Google is leading the way in the global race to create human-level artificial intelligence, according to leading AI expert Nick Bostrom. Speaking at the IP Expo conference in London on Wednesday, October 5, Bostrom said that there are several companies and organizations that are currently focused on developing human-level AI, or artificial general intelligence. "There are different bets on what approach [to developing human-level AI] is most promising, and since we don't know what approach will ultimately work, there is some uncertainty there," Bostrom said in response to a question from Newsweek . "Baidu, Open AI, and all the large tech companies have various kinds of AI efforts that if they were to become specifically directed to this aim, they have a lot of resources." When pushed to back just one company that is currently leading the field, Bostrom said that Google's DeepMind was the clear frontrunner.

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic.

Samsung Electronics has agreed to buy Viv Labs, an artificial intelligence startup created by Dag Kittlaus, Adam Cheyer, and Chris Brigham. You may not have heard of Kittlaus, Cheyer, or Brigham, but if you own an iPhone you've probably spoken with one of their creations: Siri. Apple bought their first startup, a spinoff from SRI International, in 2010. A couple of years later, they left to create Viv. Samsung's move into AI could be seen as a reaction to Google's launch of a new AI assistant on its Pixel and Pixel XL smartphones on Tuesday.

Tech billionaires' latest obsession -- outside of suing websites into oblivion and attending odd, expensive festivals in the desert -- is apparently one that gripped the country in 1999. The good news: According to a New Yorker story this week, a couple of tech billionaires are secretly funding research to break us out. Jokes aside, the idea is called the simulation hypothesis and it's growing in popularity with the Silicon Valley nouveau-riche, as well as in idiosyncratic corners of Reddit. The New Yorker piece by Tad Friend was centered on Sam Altman, CEO of the "startup accelerator" Y Combinator, but took a detour into Matrix territory. Wrote Friend: "Many people in Silicon Valley have become obsessed with the simulation hypothesis, the argument that what we experience as reality is in fact fabricated in a computer; two tech billionaires have gone so far as to secretly engage scientists to work on breaking us out of the simulation."

Artificial Intelligence(AI) continues to be the next great topic of debate. In fact, Microsoft, Amazon, IBM, Google and Facebook announced on Thursday,Sept.29 the formation of the Partnership on Artificial Intelligence to Benefit People and Society. Within the predictive analytics discipline, though, we tend to use the term "machine learning" as our reference point for artificial intelligence. Much of our thinking in this area has focused around the role of the practitioner or craftsman versus the machine and the concept of machine learning. Yet, machine learning has now evolved into the usage of higher levels of mathematics and computer science with the most recent level being deep learning.

Food security is threatened by many things. In some regions, climate variability causes droughts that make vital resources scarce. In others, political turmoil creates logistical blockades for farming, harvesting, and shipping produce. But, practically everywhere, plant disease can wipe out entire crops with little warning. A team of researchers at Pennsylvania State University and the École Polytechnique Fédérale de Lausanne, Switzerland have turned the keen eye of artificial intelligence toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.

Industry reports show advanced attacks can sit undetected for up to 200 days, waiting for security software to catch up. In today's threat environment, organizations need intelligent security solutions that continually evolve to detect the latest threats as they emerge.

Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify. These tuning knobs, the so-called hyperparameters, help us control the behavior of machine learning algorithms when optimizing for performance, finding the right balance between bias and variance. Hyperparameter tuning for performance optimization is an art in itself, and there are no hard-and-fast rules that guarantee best performance on a given dataset. In Part I and Part II, we saw different holdout and bootstrap techniques for estimating the generalization performance of a model. We learned about the bias-variance trade-off, and we computed the uncertainty of our estimates. In this third part, we will focus on different methods of cross-validation for model evaluation and model selection. We will use these cross-validation techniques to rank models from several hyperparameter configurations and estimate how well they generalize to independent datasets. Previously, we used the holdout method or different flavors of bootstrapping to estimate the generalization performance of our predictive models.

Deep Reinforcement Learning, or Deep RL, is a really hot field at the moment. If you haven't heard of it, pay attention. Combining the power of reinforcement learning and deep learning, it is being used to play complex games better than humans, control driverless cars, optimize robotic decisions and limb trajectories, and much more. And we haven't even gotten started… Deep RL has far reaching applications in business, finance, health care, and many other fields which could be improved with better decision making. It's the closest (practical) approach we have to AGI.

A shorter version of this post first appeared on Pulse, our main publishing platform at LinkedIn. At LinkedIn, we use machine learning technology widely to optimize our products: for instance, ranking search results, advertisements, and updates in the news feed, or recommending people, jobs, articles, and learning opportunities to members. An important component of this technology stack is a knowledge graph that provides input signals to machine learning models and data insight pipelines to power LinkedIn products. This post gives an overview of how we build this knowledge graph. LinkedIn's knowledge graph is a large knowledge base built upon "entities" on LinkedIn, such as members, jobs, titles, skills, companies, geographical locations, schools, etc.