Organisations across the board are looking to artificial intelligence (AI) to find ways to more accurately manage risk, enhance efficiencies to reduce operating costs, and improve experiences for clients and customers. Nvidia has conducted a survey with some of the world's leading financial institutions to find out what's on the top of their minds. Below are the top four findings gleaned from the "State of AI in Financial Services: 2023 Trends" survey taken by nearly 500 global financial services professionals. Financial services firms, like other enterprises, are looking to optimise spending for AI training and inference -- with the knowledge that sensitive data can't be migrated to the cloud. To do so cost-effectively, they're moving many of their compute-intensive workloads to the hybrid cloud.

Artificial intelligence is set to revolutionise agriculture by helping farmers meet field-hand needs and identify diseased plants. "Farmdroid" has made life a lot easier for Mark Buijze, who runs a biological farm with 50 cows and 15 hectares of land. Buijze is one of the very few owners of robots in European agriculture. His electronic field worker uses GPS and is multifunctional, switching between weeding and seeding. With the push of a button, all Buijze has to do is enter coordinates and Farmdroid takes it from there.

Artificial intelligence (AI) is not new to us. It has made its integrations into human life - in our phones, smart televisions, cars, healthcare, security, and almost everything. However, it is still early to say that artificial intelligence has taken over human life. We still have a long way to go for AI-based models to analyze and process things better than a human does. To make this possible, the majority of AI companies need data annotation services to speed up the deployment of these systems.

Building artificial intelligence (AI) models is not like building software. It requires a constant'test and learn' approach. Algorithms are continually learning and data is being refined -- and as much relevant, high-quality data as possible is key. Data labelling is an integral part of data pre-processing for machine learning. If you're training a system to identify animals in images, for example, you might provide it with thousands of images of various animals from which to learn the common features of each, which would eventually enable it to identify animals in unlabelled images.

A growing number of leaders see AI as not just a business opportunity but also as a strategic risk. How do you ensure that your competitors don't figure out how to successfully use it before you do? This year's Artificial Intelligence Global Executive Study and Research Report from MIT SMR and BCG shows early AI winners are focused on organization-wide alignment, investment, and integration. The good news is that there are more women in top-level positions at U.S. businesses than at any other point in history. But a study has found that many women face the biggest obstacle to reaching the top of the corporate ladder early in their careers, with fewer women than men getting the opportunity to take their first step into management.

Something stood out of the ordinary during a speech by China's president, Xi Jinping, in January 2018. Behind Xi, on a bookshelf, were two books on artificial intelligence (AI). Why were those books there? Similar to 2015, when Russia "accidentally" aired designs for a new weapon, the placement of the books may not have been an accident. Was China sending a message?

Sortal team members Majella Edwards and Sarah Smith met when they both attended CEA's Startup Weekend. At the time, Majella was a freelance photographer looking for a new project and Sarah was a software engineer in need of a challenge. After much discussion, the two teamed up to tackle a problem that incorporated their unique skill sets – using artificial intelligence to manage and sort large digital databases. We sat down with Majella to discuss their startup journey and the struggles they've faced along the way. To be honest, it's hard to know whether you really are ahead of the pack, or not.

Quadrotors have a reputation for being both fun and expensive, but it's not usually obvious how dangerous they can be. While it's pretty clear from the get-go that it's in everyone's best interest to avoid the spinny bits whenever possible, quadrotor safety primarily involves doing little more than trying your level best not to run into people. Not running into people with your drone is generally good advice, but the problems tend to happen when for whatever reason the drone escapes from your control. Maybe it's your fault, maybe it's the drone's fault, but either way, those spinny bits can cause serious damage. Safety-conscious quadrotor pilots have few options for making their drones safer, and none of them are all that great, due either to mediocre effectiveness or significant cost and performance tradeoffs.

At Google IO today Waymo took the stage to discuss self-driving cars. Its big pitch: Waymo and Google have "unlocked truly autonomous vehicles." First up, Waymo and Google are making driverless cars safer for pedestrians. Google Brain and Waymo were able to to reduce the error-rate for detecting pedestrians by 100X (not 100 percent, but still!). The company showed off its AI's ability to detect pedestrians in incredibly obscure situations.

In the fast-paced IT industry, new statistics and data are released daily. Each week, Enterprise Mobility Exchange publishes Fast Facts, taking a look at interesting or noteworthy information impacting businesses. The Artificial Intelligence (AI) market is about to embark on unprecedented growth due to demand, but while the explosion will further the technology to new heights, it's facing one obstacle. In a new report by MarketsandMarkets, the AI market value stands at $21.46 billion in 2018, and is forecast to reach $190.61 billion by 2025, boasting a CAGR of 36.62%. There is one major restraint for the market, the forecast says, and that's the limited number of AI technology experts to power the innovation.