We've been providing commentary for the past two years on the potential for AI and IoT to solve the huge inefficiencies in food production – a huge positive disruption…now we are seeing the first practical uses. From Engadget;" Artificial intelligence technology doesn't just have to solve grand challenges. Sometimes, it can tackle decidedly everyday problems -- like, say, improving a cucumber farm. Makoto Koike has built a cucumber sorter that uses Google's TensorFlow machine learning technology to save his farmer parents a lot of work. The system uses a camera-equipped Raspberry Pi 3 to snap photos of the veggies and send the shots to a small TensorFlow neural network, where they're identified as cucumbers. After that, it sends images to a larger network on a Linux server to classify the cucumbers by attributes like colour, shape and size. An Arduino Micro uses that info to control the actual sorting, while a Windows PC trains the neural network with images. Koike estimates that it takes about 2-3 days to train the sorting AI, even using very low-resolution (80 x 80) pictures. And even the 7,000 photos Koike used for that training probably weren't enough. While the sorter recognized 95 percent of test images, real-world sorting dipped to about 70 percent. Having said that, it's not the immediate results that matter."…more…

In the 2013 film Her, Joaquin Phoenix falls in love with a computer operating system voiced by Scarlett Johansson. The relationship embodies a shared vision of a future when human connections are radically changed by interactions with computers powered by artificial intelligence (AI). That reality is actually not far away. Amazon's Echo listening device has transformed the market. The device is controlled by a user's voice and is always listening for commands such as questions about the weather or the latest sport scores.

The journal has a broad scope and publishes high quality papers on all aspects of computational intelligence and artificial intelligence related to games. TCIAIG has been accepted into the Thomson Reuters Web of Science and will, by mid-2012, have an Impact Factor listed in the Journal Citation Report (JCR). The journal is co-sponsored by the IEEE Computational Intelligence Society, the IEEE Computer Society, the IEEE Consumer Electronics Society and the IEEE Sensors Council. It is technically co-sponsored by the IEEE Systems, Man, and Cybernetics Society, the IEEE Instrumentation and Measurement Society, the IEEE Robotics and Automation Society, and the IEEE Communications Society.

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.