If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
If you ask ten data scientists, "Which machine learning tool is best?" you'll likely get many different answers. But slightly more surprising is that if you ask any one data of those data scientists the same question, you'll likely still get many different answers. Here's a quick preview of two key observations about what makes machine learning successful: Instead they usually keep three to five machine learning options in their tool box. Let's start with the first observation. Turns out that at any point in time many organizations have adopted a range of several machine learning tools.
Until the last 5 years or so, it was infeasible to uncover topics and emotions across the web without powerful computing resources. Engineers didn't have efficient methods to make sense of words and documents at a large scale. Now, with deep learning, we can convert unstructured text to computable formats, effectively incorporating semantic knowledge for training machine learning models. Harnessing the vast data troves of the digital world can help us understand people more directly, going beyond the limitations of collecting data points through measurements and survey results. Here's a glimpse into how we achieve this at MarianaIQ.
Lots of people will tell you they're nervous about the changes artificial intelligence will bring to the world, but Andrew Ng is confident it's all for the best. And to bring about that future, Ng, now an adjunct professor at Stanford, will share what he knows best by teaching. Today, Ng is launching a new course on deep learning on Coursera, the online education site he co-founded. The syllabus will follow his popular machine learning course, which has attracted some 2 million enrollments since its launch in 2011. "There's a lot of PR and buzz focused on AI transforming large tech companies, but there's a lot of work that still needs to be done for AI to transform the non-tech companies," Ng tells The Verge.
Saw this story after it was tweeted by Puni Rajah – Bosch to invest 300m euros in AI, employ 100 experts from India, USA, Germany. As ever India has done great job building its native skills base. But the fact Bosch needs to look outside Germany is telling. We're seeing a lot of pressure owing to skills shortages, and companies, countries and cities everywhere are going to need to up their game to avoid brain drain. Silicon Valley is still the main place data scientists, in particular machine learning and AI specialists, are ending up. Pierre Etienne Bardin of Société Générale expressed the need to be more active in hiring and training succinctly in a recent post on data transformation as the new digital transformation.
Remember the recent Elon Musk and Mark Zuckerberg clash on the future of Artificial Intelligence? So, my colleague and I were discussing the topic and after a while she said she doesn't understand machine learning & Artificial Intelligence fully. Are you one of those, who understand the basics of AI, the robots and more; yet when it comes to deep and in depth technical understanding, it suddenly becomes confusing? If yes, worry not for you have landed at the right place. We'll try to understand Machine Learning like a beginner.
Have you noticed that the better you know someone, the easier it is to communicate with them? When we are particularly close, this can border on the telepathic as we start to anticipate what the other person is going to say and finish their sentences. Unconsciously, our brains are collecting, processing, storing, and recalling a huge range of verbal and nonverbal signals, then translating this learning and familiarity into actions. Of course, we're a long way from understanding – let alone replicating – the infinite complexities of the human brain. But in the simplest of terms, this is how machines can learn to interact with people.
If the next era of human progress is built using AI, who gets to engineer it? Who will have the coding skills to use the software for creating AI products, or even more importantly, the skills to write that software? In an attempt to make the answer to those questions "anyone who wants to," Andrew Ng is releasing a new set of courses teaching deep learning on Coursera, the online learning platform he co-founded in 2012. Coursera was originally set up to offer an online class in machine learning; deep learning is a variety of that, involving exceptionally large datasets. The original machine learning course attracted more than 2 million students, Ng tells MIT Tech Review.
We are going to review the next chapter of the book: http://www.deeplearningbook.org/ For participants to gain the most experience and understanding of the material, having a volunteer presenter each week was an invaluable asset. So we have decided to continue this tradition and ask that one volunteer each week take on the challenge of presenting their findings from the material to the rest of the group. This presentation can be as short as 10 min or as long as an hour depending on the depth of the materials covered. It is also up to the presenter if they would like to prepare slides or give a free form talk on the subject.
Google scientists have developed the first computer program capable of learning a wide variety of tasks independently, in what has been hailed as a significant step towards true artificial intelligence. The same program, or "agent" as its creators call it, learnt to play 49 different retro computer games, and came up with its own strategies for winning. In the future, the same approach could be used to power self-driving cars, personal assistants in smartphones or conduct scientific research in fields from climate change to cosmology. The research was carried out by DeepMind, the British company bought by Google last year for £400m, whose stated aim is to build "smart machines". Demis Hassabis, the company's founder said: "This is the first significant rung of the ladder towards proving a general learning system can work.