The positive impact of cooperative bots on cooperation within evolutionary game theory is well documented; however, existing studies have predominantly used discrete strategic frameworks, focusing on deterministic actions with a fixed probability of one. This paper extends the investigation to continuous and mixed strategic approaches. Continuous strategies employ intermediate probabilities to convey varying degrees of cooperation and focus on expected payoffs. In contrast, mixed strategies calculate immediate payoffs from actions chosen at a given moment within these probabilities. Using the prisoner's dilemma game, this study examines the effects of cooperative bots on human cooperation within hybrid populations of human players and simple bots, across both well-mixed and structured populations. Our findings reveal that cooperative bots significantly enhance cooperation in both population types across these strategic approaches under weak imitation scenarios, where players are less concerned with material gains. However, under strong imitation scenarios, while cooperative bots do not alter the defective equilibrium in well-mixed populations, they have varied impacts in structured populations across these strategic approaches. Specifically, they disrupt cooperation under discrete and continuous strategies but facilitate it under mixed strategies. These results highlight the nuanced effects of cooperative bots within different strategic frameworks and underscore the need for careful deployment, as their effectiveness is highly sensitive to how humans update their actions and their chosen strategic approach.

In virtually every industry, the Fourth Industrial Revolution (4IR) has spurred a transformative journey that is redefining the very nature of work. While technology has played an essential and often defining role, people have nonetheless remained at the core of these revolutionary transformations. While the type of work varies across different industries and functions, 4IR transformation shifts the workforce away from highly manual tasks to a much more data-driven and automated future. Repetitive, manual factory-floor duties have been replaced with higher-level tasks that involve making data-driven decisions in collaboration with automated technology, including robotics and cobotics (or collaborative robotics). Building those new skills is the greatest business challenge for 80 percent of CEOs, according to data from the Harvard Business Review. 1 1.

California‑based Farmers Insurance has invested aggressively in AI in recent years. One project frees up time for claim adjusters by using image recognition to detect anomalies and fraud in auto insurance claims. Farmers has deployed AI chatbots to interact with customers in its contact centers. Finally, Farmers uses robotic process automation (RPA) to automate mundane back‑office processes. Most of these projects bubbled up from below, often without unified support from senior management, says Tom Davenport, a fellow of the MIT Initiative on the Digital Economy and a senior advisor to Deloitte Analytics. So, Farmers execs formed two committees to start driving AI decision‑making from the top--with dual roles for the business side and IT.

While artificial intelligence is a much-talked-about enabling technology in digital innovation strategies, some issues still need addressing. Artificial intelligence is the technology story of the hour, and everyone wants to dive in. However, three recent studies suggest there's more work to be done before AI starts delivering business value. A report from McKinsey suggests many organizations require a solid infrastructure underneath it all -- it takes digital to go more digital. Data is also a vital piece of the puzzle, a survey of 2,300 executives from MIT Technology Review and PureStorage adds.

Are artificial intelligence, machine learning and robotic process automation answers to questions that haven't been asked yet? The results of a recent KPMG study suggest there is a bit of flailing about with these hot new technologies now being aggressively pushed by vendors and analysts. Digital transformation requires crossing multiple thresholds.Photo: Joe McKendrick The study suggests there is hesitation, but in the long run, high hopes -- money will start pouring into what the authors categorize as "intelligent automation." In the next three years, 40 percent of executives expect to increase their AI investments by 20 percent or more, and 32 percent will increase robotic process automation (RPA) investment by 20 percent or more. These investments are expected to reach $232 billion by 2025.

With artificial intelligence taking center stage, business leaders across all industries are discussing - and considering - the implications of automation and personalization. The excitement levels have risen as more executives begin to see the opportunities that AI can bring when differentiating their offerings, personalizing their services, designing their products, and optimizing their operations. With this interest comes the biggest question: How do we implement AI in a natural way, both for our people and our customers? A contextual understanding of AI and its relevance combined with a strong point of view and a good data science team (to execute on your vision) is only half of the battle. The biggest, ongoing obstacle that executives face is their ability to architect a process for applying data intelligence to their business and decision making.

Let's start with the basics: Accenture defines artificial intelligence (AI) as a collection of multiple technologies that together enable machines to sense, comprehend, act and learn, either on their own or to augment human activities. Indeed, we are seeing AI at a tipping point – quickly coming of age and beginning to mature at a much faster rate than ever before. This is because it is now possible, due to the availability of massive, inexpensive cloud-accessible computing power and low-cost storage, combined with algorithms, to sift rapidly through enormous volumes of data. Companies need to know how to harness AI effectively. Corporate executives seem convinced of its potential – according to Accenture's 2016 Technology Vision survey, 70 per cent of corporate executives said they are making significantly more investments in AI-related technologies than two years ago, with 55 per cent stating that they plan on using machine learning and embedded AI solutions like Amelia extensively.