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.
What exactly is artificial intelligence? In much the same way that you'd be a bit stumped if someone demanded that you provide a hard and fast definition of say, philosophy, there isn't a satisfactorily rigorous answer to this question. As the Stanford Encyclopaedia explains, AI's definition falls under the category of "remarkably difficult, maybe even eternally unanswerable, questions, especially if the target is a consensus definition". But that hasn't stopped startups from jumping on the buzzword bandwagon. To get around this problem, most AI definitions settle for a muddled approach.
His demand for a ban triggered a legal and moral quagmire, as the Pentagon faced the prospect of throwing out service members who had willingly come forward as transgender after being promised they would be protected and allowed to serve. And as legal battles blocked the ban from taking effect, the Obama-era policy continued and transgender individuals were allowed to begin enlisting in the military a little more than a year ago.
WASHINGTON – The Defense Department has approved a new policy that will largely bar most transgender troops and military recruits from transitioning to another sex, and require most individuals to serve in their birth gender. The new policy comes after a lengthy and complicated legal battle, and it falls short of the all-out transgender ban that was initially ordered by President Donald Trump. But it will likely force the military to eventually discharge transgender individuals who need hormone treatments or surgery and can't or won't serve in their birth gender. The order says the military services must implement the new policy in 30 days, giving some individuals a short window of time to qualify for gender transition if needed. And it allows service secretaries to waive the policy on a case-by-case basis.
Although the headline that gets the clicks is "AI is taking our jobs," the current reality is that "Automation is replacing some of our tasks." I know, it's nowhere near as catchy. I say automation rather than AI because it doesn't really matter what technology the underlying system uses, so long as it does the job well. For example, being a bank teller was a human job requiring intelligence, but there's no AI in an ATM cash machine (when it comes to technology evolution, some of them don't even seem to have caught up to Windows Vista yet). I also prefer "tasks" over "jobs" because mostly AI can only do certain elements.
The Industrial Revolution conjures up images of steam engines, textile mills, and iron workers. This was a defining period during the late 18th and early 19th centuries, as society shifted from primarily agrarian to factory-based work. A second phase of rapid industrialization occurred just before World War I, driven by growth in steel and oil production, and the emergence of electricity. Fast-forward to the 1980s, when digital electronics started having a deep impact on society--the dawning Digital Revolution. Building on that era is what's called the Fourth Industrial Revolution.
It's no secret that marketing today relies heavily on data analytics and data science. Endless applications have been wildly studied and successfully applied in this regard, ranging from customer segmentation and targeting to building recommender systems and predicting churn. In this blogpost, we are going to address yet another interesting application of data science in marketing, which is marketing attribution. Unlike the above examples, marketing attribution unfortunately still lacks a rigorous data-driven approach, and it is largely addressed nowadays through rigid business rules. The content of this blogpost will be very technical at times.
As we make more cashless payments for retail purchases, restaurants, and transportation – not to mention the increase in online shopping – wallets loaded with legal tender may become a thing of the past. According to 2018 research by BigCommerce, software vendor and Square payment processing solution provider, 51 percent of Americans think that online shopping is the best option. Last year, 1.66 billion people worldwide bought goods online. And the number of digital buyers is expected to exceed 2.14 billion. Unfortunately, growing sales may mean not only greater revenue but also bigger losses due to fraud.
Machine learning is becoming so smart that algorithms designed to set prices in online marketplaces are mirroring each others' behaviour to raise prices. Algorithms using self-learning AI are popular systems that have become adopted by Amazon to constantly learn and set the best prices in order to drive website profit. An experiment by researchers in Bologna used algorithms similar to those manipulated by online shopping sites and found they were able to'collude' to artificially hike up prices. The researchers showed that this could happen entirely out of human control, as the independent AI systems were able to learn each others' behaviours. Machine learning is becoming so smart that online price setting algorithms are mirroring each others' behaviour to raise prices and with a goal to raise profits.
AI for business is an incredibly helpful tool for enterprises when used correctly. Just take a look at some numbers recently published in a Forbes Magazine article: 38% of 235 enterprises the NBRI looked at are already using AI for a variety of tasks; and more importantly, 62% of these enterprises expect to be using AI by 2018. But here's the rub: AI is a massively broad catch all term. Over the last few years, people have termed all sorts of machine coding techniques as'AI;' in fact, saying that your business uses AI is kind of like saying your garden has plants. In other words, AI is an umbrella for a whole host of technologies.