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The Role of Machine Learning in Data Science: Main Use Cases

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Ever wondered how machine learning in data science can come in handy? This informative article will be your in-depth guide into the complex worlds of data science and machine learning. Below you’ll read about the most common and significant ML  applications and the challenges of machine learning in data science.

Photo by Markus Spiske: https://www.pexels.com/photo/codes-on-tilt-shift-lens-2004161/

What is Data Science?

Back in the time, businesses collected all types of data in Microsoft Excel, and that was enough. Now, the complexity of data is gradually rising; in fact, according to Forbes, nearly 2.5 quintillion bytes of data are generated every single day. Many businesses and organizations start integrating artificial intelligence into their work pipeline to stay ahead of the competition curve. 

Data science is the process of collecting, analyzing, and modeling the data received to identify trends and patterns and extract key insights to benefit the company. For instance, AI-powered recommendation systems can analyze the data representing users’ preferences and make tailored recommendations. 

What is Machine Learning?

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that is able to make predictions by learning and evolving upon past experiences and data. It uses algorithms that allow engineers to conduct statistical analyses and draw out patterns in the data. The data can be used in different areas – AI applications are used in a number of important industries, such as insurance, autonomous vehicles, finance and banking, cybersecurity, agriculture, sports, healthcare and telehealth, and many more.

 

The Role of Machine Learning in Data Science

Machine learning operates on data, and the performance of ML algorithms depends directly on the data quality and quantity of the training data. However, in its turn, modern machine learning technologies enhance and facilitate the data science processes. ML and AI now dominate the field, replacing other Data Science techniques such as ETL and data analytics. ML algorithms use the data by first analyzing chunks of it, then sorting it out into categories or a specific order, and finally making predictions without human intervention. Below we’ll present some of the most commonly used machine learning techniques that are heavily used in data science.

 

Machine Learning Applications in Data Science

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Image recognition is one of the most prominent and common applications of machine learning in data science. It allows machines to identify objects, humans, and other characteristics in images. Due to a computer vision technique called object detection computers can analyze vast amounts of data, and categorize it based on specific properties. This allows computers to understand the context or further analyze the image and draw conclusions from it. In turn, it allows automation of the business processes, as based on the recognized item, a respective action can be performed.

 

Speech Recognition includes the process of translating verbal discourse into written text. With a simple click, one might exchange words, syllables, and other characters in the text. Various speech recognition algorithms come together to interpret and process human speech, ensuring a low error rate. This opens up a whole new level of data analysis and benefits businesses with a demand for communication investigation, such as call centers, customer support departments, and so on. 

  • Recommendation Algorithms

Online recommendations become increasingly popular with each new online application. Recommendation algorithms allow engines to analyze the users’ experience and, based on the data, make relevant recommendations for the users. Machine learning models observe customer behavior, past purchases, and history to collect relevant data. Recommendation models are especially useful for the product, marketing, customer support, and success teams. On the user side, eCommerce companies are a great example of online recommendation engines with product suggestions for users. 

Fraud detection has become much easier with the help of machine learning. The models are trained on data to automatically block or allow certain user actions, such as suspicious transactions in the banks, logins into various accounts, and even identifying thefts. Machine learning offers faster detection, reduced manual work, better predictions, and effective solutions. 

 

Main Challenges Machine Learning Poses in Data Science

 

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Machine Learning has now been in Data Science for many years and has revolutionized the field entirely. Although ML can be highly beneficial in most cases, there are still some challenges that need to be addressed. 

The foundation of every ML model is collecting necessary and reliable data, which, unfortunately, is quite difficult and expensive today. The thing is that almost every company and data scientist has tons of data about almost everything, fluffy stuff included. The abundance of this useless data creates confusion and extends the period to find specific information. Data scientists have to spend hours analyzing and processing the available data and extracting the important ones from the whole mess. 

Even if the required data is there, accessing it can still be a bottleneck. Because of the recently increased cyberattacks, more and more organizations are aware of the risks and are taking preventive measures to protect their data. This, of course, is an important factor if we consider privacy concerns, but this also becomes a huge barrier for all the data scientists and experts who try to reach out to the exact dataset that they need.  

The bad news is that the trained model is not 100% reliable as it has differences between the trained and produced data. The outcome of the trained model may not be as expected and can easily come down based on various factors such as location, mobile device, and even seasonal changes. This is when you should be more attentive and spend enough time regularly updating and improving the model to avoid this challenge as much as possible. 

Machine learning algorithms can function separately without human intervention. This is true, but only partly. However advanced the algorithm becomes, we still need programmers and data scientists  to fuel the algorithms to continue creating favorable results. At this point, relying completely on machine learning may not completely solve the problem.

 

Final Thoughts

In a nutshell, machine learning allows today’s data scientists to gather and analyze vast amounts of data for actionable insights. In addition, it allows businesses to make precise predictions and recommendations. This becomes possible by analyzing historical data and utilizing it in various fields. Machine learning is especially useful for data science when large amounts of data need to be analyzed and categorized. Some common use cases of machine learning in data science include image and speech recognition, relevant online recommendations, and even fraud detection. Though machine learning engineers still may struggle with finding relevant data, accessing private data, or failing to build a model as expected, the technology is revolutionary. With no doubt, machine learning is the best solution for analyzing high volumes of data and offering solutions to increase productivity. 

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Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Politics

Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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Politics

UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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