OpenAI offers ChatGPT, an advanced general intelligence (AGI) chatbot which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.
$0
per month
TensorFlow
Score 7.7 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
I’d definitely recommend ChatGPT to anyone as a great introduction to generative AI and as a starting point in research, writing, brainstorming, or general questions or judgement questions. It can be a great tool to use when you don’t necessarily need an accurate answer. For example, I wouldn’t let it calculate my taxes, but I’d use it to ask some general tax questions, then ask for sources and then verify by checking those sources. I also love ChatGPT for writing and questions - it’s great for emails, creating templates and outlines, and for generating spreadsheet formulas.
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
Saves time by generating content about a specific topic very quickly
Allows us to quickly learn information online (from various sources or even a single lengthy article) into more summed up digestible paragraphs (and even bullet points)
Can autogenerate content on a vast amount of topics
Wish it had support for better slides generation. Sometimes we found ourselves using chatgpt to outline a presentation but build it ourselves or use a tool like Gamma
Maybe a chepear $10 plan. In some countries the US dollar can be expensive and $20 goes a long way.
I wish you could make projects with more files. They limit it. Or make the limit based on the content, not the number of files per se
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
ChatGPT is a powerful assistant. As long as you understand what it is you're looking for in its results, it can save you a lot of time due to its ability to do the heavy lifting for you. This frees your time up to enable you to concentrate on other tasks.
It's a potent utility tool, personally and professionally. It’s also very convenient - it can be used for a wide range of things and scenarios, and when used correctly, it usually produces dependable results in seconds. I would give it a full 10 if it didn't hallucinate as much as it does, and the image generator was more accurate.
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
I find that ChatGPT has the best overall system for user-friendly Ai. I like the customization options of this tool in comparison to others. The ease of setting up a custom GPT with knowledge and resources is great - you don't get that in other tools. I also like the ability to embed the application directly into the browser or computer. This is powerful and opens up addition features or tools
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
I can solve problems quickly. What would take some time, for example, writing an SPF string, ChatGPT can do it correctly in seconds if I it gives the correct information.
The best tool I've ever used for research. For example, I can ask ChatGPT to list all the villages, cities and townships in a specific county. And provide the URL link for each.
Problems that seem unsolvable, i.e. why is Gmal rejecting every message from our list server is answered with a number of suggestions that I can follow up with.
Basically, has elimited a tech support position in our company.
