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Could Moon dust block global warming?

In a paper just published in the open-access journal PLOS Climate One, scientists at the Center for Astrophysics | Harvard & Smithsonian and the University of Utah suggest a radical way to deal with global warming. In computer simulations, they found that when launched precisely, Moon dust would follow a path between Earth and the Sun, effectively creating shade, at least for a while.

The Earth’s atmosphere traps more and more of the Sun’s energy, which steadily increases the Earth’s temperature. This new strategy could reverse this trend by intercepting a fraction of sunlight before it reaches our planet.

A cool moonshot

The paper describes the different properties of dust particles, quantities of dust, and the orbits that would be best suited for shading Earth. The results were welcome news, the team says, because much less energy is needed to launch dust from the Moon than from Earth. This is important because the amount of dust required for a solar shield is large, comparable to the output of a big mining operation here on Earth. 

But wait: What if it blocks enough sunlight to create a permanently cold, uninhabitable planet? Not to worry, advise the scientists. The Sun’s radiation naturally disperses the dust particles throughout the solar system, meaning the sunshield is temporary and particles do not fall onto Earth. 

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What if robots could collaborate with artists?

Carnegie Mellon University’s Robotics Institute is exploring that idea

Their FRIDA (Framework and Robotics Initiative for Developing Arts, named for Mexican painter Frida Kahlo), uses a robotic arm with a paintbrush taped to it and AI to collaborate with humans on works of art.

FRIDA’s first paintings (credit: Carnegie Mellon University/The Robotics Institute)

FRIDA is a robotic painting system, “but FRIDA is not an artist,” says Peter Schaldenbrand, a School of Computer Science Ph.D. student in the Robotics Institute, who is working with FRIDA and exploring AI and creativity. “Instead, FRIDA is a system that an artist could collaborate with. The artist can specify high-level goals for FRIDA and then FRIDA can execute them.” Artists can direct FRIDA by inputting a text description, submitting other works of art to inspire its style; or by uploading a photograph and asking Frida  to paint a representation of it.

An artist can direct FRIDA by inputting a text description, submitting other works of art to inspire its style, or uploading a photograph and asking it to paint a representation of it.

One of the biggest technical challenges in producing a physical image is reducing the simulation-to-real gap — the difference between what FRIDA composes in simulation and what it paints on the canvas. FRIDA uses an idea known as real2sim2real. The robot’s actual brush strokes are used to train the simulator to reflect and mimic the physical capabilities of the robot and painting materials.

Beyond DALL-E-2

The robot uses AI models similar to those powering tools like OpenAI’s ChatGPT and DALL-E 2, which generate text or an image, respectively, in response to a prompt. But instead, FRIDA simulates how it would paint an image with brush strokes and uses machine learning to evaluate its progress as it works.

DALL-E 2 uses large-vision language models to produce digital images. FRIDA takes that a step further and uses its embodied robotic system to produce physical paintings. One of the biggest technical challenges in producing a physical image is reducing the simulation-to-real gap, the difference between what FRIDA composes in simulation and what it paints on the canvas. So FRIDA uses an idea known as real2sim2real. The robot’s actual brush strokes are used to train the simulator to reflect and mimic the physical capabilities of the robot and painting materials.

The team is also experimenting with other inputs, including audio. They played ABBA’s “Dancing Queen” and asked FRIDA to paint it.

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Generative AI: The Next Consumer Platform of Highly Personalized Engagement

The age of generative AI has arrived, and is quickly gaining consumer acceptance. AI has the potential to revolutionize the internet by enabling highly personalized experiences for users, as evidenced by the early applications we are now seeing in edtech and search. By providing concise and natural language answers, language models have the potential to significantly improve search engines. Furthermore, edtech can now provide highly individualized learning plans on a large scale, giving students a “teacher in their pocket” who understands their specific needs and can provide personalized solutions to their learning requirements. AI chatbots are already being used for human connections, and many users find real meaning in their interactions with these bots. Overall, AI has the potential to supercharge existing products and create new product categories, thereby improving the consumer experience and making scaling easier. With the massive cost differential between humans doing this work and AI at scale it’s easy to see how this could potentially impact many industries.

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China’s Answer to ChatGPT Is Called ‘Ernie’

Baidu, the Chinese tech giant, is developing Ernie, China’s answer to ChatGPT. The AI-powered language model, abbreviated with the clunky “Enhanced Representation through Knowledge Integration,” has been in development since 2019 and is set to debut as a standalone service in March. Ernie can understand language, generate language, and generate images from text. Baidu intends to incorporate Ernie into its search engine in the future, similar to Microsoft’s strategy with ChatGPT and Bing. Due to the inability of many Western companies to operate in China, Ernie may be the first AI chatbot with which many Chinese internet users interact. Although its capabilities are unknown, Ernie is expected to be a strong competitor to ChatGPT and Bard after four years of development.

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Generative AI is building the foundation of proptech’s next disruptive wave

As most of my readers are aware, artificial intelligence (AI) has been making headlines and progress in a variety of industries in 2022 and now in 2023. With venture capitalists investing over $49 billion in AI, there is still significant room for growth in real estate tech or proptech. By creating more efficient and personalized experiences, AI will revolutionize the way proptech operates, from search and listings to mortgages and construction. AI-powered chatbots that can provide personalized consultations will disrupt traditional real estate brokerages. The mortgage market in the United States at more than $13 trillion will benefit from AI’s ability to create infinite customer personas for more robust search and underwriting. AI will also improve the customer journey from policy administration to claims management by automating the insurance layer for renters and homeowners. The incorporation of artificial intelligence in real estate technology represents a significant opportunity for entrepreneurs and innovation and massive disruption for the traditional marketplace.

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FDA has now cleared more than 500 healthcare AI algorithms

According to the US Food and Drug Administration (FDA), as of January 2023, over 520 AI medical algorithms had been cleared in the US, with the majority of them being related to medical imaging. By analyzing vast amounts of data generated during healthcare delivery, AI and ML technologies have the potential to transform healthcare. Because of the high accuracy of AI pattern recognition, there has been a focus on medical imaging applications such as diagnostic aids, quantification, contouring, and clinical decision support. AI is also being used in non-clinical areas of healthcare, such as IT systems to sort through patient data and extract relevant information. Clinical AI requires regulatory approval from the FDA, whereas non-clinical AI does not. With the rapid growth of FDA-approved AI and ML systems, the number of AI medical algorithms is expected to grow in the future.

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Meet Bard, Google’s rival to ChatGPT

Google has introduced Bard, a new conversational AI service. Bard will be released with a lightweight version of Google’s Language Model for Dialogue Applications (LaMDA) to allow more users to experiment with it. Sundar Pichai, CEO of Google, has described Bard as a way to combine the world’s knowledge with the power and intelligence of Google’s language models to provide new and high-quality responses. The company will use external tester feedback, as well as internal testing, to ensure that Bard meets its three LaMDA objectives: quality, safety, and groundedness. Based on this feedback, Bard’s responses will improve over time.

Google intends to use AI-powered Search features to provide more helpful answers to complex queries. People are increasingly turning to Google for deeper insights and understanding, according to Pichai, and the company wants to make it easier for them to get to the heart of what they’re looking for. The goal is to help people learn about complex topics by allowing them to explore a wide range of opinions and perspectives. Bard’s release is part of Google’s larger effort to improve its AI capabilities and provide more useful and efficient services to its users.

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How AI points the way to a new gold standard for big data analytics

While many organizations have a huge amount of data available, the limitations of central processing units make it difficult to process it all (CPUs). On the other hand, artificial intelligence (AI) has made tremendous progress thanks to the use of graphics processing units (GPUs), which have greatly accelerated algorithmic performance and opened up new opportunities for algorithms. However, big data has yet to develop its own distinct computing infrastructure, as CPUs continue to be the foundation for data analytics despite their inefficiency. We are now seeing a similar “revolution” where data scientists are finding ways to run data analytics workloads on dedicated processors with the same efficiency as AI workloads run on GPUs, leading to new levels of insights at previously unattainable speeds. We’ve seen AI researchers deviate from the standard path to find GPUs. Now, big data researchers are charting their own course and pushing the limits of big data analytics.

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Latent Technology raises $2.1M to bring generative AI to video games

Latent Technology, an AI start-up based in the United Kingdom, has raised $2.1 million in pre-seed funding to bring generative AI to the video game industry. Based on reinforcement learning, the AI technology allows video game characters to react to anything that happens in the game, eliminating the need for manual animation of every possible body motion and angle. The company’s goal is to make it easier and less expensive for video game developers to create video games. According to the CEO, Jorge del Val Santos, while generative AI may become more widely used, some game developers will always prefer complete control over the creative process. In 18 to 24 months, the company intends to release a demo in collaboration with a gaming company.

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‘We’re going through a big revolution’: how AI is de-ageing stars on screen

The use of artificial intelligence to age famous actors is a growing trend in Hollywood. Harrison Ford and Tom Hanks are two actors who have undergone the procedure to appear younger. The technology, developed by the AI firm Metaphysic, creates “high-resolution photorealistic faceswaps and de-ageing effects on top of actors’ performances live and in real time without the need for further compositing or VFX work”. Both praise and criticism have been heaped on the technology. Some believe it provides a solution to the problem of replacing aging actors, while others are concerned about the technology’s potential for malicious use. Furthermore, some are concerned about the future of the film industry and the possibility of AI rendering human actors obsolete. Regardless of the consequences, it is clear that the use of AI in Hollywood will only grow.

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