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) …
Artificial Intelligence techniques such as "deep learning" and "convolutional neural networks" have made stunning advancements in image recognition, self-driving cars, and other difficult tasks. Numerous AI companies have appeared to catch the wave of excitement as funding and acquisitions have accelerated. Yet, leading AI researchers realize something is not right. Despite the impressive progress, current AI techniques are limited. For example, deep learning networks typically require millions of training examples before they start working correctly, while a human can learn something new with just a few exposures.
In an ordinary hospital room in Los Angeles, a young woman named Lauren Dickerson waits for her chance to make history. She's 25 years old, a teacher's assistant in a middle school, with warm eyes and computer cables emerging like futuristic dreadlocks from the bandages wrapped around her head. Three days earlier, a neurosurgeon drilled 11 holes through her skull, slid 11 wires the size of spaghetti into her brain, and connected the wires to a bank of computers. Now she's caged in by bed rails, with plastic tubes snaking up her arm and medical monitors tracking her vital signs. She tries not to move.
We've all heard Elon Musk speak with foreboding about the danger AI poses -- something he says may potentially bring forth a Third World War: This is of course the power of artificial intelligence (AI). Related: 5 Major Artificial Intelligence Hurdles We're on Track to Overcome by 2020 But let's put aside for a moment Musk's claims about the threat of human extinction and look instead at the present-day risk AI poses. This risk, which may well be commonplace in the world of technology business, is bias within the learning process of artificial neural networks. This notion of bias may not be as alarming as that of "killer" artificial intelligence -- something Hollywood has conditioned us to fear. But, in fact, a plethora of evidence suggests that AI has developed a system biased against racial minorities and women.
It's no understatement to say that the science fiction stories that we've told ourselves in well-thumbed paperbacks, cult movies and beloved TV series have changed the world. For years they've led the way for real scientific advances, providing the creative vision that has inspired engineers and scientists. But in an age where reality is starting to catch up with fiction… well, what happens next? "We're really starting to catch up with those ideas now in the real world," says futurologist and artificial intelligence (AI) expert Dr Ian Pearson. And it's true, science is making fiction a reality.
In this tutorial, we will demonstrate how to create scalable, end-to-end data analysis processes in R on single machines as well as in-database in SQL Server and on Hadoop clusters running Spark. We will provide hands-on exercises as well as code in a public GitHub repository for attendees to adopt in their data science practice. In particular, the attendees will see how to build, persist, and consume machine learning models using distributed machine learning functions in R. R is one of the most used languages in the data science, statistical and machine learning (ML) community. Although open-source R (CRAN library) now has in excess of 10,000 packages and functions for statics and ML, when it comes to scalable analysis using R, or deployment of trained models into production, many data scientists are blocked or hindered by (a) its limitations of available functions to handle large datasets efficiently, and (b) knowledge about the appropriate computing environments to scale R scripts from desktop analysis to elastic and distributed cloud services. In this tutorial, we will discuss how to create end-to-end data science solutions that utilize distributed compute resources.
In the economy of heterosexual online dating, where thumbs wield the ultimate power over a person's love life, height appears to be an immensely valuable currency. The listing of height in dating app profiles has become so prevalent, that many swipers come to expect it, and sometimes hypothesise when it's been omitted from the profile. In my own experience, I have grown to attach a great deal of importance to the feet and inches in a person's bio. As I idly swipe through Bumble, I will scroll through a dater's photos before perusing their bio, searching for a number that might dictate the crucial decision: to swipe left or right? I'm 5ft8, and I often swipe left (which means no) on men under 6ft.
High Performance Computing (HPC) has historically depended on numerical analysis to solve physics equations, simulating the behavior of systems from the subatomic to galactic scale. Recently, however, scientists have begun experimenting with a completely different approach. It turns out that Machine Learning (ML) models can be far more efficient and even more accurate than the time-tested, number-crunching simulations in use today. Once a Deep Neural Network (DNN) is trained, using the virtually unlimited data sets from traditional analysis and direct observation, it can predict or estimate the outcome of a simulation–without actually running it. Early results indicate that by combining ML and traditional simulation, these "synthesis models" can improve accuracy, accelerate time to solution, and significantly reduce costs.
A short film made by campaigners and scientists shows tiny drones hunting and killing with ruthless precision and without human guidance. The movie, released by the campaign group Stop Autonomous Weapons, highlights the perils of autonomous weapons falling into the wrong hands. It shows students in a school classroom being attacked by drones, armed with explosives. The drones identified and neutralized targets and did not need any instructions during the mission. This gruesome reminder of the destructive potential of Artificial Intelligence (AI)-integrated weapons displays autonomous drones that can find, follow and fire at targets independently.
We trust in science because we can verify the accuracy of its claims. We test and verify that accuracy by repeating the scientist's original experiments. What happens when those tests fail, particularly in a field that has the potential to create billions of dollars of revenue? In 2016, Nature surveyed more than 1,500 scientists and found that more than 70% of them had tried and failed to reproduce experiments by other scientists published in scientific journals. More than half couldn't even reproduce their own work.
Artificial Intelligence has been a hot word across all industries lately. Think all the fuss around self-driving cars, Google's updated Assistant and the general talks of how conversational interfaces are the future of tech. Around 54 percent of retailers already use or plan to add artificial intelligence technology to their toolkit, with 20 percent planning to introduce some AI within the next 12 months, according to the latest report from SLI Systems. The increased adoption of AI in retail can be specifically attributed to advances in the deep learning. Deep learning is a specific machine learning approach to building and training neural networks.