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New telescopes with unprecedented sensitivity and resolution are being unveiled around the world and beyond. Among them are theGiant Magellan Telescopeunder construction in Chile, and theJames Webb Space Telescope, which is parked a million and a half kilometres out in space.

This means there is a wealth of data available to scientists that simply wasnt there before. The raw data from just a single observation of the MeerKAT radio telescopein South Africas Northern Cape province can measure a terabyte. Thats enough to fill a laptop computers hard drive.

MeerKATis an array of 64 large antenna dishes. It uses radio signals from space to study the evolution of the universe and everything it contains galaxies, for example. Each dish is said to generate as muchdata in one secondas youd find on a DVD.

Machine learningis helping astronomers to work through this data quickly and more accurately than poring over it manually.

Perhaps surprisingly, despite increasing reliance on computers, up until recently the discovery of rare or new astrophysical phenomena has completely relied on human inspection of the data.

Machine learning is essentially a set of algorithms designed to automatically learn patterns and models from data. Because we astronomers arent sure what were going to find we dont know what we dont know we also design algorithms to look out for anomalies that dont fit known parameters or labels.

This approach allowed my colleagues and Ito spot a previously overlooked object in data from MeerKAT. It sits some seven billion light years from Earth a light year is a measure of how far light would travel in a year. From what we know of the object so far, it has many of the makings of whats known as an Odd Radio Circle (ORC).

Odd Radio Circles are identifiable by theirstrange, ring like structure. Only a handful of these circles have been detected since the first discovery in 2019, so not much is known about them yet.

In a newpaper we outline the features of our potential ORC, which weve named Sauron (a Steep and Uneven Ring Of Non-thermal Radiation). Sauron is, to our knowledge, the first scientific discovery made in MeerKAT data with machine learning. There have been a handful of other discoveries assisted by machine learning in astronomy.

Not only is discovering something new incredibly exciting, new discoveries are critical for challenging our understanding of thecosmos. These new objects may match our theories of how galaxies form and evolve, or we may need to change how we see the universe. New discoveries of anomalous astrophysical objects help science to make progress.

Identifying anomalies

We spotted Sauron in data from theMeerKAT Galaxy Cluster Legacy Survey.

The survey is a programme of observations conducted with South Africas MeerKAT telescope, a precursor to theSquare Kilometre Array. The array is a global project to build the worlds largest and most sensitive radio telescope within the coming decade, co-located in South Africa and Australia.

The survey was conducted between June 2018 and June 2019. It zeroed in on some 115 galaxy clusters, each made up of hundreds or even thousands of galaxies.

Thats a lot of data to sift through, which is where machine learning comes in.

We developed and used a coding framework which we calledAstronomalyto sort through the data. Astronomaly ranked unknown objects according to an anomaly scoring system. The human team then manually evaluated the 200 anomalies that interested us most. Here, we drew on vast collective expertise to make sense of the data.

It was during this part of the process that we identified Sauron. Instead of having to look at 6 000 individual images, we only had to look through the first 60 that Astronomaly flagged as anomalous to pick up Sauron.

But the question remains: what, exactly, have we found?

Is Sauron an ORC?

We know very little about ORCs. It is currently thought that their bright, blast-like emission is the wreckage of a huge explosionin their host galaxies.

The name Sauron captures the fundamentals of the objects make-up. Steep refers to its spectral slope, indicating that at higher radio frequencies the source (or object) very quickly grows fainter. Ring refers to the shape. And the Non-Thermal Radiation refers to the type of radiation, suggesting that there must be particles accelerating in powerful magnetic fields. Sauron is at least 1.2 million light years across, about 20 times the size of the Milky Way.

But Sauron doesnt tick all the right boxes for us to say its definitely an ORC. We detected a host galaxy but can find no evidence of radio emissions with the wavelengths and frequency that match those of host galaxies of the other known ORCs.

And even thoughSauron has a number of features in common with Odd Radio Circle1 the first ORC spotted it differs in others. Its strange shape and oddly behaving magnetic fields dont align well with the main structure.

One of the most exciting possibilities is that Sauron is a remnant of the explosive merger of two supermassive black holes. These are incredibly dense objects at the centre of galaxies such as our Milky Way that could cause a massive explosion when galaxies collide.

More to come

More investigation is required to unravel the mystery.

Meanwhile, machine learning is quickly becoming an indispensable tool to find more strange objects by sorting through enormous datasets from telescopes. With this tool, we can expect to unveil more of what the universe is hiding.

Michelle Lochner is Senior Lecturer in Astronomy, University of the Western Cape

This article is republished fromThe Conversationunder a Creative Commons licence. Read theoriginal articlehere.

AI Masterclass:Moneyweb has partnered with the Institute for Technology Strategy andInnovation and North-West University Business School to offer aground-breaking new artificial intelligence course.AllInsider Gold subscribersreceivereceive a 10% discount for the four-day virtual course. For more information clickhere.

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Astronomers used machine learning to mine SA's MeerKAT ... - Moneyweb

Intelligent chatbots such as ChatGPT redefine labor division, creating value in various industries, but face limitations that may affect adoption.

Within the first five days of launching in December 2022, ChatGPT reportedly gained its first million users, outperforming competitors like Googles Bard. As more people adopt or experiment with these chatbots, economists and investors are increasingly curious about their value proposition.

To assess their value, one must first differentiate between regular chatbots and intelligent chatbots like ChatGPT. Although there is no clear cutoff between the two, it is helpful to consider them as having different maturity levels and therefore different value propositions.

Traditional chatbots are programmed to address a wide yet ultimately limited range of queries. They are often used in customer service to provide information, respond to simple requests, and distinguish between standard and complex queries.

Intelligent chatbots like ChatGPT have the ability to learn. Rather than adhering to standard chatbot behavior, they study patterns from human interactions, using this information to expand and improve the services they provide.

To better understand the maturity differences between chatbots, it is worth taking a close look at ChatGPT as an example of an intelligent bot. Its primary feature is using natural languages for both input and output, making it more accessible for average consumers.

ChatGPT is an artificial intelligence (AI) system developed by San Francisco-based AI research laboratory OpenAI. It utilizes generative pre-training (GPT), which uses natural languages by combining autonomous machine learning with pre-training on extensive connected text passages.

Since its inception in 2018, GPT has undergone several upgrades. ChatGPT is based on the third generation of the technology, where unsupervised machine learning takes place. The algorithm learns from untagged data, mimics the patterns it encounters and generates new content based on this learning curve.

GPT-3 programming enables ChatGPT to converse with humans using natural language. The bot operates with the same input and output as an average human conversation. It can answer various questions, and its responses not only improve but continue to get better as the bot is trained on human interaction. In essence, ChatGPT creates its own content.

The most obvious benefit of AI applications is the improved quality of conversation between humans and these programs. The utterances of intelligent bots like ChatGPT are less awkward and cumbersome than traditional bots. However, this is not enough to create value on its own. Additional uses for ChatGPT and similar bots include:

Coding: ChatGPT is trained in formal languages, allowing it to be used for coding. As it is also trained in natural languages, it can develop new programs, apps, games and even music. The intersection of formal and natural language is increasingly important in a digital economy relying on networks and the Internet of Things.

Creating: intelligent bots can generate text for speeches, articles or even poetry. Users can specify the subject, length and target audience for the text. The bot then uses information from the internet and its own learning to produce a result, creating meaningful content for humans.

Division of labor: ChatGPTs content creation abilities make it well suited to complement human labor. It can research information, systematically organize it and tailor the output to the users needs. This enhances the division of labor between humans, who provide input and control the output quality, and the bot, which processes content.

However, there are limitations to ChatGPT and similar AI-based bots. They are not entirely new, since similar programming has been used in translation services for at least the past five years. Their value proposition lies in the quality and breadth of their uses, rather than innovation.

There are also serious concerns about output quality. As the bot learns more, it discerns more general patterns, using these to generate content at the cost of individuation. ChatGPT creates similar outputs for different queries when they fall into the same pattern.

The algorithm combines information and processing to create content, but it is unclear if it checks the credibility of the information. Based on what it produces, it does not appear to critically assess arguments and lines of thought. Due to machine learnings multilayered nature, the bot cannot explain all its sources or how it resolved discrepancies during content generation. Users also have to keep in mind that disclosing information makes it public, since their inputs can be fed into the bots learning system. And there are other issues, such as the lack of personalization or the excess wokeism in ChatGPTs free version.

Most likely, GPT development and adoption will continue incrementally. AI will improve at handling images and animations as input and output. Bot usage will increase but likely be employed within limited areas, such as translation, customer service, prototyping and pre-underwriting. The division of labor between humans and bots will improve, and the technology will make work easier by taking on the less rewarding tasks.

In one scenario, chatbots permeate almost all interactions and even substitute some human-to-human exchanges permanently. To achieve such a dispersion, ChatGPT would need to use all natural interactions not only language, but also images, animations, human-to-human contact and nonlanguage behavior patterns as inputs and outputs. Chatbots could serve as supporting elements in nearly all human-to-human interactions, such as studying, working and deciding where to go on holiday. They would replace teachers, psychologists, marketers, or investment bankers. The probability of such a scenario is low, perhaps less than 15 percent.

In another scenario, chatbots like ChatGPT do not spread beyond any market applications other than their current niche. They could even fail if the aforementioned limitations are not addressed in future development. If the programs continues producing similar, interchangeable outcomes, they would lose value for individual users seeking personalization. Moreover, if their learning mechanism remains opaque or becomes even less transparent, their legitimacy would be questioned. Lastly, the lack of privacy for users could seriously hinder business adoption. The likelihood of this worst-case scenario is around 20 percent.

Whether intelligent chatbots will unlock their full value potential depends on how they will be adopted by individuals and in businesses. And this will hinge on how programmers develop more advanced AI. Special attention will need to be paid to parameters such as information protection, individualization and more accessible and intelligible output.

The excitement about ChatGPT might wear off, but the value proposition of intelligent chatbots will remain within reasonable limits.

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Tapping into the value of chatbots - Geopolitical Intelligence Services AG

Dublin Tech Summit 2023 is returning on 31st May to host global thought leaders, established tech experts and industry disruptors for two days of exciting, interactive, engaging and inspiring content, writes Tracey Carney, Managing Director of Dublin Tech Summit.

This years Dublin Tech Summit is our biggest to date. With hundreds of tech leaders set to address over 8,000 attendees, through a range of talks, panels, interviews, demonstrations and more, DTS23 will highlight Irelands role as tech hub for the entire world.

In a very short space of time, the ongoing tech downturn is seeing mass layoffs worldwide, while the rapid growth of AI, the shift toward sustainability and banking turbulence are creating fresh challenges for many sectors. What has led to this very recent tech downturn and are new approaches required to steer economic growth back in a positive direction? New approaches to this, and many other issues, will be discussed, debated and explored with all viewpoints represented at DTS23. You will get to hear opinions for and against cutting edge AI technology, the pros and cons of extended reality and many more thought-provoking ideas.

As we look ahead to the next decade to see where we will be and what opportunities lie ahead, DTS will look closely at AI and machine learning, topics that are currently capturing the publics imagination and posing somewhat existential questions. Other themes of immediate importance include Digital & Business Transformation; Security, Privacy & Trust; Big Data, Analytics & Datafication; Enterprise Software Solutions; Sustainability & Tech For Good; Metaverse & Extended Reality; Blockchain & Web3; Fintech; Deeptech & Future Innovation; 5G, IoT & Connectivity; Diversity, Equity & Inclusion; Start-ups & Investment and the Future Workforce.

Following full days of best-in-class discussion and debate, attendees will be invited to participate in our DTS by Night programme where we have fantastic events especially designed, in venues throughout Dublin City, to allow for optimum networking, mingling, meeting, hanging out and partying with the worlds brightest minds in tech. These include the Tech On The Rocks event and the DE&I Party.

Tickets for this years event are on sale now. For more information, please visit the Dublin Tech Summit website.

About the author: Tracey Carney is Managing Director of Dublin Tech Summit

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Major tech movers and shakers added to Dublin Tech Summit 23 lineup

Dublin Tech Summit 23 announces first round of speakers

Highlights of Day 1, Dublin Tech Summit 2022

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Dublin Tech Summit 2023 to explore AI and Machine Learning ... - Business & Finance

A new technical paper titled A Survey on Machine Learning in Hardware Security was published by researchers at TU Delft.

Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine learning in hardware security in a full and organized manner. Our contributions include classification and introduction to the relevant fields of machine learning, a comprehensive and critical overview of machine learning usage in hardware security, and an investigation of the hardware attacks against machine learning (neural network) implementations.

Find the technical paper here. Published March 2023.

Kyl, Troya al, Cezar Rodolfo Wedig Reinbrecht, Anteneh Gebregiorgis, Said Hamdioui, and Mottaqiallah Taouil. A Survey on Machine Learning in Hardware Security. ACM Journal on Emerging Technologies in Computing Systems (2023).

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Overview of Machine Learning Algorithms Used In Hardware ... - SemiEngineering

PyTorch is an open-source deep learning framework created by Facebooks AI Research lab. It is used to develop and train deep learning mechanisms such as neural networks. Some of the worlds biggest tech companies, including Google, Microsoft, and Apple, use it. If youre looking to get started with PyTorch, then youve come to the right place. Well be taking a look at the 10 best PyTorch courses available online.

Everyone interested in learning more about PyTorch, from beginners to seasoned professionals, would benefit greatly from taking one of these courses. No matter what your budget is, youll be able to locate the course that meets your needs because well cover both free and paid courses.

So, if youre ready to take your PyTorch knowledge to the next level, lets dive in and explore the 10 best PyTorch courses out there.

This course is designed to equip learners with the skills to implement Machine and Deep Learning applications with PyTorch. It provides an overview of the PyTorch framework for deep learning and computer vision applications. Learners will get hands-on experience building Neural Networks from scratch. Theyll learn to build complex models through the applied theme of Advanced Imagery.

Duration: 14 hours and 14 minutes

Certificate: Yes

Cost: Paid

This PyTorch course provides an introduction to the theoretical underpinnings of deep learning algorithms and how they are implemented with PyTorch. It covers how to use PyTorch to implement common machine-learning algorithms for image classification. By the end of the course, you will have a strong understanding of using PyTorch. Youll be able to create and train deep learning models.

Duration: 6 hours and 18 minutes with 52 lectures.

Certificate: Certificate of completion

Cost: Paid

This course gives students a foundational understanding of PyTorch. Students will learn about neurons and neural networks and how activation functions. Students will also explore how to build dynamic computation graphs in PyTorch and contrast that with the approaches used in TensorFlow. By the end of this course, students will have the skills to move on to building deep learning models in PyTorch.

Duration: 2 Hours and 51 Minutes

Certificate: N/A

Cost: Paid

This Pytorch course teaches students how to deploy deep learning models using PyTorch. It begins by introducing PyTorchs tensors and the Automatic Differentiation package, then covers models such as Linear Regression, Logistic/Softmax regression, and Feedforward Deep Neural Networks. In addition, the course also deep dives into the role of different normalization, dropout layers, and different activation functions. And this isnt it; you can also explore transfer learning and convolutional Neural Networks.

Duration: 30 Hours

Certificate: Yes

Cost: Paid

This is an ideal introduction to (GANs) and provides a tutorial on building GANs with PyTorch. Students will learn to build a Generative adversarial network and understand their concepts. In the first section, you will gain an understanding of neural networks by building a simple image classifier. In the second section, you will explore the concept of adversarial training and build progressively complex GANs.

Duration: The course is expected to take about 13 hours to complete.

Certificate: Yes

Cost: Paid

This course offers an introduction to the fundamentals of deep learning and neural networks using Python and PyTorch. Students will learn the basics of deep learning and how to build deep neural networks. Theyll also learn to build deep learning pipelines for different tasks and applications. This course is suitable for students with no prior knowledge of deep learning. At the end of the course, students will be able to build deep learning models, understand their internal workings, and apply them to real-world tasks.

Duration: This course lasts for 6 weeks, with 2-4 hours of weekly study.

Certificate: Yes

Cost: N/A

This PyTorch course is a comprehensive introduction to the field of Deep Learning and its applications. In this course, you will learn the basics of deep learning and build your own deep neural networks. With practical exercises and projects, you will gain experience and learn to implement state-of-the-art AI applications such as style transfer and text generation.

Duration: The course duration is approx. two months.

Certificate: Yes

Cost: N/A

Image Segmentation is aimed at providing the fundamentals of Image Segmentation. This course covers the major techniques used in Image Segmentation, such as Understanding the Segmentation Dataset and Writing a custom dataset class for the Image-mask dataset. Teaches how to apply segmentation augmentation to images and masks. It also includes loading a pre-trained convolutional neural network for segmentation.

Duration: This course is 2 Hours.

Certification: N/A

Cost: free

Youll learn to use NumPy to format data into arrays to manipulate and clean data with pandas. The best part is that you get a quick rundown on the basic principles of machine learning. Explore more on image classification by using PyTorch Deep Learning Library for the purpose. Get practical training by using recurrent neural networks that are for the sequence time data series and create Deep Learning models to work with tabular data.

Duration: It takes around 17 hours to complete

Certificate: Yes

Cost: Paid

Students who take this course will better grasp deep learning. Deep learning basics, neural networks, supervised and unsupervised learning, and other subjects are covered. The instructor also offers advice on using deep learning models in real-world applications. Both beginners and experts can benefit from the course, which is designed for students of all skill levels.

Duration: 6 hours and 26 minutes

Certificate: Yes

Cost: Paid

PyTorch is a potent and widely used deep learning framework that provides developers with a number of advantages. With so many excellent PyTorch courses available online, theres no excuse to start your journey to mastering PyTorch!

Just consider this thought-provoking question what if PyTorch can address the most critical issues facing the globe? Might it be used, for instance, to improve climate models or contribute to forecasting and to prevent natural disasters? The possibilities are endless, and PyTorch will provide you with the necessary capabilities to take on even the most challenging tasks. So why not explore the PyTorch courses available today and build a brighter tomorrow?

PyTorch is an open-source deep-learning framework developed by Facebook. It builds and trains deep learning models such as neural networks.

PyTorch offers various benefits, such as dynamic computational graphs, ease of use, flexibility, and strong community support. It also has a Python-based interface, making it easy to learn and use.

PyTorch can be used to develop and train a variety of deep learning models, such as image and speech recognition, natural language processing, and recommender systems.

Yes, Python is a prerequisite for using PyTorch, as it is the primary language used for building and training deep learning models.

PyTorch can be relatively easy to learn, especially for those with prior experience in Python programming and deep learning. However, it may require some time and effort to fully master its advanced features and functionalities.

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Learn Pytorch With These 10 Best Online Courses In 2023 - Fordham Ram