Lisbon, Portugal - Artificial intelligence, or AI, has become a commonplace technology, helping researchers make improvements to computerised tasks such as speech recognition and robotics. Machine learning, a branch of AI, allows the flood of data collected from devices to be organised, analysed and visualised in an intelligent fashion. These powerful insights make products such as fitness trackers and climate sensors more appealing. But as the technology evolves, experts are cautioning about the potential threats AI could pose in the future. "AI could be used to deal with particular issues around privacy and surveillance and things like this," Antoine Blondeau, chief executive of Sentient Technologies, told Al Jazeera at the Web Summit in Lisbon. Watch Inside Story: How can we make the most of artificial intelligence?

About a year ago, a certain California firm quietly snapped up VocalIQ, a UK-based startup that used machine learning to build conversational virtual assistants. Subsequent reports noted that Apple kept most of the startup's employees to work out of their unmarked Cambridge, UK office on integrating VocalIQ technology into Siri. Citing sources with knowledge of the matter, Business Insider reports that Apple is now looking to increase the size of the Siri team in Cambridge. "In a bid to make Siri that bit more useful to iPhone, iPad and Mac owners, Apple intends to hire at least half a dozen software engineers in Cambridge in the coming months," reads the post. Apple's Cambridge office is currently home to a team of roughly 30 people working on voice recognition technology that would let Siri and users speak to each other in a more natural dialogue.

For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools. To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a definition that limits machine learning to software. In the enterprise, however, machine learning is software.

RiskIQ, a startup with a new kind of security technology, has raised $30.5 million in a third round of funding. Georgian Partners led the round, with participation from existing investors Summit Partners, Battery Ventures, and MassMutual Ventures. RiskIQ notes that threats outside the firewall are vast and dynamic, so the company provides clients with access to the widest range of security intelligence and applications necessary to understand exposures and how to take action. RiskIQ is one of many companies currently applying machine learning to security. The San Francisco-based company will use this capital to expand its platform, sales, and digital risk applications.

By determining how accurate a machine is relative to human reference standards, we can work on more sophisticated machine-learning methods (i.e., deep-learning convolutional neural networks) to create a narrow-field artificial intelligence system that can extract text features (e.g., acute findings) and tabulate the findings as part of a larger database that could be used for quality studies, outcomes analysis, resource utilization studies, and predictive analytics and modeling,

A robot has just set a new record for the fastest-solved Rubik's Cube, according to its makers. The Sub1 Reloaded robot took just 0.637 seconds to analyse the toy and make 21 moves, so that each of the cube's sides showed a single colour. That beats a previous record of 0.887 seconds, which was achieved by an earlier version of the same machine using a different processor. Infineon provided its chip to highlight advancements in self-driving car tech. But one expert has questioned the point of the stunt.

Cape Analytics has raised $14 million to use computer vision and machine learning to improve automated property underwriting for insurance companies. Formation 8 led the round, with participation from XL Innovate, Data Collective, Lux Capital, Khosla Ventures, Promus Ventures, and Montage Ventures. The funding is one more application of machine learning and computer vision for business automation. Palo Alto, Calif.-based Cape Analytics starts with images of a home to help assess the home's value and allow an insurance carrier to deliver more accurate and fast quotes. The funding will allow Cape Analytics to expand its world-class engineering and sales teams as it brings its proprietary data to more regions and customers around the United States.

Ever think your smartphone is vibrating or ringing when it isn't? It's such a common phenomenon that researchers gave the problem, which plagues between 70% and 90% of smartphone users, a name: phantom vibration syndrome. Some people find themselves so unhealthily attached to or distracted by their digital devices that they go on technology fasts to help them detach. Entrepreneur Rand Hindi says the problem isn't technology but the way we interact with it. Hindi, who was born in Lebanon and grew up in France, started coding at age 10, launched a web development agency at age 15, and earned his PhD in bioinformatics while working as a consultant on algorithmic trading.

Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise. The course is launched for the first time and has limited spaces. Created in partnership with H2O.ai, the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai. To sign up or learn more, email info@futuretext.com The course targets developers and Architects who want to transition their career to Enterprise AI.

A team of researchers at the Google Brain office have been working on a project that involved creating three separate neural networks that between them have the ability to create and send encrypted messages. This type of machine learning will become more prominent in the world of AI over the next few years, particularly when it comes to handling private or sensitive information. Two of the researchers involved, Martin Abadi and David Anderson, wrote in their paper that "The learning does not require prescribing a set of cryptographic algorithms, nor indicating ways of applying these algorithms: it is based only on a secrecy specification represented by the training objectives." After several thousand simulations, Alice and Bob were each able to send and decrypt messages securely. Eve on the other hand, was unable to fully decrypt the messages.