Hi everyone, I was inspired by OpenAI's "Requests for Research" project list that was released a few days ago. In particular, I'm trying to tackle Project #2 on their list, Im2Latex. Unfortunately I'm a newbie when it comes to Machine Learning, and I've only completed one online book so far. I'm asking for some tutorials or online resources that could help me learn about some of the following: sequence to sequence models, attention, and in-depth OCR among other things. If you know of any such tutorials please post them here to help steer me in the right direction.

DeepMind was established in London by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2011. Major venture capital firms Horizons Ventures and Founders Fund have invested in the company, as well as entrepreneurs Scott Banister and Elon Musk. Jaan Tallinn was an early investor and an advisor to the company. In 2014, DeepMind received the "Company of the Year" award by Cambridge Computer Laboratory. Also on 26 January 2014 Google announced that it had agreed to take over DeepMind Technologies.

Tech giants are making acquisitions in the AI space, and an increasing number of startups are working with the technology. Speaking at The Europas, a European startup conference held in London today, John Henderson, principal at Whitestar Capital, spoke about the overwhelming competition in the space and noted the need for young tech firms to stand out from the crowd. "When it comes to investing in AI startups what we look for and what is hard to find is defensibility," he said. DeepMind – acquired by Google in 2014 – Henderson argued was "a one-off". "It [DeepMind] was acquired for the research talent. Startups out there need to think about AI as an enabling technology or a platform, as opposed to every startup building their own AI technology," added Henderson.

Inspired by the biblical concept of the seven days of creation, our group sought to create a dystopian science-fiction retelling that put a spotlight on the possibility of AI evolving and eventually supplementing the role of God. Based on the assumption that artificial intelligence would learn at an exponential rate, we wanted to make sure that the actions taken by Deepmind would progressively escalate day after day. The fundamental distinction however was that Deepmind would not share the same sense of human empathy- something that led to its destructive actions in the later days. Overall, we let the readings discussed in class form the individual plot points of the specific days (automation, love & robots, fully automated luxury communism) whilst still taking certain creative freedoms. Additionally, a key point is that the seven days of creation may not be literal 24-hour days but a figurative expression which we then incorporated into our timeframe of events within our version of AI's seven days of existence.

Google's DeepMind Health is a project that utilizes artificial intelligence to learn from data and make the healthcare process efficient. Included in the agreement are plans for developing hospital support systems such as bed and demand management software, financial control products, and private messaging and task management for junior doctors. Another key area in the partnership is real-time health prediction: the use of health care data to identify the risk of patient deterioration, death, and/or readmission. The agreement intends to "maximize the benefit of bringing together two parties who are centers of excellence in their respective fields, including improvements in clinical outcomes, patient safety, patient and staff experience, as well as cost reductions."

Android N notifications are getting a new look to help provide a better user experience. They now have a fresh look, improved for custom views, and expanded functionality in the forms of Direct Reply. The default look and feel of notifications has changed, and now the fields around the notifications have been collapsed into a new header row with the app's icon and name anchoring the notification, according to Android developer advocate Ian Lake on the Android Developers Blog. This makes the title, text and large icon have a lot of space, and now the notifications are slightly larger and easier to read. Notification actions have also received a redesign and are now in a visually separate bar below the notification, according to the blog.

At DeepMind, we conduct state-of-the-art research on a wide range of algorithms, from deep learning and reinforcement learning to systems neuroscience, towards the goal of building Artificial General Intelligence. A key factor in facilitating rapid progress is the software environment used for research. For nearly four years, the open source Torch7 machine learning library has served as our primary research platform, combining excellent flexibility with very fast runtime execution, enabling rapid prototyping. Our team has been proud to contribute to the open source project in capacities ranging from occasional bug fixes to being core maintainers of several crucial components. With Google's recent open source release of TensorFlow, we initiated a project to test its suitability for our research environment.

For a while now, Google's company DeepMind has been working on an artificial intelligence (AI) which plays Atari games better than you remember your older brother playing them in the 1980s. The AI is not only extremely proficient at playing these cult classics but has also learned to play 49 of them completely on its own. Despite this impressive feat, the DeepMind's creation isn't perfect and some games have simply proved to be too complicated for it to learn them on its own, Montezuma's Revenge being one of them. However, the Google-owned company has recently been hard at work correcting the flaws in its AI which has finally mastered the unforgiving 1984 platformer developed by the now-defunct Utopia Software. As its developers explain it, they had to make the AI "curious enough" for it to want to actually win the game.

Google wants to apply its machine learning technology to NHS patient data within the next five years, TechCrunch reports. The search giant's London-based artificial intelligence research lab, DeepMind, announced a partnership with the Royal Free NHS Trust in London in February but the full extent of the arrangement is only just becoming clear. A Memorandum of Understanding (MoU) between DeepMind and the Royal Free shows that the pair envisage a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximizing the potential to work on genuinely innovative and transformational projects." The MoU -- obtained via a Freedom of Information (FoI) request from New Scientist -- states that DeepMind hopes to gain access to "data for machine learning research under appropriate regulatory and ethical approvals" within the next five years. Machine learning -- a subfield of computer science that gives computers the ability to learn without being explicitly programmed -- has the potential to speed up patient diagnosis and optimise their treatments.

If we want to create artificial intelligence that can teach itself how the world works, it needs to be curious. This has been a recurring theme in the world of AI in recent years, and newly published research from Google's DeepMind division shows exactly why this quintessentially human quality is important for making computers smart. Curiosity means rewarding the AI agent's exploration In the video above you can see DeepMind's AI agent tackling the infamously difficult Atari game Montezuma's Revenge. Unlike bots playing Unreal Tournament or StarCraft, the agent doesn't have access to all the information in the game, but is learning to play the same way humans do -- by looking at the screen, pushing buttons, and seeing what works. If this setup sounds familiar, it's because last February DeepMind unveiled an earlier iteration of the same agent, but when that bot tried to take on Montezuma's Revenge, it couldn't score a single point.