SYC

[SYC @SYCa000]

ChatGPT of course have its' own political biases. In the early stage, some have criticized that ChatGPT have some authoritarian features, and could become aggressive if given slight imply. To remain tech neutral, better monitor or intervene in this trend.
https://t.co/l46TThp7iL

[SYC @SYCa000]

Vectorization is widely used in NLP industry, while now it is a good idea to make this replace loops.
#aigraph
https://t.co/wHHXU4Wujd

[SYC @SYCa000]

Multimodality of ChatGPT is developing incredibly fast. Just months after release of GPT4.0, Chat's data analysis ability has doubled, leading to further productivity evolution in related industry.

#aigraph
https://t.co/Fy0dnFdqib

[SYC @SYCa000]

While they are effective in understanding and generating natural language, they have limitations such as producing incorrect responses, known as the hallucination effect. This can lead to potential risks in clinical settings, especially in areas like imaging appropriateness

[SYC @SYCa000]

Large language models (LLMs) like ChatGPT, developed by OpenAI, have shown success in various tasks due to their advanced architecture and training mechanisms.
#aigraph
https://t.co/oS4txQew9e

[SYC @SYCa000]

Google Bard will destroy ChatGPT because it’s a long-term game, and Google has the upper hand in terms of market dominance. ChatGPT has a better product, but that doesn’t mean they’ll win the long race.
#aigraph https://t.co/0JP5XW2HvF

[SYC @SYCa000]

The output of the OCR process is a text that contains typos, recognition mistakes, non-text symbols, and other inaccuracies.
Another challenge that machine learning engineers face is what to define as a word like Chinese, Japanese, or Arabic.
#aigraph
https://t.co/DhNjrHZU8H

[SYC @SYCa000]

Large language models have found applications in a wide range of fields, transforming the way we interact with technology and enabling new possibilities, including NLP, Chatbots and virtual assistants, automation, Language translation, etc.
#aigraph
https://t.co/eBnUCpUhdn

[SYC @SYCa000]

This blog post provide a high-level explanation of how transformers work without relying on code or mathematics, avoid confusing technical jargon and comparisons with previous architectures and a better intuition of what they do and how they do it.
https://t.co/8hmwqM0Z7O

[SYC @SYCa000]

Applying these concepts to generative AI leads to conclusions we’ll explore, including:

Don’t aim for a Hole-In-One
User feedback isn’t free
Treat chatbot interfaces skeptically

[SYC @SYCa000]

This post details how to apply three core UX concepts to generative AI products: 1) affordances, 2) feedback, and 3) constraints.
#aigraph
https://t.co/k2y1BI6sr6

[SYC @SYCa000]

The concept of AI for common people may be very different from data scientists, causing a completely false expectation of the application of AI, and this culture devalues the actual contributions of AI developers and scientists.
#aigraph
https://t.co/B9ILlBanbf

[SYC @SYCa000]

How to detect AI generated text is becoming essential, and now we have PyTorch code to check if a given text may be AI-generated using the perplexity, also there are drawbacks of this approach, including the possibility of false positives.
#aigraph
https://t.co/PI2SxCxm8g

[SYC @SYCa000]

A prototype for Topic Modelling using ChatGPT API, wild but achieved. Based on a small sample of examples, it works amazingly and gives results that can be easily interpreted.
#aigraph
https://t.co/Zli2O4YNTX

[SYC @SYCa000]

Question Answering as Document is a very attractive topic, especially when you need to grasp the central idea of a long document on specific content. Now, tools like ChatPDF have begun to give a try on certain fields, and we expect more.
#aigraph
https://t.co/np3bVC9zmc

[SYC @SYCa000]

As GPT-4 publish its' newest model, allowing users to input pictures and video clips, it became more challenging to the field of cyber security. Bad Actors, especially the criminals have made LLMs into tools to implement cyber attacks.
#aigraph
https://t.co/7WY7nyItvz

[SYC @SYCa000]

LLMs have many specific types.
Autoregressive language models, Transformer-based models, Encoder-decoder models, Pre-trained and fine-tuned models, Multilingual models, and Hybrid models. For instance, LLaMa2 we are using is transformer-based.
#aigraph
https://t.co/O9mqJ2OiCB

[SYC @SYCa000]

“Textual inference, usually modeled as entailment problem, automatically determines whether a natural-language hypothesis can be inferred from a given premise (MacCartney and Manning, 2007). Commonsense reasoning bridges premises and…” — Tim Schopf https://t.co/NOmwWWpbIv

[SYC @SYCa000]

This is an interesting article! It explains how digital money formats will affect the financial systems and central banks, and how multinational and global banks are using robotics and AI to regain profits or minimize costs.
https://t.co/xOfv206BJp

[SYC @SYCa000]

The review highlights the increasing popularity of AI-related terms such as Big Data, Business Intelligence, and Machine Learning, and the need for a robust literature review to make sense of the chaotic development of these concepts.
#aigraph
https://t.co/c9FpJEcwei