With the advancements of Artifical Intelligence (AI) and Natural Language Processing (NLP) in the past decades, especially the rising of Large Language Model (LLM) and multimodality learning, softwrare engineering fields welcome AI techniques to be employed to every aspects of software cycles. Meanwhile, the research of intelligent applications has continuously been a hotspot (Zhao et al., 2021) because of the increasing amount of data of multimodalities generated in various domains. This type of software is designed to adapt to constantly changing scenarios of rich context (Zhao et al., 2021; Yue and Smith, 2021), and some examples are listed in part C of figure 1. One primary characteristic of those applications is that a great portion of their system behaviors is learned from continuous interaction with the users and environment involving detection and analysis of states and activities (Tzafestas, 2012; Yang and Newman, 2013; Cassavia et al., 2017), unlike applications of banking or insurance with more matured and stable business logic. The rapid evolution of hardware and software wheels bring more capabilities to intelligent applications meanwhile making the creation and maintenance of that software more intricate (Chu et al., 2021; Zheng et al., 2023), both fields of software engineering and intelligent applications are eager for breakthroughs in higher-level automation (HLA) - collaboratively resolving the challenges by benefiting from AI techniques.

One of the most groundbreaking breakthroughs in recent years is artificial intelligence (AI). Its impact is set to grow even more in 2023. It is transforming the way businesses operate. AI is becoming an essential tool for businesses in various industries. Although, building AI capabilities from scratch can take time and effort.

The Internet of Things (IoT) defines the network of physical objects "things" embedded with software, sensors, and other technologies to connect and trade data with different gadgets and systems over the internet. These devices vary from standard household objects to sophisticated industrial tools. More than 7 billion are connected to IoT devices today, and specialists expect this number to expand upto 22 billion by 2025. We can combine everyday objects, thermostats, kitchen appliances, cars, baby monitors to the internet via entrenched devices; seamless communication is feasible between people, processes, and things. By Utilizing low-cost computing, big data, the cloud, analytics, and mobile technologies, material things can transfer and compile data with the tiniest human intervention. In this hyperconnected world, digital systems record, monitor, and adjust each interaction between related items.

So, how do organizations empower their employees with machine learning? One option is to completely disregard the end-user experience and make the model available behind some endpoint where the user can directly query the model. This is, of course, technically complex and not entirely intuitive. For example, a user within their workflow identifies a gap in their knowledge that can be addressed with an available machine learning model, determines what information will adequately allow the model to fill that knowledge gap, and then selects the correct input to pass into the model. Next, the user exits their workflow to e.g. a command-line interface, Jupyter notebook, etc. to make a call to the model deployed model, receives a JSON output from the model endpoint, parses the model output to determine the answer to their original question, and then returns to their original workflow armed with the model output.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. One of the main roadblocks preventing the enterprise from putting artificial intelligence (AI) into action is the transition from development and training to production environments. To gain real benefits from the technology, this must be done at the speed and scale of today's business environment, which few organizations are capable of doing. This is why the interest in merging AI with devops is gaining steam. Forward-leaning enterprises are trying to blend machine learning (ML) in particular with the traditional devops model, which creates an MLops process that streamlines and automates the way intelligent applications are developed and deployed and then updated on a continual basis to increase the value of its operations over time.

Red Hat Inc., a provider of open source solutions, today announced new certifications and capabilities for Red Hat OpenShift aimed at accelerating the delivery of intelligent applications across the hybrid cloud. These enhancements, including the certification of Red Hat OpenShift with NVIDIA AI Enterprise 2.0, as well as the general availability of Red Hat OpenShift 4.10, are intended to help organizations deploy, manage and scale artificial intelligence (AI) workloads with confidence. According to Gartner, worldwide artificial intelligence (AI) software revenue is forecast to total $62.5 billion in 2022, an increase of 21.3% from 2021.1 As enterprises integrate AI and machine learning capabilities into cloud-native applications to deliver more insight and customer value, they need a more agile, flexible and scalable platform for developing and deploying ML models and intelligent applications into production more quickly. Red Hat OpenShift is engineered to provide this foundation and, with today's updates, Red Hat OpenShift makes it easier for organizations to add AI workloads to the industry's leading enterprise Kubernetes platform. While AI is transforming how enterprises do business, operationalizing an AI infrastructure can be complex and time- and resource-intensive.

Simith Nambiar, Practice Lead, Emerging Tech, APJ, Rackspace Technology, tells us how businesses can overcome the challenges they experience with their Artificial Intelligence/Machine Learning efforts. As businesses continue to leverage cloud-based compute technologies, attention is on the explosion of new data, AI and Machine Learning (AI/ML). Through the powerful combination of new data and AI/ML technologies, organizations can deliver superior customer-centric experiences, allowing them to understand their business environment like never before, resulting in the ability to drive new levels of efficiency. In Singapore, the government has continued to invest in ambitious projects in key sectors to accelerate AI/ML adoption. For instance, through the National AI Program in Finance, financial institutions will soon leverage an AI platform to assess the environmental impact, identify emerging risks and enable financial institutions to make green investments.

In the business capture phase, you work with your business SME or analyst to frame your business problems. Once you have captured your business problem, you can attempt to frame it in terms of AI&ML at a high level. Data is the petrol to your machine learning engine, so it is important that you capture your data requirements in detail. Initially consider your requirements for training and inference. If you're training a machine learning model, what features might be predictive?

Hosted for the global cloud computing community, Amazon Web Services' re:Invent 2021 brought together developers, engineers, IT executives and the technical decision-makers that are transforming how the world around us operates. The early stages of IT infrastructure were inflexible and expensive, but this year's conference brought to light the next shift in the digital journey that highlights the cloud's leading role as an enabler in the way that businesses function with machine learning (ML) and artificial intelligence (AI). In this on-the-show-floor video from the event, we looked at the three areas that are reshaping business processes and environments -- from the intelligent applications that embed AI/ML and take advantage of data, and the system of enablers that allow them to reach scale to the chips that power them. We spoke with Tom Trahan, vice president of business development at CircleCI, Matt McIlwain, managing director at Madrona Venture Group, and Luis Ceze, CEO at OctoML. TNS Publisher Alex Williams hosted these conversations.

Editor's Note: This presentation was given by Dr. Jim Webber at GraphTour Boston in 2019. I'm going to walk you through some no-nonsense definitions of AI-cronyms, share my history with graphs and intelligent applications, and take a little peek into the future of graph AI. A Bluffer's Guide to AI-cronyms Artificial intelligence (AI) is the property of a system that appears intelligent to its users. Machine learning (ML) is a branch of artificial intelligence that analyzes historical data to guide future interactions, specifically within a given domain. Overall, achieving AI is an interesting process, whether we're using a fancy machine learning framework to do it or not.