Give us your feedback Thank you for your feedback. Do grills have girlish associations? A study has revealed how an artificial intelligence (AI) algorithm learnt to associate women with pictures of the kitchen, based on a set of photos where the people in the kitchen were more likely to be women. As it reviewed more than 100,000 labelled images from around the internet, its biased association became stronger than that shown by the data set -- amplifying rather than simply replicating bias. The work by the University of Virginia was one of several studies showing that machine-learning systems can easily pick up biases if their design and data sets are not carefully considered.

The post coincides topically with last years' first annual Conference on Robot Learning as well as the workshop on Challenges in Robot Learning at NIPS2017, the latter we had the pleasure of co-organising together with colleagues from Oxford, DeepMind, and MIT. The events, as well as this post, cover current challenges and potentials of learning across various tasks of relevance in robotics and automation. In this context, similar to the long-term discussion on how much innate structure is optimal for artificial general intelligence, there is the more short-term question of how to merge traditional programming and learning (not sure if I prefer the branding as differentiable programming or software 2.0) for more narrow applications in efficient, robust and safe automation. The question about structure as beneficial or limiting aspect becomes arguably easier to answer in the context of robotic near-term applications as we can simply acknowledge our ignorance (our missing knowledge about what will work best in the future) and focus on the present to benchmark and combine the most efficient and effective directions. Existing solutions to many tasks in mobile robotics, such as localisation, mapping, or planning, focus on prior knowledge about the structure of our tasks and environments. This may include geometry or kinematic and dynamic models, which therefore have been built into traditional programs. However, recent successes and the flexibility of fairly unconstrained, learned models shift the focus of new academic and industrial projects. Successes in image recognition (ImageNet) as well as triumphs in reinforcement learning (Atari, Go, Chess) inspire like-minded research. As the post has become a bit of a long read, I suggest to read it like a paper: intro, discussion & conclusions and then - only if you did not fall asleep after all - the rest. Similar to scientific papers, some paragraphs will require basic familiarity with the field. However, a coarse web search should be enough to illustrate most unexplained terminology. Additionally, to keep this engaging, I have added some of my favourite recent videos highlighting interesting research for each section. Finally, this is a high-level review with more details to be found in the respective references, which just represent a small subset of available work in each field, chosen based on personal interest as well as shameless self-promotion of our work.

Welcome to Tech Explorations Arduino Step by Step Getting Serious, where you will extend your knowledge of Arduino components and techniques and build up new skills in the largest, and the most comprehensive course on the Web! Arduino is the world's favorite electronics learning and prototyping platform. Millions of people from around the world use it to learn electronics, engineering, programming, and create amazing things, from greenhouse controllers to tree climbing robots remotely controlled lawnmowers. It is a gateway to a career in engineering, a tool for Science, Technology, Engineering, and Mathematics education, a vehicle for artistic and creative expression. The course is split into 40 sections and over 250 lectures spanning more than 30 hours of video content. In each section, you will learn a specific topic.

Al Martin: Hi folks, this is Al Martin from Making Data Simple, the series, if you will. Today I have Jean-Francois Puget. Jean-Francois Puget: Yes, you did great. You passed your French test. Al Martin: All right, good, I'm going to give you the [name] JFP from now on, is that all right? So JFP is the distinguished engineer for machine learning and optimization, that's the topic today and we're going to go into that. I also have with me [Steve Moore], who is a senior content designer and storage strategist. Al Martin: So Steve wanted to join the conversation, ask a few questions. So he'll ask the intelligent questions, I will ask the normal, blockhead questions, if you will. So, thank you for being here. We've done a lot, well we've done at least, I think two podcasts on machine learning. We've done one on machine 1.15 learning for dummies, one for IBM machine learning, how to [help], if you haven't heard those, go back, so we can't do enough, and I notice that on your title JFP is machine learning and optimization.

Bitcoin's mid-December boom has sparked a renewed interest in cryptocurrency investment across the world - and some extremely strange goings-on as entrepreneurs speculate on the best way to capitalise on the digicoin gold rush. The market leader's value stormed to an all-time high of $19,850 (£14,214) in the run-up to Christmas but has since dropped back to its customary $10,000 (£7,190) mark. Competitors like ethereum, litecoin, ripple and bitcoin cash have all quietly prospered out of the limelight - but cryptocurriences remain a volatile proposition. Yesterday saw all but two of CoinMarketCap's top 50 cryptos decline in value, thought to be a reaction to the US Securities and Exchange and Commission declaring that all trading platforms will ultimately need to be registered and regulated to protect consumers. All of this excitement has led to a number of bizarre developments as businesses (and scammers) think outside the box about the best way to enter a market worth a combined $405bn (£292bn).

Lexicographers have a hard time these days. With the rise of social media, they are confronted with the appearance of new terms almost in real time. As they observe search trends in online dictionaries, they must react quickly. Some of these new terms are easy to define – bitcoin, blockchain, phishing: articles that deliver contextual information are surfacing across the net like mushrooms on the damp soil of a forest, helping lexicographers to capture the various semantic flavors into a set of definitions. But what about words coined by a pundit in the frenzy of a tweet, words that leave you wondering whether they are just a misspelling, or a genuine word creation?

Apple has revealed new details about how exactly the iPhone is made – and how it is trying to improve the lives of people who are actually putting it together. The new Supplier Responsibility report for 2017 shows just how potentially damaging that process is. But it reveals how at least some of those problems can be removed from it, and what the company is doing to make sure it is taking those steps. The problem facing Apple and other technology companies is easy to explain, but incredibly difficult and complicated to solve. Those companies are mostly sincere in their desire to ensure people aren't hurt while making the phones in our pockets and the computers on our desks, and are largely held responsible when they fail to do so – but the suppliers are operating separately from companies like Apple and often in places with fewer worker protections and more abuses.

Google has made its machine learning education program available to everyone-- from researchers, to developers and companies, to students. The Learn with Google AI portal which was earlier exclusive to Google employees, called Googlers, is now available to everyone else with an interest in the field. Anyone, from novice to an expert, can learn the basics as well as the advanced art of the trade. "This site provides ways to learn about core ML (machine learning) concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to "curious cats" who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here," said Zuri Kemp who leads Google's machine learning education effort.

TensorFlow is an open-source software library developed by Google which is used for machine learning. It is capable of running on both CPU and GPU in all Linux, Windows and MacOS platforms. Tensorflow can be used to design, implement and train deep learning models which are inspired by the structure and function of the brain. In this article, I am going to give a step by step guide to implement a simple neural network using TensorFlow. The famous Iris flower data set is used here to train and then classify a given flower to the correct type.

Google is now offering an Introductory course on Artificial Intelligence(AI) and Machine Learning(ML) for free on its new Learn With AI site. Google hopes the site will be a hub of information for AI and ML. The site is intended to be a resource for everyone from beginners to advanced researchers. Google claims the site "will be a place where one can learn about core ML concepts, develop and hone your Machine learning skills, and apply ML to real-world problems" Google understands people are not familiar with these concepts, including those in fields like app development where AI and ML actually matter. The site contains a free crash course that was initially designed for Google employees.