If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Just 32 percent of organisations in Singapore currently tap machine learning, although 52 percent believe such tools' ability to make complex decisions is imperative to the success of their business. A further 87 percent said greater automation brought about by machine learning would speed up decision-making process, while 80 percent said it would improve accuracy of such decisions, revealed a survey by ServiceNow. Conducted by Oxford Economics, the study polled 500 CIOs across 11 countries, including 91 from three Asia-Pacific markets: Singapore, Australia, and New Zealand. Ten percent of the global sample were from Singapore. ServiceNow touted machine learning as software that analysed and improved its own performance without direct human intervention, enabling it to make increasingly complex decisions as it learned.
But consider an artificially intelligent gun or computer that can think for itself. Thursday, we looked at the dicey ethical issues of autonomous computers, machines and robots. In Isaac Asimov's sci-fi classic: I, Robot, robots were always required to obey three laws. A robot must obey human orders except where such orders would conflict with the First Law. A robot must protect its own existence so it does not conflict with the First or Second Laws.
This post was co-authored with Duncan Gilchrist and is Part 1 of our "Best of Both Worlds: An Applied Intro to ML for Causal Inference" series (Part 2 here). We're grateful to Evan Magnusson for his strong thought-partnership and excellent insights throughout. Over the last couple years, we've been excited to see -- and leverage -- a range of new methods that significantly improve our ability to glean causal relationships from data, especially big data. Many of these methods marry the best of machine learning and econometrics to unlock deeper and more correct inference. Applied correctly, they help us get the insights we need to make better decisions for our companies and our communities.
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you're like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don't ease the choice. In this article for Statsbot, I will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you'll find the structured overview of the main features of described algorithms.
The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate a phenomenon known as the bullwhip effect. The game consists of a serial supply chain network with four players--a retailer, a wholesaler, a distributor, and a manufacturer. In each period of the game, the retailer experiences a random demand from customers. Then the four players each decide how much inventory of "beer" to order. The retailer orders from the wholesaler, the wholesaler orders from the distributor, the distributor from the manufacturer, and the manufacturer orders from an external supplier that is not a player in the game.
During the 2008 financial crisis, the banking industry realized that their machine learning algorithms were based on flawed assumptions. So financial system regulators decided that additional controls were needed, and regulatory requirements for "model risk" management on banks and insurers were introduced. Banks also had to prove that they understood the models they were using, so, regrettably but understandably, they deliberately limited the complexity of their technology, resorting to generalized linear models that offered simplicity and interpretability above all else. In the past several years, machine learning and AI have made enormous strides in accuracy. Yet regulated industries (like banking) remain hesitant, often prioritizing regulatory compliance and algorithm interpretability over accuracy and efficiency.
Cloud computing and the Internet of Things (IoT) have spent the last several years in a sort of maximum-acceleration race where they've lapped the other players several times over and have only one another to measure against. Neither is slowing down, particularly the IoT. According to analysis firm Gartner, the number of IoT devices will hit 20.8 billion by 2020. The world population is expected to reach 8 billion in 2020, meaning there will be 2.5 IoT devices per person on the entire planet. In 2016, the IoT was growing at the rate of 5.5 million new things getting connected every day.
The huge coverage devoted to the topics of AI and edge computing sparked an idea when I recently visited JFK Airport. My journey coincided with a severe weather storm that disrupted travel along the East Coast. This situation illustrates how customer service agents assist passengers (at the edge) when dealing with uncertainty and changing circumstances (relying predictive analysis and intelligent decision-making under uncertainty). The IoT is imminent – and so are the security challenges it will inevitably bring. Get up to speed on IoT security basics and learn how to devise your own IoT security strategy in our new e-guide.
Making decisions is not always easy, especially when choosing between two options that have both positive and negative elements, such as deciding between a job with a high salary but long hours, and a lower-paying job that allows for more leisure time. MIT neuroscientists have now discovered that making decisions in this type of situation, known as a cost-benefit conflict, is dramatically affected by chronic stress. In a study of mice, they found that stressed animals were far likelier to choose high-risk, high-payoff options. The researchers also found that impairments of a specific brain circuit underlie this abnormal decision making, and they showed that they could restore normal behavior by manipulating this circuit. If a method for tuning this circuit in humans were developed, it could help patients with disorders such as depression, addiction, and anxiety, which often feature poor decision-making.
Artificial Intelligence (shortened to AI) falls under the computer science umbrella. It refers to the discipline of programming computers to become intelligent and make decisions, just as a human would do. Its primary purpose is to replace/complement humans when making sophisticated decisions, using data inputted into a system and then injecting code to help the computers make more intelligent decisions based on possible outcomes. Although AI is said to be nothing but a gimmick by some critics, it has the potential to be highly useful in the world of business, helping organisations become more automated, freeing up a human's role to make the decisions only a human brain can make. Using AI significantly speeds up the time it takes for a process to happen and with so much data being generated every single second, automating this process can make life easier for everyone.