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) …
An inference engine using forward chaining applies a set of rules and facts to deduce conclusions, searching the rules until it finds one where the IF clause is known to be true. The process of matching new or existing facts against rules is called pattern matching, which forward chaining inference engines perform through various algorithms, such as Linear, Rete, Treat, Leaps, etc. When a condition is found to be TRUE, the engine executes the THEN clause, which results in new information being added to its dataset. In other words, the engine starts with a number of facts and applies rules to derive all possible conclusions from those facts. This is where the name "forward chaining" comes from -- the fact that the inference engine starts with the data and reasons its way forward to the answer, as opposed to backward chaining, which works the other way around.
Professor Edward Feigenbaum, while explaining the meaning of Al to a distinguished and perplexed scientific review panel for a Department of Defense AI application development program in the late 1970s commented, "If it works, it isn't AI." Because AI has been a subject of considerable interest, a number of suppliers and developers of software products have embraced the technology and offer products or demonstrations that "contain AI" It is possible that some of this labeling might be controversial among those who have worked in the field for some time. Since most AI appears as a software of some sort, many practitioners of conventional software development can recognize aspects of AI programs that could be accomplished with conventional technology. An industrial engineer replaced an electromechanical controller on a large machine with an electronic controller which included a CRT display. Upon being told the rudimentary aspects of AI technology, the industrial engineer suddenly exclaimed, "Wow, I've been doing AI all along!"
Moore's Law, advocated by Gordon Moore of Intel fame, says that the computational capabilities will double every 18 to 24 months. And we've seen that really unfolding over the last 30 years (see chart). It's really stoked people's imagination, so much so that many believe that the promise of artificial intelligence (AI) could become reality, and computers could actually learn to think like humans. I believe it's still a number of years away, but it is fueling a lot of hype regarding AI. What it's truly capable of, where it can be effective, and what it takes to implement it, all of which have become somewhat inflated in the market today.
The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations between the input features and the target is presented. Based on this taxonomy, the impact of different input feature configurations on the modelling and fault detection performance is evaluated. To this end, a framework that formulates the detection of faults as a classification problem is also presented.
Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA. We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models. Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation. We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.
We consider the problem of learning rules from a data set that support a proof of a given query, under Valiant's PAC-Semantics. We show how any backward proof search algorithm that is sufficiently oblivious to the contents of its knowledge base can be modified to learn such rules while it searches for a proof using those rules. We note that this gives such algorithms for standard logics such as chaining and resolution.
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.
Machine learning (ML) is empowering average business users with superior, automated tools to apply their domain knowledge to predictive analytics or customer profiling. The article What is Automated Machine Learning (AutoML)? These are not just empty promises to worldwide business leaders; in 2017, the age of automated, ML-powered analytics and BI dawned, and has since transformed one industry sector at a time. The automation revolution has not paused and is likely to storm global businesses in years to come. The era of AutoML is beginning to enable business users to tune existing data models and apply custom models to their everyday business situations as well.
What if instead of political parties, presidents, prime ministers, kings, queens, armies, autocrats, and who knows what else, we turned everything over to expert systems? What if we engineered them to be faithful, for example, to one simple principle: "human beings regardless of age, gender, race, origin, religion, location, intelligence, income or wealth, should be treated equally, fairly and consistently"? Here's some dialogue – enabled by natural language processing (NLP) – with an expert system named "Decider" that operates from that single principle (you can imagine how it might behave if the principle was completely different – the opposite of equal and fair). The principle is supported by the data and probabilities the system collects and interprets. The "inferences" made by Decider are pre-programmed.
Take for instance a computer that records a video and can recognize objects in the video, sure it has a data warehouse somewhere of what each object is, and if it doesn't it could add one once it "learns" what it is. But realistically how is that different from humans? Humans don't know what a color is until they learn what it is, I didn't know red was red until someone told me, and red is only red because it is generally agreed upon what the word red represents. If I see a color and tell you it's red, and an a.i.