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Nvidia CEO says don’t give up learning new skills — just maybe leave programming to AI

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Nvidia CEO Jensen Huang has clarified comments he made about the supposed “death of coding”.

Huang had been criticized in the past for saying on several occasions that as AI platforms would soon be doing a lot of the heavy lifting when it comes to coding, young people today should not necessarily consider learning it as a vital skill.

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The differences between AI Programming vs Traditional Coding

The differences between AI Programming vs Traditional Coding

In the fast-paced world of technology, choosing the right approach for your project is crucial. You might be wondering whether to embrace the capabilities of artificial intelligence (AI) or to stick with the well-established methods of traditional programming. This decision is pivotal and will shape the way you handle data, solve problems, and use computational power.

Artificial intelligence is known for its remarkable ability to learn from data and make decisions autonomously. Unlike traditional programming, which operates on a set of specific rules and logic, AI systems are dynamic. They sort through data, dividing it into training, validation, and test sets, and continuously improve their performance. These systems utilize complex algorithms, such as machine learning and reinforcement learning, to predict outcomes and make decisions that can seem almost human in their intuition.

One of the key strengths of AI is its ability to tackle large data sets and run sophisticated algorithms. This requires significant computational power, often provided by GPUs, which are essential for efficiently managing these tasks. AI excels in areas that are challenging for traditional programming, such as recognizing images and understanding spoken language.

AI Programming vs Traditional Coding

IBM take a closer look and compare artificial intelligence to traditional coding methods providing more insight into the differences between both.

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On the other hand, traditional programming is valued for its predictability and stability. It’s the go-to method for tasks that require consistent and reliable outcomes. While it may not scale as effortlessly as AI, which grows and evolves with new data, traditional programming offers a level of determinism that AI can sometimes lack. This can be particularly important in situations where stability is more important than adaptability.

Control and transparency are other areas where traditional programming has an advantage. It allows developers to fully understand and trace the logic and flow of a program. AI systems, in contrast, can be somewhat mysterious. Their complex algorithms can make it difficult to discern the reasoning behind their decisions, which can be a concern for those who need clarity and accountability, as discussed in what rules AI systems should follow.

The way each approach handles data is also a distinguishing factor. Traditional programming requires data to be structured and can struggle with changes. AI, however, is adept at working with unstructured data, such as natural language, and can adjust to new information without the need for explicit reprogramming, as seen in the concept of AI of Things.

Despite the advancements in AI, traditional programming remains essential. It’s particularly important in applications where predictability and control are paramount. However, AI offers a flexible and dynamic approach that is well-suited for dealing with the abundance of data and complex problems that characterize our modern world, including managing your finances.

As you consider your options, think about the specific needs of your project. Are you looking for the adaptability and learning capabilities of AI, or do you prioritize the stability and clarity that traditional programming provides? The choice you make will not only impact your current work but will also play a role in shaping the future of technological development.

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Code Llama 70B beats ChatGPT-4 at coding and programming

Code Llama 70B beats ChatGPT-4 at coding and programming

Developers, coders and those of you learning to program might be interested to know that the latest Code Llama 70B large language model released by Meta and specifically designed to help you improve your coding. Has apparently beaten OpenAI’s ChatGPT  when asking for coding advice, code snippets and coding across a number of different programming languages.

Meta AI recently unveiled Codellama-70B, the new sophisticated large language model (LLM) that has outperformed the well-known GPT-4 in coding tasks. This model is a part of the Codellama series, which is built on the advanced Lama 2 architecture, and it comes in three specialized versions to cater to different coding needs.

The foundational model is designed to be a versatile tool for a variety of coding tasks. For those who work primarily with Python, there’s a Python-specific variant that has been fine-tuned to understand and generate code in this popular programming language with remarkable precision. Additionally, there’s an instruct version that’s been crafted to follow and execute natural language instructions with a high degree of accuracy, making it easier for developers to translate their ideas into code. If you’re interested in learning how to run the new Code Llama 70B AI model locally on your PC check out our previous article

Meta Code Llama AI coding assistant

What sets Codellama-70B apart from its predecessors is its performance on the HumanEval dataset, a collection of coding problems used to evaluate the proficiency of coding models. Codellama-70B scored higher than GPT-4, marking a significant achievement for LLMs in the realm of coding. The training process for this model was extensive, involving the processing of a staggering 1 trillion tokens, focusing on the version with 70 billion parameters.

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The specialized versions of Codellama-70B, particularly the Python-specific and instruct variants, have undergone fine-tuning to ensure they don’t just provide accurate responses but also offer solutions that are contextually relevant and can be applied to real-world coding challenges. This fine-tuning process is what enables Codellama-70B to deliver high-quality, practical solutions that can be a boon for developers.

Recognizing the potential of Codellama-70B, Meta AI has made it available for both research and commercial use. This move underscores the model’s versatility and its potential to be used in a wide range of applications. Access to Codellama-70B is provided through a request form, and for those who are familiar with the Hugging Face platform, the model is available there as well. In an effort to make Codellama-70B even more accessible, a quantized version is in development, which aims to offer the same robust performance but with reduced computational requirements.

One of the key advantages of Codellama-70B is its compatibility with various operating systems. This means that regardless of the development environment on your local machine, you can leverage the capabilities of Codellama-70B. But the model’s expertise isn’t limited to simple coding tasks. It’s capable of generating code for complex programming projects, such as calculating the Fibonacci sequence or creating interactive web pages that respond to user interactions.

For developers and researchers looking to boost coding efficiency, automate repetitive tasks, or explore the possibilities of AI-assisted programming, Codellama-70B represents a significant step forward. Its superior performance on coding benchmarks, specialized versions for targeted tasks, and broad accessibility make it a valuable asset in the toolkit of any developer or researcher in the field of AI and coding. With Codellama-70B, the future of coding looks more efficient and intelligent, offering a glimpse into how AI can enhance and streamline the development process.

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Deals: 2024 Java Programming Certification Bundle

Java Programming

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