Software bots have attracted increasing interest and popularity in both research and society. Their contributions span automation, digital twins, game characters with conscious-like behavior, and social media. However, there is still a lack of intelligent bots that can adapt to the variability and dynamic nature of digital web environments. Unlike human users, they have difficulty understanding and exploiting the affordances across multiple virtual environments. Despite the hype, bots with human user-like cognition do not currently exist. Chatbots, for instance, lack situational awareness on the digital platforms where they operate, preventing them from enacting meaningful and autonomous intelligent behavior similar to human users. In this survey, we aim to explore the role of cognitive architectures in supporting efforts towards engineering software bots with advanced general intelligence. We discuss how cognitive architectures can contribute to creating intelligent software bots. Furthermore, we highlight key architectural recommendations for the future development of autonomous, user-like cognitive bots.

Social coding platforms have revolutionized collaboration in software development, leading to using software bots for streamlining operations. However, The presence of open-source software (OSS) bots gives rise to problems including impersonation, spamming, bias, and security risks. Identifying bot accounts and behavior is a challenging task in the OSS project. This research aims to investigate bots' behavior in open-source software projects and identify bot accounts with maximum possible accuracy. Our team gathered a dataset of 19,779 accounts that meet standardized criteria to enable future research on bots in open-source projects. We follow a rigorous workflow to ensure that the data we collect is accurate, generalizable, scalable, and up-to-date. We've identified four types of bot accounts in open-source software projects by analyzing their behavior across 17 features in 5 dimensions. Our team created BotHawk, a highly effective model for detecting bots in open-source software projects. It outperforms other models, achieving an AUC of 0.947 and an F1-score of 0.89. BotHawk can detect a wider variety of bots, including CI/CD and scanning bots. Furthermore, we find that the number of followers, number of repositories, and tags contain the most relevant features to identify the account type.

The amount of time we spend doing repetitive work is mind-boggling, with manual computer tasks and data entry taking up a good portion of an office worker's day. A recent survey indicates that people estimate they waste five hours each week on tasks that should be automated. According to McKinsey, the number is even higher, with at least one-third of job activities deemed automatable in about 60% of occupations. Whether it's data collection, approvals, or updates, many tasks don't require creativity or intuition, essential attributes that serve to increase job satisfaction. Instead, the monotony of the work lowers satisfaction, leading to lower productivity and other inefficiencies.

Companies tapped more advanced bots to double check complex legal documents and contracts for irregularities at much higher speeds than remote workers. These types of efficiency gains are expected to drive growth in the software bot market for years, said Mihir Shukla, co-founder and chief executive of robotic process automation maker Automation Anywhere Inc., based in San Jose, Calif. "Most people recognize the need for automation," Mr. Shukla said at The Wall Street Journal's CIO Network Summit, held online Tuesday. Now there is an even greater appreciation at the board level, he said. The robotic process automation market is expected to grow by double digits through 2024, according to information-technology research and consulting firm Gartner Inc.

I've been writing a few articles in the last months where I've tackled the subject of artificial intelligence (AI) and its incorporation into digital business processes and our daily life. As I was carrying out my search, I came across some resources about the usage of AI to produce art, like painting and music. By letting machines learn from the human artistic work, Artificial Intelligence Virtual Artists like AIVA can compose classical and symphonic music. Today, AIVA's YouTube channel has over 18K subscribers. In her post "Top 10 AI Music Composers in 2021," Lisa Brown has listed more examples of non-human music composers.

When you as the patient visits a hospital, your major concern is always all about getting quality healthcare service on time without facing zero difficulties in terms of convenience. Same as you, hospital authorities such as doctors and nurses have a similar concern – giving you quality care while providing you with remarkable hospital experience. But unlike your concerns, their concerns require efforts of all staff members, coordination between all departments, availability of healthcare facilities, proper usage of healthcare software and easy access to medical data of patients to not bring concerns into life to create havoc! This many practices on a large scale by default seem impossible, leaving a large window open for human errors and service delay which definitely affect patient experience and health. Here is where AI and RPA technologies come to rescue.

Just getting workers to the office can be a challenge, amid ongoing travel restrictions aimed at containing the pandemic, said Gaston Silva Maldonado, project and systems analyst at Chilean food processor giant Agrosuper SA. "Our employees have been prevented from moving from one city to another, or even from one point of the city to another," Mr. Maldonado said, citing local lockdown rules. Based in Rancagua, Agrosuper employs about 3,500 office workers, in addition to thousands more in its production plants. So far, he said, only administrative staff and production plant workers deemed essential have returned to the workplace. With the Chilean government in July announcing a five-week plan to gradually ease travel restrictions within the country, the company is hoping to bring back more in the weeks ahead. To do that, Agrosuper has started using robotic process automation to scan and relay employment data on its more than 12,000 workers to a government website that issues emergency travel passes required at health checkpoints scattered throughout the country.

Last month, the pharma company Takeda began recruiting patients for a clinical trial of a promising Covid-19 treatment involving antibodies drawn from the blood of recovered patients. It normally takes several weeks to collect people's information, determine who may be suitable for the trial, and get the paperwork in order. With the coronavirus still spreading, Takeda sped things up using a quick and simple trick: using software to record tasks like opening files, selecting input fields, and cutting and pasting text. Those tasks can then be repeated for each prospective patient. The result: The paperwork got done in days instead of weeks.

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.

Now, the AI/ML market appears to be "backing off," says Craig Le Claire, analyst with Forrester Research. Delivering his remarks at an event sponsored by Automation Anywhere, Le Claire points out that intelligent software bots are increasingly taking on the automation challenges of enterprises. Indeed, the drive toward intelligent automation in all its forms is taking time to accept and adopt among enterprises. Only 17 percent of 600 CIOs responding to a KPMG survey acknowledge their firms have smart automation technologies operating at full scale, and 30 percent don't even know where to begin. At issue are lack of staffing and technology resources, along with lack of organizational support and accountability.