In the summer of 1955, while planning a now famous workshop at Dartmouth College, John McCarthy coined the term "artificial intelligence" to describe a new field of computer science. Rather than writing programs that tell a computer how to carry out a specific task, McCarthy pledged that he and his colleagues would instead pursue algorithms that could teach themselves how to do so. The goal was to create computers that could observe the world and then make decisions based on those observations--to demonstrate, that is, an innate intelligence. The question was how to achieve that goal. Early efforts focused primarily on what's known as symbolic AI, which tried to teach computers how to reason abstractly. But today the dominant approach by far is machine learning, which relies on statistics instead. Although the approach dates back to the 1950s--one of the attendees at Dartmouth, Arthur Samuels, was the first to describe his work as "machine learning"--it wasn't until the past few decades that computers had enough storage and processing power for the approach to work well. The rise of cloud computing and customized chips has powered breakthrough after breakthrough, with research centers like OpenAI or DeepMind announcing stunning new advances seemingly every week.

A Google AI has created incredibly photo-like images of animals, including spiders, dogs and frogs. The images, which were created by developers at DeepMind, were so realistic even the creator mistook ones of jaguars and bears as real images taken from a Google search. The software, which learns in a similar fashion to the human brain, is fed thousands of images, and gradually learns patterns over time. Although these images may look incredibly life-like, the network is not perfect and has been known to create spiders with up to twenty legs. A Google AI has created incredibly photo-like images of animals, including spiders, dogs and frogs.

WIRE)--Oct 4, 2018--Esri, the global leader in location intelligence, has announced that it is partnering with BuildingFootprintUSA to provide unprecedented, accurate geocoding and address matching to users of Esri's ArcGIS platform in the United States and Canada, as well as a comprehensive training dataset used to improve Esri's machine learning and artificial intelligence. Users in any industry--including government, telecom, insurance, utilities, real estate, and retail--can benefit from the increased geocoding accuracy that comes from using building footprints. Building footprints for the United States and Canada will be available for use by ArcGIS users by the end of the year, closely followed by data for the United Kingdom and Brazil. Esri will also provide the data for advanced geocoding needs through ArcGIS World Geocoding Service, StreetMap Premium for ArcGIS North America, and ArcGIS World Geocoder. This press release features multimedia.

Python is often the language of choice for developers who need to apply statistical techniques or data analysis in their work. It is also used by data scientists whose tasks need to be integrated with web apps or production environments. Its combination of consistent syntax, shorter development time and flexibility makes it well-suited to developing sophisticated models and prediction engines that can plug directly into production systems. One of Python's greatest assets is its extensive set of libraries. Libraries are sets of routines and functions that are written in a given language.

Text-to-speech technology has advanced greatly over the past two decades. Once defined by the robotic sounding voices that they produced, text-to-speech voices today can sound just as lifelike as an actual human. Today, making a natural sounding text-to-speech voice is labor intensive and expensive. The two most popular methods, HMM and USS, require hours of recordings from a voice actor. Then, computer programmers with an understanding of linguistics must break down all of that audio into the tiniest possible pieces, called phonemes, and appropriately tag them and define the rules for when each individual unit of speech should be used.

Carl Vondrick is a doctoral candidate and researcher at MIT, where he studies computer vision and machine learning. His research focuses include leveraging large-scale data with minimal annotation and its applications to predictive vision and scene understanding. Recently his work has received a lot of media attention, including features in Forbes, Wired, CNN and PopSci, and other media outlets worldwide. As part of his work with MIT CSAIL, Carl built a deep learning vision system for AI to learn and understand human behaviour and interactions, using popular TV shows like The Office, Desperate Housewives, and YouTube videos. The resulting algorithm analyzes videos, then uses what it learns to predict how humans will behave.

Google has a new competition to challenge developers to defend or attack image classification systems with adversarial examples. DeepMind are planning to test its algorithms for head and neck radiotherapy algorithms on humans. And Microsoft have acquired a new startup called Lobe. Is it a goddamn adversarial example? We're also a little obsessed with adversarial examples, and how easy it is to fool neural networks.

While it's human to let our feelings influence our decisions, can we count on AI programs to be fair? As AI helps to automate tasks and use data to support human decision making, one form of it, deep learning, is helping computers think like humans. And, like humans, AI goes through a thought process to arrive at decisions – mostly driven by data. The question of the hour is: how do you make AI accountable for its decisions – and how do you know it's fair? As a recent Wall Street Journal article notes, that's the issue on the minds of major companies, such as Uber, Xerox and other companies that are looking into an area of AI called interpretability, and conducting research into how AI makes decisions.

Artificial intelligence is one of today's hottest buzzwords. As the technology becomes more integrated into our everyday lives, it will become as essential in casework. A basic understanding of AI and its various forms will prove helpful when building a fact-pattern and a strong legal case. Much of the hype around artificial intelligence (AI) has centered on one kind of technology: deep learning. But AI actually comes in a variety of forms, and many AI-based technologies tend to rely on more than one type.

These new applications require a new way of thinking about the development process. Traditional application development has been enhanced by the idea of DevOps, which forces operational considerations into development time, execution, and process. In this tutorial, we outline a "cognitive DevOps" process that refines and adapts the best parts of DevOps for new cognitive applications. Specifically, we cover applying DevOps to the training process of cognitive systems including training data, modeling, and performance evaluation. A cognitive or artificial intelligence (AI) system fundamentally exhibits capabilities such as understanding, reasoning, and learning from data. At a deeper level, the system is built upon a combination of various types of cognitive tasks, which, when combined, make up a part of the overall cognitive application. The science upon which a cognitive system is built includes, but is not limited to, machine learning (ML) including deep learning and natural language processing.