Mediaboss Marketing

Few-shot learning is a subfield of machine learning and deep learning that aims to teach AI models how to learn from only a small number of labeled training data. The goal of few-shot learning is to enable models to generalize new, unseen data samples based on a small number of samples we give them during the training process.

In general, few-shot learning involves training a model on a set of tasks, each of which consists of a small number of labeled samples. We train the model to learn how to recognize patterns in the data and use this knowledge.

One challenge of traditional machine learning is the fact that training models require large amounts of training data with labeled training samples. Training on a large data set allows machine learning models to generalize new, unseen data samples. However, in many real-world scenarios, obtaining a large amount of labeled data can be very difficult, expensive, time consuming or all of the above. This is where few-shot learning comes into play. Few-shot learning enables machine learning models to learn from only a few labeled data samples.

More From This Expert5 Deep Learning and Neural Network Activation Functions to Know

One reason few-shot learning is important is because it makes developing machine learning models in real-world settings feasible. In many real-world scenarios, it can be challenging to obtain a large data set we can use to train a machine learning model. Learning on a smaller training data set can significantly reduce the cost and effort required to train machine learning models. Few-shot learning makes this possible because the technique enables models to learn from only a small amount of data.

Few-shot learning can also enable the development of more flexible and adaptive machine learning systems. Traditional machine learning algorithms are typically designed to perform well on specific tasks and are trained on huge data sets with a large number of labeled examples. This means that algorithms may not generalize well to new, unseen data or perform well on tasks that are significantly different from the ones on which they were trained.

Few-shot learning solves this challenge by enabling machine learning models to learn how to learn and adapt quickly to new tasks based on a small number of labeled examples. As a result, the models become more flexible and adaptable.

Few-shot learning has many potential applications in areas such as computer vision, natural language processing (NLP) and robotics. For example, when we use few-shot learning in robotics, robots can quickly learn new tasks based on just a few examples. In natural language processing, language models can better learn new languages or dialects with minimal training data.

An error occurred.

Few-shot learning has become a promising approach for solving problems where data is limited. Here are three of the most promising approaches for few-shot learning.

Meta-learning, also known as learning to learn, involves training a model to learn the underlying structure (or meta-knowledge) of a task. Meta-learning has shown promising results for few-shot learning tasks where the model is trained on a set of tasks and learns to generalize to new tasks by learning just a few data samples. During the meta-learning process, we can train the model using meta-learning algorithms such as model-agnostic meta-learning (MALM) or by using prototypical networks.

Data augmentation refers to a technique wherein new training data samples are created by applying various transformations to the existing training data set. One major advantage of this approach is that it can improve the generalization of machine learning models in many computer vision tasks, including few-shot learning.

For computer vision tasks, data augmentation involves techniques like rotation, flipping, scaling and color jittering existing images to generate additional image samples for each class. We then add these additional images to the existing data set, which we can then use to train a few-shot learning model.

Generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have shown promising results for few-shot learning. These models are able to generate new data points that are similar to the training data.

In the context of few-shot learning, we can use generative models to augment the existing data with additional examples. The model does this by generating new examples that are similar to the few labeled examples available. We can also use generative models to generate examples for new classes that are not present in the training data. By doing so, generative models can help expand the data set for training and improve the performance of the few-shot learning algorithm.

In computer vision, we can apply few-shot learning to image classification tasks wherein our goal is to classify images into different categories. In this example, we can use few-shot learning to train a machine learning model to classify images with a limited amount of labeled data. Labeled data refers to a set of images with corresponding labels, which indicate the category or class to which each image belongs. In computer vision, obtaining a large number of labeled data is often difficult. For this reason, few-shot learning might be helpful since it allows machine learning models to learn on fewer labeled data.

Few-shot learning can be applied to various NLP tasks like text classification, sentiment analysis and language translation. For instance, in text classification, few-shot learning algorithms could learn to classify text into different categories with only a small number of labeled text examples. This approach can be particularly useful for tasks in the area of spam detection, topic classification and sentiment analysis.

Related Reading From Built In ExpertsWhat Are Self-Driving Cars?

In robotics, we can apply few-shot learning to tasks like object manipulation and motion planning. Few-shot learning can enable robots to learn to manipulate objects or plan their movement trajectories by using small amounts of training data. For robotics, the training data typically consists of demonstrations or sensor data.

In medical imaging, learning from only a few exposures can help us train machine learning models for medical imaging tasks such as tumor segmentation and disease classification. In medicine, the number of available images is usually limited due to strict legal regulations and data protection laws around medical information. As a result, there is less data available on which to train machine learning models. Few-shot learning solves this problem because it enables machine learning models to successfully learn to perform the mentioned tasks on a limited data set.

See original here:
What Is Few Shot Learning? (Definition, Applications) - Built In

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.

The rest is here:
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.

Receive insights from our experts every week in your inbox.

See the original post here:
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

Read More:

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

Go here to read the rest:
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).

Original post:
Overview of Machine Learning Algorithms Used In Hardware ... - SemiEngineering