Since the recent past years, Artificial Intelligence (AI) is a buzzword in the market. Global industries are eying at Artificial intelligence to grab profitability. This is leading to increased attention towards AI and its applications. The companies are investing in emerging technologies like machine learning (ML), deep learning, predictive analytics, speech and image recognition, natural language processing (NLP) technologies to fuel their business. In particular, machine learning and deep learning are emerging as the most fastest-growing technologies of artificial intelligence.

Embedded AI can transform a tabletop speaker into a personal assistant; give a robot brains and dexterity; and turn a smartphone into a smart camera, music player, or game console. Traditional processors, however, lack the computational power to support many of these intelligent features. Chipmakers, startups, and capital are taking this opportunity to the market. According to a Gartner report, the chip market's total revenue hit US$400 billion in 2017, and the figure is expected to exceed US$459 billion in 2018. Traditional chip makers are putting an increasing focus on AI chip development, venture capital is pumping significant investments into the market, and AI chip startups are emerging.

AI and broadband are eating retail for breakfast. In the first half of 2019, we've seen 19 retailer bankruptcies. And the retail apocalypse is only accelerating. What's coming next is astounding. Why drive when you can speak?

This is an original work submission presented for completion of Udacity's Data Scientist Nanodegree capstone project. This blog shows how to build a Book Recommendation Engine using machine learning techniques, Python and its libraries. The code is structured so it can later be deployed as a web app. The goal of this project is to develop a Book Recommendation engine based on information entered by the user. The project uses a dataset containing six million ratings for the ten thousand most popular books and classified with tags.

Artificial Intelligence (AI) and Machine Learning (ML) are buzzwords that nearly everyone has heard these days. But, even those who aren't familiar with them encounter these technologies almost every day. Research shows that 77% of the devices that we currently use have AI built into them. From a bevy of "smart" devices to Netflix recommendations to products like Amazon's Alexa and Google Home, AI is the force behind many modern technological comforts that are now part of our day-to-day lives. Besides, there are tons of innovative uses for Artificial Intelligence and Machine Learning.

By now, we know that Artificial Intelligence is the simulation of human behaviour by a machine/computer. In the 21st century, AI has reached almost every house, like Alexa, Siri, product recommendations to name a few. According to a study by Creative Strategies, only 2% of iPhone owners have never used Siri, and only 4% of Android owners have never leveraged the power of OK Google. When it comes to usage, 51% use voice assistants in the car, 6% in public, and 1.3% at work. While studying AI, there is one term that you are going to come across a lot, and it is Machine Learning (ML).

It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those factors. In this paper, we introduce a mobile app called "Addict Free", which records user profiles, tracks relapse history and summarizes recovering statistics to help users better understand their recovering situations. Also, this app builds a relapse recovering community, which allows users to ask for advice and encouragement, and share relapse prevention experience. Moreover, machine learning algorithms that ingest spatial and temporal factors are utilized to predict relapse, based on which helpful addiction diversion activities are recommended by a recovering recommendation algorithm. By interacting with users, this app targets at providing smart suggestions that aim to stop relapse, especially for alcohol and tobacco addiction users.

From Siri, the virtual assistant in Apple mobile devices, to self-driving cars, artificial intelligence (AI) is progressing rapidly, outperforming humans at some tasks. As with the majority of the changes happening globally, there will be positive and negative impacts as AI continues to shape the world we live in. Every single one of us will have to reckon with our ability to balance the human way of life and the transition to the AI cosmos. According to a report by the technology research group IDC, spending on AI is expected to reach US$46 billion by 2020 with no signs of slowing down. AI is definitely on the rise in both business and life in general. The question is, will humans eventually lose control as machines become super-intelligent?

Black Friday itself might be done and dusted but the best deals for 2019 will continue all weekend, plus Cyber Monday deals are just around the corner. That's good news for anyone late to the sales party as that means there's still a significant amount of savings to be had. You'll find those savings everywhere and anywhere, both at big box retailers like Best Buy, Walmart and Target as well as online at Amazon, NewEgg and B&H Photo. So when does Black Friday well and truly end? Admittedly, some deals were for Black Friday only and sold out within minutes or hours, but many deals started a few days before Black Friday and will continue through to Cyber Monday after Thanksgiving. There are many different kinds of deals coming up from laptops to phones to AirPods (although the latter is heading quickly out of stock). We've sadly had little in the way of a cracking Nintendo Switch Black Friday deal, and even the PS4 deals - while they have existed, and haven't been too bad - were more scarce than we expected. But, based on what we saw on Friday, there's a lot more to come. If you're thinking about a deal at the moment, or you're scared it will go cheaper on Cyber Monday, then our advice is simple: if it has a good discount attached to it, go for it! If you later find the product is cheaper elsewhere, you can always buy it again and return the original. Just make sure to check the retailer's terms and conditions for returning items. It's basically been Apple products that have been the winners - with $400 cashback on an iPhone 11, lower prices on AirPods and Apple Watches and MacBook / iPads being cheaper than ever before, it's been (and still is) the time to grab yourself a bargain. There's more in there too - Dyson has been popular for Black Friday 2019, DNA kits have been a surprise package and we've even seen great discount on power tools as well. Anyway, enough of telling you what you're getting, and onto the deals themselves - and if you want something more than what we've scavenged from the depths of the web, here are all the top Black Friday sales from around the web first: HP Laptop 15t: $979.99 $519.99 from HP This HP laptop is a phenomenal price and comes with a 15-inch display paired with a powerful 10th gen Intel Core i7 processor to power the whole thing. Perfect for fitness fanatics, the Powerbeats Pro offer an adjustable ear hook design that keeps your buds firmly in place.

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.