dan fiehn
dan fiehn

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This week, how to improve Artificial Intelligence in the future.

 

Welcome to The Digital Eye, your weekly roundup of the latest technology news. 

Our team of experts have scoured the internet for the most exciting and informative articles so that you can stay up-to-date on all things digital, data, blockchain, AI & analytics.

Is Innovation Fundamentally Slowing?

AI is currently enjoying a heyday, but is innovation slowing?

Key findings

  • Economist Impact’s linguistic analysis of AI research finds that several key concepts underpinning today’s AI applications emerged and were developed in a period typically considered an “AI winter” of stagnant interest and investment.

 

  • Linguistic diversity in academic literature, a proxy for innovation intensity, has waned since 2010 while patenting activity has increased. Findings suggest that AI investment is increasingly concentrated in a narrowing field of commercial applications, which may come at the expense of more exploratory and foundational research.

 

Running multiple APIs side-by-side with AI paves way to hyper-automation

This article was contributed by Archil Cheisvili, CEO of GenesisAI.

For business, time and money are precious commodities. The rise of the Application Programming Interface, better known as APIs, has streamlined business operations and created a better customer experience. This kind of automation saves businesses both time and money, but also provides valuable data and an improved user experience.

From chatbots to checkout, APIs have become a critical part of running a business in a digital world. Yet, while technology has brought millions of applications to the market built to help businesses improve their operations, the problem is that they often must use three to five APIs separately yet together to get the information and process flow they need for their business.

Screens over shovels: Using digital technology to modernise public works

A new report shows there are more sustainable and efficient ways to maintain public infrastructure.

As people move from rural to urban areas, expanding cities are pressed to consider new ways of managing labour-intensive public works, such as building and maintaining roads, drains, streetlights, and solid waste management services. How can local governments manage sprawling infrastructure, when the need to boost resilience to natural hazards and climate change demands collecting and maintaining detailed, up-to-date geographic data about critical infrastructure?

 

In Digital Works for Urban Resilience: Supporting African Youth, our team reports on ways that cities can use digital technology to maintain public works in more efficient, cost-effective, and gender-inclusive ways. Using digital data could also help to close the digital divide, especially for youth. Funded by the Global Facility for Disaster Reduction and Recovery, the report shows how seven pilot projects used digital technology to test a new data- and technology-driven workflow to modernise public works.

 

    Harnessing Randomness in Machine Learning

    How “random” should random be?

    The Challenges of Creating Features for Machine Learning

    What are the challenges of creating features for machine learning and how can we mitigate them.

    When I decided to leave academia and re-train as a data scientist, I quickly found out that I had to learn R or Python, or well… both. That’s probably the first time I heard about Python. I never imagined that 3 years later I would be maintaining an increasingly popular open source Python library for feature engineering: Feature-engine.

    In this article, I want to discuss the challenges of feature engineering and selection both from the technical and operational side, and then lay out how Feature-engine, an open source Python library, can help us mitigate those challenges. I will also highlight the advantages and shortcomings of Feature-engine in the context of other Python libraries. Let’s dive in.

     

    Featured Article

     

    How to Improve Artificial Intelligence in the Future

    Computer scientists at the University of Essex have devised a radically different approach to improving artificial intelligence (AI) in the future.

    How to Improve Artificial Intelligence in the Future
    Article by @EurekAlert

     

    Computer scientists at the University of Essex have devised a radically different approach to improving artificial intelligence (AI) in the future.

    Published in the top machine learning journal – the Journal of Machine Learning Research – the Essex team hope this research will provide a backbone for the next generation of AI and machine learning breakthroughs.

    This could be translated to improvements in everything from driverless cars and smartphones having a better understanding of voice commands to improving automatic medical diagnoses and drug discovery.

     

    THE DIGITAL EYE

    Agile Project Failure: The Hidden Cultural Barrier Undermining Success

    Despite being a well-established methodology, agile project failure continues to plague organisations, with success rates hovering around 42%. But is the problem really with the process? This week’s feature article uncovers the hidden cultural issues stifling true agility, revealing why many agile projects fall short of their potential and how a shift in mindset could be the key to success.

    How to Maximise Knowledge Transfer Between Business and Engineering Teams

    In this week’s feature article, discover how to maximise knowledge transfer between business experts and technical engineers to ensure the success of your digital transformation. Learn about creating structured knowledge transfer plans, fostering a learning environment, employing diverse transfer methods, and building trust and collaboration. These strategies will help you bridge knowledge gaps, boost innovation, and enhance operational efficiency. Dive into our expert insights to master the art of seamless collaboration and drive your organisation’s digital transformation forward.

    Strategic Planning Approaches: Top-Down vs. Bottom-Up

    Unravel the complexities of strategic planning with our latest exploration into Strategic Planning Approaches. Whether you’re leaning towards a top-down, bottom-up, or sideways strategy, our insight will guide you through the pros and cons of each method, helping you make informed decisions that align with your business objectives. Perfect for strategists and business leaders aiming to optimize their planning process and drive effective outcomes.