Digital processing technologies We live in an analogue world, but more and more, digital processing is changing the ways in which we interact with and experience the world. Whether through satellite navigation or autonomous vehicles, … Read more
AI/ML
From the early pioneers of AI to the latest advancements in machine learning, this collection is your gateway to understanding how AI is transforming our world and propelling us into a future where the boundaries between humans and machines continue to blur.
Cyber and Data AI
Artificial Intelligence is a very powerful tool that can be used to boost your capabilities and your business. It can be used to optimize existing patterns in data and processes, or to relentlessly explore proactive approaches to finding solutions … Read more
How to Implement AI Predictive Maintenance on Edge Devices (Case Study)
Predictive maintenance helps anticipate when maintenance should be performed on machinery. In many industries, this approach uses AI and machine learning techniques, which in turn need to be run effectively on specific devices. Recently, Capgemini … Read more
How to Optimise Your IoT Device’s Power Consumption
Long battery life is a frequent concern of people purchasing Internet of Things (IoT) products — especially those for remote or harsh environments. That’s an understandable concern. Excessive power consumption could negatively impact the … Read more
How to Implement Data Version Control and Improve Machine Learning Outcomes
It may surprise you to hear this, but machine learning is in something of a crisis. In recent years, machine learning researchers have found it increasingly difficult to reproduce the findings made by algorithms. A key problem that has been … Read more
The State of AI in 2021
We know that technology tends to move in hype cycles, where certain topics and terminology are bandied around in press releases, news articles, research, and funding announcements. These include Blockchain, ICO, IoT, as well as AI, Machine Learning … Read more
The Rise of Machine Learning at the Network Edge
Over the past few months, I have shown you why machine learning at the network edge is so essential. In this final article, we look back at why machine learning is needed and look at some of the real-world cases where it has already been … Read more
8 Techniques for Efficient Data Cleaning
Data is an essential part of data analytics, data security, and data science. That’s obvious. Sometimes, however, that data can get a little dirty. No, not like in a gangster film. More like where suddenly we are having to deal with ‘dirty data’ … Read more
5 Ways To Grow Your Business Through Data Monetization
Earning revenue through big data is a fast-growing industry that all businesses need to be a part of. With more than 2.5 quintillion bytes of data created every day, a good data monetization strategy is crucial for any business, including those … Read more
7 Key Differences Between Data Analytics and Data Mining
With every second we spend online, mountains of data is generated. For every social media post, Google search, and link clicked, there is a way in which our activity can be collected for data. Experts within the data science field can utilize this, … Read more
The Future of Machine Learning at the Edge
Over the last few months, I have shown you how Machine Learning at the edge is improving our lives. I also shared some practical examples to give you a chance to try it for yourselves. In this article, I look at the current limitations of edge ML and … Read more
5 Rules of Engagement When it Comes to Data Visualization
So you’ve been out, you’ve conducted the surveys, you’ve collected a giant pot of data that unequivocally demonstrates your position and now it’s time to present. How do you do it? Well you could just dump the raw spreadsheet at the desk of your … Read more
Ludwig Toolbox Makes Deep Learning Accessible to All
The last decade has seen exponential growth in deep learning capabilities and their application in research and development. Traditionally deep learning as a discipline has been limited to those with considerable training and knowledge of machine … Read more
AI Ladder: the IBM Approach to Artificial Intelligence
TIME TO UPGRADE YOUR APPLICATION ESTATE?Tackle Modernization and Cloudification - on November 19th 2020, 10:00 am (GMT+1)The Cloud and AI Forum is an opportunity to learn how Italian Executive & Technical Leaders introduced data efficiency, … Read more
Questions and Answers in Virtual Assistants
The typical customer care virtual assistant (aka chatbot) can answer simple questions and maybe even perform some actions. But we need chatbots that really help solve customer problems instead of disappointing them with a “Sorry, I don't know.” When … Read more
The Challenge of Ethics in AI
We often hear the notion of ethics in AI thrown around with research such as autonomous cars and 'the trolley problem. An example is the creation of MIT's The Moral Machine, a platform for gathering a human perspective on moral decisions made by … Read more
Want to Learn about Genetic Algorithms? Start Life Hacking
It’s often hard to motivate ourselves to learn new tech - whether a new coding language or algorithm - when it’s not essential to our job. Tech not only moves fast, but it’s easy to get overwhelmed by the plethora of documentation, tutorials, and … Read more
What Are the Main Areas of Development for a Data Analyst’s Career?
Data analysts manipulate data and use it to help organisations make wise business decisions. They gather information from the data regarding specific aspects, interpret, analyse and share their findings with other stakeholders in the organisation … Read more
Seeing Is Believing: Image Recognition on a €10 MCU
This series is exploring the rationale for moving machine learning to the network edge. This article looks in more detail at image recognition, one of the prime use cases for ML at the edge. As explained in previous articles, there are many use … Read more
Exploring LIME Explanations and the Mathematics Behind It
The expansion of artificial intelligence (AI) relies on trust. Users will reject machine learning (ML) systems they cannot trust. We will not trust decisions made by models that do not provide clear explanations. An AI system must provide clear … Read more