Japan's first convenience store was not, as many suppose, 7-Eleven in Tokyo in 1974 but Mitsui in Kyoto in 1673. The genius behind it was Mitsui Hachirobei, heir to the sake shop his father had opened a generation earlier. Of samurai birth, the father saw warrior status as useless in the dawning age of peace. He renounced his and went commercial. He struggled, perhaps too much the warrior at heart to really make a go of it.

Artificial intelligence (AI) is no longer pure science fiction, relegated to books, television and dystopian movies - we're well past that point today. Growth and investment in AI have been astonishing, and its set to continue with Gartner predicting AI technologies will be in almost all new software by 2020. While there has definitely been a laser focus on AI in the past few years, consumers and businesses have actually been exposed to AI for a long time, perhaps without even knowing. As an example, how do you think Outlook knows which emails to put in your spam folder? Or how does your favourite online retailer know which other products you might like?

Only a handful of people waited outside Apple's flagship London store for the iPhone 8 this morning - a far cry from the snaking queues of previous years. Queues were smaller than usual because of the anticipation surrounding the ultra premium iPhone X, which offers a radical redesign, new screen and advanced camera features. That is released in November. The special edition iPhone X features Apple's first ever edge-to-edge screen and facial recognition technology. This can be used to unlock the phone and make payments via Apple Pay, as well control new animated emoji - named Animoji - using their facial expressions.

Digitization of retail industry has unveiled new realms and opportunities for the retailers globally. Over time shopping has evolved drastically and is no longer just a utility of bartering money for a product. It is predicted that by 2018 75% of developer teams will utilize Artificial Intelligence in building one or more than one services or business applications. Over time Artificial Intelligence has made an impeccable space in the market. A study reveals that by 2020, around 80% of customer interactions will be handled by Artificial Intelligence.

From the outside looking in, shopping hasn't changed all that much in the past decade. Sure, it's more common now to find a brick-and-mortar synced efficiently with its online presence (Target is great at this). But those still seem to be the exception to the rule. Proving once again that there isn't a space that can't be reshaped by AI, our friends at CB Insights mapped the startups disrupting retail and e-commerce using artificial intelligence (AI). Syncing online and real-world inventories is just the tip of the iceberg when it comes to converting retail to a 21st-century endeavor.

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products. Machine learning (ML) algorithms involving products are often used to improve the customer experience and increase revenue, e.g., product similarity, recommendation, and price estimation. The products are required to be represented as features before training an ML algorithm. In this paper, we propose an approach called MRNet-Product2Vec for creating generic embeddings of products within an e-commerce ecosystem. We learn a dense and low-dimensional embedding where a diverse set of signals related to a product are explicitly injected into its representation. We train a Discriminative Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a product title fed through a Bidirectional RNN and at the output, product labels corresponding to fifteen different tasks are predicted. The task set includes several intrinsic characteristics about a product such as price, weight, size, color, popularity, and material. We evaluate the proposed embedding quantitatively and qualitatively. We demonstrate that they are almost as good as sparse and extremely high-dimensional TF-IDF representation in spite of having less than 3% of the TF-IDF dimension. We also use a multimodal autoencoder for comparing products from different language-regions and show preliminary yet promising qualitative results.

If you're interested in drone deliveries, it's likely because you want your internet shopping dropped at your door within an hour of clicking "buy." And while companies like Amazon are working to make that happen, complicated logistics and thorny regulations mean it's likely to be years before you start hearing the whir of rotors on your front porch. Yet drones are already proving their worth with more urgent, medical, missions. The latest of these comes from Silicon Valley startup Matternet, which has been testing an autonomous drone network over Switzerland, shuttling blood and other medical samples between hospitals and testing facilities. "We have a vision of a distributed network, not hub and spoke, but true peer-to-peer," says Matternet CEO Andreas Raptopoulos.

Israel is a country full of history, which is why they have more museums per capita than any other country. They also have the oldest continuously used cemetery in the world and the oldest continuously inhabited city in the world. Hearing that, you'd think that not a whole lot has changed over the years, but one thing that has constantly been evolving is their ability to innovate and be productive. Next to the U.S. and Canada, Israel has the largest number of publicly traded companies, which shows that they can also build successful businesses. Our recent article on "The Top-10 Biggest Startups in Israel by Funding" proved to be quite popular so we decided to do another article on the top 10 Israeli artificial intelligence (AI) startups.

Amazon.com is developing its first wearable product--a pair of "smart glasses" that will allow you to use its digital assistant Alexa wherever you are, according to the Financial Times. The FT said the new glasses would connect wirelessly to a smartphone and would boast a "bone-conduction audio system" allowing the person wearing the spectacles to hear Alexa's voice without headphones. The move is a risky one, given the difficulty that the likes of Google and Apple have had in making wearables mainstream over recent years. And Amazon itself has had its fair share of expensive failures in hardware in the past --notably with its Fire smartphone, on which it took a $170 million write down. However, Jeff Bezos's company feels it's worth the risk because it can enhance in a big way a product that has already proved extremely popular.

I have been a collector of books and classes of machine learning and deep learning for the last few years. Even though I come from a strong theoretical background, I have to say one must do hands on tinkering to be able to solve one's own problem successfully. Then for deep learning one must work with Tensorflow or Theano. However, I have been searching for a good hands-on book on tensorflow and had found none until this book. I purchased the kindle version so I can dive into this book early before the book comes out.