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 8.1 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.
Whenever the problem has the demand for a neural networks based solution, Tensorflow (TF) is a great fit.
The tf.dataset API makes it really simple to create complex data pipelines in a few lines of code.
tf.estimators API abstracts all the complex computation graph creation logic making it very simple to get started.
Eager execution makes it simple to develop a TF graph as debugging the code would be like any other imperative Python program.
TF abstracts all the complexities of scaling it to multiple machines. It has various code and data distribution algorithms ready to use.
Projects like TensorBoard make monitoring the training process really easy. It also gives the ability to view embeddings without any extra code. Their What-If is extremely useful for poking and understanding a black box model. It also has tools to visualize data to quickly check for anomalies.
TF Autograph aims to covert any normal Python code into a distributed program which is quite handy to scale an existing code base.
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
Data pipeline implementation is quite good, loading large amounts of data and pre-process it in an efficient way is no more issue for us
It supports all major DL algorithms and network layouts such as ConvNets, RNN, LSTMs, Word2Vec, and even the latest transformer architecture
The abstraction for the device is perfectly done and its support seamlessly for multiple GPU and even TPU will bring a lot of performance gain for enterprise scoped solution while still keep the flexibility
The TensorBoard is amazing. I haven't seen a similar thing in other frameworks on the market. It allows us to quickly understand and debug the model with the info visualization which makes understanding much better
A very supportive community, which is the key for sharing the ideas and find the quick and best solutions
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
It would be much better if they could provide good documentation and easy ways to understand concepts.
It is difficult to understand the concept behind for example, Tensor Graph, which takes a lot of time.
As you have to write everything, it is time consuming to write the implementation of whole neural network. It would be better if they can provide some wrapper library to make things easier.
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.
Most of the time is up. Seldom do you find a down service. It also has improved in token generation (the speed at which it prints answers) so it's usability is pretty much great all the time. Images do take a bit to generate but nothing that breaks anything. New additions like Projects, custom prompts, and some privacy settings improve experience
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.
Where ChatGPT is better: ChatGPT has significantly more use cases - it's much more versatile. Some aspects of ChatGPT's user experience are better than Claude's. I prefer ChatGPT's results presentation compared to Claude's. Where Claude excels: Claude is a more skilled writer than ChatGPT. Some aspects of Claude's user experience are better than ChatGPT's. Its image, audio, and video translations are better than ChatGPT's.
Can't seem to choose any deep learning platform in the above, so I'll list it here: 1. Apache MXNet: this has been used for one of our main algorithms for search as an end-to-end pipeline. We chose this because of the Scala bindings, which makes it easier to integrate with out JVM backend. MXNet seems comparable to TensorFlow, although community support is not as good as TensorFlow, and there are issues with memory leaks that are being worked on. TensorFlow in general is easier to use, but MXNet isn't too far behind. 2. Keras: still a favorite. Often I use this when paired with TensorFlow. TensorFlow 2.0 will make it even easier. 3. PyTorch: only used it a little, so it's hard to provide a good opinion. 4. DL4J: used it initially in an early days project because it has good JVM support. Harder to used not because of poor API design, but because community support is lacking and features don't come out as fast as TensorFlow.
Positive Impact- As I mentioned before its open source. Very easy to learn for average programmer/ developer. We were able to design a POC model for understanding the patient appointment cancellation snd reasons behind it in 3 week time frame.
Negative Impact- If you are using tensor flow for small project it works fine. If you are trying to build a model for face recognition it will be hard to program and train the system. It needs data to be processed before hand cannot learn on the go.
