"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
Yesterday, tech giant Google announced its latest solution, the Cloud AutoML, that will enable developers, even those that lack machine learning expertise, to build image recognition models. It is said to be a part of the company's initiative to democratize AI learning and provide a simple approach that anyone can easily understand. "Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses," Fei-Fei Li, Google Cloud AI chief scientists, and Jia Li, Google Cloud AI Head of R&D, wrote in the company blog. According to the duo, their latest solution would help businesses with limited machine learning expertise build "their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google." The two believe that Cloud AutoML will make experts in artificial intelligence more productive and take the technology to greater heights while helping less-skilled engineers build more powerful machine learning systems.
Intel has released two ready-to-use RealSense depth cameras, the D415 and the D435, that can add 3D capabilities to any device or machine. They both come in a USB-powered form factor and are capable of processing depth in real time, thanks to the chipmaker's new RealSense vision processor D4. The models work indoors and outdoors in any lighting environment, so they can be used for almost any machine that needs a depth camera. Those include drones meant to soar the skies and robots with AR/VR features. Intel says the cameras' target audiences aren't just developers and manufacturers, but also makers and educators, since they're easy to use and will work as soon as you plug them in.
The convergence of mobility and cloud have led to a digital explosion. Now that users have anytime, anywhere, any device access, they are generating mountains of data. In fact, IDC predicts that by 2025, the world will create 160 trillion gigabytes of it. But more important than the volume is what companies do with that data – how they leverage it for heightened customer experiences, for improved day-to-day decision making, and to innovate. And that's what's behind the rush toward digital transformation.
Samsung Electronics has revealed that its Internet of Things or IoT platform now has facial recognition technology. Does this mean the South Korea tech giant could be incorporating a Face ID-like feature to its upcoming Galaxy S9 flagship? On Thursday, South Korean online news outlet Etnews learned that Samsung's IoT platform, called "ARTIK," has absorbed a technology that will allow its to recognize faces based on machine learning. This means Samsung products that support its IoT platform could also be capable of recognizing users through facial recognition. Samsung has also said that the machine learning of ARTIK can recognize faces with the help of Microsoft's "MS-Celeb-1M," a large scale real world face image dataset that already contains images of 1 million people.
The "Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camera-based), Application, End-User - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering. The overall industrial machine vision market was valued at USD 7.91 Billion in 2017 and is expected to reach USD 12.29 Billion by 2023, at a CAGR of 7.61% between 2017 and 2023. This is because of the increasing need for quality inspection and automation, growing demand for AI and IoT integrated machine vision system, increasing adoption of Industrial 4.0, development of new connected technologies, and government initiatives to support smart factories, among others. Governments of different countries worldwide are encouraging investments in manufacturing, which is necessitating the use of various automation products for structural development. Software component is expected to grow at the highest rate between 2017 and 2023.
Google released a new AI tool on Wednesday designed to let anyone train its machine learning systems on a photo dataset of their choosing. The software is called Cloud AutoML Vision. In an accompanying blog post, the chief scientist of Google's Cloud AI division explains how the software can help users without machine learning backgrounds harness artificial intelligence. All hype aside, training the AI does appear to be surprisingly simple. First, you'll need a ton of tagged images.
Indeed, one of the first synthetic data examples Schatsky encountered was for computer vision, technology that enables machines to recognize faces or identify objects in digital photos. Researchers today are building sophisticated computer vision features where the technology can follow an eye gaze or detect an emotion on someone's face. But gathering the amount of data needed -- and labeling it -- is laborious. "And, so, what researchers did is they took a 3D-digital model of a human face and then manipulated it," Schatsky said. They can generate as many permutations of facial expressions or eye positions as they want -- and they can do so "quickly and cheaply, compared to collecting a comparable number of images," he said.
AI has become "ALL IN" and pervading at a rapid speed. Technology moves at breakneck speed, and we now have more power in our pockets than we had in our homes in the 1990s. Artificial intelligence (AI) has been a fascinating concept of science fiction for decades, but many researchers think we're finally getting close to making AI a reality. NPR notes that in the last few years, scientists have made breakthroughs in "machine learning," using neural networks, which mimic the processes of real neurons. Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society.
An international team of scientists are using data on genetic material, cell surface texture and typical facial features derived by artificial intelligence methods to simulate disease models for deficiencies in the molecule glycosylphosphatidylinositol (GPI) anchor, which is known to cause various diseases. One of the diseases is Mabry syndrome, a rare disease that is triggered by a change in a single gene, causing mental retardation. "This disease belongs to a group that we describe as GPI anchor deficiencies and which includes more than 30 genes," physician and physicist Dr. Peter Krawitz from the Institute for Genome Statistics and Bioinformatics of the University Hospital Bonn, said in a statement. GPI anchors attach specific proteins to the cell membrane and if they do not properly function due to a gene mutation, signal transmission and further steps in the cell-cell communication are impaired. The researchers investigated how a diagnosis of GPI anchor deficiencies can be improved with modern and fast DNA sequencing methods, cell surface analysis and computer aided image recognition.
In Belgium, Volvo is promoting its S90 model in an unusual way - it's getting the car to do its recruitment. They refitted a Volvo S90 as the "HR90," equipping it with artificial intelligence that allows it to interview prospective technicians. The car will be "recruiting" at the Brussels Motor Show, and will then continue with a tour of job expos, schools and Volvo dealerships in search of new hires. Volvo asked candidates to submit their job application on a website in order to be considered for an interview. The car quizzes them via image recognition, mapping and analysis of preset parameters, analysing the candidate's facial expressions and word use in order to assess their knowledge, motivation and social skills.