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
Machine Learning
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
TensorFlow Furthers the Development of Machine Learning
In preparation for his talk at the upcoming Codemotion Deep Learning virtual conference next week, I spoke to Luiz GUstavo (Gus) Martins, TensorFlow Developer Advocate, at Google. He explained that his main role is to help … Read more
The Evolution of Conversational AI According to Rasa
If you're looking for a very accessible entry into the why's and how's of AI, conversational AI is a great starting point. In preparation for our Deep Learning Conference (hey, if you are thinking of organising your first online vent we have shared … Read more
How to teach Alexa to pronounce your name
Deep learning promises to deliver a true revolution in how we tackle complex problems. But to most people, deep learning seems like some arcane dark science. However, it is one of the key planks that enabled voice assistants, such as Alexa, to get so … Read more
How to build a GAN in Python
Introduction Generative Adversarial Networks (GANs) are a hot topic in machine learning for several good reasons. Here are three of the best: GANs can provide astonishing results, creating new things (images, texts, sounds, etc.) by imitating … Read more
IBM Think Digital 2020: AI for Enterprise and the Value of Language
IBM recently hosted Think Digital 2020, a series of fascinating talks, presentations and interviews all facilitated remotely during the COVID-19 stay at home order. The two-day event was an opportunity to discuss the state of business and the … Read more