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In a new study, North Carolina State University researchers had 28 high school students create their own machine-learning artificial intelligence (AI) models for analyzing data. The goals of the project were to help students explore the challenges, limitations and promise of AI, and to ensure a future workforce is prepared to make use of AI tools.

The study was conducted in conjunction with a high school journalism class in the Northeast. Since then, researchers have expanded the program to high school classrooms in multiple states, including North Carolina. NCState researchers are looking to partner with additional schools to collaborate in bringing the curriculum into classrooms.

We want students, from a very young age, to open up that black box so they arent afraid of AI, said the studys lead author Shiyan Jiang, assistant professor of learning design and technology at NCState. We want students to know the potential and challenges of AI, and so they think about how they, the next generation, can respond to the evolving role of AI and society. We want to prepare students for the future workforce.

For the study, researchers developed a computer program called StoryQ that allows students to build their own machine-learning models. Then, researchers hosted a teacher workshop about the machine learning curriculum and technology in one-and-a-half hour sessions each week for a month. For teachers who signed up to participate further, researchers did another recap of the curriculum for participating teachers, and worked out logistics.

We created the StoryQ technology to allow students in high school or undergraduate classrooms to build what we call text classification models, Jiang said. We wanted to lower the barriers so students can really know whats going on in machine-learning, instead of struggling with the coding. So we created StoryQ, a tool that allows students to understand the nuances in building machine-learning and text classification models.

A teacher who decided to participate led a journalism class through a 15-day lesson where they used StoryQ to evaluate a series of Yelp reviews about ice cream stores. Students developed models to predict if reviews were positive or negative based on the language.

The teacher saw the relevance of the program to journalism, Jiang said. This was a very diverse class with many students who are under-represented in STEM and in computing. Overall, we found students enjoyed the lessons a lot, and had great discussions about the use and mechanism of machine-learning.

Researchers saw that students made hypotheses about specific words in the Yelp reviews, which they thought would predict if a review would be positive, or negative. For example, they expected reviews containing the word like to be positive. Then, the teacher guided the students to analyze whether their models correctly classified reviews. For example, a student who used the word like to predict reviews found that more than half of reviews containing the word were actually negative. Then, researchers said students used trial and error to try to improve the accuracy of their models.

Students learned how these models make decisions, and the role that humans can play in creating these technologies, and the kind of perspectives that can be brought in when they create AI technology, Jiang said.

From their discussions, researchers found that students had mixed reactions to AI technologies. Students were deeply concerned, for example, about the potential to use AI to automate processes for selecting students or candidates for opportunities like scholarships or programs.

For future classes, researchers created a shorter, five-hour program. Theyve launched the program in two high schools in North Carolina, as well as schools in Georgia, Maryland and Massachusetts. In the next phase of their research, they are looking to study how teachers across disciplines collaborate to launch an AI-focused program and create a community of AI learning.

We want to expand the implementation in North Carolina, Jiang said. If there are any schools interested, we are always ready to bring this program to a school. Since we know teachers are super busy, were offering a shorter professional development course, and we also provide a stipend for teachers. We will go into the classroom to teach if needed, or demonstrate how we would teach the curriculum so teachers can replicate, adapt, and revise it. We will support teachers in all the ways we can.

The study, High school students data modeling practices and processes: From modeling unstructured data to evaluating automated decisions, was published online March 13 in the journal Learning, Media and Technology. Co-authors included Hengtao Tang, Cansu Tatar, Carolyn P. Ros and Jie Chao. The work was supported by the National Science Foundation under grant number 1949110.

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Note to Editors: The study abstract follows.

High school students data modeling practices and processes: From modeling unstructured data to evaluating automated decisions

Authors: Shiyan Jiang, Hengtao Tang, Cansu Tatar, Carolyn P. Ros and Jie Chao.

Published: March 13, 2023, Learning, Media and Technology

DOI: 10.1080/17439884.2023.2189735

Abstract: Its critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.

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AUSTIN, Texas The University of Texas at Austin and Amazon are launching a science and engineering research partnership to enhance understanding in a variety of areas, including video streaming, search and information retrieval and robotics.

The UT Austin-Amazon Science Hub is the sixth such alliance between the tech company and a leading university. It aims to advance research that prompts new discoveries and addresses significant challenges while creating solutions that benefit society. This will be achieved by fostering collaboration among faculty members and students along with the development of a diverse and sustainable pipeline of research talent.

We are striving to establish even more collaborations with leading companies and organizations in order to bring together more talented people, produce higher-impact research, and help our students reach their greatest ambitions. The launch of the new hub with Amazon is the latest success story in this effort, said UT Austin President Jay Hartzell. I am eager to see the discoveries that our researchers and students will create from this collaboration, and how those discoveries will change the world.

The hub will be hosted in UT Austins Cockrell School of Engineering but engage researchers in a variety of disciplines.

As part of the collaboration, Amazon will provide funding for research projects, doctoral graduate student fellowships, and community-building events designed to diversify and increase cross-disciplinary innovation.

Amazon is thrilled to establish a university hub at UT Austin, said BA Winston, vice president of technology at Prime Video. For years, our top scientists have been a resource to UT Austin graduate students, collaborating on topics such as developing objective machine learning models to predict perceptual video quality that drive smart compression and multimodal AI models that help ensure highest quality media playback experience at scale.

The hub builds on an existing partnership between the two organizations via the Amazon Scholars program. Researchers from the Cockrell School, College of Natural Sciences, McCombs School of Business and College of Liberal Arts work with Amazon through the program.

This Science Hub will strengthen the partnership between UT Austin and Amazon by leveraging our collective strengths and creating opportunities for our faculty and students and leaders at Amazon to work together to accelerate progress in the areas of computer vision, machine learning, AI and robotics, said Roger Bonnecaze, dean of the Cockrell School of Engineering.

Research into visual neuroscience, streaming and social media is one of the reasons that UT was attractive to establish the new hub. Al Bovik, director of the new science hub and a professor in the Chandra Family Department of Electrical and Computer Engineering, helped guarantee the quality and reliability of streaming video and social media worldwide with his research. Joining him in leadership of the hub is an advisory board that includes personnel from UT Austin and Amazon:

UT Austin has built an impressive program in robotics with exceptional faculty and students, said Ken Washington, vice president of Amazon Consumer Robotics. The new hub will allow us to collaborate even more closely with them in robotics and related disciplines, so Im very optimistic about our growing partnership.

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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.

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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.

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Leading AI companies will work together to make it easy to create and deploy enterprise-grade LLMs adapted to specific business needs, both for LivePerson and its customers

NEW YORK and TORONTO, April 11, 2023 /PRNewswire/ -- LivePerson(Nasdaq: LPSN), a global leader in Conversational AI, today announced a pilot program with Cohere, the natural language processing platform enabling broad access to cutting-edge language generation and understanding technology. This program will allow enterprise brands to easily create and deploy custom Large Language Models (LLMs)that improve both customer engagement and business outcomes.

While language AI technologies have attracted intense interest and even wonder, the realities and limitations of deploying them at the enterprise level have also led to deep concern about their readiness for customer-facing experiences and their ability to actually drive better business results.

LivePerson and Cohere intend to help enterprises overcome these barriers and put LLMs to work driving better customer engagement and business outcomes. The combination of LivePerson's industry-leading conversational platform and AI with Cohere's state-of-the-art language models will be designed to deliver:

"With Generative AI and LLMs, the best outcomes are driven by expansive data models and precision data sets. Combining Cohere's cutting-edge language models with our unparalleled expertise and data for customer engagement will set the new standard for using AI to communicate at the enterprise level," said LivePerson founder and CEO Rob LoCascio.

LivePerson's AI is trained on a vast and rich data set derived from a billion conversational interactions every month and informed by 20+ years of experience managing brand-to-consumer interactions for the world's largest enterprises and integrating into their backend systems. Unlike other platforms, hundreds of thousands of humans participate in LivePerson's AI learning loops, keeping conversations grounded and factual, and the company's commitment to fighting bias in AI is deep and long-standing.

This mirrors Cohere's vision to help developers and businesses tap into the massive opportunity that NLP brings and give them a competitive advantage as early adopters in this evolving market. Cohere's impressive team includes some of the world's top machine learning talent alongside business experts with experience implementing exciting new technology into products at scale. Its platform allows enterprises to fine-tune their data for stronger outcomes and more impactful business decisions.

"Our mission is to make it simple for any developer and business to build powerful language AI into their products," said Aidan Gomez, Co-Founder and CEO at Cohere. "LivePerson's leadership in conversational AI helps to further that mission, and we can help them increase access to this transformational technology, even to enterprises without extensive compute resources or machine learning expertise."

To help enterprises learn more about custom LLMs, LivePerson and Cohere will host a webinar with AI leaders Joe Bradley, Chief Scientist at LivePerson, and Matthew Dunn, Machine Learning Research expert at Cohere. The session will take place on May 24, 2023 at 12 PM ET and help shed light on "Driving better business outcomes with custom large language models."

To register for this event and learn more about how LivePerson and Cohere are bringing AI to enterprises, please click here.

About LivePerson, Inc.LivePerson(NASDAQ: LPSN) is a global leader in Conversational AI. Hundreds of the world's leading brands including HSBC, Chipotle, and Virgin Media use our Conversational Cloud platform to engage with millions of consumers as personally as they would with one. We power nearly a billion conversational interactions every month, providing a uniquely rich data set to build connections that reduce costs, increase revenue, and are anything but artificial. Fast Company named us the #1 Most Innovative AI Company in the world. To talk with us or our Conversational AI, please visit http://www.liveperson.com.

About CohereCohereis making language AI accessible to all developers and businesses, even those without massive compute resources or rare machine learning knowledge. Cohere builds state-of-the-art language models and makes them available through an API as a managed service or cloud ML platforms, turning a complex, expensive process into an easy-to-use interface. Cohere's mission is to help every developer, enterprise leader, or startup founder benefit from the power of language models, whether through copywriting, search, conversational AI, summarization, content moderation, and more. Cohere is based in Toronto, Canada, and powers customers across the globe.

Forward Looking StatementsStatements in this press release regarding LivePerson that are not historical facts are forward-looking statements and are subject to risks and uncertainties that could cause actual future events or results to differ materially from such statements. Any such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. It is routine for our internal projections and expectations to change as the quarter and year progress, and therefore it should be clearly understood that the internal projections and beliefs upon which we base our expectations may change. Although these expectations may change, we are under no obligation to inform you if they do. Some of the factors that could cause actual results to differ materially from the forward-looking statements contained herein include without limitation, our ability to execute on and deliver our current plans and goals, and the other factors described in the Risk Factors section of the Company's most recently filed Annual Report on Form 10-K for the year ended December 31, 2023, filed with the SEC on March 16, 2023 and as from time to time updated in LivePerson's Quarterly Reports on Form 10-Q. The list of Risk Factors is intended to identify only certain of the principal factors that could cause actual results to differ from those discussed in the forward-looking statements.

Media contact: Mike Tague, [emailprotected]

SOURCE LivePerson, Inc.

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While OpenAI is propelling toward artificial general intelligence, AI that's smarter than human beings, Writer cofounders May Habib (left) and Waseem Alshikh (right) hold a different opinion. If you can unplug it, it's not AGI, Habib says.

In 2013, May Habib was browsing through GitHub when she came across the work of Dubai-based tech executive Waseem Alshikh, who dabbled in then-nascent machine learning techniques to summarize large blocks of information.

Habib, who grew up in a small village in Lebanon before immigrating to Canada in the 90s to flee the civil war, was immediately taken by the similarities she shared with Alshikh, who was also forced to leave his native country, Syria but for a different reason: as a teenager, he had illegally hacked into the countrys Ministry of Interior.

Ten years and two startups later, the duo is at the cutting edge of a wave of generative AI companies that have merged into the mainstream as the rudimentary techniques Alshikh was experimenting with a decade ago have been supercharged by advancements in transformers and deep learning models. Their generative AI startup Writer uses its own large language models called Palmyra (named after an ancient Syrian city) to let enterprises and their employees write and edit content such as emails, documents, ads and summaries, all of which will adhere to a companys editorial guidelines.

Unlike the vast majority of generative AI models out there that hallucinate, or spout incorrect information a major issue for businesses that are incorporating the technology CEO Habib claims that the latest version of its language AI model will never create anything that's factually incorrect. Thats due to the models architecture, which is designed to prioritize accuracy over creativity.

Thats technically plausible to an extent. Model architecture definitely does have an impact on hallucination rates, says Pranav Reddy, an investor at Conviction who previously worked at generative AI search engine startup Neeva. But there is no model structure that guarantees that they dont hallucinate, he continued. In the rare scenario that the companys technology does hallucinate, Habib says Writer highlights the portion of the text that has no source.

Customers, which include giants like Uber UBER , Deloitte, Spotify and Accenture ACN , seem sold so far. United Healthcare is using Writers HIPAA-compliant models to examine the fine print in health insurance plans and then write blogs or emails to explain those plans to health providers and patients. Intuit INTU has it writing blog posts based on financial data, and LOral uses it to write product descriptions and push notifications. Writers customers can also feed the software a podcast or a video and it can repurpose it into written content. (One of Writers early clients was Twitter, which hasnt paid its bills, says Habib, who sued the Elon Musk-owned company in late February for defaulting on payments.)

Writers models are trained on public information as well as companys own data such as PDFs, editorial style guides and brand words, together forming 30 billion parameters. Each company gets its own customized version of the model, and the data is stored on the companys cloud. The data is actually used like an index and we have this path to explainability that shows where specific facts came from, Habib says.

The company, which is featured on the 2023 AI 50 list, directly competes with other generative AI startups on the list that are increasingly targeting the enterprise sector. But its still catching up to its competitors in terms of funding. Valued at $155 million, Writer has raised $26 million in venture funding from Insight Partners, Upfront Ventures and others. In comparison, copywriting AI tool Jasper is a unicorn with deep pockets of $125 million in venture capital. Writers main distinguisher, according to its cofounders, is that it uses its own large language models (unlike Jasper, which is built on OpenAIs GPT-3.5) and trains those models on company-specific data to get more accurate results.

Most of the companies in the market are only playing in the application layer, and they don't even do it well. They literally use someone elses technology and put a UI on top of it, says cofounder and CTO Alshikh. Jasper is an example. Its just a reseller. As an AI company, when you don't control your AI itself, how are you going to control the quality of output?

The San Francisco-based startups strategy to address generative AIs tendency to make up facts has to do with how its built. On the backend, Writer uses machine learning, natural language processing and transformers to understand text and generate more of it. Writer combines encoders, which is the part of a transformer that is good at understanding text, and decoders, the components that predict and generate text.

Unlike other models, Writers models make encoders and decoders talk to each other. Once an encoder understands the query, it grabs the information from the database uploaded by the client company and then informs the decoder how to generate a response based on it. This is how were able to be accurate and write in an inclusive language, Habib tells Forbes.

Reddy, the Conviction investor, agrees that while the encoder-decoder models arent perfect, they are better than ChatGPT, at least when it comes to accuracy. Encoder-decoder models do tend to have much lower hallucination rates than decoder-only models, which is the OpenAI GPT-3 structure, Reddy says. The tradeoff you pay for this is that these (encoder-decoder) models also tend to be less creative.

Habibs says her clients are more concerned about using the right wording and punctuation in their branded content than being able to generate stories, sonnets and poems. Any story included in content written by Writers software is a real one and not fiction, she says.

Committing to its name, Writers tools only produce text and do not generate visual content like images and videos a service that newer enterprise-focused generative AI startups like Typeface do offer. It can be plugged into many writing and content creation tools including Google Docs, Microsoft Word and Figma. Writer charges customers a platform fee anywhere between $30,000 up to $1 million. Pricing is also based on the number of words generated by the software. Through this business model, Writer expects to book an estimated $20 million in 2023 revenue.

At a relatively low price, Nvidia provides Writer the physical hardware (GPUs and CPUs) for computing purposes and training data, Alshikh says. Our goal today is to train smaller models as much as possible to keep the costs cheap for us and our customers, he tells Forbes.

The immigrant entrepreneurs say that Writer is an amalgamation of their personal experiences with the English language and their business foundations in the machine-learning realm. Habib, a Harvard graduate, was the first woman in her family to attend college and taught her family how to speak English. Alshikh taught himself how to speak English at age 20 to learn computer science. This prompted them to start their first venture in 2015: a machine learning based translation software called Qordoba that helped companies like Sephora and Visa translate their digital content to other languages and dialects, before evolving it into Writer in 2020.

The medium of language to move ahead in the world has always been something weve thought about really deeply, Habib says.

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This Startup Claims Its Models Fix A Major Problem With Generative AI - Forbes