Artificial intelligence (AI) is quite a trendy topic these days, especially sinceGoogle Alphago's victory over the world champion Lee Sedol has given a bright illustration of the potential of machine-learning. Today, everyone seems to focus on the conversational branch of AI (bots, like the ones we build at Recast.AI, or chatbots), but tons of other applications remain mostly unknown. This is why I've decided to dedicate this paper to a subject I'm passionate about: space exploration. Indeed, it appears that AI can be an extraordinary boost to the discovery of our universe, for instance when it comes to navigation systems, situation analysis or even data transmission. So let's try to anticipate some of the big chances that lie ahead!

LONDON (Alliance News) - Artificial intelligence and natural language technology firm Arria NLG PLC on Monday said it has raised GBP2.7 million through the issue of convertible loan notes. The notes will be convertible into Arria NLG shares at 40.00 pence per share and will mature in October 2019. They will carry an interest rate of the UK base rate, currently 0.25%, plus 5.0%. In addition, subscribers for the notes will get two unlisted warrants for each USD1.00 they invest in the notes. These will be exercisable up to June 2019 at 12.00p per share.

Children today have grown up with the Internet being an integral part of their lives. Babies use tablets to swipe through games and interactive programs. Toddlers can navigate apps on a smartphone. By the time children hit middle school, technology is a natural part of their everyday life, both in school and at home. And of course, their grasp on technological concepts comes, in some cases, faster for them than for their parents. How do you explain a concept like contextual search to a 12-year-old girl or boy?

Sony recently released two songs composed by AI and French composer Benoît Carré arranged, produced the songs, and wrote the lyrics. Sony has also announced a full album made by their AI to be released in 2017. Earlier, AI had written the screenplay for a short film (though it doesnt make too much sense, for now). It is already known that many media sources, including Yahoo, have been using AI to write articles for their websites. AI at Google has attempted its hand at poetry and with good result.

But I see the same mistakes in both mindset and action again and again. In this post, you will discover the 5 most common ways that I see beginners slip-up when getting started in machine learning. I firmly believe that anyone can get started and do really well with applied machine learning. Hopefully, you can identify yourself in one or more of the traps below and take some corrective action to get back on course. How NOT To Get Started With Machine Learning Photo by Bart Everson, some rights reserved.

Over the past few months, I have been making bots, a lot of bots. Here are some of the things I have learned making 10 Chatbots. For thousands of years, we solved problems directly through conversation, Chatbots are a throwback to this simpler time. Chatbots, in their current form, don't provide as rich a GUI experience as apps. As a result, the value presented by a conversational interface needs to be utterly simple in order to be truly leveraged.

The Google Research team says it has enough images to train a neural network "from scratch," so if you'd like to try your hand at a DeepDream-style project, better version of Google Photos or the next Prisma then it's ready to go. On the other hand, the YouTube8-M file points to 8 million videos (adding up to more than 500,000 hours of footage) that the group says "represents a significant increase in scale and diversity compared to existing video datasets." The idea here is to create a library for video analysis that rivals those already in existence for still images, that's also accessible for people without big data. Part of that is because Google has also extracted and tagged still images from the videos for researchers to download.

Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach. Bagging (Bootstrap aggregating) was proposed by Leo Breiman in 1994 to improve the classification by combining classifications of randomly generated training sets.

Chipmaker Nvidia has demonstrated an AI-based self-driving system that learns by observing human drivers. Nvidia claims the system can drive on any type of road as it learns by observing how humans drive in a specific situation. Dubbed'BB-8', the program uses'deep neural networks' to successfully navigate an obstacle course containing simulated construction zones and narrow country side roads.

"Robo advisors" are a hot topic in the financial advisory business these days. These software applications use advanced analytics to provide investors with "objective" advice that is based purely on data and trends. What's more, they can be programmed to automatically allocate, deploy and rebalance investments. Robo advisors are a form of artificial intelligence and, thus, have numerous advantages over human advisors. For one thing, they always give the same advice regardless of which customer they are dealing with – so there is a consistency factor that is attractive from a consumer and regulatory standpoint.