All Posts in "GenAI"

Enhance Your Coding Journey: Using ChatGPT as a Companion to MOOCs

As the tech industry continues to thrive, learning to code has become more accessible than ever, thanks to MOOCs (Massive Open Online Courses) and online resources that offer structured, comprehensive curriculums. However, while traditional courses provide essential content and a structured pathway, they often lack immediate, personalized feedback and on-the-spot troubleshooting support that can help learners at all levels.

This is where generative AI (GenAI) tools like ChatGPT shine. They serve as a highly complementary utility, providing quick explanations, debugging help, and tailored responses that enhance the learning experience. In this article, we’ll explore how you can use GenAI tools, like ChatGPT, as a valuable companion to your coding journey alongside mainstream learning platforms.

Why GenAI Tools are Ideal Learning Companions to MOOCs

Here’s why ChatGPT and similar AI tools are perfect supplements to formal online courses:

  1. Immediate Feedback: When you’re stuck on a complex concept, you don’t have to wait for instructor responses or sift through forums. ChatGPT gives instant feedback.
  2. Personalized Explanations: MOOCs present the same material to everyone, but ChatGPT can adjust explanations based on your specific needs or background.
  3. Active Debugging Partner: ChatGPT assists with real-time troubleshooting, helping you learn from errors instead of spending excessive time struggling to solve them alone.
  4. Flexible, Anytime Support: Unlike course instructors, ChatGPT is available 24/7, making it easier to learn whenever inspiration strikes.

Combined, these benefits make ChatGPT a valuable co-pilot for coding, especially when paired with the structured, guided content of MOOCs.

How to Integrate ChatGPT Into Your Coding Journey Alongside MOOCs

1. Begin with a Structured Course for Fundamentals

Start your coding journey with a high-quality MOOC. Platforms like Coursera, edX, Udemy, and Udacity offer in-depth coding courses led by professionals, covering basics like variables, control flow, data structures, and more.

Once you’ve completed a lesson, turn to ChatGPT to:

  • Clarify Concepts: If there’s a particular concept you didn’t fully grasp, ask ChatGPT to explain it in simpler terms.
  • Get Examples: Request additional examples or analogies to reinforce your understanding. For instance, after learning about loops, ask ChatGPT for examples of different loop types in the language you’re studying.

2. Use ChatGPT for Interactive Practice

Coding is best learned by doing, so practice regularly. Use ChatGPT as a tool to reinforce your knowledge by:

  • Requesting Practice Problems: Ask ChatGPT for coding challenges that match your current skill level. For instance, if you’re learning Python, ask for beginner-level exercises in lists or functions.
  • Breaking Down MOOC Exercises: Some MOOCs provide complex assignments. If you’re struggling, ChatGPT can help you break them down into simpler steps, allowing you to tackle each part confidently.

3. Leverage ChatGPT for Real-Time Debugging

One of the hardest parts of learning to code is debugging. When faced with an error, you may not always understand what’s going wrong, which can be discouraging. Here’s how to use ChatGPT effectively:

  • Error Explanations: Paste the error message into ChatGPT and ask for an explanation. For example, “I’m getting a syntax error in this code – can you help me figure out why?”
  • Debugging Assistance: ChatGPT can help you spot common errors like missing semicolons, mismatched brackets, or logical errors in loops, offering immediate feedback that speeds up your learning process.

4. Apply ChatGPT for Reinforcement and Review

Retention is key to mastering coding. At the end of each module in your MOOC, use ChatGPT to:

  • Review Concepts: Summarize the concepts you’ve learned and ask ChatGPT to quiz you or explain them back. For instance, say, “Can you quiz me on Python dictionaries and give feedback?”
  • Create Practice Exercises: Request unique exercises based on what you’ve learned. This helps you revisit concepts in different contexts, which deepens your understanding and retention.

5. Simulate Real-World Coding Scenarios with ChatGPT

As you advance, start using ChatGPT for realistic, hands-on practice:

  • Project Ideas: Ask ChatGPT for beginner-friendly project ideas. If you’ve finished a web development course, for example, it could guide you in building a simple content management system, calculator, or game.
  • Step-by-Step Guidance: For more challenging projects, ask ChatGPT to break down each step. For instance, “How do I set up a basic HTML/CSS website from scratch?”

By engaging with these types of scenarios, you’ll start connecting concepts and building confidence in your coding skills.

6. Learn Best Practices and Style from ChatGPT

Once you’ve got a handle on the basics, focus on writing clean, efficient code by:

  • Requesting Best Practices: ChatGPT can introduce you to coding best practices like DRY (Don’t Repeat Yourself), commenting guidelines, and organizing code into reusable functions.
  • Learning About Style Guides: Ask ChatGPT about specific style guides or naming conventions. For instance, ask, “What are some best practices in writing readable Python code?”

Practicing these principles early on will improve your ability to produce quality, maintainable code as you progress.

Tips for Maximizing ChatGPT’s Utility as a Coding Companion

To make the most of ChatGPT’s capabilities, here are some practical tips:

  1. Ask Detailed Questions: The more context you provide, the more helpful ChatGPT can be. Instead of “How do I use lists?” try asking, “Can you show me how to use a list to store user input in Python?”
  2. Experiment with Multiple Solutions: If ChatGPT presents one solution, ask for alternatives. Coding often has multiple solutions, and seeing different approaches builds your problem-solving flexibility.
  3. Combine Theory with Hands-On Practice: Use ChatGPT to solidify concepts, but don’t rely on it to do all the work. Attempt exercises and projects independently before seeking help, using ChatGPT as a support tool rather than a primary instructor.
  4. Save Your Sessions for Future Review: Keep track of your sessions, particularly where you learned new concepts or solved complex problems. Reviewing past sessions is a great way to reinforce knowledge.

Potential Challenges and How to Address Them

While ChatGPT is a fantastic resource, it does come with certain limitations:

  • Occasional Inaccuracies: ChatGPT can sometimes make mistakes or offer outdated solutions, especially with more niche programming issues. Use it as a learning aid but verify its answers with additional resources if needed.
  • Risk of Over-Reliance: Avoid using ChatGPT as a crutch. Practice independent problem-solving by working through challenges on your own before turning to ChatGPT.
  • Consistency Is Key: Coding isn’t something you can learn overnight. Commit to consistent, regular practice. Try scheduling study sessions, incorporating ChatGPT for assistance when needed.

Wrapping Up: ChatGPT as a Powerful, Accessible Coding Tutor

Using ChatGPT as a supplement to MOOCs and other coding resources gives you the best of both worlds: a structured, comprehensive curriculum paired with immediate, personalized support. Whether you’re debugging code, clarifying difficult concepts, or looking for additional practice exercises, ChatGPT can be your go-to partner in the learning process.

Learning to code with GenAI tools like ChatGPT doesn’t replace the rigor of a MOOC but enhances your experience, helping you understand challenging concepts, tackle exercises with confidence, and build a strong foundation in coding. By pairing structured learning with real-time guidance, you can maximize your coding journey and reach your goals faster.

Happy coding!

Shiva by — November 8, 2024 in AI Tools 0
OpenAI’s Path to Artificial General Intelligence (AGI)

OpenAI, a leading artificial intelligence research laboratory, has outlined a five-level framework to measure progress towards achieving Artificial General Intelligence (AGI). This framework provides a structured approach to understanding the complexities and potential implications of AI development.

Level 1: Conversational AI – chatbots with conversational language

  • Focus: Developing AI systems capable of engaging in natural and informative conversations.
  • Example: ChatGPT, Google Bard
  • Benefits: Revolutionize customer service, education, and mental health support. Improve accessibility to information and facilitate human-computer interaction.

Level 2: Reasoners – human-level problem solving

  • Focus: Creating AI systems that can solve complex problems, requiring reasoning, planning, and learning.
  • Example: AI systems capable of drafting legal documents, conducting scientific research, or developing complex software.
  • Benefits: Accelerate scientific discovery, increase efficiency in various fields like medicine and engineering.

Level 3: Autonomous Agents – systems that can take actions independently

  • Focus: Building AI systems capable of operating independently in complex environments, making decisions, and taking actions.
  • Example: Self-driving cars, robots capable of performing household tasks, or AI systems managing complex infrastructure.
  • Benefits: Transform transportation, improve quality of life, and enhance efficiency in industries like manufacturing and logistics.

Level 4: Innovators – AI that can aid in invention

  • Focus: Developing AI systems capable of generating new ideas and solutions, demonstrating creativity and adaptability.
  • Example: AI systems designing new drugs, creating innovative products, or composing music.
  • Benefits: Drive economic growth, foster innovation, and potentially lead to breakthroughs in fields like art, science, and technology.

Level 5: Organizational Equivalents – AI that can do the work of an organization

  • Focus: Creating AI systems capable of operating as entire organizations, making strategic decisions, and adapting to changing environments.
  • Example: AI systems managing complex businesses, governments, or non-profit organizations.
  • Benefits: Revolutionize governance, economic systems, and societal structures. However, also raises significant ethical and societal challenges.

According to Bloomberg, OpenAI believes its technology is approaching the second level of five on the path to artificial general intelligence. It’s important to note that this framework is a conceptual roadmap and the exact boundaries between levels may be fluid. Additionally, achieving each level represents a significant technological leap and will likely require substantial advancements in hardware, algorithms, and data.

While the potential benefits of AGI are immense, it’s crucial to address the associated challenges and risks, such as job displacement, bias, and the potential for misuse. OpenAI and other leading AI research organizations are actively working on developing safety protocols and ethical guidelines to ensure that AGI benefits humanity as a whole.

References:

https://www.bloomberg.com/news/articles/2024-07-11/openai-sets-levels-to-track-progress-toward-superintelligent-ai?embedded-checkout=true&sref=HrWXCALa

https://www.forbes.com/sites/jodiecook/2024/07/16/openais-5-levels-of-super-ai-agi-to-outperform-human-capability

Shiva by — July 22, 2024 in AI Trends & News 0
Figure Unveiled a Humanoid Robot in Partnership with OpenAI

A yet another milestone in the history of A.I. and Robotics!

Yes, I’m not exaggerating! What you could potentially read in a moment would be a futuristic world where humanoid robots can very well serve humanity in many ways (keeping negatives out of the picture for timebeing).

When I first heard this news, movies such as I, Robot and Enthiran, the Robot were flashing on my mind! Putting my filmy fantasies aside, the Robotics expert company Figure, in partnership with Microsoft and OpenAI, has released the first general purpose humanoid robot – Figure 01 – designed for commercial use.

Here’s the quick video released by the creators –

Figure’s Robotics expertise has been perfectly augmented by OpenAI’s multi-modal support in understanding and generating response of visual inputs such as image, audio, video. The future looks way more promising and becoming reality that these humanoids can be supplied to the manufacturing and commercial areas where there are shortage of resources for scaling the production needs.

In the video, it is seen demonstrating the ability to recognize objects such as apple and take appropriate actions. It is reported that Figure 01 humanoid robot stands at 5 feet 6 inches tall and weighs 132 pounds. It can carry up to 44 pounds and move at a speed of 1.2 meters per second.

Figure is backed by tech giants such as Microsoft, OpenAI Startup Fund, NVIDIA, Jeff Bezos (Bezos Expeditions) and more.

Lot of fascinating innovations happening around us thanks to Gen AI / LLMs, Copilot, Devin, Sora, and now a glimpse into the reality of Humanoid Robotics. Isn’t it a great time to be in?!

Shiva by — March 13, 2024 in AI in Business 0
Meet Devin, the first AI-based Software Engineer

Gen AI enables writing highly sophisticated code for the given problem statement. Developers can already take advantage of that!

What if a full-fledged tool that can write code, fix bugs, leverages online resources, collaborates with human, and solves gigs on popular freelancing sites such as Upwork?!

Is this a fiction? Well, not anymore.

Meet Devin, the first of its kind, AI-based software engineer, created by Cognition Labs, an applied AI labs company that builds apps focusing on reasoning.

The Tech World is already amazed with the capabilities of Copilot which assists in developing code snippets, however, Devin has a unique capability and is a step-up in terms of its features that it can cater to end-to-end software development.

According to the creators, Devin has the following key capabilities as of writing –

  1. Learn how to use unfamiliar technologies.
  2. Build and deploy apps end to end.
  3. Autonomously find and fix bugs in codebases.
  4. Train and fine tune its own AI models.
  5. Address bugs and feature requests in open source repositories.
  6. Contribute to mature production repositories.
  7. Solve real jobs on Upwork!

Scott Wu, the founder and CEO of Cognition, explained Devin can access common developer tools, including its own shell, code editor and browser, within a sandboxed compute environment to plan and execute complex engineering tasks requiring thousands of decisions. 

Devin resolved 13.86% of issues without human assistance in the tested GitHub repositories as per the publication by creators based on SWE-benchmark that asks agents to resolve challenging problems in the open-source projects such as scikit-learn, Django.

There’s sparkling conversation around the globe that AI could kill basic coding skills written by human and recently NVidia Founder talked about everyone is now a programmer thanks to AI. Of course, I think, human oversight is required to refine and meet user’s requirements.

Thanks to Devin, now the human can focus more on complex or interesting problems that requires our creativity and best use of our time. As of now, access to Devin is only limited to select individuals. Public access is still pending. For more info, visit cognition-labs.com/blog

Shiva by — March 13, 2024 in GenAI 0
Meta’s Large Language Model – LLaMa 2 released for enterprises

Meta, the parent company of Facebook, unveiled the latest version of LLaMa 2 for research and commercial purposes. It’s released as open-source unlike OpenAI GPT / Google Bard which is proprietary.

What is LLaMa?

LLaMa (Large Language Model Meta AI) is an open-source language model built by Meta’s GenAI team for research. LLaMa 2 which is newly released for research and commercial uses.

Difference between LLaMa and LLaMa 2

LLaMa 2 model was trained on 40% more data than its predecessor. Al-Dahle (vice president at Meta who is leading the company’s generative AI work) says there were two sources of training data: data that was scraped online, and a data set fine-tuned and tweaked according to feedback from human annotators to behave in a more desirable way. The company says it did not use Meta user data in LLaMA 2, and excluded data from sites it knew had lots of personal information. 

Newly released LLaMa 2 models will not only further accelerate the LLM research work but also enable enterprises to build their own generative AI applications. LLaMa 2 includes 7B, 13B and 70B models, trained on more tokens than LLaMA, as well as the fine-tuned variants for instruction-following and chat. 

According to Meta, its LLaMa 2 “pretrained” models are trained on 2 trillion tokens and have a context window of 4,096 tokens (fragments of words). The context window determines the length of the content the model can process at once. Meta also says that the LLaMa 2 fine-tuned models, developed for chat applications similar to ChatGPT, have been trained on “over 1 million human annotations.”

Databricks highlights the salient features of such open-source LLMs:

  • No vendor lock-in or forced deprecation schedule
  • Ability to  fine-tune with enterprise data, while retaining full access to the trained model
  • Model behavior does not change over time
  • Ability to serve a private model instance inside of trusted infrastructure
  • Tight control over correctness, bias, and performance of generative AI applications

Microsoft says that LLaMa 2 is the latest addition to their growing Azure AI model catalog. The model catalog, currently in public preview, serves as a hub of foundation models and empowers developers and machine learning (ML) professionals to easily discover, evaluate, customize and deploy pre-built large AI models at scale.

OpenAI GPT vs LLaMa

A powerful open-source model like LLaMA 2 poses a considerable threat to OpenAI, says Percy Liang, director of Stanford’s Center for Research on Foundation Models. Liang was part of the team of researchers who developed Alpaca, an open-source competitor to GPT-3, an earlier version of OpenAI’s language model. 

“LLaMA 2 isn’t GPT-4,” says Liang. Compared to closed-source models such as GPT-4 and PaLM-2, Meta itself speaks of “a large gap in performance”. However, ChatGPT’s GPT-3.5 level should be reached by Llama-2 in most cases. And, Liang says, for many use cases, you don’t need GPT-4.

A more customizable and transparent model, such as LLaMA 2, might help companies create products and services faster than a big, sophisticated proprietary model, he says. 

“To have LLaMA 2 become the leading open-source alternative to OpenAI would be a huge win for Meta,” says Steve Weber, a professor at the University of California, Berkeley.   

LLaMA 2 also has the same problems that plague all large language models: a propensity to produce falsehoods and offensive language. The fact that LLaMA 2 is an open-source model will also allow external researchers and developers to probe it for security flaws, which will make it safer than proprietary models, Al-Dahle says. 

With that said, Meta has set to make its presence felt in the open-source AI space as it has announced the release of the commercial version of its AI model LLaMa. The model will be available for fine-tuning on AWS, Azure and Hugging Face’s AI model hosting platform in pretrained form. And it’ll be easier to run, Meta says — optimized for Windows thanks to an expanded partnership with Microsoft as well as smartphones and PCs packing Qualcomm’s Snapdragon system-on-chip. The key advantage of on-device AI is cost reduction (cloud per-query costs) and data security (as data solely remain on-device)

LLaMa can turn out to be a great alternative for pricy proprietary models sold by OpenAI like ChatGPT and Google Bard.

References:

https://ai.meta.com/llama/?utm_pageloadtype=inline_link

https://www.technologyreview.com/2023/07/18/1076479/metas-latest-ai-model-is-free-for-all/

https://blogs.microsoft.com/blog/2023/07/18/microsoft-and-meta-expand-their-ai-partnership-with-llama-2-on-azure-and-windows/

https://www.qualcomm.com/news/releases/2023/07/qualcomm-works-with-meta-to-enable-on-device-ai-applications-usi

https://techcrunch.com/2023/07/18/meta-releases-llama-2-a-more-helpful-set-of-text-generating-models/

https://www.databricks.com/blog/building-your-generative-ai-apps-metas-llama-2-and-databricks

Shiva by — July 19, 2023 in AI in Business 0