By Justin Gamble

(CNN) Using artificial intelligence and archival news articles, a teenager in Northern Virginia created a program to measure media biases and in researching older news articles, she found that Black homicide victims were less likely to be humanized in news coverage.

Emily Ocasio, an 18-year-old from Falls Church, Virginia, created an AI program that analyzed FBI homicide records between 1976 and 1984 and their corresponding coverage published in The Boston Globe to determine whether victims were presented in a humanizing or impersonal way.

After analyzing 5,042 entries, the results showed that Black men under the age of 18 were 30% less likely to receive humanizing coverage than their White counterparts, Ocasio told CNN. Black women were 23% less likely to be humanized in news stories, Ocasio added.

A news article was considered humanizing when it mentioned additional information about the victim and presented them as a person, not just a statistic, Ocasio said in her project presentation.

Her findings have not been reviewed by the larger scientific community, but she told CNN she hopes to expand her research and get it published in a scientific journal.

Ocasios project earned her second place in the prestigious Regeneron Science Talent Search on March 14 as well as a $175,000 scholarship.

Every year about 1,900 high school students from across the country participate in the competition, which started in 1942 and seeks to serve as a platform for young scientists to share original research.

Ocasio was among 40 finalists from more than 2,000 applications, according to Maya Ajmera, president and CEO of the Society for Science and executive publisher of Science News, two of the competitions sponsors.

By using AI to document these biases, Emily shows that it can be safely used to help society answer complex social science questions, her biography on the Society for Science website says.

Ocasio said she has always been interested in social justice and science and saw this project as an opportunity to combine them. Without the research, and without the statistics, you have no ability of understanding that entire communities are being left behind, she said.

Ocasio analyzed The Boston Globes news coverage because the newspaper had digital copies of its articles for the 70s to 80s time period she focused on for her project, she said. CNN has reached out to the Boston Globe for comment.

Despite her findings, Ocasio believes science cant explain everything: You can never run an experiment in a lab that tells you about how racism works in society.

Ocasio, who has Puerto Rican heritage, said her own experiences helped shape her perspective of different races and cultures, and drew her to researching racism and inequalities. She wants to replicate her research to analyze other news outlets as well, she said.

The talent searchs first-place winner, Neel Moudgal, told CNN the research done by the teenagers across the US is essential to helping solve some of societys greatest challenges.

I firmly believe that science is going to be the solution to a lot of our problems, Moudgal said. His prize-winning project was a computer model that predicts the structure of RNA molecules to help develop tests and drugs for diseases such as cancer, autoimmune diseases, and viral infections.

Ajmera said seeing such projects from high school students gives her an enormous hope for the future.

Were looking for the future scientific leaders of this country, she said.

The-CNN-Wire & 2023 Cable News Network, Inc., a Warner Bros. Discovery Company. All rights reserved.

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Weve compiled a list of phrases and concepts useful to understanding artificial intelligence, in particular the new breed of A.I.-enabled chatbots like ChatGPT, Bing and Bard.

If you dont understand these explanations, or would like to learn more, you might want to consider asking the chatbots themselves. Answering such questions is one of their most useful skills, and one of the best ways to understand A.I. is to use it. But keep in mind that they sometimes get things wrong.

Bing and Bard chatbots are being rolled out slowly, and you may need to get on their waiting lists for access. ChatGPT currently has no waiting list, but it requires setting up a free account.

For more on learning about A.I., check out The New York Timess five-part series on becoming an expert on chatbots.

Anthropomorphism: The tendency for people to attribute humanlike qualities or characteristics to an A.I. chatbot. For example, you may assume it is kind or cruel based on its answers, even though it is not capable of having emotions, or you may believe the A.I. is sentient because it is very good at mimicking human language.

Bias: A type of error that can occur in a large language model if its output is skewed by the models training data. For example, a model may associate specific traits or professions with a certain race or gender, leading to inaccurate predictions and offensive responses.

A brave new world. A new crop of chatbotspowered by artificial intelligence has ignited a scramble to determine whether the technology could upend the economics of the internet, turning todays powerhouses into has-beens and creating the industrys next giants. Here are the bots to know:

ChatGPT. ChatGPT, the artificial intelligence language model from a research lab, OpenAI, has been making headlines since November for its ability to respond to complex questions, write poetry, generate code, plan vacationsand translate languages. GPT-4, the latest version introduced in mid-March, can even respond to images(and ace the Uniform Bar Exam).

Bing. Two months after ChatGPTs debut, Microsoft, OpenAIs primary investor and partner, added a similar chatbot, capable of having open-ended text conversations on virtually any topic, to its Bing internet search engine. But it was the bots occasionally inaccurate, misleading and weird responsesthat drew much of the attention after its release.

Ernie. The search giant Baidu unveiled Chinas first major rival to ChatGPT in March. The debut of Ernie, short for Enhanced Representation through Knowledge Integration, turned out to be a flopafter a promised live demonstration of the bot was revealed to have been recorded.

Emergent behavior: Unexpected or unintended abilities in a large language model, enabled by the models learning patterns and rules from its training data. For example, models that are trained on programming and coding sites can write new code. Other examples include creative abilities like composing poetry, music and fictional stories.

Generative A.I.: Technology that creates content including text, images, video and computer code by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics. Examples include ChatGPT for text and DALL-E and Midjourney for images.

Hallucination: A well-known phenomenon in large language models, in which the system provides an answer that is factually incorrect, irrelevant or nonsensical, because of limitations in its training data and architecture.

Large language model: A type of neural network that learns skills including generating prose, conducting conversations and writing computer code by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

Natural language processing: Techniques used by large language models to understand and generate human language, including text classification and sentiment analysis. These methods often use a combination of machine learning algorithms, statistical models and linguistic rules.

Neural network: A mathematical system, modeled on the human brain, that learns skills by finding statistical patterns in data. It consists of layers of artificial neurons: The first layer receives the input data, and the last layer outputs the results. Even the experts who create neural networks dont always understand what happens in between.

Parameters: Numerical values that define a large language models structure and behavior, like clues that help it guess what words come next. Systems like GPT-4 are thought to have hundreds of billions of parameters.

Reinforcement learning: A technique that teaches an A.I. model to find the best result by trial and error, receiving rewards or punishments from an algorithm based on its results. This system can be enhanced by humans giving feedback on its performance, in the form of ratings, corrections and suggestions.

Transformer model: A neural network architecture useful for understanding language that does not have to analyze words one at a time but can look at an entire sentence at once. This was an A.I. breakthrough, because it enabled models to understand context and long-term dependencies in language. Transformers use a technique called self-attention, which allows the model to focus on the particular words that are important in understanding the meaning of a sentence.

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Artificial Intelligence Glossary: AI Terms Everyone Should Learn - The New York Times

Artificial intelligence is already piquing more Americans interest, according to Morning Consult trend data.

Part of the curiosity bump in AI-supported or AI-generated applications is simply due to increased awareness of the emerging technology. Headlines over the fight for AI dominance between Microsoft and Google have continued, as Google last week opened up access to Bard its AI chatbot competitor to ChatGPT and Microsoft announced that it would integrate the AI-powered image generator DALL-E into its Bing search engine.

According to a recent Morning Consult survey, 57% of consumers said they have heard of AI chatbots in the news, up from 50% just a month ago, and 47% reported hearing about ChatGPT in the news specifically, a double-digit jump during the same time period. In the battle for clout, Microsoft has the advantage, but new AI tools will need to be relevant and helpful to consumers as well as be developed responsibly in order to be successful.

Surveys conducted Feb. 17-March 19, 2023, among representative samples of roughly 2,200 U.S. adults each, with unweighted margins of error of +/-2 percentage points.

With heightened awareness of AI tech among consumers, interest in dozens of different AI applications is also up, from AI-powered flight and hotel recommendations to AI-generated financial planning.

Average interest across the applications included in the survey is up 4 percentage points since February. The categories seeing the most growth are: AI-generated menu recommendations at restaurants (up 13 points to 53%), AI-powered therapy and life coaching (up 10 points to 40%), and AI-generated social media captions (up 10 points to 35%).

The fact that interest in AI applications is not siloed to a single or few categories shows that there is broad interest in the technology and how it might change many facets of everyday life.

Surveys conducted Feb. 17-March 19, 2023, among representative samples of roughly 2,200 U.S. adults each, with unweighted margins of error of +/-2 percentage points.

As companies adopt AI models and integrate them into their existing products and services, they will be wise to carefully thread the needle of applications that automate help shown to be popular among consumers but dont enter the uncanny valley by substituting a human connection.

Behind all the excitement and daydreams of how AI might help us is the shadow of the harm it might also cause. These very real concerns, as reported in previous Morning Consult analysis, are seemingly understood by the biggest players in the AI space.

Microsoft has published governing principles for AI development (though it reportedly laid off its AI ethics team earlier this month) and Google noted in its announcement of expanded access to Bard that the model has the potential to provide misinformation. Even the CEO of OpenAI, the company that develops ChatGPT, has expressed concerns over the direction of the technology.

As companies develop new AI models, they are also shouldering responsibility for its ethical development. Nearly 2 in 3 (65%) consumers said companies that develop AI models bear at least some responsibility for doing so ethically. Infrastructure companies, companies that use but dont develop AI and the CEOs of AI developers are also at the top of the list. The private sector is more likely than the government and regulators to be seen as having responsibility for ethical AI development, just after the Federal Trade Commission warned companies to keep their AI claims in check.

Survey conducted March 17-19, 2023, among a representative sample of 2,205 U.S. adults, with an unweighted margin of error of +/-2 percentage points. Figures may not add up to 100% due to rounding.

Even as interest and awareness of AI tech increases over time, concerns over misinformation and bias in AI search results persist. Two in 3 U.S. adults said they are concerned about the accuracy of AI search engine results, and 69% are concerned about potential misinformation included in results from search engines that use AI.

That being said, as people become more familiar and even accustomed to the idea of AI in search, the share who trust AI is increasing over (a relatively short period of) time. More than a third (35%) of consumers completely or mostly trust AI search to provide unbiased results, up from 27% a month ago, and trust in companies to develop AI responsibly is also up 8 points.

Surveys conducted Feb. 17-March 19, 2023, among representative samples of roughly 2,200 U.S. adults each, with unweighted margins of error of +/-2 percentage points.

Unlike with Web3 or the metaverse, consumers are showing increased interest and trust in AI as an emerging technology. Companies at the forefront of developing these AI models are trying to achieve a difficult balance between maintaining momentum in rolling out new products or applications and not moving so quickly that they risk weakening consumer confidence with a faulty chatbot. Developing AI responsibly will be critical to building that trust, and so far, consumers are warming up to the idea of AI as a presence in their everyday lives. But its still early days, and weve yet to see everything that AI has to offer both the good and the bad.

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Generative Artificial Intelligence Awareness, Interest Surging - Morning Consult

Five principles, including safety, transparency and fairness, will guide the use of artificial intelligence in the UK, as part of a new national blueprint for our world class regulators to drive responsible innovation and maintain public trust in this revolutionary technology.

The UKs AI industry is thriving, employing over 50,000 people and contributing 3.7 billion to the economy last year. Britain is home to twice as many companies providing AI products and services as any other European country and hundreds more are created each year.

AI is already delivering real social and economic benefits for people, from helping doctors to identify diseases faster to helping British farmers use their land more efficiently and sustainably. Adopting artificial intelligence in more sectors could improve productivity and unlock growth, which is why the government is committed to unleashing AIs potential across the economy.

As AI continues developing rapidly, questions have been raised about the future risks it could pose to peoples privacy, their human rights or their safety. There are concerns about the fairness of using AI tools to make decisions which impact peoples lives, such as assessing the worthiness of loan or mortgage applications.

Alongside hundreds of millions of pounds of government investment announced at Budget, the proposals in the AI regulation white paper will help create the right environment for artificial intelligence to flourish safely in the UK.

Currently, organisations can be held back from using AI to its full potential because a patchwork of legal regimes causes confusion and financial and administrative burdens for businesses trying to comply with rules.

The government will avoid heavy-handed legislation which could stifle innovation and take an adaptable approach to regulating AI. Instead of giving responsibility for AI governance to a new single regulator, the government will empower existing regulators - such as the Health and Safety Executive, Equality and Human Rights Commission and Competition and Markets Authority - to come up with tailored, context-specific approaches that suit the way AI is actually being used in their sectors.

The white paper outlines 5 clear principles that these regulators should consider to best facilitate the safe and innovative use of AI in the industries they monitor. The principles are:

This approach will mean the UKs rules can adapt as this fast-moving technology develops, ensuring protections for the public without holding businesses back from using AI technology to deliver stronger economic growth, better jobs, and bold new discoveries that radically improve peoples lives.

Over the next 12 months, regulators will issue practical guidance to organisations, as well as other tools and resources like risk assessment templates, to set out how to implement these principles in their sectors. When parliamentary time allows, legislation could be introduced to ensure regulators consider the principles consistently.

Science, Innovation and Technology Secretary Michelle Donelan said

AI has the potential to make Britain a smarter, healthier and happier place to live and work. Artificial intelligence is no longer the stuff of science fiction, and the pace of AI development is staggering, so we need to have rules to make sure it is developed safely.

Our new approach is based on strong principles so that people can trust businesses to unleash this technology of tomorrow.

Businesses warmly welcomed initial proposals for this proportionate approach during a consultation last year and highlighted the need for more coordination between regulators to ensure the new framework is implemented effectively across the economy. As part of the white paper published today, the government is consulting on new processes to improve coordination between regulators as well as monitor and evaluate the AI framework, making changes to improve the efficacy of the approach if needed.

2 million will fund a new sandbox, a trial environment where businesses can test how regulation could be applied to AI products and services, to support innovators bringing new ideas to market without being blocked by rulebook barriers.

Organisations and individuals working with AI can share their views on the white paper as part of a new consultation launching today which will inform how the framework is developed in the months ahead.

Lila Ibrahim, Chief Operating Officer and UK AI Council Member, DeepMind, said:

AI has the potential to advance science and benefit humanity in numerous ways, from combating climate change to better understanding and treating diseases. This transformative technology can only reach its full potential if it is trusted, which requires public and private partnership in the spirit of pioneering responsibly. The UKs proposed context-driven approach will help regulation keep pace with the development of AI, support innovation and mitigate future risks.

Grazia Vittadini, Chief Technology Officer, Rolls-Royce, said:

Both our business and our customers will benefit from agile, context-driven AI regulation. It will enable us to continue to lead the technical and quality assurance innovations for safety-critical industrial AI applications, while remaining compliant with the standards of integrity, responsibility and trust that society demands from AI developers.

Sue Daley, Director for Tech and Innovation at techUK, said:

techUK welcomes the much-anticipated publication of the UKs AI white paper and supports its plans for a context-specific, principle-based approach to governing AI that promotes innovation. The government must now prioritise building the necessary regulatory capacity, expertise, and coordination. techUK stands ready to work alongside government and regulators to ensure that the benefits of this powerful technology are felt across both society and the economy.

Clare Barclay, CEO, Microsoft UK, said:

AI is thetechnology that will define the coming decades with the potential to supercharge economies, create new industries and amplify human ingenuity. If the UK is to succeed and lead in the age of intelligence, then it is critical to create an environment that fosters innovation, whilst ensuring an ethical and responsible approach. We welcome the UKs commitment to being at the forefront of progress.

Rashik Parmar MBE, chief executive, BCS The Chartered Institute for IT, said:

AI is transforming how we learn, work, manage our health, discover our next binge-watch and even find love. The governments commitment to helping UK companies become global leaders in AI, while developing within responsible principles, strikes the right regulatory balance. As we watch AI growing up, we welcome the fact that our regulation will be cross-sectoral and more flexible than that proposed in the EU, while seeking to lead on aligning approaches between international partners. It is right that the risk of use is regulated, not the AI technology itself. Its also positive that the paper aims to create a central function to help monitor developments and identify risks. Similarly, the proposed multi-regulator sandbox [a safe testing environment] will help break down barriers and remove obstacles. We need to remember this future will be delivered by AI professionals - people - who believe in shared ethical values. Managing the risk of AI and building public trust is most effective when the people creating it work in an accountable and professional culture, rooted in world-leading standards and qualifications.

Read the AI regulation white paper.

Organisations and individuals involved in the AI sector are encouraged to provide feedback on the white paper through a consultation which launches today and will run until Tuesday 21 June.

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Generative AI describes algorithms that can be utilized to create new content

Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content such as audio, code, images, text, simulations, and videos. Recent breakthroughs in the industry could radically change the way we approach content creation. The way we approach content creation could be drastically altered by recent breakthroughs in the field.

Machine learning encompasses generative AI systems, and one such system, ChatGPT, describes its capabilities as follows:

The generative pre-trained transformer (GPT) is receiving a lot of attention right now. It is a cost-free chatbot that can respond to almost any question. It was developed by OpenAI and will be made available to the public for testing in November 2022 and already regarded as the best AI chatbot ever.

Medical imaging analysis and high-resolution weather forecasts are just two examples of the many applications of machine learning that have emerged in recent years. It is abundantly clear that generative AI tools like ChatGPT and DALL-E can alter how a variety of tasks are carried out.

Artificial intelligence is a type of machine learning. Models that can learn from data patterns without human guidance are developed through machine learning to develop artificial intelligence. The unmanageably colossal volume and intricacy of information (unmanageable by people, in any case) that is presently being produced has expanded the capability of AI, as well as the requirement for it.

Boldface-name donors have given OpenAI, the company behind ChatGPT, former GPT models, and DALL-E. Meta has released its Make-A-Video product, which is based on generative AI, and DeepMind is a subsidiary of Alphabet, the parent company of Google.

But its not just talent. Asking a model to practice using almost anything on the internet will cost you. OpenAI has not disclosed the exact cost but estimates that GPT-3 was trained on about 45 terabytes of text data-about a million square feet of bookshelf space, or a quarter of the entire Library of Congress-valued at several million dollars. These are not resources that your gardening business can use.

You may have noticed that the outputs produced by generative AI models can appear uncanny or indistinguishable from content created by humans. The match between the model and the use case, or input, and the quality of the model as we have seen, ChatGPTs outputs appear to be superior to those of its predecessors so far determine the outcomes.

On-demand, AI-generated art models like DALL-E can produce strange and beautiful images like a Raphael painting of a child and a Madonna, eating pizza. Other generative artificial intelligence models can deliver code, video, sound, or business reproductions.

However, not all of the outputs are appropriate or accurate. Generative AI outputs are combinations of the data used to train the algorithms that have been carefully calibrated. Since how much information is used to prepare these calculations is so unquestionably huge-as noted, GPT-3 was prepared on 45 terabytes of text information-the models can seem, by all accounts, to be inventive while creating yields.

In a matter of seconds, generative AI tools can produce a wide range of credible writing and respond to criticism to make the writing more useful. This has suggestions for a wide assortment of ventures, from IT and programming associations that can profit from the momentary, generally right code produced by computer-based intelligence models to associations needing promoting duplicate.

Weve seen that fostering a generative computer-based intelligence model is so asset-escalated that it is not feasible for everything except the greatest and best-resourced organizations. Companies that want to use generative AI can either use it straight out of the box or fine-tune it to do a particular job.

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What is Generative Artificial Intelligence (AI)? - Analytics Insight