The wildebeest, hippos, gazelles and warthogs of Southern Kenya have a little less to worry about. Wildlife advocates began testing an advanced system this spring to better identify and apprehend poachers. They say the technology has led to dozens of arrests that wouldn't have happened otherwise. Brian Heath, chief executive of the Mara Conservancy in Kenya, believes expanding the use of the technology throughout Africa could significantly improve anti-poaching efforts. "Our rangers now feel completely disadvantaged and blind without it," Heath said. "They get a huge amount of reassurance by having it and the ability to [better] see and identify people and animals."

Wildlife poachers who stalk endangered animals in East and South Africa have long operated under the cover of night. But lately not even a moonless sky is safe cover for stalking impalas, elephants, and rhinos. Now, the power of increasingly inexpensive infrared cameras, artificial intelligence, and drones are being used to stop illegal poaching. Rangers are rounding up veteran poachers in the middle of the night, says Colby Loucks, World Wildlife Fund's senior director of wildlife crime technology, who ask, dumbfounded, "How are you finding me?'" This spring, the World Wildlife Fund began deploying thermal sensing infrared technology from the imaging company FLIR to combat poaching in Kenya's Maasai Mara Conservancy park--and at another secret location that's home to rhinos, one of the most imperiled creatures on Earth.

The Carnegie Endowment for International Peace (CEIP) and Carnegie Mellon University held the first part of the two-part joint Carnegie Colloquium on Digital Governance and Security in Washington D.C. on Oct. 31. The first part of the colloquium was titled "The Rise of Artificial Intelligence: Implications for Military Operations and Privacy," and the second part, titled "The Future of the Internet: Governance and Conflict," will be held this year in Pittsburgh on Dec. 2. CEIP is a series of foreign policy-based research centers located in Russia, China, Europe, the Middle East, India, and the United States, with headquarters in Washington D.C., that collects itself under the phrase, "The Global Think Tank." It was established in 1960 by Andrew Carnegie and, according to a report by the University of Pennsylvania, is the third most influential think tank in the world. The colloquium, held for the benefit of both Carnegie Mellon and the CEIP, aimed to allow for communication between the academics at Carnegie Mellon and the foreign policy and ethics experts from all the CEIP stations across the world to discuss the implication of artificial intelligence on foreign policy and the challenges posed by it. The second part of the colloquium will focus on cyber-security norms and internet governance. "Designing safe software systems and attempting to create the learning abilities of the human brain are natural progressions towards the two of the modern world's most pressing concerns -- cyber security and privacy," said President Subra Suresh, in the welcome address.

Technology consultant and software developer Prakelt has announced that its new chatbot, Feersum Engine, is ready to start talking using WhatsApp, Facebook, Twitter and other established messaging platforms to interact with end users in a way that feels familiar and comfortable. Feersum Engine is a chatbot that can engage people in conversation using natural language processing and machine learning. Tipped for exponential growth, chatbots and conversational interfaces are already being adopted by many banks and financial services around the world, and their applications are becoming more varied as designers better understand the technology. Streamlining interaction Locally-built Feersum Engine brings artificial intelligence and conversational interaction to large audiences for customer support, brand marketing or public service outreach. One use case for Feersum Engine, for example, is to cut down the number of simple to answer requests that overwhelm support and service agents, and is claimed to be its flexible enough to work in any environment from product support to government healthcare initiatives.

Farming is, by far, the most mature industry mankind has created. Dating back to the dawn of civilization, farming has been refined, adjusted and adapted -- but never perfected. We, as a society, always worry over the future of farming. Today, we even apply terms usually reserved for the tech sector -- digital, IoT, AI and so on. So why are we worrying? The Economist, in its Q2 Technology Quarterly issue, proclaims agriculture will soon need to become more manufacturing-like in order to feed the world's growing population.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

The saying goes: 'Never judge a book by its cover,' but that's exactly what new AI technology has been designed to do. A controversial paper has been released, which investigates whether a computer can detect if a human could be a criminal, by analysing their facial features. The results suggest that it is bad news for people with smaller mouths, curvier upper lips and closer-set eyes, as apparently these features suggest you could be a crook. The paper investigates whether a computer can detect if a human could be a criminal, by analysing their facial features. The researchers singled out three features that they suggest can tell whether someone will be a criminal or not – lip curvature, eye inner corner distance, and the angle from the tip of the nose to the corners of the mouth.

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.

The fields of artificial intelligence and machine learning are moving so quickly that any notion of ethics is lagging decades behind, or left to works of science fiction. This might explain a new study out of Shanghai Jiao Tong University, which says computers can tell whether you will be a criminal based on nothing more than your facial features. The bankrupt attempt to infer moral qualities from physiology was a popular pursuit for millennia, particularly among those who wanted to justify the supremacy of one racial group over another. But phrenology, which involved studying the cranium to determine someone's character and intelligence, was debunked around the time of the Industrial Revolution, and few outside of the pseudo-scientific fringe would still claim that the shape of your mouth or size of your eyelids might predict whether you'll become a rapist or thief. Not so in the modern age of Artificial Intelligence, apparently: In a paper titled "Automated Inference on Criminality using Face Images," two Shanghai Jiao Tong University researchers say they fed "facial images of 1,856 real persons" into computers and found "some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle."

On a Friday morning nine (!) years ago, I published a post with just one video and one line of text on BotJunkie.com, That was the beginning of Video Friday. As more and more robot video content started showing up over the years, Video Friday turned into a way to keep you updated on everything that happened all week in one efficient (and hopefully entertaining) post. At one point Video Friday grew to include something like 30 videos (if we've crashed your browser, we're very sorry!). We've now toned it down to around 20 videos by being slightly more selective.