Facebook Inc. today updated its popular artificial intelligence software framework PyTorch with support for new features that enable a more seamless AI model deployment to mobile devices. PyTorch is used by developers to research and build AI models for software applications, and then move those apps straight to production thanks to its integration with leading public cloud platforms. PyTorch was first built by Facebook's AI research group as a machine learning library of functions for the programming language Python. It's primarily designed for use with deep learning, which is a branch of machine learning that attempts to emulate the way the human brain functions. It has led to major breakthroughs in areas such as language translation and image and voice recognition.

The demand for machine learning systems has soared over the past few years. This is majorly due to the success of Machine Learning techniques in a wide range of applications. AutoML is fundamentally changing the face of ML-based solutions today by enabling people from diverse backgrounds to use machine learning models to address complex scenarios. However, even with a clear indication that machine learning can provide a boost to certain businesses, a lot of companies today struggle to deploy ML models. This is because there is a shortage of experienced and seasoned data scientists in the industry.

In China, AI helps sort rubbish, catch criminals and improve traffic – now it's used to stop toilet paper theft

Social media platforms like Facebook use a combination of artificial intelligence and human moderators to scout out and eliminate hate speech. But now researchers have developed a new AI tool that wouldn't just scrub hate speech but would actually craft responses to it, like: "The language used is highly offensive. All ethnicities and social groups deserve tolerance." "And this type of intervention response can hopefully short-circuit the hate cycles that we often get in these types of forums." The idea, she says, is to fight hate speech with more speech--an approach advocated by the ACLU and the U.N. High Commissioner for Human Rights.

Unplanned outages are on the rise globally from both equipment failures and damages. Smaller operators do not have the excess capital to conduct the same level of planned outages. As traditional methods of predicting equipment failure can be unreliable in a dynamic operating environment, they can be hesitant to sanction significant shutdown work unless it is absolutely necessary. Thus, they are forced to ride out operational uncertainty and have unfortunately seen unplanned outages increase. Unplanned outages have severe negative cost and production consequences for the operator.

In essence, the Government Digital Service (GDS) helps government work better for everyone by leading digital transformation (1). In August 2019, Alison Pritchard was named Interim Director-General of the GDS (2), now that Kevin Cunnington has taken on a new role promoting government services around the world. Alison recently shared her journey in government so far and discussed what she looks forward to delivering over the coming months. Long before she joined any government department, Alison already had experience of meeting user needs and delivering more for less. Alison explains that she was given the chance to run the garden bar at the family pub at the age of eight.

Your data has value, but unlocking it for your own benefit is challenging. Understanding how valuable data are collected and approved for use can help you to get there. Two primary means for differentiating audiences by their data collection methods are site-authenticated data collection and people-based data collection, suggested a recent piece in BulletinHealthcare written by Justin Fadgen, chief corporate development officer for the firm. Site-authenticated data are sourced from individual authentication events, such as when a user completes an online form, and generally agrees to a privacy policy that includes a data use agreement. User data are then be combined with other data sources that add meaning, becoming the basis of advertising targeting for instance.

Almost everyone with a personal computer carries a piece of artificial intelligence (AI) interface in the form of Siri or Cortana. If not, you might have interacted with one on your smartphone today. These are the most common touchpoints between consumers and AI. Needless to say, that is just the tip of the iceberg. AI penetration has been impactful in B2B setups or at the back-end of many services that we use every day.

Even after years of experience, senior underwriters can still spend a large part of their day doing the administrative tasks that they were taught early in their careers. AntWorks, a global artificial intelligence (AI) and intelligent automation company, can transform the way they spend their time. "All the rules and learning are completely discoverable, addressing concerns often held about AI and RPA technologies." "In your first weeks of work you may have been trained to recognise insurance slips and documents that brokers might present to you as an underwriter," said Mike Hobday, AntWorks' senior vice president for Europe. "You would be trained to understand the data you need to extract, to think about the standing of the broker and the nature of the risks. You might even make some calculations and underwrite the offered risk by entering data into the core systems of the business. "As a very senior and experienced underwriter, there might still be a large part of your day spent doing the administration you were taught in those earlier days.

We've all heard it before: "Win or go home." Whether in business or on the playing field, the pressure to win is intense. And in today's financial services industry, the winner can literally take all. As banks struggle to adapt in the throes of digital disruption, executives find themselves squeezed to use artificial intelligence (AI) or machine learning (ML) models to power their digital transformation initiatives forward. The industry's use of computational finance models to make decisions is nothing new.