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.
In the short term, AI (non-linear) models will be used as a benchmark for explainable (linear) machine learning models. When validation data sets are run in parallel with linear and non-linear models and converge on same answer, the linear models can be used to approximate the decision from the neural network. Fraud, risk management and AML, CIP/KYC processes stand to benefit from this approach as compliance officers see the benefits of explainable and transparent machine learning models over their legacy, opaque and unwieldy rules-based systems.
A US federal judge has blocked new Trump administration regulations on birth control from applying across the entire country. The rules allow employers and insurers to decline to provide birth control if doing so violates their "religious beliefs" or "moral convictions". The rules were to come into effect nationwide from Monday. But the judge in Philadelphia granted an injunction requested by attorneys general in Pennsylvania and New Jersey. Judge Wendy Beetlestone ruled that the new rules would make it more difficult for many women to obtain free contraception and would be an undue burden on US states.
Artificial intelligence is a major driver of value for the enterprise. According to a recent AI study from IBM, 82 percent of organizations are now at least considering AI adoption, and the number of companies that are beyond the AI implementation stage is 33 percent higher than it was in 2016. What's more, by pairing AI with other exponential technologies such as automation, blockchain and the Internet of Things (IoT), companies are redefining their business architectures. The IBM "Cognitive Enterprise" report highlights how these technologies represent the next inflection point for the enterprise comparable in scale and scope to the introduction of the Internet and mobile technology. The cognitive enterprise is a framework for companies to define and pursue a bold vision to realize new sources of value and restructure their industries, missions, and business models.
U.S. District Judge Haywood Gilliam in Oakland granted a request by 14 Democratic attorneys general for a preliminary injunction. The rules, which are set to go into effect Jan. 14, allow businesses or nonprofits to obtain exemptions to an Obamacare requirement for contraceptive coverage on moral or religious grounds.
Since the first list went into effect on July 6, 2018, tariffs on goods imported to the U.S. from China have been a significant source of pain, particularly for those in the high-tech industry. As reported by SourceToday, the 818 categories of products listed are valued at $34 billion, with electronic components making up 58 of the categories, accounting for 15 percent of the value of targeted goods. A second list may soon be on the way and promises to hit electronic components even harder, with 27 percent of the value of goods, totaling $4.3 billion of the $16 billion, in this second wave of tariffs. Bracing for the impact of existing and future tariffs, some distributors are trying to clear the way by rolling tariffs into their prices, while others are depending on manufacturers to account for the cost after the buy. These policies continue to be in flux as the industry comes to terms with the issue, making it almost impossible to determine total expenditures.
One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google's DeepMind beat the world's best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM's Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today's human jobs in the years ahead. Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more. As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics.
Google now checks for security breaches even after a user has logged in. Last year, Microsoft Corp's Azure security team detected suspicious activity in the cloud computing usage of a large retailer: One of the company's administrators, who usually logs on from New York, was trying to gain entry from Romania. A hacker had broken in. Microsoft quickly alerted its customer, and the attack was foiled before the intruder got too far. Inc and various start-ups are moving away from solely using older "rules-based" technology designed to respond to specific kinds of intrusion and deploying machine-learning algorithms that crunch massive amounts of data on logins, behaviour and previous attacks to ferret out and stop hackers.