It is neuroscience dogma that the brain's computational mechanics are implemented by the complex dynamics of its spiking neural networks. As a consequence, detailed knowledge of the spiking activity for "as-many-neurons-as-possible" during behavior is seen as essential to understand how the brain receives and transforms information. Electrophysiological methods that record spiking activity extracellularly have been one of the most significant tools for exploring the correlations between behavior and neural activity and there has been a constant drive to record from more neurons, for longer times, from a host of neural regions, in diverse physiological conditions, and from many different species. This trend was recently accelerated by new microfabricated recording probes that extend the standard single electrode and tetrode devices (Recce 1989) with integrated electronics to produce devices with thousands of recording sites (Ruther 2015, Alivisatos 2013). The new generation of recording tools brings with it the challenge of extracting meaningful physiological signals from the resulting (big) data sets.

At the recent Spark & Machine Learning Meetup in Brussels, Koen Dejonghe of Eurocontrol delivered a lightning talk titled "Simulation and processing of data streams in telecommunications." Specifically, Koen discussed the development of a prototype for processing of data coming from cell towers, executed for a telco operator in the Middle East--with the added difficulty that the customer could not provide real data. In the end, Koen developed a data generator in Scala/Akka, a data processor with Spark Streaming, and a visualization front-end with Node.js.

Google announced a new machine learning platform for developers. The company is also open-sourcing tools such as Tensorflow to allow the community to take its internal tools, adapt them for their own uses, and improve them.

One of the latest trends in the world of technology and engineering is "machine learning" -- in fact, all of the big technology companies today have invested in artificial intelligence and machine learning projects. The term "machine learning" was first defined by Arthur Samuel, way back in 1959. He defined it as "the ability to learn without being explicitly programmed," which basically means that a machine could learn from its own mistakes and reprogram itself to improve its performance over time. The idea gained popularity in the 90s when the concept of data mining came into existence. Data mining uses algorithms to look for patterns in a given set of information, which led to data-driven predictions and decision making.

His contention made sense: If marketers are turning out exponentially-increasing amounts of content, yet are competing for the attention of prospects who each have a limited amount of bandwidth, those marketers are forced to invest more resources and money in creating competitive content.

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,