Temporal Neural Networks (TNNs), a special class of spiking neural networks, draw inspiration from the neocortex in utilizing spike-timings for information processing. Recent works proposed a microarchitecture framework and custom macro suite for designing highly energy-efficient application-specific TNNs. These recent works rely on manual hardware design, a labor-intensive and time-consuming process. Further, there is no open-source functional simulation framework for TNNs. This paper introduces TNNGen, a pioneering effort towards the automated design of TNNs from PyTorch software models to post-layout netlists. TNNGen comprises a novel PyTorch functional simulator (for TNN modeling and application exploration) coupled with a Python-based hardware generator (for PyTorch-to-RTL and RTL-to-Layout conversions). Seven representative TNN designs for time-series signal clustering across diverse sensory modalities are simulated and their post-layout hardware complexity and design runtimes are assessed to demonstrate the effectiveness of TNNGen. We also highlight TNNGen's ability to accurately forecast silicon metrics without running hardware process flow.

Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN). Most recently, chatbots, programs that allow users to interact with a machine using natural language, have been increasingly used for business process execution support. A recent category of chatbots worth mentioning is generative-based chatbots, powered by Large Language Models (LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and Google's Pathways Language Model (PaLM), which are trained on billions of parameters and support conversational intelligence. However, it is not clear whether generative-based chatbots are able to understand and meet the requirements of constructs such as those provided by BPMN for process execution support. This paper presents a case study to compare the performance of prominent generative models, GPT and PaLM, in the context of process execution support. The research sheds light into the challenging problem of using conversational approaches supported by generative chatbots as a means to understand process-aware modelling notations and support users to execute their tasks.

An effective interdisciplinary study between machine learning and solar-thermal desalination requires a sufficiently large and well-analyzed experimental datasets. This study develops a modified dataset collection and analysis process for studying solar-thermal desalination by machine learning. Based on the optimized water condensation and collection process, the proposed experimental method collects over one thousand datasets, which is ten times more than the average number of datasets in previous works, by accelerating data collection and reducing the time by 83.3%. On the other hand, the effects of dataset features are investigated by using three different algorithms, including artificial neural networks, multiple linear regressions, and random forests. The investigation focuses on the effects of dataset size and range on prediction accuracy, factor importance ranking, and the model's generalization ability. The results demonstrate that a larger dataset can significantly improve prediction accuracy when using artificial neural networks and random forests. Additionally, the study highlights the significant impact of dataset size and range on ranking the importance of influence factors. Furthermore, the study reveals that the extrapolation data range significantly affects the extrapolation accuracy of artificial neural networks. Based on the results, massive dataset collection and analysis of dataset feature effects are important steps in an effective and consistent machine learning process flow for solar-thermal desalination, which can promote machine learning as a more general tool in the field of solar-thermal desalination.

I recently visited with the CIO of a Fortune 500 company. He was touting the advances they had made in IT and corporate culture regarding the use of artificial intelligence and analytics, but he had one major concern: How do you fuse AI and analytics into the rest of your transactional line of business IT infrastructure? It hasn't been that way in his enterprise. His IT organization had started its analytics initiative with an internal Hadoop group that was responsible for processing big data internally. Meanwhile other departments in IT supported transactional data processing on an assortment of mainframes and servers in the data center. Regular IT and the Hadoop groups were somewhat siloed from each other because the parallel processing and storage management needs for big data and AI were notably different than what they were for transactional data and processing management.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

Founded in 1991, India-headquartered HCL Technologies is a global technology company that helps enterprises reimagine their businesses for the digital age. The company specializes in key areas, including digital, IoT, cloud, automation, cybersecurity, and analytics, amongst others. With the company increasingly having a presence in the RegTech space, how does it see the sector changing? How is RegTech changing compliance? According to Daryl Wilkinson – Senior Executive, Strategic Initiatives, Financial Services UK&I at HCL Technologies, "I think you can look at this through two lenses. First, there appears to be a consensus that the global RegTech market is expected to achieve $30bn by 2027 – so that alone is changing the compliance market –new investment is disrupting incumbent models and is changing the way regulators engage with businesses. The second lens is cost; financial services rely heavily on legacy technology – RegTech's nature is to find that niche to solve those problems at a much lower cost than the banks and insurers might otherwise do themselves."

Artificial Intelligence and Machine Learning are two disruptive technologies that are changing business, education, healthcare and finance in productive ways. Reports of the International Data Corporation (IDC) survey say that companies are already spending more on AI. Over half of the businesses have adopted AI in one form or other. To implement such powerful technologies and empower your business, these are the basic requirements. In order to be proactive in implementing these advanced technologies, we have to identify commonalities throughout the business.

Insurance works with large amounts of data, about many individuals, many instances requiring insurance, and many factors involved in solving the claims. To add to the complexity, not all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.

A common measure of machine intelligence is challenging AI to play complex games against humans. The first AI programs tackled checkers and progressed to beat human players at chess, Go and a wide range of multiplayer games. The thinking behind reinforcement learning (RL) is that if a computer can outwit humans by thinking, planning ahead and predicting human behavior, then the machines have the capacity to learn anything. Now, researchers are still studying how computers learn through iteration and trial and error. One of the simplest goal-driven problems that computers were first tasked with was trying to find the right path through a maze.

In particular, small soft robots at millimeter scale are of practical interest as they can be designed as a combination of miniature actuators simply driven by pneumatic pressure. They are also well suited for navigation in confined areas and manipulation of small objects. However, scaling down soft pneumatic robots to millimeters results in finer features that are reduced by more than one order of magnitude. The design complexity of such robots demands great delicacy when they are fabricated with traditional processes such as molding and soft lithography. Although emerging 3D printing technologies like digital light processing (DLP) offer high theoretical resolutions, dealing with microscale voids and channels without causing clogging has still been challenging.