Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon's AI team. Once they've been trained, they can be incredibly efficient at tasks like recognizing cats or playing Atari games, says Google DeepMind research scientist Raia Hadsell. A solution to this might be something called progressive neural networks -- this means connecting separate deep learning systems together so that they can pass on certain bits of information. One way of doing this is revisiting an old, unfashionable strand of artificial intelligence known as symbolic AI or Good Old-Fashioned Artificial Intelligence (GOFAI), says Murray Shanahan, a professor of cognitive robotics at Imperial College London (and also the scientific advisor on Ex Machina).

Designed to help understand the fundamentals of DS & Algorithms really well. A must have for programming interviews.

Chatbots help people to learn through messaging. They support learners as tutors to decrease learning risk to discover unknown things. Chatbots lead the learning process and are very adaptive to each person. Probably, these bots will be free and will be available in different countries. Compared with education apps, chatbots are easier in using.

If you'd like to learn how to run R within Azure Machine Learning and SQL Server, you may be interested in these upcoming 4-day Practical Data Science courses, presented by Rafal Lukawiecki from Project Botticelli. In this classroom-based course, you will learn machine learning, data mining, some statistics, data preparation, and how to interpret the results. You will also learn how to formulate business questions in terms of data science hypotheses and experiments, and how to prepare inputs to answer those questions. Rafal will share his decade of hands-on experience while teaching you about Azure Machine Learning (Azure ML) which is the foundation of Cortana Analytics Suite, and its highly-visual, on-premise companion, the SQL Server Analysis Services Data Mining engine, supplemented with the free Microsoft R Open and Microsoft R Server software. By the end of this course you will be able to plan and run data science projects.

With his research into artificial intelligence, he sees potential to make a significant difference in the defence and emergency management sectors. His expertise in this area also forms the basis for his teaching in both the Master of Information Technology and the Master of Computer Science at RMIT University. We spoke to him to find out more about his passion for this increasingly relevant area of IT. I'm an Associate Professor in Artificial Intelligence within the School of Science and my work is focused on conducting research in a range of topics in artificial intelligence (AI). I also teach programming and specialist AI courses in both undergraduate and postgraduate programs; supervise a number of projects in smart systems product development; and I'm the program coordinator for the Bachelor of Computer Science. In addition to this, I manage and contribute to industry and Government funded research projects and have been part of nearly 1.5 million dollars' worth of research funding in the last five years.

This post is aimed at artists and other creative people who are interested in a survey of recent developments in machine learning research that intersect with art and culture. If you've been following ML research recently, you might find some of the experiments interesting but will want to skip most of the explanations. The first AI that left me speechless was a chatbot named MegaHAL. It turns out MegaHAL was basically sleight of hand, picking a single word from your input and using a technique called Markov chains to iteratively guess the most likely words that would precede and follow based on a large corpus of example text (not unlike some Dada word games). But reading these transcripts in high school had a big effect on how I saw computers, and my interest in AI even affected where I applied to college.

Washington, D.C. - IBM, a pioneer in the advancement of artificial intelligence, today welcomed the release of a White House report on the future and promise of this exciting technology: "We commend the Administration, and the Office of Science and Technology Policy (OSTP) in particular, for launching an open and inclusive dialogue that helped shape today's report. The document recognizes what IBM has believed all along, that artificial intelligence (AI), or cognitive computing systems like IBM Watson, will jump start economic opportunity and help solve some of humanity's biggest challenges. Embedding ethical training into computer science education, as the report recommends, is a positive way to prepare the next generation of technology experts to appropriately guide the advancement of AI systems. Cybersecurity is one area in particular where IBM agrees that AI can be a true game-changer, and one where we are actively preparing IBM Watson to make a real and tangible difference in the push to better defend America's digital networks.

Researchers have tried for decades to create computers capable of learning. Recently, using the human brain as a model, they have had some success. Complicated algorithms have been developed, allowing computers to learn on a limited scale. Deep Learning (DL) is the name used for the process of computers "learning" appropriate responses as they interact with their users, or seek patterns in Big Data. This Big Data "pattern seeking aspect" has the potential to replace Data Scientists as Big Data pattern seekers.

As you may have noticed, we've been researching artificial intelligence (AI) and its economic and educational implications. From healthcare to transportation, we believe it is incredibly important for young people and adults to be learning about AI, and we are writing more about it to equip teachers and parents with information to help young people ask good questions about the implications of AI on their lives and livelihoods. To get the scoop, I sat Tom Vander Ark down for a podcast interview on AI. You'll also hear from Gerald Huff, a senior Silicon Valley software engineer, who shares his thoughts on AI and what it means for students and the transportation industry. Listen to the podcast, read excerpts from the interview below and be sure follow the campaign at #AskAboutAI.