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
Prompt Engineering for Developers: Leveraging AI as Your Coding Assistant

Gartner predicts “By 2027, 50% of developers will use ML-powered coding tools, up from less than 5% today”

In the age of AI, developers have an invaluable tool to enhance productivity: prompt engineering. This is the art and science of crafting effective inputs (prompts) for AI models, enabling them to understand, process, and deliver high-quality outputs. By leveraging prompt engineering, developers can guide AI to assist with coding, from generating modules to optimizing code structures, creating a whole new dynamic for AI-assisted development.

What is Prompt Engineering?

Prompt engineering involves designing specific, concise instructions to communicate clearly with an AI, like OpenAI’s GPT. By carefully wording prompts, developers can guide AI to produce responses that meet their goals, from completing code snippets to debugging.

Why is Prompt Engineering Important for Developers?

For developers, prompt engineering can mean the difference between an AI providing useful assistance or producing vague or off-target responses. With the right prompts, developers can get AI to help in tasks like:

  • Generating boilerplate code
  • Writing documentation
  • Translating code from one language to another
  • Offering suggestions for optimization

How Developers Can Leverage Prompt Engineering for Coding

  1. Code Generation
    Developers can use prompt engineering to generate entire code modules or functions by providing detailed prompts. For example:
    • Prompt: “Generate a Python function that reads a CSV file and calculates the average of a specified column.”
  2. Debugging Assistance
    AI models can identify bugs or inefficiencies. A well-crafted prompt describing an error or issue can help the AI provide pinpointed debugging tips.
    • Prompt: “Review this JavaScript function and identify any syntax errors or inefficiencies.”
  3. Code Optimization
    AI can suggest alternative coding approaches that might improve performance.
    • Prompt: “Suggest performance optimizations for this SQL query that selects records from a large dataset.”
  4. Documentation and Explanations
    Developers can create prompts that generate explanations or documentation for their code, aiding understanding and collaboration.
    • Prompt: “Explain what this Python function does and provide inline comments for each step.”
  5. Testing and Validation
    AI can help generate test cases by understanding the function’s purpose through prompts.
    • Prompt: “Create test cases for this function that checks for valid email addresses.”
  6. Learning New Frameworks or Languages
    Developers can use prompts to ask AI for learning resources, tutorials, or beginner-level code snippets for new programming languages or frameworks.
    • Prompt: “Explain the basics of using the Databricks framework for data analysis in Python.”

Advanced Prompt Engineering Techniques

1. Chain of Thought Prompting

Guide the AI through the development process:

Let's develop a caching system step by step:
1. First, explain the caching strategy you'll use and why
2. Then, outline the main classes/interfaces needed
3. Next, implement the core caching logic
4. Finally, add monitoring and error handling

2. Few-Shot Learning

Provide examples of desired output:

Generate a Python logging decorator following these examples:

Example 1:
@log_execution_time
def process_data(): ...

Example 2:
@log_errors(logger=custom_logger)
def api_call(): ...

Now create a new decorator that combines both features

3. Role-Based Prompting

Act as a security expert reviewing this authentication code:
[paste code]
Identify potential vulnerabilities and suggest improvements

Key Considerations for Effective Prompt Engineering

To maximize AI’s effectiveness as a coding assistant, developers should:

  • Be Clear and Concise: The more specific a prompt is, the more accurate the response.
  • Iterate on Prompts: Experiment with different phrasings to improve the AI’s response quality.
  • Leverage Context: Provide context when necessary. E.g., “In a web development project, write a function…”

Conclusion

Prompt engineering offers developers a powerful way to work alongside AI as a coding assistant. By mastering the art of crafting precise prompts, developers can unlock new levels of productivity, streamline coding tasks, and tackle complex challenges. As AI’s capabilities continue to grow, so too will the potential for prompt engineering to reshape the way developers build and maintain software.

Shiva by — October 26, 2024 in AI Trends & News 0
Unlocking the Power of Generative AI in the Travel & Hospitality Industry

Generative AI (GenAI) is transforming industries, and the Travel & Hospitality sector is no exception. GenAI models, such as GPT and LLMs (Large Language Models), offer a revolutionary approach to improving customer experiences, operational efficiency, and personalization.

According to Skift, GenAI presents a $28 billion opportunity for the travel industry. Two out of three leaders are looking to invest toward the integration of new gen AI systems with legacy systems.

Key Value for Enterprises in Travel & Hospitality:

  1. Hyper-Personalization: GenAI enables hotels and airlines to deliver customized travel itineraries, special offers, and personalized services based on real-time data, guest preferences, and behavior. This creates unique, targeted experiences that increase customer satisfaction and loyalty.
  2. Automated Customer Support: AI-powered chatbots and virtual assistants, fueled by GenAI, provide 24/7 assistance for common customer queries, flight changes, reservations, and more. These tools not only enhance service but also reduce reliance on human customer support teams.
  3. Operational Efficiency: GenAI-driven tools can help streamline back-office processes like scheduling, inventory management, and demand forecasting. In the airline sector, AI algorithms can optimize route planning, fleet management, and dynamic pricing strategies, reducing operational costs and improving profitability.
  4. Content Generation & Marketing: With GenAI, travel companies can automate content creation for marketing campaigns, travel guides, blog articles, and even social media posts, allowing for consistent and rapid content generation. This helps companies keep their marketing fresh, engaging, and responsive to real-time trends.
  5. Predictive Analytics: Generative AI’s deep learning models enable companies to predict customer behavior, future travel trends, and even identify areas of potential disruption (like weather conditions or geopolitical events). This helps businesses adapt swiftly and proactively to changes in the market.

I encourage you to read about this Accenture report. It depicts the potential of impact that GenAI creates for industries from Airlines to Cruise Lines.

Also, the report offers us more use-cases across the typical customer journey from Inspiration to Planning to Booking stage.

Conclusion

The adoption of Generative AI by enterprises in the Travel & Hospitality industry is a game changer. By enhancing personalization, improving efficiency, and unlocking new marketing opportunities, GenAI is paving the way for innovation, delivering a competitive edge in a fast-evolving landscape. Businesses that embrace this technology will be able to not only meet but exceed customer expectations, positioning themselves as leaders in the post-digital travel era.

Shiva by — October 18, 2024 in AI in Business 0
Key Trends in Data Engineering for 2025

As we approach 2025, the field of data engineering continues to evolve rapidly. Organizations are increasingly recognizing the critical role that effective data management and utilization play in driving business success.

In my professional experiences, I have observed ~60% of Data & Analytics services for enterprises revolve around Data Engineering workloads, and the rest on Business Intelligence (BI), AI/ML, and Support Ops.

Here are the key trends that are shaping the future of data engineering:

1. Data Modernization

The push for data modernization remains a top priority for organizations looking to stay competitive. This involves:

  • Migrating from legacy systems to cloud-based platforms like Snowflake, Databricks, AWS, Azure, GCP.
  • Adopting real-time data processing capabilities. Technologies like Apache Kafka, Apache Flink, and Spark Structured Streaming are essential to handle streaming data from various sources, delivering up-to-the-second insights
  • Data Lakehouses – Hybrid data platforms combining the best of data warehouses and data lakes will gain popularity, offering a unified approach to data management
  • Serverless computing will become more prevalent, enabling organizations to focus on data processing without managing infrastructure. Ex: AWS Lambda and Google Cloud Functions

We’ll see more companies adopting their modernization journeys, enabling them to be more agile and responsive to changing business needs.

2. Data Observability

As data ecosystems grow more complex, the importance of data observability cannot be overstated. This trend focuses on:

  • Monitoring data quality and reliability in real-time
  • Detecting and resolving data issues proactively
  • Providing end-to-end visibility into data pipelines

Tools like Monte Carlo and Datadog will become mainstream, offering real-time insights into issues like data drift, schema changes, or pipeline failures.

3. Data Governance

With increasing regulatory pressures and the need for trusted data, robust data governance will be crucial. Key aspects include:

  • Implementing comprehensive data cataloging and metadata management
  • Enforcing data privacy and security measures
  • Establishing clear data ownership and stewardship roles

Solutions like Collibra and Alation help enterprises manage compliance, data quality, and data lineage, ensuring that data remains secure and accessible to the right stakeholders.

4. Data Democratization

The trend towards making data accessible to non-technical users will continue to gain momentum. This involves:

  • Developing user-friendly self-service analytics platforms
  • Providing better data literacy training across organizations
  • Creating intuitive data visualization tools

As a result, we’ll see more employees across various departments becoming empowered to make data-driven decisions.

5. FinOps (Cloud Cost Management)

As cloud adoption increases, so does the need for effective cost management. FinOps will become an essential practice, focusing on:

  • Optimizing cloud resource allocation
  • Implementing cost-aware data processing strategies
  • Balancing performance needs with budget constraints

Expect to see more advanced FinOps tools that can provide predictive cost analysis and automated optimization recommendations.

6. Generative AI in Data Engineering

The impact of generative AI on data engineering will be significant in 2025. Key applications include:

  • Automating data pipeline creation and optimization
  • Generating synthetic data for testing and development
  • Enriching existing datasets with AI-generated data to improve model performance
  • Assisting in data cleansing and transformation tasks

Tools like GPT and BERT will assist in speeding up data preparation, reducing manual intervention. We’ll likely see more integration of GenAI capabilities into existing data engineering tools and platforms.

7. DataOps and MLOps Convergence

The lines between DataOps and MLOps will continue to blur, leading to more integrated approaches:

  • Streamlining the entire data-to-model lifecycle
  • Implementing continuous integration and deployment for both data pipelines and ML models
  • Enhancing collaboration between data engineers, data scientists, and ML engineers

This convergence will result in faster time-to-value for data and AI initiatives.

8. Edge Computing and IoT Data Processing

With the proliferation of IoT devices, edge computing will play a crucial role in data engineering:

  • Processing data closer to the source to reduce latency
  • Implementing edge analytics for real-time decision making, with tools like AWS Greengrass and Azure IoT Edge leading the way
  • Developing efficient data synchronization between edge and cloud

Edge computing reduces latency and bandwidth use, enabling real-time analytics and decision-making in industries like manufacturing, healthcare, and autonomous vehicles.

9. Data Mesh Architecture

The data mesh approach will gain more traction as organizations seek to decentralize data ownership:

  • Treating data as a product with clear ownership and quality standards
  • Implementing domain-oriented data architectures
  • Providing self-serve data infrastructure

This paradigm shift will help larger organizations scale their data initiatives more effectively.

10. Low-Code/No-Code

Low-code and no-code platforms are simplifying data engineering, allowing even non-experts to build and maintain data pipelines. Tools like Airbyte and Fivetran will empower more people to create data workflows with minimal coding.

It broadens access to data engineering, allowing more teams to build data solutions without deep technical expertise.

Conclusion

As we look towards 2025, these trends highlight the ongoing evolution of data engineering. The focus is clearly on creating more agile, efficient, and democratized data ecosystems that can drive real business value. Data engineers will need to continually update their skills and embrace new technologies to stay ahead in this rapidly changing field. Organizations that successfully adapt to these trends will be well-positioned to thrive in the data-driven future that lies ahead.

Shiva by — October 16, 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
Difference between traditional AI and Generative AI

Generative AI is the new buzzword since late 2022. The likes of ChatGPT, Bard, etc. is taking the AI to the all new levels with wide variety of use-cases for consumers and enterprises.

I wanted to briefly understand the difference between traditional AI and generative AI. According to a recent report published in Deloitte, GenAI’s output is of a higher complexity while compared with traditional AI.

Typical AI models would generate output in the form of a value (Ex: predicting sales for next quarter), label (Ex: classifying a transaction as legitimate or fraud). GenAI models tend to generate a full page of composed text or other digital artifact. Applications like Midjourney, DALL-E produces images, for instance.

In the case of GenAI, there is no one possible correct answer. Deloitte study reports, this results in a large degree of freedom and variability, which can be interpreted as creativity.

The underlying GenAI models are usually large in terms of resources consumption, requiring TBs of high-quality data processed on large-scale, GPU-enabled, high-performance computing clusters. With OpenAI’s innovation being plugged into Microsoft Azure Services and Office suites, it would be interesting to see the dramatic changes in consumers’ productivity!

Shiva by — May 28, 2023 in AI in Business 0