There's always been something so annoying about people who found the need to stack additional challenges onto solving a Rubik's Cube quickly, whether it was doing it blind-folded or while juggling or one-handed. While it might have just been a challenge for them, it also seemed like a need to show off. OpenAI is clearly interested in showing off what its Dactyl robotic-hand can do with a Rubik's Cube. The organization announced that the robot has learned to solve a Rubik's Cube one-handed, an accomplishment that speaks to the robot's dexterity in handling and manipulating the cube more than anything. Previously, we had seen the robot interact with unknown objects without any real-world training, only virtual simulations.

Robots with truly humanlike dexterity are far from becoming reality, but progress accelerated by AI has brought us closer to achieving this vision than ever before. In a research paper published in September, a team of scientists at Google detailed their tests with a robotic hand that enabled it to rotate Baoding balls with minimal training data. And at a computer vision conference in June, MIT researchers presented their work on an AI model capable of predicting the tactility of physical things from snippets of visual data alone. Now, OpenAI -- the San Francisco-based AI research firm cofounded by Elon Musk and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman -- says it's on the cusp of solving something of a grand challenge in robotics and AI systems: solving a Rubik's cube. Unlike breakthroughs achieved by teams at the University of California, Irvine and elsewhere, which leveraged machines tailor-built to manipulate Rubik's cubes with speed, the approach devised by OpenAI researchers uses a five-fingered humanoid hand guided by an AI model with 13,000 years of cumulative experience -- on the same order of magnitude as the 40,000 years used by OpenAI's Dota-playing bot.

Get expert knowledge on the tools and technologies you need to put your data strategies to work. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies. This is a good time to assess enterprise activities, as there are many indications a number of companies are already beginning to use machine learning. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

TF Encrypted aims to make encrypted deep learning accessible. For this reason, we are pleased to share with the community that TF Encrypted now offers a high level API, TF Encrypted Keras, which matches a subset of Keras's features. This is an important first step to supporting complex neural network architectures and use cases. François Chollet, the creator of Keras, aimed to provide the best user experience possible when designing the Keras API. As he explained in this blog post, good UX reduces cognitive load (e.g., remembering how things work).

"GoldSpot and Yamana Gold recently completed a machine learning collaboration in the area surrounding the El Penon mine site using extensive, …

Google Colaboratory is a platform built on top of the Jupyter Notebook environment which runs entirely on Google Cloud Platform (GCP). This platform provides GPU which is free of cost and supports Python 2 and 3 versions. With the help of Colab, one can not only improve machine learning coding skills but also learn to develop deep learning applications. You can also learn to work with popular deep learning libraries such as Keras, TensorFlow, OpenCV and others. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

Pix2Pix GAN has a generator and a discriminator just like a normal GAN would have. But, it is more supervised than GAN (as it has target images as output labels). For our black and white image colorization task, the input B&W is processed by the generator model and it produces the color version of the input as output. In Pix2Pix, the generator is a convolutional network with U-net architecture. It takes in the input image (B&W, single channel), passes it through a series of convolution and up-sampling layers.

Most galaxies in the universe live in low-density environments known as "the field," or in small groups, like the one that contains our Milky Way and Andromeda. Galaxy clusters are rarer, but they represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy. During 1950s the pioneer of galaxy cluster-finding, astronomer George Abell, spent many years searching for galaxy clusters by eye, using a magnifying lens and photographic plates to locate them. Abell manually analysed around 2,000 photographic plates, looking for visual signatures the of galaxy clusters, and detailing the astronomical coordinates of the dense regions of galaxies. His work resulted in the'Abell catalogue' of galaxy clusters found in the northern hemisphere.

In this article, I will share my findings while attempting to use neural networks to describe the content of images. Images greatly contribute to a website's SEO and improve the overall user experience. Fully optimizing images is about helping users, and search engines, better understand the content of an article. The SEO community has always been quite keen in recommending publishers to invest on visual elements and this has become even more important in 2019 as Google keeps on revamping Google Image Search by adding new filters and new functionalities. There are several aspects that Google mentions in its list of best practices for images but the work I've been focusing on, for this article, is about providing alt text and captions in a semi-automated way.

In the Google Search: State of the Union last May, John Mueller and Martin Splitt spent about a fourth of the address to image-related topics. They announced a big list of improvements to Google Image Search and predicted that it would be a massive untapped opportunity for SEO. SEO Clarity, an SEO tool vendor, released a very interesting report around the same time. Among other findings, they found that more than a third of web search results include images. Images are important to search visitors not only because they are visually more attractive than text, but they also convey context instantly that would require a lot more time when reading text.