Between 2010 and 2050, the global road network is expected to grow by 60 percent. Many of the new roads will emerge in areas that are still undeveloped in Africa, South America or Asia, further limiting the habitats of endangered animals. Many roads are unplanned or even illegally built. In rainforests, which are very important for the global climate, illegal road construction already leads to deforestation, slash-and-burn and colonization.

Many organisations have already embarked on a Robotics Process Automation or Robotics chatbot initiative. However, many firms also experience delays and cost over-runs, and ultimately failed initiatives. The IITPSA invites you to attend an enlightening business breakfast where you will hear from industry experts about the use of these technologies in the South African context, and have an opportunity to network with other AI and Robotics enthusiasts from the local industry. Free to IITPSA members in good standing (paid up fees).

'For species like chimpanzees, which have complex social lives and live for many years, getting snapshots of their behaviour from short-term field research can only tell us so much,' says Dan Schofield, researcher and DPhil student at Oxford University's Primate Models Lab, School of Anthropology. 'By harnessing the power of machine learning to unlock large video archives, it makes it feasible to measure behaviour over the long term, for example observing how the social interactions of a group change over several generations.' The computer model was trained using over 10 million images from Kyoto University's Primate Research Institute (PRI) video archive of wild chimpanzees in Guinea, West Africa. The new software is the first to continuously track and recognise individuals in a wide range of poses, performing with high accuracy in difficult conditions such as low lighting, poor image quality and motion blur. 'Access to this large video archive has allowed us to use cutting edge deep neural networks to train models at a scale that was previously not possible,' says Arsha Nagrani, co-author of the study and DPhil student at the Department of Engineering Science, University of Oxford.

Rightly or wrongly, the AI-driven world has come to typify the woes of modern economic life, as tech giants such as Facebook and Alphabet amass vast fortunes, due in large part to the huge quantities of data that users often freely provide them. Accusations of economic imbalance tend to be multi-faceted. Not only do these companies typically employ fewer people than the industrial titans of yore, but they also attract the ire of officials over their tax practices and have grown insanely rich off the back of something users receive no compensation for. It's helped to create a world in which the haves are increasingly well off, while the have nots make do with insecure and poorly paid work. Nowhere is this exchange more evident than in the data annotation industry, where people from around the world help to prepare and tidy up the data used by the tech giants to train the algorithms upon which their fortunes increasingly rest.

An artificial intelligence that detects, tracks and recognises chimpanzees could make studying animals in the wild more efficient. Arsha Nagrani at the University of Oxford in the UK and her colleagues have developed a facial recognition AI that can detect and identify the individual chimpanzees captured in video footage recorded in the wild. Using the AI, they can cut down the time and resources needed to track animals in their natural habitat. The algorithm could help researchers and wildlife conservationists study the complex behaviours of chimpanzees and other primates more efficiently. The team trained the AI on 50 hours of archival footage – spanning 14 years – of chimpanzees in Bossou, Guinea in West Africa.

Scientists at the University of Oxford have developed new artificial intelligence software to recognise and track the faces of individual chimpanzees in the wild. The new software will allow researchers and wildlife conservationists to significantly cut back on time and resources spent analysing video footage, according to the new paper published today in Science Advances. "For species like chimpanzees, which have complex social lives and live for many years, getting snapshots of their behaviour from short-term field research can only tell us so much," says Dan Schofield, researcher and DPhil student at Oxford University's Primate Models Lab, School of Anthropology. "By harnessing the power of machine learning to unlock large video archives, it makes it feasible to measure behaviour over the long term, for example observing how the social interactions of a group change over several generations." The computer model was trained using over 10 million images from Kyoto University's Primate Research Institute (PRI) video archive of wild chimpanzees in Guinea, West Africa.

Xero has bolstered its machine learning capabilities for document processing and extraction using technology from Hubdoc, a company that the cloud accounting firm acquired last year for $70 million. According to Xero, the new machine learning capabilities means advisors will have access to more accurate information, gain deeper understanding of financial health, and the flow of data will become more automated. Xero added it is working with advisors to pilot a short-term cash flow tool that uses data and advanced statistical methods to provide small businesses with a 30-day view of artificial intelligence (AI)-powered cash flow predictions, including impact of existing bills and invoices. In addition, the company has introduced single sign-on, touting it as making it easier for developers to build on the Xero platform, onboard new users, and integrate Xero with certified third-party apps. Updates to the Xero Advisor Directory using geo-targeting have also been made, the company said during Xerocon Brisbane 2019, noting the technology will make it easier for small businesses to find a local accountant or bookkeeper and onboard new clients.

A New Research on the Global Emotion Artificial Intelligence Market was conducted across a variety of industries in various regions to produce more than 150 page reports. This study is a perfect blend of qualitative and quantifiable information highlighting key market developments, industry and competitors' challenges in gap analysis and new opportunities and may be trending in the Emotion Artificial Intelligence market. Some are part of the coverage and are the core and emerging players being profiled IBM, Microsoft, Eyesight Technologies, Affectiva, NuraLogix, gestigon GmbH, Crowd Emotion, Beyond Verbal, nViso, Cogito Corporation, Kairos. Import and export policies that can have an immediate impact on the global Emotion Artificial Intelligence market. This study includes EXIM * related chapters for all relevant companies dealing with the Emotion Artificial Intelligence market and related profiles and provides valuable data in terms of finances, product portfolio, investment planning and marketing and business strategy. The study is a collection of primary and secondary data that contains valuable information from the major suppliers of the market.

Unfamiliar faces aren't welcome at Beijing public housing projects. To prevent illegal subletting, many have facial recognition systems that allow entry only to residents and certain delivery staff, according to state news agency Xinhua. Each of the city's 59 public housing sites is due to have the technology by year's end. Artificial intelligence startup Megvii mentioned a similar public housing security contract in an unspecified Chinese city in filing for an initial public offering in Hong Kong last week. The Chinese startup, best-known for facial recognition, touts its government dealings, including locking down public housing to curb subletting, as a selling point to potential investors.

Rightly or wrongly, the AI-driven world has come to typify the woes of modern economic life, as tech giants such as Facebook and Alphabet amass vast fortunes, due in large part to the huge quantities of data that users often freely provide them. Accusations of economic imbalance tend to be multi-faceted. Not only do these companies typically employ fewer people than the industrial titans of yore, but they also attract the ire of officials over their tax practices and have grown insanely rich off the back of something users receive no compensation for. It's helped to create a world in which the haves are increasingly well off, while the have nots make do with insecure and poorly paid work. Nowhere is this exchange more evident than in the data annotation industry, where people from around the world help to prepare and tidy up the data used by the tech giants to train the algorithms upon which their fortunes increasingly rest.