "Where is the inflation?"—to which I responded, "It already happened." We need to return to the true definition of inflation. Otherwise, the responsibility of government and the central bank is obscured.

While there has been some debate within the Austrian school, even distinguishing between Ludwig von Mises's and Murray Rothbard's positions, inflation is, generally speaking, the artificial increase of money and credit (i.E., "printing money"). This act can be perpetrated by the central bank or banks operating on fractional reserves (usually with legal permission granted by the government before the central bank). Mises wrote of the negative impact of this definition change in his essay "Inflation and Price Control":

The term inflation is used with a new connotation. What people today call inflation is not inflation, i.E., the increase in the quantity of money and money substitutes, but the general rise in commodity prices and wage rates which is the inevitable consequence of inflation. This semantic innovation is by no means harmless. (emphasis added)

Mises again referred to the consequences of such a definition in Human Action:

The semantic revolution which is one of the characteristic features of our day has also changed the traditional connotation of the terms inflation and deflation. What many people today call inflation or deflation is no longer the great increase or decrease in the supply of money, but its inexorable consequences, the general tendency toward a rise or a fall in commodity prices and wage rates. This innovation is by no means harmless. It plays an important role in fomenting the popular tendencies toward inflationism. (emphasis added)

Mises continued:

There is no longer any term available to signify what inflation used to signify. It is impossible to fight a policy which you cannot name. Statesmen and writers no longer have the opportunity of resorting to a terminology accepted and understood by the public when they want to question the expediency of issuing huge amounts of additional money. They must enter into a detailed analysis and description of this policy with full particulars and minute accounts whenever they want to refer to it, and they must repeat this bothersome procedure in every sentence in which they deal with the subject. As this policy has no name, it becomes self-understood and a matter of fact. It goes on luxuriantly. (emphasis added)

Likewise, Henry Hazlitt agreed with Mises and wrote a short essay that is not as well-known as it ought to be—"Inflation in One Page." In this masterful summary, Hazlitt explains:

Inflation is an increase in the quantity of money and credit. Its chief consequence is soaring prices. Therefore inflation—if we misuse the term to mean the rising prices themselves—is caused solely by printing more money. For this the government's monetary policies are entirely responsible. (emphasis added)

Rothbard's definition of inflation differed slightly from Mises's and was arguably a bit more precise but in the same spirit. In a recent article in the Quarterly Journal of Austrian Economics, "What Is Inflation? Clarifying and Justifying Rothbard's Definition," Kristoffer Hansen and Jonathan Newman clarify and agree with Rothbard's definition of inflation, noting that Mises defined inflation as the increase in the money supply not offset by an increase in the demand for money, but Rothbard defined it as issuing "pseudo warehouse receipts" or issuing money in excess of the stock of specie (e.G., gold). Rothbard described inflation as follows:

The process of issuing pseudo warehouse receipts or, more exactly, the process of issuing money beyond any increase in the stock of specie, may be called inflation [italics original]. . . . The profit is practically costless, because, while all other people must either sell goods and services and buy or mine gold, the government or the commercial banks are literally creating money out of thin air. They do not have to buy it. Any profit from the use of this magical money is clear gain to the issuers. (emphasis added)

In The Progressive Era, Rothbard explained further:

The terms "inflation" and "inflationary" are used throughout this article according to their original definition—an expansion of the money supply—rather than in the current popular sense of a rise in price. The former meaning is precise and illuminating; the latter is confusing because prices are complex phenomena with various causes, operating from the sides of both demand and supply. It only muddles the issue to call every supply-side price rise (say, due to a coffee blight or an OPEC cartel) "inflationary." (emphasis added)

While Mises and Rothbard differed on whether inflation should include or exclude new gold inflows, they both agreed that inflation is not an increase in prices but rather an increase in the supply of money and credit. They also both agreed that the consequences of inflation are (often) asymmetrically rising prices and business cycles.

If the definition was so simple and uncontested, why did the meaning of inflation change?

The answer can be found in the ideological and policy dispute between the British currency school and the British banking school (and their American counterparts). The currency school, which favored hard money to a greater degree, triumphed at first and had the opportunity to implement their policy prescriptions with the Bank of England. At that time, their definition of inflation was accepted. As Dr. Joseph Salerno explains in his book Money, Sound and Unsound, "The term 'inflation' was now used strictly to denote an increase in the supply of money that consisted in the creation of currency and bank deposits unbacked by gold."

Unfortunately, unlike their American counterparts, the proponents of the British currency school, despite their accurate definition of inflation, failed to consider demand deposits as part of the money supply. For that reason, their policies as adopted in Great Britain failed to prevent both price inflation and the business cycle, and the currency school fell into disrepute. Inflation would now be subtly redefined as "a supply of circulating media in excess of trade needs."

Over time, the definition would be further distorted. For example, even though it sounds technically correct, Milton Friedman's definition contains several mistaken presuppositions: "[Inflation] is always and everywhere a monetary phenomenon. It's always and everywhere a result of too much money, of a more rapid increase in the quantity of money than in output."

Friedman is correct that inflation is always a monetary phenomenon; however, monetarists have different definitions of "money" and "money supply," and monetarists view money as a policy instrument to be adjusted through inflation. Finally, the Keynesian revolution from 1936 forward would continue to change the definition of inflation to mean a general increase in the so-called price level. Salerno writes:

Before World War II, when the terms "inflation" and "deflation" were used in academic discourse or everyday speech, they generally meant an increase or a decrease in the stock of money, respectively. A general rise in prices was viewed as one of several consequences of inflation of the money supply; likewise, a decline in overall prices was viewed as one consequence of deflation of the money supply. Under the influence of the Keynesian Revolution of the mid-1930s, however, the meanings of these terms began to change radically. By the 1950s, the definition of inflation as a general rise in prices and of deflation as a general fall in prices became firmly entrenched in academic writings and popular speech. (emphasis added)

As Mises said, we cannot fight a policy that we cannot name. It is high time we clarified and implemented the true definition of inflation. If we can convince people that inflation means "printing money" artificially and then explain the consequences, this will help them understand what government has done to our money.

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