I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It was obvious that things had to change for the organization to be able to execute at speed in real time. Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence.

Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide.

Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.

I was wrong to say that Intel (INTC) doesn't need GPUs to compete with Nvidia (NVDA) on artificial intelligence/deep learning computing. Further research told me that along with FPGA (Field Programmable Field Gate Array), there's an embedded Intel Processor Graphics for deep learning inference. It's a new concept that was discussed by Intel only last May. Nvidia's GPU can be the Training Engine for deep learning computers. Intel's FPGAs and embedded Processor Graphics could be the go-to hardware accelerators for inference computing.

Software giant Microsoft has announced its Project Brainwave deep learning acceleration platform for real-time artificial intelligence (AI). With the help of ultra-low latency, the system processes requests as fast as it receives them. "Real-time AI is becoming increasingly important as cloud infrastructures process live data streams, whether they be search queries, videos, sensor streams, or interactions with users," said Doug Burger, an engineer at Microsoft, in a blog post late on Tuesday. The'Project Brainwave' uses the massive field-programmable gate array (FPGA) infrastructure that Microsoft has been deploying over the past few years. "By attaching high-performance FPGAs directly to our datacentre network, we can serve DNNs as hardware microservices, where a DNN can be mapped to a pool of remote FPGAs and called by a server with no software in the loop," Burger said.

Police officers could soon be wearing body-mounted cameras programmed to spot criminals and missing people in real-time, using artificial intelligence. The cameras, built by Motorola and similar to those already used by some US police forces to record an officer's point of view, could also help find missing objects like a stolen car, thanks to machine learning. A prototype of the AI camera is already being developed by Motorola and Neurala, a deep learning startup based in Boston, Massachusetts that recently added its software to drone cameras to help track poachers in Africa. The smart camera will learn while it is used and "automatically search for persons or objects of interest, significantly reducing the time and effort required to find a missing child or suspicious object in environments that are often crowded or chaotic," Motorola and Neurala said in a joint statement. "We see powerful potential for artificial intelligence to improve safety and efficiency for our customers, which in turn helps create safer communities," said Paul Steinberg, chief technology officer of Motorola Solutions.