How Coders Can Survive—and Thrive—in a ChatGPT World

Artificial intelligence, particularly generative AI powered by large language models (LLMs), could upend many coders’ livelihoods. But some experts argue that AI won’t replace human programmers—not immediately, at least.

“You will have to worry about people who are using AI replacing you,” says Tanishq Mathew Abraham, a recent Ph.D. in biomedical engineering at the University of California, Davis and the CEO of medical AI research center MedARC.

Here are some tips and techniques for coders to survive and thrive in a generative AI world.

Stick to Basics and Best Practices
While the myriad AI-based coding assistants could help with code completion and code generation, the fundamentals of programming remain: the ability to read and reason about your own and others’ code, and understanding how the code you write fits into a larger system.

Find the Tool That Fits Your Needs
Finding the right AI-based tool is essential. Each tool has its own ways to interact with it, and there are different ways to incorporate each tool into your development workflow—whether that’s automating the creation of unit tests, generating test data, or writing documentation.

Clear and Precise Conversations Are Crucial
When using AI coding assistants, be detailed about what you need and view it as an iterative process. Abraham proposes writing a comment that explains the code you want so the assistant can generate relevant suggestions that meet your requirements.

Be Critical and Understand the Risks
Software engineers should be critical of the outputs of large language models, as they tend to hallucinate and produce inaccurate or incorrect code. “It’s easy to get stuck in a debugging rabbit hole when blindly using AI-generated code, and subtle bugs can be difficult to spot,” Vaithilingam says.

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Nvidia delays next gen AI chip as investors issue ‘bubble’ warning

Nvidia highly anticipated “Blackwell” B-200 artificial intelligence chip will reportedly be delayed, sending the near-term future of the entire AI industry into a state of uncertainty.

Tech news outlet The Information claims that a Microsoft employee and at least two other people familiar with the situation have stated that the new chip’s launch date has been pushed back by at least three months due to a design flaw.

While Nvidia hadn’t given a public launch date, CEO Jensen Huang recently announced that the company would begin sending engineering samples “this week” on July 31 at the SIGGRAPH event in Denver, Colorado.

Source-Link : MSN

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Meta just announced a new LLM Evaluation Research Grant aimed at boosting innovation in the field of LLM evaluations. This grant offers *$200K* in funding to selected recipients to accelerate their research, particularly in areas like complex reasoning, emotional & social intelligence, and agentic behavior.

Proposals are being accepted until September 6th. You can check out all the details here [https://llama.meta.com/llm-evaluation-research-grant/?utm_source=linkedin&utm_medium=organic_social&utm_content=image&utm_campaign=llama].

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Future Trends in Artificial Intelligence 👇👇

1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.

2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.

3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.

4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.

5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.

6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.

7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.

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Artificial Intelligence

🧱 Large Language Models with Python

Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python.


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Will LLMs always hallucinate?

As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.

A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.

While the idea of hallucinations as features isn't new, the researchers' explanation is.

They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.

In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.

This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.

So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.

It also suggests that research into making models more robust and understanding their failure modes is crucial.

No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases

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HandsOnLLM/Hands-On-Large-Language-Models
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New research out of Hong Kong suggests LLMs and humans remember things in similar ways.

Both humans and AI recall memories when triggered by input, rather than static info storage.

If proven correct, it suggests a lesser fundamental difference between AI and human cognition.

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Over 87 former employees have launched around 32 AI startups and OpenAI mafia just getting bigger and bigger!

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Today Several former OpenAI employees have launched their own AI startups. Companies such as Anthropic, Pilot, and Perplexity, have collectively raised almost $10 billion. Many of these startups focus on AI safety, robotics, and AI applications in various industries.

OpenAI had approximately 2600 employees as of last month & who knows how many more AI startups would spin out of the company. It is fascinating to see new tech entrepreneurs being born out of the OpenAI ecosystem, which is acting as a training ground for future AI leaders.

Stanford just uploaded their new "Building LLMS" lecture.

"This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).

For each component, it explores common practices in data collection, algorithms, and evaluation methods." https://www.youtube.com/watch?v=9vM4p9NN0Ts

Towards Natural Image Matting in the Wild via Real-Scenario Prior


Publication date: 9 Oct 2024

Topic: Semantic Segmentation

Paper: https://arxiv.org/pdf/2410.06593v1.pdf

GitHub: https://github.com/xiarho/semat

Description:

We propose SEMat which revamps the network architecture and training objectives. For network architecture, the proposed feature-aligned transformer learns to extract fine-grained edge and transparency features. The proposed matte-aligned decoder aims to segment matting-specific objects and convert coarse masks into high-precision mattes. For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information. Extensive experiments across seven diverse datasets demonstrate the superior performance of our method, proving its efficacy in interactive natural image matting.

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Generative AI isn't easy!

It’s the groundbreaking technology that creates new content—whether it’s images, text, music, or even entire virtual worlds.

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0. Understanding the Basics: Learn the foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.


1. Mastering Neural Networks: Dive deep into the types of neural networks used in generative AI, such as convolutional neural networks (CNNs) for image generation and transformer models for text.


2. Exploring Text Generation Models: Understand the mechanics behind language models like GPT and BERT, and how they generate human-like text.


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4. Working with Audio and Music Generation: Explore models like Jukedeck and OpenAI’s MuseNet to create music and sound using AI.


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6. Fine-Tuning Pre-Trained Models: Learn how to adapt large pre-trained models to specific tasks by fine-tuning them with domain-specific data.


7. Ethics and Bias in Generative AI: Understand the ethical implications of creating content using AI, including issues of plagiarism, bias, and misinformation.


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Generative AI is a creative force that blends technology with imagination.

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