Why is it that every time humans develop a really clever computer system in the movies, it seems intent on killing every last one of us at its first opportunity? In Stanley Kubrick's masterpiece, 2001: A Space Odyssey, HAL 9000 starts off as an attentive, if somewhat creepy, custodian of the astronauts aboard the USS Discovery One, before famously turning homicidal and trying to kill them all. In The Matrix, humanity's invention of AI promptly results in human-machine warfare, leading to humans enslaved as a biological source of energy by the machines. In Daniel H. Wilson's book Robopocalypse, computer scientists finally crack the code on the AI problem, only to have their creation develop a sudden and deep dislike for its creators. Is Siri just a few upgrades away from killing you in your sleep? And you're not an especially sentient being yourself if you haven't heard the story of Skynet (see The Terminator, T2, T3, etc.) The simple answer is that -- movies like Wall-E, Short Circuit, and Chappie, notwithstanding -- Hollywood knows that nothing guarantees box office gold quite like an existential threat to all of humanity. Whether that threat is likely in real life or not is decidedly beside the point. How else can one explain the endless march of zombie flicks, not to mention those pesky, shark-infested tornadoes? The reality of AI is nothing like the movies. Siri, Alexa, Watson, Cortana -- these are our HAL 9000s, and none seems even vaguely murderous. The technology has taken leaps and bounds in the last decade, and seems poised to finally match the vision our artists have depicted in film for decades. Is Siri just a few upgrades away from killing you in your sleep, or is Hollywood running away with a tired idea? Looking back at the last decade of AI research helps to paint a clearer picture of a sometimes frightening, sometimes enlightened future. An increasing number of prominent voices are being raised about the real dangers of humanity's continuing work on so-called artificial intelligence.

We will be talking about Neural Networks. We will start by introducing Neural Networks and talk about applications where it shines. After going through the basics of Neural Networks, we will move into speaking about Deep Neural Networks, we will try to go through implementation examples of Neural Networks using the TensorFlow library. Here we will introduce the constitutive element of a Neural Network, the neuron. We will talk about how it is the building block of neural nets.

Machine Learning, somehow, emerged from Artificial Intelligence.

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Self-learning, machine learning, and AI are all buzzwords in the tech field today. They all represent the next generation in software development and management. In this brave new world, programmers will often set up the application -- and the software will do the rest. Driven by big data, deep learning systems, and consumer demand, you may be investing in self-learning programs sooner than you think. Self-learning, often referred to as machine learning, is a form of AI.

Artificial intelligence is everywhere, at times obscured and sometimes fully hidden. It lurks in the Facebook newsfeed algorithm that curates the news you see, it's being implemented in the programs of semi-autonomous vehicles that decide who lives in case of an accident, and it spectacularly beat the top Go champions in the world with its deep neural network technology. The applications of AI are evolving with increased sophistication, sparking considerable, complex questions related the social impact, governance, and ethics of its technology. These questions are particularly salient as accountability mechanisms for algorithms are yet in a nascent stage, where the balance of power is skewed towards industry giants who control these technologies. In this particular moment, the research, development, and deployment of AI is primarily taking place in the private sector, while governments around the world are increasingly contracting out their own use of these powerful technologies. In this context, the future role of universities emerges as one that is particularly meaningful when it comes to addressing these questions of social impact, ethics, and governance of AI.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. Over the past few weeks, we published a series of posts outlining expert opinions in data science, machine learning, artificial intelligence, and related fields. In an encore post of this series, we bring you the collected responses to an amalgam question -- including experts from all of the previous posts' fields -- while adding a second dimension this time around. I'd like to thank one of my researchers, Alekh Agarwal, for great input here. The way to increase the number of women in AI, ML and data science is two-fold. First, we must expand the definitions of the fields to include their interaction with the other sciences, including the biological and social sciences.

Massimiliano "Max" Versace traces the birth date of his startup to when NASA came knocking in 2010. The U.S. space agency had caught wind of his military-funded Boston University research on making software for a brain-inspired microprocessor through an IEEE Spectrum article, and wanted to see if Versace and his colleagues could help develop a software controller for robotic rovers that could autonomously explore Mars. NASA's vision proved no easy challenge. Mars rovers have limited computing, communications, and power resources. NASA engineers wanted artificial intelligence that could rely solely on images from a low-end camera to navigate different environments.