The use of artificial intelligence in the development of video games has been met with both excitement and dread.
According to a recent industry report by game engine developer Unity, studios are already using AI to save time and boost productivity by whipping up assets and code.
But given enough time, the video games of the future could soon be entirely created with the use of AI — maybe even within just ten years, according to Nvidia CEO Jensen Huang, the man behind a company that’s greatly benefitting from selling thousands of graphics processing units (GPUs) to some of the biggest players in the AI industry.
Using artificial intelligence, researchers have discovered mysterious “fairy circles” in hundreds of locations across the globe.
These unusual round vegetation patterns have long puzzled experts, dotting the landscapes in the Namib Desert and the Australian outback.
But according to a new study published in the journal Proceedings of the National Academy of Sciences, the unusual phenomenon could be far more widespread than previously thought, cracking the case wide open and raising plenty more questions than answers.
The pursuit of artificial intelligence that can navigate and comprehend the intricacies of three-dimensional environments with the ease and adaptability of humans has long been a frontier in technology. At the heart of this exploration is the ambition to create AI agents that not only perceive their surroundings but also follow complex instructions articulated in the language of their human creators. Researchers are pushing the boundaries of what AI can achieve by bridging the gap between abstract verbal commands and concrete actions within digital worlds.
Researchers from Google DeepMind and the University of British Columbia focus on a groundbreaking AI framework, the Scalable, Instructable, Multiworld Agent (SIMA). This framework is not just another AI tool but a unique system designed to train AI agents in diverse simulated 3D environments, from meticulously designed research labs to the expansive realms of commercial video games. Its universal applicability sets SIMA apart, enabling it to understand and act upon instructions in any virtual setting, a feature that could revolutionize how everyone interacts with AI.
Creating a versatile AI that can interpret and act on instructions in natural language is no small feat. Earlier AI systems were trained in specific environments, which limits their usefulness in new situations. This is where SIMA steps in with its innovative approach. Training in various virtual settings allows SIMA to understand and execute multiple tasks, linking linguistic instructions with appropriate actions. This enhances its adaptability and deepens its understanding of language in the context of different 3D spaces, a significant step forward in AI development.
Long before Archimedes suggested that all phenomena observable to us might be understandable through fundamental principles, humans have imagined the possibility of a theory of everything. Over the past century, physicists have edged nearer to unraveling this mystery. Albert Einstein’s theory of general relativity provides a solid basis for comprehending the cosmos at a large scale, while quantum mechanics allows us to grasp its workings at the subatomic level. The trouble is that the two systems don’t agree on how gravity works.
Today, artificial intelligence offers new hope for scientists addressing the massive computational challenges involved in unraveling the mysteries of something as complex as the universe and everything in it, and Kent Yagi, an associate professor with the University of Virginia’s College and Graduate School of Arts & Sciences is leading a research partnership between theoretical physicists and computational physicists at UVA that could offer new insight into the possibility of a theory of everything or, at least, a better understanding of gravity, one of the universe’s fundamental forces. The work has earned him a CAREER grant from the National Science Foundation, one of the most prestigious awards available to the nation’s most promising young researchers and educators.
A hot potato: ChatGPT, the chatbot that turned machine learning algorithms into a new gold rush for Wall Street speculators and Big Tech companies, is merely a “storefront” for large language models within the Generative Pre-trained Transformer (GPT) series. Developer OpenAI is now readying yet another upgrade for the technology.
OpenAI is busily working on GPT-5, the next generation of the company’s multimodal large language model that will replace the currently available GPT-4 model. Anonymous sources familiar with the matter told Business Insider that GPT-5 will launch by mid-2024, likely during summer.
OpenAI is developing GPT-5 with third-party organizations and recently showed a live demo of the technology geared to use cases and data sets specific to a particular company. The CEO of the unnamed firm was impressed by the demonstration, stating that GPT-5 is exceptionally good, even “materially better” than previous chatbot tech.
Because we live in a dystopian healthcare hell, AI chip manufacturer Nvidia has announced a partnership with an AI venture called Hippocratic AI to replace nurses with freaky AI “agents.”
These phony nursing robots cost hospitals and other health providers $9 an hour, a fee that barely falls above the US minimum hourly wage, and far below the average hourly wage for registered nurses (RNs.)
In a press release, Hippocratic AI described the disturbingly cheap nurses as part of an effort to mitigate staffing issues. The company also claims that the agents won’t be doing any diagnostic work, and will instead be doing “low-risk,” “patient-facing” tasks that can take place via video call.
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Whether it’s a powered prosthesis to assist a person who has lost a limb or an independent robot navigating the outside world, we are asking machines to perform increasingly complex, dynamic tasks. But the standard electric motor was designed for steady, ongoing activities like running a compressor or spinning a conveyor belt – even updated designs waste a lot of energy when making more complicated movements.
Researchers at Stanford University have invented a way to augment electric motors to make them much more efficient at performing dynamic movements through a new type of actuator, a device that uses energy to make things move. Their actuator, published March 20 in Science Robotics, uses springs and clutches to accomplish a variety of tasks with a fraction of the energy usage of a typical electric motor.
“Rather than wasting lots of electricity to just sit there humming away and generating heat, our actuator uses these clutches to achieve the very high levels of efficiency that we see from electric motors in continuous processes, without giving up on controllability and other features that make electric motors attractive,” said Steve Collins, associate professor of mechanical engineering and senior author of the paper.
Today we’re introducing SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces from @RealityLabs Research.
The term “artificial general intelligence” (AGI) has become ubiquitous in current discourse around AI. OpenAI states that its mission is “to ensure that artificial general intelligence benefits all of humanity.” DeepMind’s company vision statement notes that “artificial general intelligence…has the potential to drive one of the greatest transformations in history.” AGI is mentioned prominently in the UK government’s National AI Strategy and in US government AI documents. Microsoft researchers recently claimed evidence of “sparks of AGI” in the large language model GPT-4, and current and former Google executives proclaimed that “AGI is already here.” The question of whether GPT-4 is an “AGI algorithm” is at the center of a lawsuit filed by Elon Musk against OpenAI.
Given the pervasiveness of AGI talk in business, government, and the media, one could not be blamed for assuming that the meaning of the term is established and agreed upon. However, the opposite is true: What AGI means, or whether it means anything coherent at all, is hotly debated in the AI community. And the meaning and likely consequences of AGI have become more than just an academic dispute over an arcane term. The world’s biggest tech companies and entire governments are making important decisions on the basis of what they think AGI will entail. But a deep dive into speculations about AGI reveals that many AI practitioners have starkly different views on the nature of intelligence than do those who study human and animal cognition—differences that matter for understanding the present and predicting the likely future of machine intelligence.
The original goal of the AI field was to create machines with general intelligence comparable to that of humans. Early AI pioneers were optimistic: In 1965, Herbert Simon predicted in his book The Shape of Automation for Men and Management that “machines will be capable, within twenty years, of doing any work that a man can do,” and, in a 1970 issue of Life magazine, Marvin Minsky is quoted as declaring that, “In from three to eight years we will have a machine with the general intelligence of an average human being. I mean a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight.”
