17
Apr
/
Building Ethical AI: A Guide for Developers on Avoiding Bias and Designing Responsible Systems
The intersection of philosophy and artificial intelligence may seem obvious, but there are many different levels to be considered. We talked to Katleen Gabriels, Assistant Professor in Ethics and Philosophy of Technology and author of the 2020 book “Conscientious AI: Machines Learning Morals”. We asked her about the intersection of philosophy and AI, about the ethics of ChatGPT, AGI and the singularity.
19
Feb
/
OpenAI Embeddings
Embedding vectors (or embeddings) play a central role in the challenges of processing and interpretation of unstructured data such as text, images, or audio files. Embeddings take unstructured data and convert it to structured, no matter how complex, so they can be easily processed by software. OpenAI offers such embeddings, and this article will go over how they work and how they can be used.
2
Feb
/
Address Matching with NLP in Python
Discover the power of address matching in real estate data management with this comprehensive guide. Learn how to leverage natural language processing (NLP) techniques using Python, including open-source libraries like SpaCy and fuzzywuzzy, to parse, clean, and match addresses. From breaking down data silos to geocoding and point-in-polygon searches, this article provides a step-by-step approach to creating a Source-of-Truth Real Estate Dataset. Whether you're in geospatial analysis, real estate data management, logistics, or compliance, accurate address matching is the key to unlocking valuable insights.
12
Oct
/
What is Data Annotation and how is it used in Machine Learning?
What is data annotation? And how is data annotation applied in ML? In this article, we are delving deep to answer these key questions. Data annotation is valuable to ML and has contributed immensely to some of the cutting-edge technologies we enjoy today. Data annotators, or the invisible workers in the ML workforce, are needed more now than ever before.
14
Sep
/
Neuroph and DL4J
In this article, we would like to show how neural networks, specifically the multilayer perceptron of two Java frameworks, can be used to detect blood cells in images.
20
Jul
/
Top 5 reasons to attend ML Conference
So you’ve decided to attend ML Conference but you don’t know how to break it to your boss that it is a win-win situation? Don’t worry, we’ve got you covered. Follow 4 simple steps and use these 5 arguments to show why your organization needs to invest in ML Conference!
9
Jun
/
Anomaly Detection as a Service with Metrics Advisor
We humans are usually good at spotting anomalies: often a quick glance at monitoring charts is enough to spot (or, in the best case, predict) a performance problem. A curve rises unnaturally fast, a value falls below a desired minimum or there are fluctuations that cannot be explained rationally. Some of this would be technically detectable by a simple automated if, but it's more fun with Azure Cognitive Services' new Metrics Advisor.
26
May
/
Tools & Processes for MLOps
Training a machine learning model is getting easier. But building and training the model is also the easy part. The real challenge is getting a machine learning system into production and running it reliably. In the field of software development, we have gained a significant insight in this regard: DevOps is no longer just nice to have, but absolutely necessary. So why not use DevOps tools and processes for machine learning projects as well?
16
Feb
/
Understanding Language is a Frontier for AI
In recent years we have seen a lot of breakthroughs in AI. We now have deep learning algorithms beating the best of the best in games like chess and go. In computer vision these algorithms now recognise faces with the same accuracy as humans. Except they don’t, they can do it for millions of faces while humans struggle to recognize more than a few hundred people.