... includes all of the major AI methods for (a) representing knowledge about a task or a problem area, and (b) reasoning about a problem.
Dynamic programming is a method developed by Richard Bellman in 1950s. The main idea behind the dynamic programming is to break a complicated problem into smaller sub-problems in a recursive manner. In computer science and programming, the dynamic programming method is used to solve some optimization problems. The dynamic programming is a general concept and not special to a particular programming language. But, we will do the examples in Python.
The world of work is witnessing tidal changes in business transformation made possible by the revolution in technology. Most notable is Artificial Intelligence (AI) which can be used to create new paradigms of collaboration and creativity. Here's how it can be a positive impact on an important and often overlooked segment of your employee population- your managers. Manager engagement begins by ensuring that they feel empowered to lead, make independent decisions and shoulder responsibilities. And if employee engagement is considered an art, then AI can bring the brushstrokes to your canvas.
If you're confused about which smart speaker to invest in, I don't blame you. There have been quite a few to hit the scene in the last few months. It's highly likely, though, that you're stuck choosing between the big three: Apple's HomePod mini, Google's Nest Audio, and Amazon's Echo. All of which are competitively priced at $99. I know... that shared price point doesn't help make the decision any easier. But beyond pricing, there are a few other things to take into consideration when choosing which smart speaker is right for you, specifically design, audio quality, connected apps, and voice assistant capabilities.
Quantum computing--considered to be the next generation of high-performance computing--is a rapidly-changing field that receives equal parts attention in academia and in enterprise research labs. Honeywell, IBM, and Intel are independently developing their own implementations of quantum systems, as are startups such as D-Wave Systems. In late 2018, President Donald Trump signed the National Quantum Initiative Act that provides $1.2 billion for quantum research and development. TechRepublic's cheat sheet for quantum computing is positioned both as an easily digestible introduction to a new paradigm of computing, as well as a living guide that will be updated periodically to keep IT leaders informed on advances in the science and commercialization of quantum computing. SEE: The CIO's guide to quantum computing (ZDNet/TechRepublic special feature) Download the free PDF version (TechRepublic) SEE: All of TechRepublic's cheat sheets and smart person's guides Quantum computing is an emerging technology that attempts to overcome limitations inherent to traditional, transistor-based computers. Transistor-based computers rely on the encoding of data in binary bits--either 0 or 1. Quantum computers utilize qubits, which have different operational properties.
There are many approaches to determining whether a particular transaction is fraudulent. From rule-based systems to machine learning models - each method tends to work best under certain conditions. Successful anti-fraud systems should reap the benefits of all the approaches and utilize them where they fit the problem best. The notion of networks and connection analysis in the world of anti-fraud systems is paramount since it helps uncover hidden characteristics of transactions that are not retrievable any other way. In this blog post, we will try to shed some light on the way networks are created and then used to detect fraudulent transactions.
AutoML enjoys a steadily increasing popularity (see Forbes). Not least driven by the numerous successes in practical analyses. In a world in which more and more devices produce data and are networked with each other, the data "produced" grows disproportionately. Therefore AutoML is of urgent necessity to gain knowledge from these rapidly increasing data on time. We assume that AutoML becomes even more critical in the coming years and that the analysis methods deliver even more precise and faster results. The field of activity of the data scientist will not disappear, but rather, his focus will shift to more specific or sophisticated analysis techniques.
Data is the new game-changer, everywhere. According to reports, data-driven organizations are 19 times more likely to be profitable. Data and analytics are critical components of digital transformation. Considering the rate at which data is being generated, its analysis is becoming a hefty task. Organizing large volumes of real-time data from several sources is time-consuming and tedious. To reduce the human effort involved in this and decrease the required time, AI and ML are being employed.
The term artificial intelligence (AI) refers to computing systems that perform tasks normally considered within the realm of human decision making. These software-driven systems and intelligent agents incorporate advanced data analytics and Big Data applications. AI systems leverage this knowledge repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. AI, which was introduced as an area of science in the mid 1950s, has evolved rapidly in recent years. It has become a valuable and essential tool for orchestrating digital technologies and managing business operations.
Cyber-Physical Systems (CPS) play a crucial role in the era of the 4thIndustrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of their intrinsic requirement is the capability to interface and cooperate with other systems. On the other hand, to fully realize theIndustry 4.0 vision, it is required to address horizontal, vertical, and end-to-end integration enabling a complete awareness through the entire supply chain. In this context, Semantic Web standards and technologies may have a promising role to represent manufacturing knowledge in a machine-interpretable way for enabling communications among heterogeneous Industrial assets.