Goto

Collaborating Authors


Make predictions with AutoML ONNX Model in .NET - Azure Machine Learning

#artificialintelligence

In this sample, the pipeline is defined and used within the same application. However, it is recommended that you use separate applications to define and use your pipeline to make predictions. In ML.NET your pipelines can be serialized and saved for further use in other .NET end-user applications. ML.NET supports various deployment targets such as desktop applications, web services, WebAssembly applications*, and many more. To learn more about saving pipelines, see the ML.NET save and load trained models guide.


Designing a Python interface for machine learning engineering

#artificialintelligence

In order to do machine learning engineering, a model must first be deployed, in most cases as a prediction API. In order to make this API work in production, model serving infrastructure must first be built. This includes load balancing, scaling, monitoring, updating, and much more. At first glance, all of this work seems familiar. Web developers and DevOps engineers have been automating microservice infrastructure for years now.


Tutorial: Get started with machine learning - Python - Azure Machine Learning

#artificialintelligence

This tutorial series focuses on the Azure Machine Learning concepts required to submit batch jobs - this is where the code is submitted to the cloud to run in the background without any user interaction. This is useful for finished scripts or code you wish to run repeatedly, or for compute-intensive machine learning tasks. If you are more interested in an exploratory workflow, you could instead use Jupyter or RStudio on an Azure Machine Learning compute instance.


Can Auditing Eliminate Bias from Algorithms? – The Markup

#artificialintelligence

For more than a decade, journalists and researchers have been writing about the dangers of relying on algorithms to make weighty decisions: who gets locked up, who gets a job, who gets a loan--even who has priority for COVID-19 vaccines. Rather than remove bias, one algorithm after another has codified and perpetuated it, as companies have simultaneously continued to more or less shield their algorithms from public scrutiny. The big question ever since: How do we solve this problem? Lawmakers and researchers have advocated for algorithmic audits, which would dissect and stress-test algorithms to see how they work and whether they're performing their stated goals or producing biased outcomes. And there is a growing field of private auditing firms that purport to do just that.


10 Must Read ML Blog Posts

#artificialintelligence

I have been doing NLP/ML research for the last 6 years. I have come across a lot of machine learning resources and papers. Today, I kept thinking about the machine learning / NLP / deep learning related blog posts (not papers) that have been transformational for me. In this blog post, I provide a short collection of a few high-impact blog posts that come to mind. This post was originally a Twitter thread.


Sailing without a crew: Saildrone aiming to replace manned ships on mapping expeditions

#artificialintelligence

They are also using machine learning to help the Surveyor sense and respond to its surroundings when it's out at sea. "We have to make sure the sonar …


'Black Swan event has rewritten global business rules'

#artificialintelligence

… are ensuring that our team in India is future-ready and is constantly leveraging newer technologies including machine learning, artificial intelligence, …


AI Content Creator: 5 Ways it Helps Your Blog Grow

#artificialintelligence

Every blogger wants their blog to rank in the top position in Google search results since users commonly select results contained on the first page, especially those in one of the top 3 positions, as you can see in the graphic below. And, for years, the Google search algorithm made content king. This explains why companies invest more into content creation, with 24% of marketers planning to increase their budget for content marketing from 2020 levels. But, content creation is expensive; costing between $2000 and $10,000 a month for the average SME (small and mid-sized enterprise). If you want to get those costs down, consider using an AI-fueled content creator to make your job efficient at a lower cost. Moreover, if you write the content yourself, or you hire a writer to create content, it doesn't take long before you run out of topic ideas.


This Week's Awesome Tech Stories From Around the Web (Through February 27)

#artificialintelligence

An Extinct Cave Bear's DNA Was Still Readable After 360,000 Years George Dvorsky Gizmodo "The bone analyzed in the new study--a petrous bone from the inner ear of an extinct cave bear--was approximately seven times older than any the team had studied before, 'showing that genome data can be recovered from temperate zone samples spanning more than 300 millennia,' [Axel Barlow] said. Indeed, older DNA samples exist, but they were all sourced from fossils found in permafrost, like the astounding million-year-old mammoth teeth that recently made headlines." Some, such as mRNA vaccines, are already changing our lives, while others are still a few years off. Below, you'll find a brief description along with a link to a feature article that probes each technology in detail. We hope you'll enjoy and explore--taken together, we believe this list represents a glimpse into our collective future." She Beat Cancer at 10. Now She's Set to Be the Youngest American in Space. Arceneaux, a physician assistant at St. Jude Children's Research Hospital in Memphis, will be one of four people on a SpaceX Falcon 9 rocket lifting off from Florida. Scheduled to launch late this year, it is to be the first crewed mission to circle Earth in which no one on board is a professional astronaut."


IBM Watson: Why Is Healthcare AI So Tough?

#artificialintelligence

UKRAINE - 2021/02/19: In this photo illustration an IBM logo is seen on a smartphone screen. A pivotal event for AI happened when IBM's Watson beat two all-time champions of Jeopardy! in 2011. This showed that the technology was far from being experimental. IBM would soon go on to make Watson the centerpiece of its AI strategy. And a big part of this was to focus on healthcare.