Interviewing AI. AI has thus answered the questions on AI in Software Development!

Me: Describe to me how AI is changing software development and the work developers do daily.

The AI: AI is transforming software development in various ways, ranging from automating repetitive tasks to enhancing software performance and user experience. Here are some specific ways that AI is changing software development and the work developers do daily:

  1. Automating mundane tasks: AI has enabled developers to automate various repetitive tasks that were previously done manually. For instance, using machine learning algorithms, AI can automate code review and testing, freeing up developers’ time to focus on more complex tasks.
  2. Enhancing software performance: With AI, developers can create more robust and efficient software. AI algorithms can analyze code and identify performance issues, potential bugs, and other areas that can be improved. This makes it easier for developers to optimize code and improve overall software performance.
  3. Improving user experience: AI can be used to enhance user experience in various ways. For instance, AI can be used to analyze user behavior and preferences, allowing developers to create more personalized and intuitive interfaces. AI can also be used to provide real-time feedback and recommendations to users, making software more engaging and interactive.
  4. Increasing productivity: AI-powered tools and frameworks can help developers be more productive by automating repetitive tasks, suggesting solutions to problems, and speeding up the development process.
  5. Facilitating collaboration: AI-powered tools can facilitate collaboration among developers by identifying areas where different developers can work together, highlighting common issues, and recommending solutions.

Overall, AI is transforming software development by making it more efficient, effective, and collaborative. As AI continues to evolve, developers will likely see even more changes in the way they work and the software they produce.

Me: What are ways that software developers can prepare for the changes AI is bringing to software development?

The AI: As AI continues to disrupt the software development industry, software developers need to prepare for the changes that are coming. Here are some ways that software developers can prepare for the changes AI is bringing to software development:

  1. Stay informed: Developers need to keep up with the latest trends and developments in AI and machine learning. This includes reading industry publications, attending conferences, and participating in online forums.
  2. Learn new skills: Developers should invest in learning new skills that will be valuable in an AI-driven future. This includes understanding machine learning algorithms, natural language processing, and data analysis techniques.
  3. Collaborate with AI experts: Developers can collaborate with AI experts to learn more about AI technologies and how they can be applied to software development. This can involve working on joint projects, attending meetups, and networking with AI experts.
  4. Embrace automation: Developers need to be open to automation and embrace tools that can automate repetitive tasks. This includes using AI-powered code review and testing tools, as well as automated build and deployment systems.
  5. Focus on the user: Developers should focus on creating software that delivers value to users. AI can be used to create more personalized and intuitive interfaces that enhance the user experience. By focusing on the user, developers can stay ahead of the curve and ensure that their software remains relevant.
  6. Develop a growth mindset: Lastly, developers should develop a growth mindset that enables them to embrace change and learn new skills. This includes being open to feedback, seeking out new challenges, and continuously improving their skills.

In summary, by staying informed, learning new skills, collaborating with AI experts, embracing automation, focusing on the user, and developing a growth mindset, software developers can prepare themselves for the changes AI is bringing to software development.

Me: What effects might AI cause in the developer job market with the reduction and automation of work that many software developers do today?

The AI: The increasing adoption of AI in software development is likely to have a significant impact on the job market for software developers. While AI will create new job opportunities in areas such as data science and machine learning, it may also lead to the reduction and automation of work that many software developers do today. Here are some potential effects:

  1. Automation of repetitive tasks: As AI-powered tools become more prevalent, developers may find that many of the tasks they previously performed manually are automated. This could include tasks such as testing, debugging, and code review. While this will free up developers’ time to focus on more complex tasks, it may also lead to a reduction in the number of jobs available.
  2. Shift in skill requirements: As AI becomes more prevalent, the skills required to be a successful software developer may change. Developers may need to have a greater understanding of machine learning algorithms, data analysis, and natural language processing. This may lead to a shift in the education and training required for software development.
  3. Emergence of new job roles: While some jobs may be automated, AI is also likely to create new job opportunities in areas such as data science, machine learning engineering, and AI architecture. These jobs may require a different set of skills and expertise than traditional software development roles.
  4. Increased competition: As the number of jobs available in software development potentially decreases, competition for the remaining jobs may increase. This may put pressure on developers to constantly update their skills and stay relevant in the job market.

Overall, the impact of AI on the job market for software developers is likely to be complex and multifaceted. While some jobs may be automated or reduced, new job opportunities may emerge. Developers who stay up-to-date with the latest trends and developments in AI and machine learning will be better positioned to take advantage of these new opportunities.

Me: Is there any evidence that there will be a net loss of jobs when AI becomes a larger part of software development?

The AI: There is some debate among experts about the impact of AI on jobs in the software development industry. While AI will certainly lead to the automation of some tasks, it may also create new job opportunities in areas such as data science, machine learning engineering, and AI architecture.

There have been several studies on the impact of AI on jobs in the broader economy, and the results have been mixed. Some studies suggest that AI will lead to significant job losses, while others suggest that it will create new job opportunities and lead to a net gain in jobs.

In the software development industry specifically, there is evidence that the impact of AI on jobs will depend on the specific tasks and skills involved. For example, a study by Deloitte found that while AI may automate some routine programming tasks, it is unlikely to replace developers altogether. Instead, AI is likely to shift the focus of developers towards more complex tasks that require human creativity and problem-solving skills.

Another study by the World Economic Forum found that AI is likely to create new job roles in software development, such as data analysts, machine learning engineers, and AI architects. However, the study also found that there may be some displacement of jobs, particularly for those with lower skill levels.

Overall, while it is difficult to predict the exact impact of AI on jobs in software development, it is likely that there will be both job losses and job gains. Developers who are able to adapt and acquire new skills in areas such as machine learning and data analysis are likely to be better positioned to take advantage of the new job opportunities created by AI.


Funny thing, and maybe it’s all a simulation right, but AI has given you basically the same exact answers I’d give you if asked these same questions. However, I’m writing an article to add more detail that AI (at least chatGPT) has not been able to provide. If you’ve used it, you’ve probably noticed, but there are distinctive limitations to the AI’s learning so far that just isn’t equitable to a well researched and well read human writing up a piece on a topic they understand well. It appears, so far, we’re not entirely replaceable yet!

Zhi Yang Presenting “Hierarchical Topic Modeling in Cancer Patients’ Mutational Profiles”

UPDATED: Video Added from the Conference!


Introducing Zhi Yang > @zhiiiyang < presenting “Hierarchical Topic Modeling in Cancer Research”.

Topic models have been widely applied to extract topics from various range of documents or collections of texts, i.e., online customers reviews, medical records, scientific
journals, legal documents, books and etc. Its application facilitates the process for us to quickly understand the most featured and commonly shared information embedded texts without actually reading through the entire collection. In addition, topic models also allow us to access the contribution of each topic and its representations across different documents. Human genomes have been exposed to an assortment of mutational processes by contributing to unique patterns of somatic mutations. What would happen if we apply the same concept to the somatic mutations obtained from the cancer patients and look for “topics” of mutations? What would these “topics” tell us about the most important information for our health, genetic, risk factors for cancer and
something more that slip under the radar?

Shiraishi et al’s have proposed a topic model targeted for somatic mutations to capture the characteristics and burdens contributed by mutational processes. By closely examining the burdens, we’d like to compare them across different categories, say, for example, time, cancer subtype, ethnicity, smoking history, etc. Then, we’d like to develop the statistical machinery to infer the difference between the mutational profiles across different categories and associate the variations with the know exposures. This tool is potentially useful for identifying novel and existing mutational processes and correlating them with risk factors in which later can be used to monitor any treatment effects in personalized medicine and targeted therapy.

Read the publication here at biorxiv and come check out Zhi Yang’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

ML Spends A Year In Burgundy with Jon Oropeza at ML4ALL

We’re building up to ML4ALL 2019, and in the meanwhile I want to re-introduce some of the past speakers and show you their talks. This first, of the many, is Jon Oropeza. I introduced him last year here, so check out his talk and work, he’s got a lot of good stuff he’s put together!.

The Talk

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ML4ALL Speakers – Meet Paige & Suz

Hello everybody and especially the upcoming ML4ALL attendees! For those that haven’t registered, here’s the link for that, and for those that have welcome aboard! Here’s two of the excellent speakers that will be presenting at ML4ALL Paige (@DynamicWebPaige) and Suz (@Noopkat)!

Paige & Suz both work on the amazing, now somewhat famous Microsoft Developer Advocacy team others like Peter Bright (@drpizza) wrote about in “Microsoft’s renewed embrace of developers developers developers” and James Governor (@monkchips) wrote in “On Hiring Jesse Frazelle: Microsoft’s developer advocacy hot streak continues” and I even wrote a blurb about many months ago. They’ve added even more awesome to a great team and will be coming to ML4ALL to do more of that.

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