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What is Mojo language? Mojo is a new language for AI development. The language is being developed by the Modular company. The developer says, that it combines the best of Python syntax with systems programming and metaprogramming and lets you write portable code that's faster than C and interoperates seamlessly with the Python ecosystem. programming According to the official website, Mojo supports parallel threading and is 68,000 times faster than Python. Why this project is important? Mojo is becoming more and more popular and the community is growing actively. So, I decided to try to write my first data science project, but in Mojo language. I am excited to try a new programming language and start learning it before its popularity explodes. This article is being written in December 2023, so this code is valid for that period. So, I strongly believe this little guide will be valuable for someone interested in Mojo. Code from the article is available in this GitHub repo https://github.com/OberemokAlexandra/mojo_experiments/tree/main How to install Mojo language and start experiments? There are two ways to get started with Mojo. The first is to install it manually on your laptop. Instructions for each operating system can be found here https://developer.modular.com/download Installation takes time - be prepared But there is an easier and faster way to try out Mojo on your own: the free JupiterLab playground is available here: https://playground.modular.com/ I set up all the components on my laptop, and all the code below was tested on the environment, which was configured by me on my laptop. Choose of the dataset As it is my very first data project on Mojo - a regression task, I decided to choose a dataset from the sklearn library - diabetes dataset - https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html Iris and Titanic datasets are the basis for classification tasks, so not today 🙂 . This dataset contains measurements from 442 diabetic patients. There are 10 features with different magical measurements, and the target is a quantitative measure of disease progression over a year. A great explanation of the features is given here https://rowannicholls.github.io/python/data/sklearn_datasets/diabetes.html Installation of Python modules with Mojo Mojo has built-in support for Python infrastructure - so it's quite easy to import a required module. For example, a numpy import will look like this. # first line imports a Python module in Mojo
from python import Python
# This line imports a numpy module itself
let np = Python.import_module("numpy") Don’t forget to install the necessary  modules in your environment first. For example, pip install numpy Get the dataset Variables in Mojo can be declared with two keywords - let and var. The main difference between them is that “let” is used to declare an immutable variable, while “var” is needed to state a mutable variable. In a Mojo, there are two ways to declare a function - by using def and fn keywords. More detailed information about the difference between the keywords can be found here: https://docs.modular.com/mojo/manual/functions.html Here is a function to return a dataset for further modeling. fn get_data() raises -> PythonObject:
    # Import of sklearn module with ready datasets
    # It’s possible to import a whole module
    let datasets = Python.import_module("sklearn.datasets")  
    let ds = datasets.load_diabetes()
    return ds Function to split, build an ml model, and evaluate its performance fn main() raises:
    # gets data from previous function
    let ds = get_data()
    let X = ds['data']
    let y = ds['target']
    # import needed sklearn module for train_test split
    let model_selection = Python.import_module("sklearn.model_selection")
    let split = model_selection.train_test_split(X, y)
    # Unpacking looks much harder than in python
    let X_train = split[0]
    let X_test = split[1]
    let y_train = split[2]
    let y_test = split[3]
    # modeling process
    let linear_model = Python.import_module("sklearn.linear_model")
    var regr = linear_model.LinearRegression()
    regr.fit(X_train, y_train)
    let pred = regr.predict(X_test) Metrics and results Let’s evaluate the regression model and see the results let metrics = Python.import_module("sklearn.metrics")
print("mean")
print(np.mean(y_test))
print("mse")
print(metrics.mean_squared_error(y_test, pred))
print("mae")
print(metrics.mean_absolute_error(y_test, pred))
print("r2")
print(metrics.r2_score(y_test, pred)) The results look like this Warning: your results may differ from those given here due to the random splitting of the data into training and test samples and model evaluation procedure Brief metrics analysis and explanation: Mean shows the mean value in the test split A huge value of the mse metric compared to a relatively small mae value may indicate that there are outliers in the test sample. The R-squared metric represents how much of the variance from the mean is accounted for by the model. In our case, it equals approximately 0.6, and it means that the model deserves to be considered. Mojo vs Python I’ve rewritten the same code in Python. To make the experiment clear and honest, I maintained the same settings on Python (trust me, you can make much more using Python). Here is the code listed below from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np

def get_data():
    """returns dataset for modeling"""
    return load_diabetes()


def main():
    """performs all the modeling actions"""
    ds = get_data()
    X, y = ds['data'], ds['target']
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    regr = LinearRegression()
    regr.fit(X_train, y_train)
    pred = regr.predict(X_test)
    print("mean")
    print(np.mean(y_test))
    print("mse")
    print(mean_squared_error(y_test, pred))
    print("mae")
    print(mean_absolute_error(y_test, pred))
    print("r2")
    print(r2_score(y_test, pred)) It is seen that modeling results using Python are comparable. Challenges The challenges below are valid for December 2023. By the time you are reading this article, they may have already been fixed For now, the hardest part - is that it’s almost impossible to pass arguments easily to a Python module in Mojo data = load_diabetes(as_frame=True) Nowadays, I can’t pass parameters like as_frame=True in Mojo code. It can lead to difficulties when using and tuning complex models Based on information from Github, this feature is on request to develop issue link - https://github.com/modularml/mojo/issues/702 I couldn't unpack values in “the Python way.” I can't write a line of code like this X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) I only could do something like this let split = model_selection.train_test_split(X, y)
let X_train = split[0]
let X_test = split[1]
let y_train = split[2]
let y_test = split[3] Conclusion Mojo is a new programming language under development. Today, it is possible to write code with a simple regression model. Some features need to be introduced for more development abilities, but I strongly believe that the new programming language has potential and will gain more popularity in the future.

A New Programming Language For AI: Linear Regression, But With Mojo Language

Similar to traditional , a security token performs the same function except that it confirms ownership through transactions and also make fractional ownership possible. Federal laws that govern securities also apply to Security tokens with the intention of protecting investors on some levels. Security tokens are programmable. Tokenizing securities, in theory, remove the need of a third party by using smart contracts. For example, a loan “tokenized” on a blockchain could automatically make payments without the use of a traditional middleman like a bank. We discussed deeper on the various regulatory requirements around previously on Hackernoon. security blockchain security tokens _STO is the new IPO_hackernoon.com Security Token Offerings (STOs) — What You Need To Know Originally, ICOs were originally thought to be killers of the space. Due to the number of scams, regulations were quick to address the issue. STO is, simply put, the legalized version of ICOs. , one of the key reasons to why ICOs have crashed is due to their flawed business concepts. The number of great and credible projects is severely low as compared to scams and projects seemingly “claim” to revolutionize an industry. As a result, these companies are facing trouble in terms of compliance issues and utilizing their funds properly. Which leads to the rise of STOs. Companies or projects have to go through proper legal channels with governing entities before raising funds. Ideally, should the company ended up closing, it would ultimately be a failed investment. And if said company were a scam, they can be traced back by authorities. According to an article from Securities.io Let’s take a look at three commonly known types of security tokens: Equity Token Debt Token Real Assets Token Equity Token Equity tokens represent the value of shares issued by a company on the blockchain. The difference between an equity token and a traditional stock lies in its method of recording ownership. A traditional stock is logged into a database and the records are then represented by a paper certificate. For an equity token, however, it is recorded on an immutable blockchain essentially digitizing the traditional means of recording. Owning an equity token entitles the investor a portion of the company’s profits and a right to vote. It is important to note that these tokens are not limited to only the early stages of funding though it is more common to find companies offering its tokens during seed round. There are three benefits to this system: Enables investors to invest in blockchain companies while staying in compliance with securities law New fundraising model for early startups Framework for regulators to evaluate the project’s fundraising ICOs provided an opportunity for early startups to seek funding through utility tokens. However, it came under major scrutiny by authorities as utility tokens do not represent ownership to the company. An STO ensures their fundraising efforts are compliant with securities law. One example of an equity token offering is . They will be launching one of the world’s first equity token offerings to fund its business initiative to drive the mass adoption of paperless document technologies. Their tokens DCMO represents actual equity ownership in Documo. Documo Debt Token Debt-based security tokens represent debt instruments such as real estate mortgages and corporate bonds. The prices of these tokens are dictated by two factors: Risk and Dividend. A medium risk of default in a real estate mortgage cannot be priced the same way as a bond of a pre-IPO company. Therefore, modeling the price of a security token after risk and dividend is key. In blockchain terms, the smart contract representing a debt security token should include operations such as repayment terms that dictate the dividend model but also incorporate the different risk factors of the underlying debt. The benefits of tokenizing debt include: Fractionalization Fractionalizing debt vehicles brings new opportunities to a larger scope of investors Futures Tokenizing futures contracts and derivatives could open up a whole world of new opportunities. As a result, they can bring massive liquidity into the tokenized market, thanks to its highly leveraged nature. It also provides a great way of hedging portfolios. Market Size The current public market that includes bond and debt security is worth $100 trillion dollars. Should tokenization be the next evolutionary step for financial instruments, the potential for debt-based tokens can be massive. Dividends The difference between dividends from equity and from debt is its regularity. Dividends from bonds are typically more frequent than equity because dividends from equity heavily depend on the underlying companies’ performance. Real Asset Tokens This type of token represents ownership to a certain asset such as real estate or commodities. Commodity-backed tokens address issues of trust, their inefficiencies and the complexity of transactions, which typically involve multiple parties. Blockchain technology allows a transparent record of complicated transactions, track goods, and reduce fraud, which seems to make it a natural fit for the commodity business. Tokens can be used as virtual currencies, which have the same characteristics as any commodity (like gold) that can be traded with profit-making intentions. Commodity-backed cryptocurrencies included tokens linked to gold, silver, and oil . And each of those commodity has its own advantage and disadvantage. Commodity-backed stablecoins are one of the most exciting developments in the crypto world. Commodities such as gold or diamonds giving the tokens stability and value. Some examples: — a cryptocurrency backed by bananas. Bananas — backed by liquid cannabis extracts from legal sources Cannabium — backed by renewable energy of the sun. PowerLedger “ ” — Oil-backed. Venezuela’s economy has been plummeting. El Petro Diamonds are an easy-to-redeem commodity with a good potential of a soon-to-be unlocked market. Among commodities, diamonds are one of the most stable in value. While gold, silver, and other commodities are exposed to financial markets and speculators’ whims, diamonds have remained steady for over three decades (enjoying a positive appreciation). The STO Market Today With regards to these three types of security tokens, the majority of projects that offer the token is lacking in quality. There are several great examples of real asset tokens such as the project led by Inveniam Capital Partners to . However, buyers have to hold at least $10 million in Crypto to participate and purchase a minimum of $500,000 worth. It is clear that the world of security tokens are fundamentally geared more towards institutions. tokenize $260 million worth in real estate and debt transactions It will take a considerable amount of time to incorporate the true beauty of initial coin offerings into security tokens. Initial coin offerings democratize the fundraising process which was only open to larger institutions and accredited investor. Imagine a world where a student living in Argentina being able to own equities to a company based in Russia simply from their phones. But for now, the Argentinian student has to wait and proceed with the current . The STO market is definitely one to watch for in the next coming years as we attempt to revolutionize the financial markets. ways to invest in security tokens About me: Iliya Zaki is the Head of Marketing & Business Development for Moonwhale Ventures , an STO Advisory Firm based in the U.S. Moonwhale Ventures is an offering companies strategic advice on STO process & structure, as well as token issuance incl. lifecycle management and secondary market on-boarding for their projects. Moonwhale is also building an End-to-End Security Token Offering (STO) Investment Platform that will cater to investors looking to invest in STO projects, and to companies looking to raise capital through STO to finance business expansion or new ventures. STO Financial Advisory Follow us on our various social media platform. — | | | Twitter LinkedIn Facebook

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