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Singularity Future Technology Ltd. (NASDAQ:SGLY Get Free Report) was the recipient of a large decrease in short interest in the month of July. As of July 15th, there was short interest totalling 40,800 shares, a decrease of 14.8% from the June 30th total of 47,900 shares. Currently, 1.2% of the shares of the company are sold short. Based on an average trading volume of 26,700 shares, the short-interest ratio is currently 1.5 days.

Shares of SGLY opened at $4.01 on Friday. The businesss 50-day moving average is $4.75 and its two-hundred day moving average is $4.56. The company has a market capitalization of $14.04 million, a P/E ratio of -1.06 and a beta of 1.01. Singularity Future Technology has a fifty-two week low of $2.00 and a fifty-two week high of $8.00.

Singularity Future Technology (NASDAQ:SGLY Get Free Report) last announced its quarterly earnings results on Wednesday, May 15th. The company reported ($0.32) earnings per share for the quarter. The company had revenue of $0.45 million for the quarter. Singularity Future Technology had a negative return on equity of 97.21% and a negative net margin of 255.35%.

Singularity Future Technology Ltd. operates as an integrated logistics solutions provider in China and the United States. It offers freight logistics services, including shipping, transportation, warehouse, collection, last-mile delivery, drop shipping, customs clearance, and overseas transit delivery services.

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Short Interest in Singularity Future Technology Ltd. (NASDAQ:SGLY) Drops By 14.8% - Defense World

Google DeepMinds New AI Systems Can Now Solve Complex Math Problems Rhiannon Williams | MIT Technology Review AI models can easily generate essays and other types of text. However, theyre nowhere near as good at solving math problems, which tend to involve logical reasoningsomething thats beyond the capabilities of most current AI systems. But that may finally be changing. Google DeepMind says it has trained two specialized AI systems to solve complex math problems involving advanced reasoning.

This Startup Is Building the Countrys Most Powerful Quantum Computer on Chicagos South Side Adam Bluestein | Fast Company PsiQuantums approach is radically different from that of its competitors. Its relying on cutting-edge silicon photonics to manipulate single particles of light for computation. And instead of taking an incremental approach to building a supercomputer, its focused entirely on coming out of the gate with a full-blown, fault tolerant system that will be far larger than any quantum computer built to date. The company has vowed to have its first system operational by late 2027, years earlier than other projections.

The Race for the Next Ozempic Emily Mullin | Wired These drugs are now wildly popular, in shortage as a result, and hugely profitable for the companies making them. Their success has sparked a frenzy among pharmaceutical companies looking for the next blockbuster weight-loss drug. Researchers are now racing to develop new anti-obesity medications that are more effective, more convenient, or produce fewer side effects than the ones currently on the market.

Watch a Robot Peel a Squash With Human-Like Dexterity Alex Wilkins | New Scientist Pulkit Agrawal at the Massachusetts Institute of Technology and his colleagues have developed a robotic system that can rotate different types of fruit and vegetable using its fingers on one hand, while the other arm is made to peel.

Heres What Happens When You Give People Free Money Paresh Dave | Wired The initial results from what OpenResearch, an Altman-funded research lab, describes as the most comprehensive study on unconditional cash show that while the grants had their benefits and werent spent on items such as drugs and alcohol, they were hardly a panacea for treating some of the biggest concerns about income inequality and the prospect of AI and other automation technologies taking jobs.

Meta Releases the Biggest and Best Open-Source AI Model Yet Alex Heath | The Verge Meta is releasing Llama 3.1, the largest-ever open-source AI model, which the company claims outperforms GPT-4o and Anthropics Claude 3.5 Sonnet on several benchmarks. CEO Mark Zuckerberg now predicts that Meta AI will be the most widely used assistant by the end of this year, surpassing ChatGPT.

US Solar Production Soars by 25 Percent in Just One Year John Timmer | Ars Technica In terms of utility-scale production, the first five months of 2024 saw it rise by 29 percent compared to the same period in the year prior. Small-scale solar was only up by 18 percent, with the combined number rising by 25.3 percent. Its worth noting that this data all comes from before some of the most productive months of the year for solar power; overall, the EIA is predicting that solar production couldrise by as much as 42 percent in 2024.

SearchGPT Is OpenAIs Direct Assault on Google Reece Rogers and Will Knight | Wired After months of speculation about its search ambitions, OpenAIhas revealedSearchGPT, a prototype search engine that could eventually help the company tear off a slice of Googles lucrative business. OpenAI said that the new tool would help users find what they are looking for more quickly and easily by using generative AI to gather links and answer user queries in a conversational tone.

Wafer-Thin Light Sail Could Help Us Reach Another Star Sooner Alex Wilkins | New Scientist Alight sail designed using artificial intelligence is about 1000 times thinner than a human hair and weighs as much as a grain of sandand it could help us create a spacecraft capable of reaching another star sooner than we thought.

AI Cant Make Music Matteo Wong | The Atlantic While AI models are starting to replicate musical patterns, it is the breaking of rules that tends to produce era-defining songs. Algorithms are great at fulfilling expectations but not good at subverting them, but thats what often makes the best music, Eric Drott, a music-theory professor at the University of Texas at Austin, told me.

Image Credit: David Clode /Unsplash

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This Weeks Awesome Tech Stories From Around the Web (Through July 27) - Singularity Hub

Vernor Vinge proposes an interesting and potentially terrifying prediction in his essay titled "The Coming Technological Singularity: How to Survive in the Post-Human Era." He asserts that mankind will develop a superhuman intelligence before 2030.

The essay specifies four ways in which this could happen:

Out of those four possibilities, the first three could lead to machines taking over. While Vinge addresses all the possibilities in his essay, he spends the most time discussing the first one.

Computer technology advances at a faster rate than many other technologies. Computers tend to double in power every two years or so. This trend is related to Moore's Law, which states that transistors double in power every 18 months.

Vinge says that at this rate, it's only a matter of time before humans build a machine that can "think" like a human.

But hardware is only part of the equation. Before artificial intelligence becomes a reality, someone will have to develop software that will allow a machine to analyze data, make decisions and act autonomously.

If that happens, we can expect to see machines begin to design and build even better machines. These new machines could build faster, more powerful models.

Yoshikazu Tsuno/AFP/Getty Images

Robots like this might look cute, but could they be plotting your downfall?

Technological advances would move at a blistering pace. Machines would know how to improve themselves. Humans would become obsolete in the computer world. We would have created a superhuman intelligence.

Advances would come faster than we could recognize them. In short, we would reach the singularity.

Vinge says it's impossible to say. The world would become such a different landscape that we can only make the wildest of guesses. Vinge admits that while it's probably not fruitful to suggest possible scenarios, it's still a lot of fun. Maybe we'll live in a world where each person's consciousness merges with a computer network.

Or perhaps machines will accomplish all our tasks for us and let us live in luxury. But what if the machines see humans as redundant or worse? When machines reach the point where they can repair themselves and even create better versions of themselves, could they come to the conclusion that humans are not only unnecessary, but also unwanted?

It certainly seems like a scary scenario. But is Vinge's vision of the future a certainty? Is there any way we can avoid it?

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What Is the Singularity? And Should You Be Worried? - Electronics | HowStuffWorks

Here's some fun news for your day: By 2045, human beings will become second-banana to machines that have surpassed the intelligence of mankind. So, in less than 30 years, artificial intelligence will become smarter than human intelligence, and robots will rule us all. (Or something like that.) And we know it's true, because Ray Kurzweil says so.

Kurzweil isn't some cult leader. He's a director of engineering at Google. But he is in the business of predictions, as a futurist. And while his most recent declaration is that the singularity (artificial intelligence surpassing human intelligence) will happen by 2045, it's only the most recent of his predictions, of which he claims an 86 percent accuracy rate, as of 2010.

Now seems like a good time to review a few more of Kurzweil's predictions:

1. In 1990, Kurzweil predicted that computers would beat chess players by 2000. Deep Blue slayed Garry Kasparov in 1997.

2. He predicted in 1999 that personal computers would be embedded in jewelry, watches and all sorts of other sizes and shapes. Uh, yeah.

3. In 1999, Kurzweil said that by 2009 we'd mostly be using speech recognition programs for the text we write. Not really happening, because it turns out that speech recognition software is super hard to perfect.

4. By 2029, Kurzweil says that advanced artificial intelligence will lead to a political and social movement for robots, lobbying for recognition and certain civil rights.

5. By the 2030s, most of our communication will not be between humans, but instead human to machine.

6. By 2099, the entire brain will be entirely understood. Period. Done.

Watch Neil deGrasse Tyson, who calls himself Kurzweil's biggest skeptic in this video, talk to the inventor and futurist in this "Cosmology Today" 2016 episode:

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The Singularity by 2045, Plus 6 Other Ray Kurzweil Predictions - Electronics | HowStuffWorks

A new system for forecasting weather and predicting future climate uses artificial intelligence to achieve results comparable with the best existing models while using much less computer power, according to its creators.

In a paper published in Nature yesterday, a team of researchers from Google, MIT, Harvard, and the European Center for Medium-Range Weather Forecasts say their model offers enormous computational savings and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.

The NeuralGCM model is the latest in a steady stream of research models that use advances in machine learning to make weather and climate predictions faster and cheaper.

The NeuralGCM model aims to combine the best features of traditional models with a machine-learning approach.

At its core, NeuralGCM is whats called a general circulation model. It contains a mathematical description of the physical state of Earths atmosphere and solves complicated equations to predict what will happen in the future.

However, NeuralGCM also uses machine learninga process of searching out patterns and regularities in vast troves of datafor some less well-understood physical processes, such as cloud formation. The hybrid approach makes sure the output of the machine learning modules will be consistent with the laws of physics.

The resulting model can then be used for making forecasts of weather days and weeks in advance, as well as looking months and years ahead for climate predictions.

The researchers compared NeuralGCM against other models using a standardized set of forecasting tests called WeatherBench 2. For three- and five-day forecasts, NeuralGCM did about as well as other machine-learning weather models such as Pangu and GraphCast. For longer-range forecasts, over 10 and 15 days, NeuralGCM was about as accurate as the best existing traditional models.

NeuralGCM was also quite successful in forecasting less-common weather phenomena, such as tropical cyclones and atmospheric rivers.

Machine learning models are based on algorithms that learn patterns in the data fed to them and then use this learning to make predictions. Because climate and weather systems are highly complex, machine learning models require vast amounts of historical observations and satellite data for training.

The training process is very expensive and requires a lot of computer power. However, after a model is trained, using it to make predictions is fast and cheap. This is a large part of their appeal for weather forecasting.

The high cost of training and low cost of use is similar to other kinds of machine learning models. GPT-4, for example, reportedly took several months to train at a cost of more than $100 million, but can respond to a query in moments.

A weakness of machine learning models is that they often struggle in unfamiliar situationsor in this case, extreme or unprecedented weather conditions. To improve at this, a model needs to generalize, or extrapolate beyond the data it was trained on.

NeuralGCM appears to be better at this than other machine learning models because its physics-based core provides some grounding in reality. As Earths climate changes, unprecedented weather conditions will become more common, and we dont know how well machine learning models will keep up.

Nobody is actually using machine learning-based weather models for day-to-day forecasting yet. However, it is a very active area of researchand one way or another, we can be confident that the forecasts of the future will involve machine learning.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Kochov et al. / Nature

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AI-Powered Weather and Climate Models Are Set to Change Forecasting - Singularity Hub