Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him. Lenat fed the massive Traveller rulebook into the system and asked it to find the best way of winning.

Since announcements late last year about Google open-sourcing TensorFlow, the company's open-source library for machine learning, and previous coverage at InfoQ, the data-science community has had an opportunity to try out TensorFlow for their own projects. Databricks' Tim Hunter demonstrates TensorFlow-generated model selection and at-scale neural network processing with Spark. Hunter describes an artificial neural network as mimicking the neurons in the visual cortex of the human brain, which when adequately trained can be used for processing complex input data like imagery or audio. Hunter detailed how he ran TensorFlow on various Spark configurations to parallelize hyperparameter tuning. Hunter stated that TensorFlow, currently available with Python and C support helped "automate the creation of training algorithms for neural networks of various shapes and sizes" for the purpose of training a neural network to process large amounts of data with high accuracy and optimal runtime performance.

With the Watson visual-recognition service, you can analyse images or video frames to understand their contents. Image recognition is a hot area of research using artificial intelligence, and now IBM offers an online demo to let anyone test out the capabilities offered by its Watson cognitive computing system. Six sample photos are provided for illustration, or you can upload your own and ask Watson to analyze them. Either way, the cognitive system will produce a series of "classifiers" offering descriptions of the image's contents along with confidence scores for each of them. You can also create custom classifiers tailored for specific purposes.

As many of you know by now, on Wednesday we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values. I want to share what we learned and how we're taking these lessons forward. For context, Tay was not the first artificial intelligence application we released into the online social world.

One of the things that set statistics apart from the rest of applied mathematics is an interest in the problems introduced by sampling: how can we learn about a model if we're given only a finite and potentially noisy sample of data? Although frequently important, the issues introduced by sampling can be a distraction when the core difficulties you face would persist even with access to an infinite supply of noiseless data. For example, if you're fitting a misspecified model \(m_1\) to data generated by a model \(m_2\), this misspecification will persist even as the supply of data becomes infinite. In this setting, the issues introduced by sampling can be irrelevant: it's often more important to know whether or not the misspecified model, \(m_1\), could ever act as an acceptable approximation to the true model, \(m_2\). Until recently, I knew very little about these sorts of issues.

Yesterday we reported that Google is aiming to use machine learning in spam algorithms, to better fight spam. Shortly after I wrote that, former Google spam fighter Murat Yatagan, said machine learning is already baked into the spam algorithms. Murat worked at Google for several years and many of those years specifically on search quality and spam. His focus was on the Turkish market. He is now doing SEO like many former Google spam fighters.

How far is too far when it comes to machine learning? We live in the digital age where companies like Google collect information which feeds and informs their algorithms, potentially advancing their technology into the realm of the uncomfortable. When it comes to digital AI and algorithmic predictions, when do we say enough is enough? Algorithms are digital codes that sort, predict and filter information in a fashion that is similar to the way the human brain works. They are in effect all over the internet, from Google's search predictions to Netflix's recommendation section.

This article was taken from The WIRED World in 2016 -- our fourth annual trends report, a standalone magazine in which our network of expert writers and influencers predicts what's coming next. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. When assessing a patient, medics look at snapshots of physiological data that are manually taken by doctors or nurses, and make decisions against patient history, family background and test results, as well as their own knowledge and experience. But what if this data was constantly being taken, every second of every day? And what if a system was clever enough to compare these readings to thousands of patients worldwide with a similar history and disorder, as well as all the current clinical guidelines and studies, and make clinical suggestions to doctors?

Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade!

The Internet of Things (IoT), Artificial Intelligence (AI) and cloud computing are three technologies that are converging to disrupt nearly every industry. IoT refers to a connected network of objects embedded with technology that enables the collection and exchange of data. Cloud computing is the storing and retrieval of data, and accessing application programs via the Internet. Artificial Intelligence is the simulation of human intelligence by machines. We are currently in the midst of the rise of the first wave of this technological convergence.