Collaborating Authors

Machine Learning

How Google Might Rank Image Search Results - SEO by the Sea


We are seeing more references to machine learning in how Google is ranking pages and other documents in search results. That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind. It's still worth considering some of those older ranking signals because they may play a role in how things are ranked. As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images. Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.

How COVID-19 sparked a revolution in healthcare machine learning and AI – IAM Network


In the past six months, COVID-19 has evolved from a speck on the world radar to a full-blown pandemic. While it has claimed the lives of many and shed a massive spotlight on some of the major issues in healthcare, it has also served as a catalyst for innovation. As with nearly every element of the healthcare system, applications of machine learning and artificial intelligence (AI) have also been transformed by the pandemic. Although the power of machine learning and AI was being put to significant use prior to the Coronavirus outbreak, there is now increased pressure to understand the underlying patterns to help us prepare for any epidemic that might hit the world in the future. How have AI interventions fared so far?

MLflow Project Joins the Linux Foundation » ADMIN Magazine


The platform was created in response to the complicated process of machine learning model development. Unlike traditional software development, machine learning models must not only track code versions, but versions of data sets, model parameters, and algorithms. Because of this, the variables that must be tracked and managed can grow exponentially. MLflow prevents the process from overwhelming scientists, educators, developers, and other users by providing a platform to fully manage end-to-end machine learning development lifecycle, which includes everything from data prep, deployment, experiment tracking, creating reproducible runs, model sharing, and collaboration. Matei Zaharia, the original creator of Apache Spark and creator of MLflow, said of his platform, "MLflow has become the open source standard for machine learning platforms because of the community of contributors, which consists of hundreds of engineers from over a hundred companies. " Zaharia concludes with, "Machine learning is transforming all major industries and driving billions of decisions in retail, finance, and health care. Our move to contribute MLflow to the Linux Foundation is an invitation to the machine learning community to incorporate the best practices for ML engineering into a standard platform that is open, collaborative, and end-to-end."

Artist uses AI to reveal what historical figures really looked like


A Dutch artist is using modern technology to create realistic photo-style portraits of famous figures only depicted in paint and sculpture. Bas Uterwijk, from Amsterdam, explained that he wanted to see if he could create realistic digital renderings of key faces in history, including Vincent Van Gogh and Napoleon. He also turned his talents to statues like Michelangelo's David and the Statue of Liberty. Bas uses Artbreeder, a'deep-learning' software which can create life-like images from scratch or based on a composite of different portraits. Bas Uterwijk, from Amsterdam, can create likenesses of famous historical figures using'deep-learning' technology.

Tools to Spot Deepfakes and AI-Generated Text - KDnuggets


With the emergence of incredibly powerful machine learning technologies, such as Deepfakes and Generative Neural Networks, it is all the easier now to spread false information. In this article, we will briefly introduce deepfakes and generative neural networks, as well as a few ways to spot AI-generated content and protect yourself against misinformation. I have many elderly relatives and some middle-aged relatives that just aren't well-versed with technology. Some of these people believe nearly anything they read, or at least believe it enough to share it on social media. While that doesn't sound so bad, it depends on what you are sharing.

Startup Tenstorrent shows AI is changing computing and vice versa


That year, numerous experienced computer chip designers set out on their own to design novel kinds of parts to improve the performance of artificial intelligence. It's taken a few years, but the world is finally seeing what those young hopefuls have been working on. The new chips coming out suggest, as ZDNet has reported in past, that AI is totally changing the nature of computing. It also suggests that changes in computing are going to have an effect on how artificial intelligence programs, such as deep learning neural networks, are designed. Case in point, startup Tenstorrent, founded in 2016 and headquartered in Toronto, Canada, on Thursday unveiled its first chip, "Grayskull," at a microprocessor conference run by the legendary computer chip analysis firm The Linley Group.

MIT pulls massive AI dataset over racist, misogynistic content

FOX News

Fox News Flash top headlines are here. Check out what's clicking on The Massachusetts Institute of Technology permanently took down its 80 Million Tiny Images dataset--a popular image database used to train machine learning systems to identify people and objects in an environment--because it used a range of racist, misogynistic, and other offensive terms to label photos. In a letter published Monday to MIT's CSAIL website, the three creators of the huge dataset, Antonio Torralba, Rob Fergus, and Bill Freeman, apologized and said they had decided to take the dataset offline. "It has been brought to our attention that the Tiny Images dataset contains some derogatory terms as categories and offensive images. This was a consequence of the automated data collection procedure that relied on nouns from WordNet. We are greatly concerned by this and apologize to those who may have been affected," they wrote in the letter.

CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design


The emergence and outbreak of SARS-CoV-2, the causative agent of COVID-19, has rapidly become a global concern and has highlighted the need for fast, sensitive, and specific tools to surveil circulating viruses. Here we provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance. We provide assay designs for detection of 67 viral species and subspecies, including: SARS-CoV-2, phylogenetically-related viruses, and viruses with similar clinical presentation. The designs are outputs of algorithms that we are developing for rapidly designing nucleic acid detection assays that are comprehensive across genomic diversity and predicted to be highly sensitive and specific. Of our design set, we experimentally screened 4 SARS-CoV-2 designs with a CRISPR-Cas13 detection system and then extensively tested the highest-performing SARS-CoV-2 assay.

Simplifying AI Deployment for Quality Inspection


Artificial intelligence (AI) is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications, deployment remains a challenge. Part of the technology trepidation stems from uncertainty around the terms and definitions of'AI' and'machine learning.' Organizations are also unsure how to deploy new AI capabilities alongside existing infrastructure and processes. This is especially true in inspection systems, where there are significant investments in cameras, specialized sensors, and analysis software with well-established processes for end-users. The cost and complexity of algorithm training is also a concern for businesses evaluating AI.

7 Ground-Breaking Machine Learning Applications for Utilities


Digital Transformation is an ongoing process for utilities today. However, to be successful they must focus on technologies that deliver the services customers want. Machine Learning offers enormous potential for utilities to discover more about their customers and for solving the common issues utilities face every day. Today, it is undisputed that Digital Transformation is essential for utilities. However, organizations often find the results of their Digital Transformation efforts disappointing.