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How to use Machine Learning for IoT Analysis – ReadWrite

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How to use Machine Learning for IoT Analysis - ReadWrite


Machine Learning and the Internet of Things (IoT) have been the buzzwords for the decade. These technologies find application in almost all industries, from enabling artificially intelligent powered digital assistants to the supply chain’s automation. They have revolutionized not only how we interact on social media but also how we pay the bills. Here is how to use Machine Learning for IoT Analysis.

Looking at the google trends analysis below, one can be sure that these technologies offer a lucrative career, so many people are interested in learning about them.

You already know what Machine Learning and IoT are.

Machine Learning is the process of getting computers to learn and act as humans & automatically improve with experience, without explicitly programming it. On the other contrary, the Internet of Things refers to a system of internet-connected objects that can communicate over wireless networks.

Now, it is exciting to note that the base of both these technologies is ‘Data.’ IoT devices generate a lot of data, which may seem useless to us, but this is where the role of Machine Learning comes into the picture.

How Can Machine Learning be Applied to IoT?

Talking about data analytics, Predictive and Prescriptive Analytics both utilize machine learning and find application in the world of IoT.

  • Predictive Analytics uses different statistical and Machine Learning Models to predict future outcomes based on past data.
  • For example, in smart lighting systems, the sensors can collect information about illuminance, movement of people and vehicles and public transport schedule, time of the day, year, etc. Based on the data received coupled with the historical data, the Machine Learning Algorithms can predict the appropriate lighting based on the conditions & this will enable the city administration to cut down their electricity costs.
  • Prescriptive Analytics uses a combination of business rules, computational modeling, and Machine Learning to roll out individual recommendations to a user for any pre-specified outcome.
  • SmartWatch using a wide range of sensors is an example of Prescriptive Analytics. The watch would record all your information and utilize machine learning models to roll out individual recommendations for you and alert you when it finds an abnormality in the reading.

Tesla Vehicles have always been in the news and even more so now. Probably it is a dream car for many of us. It is a pioneer in technology & they also have rolled out the concept of ‘Self Driving Mode’ on a pilot basis in some of their vehicles.

Have you ever imagined how these Self Driving Cars work? These vehicles have many sensors like lidars, radars, cameras, IoT devices that communicate with each other and send out the data in the form of images and numerical values to a dedicated server.

Based on the data received, various Deep Learning models like Convolutional Neural Network and VGG16 are applied to make the car learn automatically and improve over-time with experience.

Benefits of Using Machine Learning for IoT Data Analysis

  1. Machine Learning can be used to identify patterns in data and make real-time predictions. For example, it can help create a better user experience when coupled with appliances like Air Conditioning. The machine learning models can learn from the past data at what temperatures you are more comfortable with.

It can automatically optimize the room temperature according to your requirements when returning home from work by utilizing past data and current temperature.

2. Machine Learning and IoT can automate some industrial processes and ensure worker safety in hazardous areas by using IoT, and Machine Learning enabled instruments to monitor and optimize processes.

3. IoT Analysis helps in taking cost-saving measures in Industrial Applications. We are now done with the old school concept of ‘Scheduled Maintenance,’ and we are now looking forward to reducing the surprise downtime using Predictive Maintenance.

The problem with Scheduled Maintenance is that the production halts when the machine breaks down, resulting in a substantial revenue loss. It is also possible that while doing the maintenance, some parts which were working perfectly before were removed and exchanged with the new parts. It results in an overhead expenditure which no business person would find suitable in the right mind. This is where IoT is looking to cut the cost of Industrial Applications.

Sensors

Modern machines now use sensors that monitor a wide variety of data, including usage, uptime, energy consumption, and a log of system disruptions. In case of a problem, t the historical data coupled with the predictive analysis done by Machine Learning Models notifies the concerned person about the life cycle of the component and how the quality of the production due to the faulty component.

4. IoT and Machine Learning together can help in efficient Risk Management. Machine learning can be used to predict risks by utilizing past data and automate responses to this risk.

5. You can achieve process efficiency by utilizing Machine Learning along with IoT. Machine Learning models can optimize a process to maintain the desired output utilizing data from the past to adjust parameters in real-time. For example, In the case of a Smart Traffic Management System, CCTV Cameras fitted on the top of traffic lights can capture real-time images and, based on the Algorithm it is trained on, can detect whether a road is congested or not.

At the same time, this information can be intimated to the citizen and suggest a better route to reach their destination.

An automated robot arm machine in a smart industrial factory with tablet real-time process control monitoring system application. Source – Canva.com

Security

The grass is always greener on the other side. While we have talked a lot about IoT’s advantages and how fantastic it is, there is a definite question mark in the form of its security.

A report published by Thales Group, one of the leaders in Cyber Security, says that 90% of the consumers lack confidence in IoT Devices’ security. Moreover, about 63% of the users from the developed world have termed these devices as ‘creepy.’ With increasing Data Breach cases reported now and then, the end-users are even more worried about whether their data is misused or not.

IoT Devices contain a lot of personal information and even the slightest breach might mean all your data is compromised. Therefore, there is an ever-increasing need to make these smart devices even more secure.

The first step for any IoT business is to undergo a thorough security risk assessment that examines vulnerabilities in devices and network systems and user and customer backend systems.

To address these security challenges, IoT devices and manufacturing companies should have a solid strategy.

Conclusion:

We have thus seen how the combination of ML & IoT is changing our lives and we expect to see some of the more technological advancements in this field. We also discussed the advantages and some challenges faced in implementing Machine Learning to IoT devices.

Soon, using IoT and ML, we might predict unfortunate events like train crashes and crimes even before they happen. These technologies are, for sure, opening the door to boundless opportunities.

Image Credit: andrew neel; pexels; thank you!

Katrine Osborne

Katrine Osborne is a head of the content department in an Artofvisualization. She is fond of technologies and programming and has experience working as a Data Scientist. She is a persistent traveler and dedicated to her family, work, and friends.

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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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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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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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