SpaceX CEO Elon Musk hasn't been shy about his plans to colonize Mars. AP

Elon Musk hasn't been shy about his plans to colonize Mars.

For years, the billionaire has argued that humans must become a multiplanet species as quickly as possible to escape threats on Earth such as overpopulation.

Musk has said that by 2050, he plans to put 1 million people on the neighboring planet, with help from his space-exploration company, SpaceX.

Some of his contemporaries aren't so sure about the plan.

DeepMind CEO Demis Hassabis was among the latest to point out an issue in the plans, The New York Times reported.

While Hassabis agreed the plan could work in theory, Musk was left speechless by Hassabis' suggestion that superintelligent artificial intelligence could follow him to Mars and destroy humanity, the Times reported.

The billionaire hadn't considered that risk and was so concerned he later invested in DeepMind to stay close to the technology, the report said.

Musk, a vocal AI doomsayer, has since launched his own AI company and an AI-powered chatbot.

Hassabis is far from the first to poke holes in the Tesla CEO and X owner's ambitions.

The Microsoft cofounder Bill Gates previously said Musk's ambition to colonize Mars wasn't a good use of money. Gates said funding important healthcare such as vaccine development was a better use of funds.

Four scientists previously told Business Insider that the plan suffered from technical, scientific, and ethical flaws. They said forming a colony on another planetary body, such as the moon, was more realistic than settling on the red planet.

The filmmaker Werner Herzog also took aim at the plans, once saying he thought the proposal was a "mistake" and an "obscenity."Herzog said he thought humans should focus on keeping Earth habitable, rather than looking for a new home.

Hassabis and Musk did not immediately respond to requests for comment made outside normal working hours.

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DeepMind boss found a flaw in Musk's Mars plan. He's not the first. - Business Insider

Teaching algorithms to mimic humans typically requires hundreds or thousands of examples. But a new AI from Google DeepMind can pick up new skills from human demonstrators on the fly.

One of humanitys greatest tricks is our ability to acquire knowledge rapidly and efficiently from each other. This kind of social learning, often referred to as cultural transmission, is what allows us to show a colleague how to use a new tool or teach our children nursery rhymes.

Its no surprise that researchers have tried to replicate the process in machines. Imitation learning, in which AI watches a human complete a task and then tries to mimic their behavior, has long been a popular approach for training robots. But even todays most advanced deep learning algorithms typically need to see many examples before they can successfully copy their trainers.

When humans learn through imitation, they can often pick up new tasks after just a handful of demonstrations. Now, Google DeepMind researchers have taken a step toward rapid social learning in AI with agents that learn to navigate a virtual world from humans in real time.

Our agents succeed at real-time imitation of a human in novel contexts without using any pre-collected human data, the researchers write in a paper in Nature Communications. We identify a surprisingly simple set of ingredients sufficient for generating cultural transmission.

The researchers trained their agents in a specially designed simulator called GoalCycle3D. The simulator uses an algorithm to generate an almost endless number of different environments based on rules about how the simulation should operate and what aspects of it should vary.

In each environment, small blob-like AI agents must navigate uneven terrain and various obstacles to pass through a series of colored spheres in a specific order. The bumpiness of the terrain, the density of obstacles, and the configuration of the spheres varies between environments.

The agents are trained to navigate using reinforcement learning. They earn a reward for passing through the spheres in the correct order and use this signal to improve their performance over many trials. But in addition, the environments also feature an expert agentwhich is either hard-coded or controlled by a humanthat already knows the correct route through the course.

Over many training runs, the AI agents learn not only the fundamentals of how the environments operate, but also that the quickest way to solve each problem is to imitate the expert. To ensure the agents were learning to imitate rather than just memorizing the courses, the team trained them on one set of environments and then tested them on another. Crucially, after training, the team showed that their agents could imitate an expert and continue to follow the route even without the expert.

This required a few tweaks to standard reinforcement learning approaches.

The researchers made the algorithm focus on the expert by having it predict the location of the other agent. They also gave it a memory module. During training, the expert would drop in and out of environments, forcing the agent to memorize its actions for when it was no longer present. The AI also trained on a broad set of environments, which ensured it saw a wide range of possible tasks.

It might be difficult to translate the approach to more practical domains though. A key limitation is that when the researchers tested if the AI could learn from human demonstrations, the expert agent was controlled by one person during all training runs. That makes it hard to know whether the agents could learn from a variety of people.

More pressingly, the ability to randomly alter the training environment would be difficult to recreate in the real world. And the underlying task was simple, requiring no fine motor control and occurring in highly controlled virtual environments.

Still, social learning progress in AI is welcome. If were to live in a world with intelligent machines, finding efficient and intuitive ways to share our experience and expertise with them will be crucial.

Image Credit: Juliana e Mariana Amorim / Unsplash

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This DeepMind AI Rapidly Learns New Skills Just by Watching Humans - Singularity Hub

Inorganic crystals are essential to many contemporary technologies, including computer chips, batteries, and solar panels. Every new, stable crystal results from months of meticulous experimentation, and stable crystals are essential for enabling new technologies since they do not dissolve.

Researchers have engaged in costly, trial-and-error experiments that yielded only limited results. They sought new crystal structures by modifying existing crystals or trying other element combinations. 28,000 novel materials have been found in the past ten years thanks to computational methods spearheaded by the Materials Project and others. The capacity of emerging AI-guided techniques to reliably forecast materials that may be experimentally viable has been a major limitation up until now.

Researchers from the Lawrence Berkeley National Laboratory and Google DeepMind have published two papers in Nature demonstrating the potential of our AI predictions for autonomous material synthesis. The study shows a finding of 2.2 million more crystals, the same as approximately 800 years worth of information. Their new deep learning tool, Graph Networks for Materials Exploration (GNoME), predicts the stability of novel materials, greatly improving the speed and efficiency of discovery. GNoME exemplifies the promise of AI in the large-scale discovery and development of novel materials. Separate yet contemporaneous efforts by scientists in different laboratories across the globe have produced 736 of these novel structures.

The number of technically feasible materials has been increased by a factor of two thanks to GNoME. Among its 2.2 million forecasts, 380,000 show the greatest promise for experimental synthesis because of their stability. Materials with the ability to create next-generation batteries that improve the efficiency of electric vehicles and superconductors that power supercomputers are among these contenders.

GNoME is a model for a state-of-the-art GNN. Because GNN input data is represented by a graph analogous to atomic connections, GNNs are well suited to finding novel crystalline materials.

Data on crystal structures and their stability, initially used to train GNoME, are publicly available through the Materials Project. The use of active learning as a training method significantly improved GNoMEs efficiency. The researchers generated new crystal candidates and predicted their stability using GNoME. They used Density Functional Theory (DFT), a well-established computational method in physics, chemistry, and materials science for understanding atomic structurescrucial for evaluating crystal stabilityto repeatedly check their models performance throughout progressive training cycles to evaluate its predictive power. The model training went back into the process using the high-quality training data.

The findings show that the research increased the rate of materials stability prediction discovery from approximately 50% to 80%, using an external benchmark set by earlier state-of-the-art models as a guide. Enhancements to this models efficiency allowed the discovery rate to be boosted from below 10% to over 80%; these gains in efficiency may have a major bearing on the computing power needed for each discovery.

The autonomous lab produced over forty-one novel materials using ingredients from the Materials Project and stability information from GNoME, paving the way for further advancements in AI-driven materials synthesis.

The GNoMEs forecasts have been released to the scientific community. The researchers will provide the Materials Project, which analyzes the compounds and adds them to its online database with 380,000 materials. With the help of these resources, they hope that the community will seek to study inorganic crystals further and realize the potential of machine learning technologies as experimental guidelines.

Check out thePaper 1 and Paper 2andReference Article.All credit for this research goes to the researchers of this project. Also,dont forget to joinour 33k+ ML SubReddit,41k+ Facebook Community,Discord Channel,andEmail Newsletter, where we share the latest AI research news, cool AI projects, and more.

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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in todays evolving world making everyone's life easy.

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Google DeepMind Introduces GNoME: A New Deep Learning Tool that Dramatically Increases the Speed and Efficiency of Discovery by Predicting the...

It said Hurricane Lee would make landfall in Nova Scotia three days sooner than traditional methods predicted.

This year the Earth has been hit by a record number of unpredictable extreme weather events made worse by climate change. Predicting them faster and with greater accuracy could enable us to prepare better for natural disasters and help save lives. A new AI model from Google DeepMind could make that easier.

In research published in Science today, Google DeepMinds model, GraphCast, was able to predict weather conditions up to 10 days in advance, more accurately and much faster than the current gold standard. GraphCast outperformed the model from the European Centre for Medium-Range Weather Forecasts (ECMWF) in more than 90% of over 1,300 test areas. And on predictions for Earths tropospherethe lowest part of the atmosphere, where most weather happensGraphCast outperformed the ECMWFs model on more than 99% of weather variables, such as rain and air temperature

Crucially, GraphCast can also offer meteorologists accurate warnings, much earlier than standard models, of conditions such as extreme temperatures and the paths of cyclones. In September, GraphCast accurately predicted that Hurricane Lee would make landfall in Nova Scotia nine days in advance, says Rmi Lam, a staff research scientist at Google DeepMind.Traditional weather forecasting models pinpointed the hurricane to Nova Scotia only six days in advance.

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Weather prediction is one of the most challenging problems that humanity has been working on for a long, long time. And if you look at what has happened in the last few years with climate change, this is an incredibly important problem, says Pushmeet Kohli, the vice president of research at Google DeepMind.

Traditionally, meteorologists use massive computer simulations to make weather predictions. They are very energy intensive and time consuming to run, because the simulations take into account many physics-based equations and different weather variables such as temperature, precipitation, pressure, wind, humidity, and cloudiness, one by one.

GraphCast uses machine learning to do these calculations in under a minute. Instead of using the physics-based equations, it bases its predictions on four decades of historical weather data. GraphCast uses graph neural networks, which map Earths surface into more than a million grid points. At each grid point, the model predicts the temperature, wind speed and direction, and mean sea-level pressure, as well as other conditions like humidity. The neural network is then able to find patterns and draw conclusions about what will happen next for each of these data points.

For the past year, weather forecasting has been going through a revolution as models such as GraphCast, Huaweis Pangu-Weather and Nvidias FourcastNet have made meteorologists rethink the role AI can play in weather forecasting. GraphCast improves on the performance of other competing models, such as Pangu-Weather, and is able to predict more weather variables, says Lam.The ECMWF is already using it.

When Google DeepMind first debuted GraphCast last December, it felt like Christmas, says Peter Dueben, head of Earth system modeling at ECMWF, who was not involved in the research.

It showed that these models are so good that we cannot avoid them anymore, he says.

GraphCast is a reckoning moment for weather prediction because it shows that predictions can be made using historical data, says Aditya Grover, an assistant professor of computer science at UCLA, who developed ClimaX, a foundation model that allows researchers to do different tasks relating to modeling the Earths weather and climate.

DeepMinds model is great work and extremely exciting, says Oliver Fuhrer, the head of the numerical prediction department at MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology. Fuhrer says that other weather agencies, such as the ECMWF and the Swedish Meteorological and Hydrological Institute, have also used the graph neural network architecture proposed by Google DeepMind to build their own models.

But GraphCast is not perfect. It still lags behind conventional weather forecasting models in some areas, such as precipitation, Dueben says. Meteorologists will still have to use conventional models alongside machine-learning models to offer better predictions.

Google DeepMind is also making GraphCast open source. This is a good development, says UCLAs Grover.

With climate change on the rise, its very important that big organizations, which have had the luxury of so much compute, also think about giving back [to the scientific community], he says.

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Google DeepMinds weather AI can forecast extreme weather faster and more accurately - MIT Technology Review

A new AI model from Google DeepMind is the worlds most accurate 10-day global weather forecasting system, according to the London-based lab.

Named GraphCast, the model promises medium-range weather forecasts of unprecedented accuracy. In a study published today, GraphCast was shown to be more precise and faster than the industry gold standard for weather simulation, the High-Resolution Forecast (HRES).

The system also predicted extreme weather further into the future than was previously possible.

These insights were analysed by the European Centre for Medium-Range Weather Forecasts (ECMWF), an intergovernmental organisation that produces the HRES.

A live version of GraphCast was deployed on the ECMWF website. In September, the system accurately predicted around nine days in advance that Hurricane Lee would make landfall in Nova Scotia.

In contrast, traditional forecasting methods only spotlighted Nova Scotia about six days beforehand. They also provided less consistent predictions of the time and location of landfall.

Intriguingly, GraphCast can identify dangerous weather events without being trained to find them. After integrating a simple cyclone tracker, the model predicted cyclone movements more accurately than the HRES method.

Such data could save lives and livelihoods. As the climate becomes more extreme and unpredictable, fast and accurate forecasts will provide increasingly vital insights for disaster planning.

Matthew Chantry, a machine learning coordinator at the ECMWF, believes his industry has reached an inflection point.

Theres probably more work to be done to create reliable operational products, but this is likely the beginning of a revolution, Chantry said at a press briefing.

Meteorological organisations, he added, had previously expected AI to be most useful when merged with physics. But recent breakthroughs show that machine learning can also directly forecast the weather.

Conventional weather forecasts are based on intricate physics equations. These are then adapted into algorithms that run on supercomputers.

The process can be painstaking. It also requires specialist knowledge and vast computing resources.

GraphCast harnesses a different technique. The model combines machine learning with Graph Neural Networks (GNNs), an architecture thats adept at processing spatially structured data.

To learn the causes and effects that determine weather changes, the system was trained on decades of weather information.

Traditional approaches are also incorporated. The ECMWF supplied GraphCast with training data from around 40 years of weather reanalysis, which encompassed monitoring from satellites, radars and weather stations.

When there are gaps in the observations, physics-based prediction methods fill them in. The result is a detailed history of global weather. GraphCast uses these lessons from the past to predict the future.

GraphCast makes predictions at a spatial resolution of 0.25-degrees latitude/longitude.

To put that into perspective, imagine the Earth divided into a million grid points. At each point, the model predicts five Earth-surface variable and six atmospheric variables. Together, they cover the planets entire atmosphere in 3D over 37 levels.

The variables encompass temperature, wind, humidity, precipitation, and sea-level pressure. They also incorporate geopotential the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level.

In tests, the results were impressive. GraphCast significantly outperformed the most accurate operational deterministic systems on 90% of 1,380 test targets.

The disparity was even starker in the troposphere the lowest layer of Earths atmosphere and the location of most weather phenomena. In this region, GraphCast outperformed HRES on 99.7% of the test variables for future weather.

GraphCast is also highly efficient. A 10-day forecast takes under a minute to complete on a single Google TPU v4 machine.

A conventional approach, by comparison, can take hours of computation in a supercomputer with hundreds of machines.

Despite the promising early results, GraphCast could still benefit from further refinement. In the cyclone predictions, for instance, the model proved accurate at tracking movements, but less effective at measuring intensity.

Gentry is keen to see how much this can improve.

At the moment, thats an area where GraphCast and machine learning models still lag a little bit behind physical models Im hopeful that this can be an area for further improvement, but this shows that its still a nascent technology, he said.

Those improvements could now come from anywhere, because DeepMind has open-sourced the model code. Global organisations and individuals alike can now experiment with GraphCast and add their own improvements.

The potential applications are, ironically, unpredictable. The forecasts could, for instance, inform renewable energy production and air traffic routing. But they could also be applied to tasks that havent even been imagined.

Theres a lot of downstream use cases for weather forecasts, said Peter Battaglia, Google DeepMinds research director. And were not aware of all of those.

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DeepMind says new AI is world's most accurate 10-day weather forecaster - TNW