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.

A federal health official says a nationwide injunction on new Trump administration rules that allowed more employers to opt out of providing women with no-cost birth control will force Americans to "violate" their consciences.

U.S. District Judge Wendy Beetlestone in Philadelphia issued a nationwide injunction preventing the rules from taking effect, a day after another judge issued a more limited ruling blocking their enforcement in 13 states and the District of Columbia.

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.

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. If we don't have common definitions for and understanding of what we are talking about, it's likely we will create an increasing number of problems going forward.