For fusion reactions to become practical, parameters such as plasma density and shape must be monitored in real time and impending disruptions responded to instantly.

Nuclear fusion is widely regarded as one of the most promising sources of clean and sustainable energy of the future. In a fusion reaction, two light atomic nuclei combine to form another, whose mass is less than the total mass of the original pair, and according to Einsteins famous formula E = mc2, this mass difference gets transformed into energy that can be utilized.

The problem with this source of energy is that for positively charged nuclei to fuse, they have to overcome the electrical repulsion between them. For this, the velocity of colliding nuclei must be very high, which is achieved by heating the substance in which the reaction takes place to an enormous temperature, at least tens of millions of degrees Kelvin.

Of course, no material can withstand contact with matter at such temperature, so in all prototype fusion reactors, a magnetic field is used to contain the hot plasma, limiting its movement and preventing it from coming into contact with the walls of the reactor. However, in a hot plasma instabilities constantly arise, which can force it to leave the region of the magnetic container and collide with the walls of the reactor, damaging them. Such contacts also guarantee the cooling of the plasma and the termination of the fusion reaction.

In order to prevent these violent plasma disruptions, it is necessary to monitor plasma parameters such as its density and shape in real time and respond instantly to impending disruptions. To achieve this, a team of American and British scientists led by William Tang of Princeton University, has developed a machine learning-based software that can predict the disruptions and analyze the physical conditions which result in them.

In their work, the physicists used a large amount of data from the British JET facility and the American DIII-D machine, which are tokamaks, fusion reactors in which the plasma has the shape of a donut. To be more precise, the researchers used some of the data they had on the state of the plasma in the reactors during their operation to train the program. This training allows the software to to predict when a disruption would occur. The accuracy of these predictions could then be tested using real world data not used in the training set.

The team not only trained their software to correctly predict the disruptions, but also to analyze the physical processes occurring in the plasma that led to these events. This property of the algorithm is essential, since in the operation of a real fusion reactor it is important not only to understand that a disruption is approaching, but also to be able to prevent it by changing the parameters of the plasma in the reactor within milliseconds.

With a larger dataset and more powerful supercomputers, such as those currently being built at Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and Argonne National Laboratory, the researchers hope they can make their algorithm even more sensitive to the processes occurring in the plasma, and hence more accurately predict and respond to impending disruptions.

They expect that the software they have developed will be implemented on the current prototype tokamaks, whose data they used in their study, as well as on future more powerful machines such as ITER, currently under construction in France. If this happens, then this may lead to earlier stable energy production from fusion reactions.

References: William Tang et al, Implementation of AI/DEEP learning disruption predictor into a plasma control system, Contributions to Plasma Physics (2023), DOI: 10.1002/ctpp.202200095.

Julian Kates-Harbeck, et al, Predicting disruptive instabilities in controlled fusion plasmas through deep learning, Nature (2019), DOI: 10.1038/s41586-019-1116-4.

Feature image credit: TheDigitalArtist on Pixabay

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Using machine learning to tame plasma in fusion reactors - Advanced Science News

Exploring the Impact of Reinforcement Learning on the Evolution of Telecommunications

The role of reinforcement learning in advancing telecommunications is a topic of increasing interest and relevance in todays digital age. As the world becomes more interconnected, the demand for efficient, reliable, and advanced telecommunications systems is growing. Reinforcement learning, a type of machine learning where an agent learns to make decisions by interacting with its environment, is playing a pivotal role in meeting this demand.

Reinforcement learning is a powerful tool that can help telecommunications companies optimize their networks, improve service quality, and reduce costs. It works by using algorithms to learn from past experiences and make better decisions in the future. This approach is particularly useful in telecommunications, where networks are complex and constantly changing.

One of the key areas where reinforcement learning is making a significant impact is in network optimization. Telecommunications networks are incredibly complex, with a multitude of variables and parameters that need to be managed and optimized. Traditional methods of network management are often manual, time-consuming, and prone to errors. Reinforcement learning, on the other hand, can automate this process, learning from past network states to make optimal decisions about how to manage the network in the future.

For instance, reinforcement learning can be used to optimize the allocation of resources in a network, such as bandwidth or power. By learning from past network states, the reinforcement learning algorithm can determine the best way to allocate these resources to maximize network performance and minimize costs. This can result in significant improvements in service quality and efficiency.

Another area where reinforcement learning is making a difference is in the management of network traffic. With the explosion of data traffic due to the proliferation of smartphones, IoT devices, and other connected technologies, managing network traffic has become a major challenge for telecommunications companies. Reinforcement learning can help address this challenge by learning from past traffic patterns and making intelligent decisions about how to route traffic to avoid congestion and ensure smooth service.

Moreover, reinforcement learning can also play a crucial role in the development of next-generation telecommunications technologies, such as 5G and beyond. These technologies require highly dynamic and flexible network management, which is exactly what reinforcement learning can provide. By continuously learning and adapting to changes in the network environment, reinforcement learning can help these technologies reach their full potential.

In conclusion, reinforcement learning is playing a crucial role in advancing telecommunications. By automating network management, optimizing resource allocation, managing network traffic, and supporting the development of next-generation technologies, reinforcement learning is helping telecommunications companies meet the growing demand for efficient, reliable, and advanced services. As the world becomes more interconnected, the role of reinforcement learning in telecommunications is only set to grow. It is an exciting time for both the fields of machine learning and telecommunications, as they work together to shape the future of digital communication.

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The Role of Reinforcement Learning in Advancing ... - Fagen wasanni

Animal communication signals have always been a complex field to decipher. Researchers rely on careful observation and experimentation to understand their meaning. However, this process is time-consuming, and even experienced biologists struggle with differentiating similar signal types.

AI may offer a solution to expedite this process. Machine learning algorithms, known for their pattern detection abilities, can potentially decode the communication systems of various animals like whales, crows, and bats. These algorithms have proven their effectiveness in processing human language and can also identify and classify animal signals from audio and video recordings.

One of the main challenges with machine learning methods is the need for vast amounts of data. For instance, the Chat GPT-3 language model was trained using billions of tokens or words. This means creative solutions are necessary to collect data from wild animals.

Despite these challenges, there are ongoing research projects exploring the use of AI in animal communication. Project CETI (Cetacean Translation Initiative) focuses on the communicative behavior of sperm whales. Utilizing bioinspired whale-mounted tags, underwater robots, and other methods, researchers aim to map the full richness of these animals communication.

Understanding who talks to whom and the environmental and social conditions are essential for decoding animal conversations. By combining machine learning approaches with well-designed experiments, researchers hope to discover which signals animals use and potentially their meanings. This knowledge can then be applied to improve animal welfare in captivity and develop more effective conservation strategies.

In the future, machine learning could even enable the ability to listen in on entire communities of animals. Detailed comparisons of communication could be made, including historical baseline recordings of the last surviving individuals held in conservation breeding centers. This research has the potential to reintroduce lost calls and restore cultural practices among animal populations.

Moreover, the use of passive acoustic monitoring systems could help identify communication signals associated with distress or avoidance. This could provide insights into the well-being of animals at a landscape level and aid in conservation efforts.

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Machine Learning Tools: Transformative Insights into Animal ... - Fagen wasanni

The worldwide digital space was gripped by an unprecedented surge in online scams and phishing attacks in 2022. Cybersecurity company Group-IB unveiled an alarming analysis detailing this rising threat.

Their recently launched study showed that the number of scam resources created per brand soared by 162% globally, and even more drastically in the Asia-Pacific region, with a whopping increase of 211% from 2021. The report also disclosed a more than three-fold increase in detected phishing websites over the last year.

These findings underscore the persistent cyber threat landscape, shedding light on a cyber menace that cost more than USD55 billion in damages last year, according to the Global Anti Scam Alliance and ScamAdviser's 2022 Global State of Scams Report. With these alarming trends, the scamdemic shows no signs of slowing down.

Scam campaigns are not just affecting more brands each year; the impact that each individual brand faces is growing larger. Scammers are using a vast amount of domains and social media accounts to not only reach a greater number of potential victims but also evade counteraction, explained Afiq Sasman, head of the digital risk protection analytics team in the Asia Pacific at Group-IB.

The rise in scams was attributed to increased social media use and the growing automation of scam processes. Social media platforms often serve as the first point of contact between scammers and potential victims, with 58% of scam resources created on such platforms in the Asia-Pacific region last year. Group-IB's Digital Risk Protection analysts found that more than 80% of operations are now automated in scams like Classiscam.

Using automation and AI-driven text generators by cybercriminals to craft convincing scam and phishing campaigns poses an escalating threat. Such advancements allow cybercriminals to scale operations and provide increased security within their illicit ecosystems.

The study also highlighted the uptick in scam resources hosted on the .tk domain, accounting for 38.8% of all scam resources examined by Group-IB in the second half of 2022. This development reveals the increasing impact of automation in the scam industry, as affiliate programs automatically generate links on this domain zone.

The research underscores the urgent need for robust and innovative cybersecurity measures. By leveraging advanced technologies such as neural networks and machine learning, organizations can monitor millions of online resources to guard against external digital risks, protecting their intellectual property and brand identity. Only through such proactive measures can we hope to turn the tide against the rising tide of this digital 'scamdemic.

Image credit: iStockphoto/Dragon Claws

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The Scamdemic: Can Machine Learning Turn the Tide? - CDOTrends

Energy Consumption in Machine Learning: An Unseen Cost of Innovation

In recent years, machine learning has emerged as a driving force behind many technological advancements, from self-driving cars to facial recognition systems. As these innovations continue to transform our world, there is a growing concern about the environmental impact of the energy consumption required to power these advancements. The energy consumption in machine learning is an unseen cost of innovation that needs to be addressed in order to ensure a sustainable future.

Machine learning, a subset of artificial intelligence, involves the development of algorithms that enable computers to learn from and make predictions or decisions based on data. These algorithms require vast amounts of computational power to process and analyze the data, which in turn requires significant energy resources. As the demand for machine learning applications grows, so does the need for more powerful hardware and energy to fuel these computations.

One of the most energy-intensive aspects of machine learning is the training process, during which an algorithm is exposed to a large dataset and learns to recognize patterns and make predictions. This process can take days, weeks, or even months to complete, depending on the complexity of the task and the size of the dataset. During this time, the hardware used to run the algorithms consumes a considerable amount of electricity, contributing to greenhouse gas emissions and exacerbating climate change.

The energy consumption of machine learning is not only an environmental concern but also a financial one. As the cost of electricity continues to rise, companies and researchers may find it increasingly difficult to afford the energy required to develop and deploy machine learning applications. This could potentially slow down the pace of innovation and hinder the adoption of new technologies that could improve our lives.

Recognizing the need to address this issue, researchers and technology companies are exploring ways to reduce the energy consumption of machine learning. One approach is to develop more energy-efficient hardware, such as specialized processors designed specifically for machine learning tasks. These processors can perform computations more efficiently than traditional CPUs or GPUs, reducing the amount of energy required to run machine learning algorithms.

Another approach is to optimize the algorithms themselves, making them more efficient and requiring less computational power to achieve the same results. This can be achieved through techniques such as pruning, which involves removing unnecessary connections in a neural network, and quantization, which reduces the precision of the numerical values used in the computations. Both of these techniques can lead to significant reductions in energy consumption without sacrificing the accuracy of the machine learning model.

In addition to these technological solutions, there is also a growing awareness of the need for more sustainable practices in the field of machine learning. Researchers and companies are increasingly considering the environmental impact of their work and taking steps to minimize their energy consumption. This can include using renewable energy sources to power their data centers, implementing energy-efficient cooling systems, and recycling or repurposing old hardware.

As machine learning continues to advance and become more prevalent in our daily lives, it is crucial that we address the issue of energy consumption in order to ensure a sustainable future. By developing more energy-efficient hardware and algorithms, adopting sustainable practices, and raising awareness of the environmental impact of machine learning, we can continue to enjoy the benefits of these innovations while minimizing their impact on our planet. The unseen cost of innovation must be acknowledged and addressed to ensure that the progress we make does not come at the expense of our environment.

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Energy Consumption in Machine Learning: An Unseen Cost of ... - EnergyPortal.eu