As 5G networks continue to expand in cities and countries across the globe, key researchers have already started to lay the foundation for 6G deployments roughly a decade from now. This time, they say, the key selling point won't be faster phones or wireless home internet service, but rather a range of advanced industrial and scientific applications -- including wireless, real-time remote access to human brain-level AI computing. That's one of the more interesting takeaways from a new IEEE paper published by NYU Wireless's pioneering researcher Dr. Ted Rappaport and colleagues, focused on applications for 100 gigahertz (GHz) to 3 terahertz (THz) wireless spectrum. As prior cellular generations have continually expanded the use of radio spectrum from microwave frequencies up to millimeter wave frequencies, that "submillimeter wave" range is the last collection of seemingly safe, non-ionizing frequencies that can be used for communications before hitting optical, x-ray, gamma ray, and cosmic ray wavelengths. Dr. Rappaport's team says that while 5G networks should eventually be able to deliver 100Gbps speeds, signal densification technology doesn't yet exist to eclipse that rate -- even on today's millimeter wave bands, one of which offers access to bandwidth that's akin to a 500-lane highway. Consequently, opening up the terahertz frequencies will provide gigantic swaths of new bandwidth for wireless use, enabling unthinkable quantities and types of data to be transferred in only a second.

It's been a big week for 5G, the next generation of wireless networks. Samsung announced its first 5G capable phone, the S10, on Wednesday. Qualcomm announced a new 5G modem on Tuesday. But President Trump is aiming higher. "I want 5G, and even 6G, technology in the United States as soon as possible," Trump wrote in a tweet urging carriers to pick up their pace.

Recently a team of researchers in the US revolutionised artificial intelligence when they made the world's first neural network made from DNA, or "AI in a test tube," and now a team of electrical and computer engineers from the University of California Los Angeles (UCLA) have announced they've 3D printed the world's first physical Artificial Intelligence (AI) neural network that uses light, and not electrons like it's traditional computer cousins, to analyse large volumes of data and identify objects. Additionally though because the new neural network uses light to work, and not electrons, it's passive and doesn't need any external power source to work. Numerous devices in everyday life today use computerised cameras to identify objects -- think of automated teller machines that can "read" handwritten dollar amounts when you deposit a check, or internet search engines that can quickly match photos to other similar images in their databases. But those systems rely on a piece of equipment to image the object, first by "seeing" it with a camera or optical sensor, then processing what it sees into data, and finally using computing programs to figure out what it is, and this is where the UCLA developed device gets a head start. Called a "Diffractive neural network," it uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply "see" the object.

The network, composed of a series of polymer layers, works using light that travels through it. Each layer is 8 centimeters square. A team of UCLA electrical and computer engineers has created a physical artificial neural network -- a device modeled on how the human brain works -- that can analyze large volumes of data and identify objects at the actual speed of light. The device was created using a 3D printer at the UCLA Samueli School of Engineering. Numerous devices in everyday life today use computerized cameras to identify objects -- think of automated teller machines that can "read" handwritten dollar amounts when you deposit a check, or internet search engines that can quickly match photos to other similar images in their databases.