A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
At many firms, the marketing function is rapidly embracing artificial intelligence. But in order to fully realize the technology's enormous potential, chief marketing officers must understand the various types of applications--and how they might evolve. Classifying AI by its intelligence level (whether it is simple task automation or uses advanced machine learning) and structure (whether it is a stand-alone application or is integrated into larger platforms) can help firms plan which technologies to pursue and when. Companies should take a stepped approach, starting with rule-based, stand-alone applications that help employees make better decisions, and over time deploying more-sophisticated and integrated AI systems in customer-facing situations. Of all a company's functions, marketing has perhaps the most to gain from artificial intelligence.
Compared with previous types of networks, 5G networks are both more in need of automation and more amenable to automation. Automation tools are still evolving and machine learning is not yet common in carrier-grade networking, but rapid change is expected. Emerging standards from 3GPP, ETSI, ITU and the open source software community anticipate increased use of automation, artificial intelligence (AI) and machine learning (ML). And key suppliers' activities add credibility to the vision and promise of artificially intelligent network operations. "Growing complexity and the need to solve repetitive tasks in 5G and future radio systems necessitate new automation solutions that take advantage of state-of-the-art artificial intelligence and machine learning techniques that boost system efficiency," wrote Ericsson's chief technology officer (CTO), Erik Ekudden, recently.
Machine Learning (ML) has found its place into cybersecurity a long time ago and usage of ML has given cybersecurity teams much-needed insights into the malware network and effective ways to curb cyber attacks. Most ML-based solutions are proprietary or designed for specific feature representations. In 2017, one of the most prominent credit reporting agencies (CRA) of the United States– Equifax, suffered a huge malicious attack that led to a data breach that is famous for all the wrong reasons. Personal and sensitive data worth 148 million was lost to a data breach. Such data breach and data risks are still prevalent irrespective of the endpoint protection and other monitoring techniques deployed by enterprises worldwide.
Foreign Minister Taro Kono agreed Wednesday with his German counterpart to promote free trade amid a rising protectionist tide, while supporting a rules-based international order. During talks in Tokyo, Kono and German Foreign Minister Heiko Maas stressed the importance of closer economic ties just days after the signing of a free trade agreement between Japan and the European Union. "The free, open and rules-based international order faces a serious challenge," Kono said during a joint press briefing with Maas. "Closer cooperation between Japan and Germany, (countries) that share the same values such as democracy, and lead Asia and Europe … is taking on greater importance than ever." The signing earlier this month of the free trade deal, which covers about a third of the world's economy, has been seen as symbolic of the concerted effort to counter the increasingly protectionist steps taken by U.S. President Donald Trump.
Machines are learning to process simple commands by exploring 3-D virtual worlds. Devices like Amazon's Alexa and Google Home have brought voice-controlled technology into the mainstream, but these still only deal with simple commands. Making machines smart enough to handle a real conversation remains a very tough challenge. And it may be difficult to achieve without some grounding in the way the physical world works. Attempts to solve this problem by hard-coding relationships between words and objects and actions requires endless rules, making a machine unable to adapt to new situations.