I've published a couple of articles lately about the need for organizations to have an artificial intelligence (AI) strategy and how AI can help organizations with employee development. It's possible that with all the conversation about AI technologies in today's news, organizations are talking about what AI could mean for their operation and how to get started. So, I wanted to bring in another technology expert to talk specifically about the things that organizations need to consider when looking at AI tools. Matthew Geohring, MS, is a technology solutions consultant for global insurance brokerage Hub International's HUB People & Technology Consulting Practice. Prior to joining HUB, Matthew spent time as both a human resources generalist and an in-house senior HRIS analyst. I'm excited to be sharing his thoughts with you today.

This time I'm going to introduce you to the PyTorch Wrapper, a great tool that makes developing and training PyTorch models much easier and faster. This wrapper allows us to build and train complex neural networks in blocks, so we don't have to manually set all the code. This is a huge benefit because it saves us time and energy. In my last tutorial, I showed you how to train and build a simple PyTorch model. We used Convolutional Neural Networks to classify MNIST data and achieved an accuracy rate of 97–98%, proving that PyTorch is a powerful tool for deep learning.

AI won't take your job, but the developers who use AI will. Artificial Intelligence is changing the software landscape with tools that have been a great relief to developers, in that it reduces the burden to build from scratch and increases productivity. ChatGPT, a recent AI tool by OpenAI has been the most talked about assistive tool not just in the tech space, but almost in every facet of life. It was adopted by writers, teachers, data scientists and almost all professionals in every field you can think of. The AI tools preceding ChatGPT didn't get as much popularity and adoption as this, but it has been a great standout tool no doubt. AI tools have a large resource of data that makes them pull out our requests in seconds.

GPT-3 is the third generation Generative Pre-trained Transformer machine learning model that was developed by OpenAI and released in November 2021. It is a deep learning neural network with well over 175 billion machine learning parameters. Wait… but what does this all mean? GPT-3 is a major game changer in the way that artificial intelligence produces written text. It's the closest AI has ever been to mimicking a human being.

KNIME is an open source data analytics platform for data science, ML, AI, AutoML, big data, & more. This is a course for Business Enthusiasts who look for data-driven decision-making techniques for different business scenarios. This would provide a basic and intermediate level understanding of different Machine Learning Algorithms and how they can be implemented in KNIME. It would also teach the students how to judge the different Machine Learning Algorithms and which ones will fit your business scenario. KNIME is free and powerful software that has a vast number of business use cases.

KNIME is an open source data analytics platform for data science, ML, AI, AutoML, big data, & more. This is a course for Business Enthusiasts who look for data-driven decision-making techniques for different business scenarios. This would provide a basic and intermediate level understanding of different Machine Learning Algorithms and how they can be implemented in KNIME. It would also teach the students how to judge the different Machine Learning Algorithms and which ones will fit your business scenario. KNIME is free and powerful software that has a vast number of business use cases.

A successful data scientist understands a wide range of Machine Learning algorithms and can explain the results to stakeholders. But, unfortunately, not every stakeholder has a sufficient amount of training to grasp the complexities of ML. Luckily, we can aid our explanations by using dimensionality reduction techniques to create visual representations of high dimensional data. This article will take you through one such technique called t-Distributed Stochastic Neighbor Embedding (t-SNE). Perfect categorization of Machine Learning techniques is not always possible due to the flexibility demonstrated by specific algorithms, making them useful when solving different problems (e.g., one can use k-NN for regression and classification).

There are two kinds of people in the world -- those who divide the world into groups of two and those who don't. And, here is, now, my take on AI. There are two kinds of AIs -- those that are built using a large set of samples, and the others that "learn" using just the minimal required set of samples. The former is known as Deep Learning or BIG DATA AI, and I call the other kind of AI that learns with minimal data as "MIN DATA AI". In the BIG Data AI, the learning is basically a process that examines a large set of samples and creates a formula using a large number of parameters many of them seem indistinguishable to the naked eye.

TechRepublic's Karen Roby spoke with Chris Ford, VP of product for Threat Stack, about supervised and unsupervised machine learning. The following is an edited transcript of their conversation. Christopher Ford: Supervised and unsupervised learning are techniques that help to facilitate different use cases within the sphere of machine learning. As your viewers know, machine learning is used to gain insights out of data sets. I would say that the crucial difference between unsupervised learning and supervised learning is that the former, unsupervised learning, it's easier to get started with because it does not require labeled data.

He's 11-years-old and, until he can receive his shots, Gronvall's been using at-home COVID-19 test kits in order to determine if his sniffles are more than allergies or a slight cold. The test swabs are longer than a Q-tip, but easier on the nasal cavity than a flu diagnostic or the original "brain swab" used to test for COVID since early in the pandemic. "There's often a lot of stuff coming out of their nose," Gronvall said of her kids, with a slight chuckle, when we talked recently. As an associate professor at the Johns Hopkins Bloomberg School of Public Health, Gronvall knows the importance of testing. "We can't all rely on everybody being extra scrupulous and paying attention to all of the COVID restrictions," she said.