Retrieval-Augmented Generation (RAG) systems enhance text generation by incorporating external knowledge but often struggle when retrieving context across different text modalities due to semantic gaps. We introduce a generalized projection-based method, inspired by adapter modules in transfer learning, that efficiently bridges these gaps between various text types, such as programming code and pseudocode, or English and French sentences. Our approach emphasizes speed, accuracy, and data efficiency, requiring minimal resources for training and inference. By aligning embeddings from heterogeneous text modalities into a unified space through a lightweight projection network, our model significantly outperforms traditional retrieval methods like the Okapi BM25 algorithm and models like Dense Passage Retrieval (DPR), while approaching the accuracy of Sentence Transformers. Extensive evaluations demonstrate the effectiveness and generalizability of our method across different tasks, highlighting its potential for real-time, resource-constrained applications.

We systematically evaluated the performance of seven large language models in generating programming code using various prompt strategies, programming languages, and task difficulties. GPT-4 substantially outperforms other large language models, including Gemini Ultra and Claude 2. The coding performance of GPT-4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4 employing the optimal prompt strategy outperforms 85 percent of human participants. Additionally, GPT-4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT-4 is comparable to that of human programmers. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development.

Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.

It's taken Amazon a while, but the tech giant has finally jumped on the artificial intelligence (AI) chatbot bandwagon. The firm has just unveiled its version of ChatGPT called'Q' – which may be a reference to the ingenious tech boffin in the James Bond films. Q – designed for employees in fields such as IT, software, customer service and more – lets workers ask questions that are specific to their companies. Among its skills are summarising meetings, explaining programming code and locating information from hundreds of company documents. Q comes soon after Elon Musk announced his own'sarcastic' AI bot called Grok that will be integrated within X (formerly known as Twitter).

For the past two months, anybody wanting to try out Google's new chatbot AI, Bard, had to first register their interest and join a waitlist before being granted access. On Wednesday, the company announced that those days are over. Bard will immediately be dropping the waitlist requirement as it expands to 180 additional countries and territories. What's more, this expanded Bard will be built atop Google's newest Large Language Model, PaLM 2, making it more capable than ever before. Google hurriedly released the first generation Bard back in February after OpenAI's ChatGPT came out of nowhere and began eating the industry's collective lunch like Gulliver in a Lilliputian cafeteria. Matters were made worse when Bard's initial performances proved less than impressive -- especially given Google's generally accepted status at the forefront of AI development -- which hurt both Google's public image and its bottom line.

GPT-3 is a neural network machine learning model trained using internet data to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate a large volume of relevant and complex machine-generated text. GPT-3's deep learning neural network is a model with over 175 billion machine learning parameters. To put things at scale, the largest trained language model before GPT-3 was Microsoft's Turing NLG model with 10 billion parameters. As of early 2021, GPT-3 is the largest neural network ever produced.

A new'generative' artificial intelligence (AI) tool is in town. And it is quite significant. 'Generative' AI has created a frenzy among tech enthusiasts twice this year, first with DALL-E in April and now with ChatGPT. ChatGPT – an AI language model from OpenAI – has taken the internet by storm garnering over a million users within a week of its release on 30 November. This AI bot can provide answers to your questions – being able to write essays and programming codes.

A functioning AI product contains more than just a Machine Learning model. You need to add a lot of ingredients to your ML soup to make it bring value to customers. We call all of these ingredients together a Machine Learning System. But we tend to pay too much attention to our ML models while neglecting other parts of the bigger picture. I call this phenomenon a model-centric bias.

This Article was co-authored with Muhammad Abutahir, You can find him on linkedin and instagram. So recently I was surfing the web when I came across a YouTube video on GitHub copilot. It amazed me to see how AI is transforming the lives of programmers all around the globe. The person was boasting about it too much and it didn't seem right for a test version of the software, so I thought of taking a deep dive into the system about how it works. If you don't know what GitHub copilot is, then let me tell you, GitHub copilot is an intelligent AI system released by GitHub and OpenAI organization that gives you appropriate suggestions for your code as well as it can generate an entire function based on the comments you provide! That gives it another name called AI pair programmer.

Artificial intelligence research company OpenAI has announced the development of an AI system that translates natural language to programming code--called Codex, the system is being released as a free API, at least for the time being. Codex is more of a next-step product for OpenAI, rather than something completely new. It builds on Copilot, a tool for use with Microsoft's GitHub code repository. With the earlier product, users would get suggestions similar to those seen in autocomplete in Google, except it would help finish lines of code. Codex has taken that concept a huge step forward by accepting sentences written in English and translating them into runnable code.