Facial-recognition software from major tech companies is apparently ill-equipped to work on transgender and non-binary people, according to new research. A recent study by computer-science researchers at the University of Colorado Boulder found that major AI-based facial analysis tools--including Amazon's Rekognition, IBM's Watson, Microsoft's Azure, and Clarifai--habitually misidentified non-cisgender people. They eliminated instances in which multiple individuals were in the photo, or where at least 75% of the person's face wasn't visible. The images were then divided by hashtag, amounting to 350 images in each group. Scientists then tested each group against the facial analysis tools of the four companies.

It is true that since the start of developing AI, it has replaced certain occupations and has the potential to seriously disrupt labour. However, seeing that AI is aiming to replace all jobs instantaneously of labour from humans to machines is a tremendous over-simplification. There have been certain transformations of employment since nineteenth century and there has been a number of occupations since the rapid development of population which has generally been consistent. Regardless of what is being said, there is exceptionally little proof to propose any mass unemployment or widespread redundancy of human workforce's is likely. It is just as possible that a more productive and beneficial economy can take place increasing the effectiveness and reduction of waste from automation promises, this in turn can grant more alternatives in investing time on productive and profitable pursuits.

The situation called for a new approach. They needed information services that would help them decide what varieties to plant, when they should sow and how they should manage their crops. A consortium formed with the government, Colombia's National Cereals and Legumes Federation (FENALCE), and big-data scientists at the International Center for Tropical Agriculture (CIAT). The researchers used big-data tools, based on the data farmers helped collect, and yields increased substantially. The study, published in September in Global Food Security, shows how machine learning of data from multiple sources can help make farming more efficient and productive even as the climate changes. "Today we can collect massive amounts of data, but you can't just bulk it, process it in a machine and make a decision," said Daniel Jimenez, a data scientist at CIAT and the study's lead author.

Namaste, says India's first lip-syncing robot Also the world's first Hindi-speaking robot, Rashmi addressed an excited audience of 15,000 people with a warm "Namaste". Apart from Hindi, Rashmi also speaks English, Marathi and Bhojpuri. The humanoid uses Artificial Intelligence, Linguistic Interpretation, Visual Data and Face Recognition systems to converse like a human being. Rashmi was developed at a cost of Rs 5 lakh. She said Isro had inspected her, and planned to send her to Mars in 2022.

We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing "at scale" within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks.

In this week's episode of Growth Interviews, we invite you to join our podcast conversation with Miroslav Varga, search engine advertising expert, professor and MENSA member, experienced and specialized in Google Ads account optimization and statistical analysis (data mining). Our mission is to provide insights and ideas from world-class professionals on the topic of growth and to cut through the noise of so-called marketing tips and tricks, revealing the money-making strategies behind e-commerce. Welcome to Growth Interviews, the fun, stimulating and engaging series of conversations driven by digital business growth. Each episode is an intriguing challenge involving an insightful expert who reveals some of their best-kept secrets, which you can use right away to boost your business. Miroslav is a Google AdWords Certified Trainer – GCT and online marketing lecturer at several schools and institutions and probably the only Google Ads certified Trainer and GAIQ triple grandfather in the world.

It's a bright April day in Boston, and Gabi Zijderveld, a pioneer in the field of emotional artificial intelligence, is trying to explain why teaching robots to feel is as important as teaching them to think. "We live in a world surrounded by all these super-advanced technologies, hyper-connected devices, AI systems with super cognitive abilities -- or, as I like to say, lots of IQ but absolutely no EQ," says Zijderveld, chief marketing officer of Affectiva, the startup that spun out of the MIT Media Lab 10 years ago to build emotionally intelligent machines. "Just like humans that are successful in business and in life -- they have high emotional intelligence and social skills -- we should expect the same with technology, especially for these technologies that are designed to interact with humans." Giving machines a soul has been a dream of scientists, and sci-fi writers, for decades. But until recently, the idea of robots with heart was the stuff of moviemaking.

The graph represents a network of 3,752 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 17 October 2019 at 01:35 UTC. The requested start date was Monday, 14 October 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 2-day, 16-hour, 29-minute period from Friday, 11 October 2019 at 07:31 UTC to Monday, 14 October 2019 at 00:00 UTC.

The presentation will introduce contemporary brain-computer interface (BCI) techniques. Dr Rutkowski will explain auditory, visual, and tactile reactive BCI examples with applications for communication and passive solutions for cognitive-load/dementia biomarker elucidation. He will also discuss future research directions of the so-called neurotechnology applications for healthcare and especially cognitive monitoring solutions. Tomasz Rutkowski received his M.Sc. in Electronics and Ph.D. in Telecommunications and Acoustics from Wroclaw University of Technology, Poland, in 1994 and 2002, respectively. He received postdoctoral training at the Multimedia Laboratory, Kyoto University, and in 2005-2011 he worked as a research scientist at RIKEN Brain Science Institute, Japan.

As befits the topic, we start our list with a comprehensive introduction into AI technology: "Introduction to Artificial Intelligence." Written by Phillip C. Jackson, Jr., the book is one of the classics that's still read by experts in the field and non-specialists alike. This book provides a summary of the previous two decades of research into the science of computer reasoning, and where it could be heading. Published in 1985, some of the information might be outdated, but if nothing else, the book could serve as a valuable historical document.