In the second article in this series co-produced with Mouser Electronics, we saw how to implement embedded processors with programmable logic devices. Now, let’s see how vendors have lowered programmable logic tools' learning curve. Design … Read more
Machine Learning
Bring More AI/ML Projects to Production by Increasing Access to Primary Data
Building a machine learning model is simple: you collect data, split them into datasets, feed the model with data from an open-source framework, refine the model, retest it, and hey presto!, magic in the making - right? Well, if that’s your … Read more
How Augmented Reality (AR) Enables Remote Troubleshooting
Thinking about a production chain within big industry, it is easy to imagine how many machines must work together to provide high productivity levels. In such contexts, a single faulty mechanism can have a huge impact on the whole chain, causing … 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 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 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
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
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
Voice Control: Building Your Voice Assistant
Voice control was the stuff of science fiction throughout the 20th Century. But in the last two decades, voice control has entered the mainstream. Voice assistants like Siri and Alexa are embedded in home devices, headphones, and even … 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
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
How AI Can Help Solve the Challenges of Economic and Financial Inequality
Table Of ContentsWhy AI for tax policies?Economic AI Simulation as a Learning EnvironmentCritical collaboration in response to social and economic challenges Disciplines such as machine learning, data science, and AI are heavily steeped in … Read more
ML at the Edge: a Practical Example
Machine learning is the primary methodology for delivering AI applications. In previous articles, I discussed the main reasons behind moving machine learning to the network edge. These include the need for real-time performance, security … Read more
Google AI Hub: what, why, how
Artificial intelligence (AI) and machine learning (ML) increasingly seem to be indispensable tools that developers need to be able to handle. There are many ways these tools can be put to use, applied to applications and products. In research and … Read more
Getting started with edge machine learning
In this six-article series from Mouser Electronics, we explore why AI is moving to the network edge and the technology that’s making it possible. The first article examined why AI needs to move to the edge. Here, we dive in to the hardware and tools … Read more
The cutting edge of real-time AI
Machine learning is traditionally processor-intensive; ML algorithms require large numbers of parallel operations. As a result, the models usually run in data centres at the core of the network. However, this has a direct impact in terms of latency, … Read more
Machine Learning and the Digital Transformation of Banking
In preparation for Codemotion Deep Learning conference, we had spoken to one of the speakers, Manuel Morales. He wears a variety of hats in machine learning. Since 2018, he has been General Director of the FinML Network. He is currently an … Read more