Everybody in the IT industry is fascinated (or scared) of artificial intelligence. The fear, however, is more a result of misinformation than anything rooted in reality. Thankfully, machine learning has a better reputation, even though it's the most important approach to achieving artificial intelligence. Machine learning consists of algorithms that are capable of consuming massive amounts of data. These algorithms understand patterns from the data and then translate the insight into actions.

You've probably heard about "machine learning" and "artificial intelligence". We break down everything you need to know. Machine learning and artificial intelligence (known as A.I.) both sound like futuristic terms for some dystopian future where robots take over the planet. There are lots of similarities and there is much overlap between different types of computer automated learning, inference, and autonomy, and each one comes with its own set of pros and cons. Sci-fi movies aside, there are lots of important differences between deep learning, machine learning, and artificial intelligence that highlight the different ways in which they work and the different applications they're best suited for.

Machine learning is an algorithm or model that learns patterns in data and then predicts similar patterns in new data. For example, if you want to classify children's books, it would mean that instead of setting up precise rules for what constitutes a children's book, developers can feed the computer hundreds of examples of children's books. The computer finds the patterns in these books and uses that pattern to identify future books in that category. Essentially, ML is a subset of artificial intelligence that enables computers to learn without being explicitly programmed with predefined rules. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

After being in a limited beta run, Amadeus Code has just opened up to the public, ready to turn would-be artists into hit-making musicians. How it works: Amadeus Code's AI churns through music libraries, breaking down music into tiny units and looking for patterns. When a songwriter uses the app, the AI can then pull up those patterns and suggest new notes, slowly building the composer's melodies into music. "AI has this peculiar ability to find novel solutions--some successful, some not so much," says Amadeus Code cofounder Taishi Fukuyama in a statement. "These are suggestions which a composer can take or leave. Its decisions can spark a new idea for a composer, getting her into new creative territory."

I have recently read this paper from KDD 2018 and wanted to implement the paper and tried to see if I can reproduce the results. This is my first time implementing a paper and I don't think my implementation was perfect. However, I'm excited about this work and I would really appreciate it if y'all can take a look at it and give some feedback on the implementation! Please feel free to submit issues/PRs and I'm more than happy to discuss them and make the implementation better:)

More than 14 trillion photos are taken annually and most people have no formal training in photography. Most photographs come out subpar and it can easily be fixed with better lighting. Unfortunately, smartphone cameras do not provide sufficient lighting to accurately frame the subject, but Relonch AI changes that. This startup launched its latest product Relonch Alfred, which imitates light sources that don't exist in real life to give your camera phone photographs look as if they are professional quality. The company is inspired by the great NYC photographer Alfred Steiglitz, who revolutionized photography as an artistic medium.

The NFL is back in action, and along with it we have a slew of fall TV shows returning. That includes bingeable (it's a word) options on Netflix, Amazon and Hulu like Bojack Horseman season five, The First, Forever and American Vandal season two. For gamers, the standard edition of NBA 2K19 is here, plus the latest Tomb Raider game, while Blu-ray fans can get Oceans 8 or Batman: The Killing Joke on 4K Blu-ray. Look after the break to check out each day's highlights, including trailers and let us know what you think (or what we missed). Richard's been tech-obsessed since first laying hands on an Atari joystick.

SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. The code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL. If you previously used SQL Server 2016 R Services, Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016. The key value proposition of Machine Learning Services is the power of its enterprise R and Python packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network. SQL Server 2017 supports R and Python.

Tom was discussing the film star Tang Wei with a chatbot named XiaoIce, and the bot was excited: "A goddess! She stole my heart … and then went off and married!" Married who? "Haven't you heard?" XiaoIce replied. "Tang Wei is engaged to famous Korean director Kim Tae-yong." XiaoIce is a massive hit on social networks in Asia. Introduced in 2014 by Microsoft Research and Bing in Beijing, it can answer simple questions, like a stripped-down version of Cortana.

Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacker desires. However, these poisoning attacks are either agnostic to recommendation algorithms or optimized to recommender systems that are not graph-based. Like association-rule-based and matrix-factorization-based recommender systems, graph-based recommender system is also deployed in practice, e.g., eBay, Huawei App Store. However, how to design optimized poisoning attacks for graph-based recommender systems is still an open problem. In this work, we perform a systematic study on poisoning attacks to graph-based recommender systems. Due to limited resources and to avoid detection, we assume the number of fake users that can be injected into the system is bounded. The key challenge is how to assign rating scores to the fake users such that the target item is recommended to as many normal users as possible. To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users. We also propose techniques to solve the optimization problem. We evaluate our attacks and compare them with existing attacks under white-box (recommendation algorithm and its parameters are known), gray-box (recommendation algorithm is known but its parameters are unknown), and black-box (recommendation algorithm is unknown) settings using two real-world datasets. Our results show that our attack is effective and outperforms existing attacks for graph-based recommender systems. For instance, when 1% fake users are injected, our attack can make a target item recommended to 580 times more normal users in certain scenarios.