When you're providing content, you want to make sure that it's safe for your audience and appropriate for the topic at hand. Flagging and removing content that is unacceptable or NSFW keeps the focus on acceptable content and boosts productivity, but doing so takes time. Machine learning can help curb the use of NSFW content where it's not appropriate. See how machine learning detects inappropriate content and how this technology can be useful to ensure your digital assets are on message and boost productivity. Machine learning is a subset of AI that uses algorithms to help computers learn by making predictions based on data, and it is already widely used in the workplace.

Generic OCR programs are adequate for simple text scooping and editing. For example, with Microsoft OneNote, you can import a PDF or JPEG image and use the OCR tool to output text. General purpose OCR lacks the tools for your business to take full advantage of the tremendous labor-saving and machine learning capabilities that full-powered OCR provides. You need OCR that scales to your needs and is specific for your business context. Think of OCR as a data collection tool.

At its core, AI is about automation and augmentation: it improves upon and speeds up many of the core processes of a business. Whether your business uses AI or not, it's going to be impacted by artificial intelligence in 2019. It's important to know how. As of early January, 61% of all businesses had already started implementing AI-based technologies. Even if you aren't using AI to streamline your operations and reduce your costs, your competition is.

Getting past the buzzwords to the meat of what is on offer in the machine learning services landscape isn't easy. That's why we've put together a little guide to help you understand how you can start utilizing real and true machine learning to accelerate your business. Lots of buzzwords these days actually reference open-source machine learning platforms and libraries, which often require knowledge of computer science, statistics, linear algebra, calculus and sometimes even more arcane math to fully utilize. These are separate from the technologies built on top of them, but can get conflated in the media landscape. Tensorflow is the (mostly) open-source library that Google maintains and presumably builds their internal and external machine learning services with.

Machine learning platforms are not the wave of the future. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools -- listed in no certain order -- are available now as resources to seamlessly integrate the power of ML into daily tasks. H2O was designed for the Python, R and Java programming languages by H2O.ai. By using these familiar languages, this open source software makes it easy for developers to apply both predictive analytics and machine learning to a variety of situations.

In just a few short years, machine learning has advanced to the point where it can make both our personal and our professional lives easier in a wide range of different ways. As a subset of the larger topic of artificial intelligence, machine learning is simply classified as the use of various statistical techniques that give computers and software applications the ability to "learn" without actually being programed to do so. In the world of business, this has the potential to unlock an incredible amount of value. Using past customer data, you can use machine learning to better interpret their own behaviors and predict opportunities for up-sells or upgrades, make personal recommendations based on order histories and more. The manufacturing industry has been using machine learning to great effect, using it to increase the efficiency of predictive maintenance on mission critical assets in environments like factories and warehouses.

Machine learning is the idea that describes computers that can essentially "learn" and process new information without specifically being programed to do so. If you give a computer a task, it will more-or-less get better at that task the more it has a chance to engage in it. Object detection is a subset of this idea and is of particular relevance to photos. Not only does object detection let you know which objects are in a photo (hence the name), it also gives you insight into precisely where they are, too. But out of all the industries and activities where object detection is poised to make a big impact, drone services are undoubtedly right at the top.

If you had to make a list of the major things that business leaders in nearly every industry are concerned about on a daily basis, tax preparation would undoubtedly be right at the top. It's a major source of concern – whether they're worried about the effects of tax reform, the consequences of making a mistake or the sheer volume of time and effort involved in preparation to begin with, it's something that many cite as one of the biggest challenges of running a business, year after year. But what if there was a way to streamline the tax preparation process? If there was a solution that not only allowed you to automate a lot of the administrative side of tax preparation, but also reduce the possibility of human error and extract relevant information from W2s and other documents in real-time, that would be a solution worth investigating, right? The good news is that this solution already exists: it's called OCR and if your business isn't already using it to help manage your tax documents, there are a number of compelling reasons as to why now would be an excellent time to start.

Not long ago, Amazon unveiled Sagemaker, their machine learning training and deployment infrastructure. To understand why it might be useful, its worth considering the current difficulties of scaling out machine learning services to the cloud. The wild successes of deep learning have increased its demand and taught people to demand its accuracy, which is not easy to achieve without troves of data and the GPU-backed, distributed training platforms to ingest them. A few services out there attempt to help you evade this data barrier: Google ML Engine (and soon AutoML) allow you to train and deploy custom Tensorflow models on Google's cloud infrastructure, Azure has an anaytics platform, BitFusion is trying to help distribute GPUs across cloud providers. There isn't exactly a mad-dash to become the AWS of machine learning, but there is a healthy competition. That being said, there already is an AWS of machine learning: AWS.

A few weeks ago, Facebook open-sourced its platform for object detection research, which they are calling Detectron. Object detection, wherein a machine learning algorithm detects the coordinates of objects in images, remains an ongoing challenge. To find algorithms that provide both sufficient speed and high accuracy is far from a solved problem. Detectron's ostensible purpose is to move a bit closer to that goal using community-led contributions. Google did something similar last year when they released the Tensorflow Object Detection API, which Filestack utilizes for its object detection models, so we thought we'd take a look at Detectron and see how it compares.