Collaborating Authors

Machine Learning

Edge AI Chips Take to the Field


Airplanes and automobiles, databases and personal computers – all entities with ubiquitous form factors today, but that started out with diverging architectures. So it's not surprising that the shape of edge AI chip technology is similarly diversified. These are nascent days for AI chips. And with numerous designs in the market, there's unlikely to be a common architecture anytime soon. Today, established vendors and startup chip houses alike have jumped into the fray in a bid to complement or displace conventional microprocessors and controllers.

Machine Learning development with AWS Sage Maker


We can easy to set up a training environment from a notebook with "click" for elastic of CPUs/GPUs * Connectivity and easy to deploy – AWS SageMaker is AWS managed service and it easy to integrate with other AWS services inside of a private network. Which also impact to big data solution, ETL processed with data can be processing inside of a private network and reduce cost for the transfer. AWS managed service will help to reduce the resource we need to create.

Artificial Intelligence, a Transformational Force for the Healthcare Industry


Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.

3 ways to evaluate and improve machine learning models


When solving machine learning problems, simply training a model based on a problem-specific training machine learning algorithm does not guarantee either that the resulting model fully captures the underlying concept hidden in the training data or that the optimum parameter values were chosen for model training. Failing to test a model's performance means an underperforming model could be deployed on the production system, resulting in incorrect predictions. Choosing one model from the many available options based on intuition alone is risky. By generating different metrics, the efficacy of the model can be assessed. Use of these metrics reveals how well the model fits the data on which it was trained.

Artificial Intelligence is revolutionizing veterinary medicine. Are you missing out?


Artificial intelligence has several subdisciplines, including machine learning, unsupervised learning, and deep learning.

Wavicle Data Solutions Introduces Augment, Its New Machine Learning-Powered Augmented Data …


Augment uses machine learning and NLP capabilities to pre-emptively secure sensitive data before/after ingestion, ensuring that it doesn't end up …

UW to lead new NSF institute for using artificial intelligence to understand dynamic systems


The UW will lead a new artificial intelligence research institute that will focus on fundamental AI and machine learning theory, algorithms and …

Army Researchers Develop New ML Framework to Improve In-Vehicle Network Cybersecurity


The deep reinforcement learning-based resource allocation and moving … Since it is based on machine learning, the framework can be modified by …

OpenAI debuts Python-based Triton for GPU-powered machine learning


This allows integration with the rest of the Python ecosystem, currently the biggest destination for developing machine learning solutions.