Tag: Generative AI

Categories
Community

Generative AI and Its Evolving Role in Software Development

Remember the days when software development was solely the domain of humans painstakingly writing lines of code? Those days are evolving rapidly. Generative AI, a branch of artificial intelligence capable of creating original content, is quickly becoming the co-pilot for software developers worldwide. This technology, leveraging advanced models like GPT-4, is not only automating mundane tasks but is also opening doors to unprecedented creativity and efficiency in the software development lifecycle.

 The Rise of AI Coding Companions

Generative AI models, like OpenAI’s ChatGPT or GitHub’s Copilot, have emerged as powerful allies for developers. These models can:

  • Generate Code: Need a function to sort a list? Just describe what you need, and the AI can generate the code for you.
  • Complete Code: Start typing a line of code, and the AI can suggest how to finish it, saving you keystrokes and brainpower.
  • Refactor Code: Want to clean up or optimize your code? The AI can suggest improvements.
  • Explain Code: Encountered a complex piece of code? Ask the AI to break it down for you in simple terms.
  • Detect Bugs: The AI can scan your code for potential bugs and suggest fixes, reducing the time spent on debugging.

NEW Developer Nation survey is live! Participate, shape the trends in software development, and win big. Start here!

Boosting Productivity and Creativity

The benefits of these AI coding companions are clear:

  • Increased Efficiency: Developers can accomplish tasks much faster, from writing boilerplate code to debugging complex issues.
  • Enhanced Creativity: The AI can offer alternative solutions or suggest innovative approaches, sparking new ideas.
  • Reduced Tedium: Developers can focus on higher-level problem-solving instead of getting bogged down in repetitive tasks.
  • Accelerated Learning: Newcomers can learn faster by getting instant feedback and explanations from the AI.

Real-World Applications

Generative AI isn’t just a theoretical concept; it’s already being used in the real world:

  • GitHub Copilot: This popular tool integrates with various code editors, providing code suggestions and completions in real time.
  • Tabnine: Another AI-powered code completion tool that supports multiple languages and frameworks.
  • Replit Ghostwriter: This tool offers AI-powered code generation, completion, and transformation features.
  • Deep TabNine: A deep learning-based code completion tool that can be integrated with various IDEs and text editors.

Challenges and Considerations

While the potential of generative AI is exciting, there are also challenges to address:

  • Accuracy: AI-generated code might not always be perfect and may require human review and correction. While these tools can significantly speed up the coding process, developers should still verify the AI’s suggestions to ensure they meet required standards and project specifications.
  • Bias: AI models can inherit biases from their training data, leading to potentially biased or unfair code suggestions. This requires developers to remain vigilant and critically assess any suggestions made, ensuring equitable and inclusive coding practices.
  • Security: The security of code generated by AI needs careful consideration to prevent vulnerabilities. Developers must be aware of potential security gaps and rigorously test AI-generated code to protect against cyber threats and maintain the integrity of their applications.
  • Ethics: As with any technology, the ethical implications of AI in coding should be carefully evaluated and addressed. This entails considering the broader impact of AI-generated solutions and ensuring that their use aligns with ethical standards and promotes positive societal outcomes.

The Future of AI-Assisted Development

The future of AI software development services is undoubtedly intertwined with generative AI. As these models continue to improve, we can expect even more sophisticated tools that will:

  • Understand Natural Language Better: Allowing developers to communicate with AI in a more intuitive way. As natural language processing capabilities advance, developers will be able to describe the functionality they need in plain English, and the AI will generate the corresponding code, reducing the need for detailed programming knowledge.
  • Generate More Complex Code: Tackling larger, more complex programming tasks. Future AI models will be capable of handling intricate logic, cross-functional dependencies, and larger codebases, thus enabling the automation of more sophisticated software projects.
  • Integrate with More Development Tools: Becoming a seamless part of the developer’s workflow. As generative AI tools continue to evolve, their integration with a wider range of development environments, version control systems, and project management tools will ensure a smoother and more cohesive development experience.

The Developer Nation Survey, a comprehensive look at developer trends, already highlights a growing interest in AI tools for coding. This indicates a shift in how developers perceive and use AI, moving from skepticism to embracing its potential.

Conclusion

Generative AI is a game-changer for software development, offering a glimpse into a future where humans and AI collaborate to create more efficient, innovative, and secure software. While challenges remain, the potential benefits are too significant to ignore. As we move forward, developers who embrace these AI-powered tools will be well-positioned to thrive in the ever-evolving landscape of software development.

NEW Developer Nation survey is live! Participate, shape the trends in software development, and win big. Start here!

Categories
Developer Nation Broadcast

The state of Data Science and future of Generative AI with Anand Mishra

In this captivating episode, we delve into the dynamic journey of Anand Mishra, the CTO of Analytics Vidhya, a frontrunner in the Data Science realm. Anand shares his transformative evolution from a Data Scientist to assuming the pivotal role of CTO, illuminating the intricate pathways and milestones that shaped his career trajectory. As we navigate through his experiences, listeners gain invaluable insights into the evolving landscape of Data Science, particularly amidst the burgeoning influence of AI.

Anand provides a compelling narrative on where the field of Data Science is headed, painting a vivid picture of its metamorphosis under the relentless march of artificial intelligence. From the intricate nuances of modern data analytics to the potential unleashed by generative AI, Anand’s perspective offers a glimpse into the future of this rapidly evolving domain.

With each anecdote and observation, Anand weaves a narrative that not only captures the essence of his personal journey but also serves as a compass for those navigating the ever-changing seas of Data Science and AI. Join us as we unravel the tapestry of innovation and exploration in this thought-provoking conversation with one of the foremost voices in the field.

Tune in to uncover the untold stories, gain exclusive insights, and embark on a journey of discovery that promises to illuminate the path ahead in the enthralling world of Data Science and AI.

Categories
Community

How Generative AI will affect developers’ work

We all remember back in March when prominent leaders, researchers, and figures in tech, most notably Elon Musk and Steve Wozniak, signed a letter advocating for a six-month pause of giant artificial intelligence (AI) experiments. Despite the letter prompting a wide discussion and raising both ethical and practical concerns that were acknowledged by many in the field, few were surprised that the letter had a negligible impact on slowing the pace of research.  

Now, approximately six months following the letter’s initial publication, we check in with those who are among the closest to the subject in question: developers. In our latest Developer Nation survey, we collected insights, perspectives, and real-world experiences from over 17,000 developers worldwide, delving into the impact of generative AI on their careers and work now and in the future. 

In this article, we present our analysis from a snapshot of the data collected to offer insight into how developers perceive the future of generative AI; specifically how it relates to their role as a developer.

Various studies that measured AI’s impact on worker productivity in different roles have been published this year. In this chapter, we do not delve into productivity metrics, but rather, we present developers’ views and perceptions about how generative AI will affect their productivity, work, and the world. 

Highlight

80% of developers believe that generative AI will increase their potential and productivity at work

💡Checkout our recent meet-up where developers from our community discussed about Impact of Generative AI in more details: https://youtu.be/OMHlve6d1bI?feature=shared

Regarding AI’s impact on their work, developers are overwhelmingly optimistic. The vast majority (80%) agree or strongly agree that AI will increase their potential and productivity, while 70% agree or strongly agree that it will give them access to new programming tools. Throughout our many years of tracking and researching developers’ preferences and behaviours, we have found that the community is incredibly heterogeneous. There is substantial variation in developers’ educational backgrounds, technology choices, and professional preferences. Hence, this remarkable consensus regarding the impact of AI on their workflow is striking. It sends a clear message about generative AI’s positive potential: only 7% of developers disagree or strongly disagree that AI’s net impact on the world will be positive!

While generative AI’s potential is great, developers clearly still harbour some reservations as well. 61% of developers agree or strongly agree that generative AI raises many ethical concerns. While we did not ask our survey respondents to specifically identify which ethical concerns they are most preoccupied with, one of the primary ethical concerns regarding AI that has received considerable attention this past year is its potential to displace workers.

Developers are somewhat split about generative AI’s potential to displace them in their current roles. 32% of developers strongly agree that AI will surpass their skills and render their jobs obsolete, while 40% either disagree or strongly disagree that this is actually a feasible outcome. How developers feel about AI’s potential to replace their jobs depends on a number of factors, but one critical factor is their current role. The following section examines the differences in perceptions across various roles. 

Will AI replace developers?

Segmenting developers by ten of the most popular roles, we examine which roles have the highest concerns about potential replacement. CIOs, CTOs, and IT managers are the most likely (40%) to strongly agree that generative AI could surpass their skills and render their role obsolete. Initially, this result is somewhat counter-intuitive as managerial skills are unlikely to be replaced in the foreseeable future by generative AI due to the variety and complexity of tasks that managers often face. However, these specific roles are frequently responsible for monitoring a company’s technology, infrastructure, and data for – among other metrics – accuracy, efficiency, security, and efficacy; all of which are quantifiable. 

While AI surpasses a human’s ability in terms of speed and accuracy of monitoring various quantitative metrics, we do not foresee a future where these roles are no longer present, rather, the technical aspect of the roles are altered; a notion supported by 41% of developers in these roles. These roles are more than likely going to evolve or be reimagined to compensate for the additional capacity granted by AI. 

Developers working or studying as data analysts, scientists, and/or researchers make up the next three roles that are most likely (37%-40%) to strongly agree their skills will be surpassed and their jobs could be rendered obsolete. However, roughly an equal or greater number of developers in these data-driven roles strongly disagree.

Generative AI has made great strides in several areas, including data analysis and code generation. While the progress is impressive, what is equally striking is how difficult it can be to differentiate correct from false/hallucinated answers and incorrect analytical applications or interpretations unless the developer has domain-specific knowledge. Hence, while these researcher and data-handling roles have already been substantially impacted by AI and will surely continue to be, developers are split on the future of these types of roles.

On the opposite end of the spectrum, architects and programmers are the most likely to strongly disagree that their roles and skills can be replaced by generative AI. Most of these developers, 61% and 55%, respectively, feel their role and skills are safe from the threat of generative AI. These developers are some of the most likely to be technical experts and recognise that while AI can excel at quantifiable solutions, complex or multi-faceted problems are likely to continue to require substantial human input for the foreseeable future.

It is inevitable, however, that these roles will still feel its impact and influence in their work. Hence, in the next section, we take a look into who the developers are who feel that they can benefit from AI and gain access to additional tools through its use. 

Highlight

61% and 55% of architects and programmers respectively, disagree or strongly disagree that Generative AI will surpass their skills and render their jobs obsolete

Will AI allow developers to access new tools and technologies?

One of the factors that significantly impact developers’ perceptions on whether generative AI will allow them to use programming tools that they previously could not is their level of experience. Overwhelmingly, 80% of developers with less than a year of software development experience agree or strongly agree that AI will give them access to new tools that would otherwise not be available. The proportion of developers who agree steadily declines to 60% as developers gain more experience, where, in turn, more experienced developers are more likely to strongly disagree with this sentiment. 

More experienced developers also have greater programming skills and are, therefore, less likely to expect that generative AI will create new opportunities for them to access additional tools. It is not a new phenomenon that younger, less experienced individuals enter a field or company and are more open to learning novel techniques or new methods, some of which can be in contrast to the established, institutionalised way of doing things. This distribution of developers’ AI perspective below conforms to this trend and demonstrates that less experienced developers perceive AI in a different light compared to the more seasoned ones. 

Insight

80% of developers in their first year of developing software strongly agree that AI will be/ is a gateway granting them access to new programming tools

However, a finding worth highlighting here is that the experts in the field – those with more than 16 years of experience – are the most likely (28%) to report being unsure, neither agreeing or disagreeing, about AI’s potential to provide them access to new tools. This degree of uncertainty from the most practised group of developers is a good indicator that the future of generative AI is still very much evolving and points to an exciting but somewhat uncertain future of how AI advancements will continue to shape the role of developers. 

Categories
Community Tips

Navigating the New Era of Learning: Top Generative AI Books for Programmers

Hello, we’re Computer Science Professors Dr. Leo Porter and Dr. Daniel Zingaro. We’ve dedicated our careers to helping students succeed in programming and computer science courses. There are approaches we know are effective in teaching novices, such as learning from worked-out examples and using real-world problems that resonate with students. When we’re reading a book, we’re always thinking: will this book help people learn? Does it use what we know about learning to serve as an effective teaching aid? Can we use this with our students? Can we for once stop analyzing the book and just read for fun? (The answer to that last question is, unfortunately, ‘no,’ 😀 We can’t help it!)

With massive changes happening due to generative AI tools like ChatGPT and GitHub Copilot, you won’t be surprised that there’s a swarm of new books that use generative AI to teach programming to beginners or to enhance what programmers can do.

In this article, we wanted to cover our top four generative AI books that are being published by Manning Publications.

Special Deal! Get 35% Off all Manning Products at checkout with code bldevnat23 @ Manning.com

Introduction to Generative AI
Introduction to Generative AI: An ethical, societal, and legal overview Numa Dhamani and Maggie Engler

We know, we know: you just want to use generative AI to supercharge your programming productivity. We want that, too! But we’re at the dawn of a programming revolution here, and we strongly encourage you to take the time to understand the ethical and legal concerns wrought by generative AI. 

What happens when generative AI models ingest objectionable speech or personal data? Why are these models apt to produce hallucinations, and why should we care? Why is it so difficult to address bias in machine learning? What is the critical role that human feedback plays in LLM training, and what are the associated costs to humans? Does generative AI’s use of copyrighted work fall under fair use?

As informed end-users of generative AI, it’s up to us to answer these questions–to understand what data we might be using, how that data was produced, and the societal and ethical impacts of these tools. This book helps us toward those answers.

We appreciate that many claims in the book are supported with references that the reader can check for additional details. We also benefited from numerous powerful examples throughout the book, such as racial bias in movie sentiment scores, a ChatGPT data breach, and a famous virtual influencer.

We’d also like to emphasize that while the focus of the book is on the responsible use of generative AI, there is also a non-mathematical overview coverage of how generative AI tools work, which we suspect will be of interest to many readers. For example, you’ll learn more about many concepts you’ve probably heard about in passing, such as foundation models, fine-tuning, emergent properties of LLMs, zero-shot and few-shot learning, and chain-of-thought prompting.

Finally, we applaud the balanced discussion of the pros and cons of synthetic media, the ways that LLMs are and will be misused, the ways that professionals are using LLMs and–of course!–the coverage of the impacts on education.

Dhamani and Engler’s Introduction to Generative AI is a must-read foundational guide not only to understand how generative AI works but also to understand its broader societal implications.

Learn AI-Assisted Python Programming
Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT Leo Porter and Daniel Zingaro

The two of us (Daniel Zingaro and Leo Porter) wrote this book because we believe that the way new programmers learn to program has changed dramatically now that generative AI is here. We’ve both taught thousands of students to program over the years and a lot of our time needed to revolve around teaching syntax, which is the ways that words and symbols are put together to create programs that run. But generative AI handles syntax extremely well (which is a good thing, because many learners find syntax boring and frustrating). So, in writing this book, our guiding question was: what are the main skills that new programmers need to learn now?

In this book, written for absolute beginners, you’ll be writing programs that work from day one, in contrast to the before times when you would have had to learn lots of syntax first. You’ll learn how to test code that comes from the generative AI to check whether it is correct, break down large problems into smaller bits that the AI can better solve, and use a debugger to trace your code very carefully to see what it’s doing. Oh, and you’ll be learning Python along the way, too, in case you need that for your resume 🙂

The Complete Obsolete Guide to Generative AI
The Complete Obsolete Guide to Generative AI
David Clinton

Why would you buy and read a book with ‘obsolete’ in the title? What the author is getting at with this irreverent title is that generative AI is moving so quickly that everything written about it will be obsolete quickly. We may as well understand the foundations of effectively interacting with these tools, which is what this book focuses on.

The book starts by explaining the background concepts you need to know when working with generative AI tools. What’s a token? What are the differences between all of those GPT models? What the heck is temperature and Top P?

You need Python experience to read this one. This isn’t a programming book, though. It’s a “let’s see what we can do with generative AI!” book. You’ll generate fiction (not very good fiction… yet?), generate book cover images, convert slides to videos, and quickly obtain summaries of boring meetings and long PDF documents. The book tours many powerful generative AI tools that you may not have been aware of–it goes way beyond what the general public is doing with ChatGPT.

The key takeaway of the book is that the best results come from pairing your domain knowledge with the explosion of content you can create with generative AI.

AI-Powered Developer: Build great software with ChatGPT and Copilot Nathan B. Crocker

OK — so you’re already a Python developer and you want to start using LLMs to rocket your productivity. How? By reading Crocker’s new book 😀

This book shows you how to use GitHub Copilot, ChatGPT, and Amazon CodeWhisperer (and when to use each). It assumes that you already know Python, and we further suggest that familiarity with building APIs in Python would be a plus.

Through its chapters, you’ll build an Information Technology Asset Management (ITAM) system, using generative AI for each step… from designing the system to writing the code, generating data, testing and managing the deployment, and helping with security. (Yes: generative AI is useful way beyond writing code for you!)

The pro of writing the book as one comprehensive example is that you see how a complete application is built and deployed with generative AI help. The cons are that it makes it difficult to jump around the book and that if you are not motivated by the chosen example then the book itself may not be as motivating as a collection of smaller examples. We need books of both types!

For us, the material on system design is of particular interest, because in our time working with generative AI, we have done the high-level design and left the low-level code to the AI. Crocker’s book shows that experienced programmers can indeed push generative AI into the design realm as well, including proposing designs, creating class diagrams for designs, and comparing and contrasting potential designs.

Whether you want to understand generative AI at a societal level, to learn programming from scratch “the new way,” to add generative AI to your programming toolbox, or to be inspired to use generative AI to … generate (sorry!) content, we’re confident that you’ll find value in one or more of these books.

Manning Publications is a premier publisher of technical books on computer and software development topics for both experienced developers and new learners alike.

Manning prides itself on being independently owned and operated, and for paving the way for innovative initiatives, such as early access book content and protection-free PDF formats that are now industry standard.