Google DeepMind announced Monday (Jan. 6) that it is creating a new team to work on “massive” generative models that would “simulate the world.” These models represent the next stage of advancement in artificial intelligence (AI) capabilities in decision-making, planning and creativity.
World models are computational frameworks that help AI systems understand and simulate the real or virtual world. They are key to helping teach AI systems to navigate an environment and have widespread applications in robotics, gaming and autonomous systems.
For example, autonomous vehicles use these world models to simulate traffic and road conditions. They can also train generalist AI robots in different environments. A common problem is the lack of rich, diverse and safe training environments for so-called embodied AI.
DeepMind’s job posting on Monday said that scaling AI models is also important to the tech’s evolution.
“We believe scaling pretraining on video and multimodal data is on the critical path to artificial general intelligence. World models will power numerous domains, such as visual reasoning and simulation, planning for embodied agents, and real-time interactive entertainment,” the job posting said. PYMNTS reached out to Google but has yet to receive a reply.
Tim Brooks, who left OpenAI in October to join Google DeepMind, will lead the team. At OpenAI, Brooks co-led the development of Sora, its video generation model that went viral upon unveiling because of its sophistication.
According to job listings for the team, the new hires will “collaborate with and build on” the work from Gemini, Google’s flagship large multimodal model, Veo (video generation model), and Genie (world model) teams.
Google DeepMind’s focus on world models comes as AI startup World Labs said it raised $230 million when it came out of stealth last September. The startup is developing large world models. Led by Stanford AI pioneer Fei Fei Li, the startup is funded by AI pioneer and Nobel laureate Geoffrey Hinton, Salesforce CEO Marc Benioff, LinkedIn co-founder Reid Hoffman, former Google Chairman Eric Schmidt as well as Andreessen Horowitz, NEA, NVentures and others.
Google DeepMind has already developed several world models, including Genie and Genie 2. Genie 2 can turn text and image into 3D worlds that react according to a user’s actions in this environment. (Genie created only 2D worlds).
Genie 2 is a powerful AI model that learns from a large video dataset and uses a process that compresses video frames into simpler, meaningful representations through an autoencoder. These compressed frames are then analyzed by a transformer model that predicts how the video should progress, step-by-step, using a method similar to how text-generating models like ChatGPT work.
Trained on a large-scale video dataset, Genie 2 can display object interactions, complex character animation, physics (such as gravity and splashing water effects) and behavior modeling of other agents. The world it creates can last up to a minute, with most in the 10- to 20-second range.
Google DeepMind’s expanded focus on world models will further sharpen its AI systems’ capabilities as it competes with OpenAI, Meta, Microsoft and Amazon in serving enterprises.
The latest innovation adds to its already rich array of innovations, one of which most recently led to Nobel Prize nods for CEO Demis Hassabis and John M. Jumper: AlphaFold2. It is an AI model that predicted the nature of all known proteins, solving a 50-year biochemistry challenge.
In a paper published in October, Google DeepMind researchers said they trained a large language model called the Habermas Machine to serve as an AI mediator that helped small U.K. groups find common ground on controversial issues such as Brexit or immigration. It did so by writing a “group statement” that captured their shared viewpoints.
We get a lot of press releases here at PYMNTS. We consider all of them, and some are more newsworthy than others. But this one really got our attention. This past week, Diebold Nixdorf made headlines with its announcement of successfully installing two new automated teller machines (ATMs) at the U.S. National Science Foundation’s McMurdo Station in Antarctica. This achievement marks a significant milestone in banking accessibility, to be sure. We would like to meet the crew that installed them. We’d also like to know why they needed two. Was there a line at the first one? More to come on that.
According to Diebold, McMurdo Station is Antarctica’s largest research and logistics hub, supporting a fluctuating population that ranges from fewer than 200 residents during the winter months to up to 1,100 individuals during the summer (October through February). The presence of these ATMs is crucial, it says, as the next closest banking facilities are thousands of miles away, making them the only ATMs on the entire continent. How’s that for a value proposition?
The DN Series ATMs are designed for always-on availability. And why do they need two? One ATM is actively in use, while the second serves as a backup for spare parts, ensuring uninterrupted service in this isolated area. These machines are connected to the DN AllConnect Data Engine, which leverages Internet of Things (IoT) connectivity, machine learning, and artificial intelligence (AI) to monitor their performance. A dedicated team continuously aggregates and analyzes technical data to identify potential issues, enabling remote diagnostics and repairs. The ATM can be maintained by trained staff at NSF McMurdo Station, or the Diebold Nixdorf service team can remotely guide them through the repair process.
Anyway, it got us thinking. Are there other surprising ATMs in extreme locations? Well, of course, there are. Here’s a sampling of what we found.
At an altitude of about 5,364 meters (17,600 feet), the Mount Everest Base Camp in Nepal is another unexpected place to find an ATM. Although it’s not a permanent fixture and is often set up seasonally, it caters to climbers and trekkers who need cash for local transactions. This temporary ATM service underscores the adaptability of banking services in extreme environments.
In some parts of the Amazon rainforest, particularly in Brazil and Peru, ATMs can be found in small villages and towns. These machines are vital for local communities, providing access to cash in areas where digital payment options might be limited. The presence of ATMs here demonstrates how banking services can reach even the most remote communities.
Located in the Tibet Autonomous Region, Nagqu is home to one of the highest ATMs in the world. This region is very remote, with limited infrastructure, making the presence of an ATM a notable example of banking accessibility in extreme environments.
In the Thousand Islands (Kepulauan Seribu) off the coast of Jakarta, Indonesia, there’s a floating ATM. This unique ATM serves the local community and tourists on the islands, demonstrating how banking services can adapt to isolated marine environments.
Longyearbyen, the administrative center of the Svalbard archipelago in Norway, boasts the most northerly ATM. This location is one of the most remote inhabited places on Earth, with limited access to mainland Norway, making the ATM a vital service for residents and visitors.
On a more serious note, the installation of ATMs in places like Antarctica and other remote locations highlights the evolving nature of banking technology. With advancements in IoT, AI and remote diagnostics, it’s becoming increasingly feasible to provide banking services in areas previously considered inaccessible. As we look to the future, it will be interesting to see where else ATMs might appear. Whether it’s on a remote island, at the top of a mountain or even in space, the ability to access cash is becoming more universal than ever. And who knows? Maybe one day, we’ll see an ATM on Mars, serving the first interplanetary travelers.
For now, the presence of ATMs in unexpected places reminds us that banking is not just about transactions; it’s about connecting people and communities across the globe, no matter how remote they might be.
