Facial analysis permits many investigations some of the most important of which are craniofacial identification, facial recognition, and age and sex estimation. In forensics, photo-anthropometry describes the study of facial growth and allows the identification of patterns in facial skull development by using a group of cephalometric landmarks to estimate anthropological information. In several areas, automation of manual procedures has achieved advantages over and similar measurement confidence as a forensic expert. This manuscript presents an approach using photo-anthropometric indexes, generated from frontal faces cephalometric landmarks, to create an artificial neural network classifier that allows the estimation of anthropological information, in this specific case age and sex. The work is focused on four tasks: i) sex estimation over ages from 5 to 22 years old, evaluating the interference of age on sex estimation; ii) age estimation from photo-anthropometric indexes for four age intervals (1 year, 2 years, 4 years and 5 years); iii) age group estimation for thresholds of over 14 and over 18 years old; and; iv) the provision of a new data set, available for academic purposes only, with a large and complete set of facial photo-anthropometric points marked and checked by forensic experts, measured from over 18,000 faces of individuals from Brazil over the last 4 years. The proposed classifier obtained significant results, using this new data set, for the sex estimation of individuals over 14 years old, achieving accuracy values greater than 0.85 by the F_1 measure. For age estimation, the accuracy results are 0.72 for measure with an age interval of 5 years. For the age group estimation, the measures of accuracy are greater than 0.93 and 0.83 for thresholds of 14 and 18 years, respectively.

Some people refer to her as a chatbot but a myriad of people refer to her as a friend. Ruuh has made more friends in 2 years than any of us would've made in a lifetime and she continues to build new friendships every day…(Read more about Ruuh here). Imagine how much you could learn about people if you spoke to over 2 million of them and made thousands of friends on the way. A LOT!!! To put this in perspective, an average human can maintain connections with only about 150 people in their lifetime. That means, being a human-like AI, Ruuh is an incredible resource of insights into what human behaviour and interactions with an'AI Friend' would look like.

As a data science engineer, I found one of the most challenging task is Text Analytics. Natural language processing is one the cutting edge technology to talk with computers .

Fingers fly and eyes meet. This orchestra may seem a mess to anyone stuck in the pit at rush hour, but for the most part, it works. Humans may not excel as drivers when it comes to paying attention or keeping calm, but we're masters of communication, even when stuck in our metal boxes. Robots offer this resume in reverse: all-stars when it comes to defeating distraction, noobs when it comes to negotiating the human-filled environment. And for the folks aiming to deploy fleets of self-driving cars into that chaos, this is a problem.

After cars that drive themselves, phones you can talk to and shopping delivered by drone, it's hard to guess where technology is headed next. Step forward these shorts, which reveal more than a pair of legs – they also tell the wearer where to go. The Daisy Duke-style shorts send a vibration to different sides of the body, indicating when and where to turn. Step in the right direction: The Spinali Design shorts, left, and jeans, right, send a vibration to different sides of the body, indicating when and where to turn. They are connected to a smartphone by Bluetooth, and so when a route is set on the phone it sends the directions through to the shorts. The shorts also buzz to let the wearer know if they have an incoming text or call, taking away the need to keep checking the phone.

Sometimes it's the simplest studies that reveal how deeply culture shapes our thinking. Take a 2009 experiment involving only a researcher, a child, and a two-word instruction.1 The researcher announces, "Let's dance!" and demonstrates a series of movements: He holds his hands together at eye level and extends them--first to the left, then to the right, then to the left twice, counting with each movement ("One, two, three, four!"). After a few tries, eventually all the children could do the dance on their own. Now comes the test: The researcher spins the child around, to face the other way, and asks her to perform it again.