Lifeboat Foundation

In recent years, computer scientists have been trying to create increasingly advanced dialogue and information systems. The release of ChatGPT and other highly performing language models are demonstrating just how far artificial intelligence can go in answering user questions, writing texts and conversing with humans.

Researchers at University of California-Riverside, École Normale Supérieure (ECN) in Paris, and Ludwig-Maximilians-Universität München developed a large language model that can answer philosophical questions in the voice of a specific philosopher. This model, presented in a paper published on the pre-print server arXiv, can autonomously generate answers that closely resemble those produced by human philosophers.

“Anna Strasser, Matthew Crosby and I had noticed that people were creating GPT-3 outputs in the style of various writers or other philosophers,” Eric Schwitzgebel, one of the researchers who carried out the study, told Tech Xplore. “We thought it would be interesting to see if we could fine-tune GPT-3 (Generative Pre-trained Transformer 3) on the body of work of a philosopher, then ask it questions and see if it said things that the real philosopher might have said.”

Google was referring to its dominant position in the search market. In the early 2000s, several search engines existed in the market, but Google now has 90 per cent of the market. It is also not the pioneer of Android, though it purchased it at an early stage. At that time, the market was dominated by Blackberry and Nokia custom OS (operating systems), but today, Android has the largest mobile OS market share.

In the email, Pichai reportedly remained optimistic and said that AI has “gone through many winters and springs”, adding that the “most important thing we can do right now is to focus on building a great product and developing it responsibly.”

Continue reading “Sundar Pichai tells Google employees to spend 4 hours with Bard to make it a worthy ChatGPT opponent” »

Reconfigurable antennas — those that can tune properties like frequency or radiation beams in real-time, from afar — are integral to future communication network systems, like 6G. But many current reconfigurable antenna designs can fall short: they dysfunction in high or low temperatures, have power limitations, or require regular servicing.

To address these limitations, electrical engineers in the Penn State College of Engineering combined electromagnets with a compliant mechanism, which is the same mechanical engineering concept behind binder clips or a bow and arrow. They published their proof-of-concept reconfigurable compliant mechanism-enabled patch antenna today (February 13, 2023) in the journal Nature Communications.

<em>Nature Communications</em> is a peer-reviewed, open-access, multidisciplinary, scientific journal published by Nature Portfolio. It covers the natural sciences, including physics, biology, chemistry, medicine, and earth sciences. It began publishing in 2010 and has editorial offices in London, Berlin, New York City, and Shanghai.

Quantum computing has entered a bit of an awkward period. There have been clear demonstrations that we can successfully run quantum algorithms, but the qubit counts and error rates of existing hardware mean that we can’t solve any commercially useful problems at the moment. So, while many companies are interested in quantum computing and have developed software for existing hardware (and have paid for access to that hardware), the efforts have been focused on preparation. They want the expertise and capability needed to develop useful software once the computers are ready to run it.

For the moment, that leaves them waiting for hardware companies to produce sufficiently robust machines—machines that don’t currently have a clear delivery date. It could be years; it could be decades. Beyond learning how to develop quantum computing software, there’s nothing obvious to do with the hardware in the meantime.

But a company called QuEra may have found a way to do something that’s not as obvious. The technology it is developing could ultimately provide a route to quantum computing. But until then, it’s possible to solve a class of mathematical problems on the same hardware, and any improvements to that hardware will benefit both types of computation. And in a new paper, the company’s researchers have expanded the types of computations that can be run on their machine.

Black holes are the source of dark energy, the mysterious force behind the accelerating expansion of the universe, says a new study. This claim comes from an international team that compared growth rates of black holes in different galaxies. The team concluded that the spread of the masses observed could be explained by black holes bearing cores of ‘dark energy’, a report by the Guardian said.

Seventeen researchers in nine countries shared their findings in two papers published in The Astrophysical Journal and The Astrophysical Journal Letters. One of the researchers, Duncan Farrah from the University of Hawaii, said, “We propose that black holes are the source for dark energy.” Farrah added that this dark energy is produced when the normal matter is compressed during the death and collapse of large stars, the Guardian report added.

The researchers said the findings could be explained if black holes grow as the universe expands. They said that observations found black holes expanding 10 orders of magnitude in mass across most of cosmic history.

Google developed the language model known as Google Bard AI (Bidirectional Encoder Representations from Transformers) to produce high-quality text by anticipating the next word in a given phrase based on context. It is trained on a sizable corpus of text and may be tailored using smaller datasets to produce material in a particular style or domain.

The third version of OpenAI’s language model, GPT-3 (Generative Pretrained Transformer 3), was trained on enormous volumes of text data and is capable of producing text, summarising text, translating text, responding to inquiries, and carrying out a range of other natural language tasks.

Let’s take a look at the top 5 differences between ChatGPT and Google Bard AI.

It is truly an exciting time for the world of computing. Imagine being able to complete complex computing tasks in just a matter of hours, or even minutes, instead of waiting for days on end. L Venkata Subramaniam, a Quantum Distinguished Ambassador at IBM, tells AIM that this dream could become a reality thanks to the incredible power of quantum computing.

“Quantum naturally works in a higher dimensional space where data is better viewed or separated, or you can understand more about the data. Therefore, it is easier to work in quantum on AI problems,” said Subramaniam.

Quantum computing can also be effective in working with fundamental models, such as ChatGPT. Certain early observations suggest that quantum computing can achieve comparable results to classical AI using less training data and has the potential to accelerate the training process for AI models.

Though mitochondrial dysfunction is a known marker of aging and neurodegenerative diseases, the exact mechanism behind it remains unknown. Our study suggests that the decay of SIRT6 levels during aging [18] and in Alzheimer’s disease [18, 23, 46] could be a key mechanism causing the deterioration of mitochondrial functions. The changes induced by the SIRT6 knockout that we observe at the metabolite level support this claim: metabolites related to mitochondrial energy system pathways (in particular, OXPHOS and TCA cycle) are significantly overrepresented among differentially abundant metabolites. In line with the discussed mitochondrial dysfunction in aging, all these metabolites are downregulated in the SIRT6-KO samples. Importantly, the dramatic decline of one of them, NAD⁺, was also associated with pro-senescence mechanisms in various species [47, 48], as well as with limited neuroprotective activity of sirtuins [49].

Accordingly, the vast majority of differentially expressed mitochondria-related genes were downregulated in our gene expression analysis. As they were strongly enriched with mitochondrial respiratory chain complexes, we measured the mitochondrial membrane potential and mitochondrial content in SIRT6-KO cells because reduced gene expression might indicate the loss of mitochondria. Both measured characteristics were significantly decreased, validating the suggested impairment of mitochondrial oxidative phosphorylation and mitochondrial biogenesis in SIRT6-deficient brains. Interestingly, the average decrease of mtDNA gene expression (~19.7%) in SIRT6-KO was in good agreement with the corresponding reduction of mitochondrial content (21.8%), suggesting impaired mitochondrial biogenesis as a primary cause of the observed transcriptional dysregulation in mitochondria upon SIRT6 knockout.

Concordantly, the impaired membrane potential upon SIRT6-KO can be partially rescued by restoring SIRT3 and SIRT4 levels, which were significantly downregulated in SIRT6-deficient brains. Both of them are localized in mitochondria and impact mitochondrial pathways related to redox homeostasis and cellular metabolism [38] and have important roles in mitochondria metabolism ROS balance and lifespan [50,51,52]. The analysis of our and publicly available gene expression data [39] confirms that SIRT6 transcriptionally regulates SIRT3 and SIRT4. Our analysis further indicates that SIRT6 regulates mitochondrial gene expression through the transcription factor YY1. We have previously shown that SIRT6 and YY1 form a complex that regulates many shared target genes [24]. Our analysis of YY1 ChIP-seq data [53] suggests that SIRT6 and YY1 regulate mitochondrial processes coordinately.

Comprehending that other people might think differently from you is a form of intelligence known as theory of mind – what does it mean that the artificial intelligence behind ChatGPT can do as well on tests of it as a 9-year-old child?