Deep learning uses algorithms inspired by the way human brains operate to put computers to work on tasks too big for organic gray matter. On Monday, IBM announced that a new record for the performance of a large neural network working with a large data set.
The company’s new deep-learning software brings together more than 256 graphics processing units across 64 IBM Power systems. The speed improvements brought about by the research come as a result of better communication between the array of GPUs.
Faster GPUs provide the necessary muscle to take on the kind of large scale problems today’s deep-learning systems are capable of tackling. However, the faster the components are, the more difficult it is to ensure that they are all working together as one cohesive unit.
As individual GPUs work on a particular problem, they share their learning with the other processors that make up the system. Conventional software is not capable of keeping up with the speed of current GPU technology, which means that time is wasted as they wait around for one another’s results.
Hillery Hunter, IBM’s director of systems acceleration and memory, compared the situation to the well-known parable of the blind men and the elephant. The company’s distributed deep-learning project has resulted in an API that developers can be used in conjunction with deep-learning frameworks to scale to multiple servers, making sure that their GPUs remain synchronized.
IBM recorded image recognition accuracy of 33.8 percent on a test run using 7.5 million images from the ImageNet-22K database. The previous best-published result was 29.8 percent, which was posted by Microsoft in October 2014 — in the past, accuracy has typically edged forward at a rate of about one percent in new implementations, so an improvement of four percent is considered to be a very good result.
Crucially, IBM’s system managed to achieve this in seven hours; the process that allowed Microsoft to set the previous record took 10 days to complete.
“Speed and scalability, which means higher accuracy, means that we can quickly retrain an AI model after there is a new cyber-security hack or a new fraud situation,” Hunter told Digital Trends. “Waiting for days or weeks to retrain the model is not practical — so being able to train accurately and within hours makes a big difference.”
These massive improvements in terms of speed, combined with advances in terms of accuracy make IBM’s distributed deep-learning software a major boon for anyone working with this technology. A technical preview of the API is available now as part of the company’s PowerAI enterprise deep-learning software.
The absolutely absurd idea of putting a touchscreen display on a mouse isn't exactly new. Turtle Beach already did it with its Command Series MC7, which features a 2.25-inch control screen. But apparently, even that wasn't ridiculous enough for Arctic Fox Technologies. Its new Super Mouse looks like someone took the chunky old Atari ST mouse, slapped a touchscreen and scroll wheel on it, and called it an upgrade.
The result is a rather unusual-looking mouse that is now on Kickstarter, starting at $199. And as ridiculous as it looks, some of its features are genuinely intriguing.
Apple is less than a day away from launching its highly anticipated OLED MacBook Pro with a touch-sensitive screen. But it appears that the upgrade is going to cost a pretty penny. According to a report by Bloomberg, Apple is reportedly eyeing a starting price of $2,899 for the 14-inch MacBook Pro, while the 16-inch version could start at $3,399 in the US.
"At $2,899, the 14-inch model would mark the highest starting price for a new MacBook Pro model, surpassing the $2,799 Apple charged for a 17-inch version introduced in 2006," says the report. The report claims that Apple is targeting a minimum price hike of $400 for its upcoming redesigned 14-inch and 16-inch MacBook Pro models.
I've spent a good amount of time digging into the spec sheet of the new M6 MacBook Pro (14-inch), and for all intents and purposes, this is a classic Apple "chip and connectivity" refresh. It's not a full redesign. The chassis, Liquid Retina XDR panel, speakers, ports, and battery, all of them carry over unchanged.
However, the M6 brings more cores, stronger on-device AI, higher memory bandwidth on the larger configs, and modern wireless. Here’s the practical rundown of what actually improved, and whether those gains are worth your money.