Machine learning is often thought to be too complicated for everyday development tasks. I have always felt like we can benefit from using machine learning for simple tasks that we do regularly. At Mailgun, we work with e-mail and as part of our offering, we parse HTML quotations. This allows a user to grab the latest reply instead of the entire conversation, which is returned as part of our webhook response. For those of you who don't know, here's what parsing HTML from the public Internet looks like: Changing the parsing library can help, but it won't solve the issue completely because every library has its limitations.

In-memory data store Redis recently acquired a module architecture to expand functionality. The latest module is a machine learning add-on that accelerates delivery of results from trained data rather than training itself. Redis-ML, or the Redis Module for Machine Learning, comes courtesy of the commercial outfit that drives Redis development, Redis Labs. It speeds the execution of machine learning models while still allowing those models to be trained in familiar ways. Redis works as an in-memory cache backed by disk storage, and its creators claim machine learning models can be executed orders of magnitude more quickly with it.

In recent years, there has been a big leap in machine learning due to the capabilities of graphic processing units (GPUs) evolving at lightning speed. Even though machine learning is never going to be exactly human-like, we are getting closer in a few areas, like imagenet large scale challenge, where the error rates are on par with or better than humans. A recent announcement of SAP human resources implementing bias filters is another great example of machine learning performing better than humans. Machine learning promises a next wave of innovations for every application, business process, and interaction of the enterprise with its customers. To disrupt, companies need to have a "machine learning-first" mentality to differentiate them in the marketplace.

Leveraging its machine learning, artificial intelligence (AI) and deep learning capabilities, software major Adobe Systems has unveiled Adobe Sensei -- a new framework and set of intelligent services that improve the design and delivery of digital experiences -- at its ongoing annual creativity conference here. Adobe "Sensei", which means "master" or "teacher" in Japanese, tackles complex experience challenges, including image matching across millions of images, understanding the meaning and sentiment of documents and finely targeting important audience segments. "Adobe Sensei is uniquely focused on solving today's complex experience challenges in the design, document and marketing fields, where only Adobe has decades of expertise and market leadership," Shantanu Narayen, President and CEO of Adobe said on Wednesday. "Leveraging our machine learning and AI capabilities, as well as trillions of content and data assets, Adobe Sensei will be one of our biggest strategic investments. We're excited to open it up to our broader ecosystem of partners, ISVs and developers to enable even more innovation," he added. Adobe Sensei, unveiled at the Adobe Max 2016 creativity conference, includes a unified AI/machine learning framework that power Creative Cloud, Adobe Document Cloud and Adobe Marketing Cloud and automates mundane tasks, drive predictive and personalization capabilities and boost productivity.

NEC Corporation has launched a new software program that uses artificial intelligence (AI) in video footage search to quickly identify a person by facial recognition. NeoFace Image Data Mining (Idm) is a new product offering from NEC that can use video footage, for example, data gathered by CCTV cameras, and scan it to accurately identify an individual whose image is captured on camera. It can also be used to search for people who appear at a certain time and place, or who appear with other specified individuals. A complete search for a specific person among one million captured images can be concluded in under 10 seconds. Idm combines existing facial recognition technology with profiling parameters – what NEC refers to as'Profiling Across Spatio-Temporal Data' technology.

Investors poured a record high $1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

Sophos has announced the acquisition of Irish security firm Barricade, adding behavior-based analytics to its endpoint offering. Barricade offer a technology platform that it claims can enhance the ability to identify malicious or suspicious behaviour by using machine learning and artificial intelligence. It said that this works by extending the capabilities of rule-based detection technologies, that will be increasingly challenged to keep up with the growth of sophisticated and complex attack patterns. Sophos will maintain the offices in the Republic of Ireland with Barricade CEO David Coallier and the team of developers, data scientists and engineers joining the Sophos Cloud group. Coallier said: "We are proud of the technology we have built and are pleased to join the team at Sophos focused on artificial intelligence and machine learning based security analytics. Driving the development of our technology into a comprehensive security solution that every IT professional can use presents us with the next phase in our exciting journey."

Please join us for an exciting day discussing the impact artificial intelligence will have on business and important key industries within the UK, including manufacturing, the financial sector and creative industries. From writing new Beatles-esque songs to making increasingly accurate medical diagnoses, few industries appear safe from the disruptive impact of rapidly developing Artificial Intelligence technology. In fact, a recent Australian study predicted that as many as 60% of students are pursuing careers that will be rendered obsolete by the time they graduate. With 80-100 people due to attend the AI conference, we will look at the wide reaching social and economic impact that deep learning and artificial intelligence is likely to have traditionally conservative'human to human' industries. There is little doubt that AI and deep learning represent a hugely positive opportunity for business, we will discuss these opportunities, what businesses need to do next and the economic transformation we may be facing in our lifetime.

The Snasci Logo comprises of three smaller rings, intersected by a large ring. Symbolically, this represents an adaptation of the Three Laws of Robotics by the science fiction author Isaac Asimov. The rules first appeared in his short story "Runaround" (1942). Quoting from the "Handbook of Robotics, 56th Edition, 2058 A.D.", the laws are: Whilst these laws are broadly acceptable for a robot, they are too narrow for an Artificial General Intelligence. An artificial General Intelligence must deal with scenarios that go beyond physical interaction with humans.

Last week, Microsoft announced a speech recognition breakthrough: a transcription system that can match humans, with a word error rate of 5.9 percent for conversational speech. This new system is built on an open source toolkit that Microsoft already developed. A major new update to the toolkit, now called the Cognitive Toolkit, was released today in beta. Formerly called the Computational Network Toolkit (CNTK), the MIT-licensed, GitHub-hosted project gives researchers some of the building blocks, such as neural networks, to develop their own machine learning systems. These machine learning applications can run on both CPUs and GPUs, and the toolkit has support for compute clusters.