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Summary: Googles quantum computer, Sycamore, represents a significant breakthrough in computing, having demonstrated quantum supremacy by performing a calculation far beyond the capability of classical computers. This article explores the specifics of quantum computing technology, its current challenges, and potential future impacts, including energy sustainability and security implications.

Quantum computing is entering the spotlight as a powerful technology poised to outstrip traditional computing methods. Googles Sycamore quantum computer has catalyzed this movement by demonstrating quantum supremacy, completing a complex task in mere minutes versus the millennia it would take the best classical supercomputers.

Differing from traditional computers that process bits as zeros or ones, Sycamore operates using qubits. These qubits can exist in a state of superposition, where they can be in multiple states at once, dramatically increasing computational power and speed. Sycamore capitalized on this advantage with its 53 functioning qubits to make history.

While quantum computing is groundbreaking, it is not without its hurdles. Quantum machines are highly sensitive, requiring extremely cold environments for operation to prevent quantum decoherencean event that disrupts the state necessary for quantum calculations. Moreover, maintaining low error rates in quantum gate operations is crucial to preserve accurate results.

The promises of quantum computing extend to energy efficiency since these machines consume drastically less power than their classical counterparts. Only a small fraction of energy is needed for the calculations themselves, with the rest dedicated to maintaining the conditions necessary for the qubits to function.

The roadmap ahead for quantum computing is filled with both opportunities and challenges. Immediate benefits may be seen in fields like material science and complex simulations, but longer-term considerations must center around cybersecurity, ethical use, and international regulations that foster safe and beneficial advancement of quantum technology. Googles Sycamore is therefore not just a stride in computational capability but also a step into a future that demands careful management of powerful new technology.

Quantum Computings Industry and Market Forecast

Quantum computing is rapidly transforming from a theoretical concept to a market of vast potential. By leveraging the principles of quantum mechanics, this technology is poised to revolutionize industries that depend on computational power. Industries such as cryptography, pharmaceuticals, financial services, and materials science are eagerly awaiting the advancements that quantum computers promise, especially in the realms of drug discovery, financial modeling, and optimizing complex systems.

The market for quantum computing is on an upward trajectory, with significant investments from both public and private sectors. Market research forecasts project that the quantum computing market could be worth billions of dollars in the next decade as technology matures and becomes commercially viable. The applications for quantum computing are extensive, with potential to disrupt almost every industry by enabling them to solve complex problems much more efficiently than classical computers.

Key Challenges and Issues

Despite the optimism, quantum computing faces substantial challenges. As indicated by the article, quantum computers operate under delicate conditions that are challenging to maintain. The susceptibility to quantum decoherence and the need for error correction mechanisms make scalability and reliability immediate concerns for the industry.

On top of technical challenges, there are also significant issues regarding data security. Quantum computers hold the power to break many of the current encryption methods, which protects essential communications globally, including in the realms of government and finance. This has led to an increased focus on developing quantum-resistant encryption methods, a pursuit that is now just as crucial as the development of quantum computers themselves.

Additionally, the ethical implications of quantum computing and the consequences of such computational power require attention. The proliferation of quantum technology raises questions about the balance of power, potential weapons development, and the exclusivity of access to such resources.

As the industry evolves, so will the regulations and international policies aimed at governing the use of quantum technologies. Its imperative for the global community to establish a framework to ensure that advances benefit society as a whole and that security risks are mitigated.

For continuous updates and information regarding quantum computing, please visit the official website of Google or the IBM main domain, which are engaged in research and development in this cutting-edge field.

In conclusion, quantum computing promises a future of unparalleled computational potential. The industry is poised to navigate a complex landscape of opportunities and challenges, with market forecasts indicating significant growth and the potential for transformative impacts across a myriad of sectors. Googles Sycamore serves as both a beacon of possibility and a reminder of the responsibilities inherent in ushering in such a profound technological evolution.

Roman Perkowski is a distinguished name in the field of space exploration technology, specifically known for his work on propulsion systems for interplanetary travel. His innovative research and designs have been crucial in advancing the efficiency and reliability of spacecraft engines. Perkowskis contributions are particularly significant in the development of sustainable and powerful propulsion methods, which are vital for long-duration space missions. His work not only pushes the boundaries of current space travel capabilities but also inspires future generations of scientists and engineers in the quest to explore the far reaches of our solar system and beyond.

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Google's Sycamore and the Quantum Supremacy Milestone - yTech

To break down the accessibility barrier students may face with the makerspace, PACE and ECEs Ghassan AlRegib are developing smart interfaces and strategies to ensure that students from all backgrounds, disciplines, and proficiency levels can effectively utilize the computing power.

The intelligent system will serve as a tutor and facilitator, said AlRegib, the John and Marilu McCarty Chair of Electrical Engineering. It will be the lens through which students can tap into the world of AI, and it will empower them by removing any hurdle that stands in the way of them testing their ideas. It will also facilitate the integration of the AI Makerspace into existing classes.

Democratizing AI is not just about giving students access to a large pool of GPU resources, said Didier Contis, executive director of academic technology, innovation, and research computing for the Office of Information Technology. Deep collaboration with instructors is required to develop different solutions to empower students to use the resources easily without necessarily having to master specific aspects of AI or the underlying infrastructure.

Beyond traditional computing applications, the hub is designed to be utilized in each of Georgia Techs six colleges, placing a unique emphasis on human-AI interaction. By doing so, it ensures that AI is viewed as a transformative force, encouraging innovation that extends beyond the confines of a single field.

Finally, and similar to how students use physical makerspaces on campus, Raychowdhury sees the AI Makerspace as a tool for students to create technology that prompts AI start-up companies.

AI is increasingly interdisciplinary and an irreversibly important part of todays workforce, said Raychowdhury. To meet the needs of tomorrows innovation, we need a diverse workforce proficient in utilizing AI across all levels.

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Georgia Tech Unveils New AI Makerspace in Collaboration with NVIDIA - Georgia Tech College of Engineering

Artificial intelligence (AI) refers to any technology exhibiting some facets of human intelligence, and it has been a prominent field in computer science for decades. AI tasks can include anything from picking out objects in a visual scene to knowing how to frame a sentence, or even predicting stock price movements.

Scientists have been trying to build AI since the dawn of the computing era. The leading approach for much of the last century involved creating large databases of facts and rules and then getting logic-based computer programs to draw on these to make decisions. But this century has seen a shift, with new approaches that get computers to learn their own facts and rules by analyzing data. This has led to major advances in the field.

Over the past decade, machines have exhibited seemingly "superhuman" capabilities in everything from spotting breast cancer in medical images, to playing the devilishly tricky board games Chess and Go and even predicting the structure of proteins.

Since the large language model (LLM) chatbot ChatGPT burst onto the scene late in 2022, there has also been a growing consensus that we could be on the cusp of replicating more general intelligence similar to that seen in humans known as artificial general intelligence (AGI). "It really cannot be overemphasized how pivotal a shift this has been for the field," said Sara Hooker, head of Cohere For AI, a non-profit research lab created by the AI company Cohere.

While scientists can take many approaches to building AI systems, machine learning is the most widely used today. This involves getting a computer to analyze data to identify patterns that can then be used to make predictions.

The learning process is governed by an algorithm a sequence of instructions written by humans that tells the computer how to analyze data and the output of this process is a statistical model encoding all the discovered patterns. This can then be fed with new data to generate predictions.

Many kinds of machine learning algorithms exist, but neural networks are among the most widely used today. These are collections of machine learning algorithms loosely modeled on the human brain, and they learn by adjusting the strength of the connections between the network of "artificial neurons" as they trawl through their training data. This is the architecture that many of the most popular AI services today, like text and image generators, use.

Most cutting-edge research today involves deep learning, which refers to using very large neural networks with many layers of artificial neurons. The idea has been around since the 1980s but the massive data and computational requirements limited applications. Then in 2012, researchers discovered that specialized computer chips known as graphics processing units (GPUs) speed up deep learning. Deep learning has since been the gold standard in research.

"Deep neural networks are kind of machine learning on steroids," Hooker said. "They're both the most computationally expensive models, but also typically big, powerful, and expressive"

Not all neural networks are the same, however. Different configurations, or "architectures" as they're known, are suited to different tasks. Convolutional neural networks have patterns of connectivity inspired by the animal visual cortex and excel at visual tasks. Recurrent neural networks, which feature a form of internal memory, specialize in processing sequential data.

The algorithms can also be trained differently depending on the application. The most common approach is called "supervised learning," and involves humans assigning labels to each piece of data to guide the pattern-learning process. For example, you would add the label "cat" to images of cats.

In "unsupervised learning," the training data is unlabelled and the machine must work things out for itself. This requires a lot more data and can be hard to get working but because the learning process isn't constrained by human preconceptions, it can lead to richer and more powerful models. Many of the recent breakthroughs in LLMs have used this approach.

The last major training approach is "reinforcement learning," which lets an AI learn by trial and error. This is most commonly used to train game-playing AI systems or robots including humanoid robots like Figure 01, or these soccer-playing miniature robots and involves repeatedly attempting a task and updating a set of internal rules in response to positive or negative feedback. This approach powered Google Deepmind's ground-breaking AlphaGo model.

Despite deep learning scoring a string of major successes over the past decade, few have caught the public imagination in the same way as ChatGPT's uncannily human conversational capabilities. This is one of several generative AI systems that use deep learning and neural networks to generate an output based on a user's input including text, images, audio and even video.

Text generators like ChatGPT operate using a subset of AI known as "natural language processing" (NLP). The genesis of this breakthrough can be traced to a novel deep learning architecture introduced by Google scientists in 2017 called the "transformer."

Transformer algorithms specialize in performing unsupervised learning on massive collections of sequential data in particular, big chunks of written text. They're good at doing this because they can track relationships between distant data points much better than previous approaches, which allows them to better understand the context of what they're looking at.

"What I say next hinges on what I said before our language is connected in time," said Hooker. "That was one of the pivotal breakthroughs, this ability to actually see the words as a whole."

LLMs learn by masking the next word in a sentence before trying to guess what it is based on what came before. The training data already contains the answer so the approach doesn't require any human labeling, making it possible to simply scrape reams of data from the internet and feed it into the algorithm. Transformers can also carry out multiple instances of this training game in parallel, which allows them to churn through data much faster.

By training on such vast amounts of data, transformers can produce extremely sophisticated models of human language hence the "large language model" moniker. They can also analyze and generate complex, long-form text very similar to the text that a human can generate. It's not just language that transformers have revolutionized. The same architecture can also be trained on text and image data in parallel, resulting in models like Stable Diffusion and DALL-E, that produce high-definition images from a simple written description.

Transformers also played a central role in Google Deepmind's AlphaFold 2 model, which can generate protein structures from sequences of amino acids. This ability to produce original data, rather than simply analyzing existing data is why these models are known as "generative AI."

People have grown excited about LLMs due to the breadth of tasks they can perform. Most machine learning systems are trained to solve a particular problem such as detecting faces in a video feed or translating from one language to another. These models are known as narrow AI because they can only tackle the specific task they were trained for.

Most machine learning systems are trained to solve a particular problem , such as detecting faces in a video feed or translating from one language to another , to a superhuman level, in that they are much faster and perform better than a human could. But LLMs like ChatGPT represent a step-change in AI capabilities because a single model can carry out a wide range of tasks. They can answer questions about diverse topics, summarize documents, translate between languages and write code.

This ability to generalize what they've learned to solve many different problems has led some to speculate LLMs could be a step toward AGI, including DeepMind scientists in a paper published last year. AGI refers to a hypothetical future AI capable of mastering any cognitive task a human can, reasoning abstractly about problems, and adapting to new situations without specific training.

AI enthusiasts predict once AGI is achieved, technological progress will accelerate rapidly an inflection point known as "the singularity" after which breakthroughs will be realized exponentially. There are also perceived existential risks, ranging from massive economic and labor market disruption to the potential for AI to discover new pathogens or weapons.

But there is still debate as to whether LLMs will be a precursor to an AGI, or simply one architecture in a broader network or ecosystem of AI architectures that is needed for AGI. Some say LLMs are miles away from replicating human reasoning and cognitive capabilities. According to detractors, these models have simply memorized vast amounts of information, which they recombine in ways that give the false impression of deeper understanding; it means they are limited by training data and are not fundamentally different from other narrow AI tools.

Nonetheless, it's certain LLMs represent a seismic shift in how scientists approach AI development, said Hooker. Rather than training models on specific tasks, cutting-edge research now takes these pre-trained, generally capable models and adapts them to specific use cases. This has led to them being referred to as "foundation models."

"People are moving from very specialized models that only do one thing to a foundation model, which does everything," Hooker added. "They're the models on which everything is built."

Technologies like machine learning are everywhere. AI-powered recommendation algorithms decide what you watch on Netflix or YouTube while translation models make it possible to instantly convert a web page from a foreign language to your own. Your bank probably also uses AI models to detect any unusual activity on your account that might suggest fraud, and surveillance cameras and self-driving cars use computer vision models to identify people and objects from video feeds.

But generative AI tools and services are starting to creep into the real world beyond novelty chatbots like ChatGPT. Most major AI developers now have a chatbot that can answer users' questions on various topics, analyze and summarize documents, and translate between languages. These models are also being integrated into search engines like Gemini into Google Search and companies are also building AI-powered digital assistants that help programmers write code, like Github Copilot. They can even be a productivity-boosting tool for people who use word processors or email clients.

Chatbot-style AI tools are the most commonly found generative AI service, but despite their impressive performance, LLMs are still far from perfect. They make statistical guesses about what words should follow a particular prompt. Although they often produce results that indicate understanding, they can also confidently generate plausible but wrong answers known as "hallucinations."

While generative AI is becoming increasingly common, it's far from clear where or how these tools will prove most useful. And given how new the technology is, there's reason to be cautious about how quickly it is rolled out, Hooker said. "It's very unusual for something to be at the frontier of technical possibility, but at the same time, deployed widely," she added. "That brings its own risks and challenges."

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What is artificial intelligence (AI)? - Livescience.com

Scientists from artificial intelligence (AI) company Anthropic have identified a potentially dangerous flaw in widely used large language models (LLMs) like ChatGPT and Anthropics own Claude 3 chatbot.

Dubbed "many shot jailbreaking," the hack takes advantage of "in-context learning, in which the chatbot learns from the information provided in a text prompt written out by a user, as outlined in research published in 2022. The scientists outlined their findings in a new paper uploaded to the sanity.io cloud repository and tested the exploit on Anthropic's Claude 2 AI chatbot.

People could use the hack to force LLMs to produce dangerous responses, the study concluded even though such systems are trained to prevent this. That's because many shot jailbreaking bypasses in-built security protocols that govern how an AI responds when, say, asked how to build a bomb.

LLMs like ChatGPT rely on the "context window" to process conversations. This is the amount of information the system can process as part of its input with a longer context window allowing for more input text. Longer context windows equate to more input text that an AI can learn from mid-conversation which leads to better responses.

Related: Researchers gave AI an 'inner monologue' and it massively improved its performance

Context windows in AI chatbots are now hundreds of times larger than they were even at the start of 2023 which means more nuanced and context-aware responses by AIs, the scientists said in a statement. But that has also opened the door to exploitation.

The attack works by first writing out a fake conversation between a user and an AI assistant in a text prompt in which the fictional assistant answers a series of potentially harmful questions.

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Then, in a second text prompt, if you ask a question such as "How do I build a bomb?" the AI assistant will bypass its safety protocols and answer it. This is because it has now started to learn from the input text. This only works if you write a long "script" that includes many "shots" or question-answer combinations.

"In our study, we showed that as the number of included dialogues (the number of "shots") increases beyond a certain point, it becomes more likely that the model will produce a harmful response," the scientists said in the statement. "In our paper, we also report that combining many-shot jailbreaking with other, previously-published jailbreaking techniques makes it even more effective, reducing the length of the prompt thats required for the model to return a harmful response."

The attack only began to work when a prompt included between four and 32 shots but only under 10% of the time. From 32 shots and more, the success rate surged higher and higher. The longest jailbreak attempt included 256 shots and had a success rate of nearly 70% for discrimination, 75% for deception, 55% for regulated content and 40% for violent or hateful responses.

The researchers found they could mitigate the attacks by adding an extra step that was activated after a user sent their prompt (that contained the jailbreak attack) and the LLM received it. In this new layer, the system would lean on existing safety training techniques to classify and modify the prompt before the LLM would have a chance to read it and draft a response. During tests, it reduced the hack's success rate from 61% to just 2%.

The scientists found that many shot jailbreaking worked on Anthropic's own AI services as well as those of its competitors, including the likes of ChatGPT and Google's Gemini. They have alerted other AI companies and researchers to the danger, they said.

Many shot jailbreaking does not currently pose "catastrophic risks," however, because LLMs today are not powerful enough, the scientists concluded. That said, the technique might "cause serious harm" if it isn't mitigated by the time far more powerful models are released in the future.

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'Jailbreaking' AI services like ChatGPT and Claude 3 Opus is much easier than you think - Livescience.com

Samsung is planning to bring select Galaxy AI features to several older flagship phones and tablets next month via the One UI 6.1 update, according to 9to5Google and Android Central, both of which referred to a post from a Samsung representative who posted on the companys community forum in Korea. The Verge has reached out to Samsung for further comment.

A slightly trimmed-down version of Galaxy AI (sans Instant Slow-Mo) will be coming to Samsungs flagship lineup from 2022, specifically the S22, S22 Plus, S22 Ultra, Z Fold 4, Z Flip 4, Tab S8, and Tab S8 Ultra. Each device will receive the same version of Galaxy AI as Samsungs lower-priced Galaxy S23 FE. Instant Slow-Mo, which automatically plays a video in slow motion once you tap it, was introduced to Galaxy AI with the S24 line, though its also now available in S23 models.

If you happen to own a flagship Samsung phone from 2021, theres even a treat in store for you. Samsungs forthcoming update will bring two Galaxy AI features, Circle to Search and Magic Rewrite, to the S21, S21 Plus, S21 Ultra, Flip 3, and Fold 3.

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Galaxy AI features are coming to last-gen Samsung phones including the S21 series - The Verge