The current demand for pursuing a career in the field of AI and computer vision is at an all-time high. As with various other aspects of the digital realm, a comprehensive understanding of these areas can be attained through online resources. It is often presumed that the quality of online courses could be better than traditional methods, such as college-level programs, practical experience in the field, and offline studies. However, online learning has advanced beyond this misconception. Paid and free online courses can teach fundamental computer vision principles and specific elements of the discipline.

Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intelligent services-such as computer vision-continually learn with time. When the internals within the abstracted 'black box' become hidden and evolve, pitfalls emerge in the robustness of applications that depend on these evolving services. Without adapting the way developers plan and construct projects reliant on intelligent services, significant gaps and risks result in both project planning and development. Therefore, how can software engineers best mitigate software evolution risk moving forward, thereby ensuring that their own applications maintain quality? Our proposal is an architectural tactic designed to improve intelligent service-dependent software robustness. The tactic involves creating an application-specific benchmark dataset baselined against an intelligent service, enabling evolutionary behaviour changes to be mitigated. A technical evaluation of our implementation of this architecture demonstrates how the tactic can identify 1,054 cases of substantial confidence evolution and 2,461 cases of substantial changes to response label sets using a dataset consisting of 331 images that evolve when sent to a service.

Amazon's controversial Rekognition computer vision technology is now being used to rid food sites of surprise dick pics. The London-based food delivery service Deliveroo has a definite content moderation challenge. It seems that when there's a problem with a food order, Deliveroo customers often send in a photo of the food with their complaint. Or they arrange the food into the shapes of private parts. Deliveroo's employees, it turns out, don't necessarily want to see stuff like that. So the company uses Rekognition to recognize unsavory photos and blur or delete them before they reach human eyes.

Recent advances in artificial intelligence (AI) and machine learning (ML), such as computer vision, are now available as intelligent services and their accessibility and simplicity is compelling. Multiple vendors now offer this technology as cloud services and developers want to leverage these advances to provide value to end-users. However, there is no firm investigation into the maintenance and evolution risks arising from use of these intelligent services; in particular, their behavioural consistency and transparency of their functionality. We evaluated the responses of three different intelligent services (specifically computer vision) over 11 months using 3 different data sets, verifying responses against the respective documentation and assessing evolution risk. We found that there are: (1) inconsistencies in how these services behave; (2) evolution risk in the responses; and (3) a lack of clear communication that documents these risks and inconsistencies.

Recent advances in artificial intelligence (AI) and machine learning (ML), such as computer vision, are now available as intelligent services and their accessibility and simplicity is compelling. Multiple vendors now offer this technology as cloud services and developers want to leverage these advances to provide value to end-users. However, there is no firm investigation into the maintenance and evolution risks arising from use of these intelligent services; in particular, their behavioural consistency and transparency of their functionality. We evaluated the responses of three different intelligent services (specifically computer vision) over 11 months using 3 different data sets, verifying responses against the respective documentation and assessing evolution risk. We found that there are: (1) inconsistencies in how these services behave; (2) evolution risk in the responses; and (3) a lack of clear communication that documents these risks and inconsistencies. We propose a set of recommendations to both developers and intelligent service providers to inform risk and assist maintainability.

Tricking Google's computer vision AI into seeing a dog as a pair of human skiers may seem mostly harmless. But the possibilities become more unnerving when considering how hackers could trick a self-driving car's AI into seeing a plastic bag instead of a child up ahead. Or making future surveillance systems overlook a gun because they see it as a toy doll. An independent AI research group run by MIT students has demonstrated a new way to fool the computer vision algorithms that enable AI systems to see the world--an approach that could prove up to 1000 times as fast as other existing ways of hacking "black box" systems whose inner workings remain hidden to outsiders. That idea of a black box perfectly describes the neural networks behind the deep learning algorithms enabling computer vision services for Google, Facebook, and other companies.

Amazon touts Alexa, Google has its Assistant, Microsoft has Cortana. But at Adobe, an often overlooked player in this contest, it's all about Sensei. Launched last fall, Sensei is a series of AI services and a voice-powered virtual assistant being added to Creative Cloud (formerly Creative Suite) apps and services like Photoshop and Premiere. Some Sensei services are already available, like the ability to change a facial expression with Face Aware Editing in Photoshop, while others, like the ability to control Photoshop with your voice, are still prototypes. Sensei will be able to talk you through how to edit photos and videos like a pro because Adobe has tracked millions of photo and video editing sessions.

Amazon touts Alexa, Google has its Assistant, Microsoft has Cortana. But at Adobe, an often overlooked player in this contest, it's all about Sensei. Launched last fall, Sensei is a series of AI services and a voice-powered virtual assistant being added to Creative Cloud (formerly Creative Suite) apps and services like Photoshop and Premiere. Some Sensei services are already available, like the ability to change a facial expression with Face Aware Editing in Photoshop, while others, like the ability to control Photoshop with your voice, are still prototypes. Sensei will be able to talk you through how to edit photos and videos like a pro because Adobe has tracked millions of photo and video editing sessions.