Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
Here is another quick note before we move on to GPT-4 assisted automation journey. Below are some "little" helps ChatGPT had already been offering, here and there, during daily works.
And what could be the perceived gaps, risks and traps to LLMs assisted automation, if you happen to explore this path too. I'd also love to hear anyone's use cases and experiences on this front too.
Large language models are stirring up some phenomena in recent months. So inevitably I was playing ChatGPT too over last weekend, to probe whether it would be a complimentary to some BERT based "traditional" AI chatbots I was knocking up, or rather would it simply sweep them away.
I got access to the new Bing this morning and thought I would try out some things on it. Below are a series of screenshots of what I searched and the results. Please share your experiences here too.
In our latest episode of Data Points, I had a conversation with @Thomas Dyarabout AI Link, which helps bridge the gap between data scientists and business analysts. Our conversation talks about how AI Link fits with IntegratedML and Adaptive Analytics, as well, as what new features are on the horizon for IntegratedML. Take a listen!
Hello everyone, this is with great pleasure that I announce the V2 of my application 'Contest-FHIR'.
In this new version, I used new tools and techniques I discovered at the EUROPEAN HEALTHCARE HACKATHON in which I was invited by InterSystems as a guest and as a mentor to display the multiple projects I did in my intership back in April 2022.
Today I present to you the V2 of my application, it can now transform CSV to FHIR to SQL to JUPYTER notebook.
Several models, such as DALL-E, Midjourney, and StableDiffusion, became available recently. All these models generate digital images from natural language descriptions. The most interesting one, in my opinion, is StableDiffusion which is open source - released barely a few weeks ago. There's now an entire community trying to leverage it for various use cases.
In this article, I am trying to identify the multiple areas to develop the features we can able to do using python and machine learning.
Each hospital is every moment trying to improve its quality of service and efficiency using technology and services.
The healthcare sector is one of the very big and vast areas of service options available and python is one of the best technology for doing machine learning.
In every hospital, humans will come with some feelings, if this feeling will understand using technology is make a chance to provide better service.
Maternal Risk can be measured from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Yasir Hussein Shakir published a very useful dataset for training ML algorithms in the detection/prediction of Maternal Risk.
Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.
Diabetes can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the National Institute of Diabetes and Digestive and Kidney Diseases published a very useful dataset for training ML algorithms in the detection/prediction of diabetes. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/mathchi/diabetes-data-set.
any news about support of the IRIS platform in the DBeaver? I only found this https://github.com/dbeaver/dbeaver/issues/6289 Github issue. They are waiting for a new version of the JDBC driver.
All of us know that money is important. We constantly need to monitor all expenses to avoid looking back to the bank statement and thinking: “So, where’s my money?”
Uber type application and database management system wanted.
Reaching out to the development community. Here at POLC we are planning to soon launch a new ride hailing app called POLC. Wanted to know if any developers on this forum has built a similar system before? Please DM me on +27 66 243 1618
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