Build your own best friend! Students design $3,000 kit robo-dog that can jump, flip and dance

Daily Mail - Science & tech

A robotic dog that can dance, do flips and jump has been created by a team of students - and they are encouraging people to build their own. The robo-dog senses when it is out of position and uses'virtual springs' to pop upright with precision. It has been created with the goal of being reproduced by anyone and the team has published their designs and blueprints online to encourage people to make their own robots. Doggo's creators wanted to share their joy so much they have made the plans, code and a supply list all freely available on GitHub, a specialist platform for developers to share computer code. On the Stanford Doggo Project Github blog, the students describe themselves as undergraduate and graduate students in the Stanford Student Robotics club and part of the club's'Extreme Mobility team'.

Beyond Deepfakes – AI, Security, and Programmatically Generated Everything Emerj


Deepfakes have made their way into the radar of much of the First World. As with many technology phenomena, deepfakes have their origins in pornography – editing (the Reddit page that originally popularized deepfakes was banned in early 2018). In April of this year, I was asked by UNICRI (the crime and justice wing of the UN) to present the risks and opportunities of deepfakes and programmatically generated content at United Nations headquarters for a convening titled: Artificial Intelligence and Robotics: Reshaping the Future of Crime, Terrorism, and Security. Instead of speaking about the topic, we decided it would be better to showcase the technology to the UN, IGO, and law enforcement leaders attending the event. So we took a video of UNICRI Director Ms. Bettina Tucci Bartsiotas, and created a deepfake, altering her words and statements by using a model of her face on another person.

A Discussion about Accessibility in AI at Stanford ·


I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here. Q: What 3 things would you most like the general public to know about AI? AI is easier to use than the hype would lead you to believe. In my recent talk at the MIT Technology Review conference, I debunked several common myths that you must have a PhD, a giant data set, or expensive computational power to use AI. Most AI researchers are not working on getting computers to achieve human consciousness.

Minecraft at 10: a decade of building things and changing lives

The Guardian

Hidden away somewhere in my attic is an old Xbox 360 that I'll never throw away. On its hard drive is a Minecraft save file that contains the first house my oldest son ever built in the game. He was seven and, coming from a boy on the autism spectrum with a limited vocabulary and no patience to draw and paint, his creation was a revelation. Sure, it is a monstrous carbuncle, a mess of wooden planks, cobblestone and dirt. But it is also the greatest building I ever saw.

Match app offers free dating coaches to help send messages, get over breakups, and find love

Daily Mail - Science & tech

Match is becoming the first major dating app to provide its premium users with personally-tailored advice through a free human coach. The company announced today that it is beginning to roll out a new service called AskMatch which allows its paid users to chat on the phone with one of the company's dating hired'experts.' According to a report from TechCrunch, Match members can pick their coach's brains on a variety of topics that include how to set up a good dating profile, getting over a break up, or more general advice on dating. In multiple phone interviews, Match CEO, Hesam Hosseini said that the service will help to push the online dating platform, which has been in existence since 1995, into the future. 'Match's mission has always been around relationships and bringing people together.

Nike says you might be wearing the wrong size shoe, so it created an AR tool to help

USATODAY - Tech Top Stories

Nike Fit will utilize smartphone cameras and augmented reality to scan users' feet and measure the full shape of both feet. If you don't know which size shoe to buy when ordering sneakers online, the world's largest shoe company is rolling out a possible solution. Nike is introducing a feature to its app that lets you scan your feet using your smartphone camera to determine what size shoe will be the perfect fit. Aptly titled, Nike Fit, the AR tool seeks to replace the steel measurement device that you find under the seats at your local shoe store. It's a timely development as more consumers shift their shopping habits online.

Apple and Google pull dating apps after they allowed CHILDREN as young as 12 to create profiles

Daily Mail - Science & tech

Following warnings from the FTC, both Apple and Google have removed several dating apps from their platforms that they say allowed children to join. According to a letter sent by the FTC to Ukraine-based Wildec LLC, which owns FastMeet, Meet24 and Meet4U, the trio of dating apps allowed children as young as 12 to participate in the service and communicate with adults. In an FTC investigation of the app, the organization says they were also able to identify and confirm multiple children within the service using a built-in age filter that allows users to search by age. A trio of dating apps was removed by Google and Apple for allowing children under 13-years-old to participate. The FTC says the apps have already been used by sexual predators.

Online Learning for Latent Dirichlet Allocation

Neural Information Processing Systems

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, including those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a fraction of the time.

How AI technology is influencing Gen Z engagement strategies


The past decade has seen artificial intelligence develop from a mere fantasy to a fully integrated part of a marketing strategy, for brands that look to differentiate and improve their customer experiences and online strategies. Take Farfetch for example, which utilized RFID-enabled clothing racks and digital mirrors to allow its customers the choice of size and colour before directly checking out online. This particular use of AI shows the seamless integration of online and offline experiences, and proves that this technology has no end to the benefits and creativity it can bring for a brands engagement efforts. Found at the core of AI technology is data and analytics, allowing brands to streamline digital ads and offer a personalized customer service. This can result in a significant lift to brands engagement efforts and empowers them to fully engage with customer at every stage of the purchase lifecycle.

Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem Machine Learning

Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers' preferences through offering recommendations, which may contain existing products and new products that are launched in the middle of a selling period. We propose a sequential multinomial logit (SMNL) model to characterize customers' behavior when product recommendations are presented in tiers. For the offline version with known customers' preferences, we propose a polynomial-time algorithm and characterize the properties of the optimal tiered product recommendation. For the online problem, we propose a learning algorithm and quantify its regret bound. Moreover, we extend the setting to incorporate a constraint which ensures every new product is learned to a given accuracy. Our results demonstrate the tier structure can be used to mitigate the risks associated with learning new products.