What Is Artificial Intelligence?
Artificial intelligence (AI) is the study and development of intelligent machines that work and react like humans. Related to AI is machine learning (ML), a process by which machines acquire “knowledge” more efficiently by using algorithms to learn. Activities that programmers use computers with artificial intelligence and machine learning include speech recognition, image recognition, and other types of data analysis.
Which Developers Care About AI?
The developers who care about AI and ML vary widely based on application space and expertise. For example AI and ML can be applied to something as commonplace as e-commerce and entertainment streaming platforms or as forward thinking as autonomous vehicles like cars. AI can be a part of an entirely software-based application or the internet-of-things (IoT).
What Are Their Most Pressing Problems?
Regardless of application or expertise, there are a few problems that can be identified.
- Most AI and ML developers rely on language and speech based Application Programmer Interfaces (APIs), topics related to the specifics of using AI and ML with language and speech APIs are sure to have an audience.
- 50% of developers surveyed said gathering or generating data is the most challenging aspect of continually training and fine-tuning AI models.
- The AI and machine learning developers surveyed revealed that the complexity of managing operations is their biggest pain point when developing AI applications.
How Did We Discover These Problems?
Just as in our search for topics in digital transformation, we consulted our usual sources. One of the best sources of information about what technical issues are on developers minds are the developers themselves. Thanks to a survey by Evans and the detailed reporting of those findings, a rich tapestry of detail is available to suggest topics that might be of interest to AI and ML developers.
Mind-Blowing or Meh?
Is one type of information source sufficient for finding a hot topic? Survey data is probably the best place to start, but used alone it is difficult to assess the other elements that make a topic mind-blowing – chiefly specific relevance to your segment of the AI developer audience and timeliness. Check out our recent feature of the selection process insight offered by Weavr AI founder Avinash Harsh.
Data does give shape to the topic areas that might require a brand’s attention. Using data as a starting point, one can then search developer websites, thought leaders, and forums, focusing particularly on topics where there is robust Q&A and discussion. Narrowing the topics down to something technically specific is key, and it will require the input technical experts to brainstorm and validate riveting and relevant topics.