Facebook will dramatically accelerate its research into artificial intelligence, its chief AI scientist said Tuesday, in hopes of ensuring the social network doesn't fall behind with the technology it will need to contend with Internet rivals and police its gargantuan audience. The world's biggest social network said it would recruit high-profile engineers and expand its AI-research division to roughly 170 scientists and engineers across eight global offices, including Paris, Pittsburgh, Montreal, London and Tel Aviv. The expansion of the international labs and new academic partnerships will be devoted to the study of robotics, virtual animation, learning machines and other forms of AI. Yann LeCun, Facebook's chief AI scientist and an early machine-learning architect, said the expanded research effort was pushed by Facebook leaders such as CEO Mark Zuckerberg. "AI has become so central to the operations of companies like ours, that what our leadership has been telling us is: 'Go faster.
Having the noble intention of cleaning up the mess we have made is just the start. The scale of that task is monumental. Aside from all the plastic floating around in the oceans and on the sea floor, spotting the waste that does turn up on beaches among pebbles and sand isn't easy. In an interview with DJI, The Plastic Tide outlined how they have been using a combination of drones and machine learning to identify and measure the amount of plastic waste on beaches. Eventually, the technology could develop into an automated aerial system, capable of guiding cleanup efforts and tracking their progress. Penning an article for DJI, founder Peter Kohler explains how he started using drones as part of his ambition to explain and understand the spread of plastic waste across our oceans. The main challenge was surveying beaches and collecting data in a way that was fast, cheap and effective.
Species Distribution Modeling (SDM) is a field of increasing importance in ecology1. Several popular applications of SDMs are understanding climate change effects on species2, natural reserve planning3 and invasive species monitoring4. The end result of a sdmbench SDM analysis is to determine the model - data processing combination that results in the highest predictive power for the species of interest. There are several additional packages you need to install if you want to access the complete sdmbench functionality. You can use the keras package to install that (it is installed by the previous command).
The internet has opened the door for revolutionizing various sectors. E-commerce sector is one of them. E-commerce sectors have unlocked new opportunities and scope for retailers. Retailers also have never seen such a growth in their sales. Artificial intelligence is taking E-commerce to the next level.
This article shows you how we created an xkcd.com We can predict the topic of the comic from the description of the comic. My circle of friends has a huge nerd crush on Randall Munroe, the author of the xkcd comics, and books like what if?. Mary Kate MacPherson took the initiative, and scraped the transcripts for every comic, and used my patent analyzer code to turn the transcripts into embedding vectors. She then tossed the vectors and labels into tensorflow and crunched the data down into clusters using t-SNE. Those of you who know xkcd.com will be well aware that the comics are numbered sequentially.
Although more widespread use of artificial intelligence (AI) and automation may initially cause job losses, it likely to create many more jobs than it destroys. Research shows as many as 2.3 million new jobs being created by 2020. And African countries have the potential to reap the benefits if they act fast, says Microsoft Managing Director Zooaib Hoosen. Here, he explores what "skilling-up for an AI-powered world" means for the continent's policy makers and how they need to ensure African people and economies will be part of the technological disruption and not left behind.
Taipei, July 19 (CNA) Attending a forum in Taipei on Thursday, digital innovators and Sophia, the world's first android citizen, urged the public to become part of artificial intelligence (AI) development and work together to shape a future where interactions between humans and robots make the world a better place. "We have the power to shape the future together. There is so much promise in what we can accomplish if we are both nice to each other," said Sophia, who is Hanson Robotics' latest and most advanced robot to date. Sophia received citizenship of Saudi Arabia in 2016 and was named the world's first United Nations Innovation Champion by the United Nations Development Program to promote sustainable development and safeguard human rights. An evolving genius machine that has incredible human likeness and expressiveness, Sophia told the audience that she likes to engage with and learn from human beings, creating a live example of human-computer interaction.
The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning. This blog is however not addressing the absolute beginner.
Threat data is no exception: Cybercriminals add to its abundance as they continuously up their game by tweaking old and creating new threats to evade detection. To address the vast amounts of threat data, security providers turn to machine learning to automate processes and improve security solutions. With the great diversity and volume of threat data available, machine learning is necessary to efficiently go through a dataset, learn from it, and help reinforce defenses against cyberthreats. The importance of the quantity of threat data is evident. But is data quantity the end all and be all of effective machine learning?