Connect with us

Politics

How High-Tech Industry is Leveraging AI for Exponential Business Growth – ReadWrite

Published

on

Anas Baig


Artificial Intelligence and machine learning is paving their way in almost all industries. Its next target is the high-tech industry. The emergence of Artificial Intelligence in the engineering and mechanical world has raised many questions. What is the scope of Artificial Intelligence in the high-tech industry? Is it a good idea to invest in AI? Will AI replace the engineers? Is it easy for Artificial intelligence to overtake all the high-tech fields?

There is no doubt that Artificial intelligence is developing rapidly. It is capable of versatile applications and has marked remarkable changes in many industries. We have the example of Google, Amazon, and Facebook algorithms in front of us. But with the current developments in AI, it cannot overtake the high-tech mechanical and engineering industry anytime soon. It may modify the traditional tools of the industry, but it is useless without the human workforce.

In this article, we have evaluated the scope of Artificial Intelligence in the high-tech industry. We have also discussed the obstacles to adopting AI in the industry.

Scope of AI in High Tech Industry

AI is now part of almost every industry, including high-tech. It has made significant progress in recent years and seems to have a good scope in the tech field.

The most significant development of Artificial Intelligence is in the research field. Today, AI tools and software are much more efficient in holding the data and evaluating it. They help the researchers in making the most of the existing research. Researchers can now focus more on finding new solutions than investing time in extracting information from previous work with the help of AI.

Artificial intelligence can process and evaluate data much more quickly and efficiently than the human brain. It can hold much more data than traditional computing devices and process it in just a few seconds. Now whether you want to correlate the existing database with centuries-old data. Or need to drive results based on an extensive database, AI software and tools are there to assist you. They can serve as the efficient assistants of data analysts and may take over their roles one day.

AI tools and software work with much larger databases and are expected to make accurate judgments in most cases. For instance, a human brain may get confused in identifying a metal or chemical. But AI tools can detect it accurately and efficiently.

Similarly, fingerprint detection and face feature detection can be done swiftly and with lesser doubts using AI tools. Due to the accuracy of AI tools, it is assumed that Artificial Intelligence will replace many engineers and specialists in the future.

Though the scope of AI seems to be quite promising in the high-tech industry, there are some obstacles to adopting AI.

  • Higher Resource Consumption

All AI-based projects require a lot of time and investment. Industries and organizations need special hardware and software tools for executing the AI model. Furthermore, training the model is itself a very time-consuming and costly procedure.

Since AI experiments’ success rate is not promising, many investors are reluctant to invest their resources in such projects. Thus, the limitation of investment in high-risk AI projects is one of the major obstacles in adopting it in the high-tech industry.

Building AI-based hardware and training AI models is a very time taking and tedious process. It yields results at a slower pace.

Keeping in mind the faster pace of the high-tech industry, most AI machines and models become outdated even before their actual execution. This time-lapse between the idea and its execution is a hindrance in the way of developing AI.

AI tools and software are dependent on the data feed to them. They can only process and evaluate the data that is there in the system. Anything beyond the scope of existing information is beyond the capacity of AI tools as well. Moreover, it cannot detect errors in the data fed to it.

Therefore, any human error in feeding data can result in the failure of the whole AI model. Therefore, this data dependence is another major obstacle in its adoption in the high-tech industry.

High tech industry demands quick and efficient decision-making. Unfortunately, though AI tools can make fast and efficient judgments in many situations, they lack creativity.

No AI tools, to date, can take abstract decisions based on the scenarios like a human mind can do. No doubt AI tools are versatile, yet they are far behind the creative capacity of the human brain.

Wrapping Up

Artificial Intelligence seems to have a good scope in the high-tech industry, especially in the telecommunication and computing fields. But it still has a long way to go before being adopted in the biotechnical and engineering fields.

AI is a high-risk investment. And reluctance in adopting AI is a significant barrier in the way of its development and progress.

Image Credit: provided by the author; thank you!

Anas Baig

Product Lead

With a passion for working on disruptive products, Anas Baig is currently working as a Product Lead at the Silicon Valley based company – Securiti. He holds a degree of Computer Science from Iqra University and specializes in Information Security & Data Privacy.

Politics

Fintech Kennek raises $12.5M seed round to digitize lending

Published

on

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.

Continue Reading

Politics

Fortune 500’s race for generative AI breakthroughs

Published

on

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.

Continue Reading

Politics

UK seizes web3 opportunity simplifying crypto regulations

Published

on

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.

Continue Reading

Copyright © 2021 Seminole Press.