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Introducing AI to IoT Data Analytics – ReadWrite



Nora Leary

The availability of high-speed internet connectivity has transformed the way we interact with and benefit from technology. The ability to send and receive vast amounts of data quickly and reliably supports the expanding Internet of Things (IoT).

All devices are gathering data — non-stop.

Google Home, Alexa, Furbo, and Ring are just a few of the players that make up the realm of Internet-enabled devices. These home-based brands, smartphones, sensors, and wearable devices — among many others — all gather valuable data analytics that acts as the foundation for future decision-making.

However, to extrapolate these insights, we must first analyze data.

While legacy systems can adequately handle this task, new solutions are necessary to support the IoT’s rapid expansion. Estimates suggest there will be 1.3 billion IoT device subscriptions by 2023 and 35 billion IoT devices installed worldwide by 2021.

The arrival of 5G cellular networks will further amplify the data generated by this growing technology pool. But what’s the most appropriate solution for handling all of this information?

Data Analytics and Artificial Intelligence

For many, the answer is artificial intelligence (AI) — a term now synonymous with the concept of machines carrying out tasks in a way humans deem intelligent. Machine Learning (ML), a subset of AI, can generate even more value as machines learn for themselves instead of relying on a preprogrammed algorithm.

Given the vast pools of IoT data, leveraging the power of ML is now a real possibility.

Forecasts suggest the world’s data will amount to around 44 zettabytes by 2020, with 10% coming from IoT. This database provides an ample supply of reference material.

Data analysis is sped up dramatically through ML or AI algorithms, which benefits all of those looking to extrapolate insights — consumers, businesses, and governments. The resulting Artificial Intelligence of Things (AIoT) accelerates decision-making and bolsters valuable information exchange.

However, there are specific ways in which the merger of these technologies delivers such results.

How AI Handles Data

Conventional data analysis facilitates IoT deployment, but AI can do it faster and with greater accuracy. More specifically, AI can structure a data set, improve IoT device interoperability, and draw conclusions in real-time.

Unstructured Data: The IoT ecosystem is diverse, which means the format of data is too. In contrast to many existing data analysis techniques, AI algorithms can save valuable time by aggregating unstructured data from multiple sources, processing it, and representing it in a cohesive format. Making this process less cumbersome offers an immediate benefit and allows stakeholders to take action faster.

Metadata: Metadata is data about data and enables IoT devices to communicate with one another. For instance, metadata might include the model number of one device, which tells another which communication protocol to use and organizes the resulting data. Here, AI might also contribute to the organization of data analytics while streamlining interoperability through its learnings.

Transformed Data: After AI processes unstructured data, systems can draw further insights. While traditional data analysis achieves the same outcome, AI or ML hold the potential to deliver this information dynamically and with greater context and even in real-time. This functionality expands the potential applications of IoT.

The Current AIoT Ecosystem

Today, several examples of companies are entering the AIoT space — an industry that’s estimated to reach a value of $5.7 billion globally by 2025. In a recent development, the Honeywell Connected Life Safety Services (CLSS) was launched as a commercial fire safety solution. The cloud platform transforms the way fire systems are designed, commissioned, monitored, and maintained.

The system’s IoT components generate constant feedback that AI processes to provide actionable insights and informed recommendations.

Honeywell defines this category as enterprise performance management (EPM) and has recently entered a partnership with Microsoft to bolster its efforts. Microsoft has also put together an independent team that explores the integration of IoT and AI to provide greater visibility and better control of internet-enabled devices and sensors.

Integrations of the Future

Although traditional IoT solutions continue to generate immense value, the next iteration of this technology expands on system monitoring and data collection.

Through the integration of AI and IoT, real-time data synthesizing is possible. AI and ML technologies hold the potential to process vast amounts of data quickly while structuring data and improving interoperability.

The merger of these technologies will facilitate the decision-making necessary to support smart cities of the future while accelerating digital transformation. The resulting benefits will dramatically impact the way consumers, businesses, and governments operate as real-time data is leveraged to add a new dimension of logic.

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Nora Leary

Growth Director

Nora Leary is the Growth Director at Ironpaper, a B2B growth agency that focuses on lead generation and sales for B2B companies that have a long sales cycle.


Fintech Kennek raises $12.5M seed round to digitize lending



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 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.

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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



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



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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