Artificial Intelligence evokes a whole gamut of reactions. The cinematic world has been taking unrestrained creative liberty for ages. Such ambiguities that hound artificial intelligence (AI) clearly stem from an inherent lack of understanding of its root concepts. Interestingly, in one form or the other, the human race is already surrounded with AI. The era of Artificial Intelligence has begun. The truest form of AI is referred to as Strong AI or True AI, which is the stage when machines can behave as skillfully and flexibly as humans.

Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. The inspiration for the tool came from projects that use social media to identify citizens' concerns -- for instance, what concerns people have about an immunization drive, or how often they suffer power outages. But at the Global Pulse lab in Kampala, Uganda, social media analysis wouldn't work, says lab manager Paula Hidalgo-Sanchis -- especially if the U.N. wanted to listen to rural voices.

Graphics chipmaker Nvidia blew past its third quarter earnings targets Thursday after the bell. The company posted record-revenue for the quarter, thanks to strong sales of Nvidia's new Pascal GPUs. Nvidia co-founder and CEO Jen-Hsun Huang said the GPUs are fully ramped and rolling out in gaming, VR, self-driving cars and datacenter AI computing applications. "We have invested years of work and billions of dollars to advance deep learning. Our GPU deep learning platform runs every AI framework, and is available in cloud services from Amazon, IBM, Microsoft and Alibaba, and in servers from every OEM. GPU deep learning has sparked a wave of innovations that will usher in the next era of computing," he said.

Facebook has succeeded in transmitting data at almost 20Gbps between two towers in Southern California in tests of a technology key to its plans to deliver internet service to rural areas using drones. The tests were conducted earlier this year and made use of frequencies in the so-called E-band, a group of millimeter wave frequencies between 60 and 90GHz. Such signals are capable of high-bandwidth data transmission but are susceptible to attenuation from distance, weather, and obstacles, so they are typically used for short-range, point-to-point transmissions. Facebook used a 60-centimeter dish to send data over a 13-kilometer link between Malibu and Woodland Hills. That test initially shot data at between 100Mbps and 3Gbps and allowed engineers to collect transmission data on clear days and during clouds, fog, high winds, and rain, Facebook said in a Thursday blog post.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10 year and am very satisfied with its benefits. Recently, I've been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning.

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.