Artificial intelligence (AI)-driven chatbot ChatGPT has made headlines in recent weeks. It has been in the news for writing academic pieces, cracking exams, and even producing news stories. In fact, the journey of AI-driven tools dates back to 1950s and '60s when first such tools was built. From ELIZA in 1966 to ChatGPT, AI researchers covered a long road to produce tools that could potentially mimic human resposes. However, even though ChatGPT appears to be able to do just about anything, it has its limitations.

From increasingly capable hardware to TinyML, embedded machine learning will make strides in 2021. Despite silicon shortages, several new capabilities for embedded machine learning on Internet of Things devices will emerge in 2021, industry watchers predict. Together with hardware advancement and progress in ML development, microcontrollers can now run increasingly complex ML models directly on the hardware and without a round trip to the cloud. To further expand TinyML on device, many enterprises will turn to TinyML as a service (TinyMLaaS)., in which an IoT device concretely takes part in the execution of intelligent services. "The adoption of way more intelligence at the device level bypasses a lot of the issues, especially with bandwidth and latency," said Lucy Lee, a senior associate at Volition Capital who tracks embedded AI/ML on IoT. Lee said that enterprises' current overpopulation of IoT devices The thousands of devices already installed will be eclipsed by the next generation of smarter hardware.

Our Company Changing the world through digital experiences is what Adobe's all about. We give everyone--from emerging artists to global brands--everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real time auto race analytics systems.

From the smallest local business to the largest global players, I believe every organization must embrace the AI revolution, and identify how AI (artificial intelligence) will make the biggest difference to their business. But before you can develop a robust AI strategy – in which you work out how best to use AI to drive business success – you first need to understand what's possible with AI. To put it another way, how are other companies using AI to drive success? Let's briefly look at each area in turn. Thanks to the Internet of Things, a whole host of everyday products are getting smarter.

From the smallest local business to the largest global players, I believe every organization must embrace the AI revolution, and identify how AI (artificial intelligence) will make the biggest difference to their business. But before you can develop a robust AI strategy – in which you work out how best to use AI to drive business success – you first need to understand what's possible with AI. To put it another way, how are other companies using AI to drive success? Let's briefly look at each area in turn. Thanks to the Internet of Things, a whole host of everyday products are getting smarter.

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.

Artificial intelligence (AI) is often viewed as a panacea for a wide range of potential applications and use cases. However, the broad applicability of AI technology has made it difficult to develop actionable development plans for utilizing it. Adobe is changing this dynamic with the announcement of Intelligent Services, which leverage Adobe Experience Platform with specific use cases and focus. "Adobe's introduction of Intelligent Services, powered by Sensei, that are aligned with common marketing tasks not only makes it much simpler to utilize AI, but they also bring the power of AI to more of the marketing team," says Monica Lay, principal product marketing manager at Adobe. "This increased utility will allow more brands to take the next step for enhancing the customer journey and improving their interaction with brands."

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour - the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.

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.