Goto

Collaborating Authors

Results


Google Open-Sources Trillion-Parameter AI Language Model Switch Transformer

#artificialintelligence

Researchers at Google Brain have open-sourced the Switch Transformer, a natural-language processing (NLP) AI model. The model scales up to 1.6T parameters and improves training time up to 7x compared to the T5 NLP model, with comparable accuracy. The team described the model in a paper published on arXiv. The Switch Transformer uses a mixture-of-experts (MoE) paradigm to combine several Transformer attention blocks. Because only a subset of the model is used to process a given input, the number of model parameters can be increased while holding computational cost steady.


Streamlining data science with open source: Data version control and continuous machine learning

ZDNet

MLOps, short for machine learning operations, is the equivalent of DevOps for machine learning models: Taking them from development to production, and managing their lifecycle in terms of improvements, fixes, redeployments, and so on. Achieving MLOps nirvana is a major barrier to getting value out of machine learning and data science. Version control systems like Git and practices like continuous integration / continuous deployment (CI/CD) have helped operationalize software development. What if those systems and practices could also be used for MLOps? Data engineers, machine learning, and data science practitioners work with a wide range of data.


You don't code? Do machine learning straight from Microsoft Excel

#artificialintelligence

Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasn't touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence. But mastering machine learning is a difficult process.


Cuttlefish pass the 'marshmallow test' in US experiments

Daily Mail - Science & tech

In an amazing show of self-control, cuttlefish can resist the impulse to eat a morsel of food if it means getting to eat two morsels later on, a new study shows. In experiments, the marine molluscs passed a variation of the'marshmallow test' – originally used in the 1970s to measure a child's ability to delay gratification. In the original Stanford experiment, pre-school kids were given one marshmallow and told they could eat it straight away, or, if they waited 20 minutes, have two marshmallows instead. For this new study, scientists performed a'fishy version' of the legendary experiment using shrimp instead of marshmallows. They found the creatures could wait over two minutes to get their preferred type of shrimp – and that the cuttlefish that could delay gratification the longest were the most intelligent, as determined by a another learning task.


Insights Discovery in Data Science Through Novel Machine Learning Approaches

#artificialintelligence

I have always appreciated the unusual, unexpected, and surprising in science and in data. As famous science author Arthur C. Clarke once said, "The most exciting phrase to hear in science, the one that heralds new discoveries, is not'Eureka!' (I found it) but'That's funny!'" This is the primary reason that I motivated most of the doctoral students that I mentored at GMU to work on some variation of Novelty Discovery (or Surprise Discovery) for their Ph.D. dissertations. "Surprise discovery" for me is a much more positive, exciting phrase than "outlier detection" or "anomaly detection", and it is much richer in meaning, in algorithms, and in new opportunities. Finding the surprising unexpected thing in your data is what inspires our exclamation "That's funny!" that may be signaling a great discovery (either about your data's quality, or about your data pipeline's deficiencies, or about some wholly new scientific concept). As famous astronomer, Vera Rubin said, "Science progresses best when observations force us to alter our preconceptions."


Udacity Machine Learning vs. Simplilearn Machine Learning - for your ML Career

#artificialintelligence

You will receive 58 hours of applied instructor-led training. To earn the certification, you should attend a full batch of online training and submit a completed project for the flexi-pass learners or complete at least 85% of the course and submit one completed project for the self-paced learners. The machine learning certification course by Simplilearn is designed for learners with intermediate-level machine learning knowledge and skills in various roles, including business analysis, data analysis, information architecture, data science, machine learning, and others. To take this course, you need a college-level understanding of statistics and mathematics as well as Python programming knowledge. Simplilearn offers a blended learning approach that gives learners access to both live instructor-led training and recorded-videos.


Linear Regression and Logistic Regression using R Studio

#artificialintelligence

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Slow-Reading is The New Deep Learning

#artificialintelligence

I was just a youth when Evelyn Wood debuted her speed-reading course back in 1959. For years, I was fascinated with the prospect of getting my reading assignments over with as quickly as possible so that I could get on to the fun part of life. Fortunately, I massively turned that around. The Evelyn Wood Reading Dynamics course became a huge sensation. So much so that the Kennedy White House sent staff members to take the course.


Classification with Localization: Convert any Keras Classifier to a Detector

#artificialintelligence

Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. There are innumerable possibilities to explore using Image Classification. If you have completed the basic courses on Computer Vision, you are familiar with the tasks and routines involved in Image Classification tasks. Image Classification tasks follow a standard flow – where you pass an image to a deep learning model and it outcomes the class or the label of the object present. While learning Computer Vision, most often a project that would be equivalent to your first hello world project, will most likely be an image classifier. You attempt to solve something like the digit recognition on MNIST Digits dataset or maybe the Cats and Dog Classification problem.


The Use of AI for Accessible Education

#artificialintelligence

Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.