Will artificial intelligence take our jobs? If you listen to Silicon Valley executives talking about the capabilities of today's cutting edge AI systems, you might think the answer is "yes, and soon." But a new paper published by MIT researchers suggests automation in the workforce might happen slower than you think. The researchers at MIT's computer science and artificial intelligence laboratory studied not only whether AI was able to perform a task, but also whether it made economic sense for firms to replace humans performing those tasks in the wider context of the labor market. They found that while computer vision AI is today capable of automating tasks that account for 1.6% of worker wages in the U.S. economy (excluding agriculture), only 23% of those wages (0.4% of the economy as a whole) would, at today's costs, be cheaper for firms to automate instead of paying human workers.

DTiQ, the world's leading provider of next-generation video intelligence, analytics, and managed video services for restaurants, convenience stores, and specialty retailers selects computer vision platform leader, alwaysAI, to help power their video analytics AI. With alwaysAI, DTiQ's video analytics are able to provide customers even greater results, creating tremendous ROI for DTiQ's customers and revolutionizing how restaurant and retail locations can be managed. "We've long since believed that existing video can do so much more to help run a better business, and our customers are seeking just that – accurate and reliable real-time data of their operations and processes. This partnership helps DTiQ fast-track the next generation of our Computer Vision AI, analytics, IoT, and machine learning solutions to increase value for our 45,000 customers," says Mike Coffey, CEO of DTiQ. "Computer Vision applications, particularly with the alwaysAI platform, provide the powerful solutions to deliver cutting-edge video analytics, together with DTiQ industry solutions and expertise, it creates completely new value on the market".

TechSee provides a visual engagement platform powered by deep learning and computer vision, enabling the auto-recognition of devices and issues in order to offer proven resolutions. Customers receive precise AR visual guidance in both assisted service and self-service modes at every stage of the journey, from sales, registration and onboarding to claims and upsell. They serve tier 1 companies and global groups in 24 markets around the world, including leading P&C insurers, telecoms and consumer electronics manufacturers. The Israeli startup was founded to help businesses better support their operations from all perspectives: customers, contact center agents, field technicians, and self-service channels. Over two decades of experience across CX technologies, visual computing, augmented reality, and big data enables TechSee to follow through on this commitment.

A modern contact center relies on its knowledge base to streamline its operations. When a new type of issue has been successfully resolved, the challenge is to make the relevant information readily available across the organization. By automatically generating textual descriptions of objects or issues identified within images, computer vision platforms make it easier for everyone to search the company system and find exactly the solution they need. For example, an insurance agent can simply type "fender bender" or "cracked windshield" into the search bar and instantly find relevant images of similar incidents, enabling them to estimate the cost of the damage in no time flat.

The world may be enamored with bots at the moment, but they've actually been around for quite some time. The first bots were used in the finance industry more than a decade ago, automatically buying and selling equities based on key market indicators. It was a novel concept at the time, but the technology is now ubiquitous in the industry, with the financial robo-advice market projected to grow to $7 trillion by 2025, according to CNBC. Today's bots have evolved to become much more capable than their ancestors. Conversational AI platforms, known as chatbots, automate and scale one-on-one conversations -- with massive use cases that extend well beyond the finance industry, into the sales, marketing and customer support domains. What's more, they're continuing to evolve from their predecessors; just a few years ago, the notion that a bot could answer a text message or suggest a product for purchase was revolutionary.

Researchers at the Massachusetts Institute of Technology (MIT) have demonstrated a new way to fool computer vision algorithms that enable artificial intelligence systems to see. The researchers exploited the Google Cloud Vision API that enables anyone to perform image labeling, face and landmark detection, optical character recognition, and tagging of explicit content. Traditional hacking approaches are inefficient and impractical when targeting large images with tens of thousands of pixels. To overcome this problem, the MIT team adapted a "natural evolution strategies" method that generates smaller populations of images around the larger image, with large random groups of pixels being perturbed instead of single pixels. Then, given the classifier's output on these randomly perturbed images, the system recovers what the contribution of each individual pixel is to the classification output, according to MIT researcher Andrew Ilyas.

Tricking Google's computer vision AI into seeing a dog as a pair of human skiers may seem mostly harmless. But the possibilities become more unnerving when considering how hackers could trick a self-driving car's AI into seeing a plastic bag instead of a child up ahead. Or making future surveillance systems overlook a gun because they see it as a toy doll. An independent AI research group run by MIT students has demonstrated a new way to fool the computer vision algorithms that enable AI systems to see the world--an approach that could prove up to 1000 times as fast as other existing ways of hacking "black box" systems whose inner workings remain hidden to outsiders. That idea of a black box perfectly describes the neural networks behind the deep learning algorithms enabling computer vision services for Google, Facebook, and other companies.