According to Grady Booch, most current AI systems are about pattern matching of signals at the edge and inductive reasoning, not true Artificial Intelligence. During his second day keynote at the 2018 QCon San Francisco, "Building the Enchanted Land", he explained his view that AI today is a "system engineering problem with AI components." True AI uses decision making and abductive reasoning, which allows those systems to reason and learn. Current artificial intelligence applications are far from being able to accomplish that, they are just components in larger systems. Contemporary AI is not of recent origin either because many of the architectures and algorithms are decades old. The difference today is the abundance of computational power, and the existence of large bodies of tagged data.

For decades, the police and drivers with lead feet have engaged in a war of radars and radar detectors. Every time police radar technology improves, so do radar detectors to outsmart it. The same is true with cybersecurity. In turn, the technology that hackers use is also improved. It makes little sense to continue this way.

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.

Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with several possibilities neural network may in such cases still be useful if instead of a classification elimination of improbable classes is done. Eliminators may be constructed using classifiers assigning new cases to a pool of several classes instead of just one winning class. Elimination may be done with the help of several classifiers using modified error functions. A real life medical application of neural network is presented illustrating the usefulness of elimination.

Here at BETSOL, we're big on machine learning, so it's always exciting to see a great consumer-oriented application of it hit the mainstream. This post will dive into the machine learning technology you can use right now in Gmail's "Smart Reply" feature. In case you haven't seen it, when you read emails in a Gmail app, you are offered three auto reply options. At first glance, these might seem like basic canned responses. But as you start using them, you realize there is something much more sophisticated going on. Upon choosing a Smart Reply, Gmail starts a reply email and auto-inserts your selected text.

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving their goals. Thus, a certain level of freedom to choose the best path to a specific goal is necessary in making AI robust and flexible enough to be deployed successfully in real-life scenarios. This is especially true when AI systems tackle difficult problems whose solution cannot be accurately defined by a traditional rule-based approach but require the data-driven and/or learning approaches increasingly being used in AI. Indeed, data-driven AI systems, such as those using machine learning, are very successful in terms of accuracy and flexibility, and they can be very "creative" in solving a problem, finding solutions that could positively surprise humans and teach them innovative ways to resolve a challenge. However, creativity and freedom without boundaries can sometimes lead to undesired actions: the AI system could achieve its goal in ways that are not considered acceptable according to values and norms of the impacted community.

There is much debate in machine ethics about the most appropriate way to introduce ethical reasoning capabilities into intelligent autonomous machines. Recent incidents involving autonomous vehicles in which humans have been killed or injured have raised questions about how we ensure that such vehicles have an ethical dimension to their behaviour and are therefore trustworthy. The main problem is that hardwiring such machines with rules not to cause harm or damage is not consistent with the notion of autonomy and intelligence. Also, such ethical hardwiring does not leave intelligent autonomous machines with any course of action if they encounter situations or dilemmas for which they are not programmed or where some harm is caused no matter what course of action is taken. Teaching machines so that they learn ethics may also be problematic given recent findings in machine learning that machines pick up the prejudices and biases embedded in their learning algorithms or data. This paper describes a fuzzy reasoning approach to machine ethics. The paper shows how it is possible for an ethics architecture to reason when taking over from a human driver is morally justified. The design behind such an ethical reasoner is also applied to an ethical dilemma resolution case. One major advantage of the approach is that the ethical reasoner can generate its own data for learning moral rules (hence, autometric) and thereby reduce the possibility of picking up human biases and prejudices. The results show that a new type of metric-based ethics appropriate for autonomous intelligent machines is feasible and that our current concept of ethical reasoning being largely qualitative in nature may need revising if want to construct future autonomous machines that have an ethical dimension to their reasoning so that they become moral machines.

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.

Cognitive technologies like machine learning and AI (artificial intelligence) certainly have proven to be an important part of the IoT (Internet of Things) sector because they can help make products and services smarter and, therefore, more valuable. These technologies can also help support human workers and decisionmakers and, in general, improve business operations. For this column, I am going to take a closer look into these technologies, exploring the current adoption landscape and prospects for the future, as well as discuss their business value. Some new research has come out that gives us some really interesting insight into the cognitive-tech landscape. There are also some cool examples of how companies are leveraging machine learning and AI to create value for people's lives and businesses, and I'll share an example of that as well.

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. Her decision follows a similar verdict by a judge in California on Sunday.