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By Leo Porter, University of California, San Diego and Daniel Zingaro, University of Toronto 

What do you think there are more of: professional computer programmers or computer users who do a little programming?

It’s the second group. There are millions of so-called end-user programmers. They’re not going into a career as a professional programmer or computer scientist. They’re going into business, teaching, law, or any number of professions – and they just need a little programming to be more efficient. The days of programmers being confined to software development companies are long gone.

If you’ve written formulas in Excel, filtered your email based on rules, modded a game, written a script in Photoshop, used R to analyze some data, or automated a repetitive work process, you’re an end-user programmer.

As educators who teach programming, we want to help students in fields other than computer science achieve their goals. But learning how to program well enough to write finished programs can be hard to accomplish in a single course because there is so much to learn about the programming language itself. Artificial intelligence can help.

Lost in the weeds

Learning the syntax of a programming language – for example, where to place colons and where indentation is required – takes a lot of time for many students. Spending time at the level of syntax is a waste for students who simply want to use coding to help solve problems rather than learn the skill of programming.

As a result, we feel our existing classes haven’t served these students well. Indeed, many students end up barely able to write small functions – short, discrete pieces of code – let alone write a full program that can help make their lives better.

Tools built on large language models such as GitHub Copilot may allow us to change these outcomes. These tools have already changed how professionals program, and we believe we can use them to help future end-user programmers write software that is meaningful to them.

These AIs almost always write syntactically correct code and can often write small functions based on prompts in plain English. Because students can use these tools to handle some of the lower-level details of programming, it frees them to focus on bigger-picture questions that are at the heart of writing software programs. Numerous universities now offer programming courses that use Copilot.

At the University of California, San Diego, we’ve created an introductory programming course primarily for those who are not computer science students that incorporates Copilot. In this course, students learn how to program with Copilot as their AI assistant, following the curriculum from our book. In our course, students learn high-level skills such as decomposing large tasks into smaller tasks, testing code to ensure its correctness, and reading and fixing buggy code.

Freed to solve problems

In this course, we’ve been giving students large, open-ended projects and couldn’t be happier with what they have created.

For example, in a project where students had to find and analyze online datasets, we had a neuroscience major create a data visualization tool that illustrated how age and other factors affected stroke risk. Or, for example, in another project, students were able to integrate their personal art into a collage, after applying filters that they had created using the programming language Python. These projects were well beyond the scope of what we could ask students to do before the advent of large language model AIs.

Given the rhetoric about how AI is ruining education by writing papers for students and doing their homework, you might be surprised to hear educators like us talking about its benefits. AI, like any other tool people have created, can be helpful in some circumstances and unhelpful in others.

In our introductory programming course with a majority of students who are not computer science majors, we see firsthand how AI can empower students in specific ways – and promises to expand the ranks of end-user programmers.

About the Author:

Leo Porter, Teaching Professor of Computer Science and Engineering, University of California, San Diego and Daniel Zingaro, Associate Professor of Mathematical and Computational Sciences, University of Toronto

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Ambuj Tewari, University of Michigan 

State-of-the-art artificial intelligence systems like OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude have captured the public imagination by producing fluent text in multiple languages in response to user prompts. Those companies have also captured headlines with the huge sums they’ve invested to build ever more powerful models.

An AI startup from China, DeepSeek, has upset expectations about how much money is needed to build the latest and greatest AIs. In the process, they’ve cast doubt on the billions of dollars of investment by the big AI players.

I study machine learning. DeepSeek’s disruptive debut comes down not to any stunning technological breakthrough but to a time-honored practice: finding efficiencies. In a field that consumes vast computing resources, that has proved to be significant.

Where the costs are

Developing such powerful AI systems begins with building a large language model. A large language model predicts the next word given previous words. For example, if the beginning of a sentence is “The theory of relativity was discovered by Albert,” a large language model might predict that the next word is “Einstein.” Large language models are trained to become good at such predictions in a process called pretraining.

Pretraining requires a lot of data and computing power. The companies collect data by crawling the web and scanning books. Computing is usually powered by graphics processing units, or GPUs. Why graphics? It turns out that both computer graphics and the artificial neural networks that underlie large language models rely on the same area of mathematics known as linear algebra. Large language models internally store hundreds of billions of numbers called parameters or weights. It is these weights that are modified during pretraining.

Pretraining is, however, not enough to yield a consumer product like ChatGPT. A pretrained large language model is usually not good at following human instructions. It might also not be aligned with human preferences. For example, it might output harmful or abusive language, both of which are present in text on the web.

The pretrained model therefore usually goes through additional stages of training. One such stage is instruction tuning where the model is shown examples of human instructions and expected responses. After instruction tuning comes a stage called reinforcement learning from human feedback. In this stage, human annotators are shown multiple large language model responses to the same prompt. The annotators are then asked to point out which response they prefer.

It is easy to see how costs add up when building an AI model: hiring top-quality AI talent, building a data center with thousands of GPUs, collecting data for pretraining, and running pretraining on GPUs. Additionally, there are costs involved in data collection and computation in the instruction tuning and reinforcement learning from human feedback stages.

All included, costs for building a cutting edge AI model can soar up to US$100 million. GPU training is a significant component of the total cost.

The expenditure does not stop when the model is ready. When the model is deployed and responds to user prompts, it uses more computation known as test time or inference time compute. Test time compute also needs GPUs. In December 2024, OpenAI announced a new phenomenon they saw with their latest model o1: as test time compute increased, the model got better at logical reasoning tasks such as math olympiad and competitive coding problems.

Slimming down resource consumption

Thus it seemed that the path to building the best AI models in the world was to invest in more computation during both training and inference. But then DeepSeek entered the fray and bucked this trend.

Their V-series models, culminating in the V3 model, used a series of optimizations to make training cutting edge AI models significantly more economical. Their technical report states that it took them less than $6 million dollars to train V3. They admit that this cost does not include costs of hiring the team, doing the research, trying out various ideas and data collection. But $6 million is still an impressively small figure for training a model that rivals leading AI models developed with much higher costs.

The reduction in costs was not due to a single magic bullet. It was a combination of many smart engineering choices including using fewer bits to represent model weights, innovation in the neural network architecture, and reducing communication overhead as data is passed around between GPUs.

It is interesting to note that due to U.S. export restrictions on China, the DeepSeek team did not have access to high performance GPUs like the Nvidia H100. Instead they used Nvidia H800 GPUs, which Nvidia designed to be lower performance so that they comply with U.S. export restrictions. Working with this limitation seems to have unleashed even more ingenuity from the DeepSeek team.

DeepSeek also innovated to make inference cheaper, reducing the cost of running the model. Moreover, they released a model called R1 that is comparable to OpenAI’s o1 model on reasoning tasks.

They released all the model weights for V3 and R1 publicly. Anyone can download and further improve or customize their models. Furthermore, DeepSeek released their models under the permissive MIT license, which allows others to use the models for personal, academic or commercial purposes with minimal restrictions.

Resetting expectations

DeepSeek has fundamentally altered the landscape of large AI models. An open weights model trained economically is now on par with more expensive and closed models that require paid subscription plans.

The research community and the stock market will need some time to adjust to this new reality.

About the Author:

Ambuj Tewari, Professor of Statistics, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Chiara Longoni, Bocconi University; Gil Appel, George Washington University, and Stephanie Tully, University of Southern California 

The rapid spread of artificial intelligence has people wondering: who’s most likely to embrace AI in their daily lives? Many assume it’s the tech-savvy – those who understand how AI works – who are most eager to adopt it.

Surprisingly, our new research (published in the Journal of Marketing) finds the opposite. People with less knowledge about AI are actually more open to using the technology. We call this difference in adoption propensity the “lower literacy-higher receptivity” link.

This link shows up across different groups, settings and even countries. For instance, our analysis of data from market research company Ipsos spanning 27 countries reveals that people in nations with lower average AI literacy are more receptive towards AI adoption than those in nations with higher literacy.

Similarly, our survey of US undergraduate students finds that those with less understanding of AI are more likely to indicate using it for tasks like academic assignments.

The reason behind this link lies in how AI now performs tasks we once thought only humans could do. When AI creates a piece of art, writes a heartfelt response or plays a musical instrument, it can feel almost magical – like it’s crossing into human territory.

Of course, AI doesn’t actually possess human qualities. A chatbot might generate an empathetic response, but it doesn’t feel empathy. People with more technical knowledge about AI understand this.

They know how algorithms (sets of mathematical rules used by computers to carry out particular tasks), training data (used to improve how an AI system works) and computational models operate. This makes the technology less mysterious.

On the other hand, those with less understanding may see AI as magical and awe inspiring. We suggest this sense of magic makes them more open to using AI tools.

Our studies show this lower literacy-higher receptivity link is strongest for using AI tools in areas people associate with human traits, like providing emotional support or counselling. When it comes to tasks that don’t evoke the same sense of human-like qualities – such as analysing test results – the pattern flips. People with higher AI literacy are more receptive to these uses because they focus on AI’s efficiency, rather than any “magical” qualities.

It’s not about capability, fear or ethics

Interestingly, this link between lower literacy and higher receptivity persists even though people with lower AI literacy are more likely to view AI as less capable, less ethical, and even a bit scary. Their openness to AI seems to stem from their sense of wonder about what it can do, despite these perceived drawbacks.

This finding offers new insights into why people respond so differently to emerging technologies. Some studies suggest consumers favour new tech, a phenomenon called “algorithm appreciation”, while others show scepticism, or “algorithm aversion”. Our research points to perceptions of AI’s “magicalness” as a key factor shaping these reactions.

These insights pose a challenge for policymakers and educators. Efforts to boost AI literacy might unintentionally dampen people’s enthusiasm for using AI by making it seem less magical. This creates a tricky balance between helping people understand AI and keeping them open to its adoption.

To make the most of AI’s potential, businesses, educators and policymakers need to strike this balance. By understanding how perceptions of “magicalness” shape people’s openness to AI, we can help develop and deploy new AI-based products and services that take the way people view AI into account, and help them understand the benefits and risks of AI.

And ideally, this will happen without causing a loss of the awe that inspires many people to embrace this new technology.

About the Author:

Chiara Longoni, Associate Professor, Marketing and Social Science, Bocconi University; Gil Appel, Assistant Professor of Marketing, School of Business, George Washington University, and Stephanie Tully, Associate Professor of Marketing, USC Marshall School of Business, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Michael Timothy Bennett, Australian National University and Elija Perrier, Stanford University 

A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure “general intelligence”.

On December 20, OpenAI’s o3 system scored 85% on the ARC-AGI benchmark, well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test.

Creating artificial general intelligence, or AGI, is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal.

While scepticism remains, many AI researchers and developers feel something just changed. For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right?

Generalisation and intelligence

To understand what the o3 result means, you need to understand what the ARC-AGI test is all about. In technical terms, it’s a test of an AI system’s “sample efficiency” in adapting to something new – how many examples of a novel situation the system needs to see to figure out how it works.

An AI system like ChatGPT (GPT-4) is not very sample efficient. It was “trained” on millions of examples of human text, constructing probabilistic “rules” about which combinations of words are most likely.

The result is pretty good at common tasks. It is bad at uncommon tasks, because it has less data (fewer samples) about those tasks.

Until AI systems can learn from small numbers of examples and adapt with more sample efficiency, they will only be used for very repetitive jobs and ones where the occasional failure is tolerable.

The ability to accurately solve previously unknown or novel problems from limited samples of data is known as the capacity to generalise. It is widely considered a necessary, even fundamental, element of intelligence.

Grids and patterns

The ARC-AGI benchmark tests for sample efficient adaptation using little grid square problems like the one below. The AI needs to figure out the pattern that turns the grid on the left into the grid on the right.

Each question gives three examples to learn from. The AI system then needs to figure out the rules that “generalise” from the three examples to the fourth.

These are a lot like the IQ tests sometimes you might remember from school.

Weak rules and adaptation

We don’t know exactly how OpenAI has done it, but the results suggest the o3 model is highly adaptable. From just a few examples, it finds rules that can be generalised.

To figure out a pattern, we shouldn’t make any unnecessary assumptions, or be more specific than we really have to be. In theory, if you can identify the “weakest” rules that do what you want, then you have maximised your ability to adapt to new situations.

What do we mean by the weakest rules? The technical definition is complicated, but weaker rules are usually ones that can be described in simpler statements.

In the example above, a plain English expression of the rule might be something like: “Any shape with a protruding line will move to the end of that line and ‘cover up’ any other shapes it overlaps with.”

Searching chains of thought?

While we don’t know how OpenAI achieved this result just yet, it seems unlikely they deliberately optimised the o3 system to find weak rules. However, to succeed at the ARC-AGI tasks it must be finding them.

We do know that OpenAI started with a general-purpose version of the o3 model (which differs from most other models, because it can spend more time “thinking” about difficult questions) and then trained it specifically for the ARC-AGI test.

French AI researcher Francois Chollet, who designed the benchmark, believes o3 searches through different “chains of thought” describing steps to solve the task. It would then choose the “best” according to some loosely defined rule, or “heuristic”.

This would be “not dissimilar” to how Google’s AlphaGo system searched through different possible sequences of moves to beat the world Go champion.

You can think of these chains of thought like programs that fit the examples. Of course, if it is like the Go-playing AI, then it needs a heuristic, or loose rule, to decide which program is best.

There could be thousands of different seemingly equally valid programs generated. That heuristic could be “choose the weakest” or “choose the simplest”.

However, if it is like AlphaGo then they simply had an AI create a heuristic. This was the process for AlphaGo. Google trained a model to rate different sequences of moves as better or worse than others.

What we still don’t know

The question then is, is this really closer to AGI? If that is how o3 works, then the underlying model might not be much better than previous models.

The concepts the model learns from language might not be any more suitable for generalisation than before. Instead, we may just be seeing a more generalisable “chain of thought” found through the extra steps of training a heuristic specialised to this test. The proof, as always, will be in the pudding.

Almost everything about o3 remains unknown. OpenAI has limited disclosure to a few media presentations and early testing to a handful of researchers, laboratories and AI safety institutions.

Truly understanding the potential of o3 will require extensive work, including evaluations, an understanding of the distribution of its capacities, how often it fails and how often it succeeds.

When o3 is finally released, we’ll have a much better idea of whether it is approximately as adaptable as an average human.

If so, it could have a huge, revolutionary, economic impact, ushering in a new era of self-improving accelerated intelligence. We will require new benchmarks for AGI itself and serious consideration of how it ought to be governed.

If not, then this will still be an impressive result. However, everyday life will remain much the same.

About the Author:

Michael Timothy Bennett, PhD Student, School of Computing, Australian National University and Elija Perrier, Research Fellow, Stanford Center for Responsible Quantum Technology, Stanford University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

I research the intersection of artificial intelligence, natural language processing and human reasoning as the director of the Advancing Human and Machine Reasoning lab at the University of South Florida. I am also commercializing this research in an AI startup that provides a vulnerability scanner for language models.

From my vantage point, I observed significant developments in the field of AI language models in 2024, both in research and the industry.

Perhaps the most exciting of these are the capabilities of smaller language models, support for addressing AI hallucination, and frameworks for developing AI agents.

Small AIs make a splash

At the heart of commercially available generative AI products like ChatGPT are large language models, or LLMs, which are trained on vast amounts of text and produce convincing humanlike language. Their size is generally measured in parameters, which are the numerical values a model derives from its training data. The larger models like those from the major AI companies have hundreds of billions of parameters.

There is an iterative interaction between large language models and smaller language models, which seems to have accelerated in 2024.

First, organizations with the most computational resources experiment with and train increasingly larger and more powerful language models. Those yield new large language model capabilities, benchmarks, training sets and training or prompting tricks. In turn, those are used to make smaller language models – in the range of 3 billion parameters or less – which can be run on more affordable computer setups, require less energy and memory to train, and can be fine-tuned with less data.

No surprise, then, that developers have released a host of powerful smaller language models – although the definition of small keeps changing: Phi-3 and Phi-4 from Microsoft, Llama-3.2 1B and 3B, and Qwen2-VL-2B are just a few examples.

These smaller language models can be specialized for more specific tasks, such as rapidly summarizing a set of comments or fact-checking text against a specific reference. They can work with their larger cousins to produce increasingly powerful hybrid systems.

Wider access

Increased access to highly capable language models large and small can be a mixed blessing. As there were many consequential elections around the world in 2024, the temptation for the misuse of language models was high.

Language models can give malicious users the ability to generate social media posts and deceptively influence public opinion. There was a great deal of concern about this threat in 2024, given that it was an election year in many countries.

And indeed, a robocall faking President Joe Biden’s voice asked New Hampshire Democratic primary voters to stay home. OpenAI had to intervene to disrupt over 20 operations and deceptive networks that tried to use its models for deceptive campaigns. Fake videos and memes were created and shared with the help of AI tools.

Despite the anxiety surrounding AI disinformation, it is not yet clear what effect these efforts actually had on public opinion and the U.S. election. Nevertheless, U.S. states passed a large amount of legislation in 2024 governing the use of AI in elections and campaigns.

Misbehaving bots

Google started including AI overviews in its search results, yielding some results that were hilariously and obviously wrong – unless you enjoy glue in your pizza. However, other results may have been dangerously wrong, such as when it suggested mixing bleach and vinegar to clean your clothes.

Large language models, as they are most commonly implemented, are prone to hallucinations. This means that they can state things that are false or misleading, often with confident language. Even though I and others continually beat the drum about this, 2024 still saw many organizations learning about the dangers of AI hallucination the hard way.

Despite significant testing, a chatbot playing the role of a Catholic priest advocated for baptism via Gatorade. A chatbot advising on New York City laws and regulations incorrectly said it was “legal for an employer to fire a worker who complains about sexual harassment, doesn’t disclose a pregnancy or refuses to cut their dreadlocks.” And OpenAI’s speech-capable model forgot whose turn it was to speak and responded to a human in her own voice.

Fortunately, 2024 also saw new ways to mitigate and live with AI hallucinations. Companies and researchers are developing tools for making sure AI systems follow given rules pre-deployment, as well as environments to evaluate them. So-called guardrail frameworks inspect large language model inputs and outputs in real time, albeit often by using another layer of large language models.

And the conversation on AI regulation accelerated, causing the big players in the large language model space to update their policies on responsibly scaling and harnessing AI.

But although researchers are continually finding ways to reduce hallucinations, in 2024, research convincingly showed that AI hallucinations are always going to exist in some form. It may be a fundamental feature of what happens when an entity has finite computational and information resources. After all, even human beings are known to confidently misremember and state falsehoods from time to time.

The rise of agents

Large language models, particularly those powered by variants of the transformer architecture, are still driving the most significant advances in AI. For example, developers are using large language models to not only create chatbots, but to serve as the basis of AI agents. The term “agentic AI” shot to prominence in 2024, with some pundits even calling it the third wave of AI.

To understand what an AI agent is, think of a chatbot expanded in two ways: First, give it access to tools that provide the ability to take actions. This might be the ability to query an external search engine, book a flight or use a calculator. Second, give it increased autonomy, or the ability to make more decisions on its own.

For example, a travel AI chatbot might be able to perform a search of flights based on what information you give it, but a tool-equipped travel agent might plan out an entire trip itinerary, including finding events, booking reservations and adding them to your calendar.

In 2024, new frameworks for developing AI agents emerged. Just to name a few, LangGraph, CrewAI, PhiData and AutoGen/Magentic-One were released or improved in 2024.

Companies are just beginning to adopt AI agents. Frameworks for developing AI agents are new and rapidly evolving. Furthermore, security, privacy and hallucination risks are still a concern.

But global market analysts forecast this to change: 82% of organizations surveyed plan to use agents within 1-3 years, and 25% of all companies currently using generative AI are likely to adopt AI agents in 2025.

About the Author:

John Licato, Associate Professor of Computer Science, Director of AMHR Lab, University of South Florida

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Tamilla Triantoro, Quinnipiac University 

Online shopping often involves endless options and fleeting discounts. A single search for running shoes can yield hundreds of results across multiple platforms, each promising the “best deal.” The holiday season brings excitement, but it also brings a blend of decision fatigue and logistical nightmares.

What if there were a tool capable of hunting for the best prices, navigating endless sales and making sure your purchases arrive on time?

The next evolution in artificial intelligence is AI agents that are capable of autonomous reasoning and multistep problem-solving. AI shopping agents not only suggest what you might like, but they can also act on your behalf. Major retailers and AI companies are developing AI shopping assistants, and the AI company Perplexity released Buy with Pro on Nov. 18, 2024.

Picture this: You prompt AI to find a winter coat under $200 that’s highly rated and will arrive by Sunday. In seconds, it scans websites, compares prices, checks reviews, confirms availability and places the order, all while you go about your day.

Unlike traditional recommendation engines, AI agents learn your preferences and handle tasks autonomously. The agents are built with machine learning and natural language processing. They learn from their interactions with the people using them and become smarter and more efficient over time from their collective interactions.

Looking ahead, AI agents are likely to not only master personal shopping needs but also negotiate directly with corporate AI systems. They will not only learn your preferences but will likely be able to book tailored experiences, handle payments across platforms and coordinate schedules.

As a researcher who studies human-AI collaboration, I see how AI agents could make the future of shopping virtually effortless and more personalized than ever.

How AI agents help shoppers

Marketplaces such as Amazon and Walmart have been using AI to automate shopping. Google Lens offers a visual search tool for finding products.

Perplexity’s Buy with Pro is a more powerful AI shopping agent. By providing your shipping and billing information, you can place orders directly on the Perplexity app with free shipping on every order. The shopping assistant is part of the company’s Perplexity Pro service, which has free and paid tiers.

For those looking to build custom AI shopping agents, AutoGPT and AgentGPT are open-source tools for configuring and deploying AI agents.

Consumers today are focused on value, looking for deals and comparing prices across platforms. Having an assistant perform these tasks could be a tremendous time saver. But can AI truly learn your preferences?

A recent study using the GPT-4o model achieved 85% accuracy in imitating the thoughts and behaviors of over 1,000 people after they interacted with the AI for just two hours. This breakthrough finding suggests that digital personas can understand and act on people’s preferences in ways that will transform the shopping experience.

How AI shopping reshapes business

AI agents are moving beyond recommendations to autonomously executing complex tasks such as automating refunds, managing inventory and approving pricing decisions. This evolution has already begun to reshape how businesses operate and how consumers interact with them.

Retailers using AI agents are seeing measurable benefits. Since October 2024, data from the Salesforce shopping index reveals that digital retailers using generative AI achieved a 7% increase in average order revenue and attributed 17% of global orders to AI-driven personalized recommendations, targeted promotions and improved customer service.

Meanwhile, the nature of search and advertising is undergoing a major shift. Amazon is capturing billions of dollars in ad revenue as shoppers bypass Google to search directly on its platform. Simultaneously, AI-powered search tools such as Perplexity and OpenAI’s web-enabled chat deliver instant, context-aware responses, challenging traditional search engines and forcing advertisers to rethink their strategies.

The outcome of the battle between Big Tech and open-source initiatives to shape the AI ecosystem is also likely to affect how the shopping experience changes.

The risks: Privacy, manipulation and dependency

While AI agents offer significant benefits, they also raise critical privacy concerns. AI systems require extensive access to personal data, shopping history and financial information. This level of access increases the risk of misuse and unauthorized sharing.

Manipulation is another issue. AI can be highly persuasive and may be optimized to serve corporate interests over consumer welfare. Such technology can prioritize upselling or nudging shoppers toward higher-margin products under the guise of personalization.

There’s also the risk of dependency. Automating many aspects of shopping could diminish the satisfaction of making choices. Research in human-AI interaction indicates that while AI tools can reduce cognitive load, increased reliance on AI could impair people’s ability to critically evaluate their options.

What’s next?

AI-based shopping is still in its infancy, so how much trust should you place in it?

In our book “Converging Minds,” AI researcher Aleksandra Przegalinska and I argue for a balanced and critical approach to AI adoption, recognizing both its potential and its pitfalls.

As cognitive scientist Gary Marcus points out, AI’s moral limitations stem from technical constraints: Despite efforts to prevent errors, these systems remain imperfect.

This cautious perspective is reflected in the responses from my MBA class. When I asked students whether they were ready to outsource their holiday shopping to AI, the answer was an overwhelming no. Ethan Mollick, a professor at the Wharton School at the University of Pennsylvania, has argued that the adoption of AI in everyday life will be gradual, as societal change typically lags behind technological advancement.

Before people are willing to hand over their credit cards and let AI take the reins, businesses will have to ensure that AI systems align with human values and priorities. The promise of AI is vast, but to fulfill that promise I believe that AI will need to be an extension of human intention – not a replacement for it.

About the Author:

Tamilla Triantoro, Associate Professor of Business Analytics and Information Systems, Quinnipiac University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Lauren Labrecque, University of Rhode Island 

Artificial intelligence is revolutionizing the way companies market their products, enabling them to target consumers in personalized and interactive ways that not long ago seemed like the realm of science fiction.

Marketers use AI-powered algorithms to scour vast amounts of data that reveals individual preferences with unrivaled accuracy. This allows companies to precisely target content – ads, emails, social media posts – that feels tailor-made and helps cultivate companies’ relationships with consumers.

As a researcher who studies technology in marketing, I joined several colleagues in conducting new research that shows AI marketing overwhelmingly neglects its potential negative consequences.

Our peer-reviewed study reviewed 290 articles that had been published over the past 10 years from 15 high-ranking marketing journals. We found that only 33 of them addressed the potential “dark side” of AI marketing.

This matters because the imbalance creates a critical gap in understanding the full impact of AI.

AI marketing can perpetuate harmful stereotypes, such as producing hypersexualized depictions of women, for example. AI can also infringe on the individual rights of artists. And it can spread misinformation through deepfakes and “hallucinations,” which occur when AI presents false information as if it were true, such as inventing historical events.

It can also negatively affect mental health. The prevalence of AI-powered beauty filters on social media, for instance, can foster unrealistic ideals and trigger depression.

These concerns loom large, prompting anxiety about the potential misuse of this powerful technology. Many people experience these worries, but young women are notably vulnerable. As AI apps gain acceptance, beauty standards are moving further from reality.

Our research finds there is an urgent need to address AI’s ethical considerations and potential negative consequences. Our intent is not to discredit AI. It’s to make sure that AI marketing benefits everyone, not just a handful of powerful companies.

I believe researchers should consider exploring the ethical problems with AI more thoroughly, and how to use it safely and responsibly.

This is important because AI is suddenly being used everywhere – from social media to self-driving cars to making health decisions. Understanding its potential negative effects empowers the public to be informed consumers and call for responsible AI use.

About the Author:

Lauren Labrecque, Professor of Marketing, University of Rhode Island

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Jaeyeon Chung, Rice University 

Think back to a time when you needed a quick answer, maybe for a recipe or a DIY project. A few years ago, most people’s first instinct was to “Google it.” Today, however, many people are more likely to reach for ChatGPT, OpenAI’s conversational AI, which is changing the way people look for information.

Rather than simply providing lists of websites, ChatGPT gives more direct, conversational responses. But can ChatGPT do more than just answer straightforward questions? Can it actually help people be more creative?

I study new technologies and consumer interaction with social media. My colleague Byung Lee and I set out to explore this question: Can ChatGPT genuinely assist people in creatively solving problems, and does it perform better at this than traditional search engines like Google?

Across a series of experiments in a study published in the journal Nature Human Behavour, we found that ChatGPT does boost creativity, especially in everyday, practical tasks. Here’s what we learned about how this technology is changing the way people solve problems, brainstorm ideas and think creatively.

ChatGPT and creative tasks

Imagine you’re searching for a creative gift idea for a teenage niece. Previously, you might have googled “creative gifts for teens” and then browsed articles until something clicked. Now, if you ask ChatGPT, it generates a direct response based on its analysis of patterns across the web. It might suggest a custom DIY project or a unique experience, crafting the idea in real time.

To explore whether ChatGPT surpasses Google in creative thinking tasks, we conducted five experiments where participants tackled various creative tasks. For example, we randomly assigned participants to either use ChatGPT for assistance, use Google search, or generate ideas on their own. Once the ideas were collected, external judges, unaware of the participants’ assigned conditions, rated each idea for creativity. We averaged the judges’ scores to provide an overall creativity rating.

One task involved brainstorming ways to repurpose everyday items, such as turning an old tennis racket and a garden hose into something new. Another asked participants to design an innovative dining table. The goal was to test whether ChatGPT could help people come up with more creative solutions compared with using a web search engine or just their own imagination.

The results were clear: Judges rated ideas generated with ChatGPT’s assistance as more creative than those generated with Google searches or without any assistance. Interestingly, ideas generated with ChatGPT – even without any human modification – scored higher in creativity than those generated with Google.

One notable finding was ChatGPT’s ability to generate incrementally creative ideas: those that improve or build on what already exists. While truly radical ideas might still be challenging for AI, ChatGPT excelled at suggesting practical yet innovative approaches. In the toy-design experiment, for example, participants using ChatGPT came up with imaginative designs, such as turning a leftover fan and a paper bag into a wind-powered craft.

Limits of AI creativity

ChatGPT’s strength lies in its ability to combine unrelated concepts into a cohesive response. Unlike Google, which requires users to sift through links and piece together information, ChatGPT offers an integrated answer that helps users articulate and refine ideas in a polished format. This makes ChatGPT promising as a creativity tool, especially for tasks that connect disparate ideas or generate new concepts.

It’s important to note, however, that ChatGPT doesn’t generate truly novel ideas. It recognizes and combines linguistic patterns from its training data, subsequently generating outputs with the most probable sequences based on its training. If you’re looking for a way to make an existing idea better or adapt it in a new way, ChatGPT can be a helpful resource. For something groundbreaking, though, human ingenuity and imagination are still essential.

Additionally, while ChatGPT can generate creative suggestions, these aren’t always practical or scalable without expert input. Steps such as screening, feasibility checks, fact-checking and market validation require human expertise. Given that ChatGPT’s responses may reflect biases in its training data, people should exercise caution in sensitive contexts such as those involving race or gender.

We also tested whether ChatGPT could assist with tasks often seen as requiring empathy, such as repurposing items cherished by a loved one. Surprisingly, ChatGPT enhanced creativity even in these scenarios, generating ideas that users found relevant and thoughtful. This result challenges the belief that AI cannot assist with emotionally driven tasks.

Future of AI and creativity

As ChatGPT and similar AI tools become more accessible, they open up new possibilities for creative tasks. Whether in the workplace or at home, AI could assist in brainstorming, problem-solving and enhancing creative projects. However, our research also points to the need for caution: While ChatGPT can augment human creativity, it doesn’t replace the unique human capacity for truly radical, out-of-the-box thinking.

This shift from Googling to asking ChatGPT represents more than just a new way to access information. It marks a transformation in how people collaborate with technology to think, create and innovate.

About the Author:

Jaeyeon Chung, Assistant Professor of Business, Rice University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Gertjan Verdickt, University of Auckland, Waipapa Taumata Rau 

When it comes to investing and planning your financial future, are you more willing to trust a person or a computer?

This isn’t a hypothetical question any more.

Big banks and investment firms are using artificial intelligence (AI) to help make financial predictions and give advice to clients.

Morgan Stanley uses AI to mitigate the potential biases of its financial analysts when it comes to stock market predictions. And one of the world’s biggest investment banks, Goldman Sachs, recently announced it was trialling the use of AI to help write computer code, though the bank declined to say which division it was being used in. Other companies are using AI to predict which stocks might go up or down.

But do people actually trust these AI advisers with their money?

Our new research examines this question. We found it really depends on who you are and your prior knowledge of AI and how it works.

Trust differences

To examine the question of trust when it comes to using AI for investment, we asked 3,600 people in the United States to imagine they were getting advice about the stock market.

In these imagined scenarios, some people got advice from human experts. Others got advice from AI. And some got advice from humans working together with AI.

In general, people were less likely to follow advice if they knew AI was involved in making it. They seemed to trust the human experts more.

But the distrust of AI wasn’t universal. Some groups of people were more open to AI advice than others.

For example, women were more likely to trust AI advice than men (by 7.5%). People who knew more about AI were more willing to listen to the advice it provided (by 10.1%). And politics mattered – people who supported the Democratic Party were more open to AI advice than others (by 7.3%).

We also found people were more likely to trust simpler AI methods.

When we told our research participants the AI was using something called “ordinary least squares” (a basic mathematics technique in which a straight line is used to estimate the relationship between two variables), they were more likely to trust it than when we said it was using “deep learning” (a more complex AI method).

This might be because people tend to trust things they understand. Much like how a person might trust a simple calculator more than a complex scientific instrument they have never seen before.

Trust in the future of finance

As AI becomes more common in the financial world, companies will need to find ways to improve levels of trust.

This might involve teaching people more about how the AI systems work, being clear about when and how AI is being used, and finding the right balance between human experts and AI.

Furthermore, we need to tailor how AI advice is presented to different groups of people and show how well AI performs over time compared to human experts.

The future of finance might involve a lot more AI, but only if people learn to trust it. It’s a bit like learning to trust self-driving cars. The technology might be great, but if people don’t feel comfortable using it, it won’t catch on.

Our research shows that building this trust isn’t just about making better AI. It’s about understanding how people think and feel about AI. It’s about bridging the gap between what AI can do and what people believe it can do.

As we move forward, we’ll need to keep studying how people react to AI in finance. We’ll need to find ways to make AI not just a powerful tool, but a trusted advisor that people feel comfortable relying on for important financial decisions.

The world of finance is changing fast, and AI is a big part of that change. But in the end, it’s still people who decide where to put their money. Understanding how to build trust between humans and AI will be key to shaping the future of finance.

About the Author:

Gertjan Verdickt, Lecturer, Business School, University of Auckland, Waipapa Taumata Rau

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Veera Sundararaghavan, University of Michigan 

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work has been fundamental in developing neural network theories that underpin generative artificial intelligence.

A neural network is a computational model consisting of layers of interconnected neurons. Like the neurons in your brain, these neurons process and send along a piece of information. Each neural layer receives a piece of data, processes it and passes the result to the next layer. By the end of the sequence, the network has processed and refined the data into something more useful.

While it might seem surprising that Hopfield and Hinton received the physics prize for their contributions to neural networks, used in computer science, their work is deeply rooted in the principles of physics, particularly a subfield called statistical mechanics.

As a computational materials scientist, I was excited to see this area of research recognized with the prize. Hopfield and Hinton’s work has allowed my colleagues and me to study a process called generative learning for materials sciences, a method that is behind many popular technologies like ChatGPT.

What is statistical mechanics?

Statistical mechanics is a branch of physics that uses statistical methods to explain the behavior of systems made up of a large number of particles.

Instead of focusing on individual particles, researchers using statistical mechanics look at the collective behavior of many particles. Seeing how they all act together helps researchers understand the system’s large-scale macroscopic properties like temperature, pressure and magnetization.

For example, physicist Ernst Ising developed a statistical mechanics model for magnetism in the 1920s. Ising imagined magnetism as the collective behavior of atomic spins interacting with their neighbors.

In Ising’s model, there are higher and lower energy states for the system, and the material is more likely to exist in the lowest energy state.

One key idea in statistical mechanics is the Boltzmann distribution, which quantifies how likely a given state is. This distribution describes the probability of a system being in a particular state – like solid, liquid or gas – based on its energy and temperature.

Ising exactly predicted the phase transition of a magnet using the Boltzmann distribution. He figured out the temperature at which the material changed from being magnetic to nonmagnetic.

Phase changes happen at predictable temperatures. Ice melts to water at a specific temperature because the Boltzmann distribution predicts that when it gets warm, the water molecules are more likely to take on a disordered – or liquid – state.

In materials, atoms arrange themselves into specific crystal structures that use the lowest amount of energy. When it’s cold, water molecules freeze into ice crystals with low energy states.

Similarly, in biology, proteins fold into low energy shapes, which allow them to function as specific antibodies – like a lock and key – targeting a virus.

Neural networks and statistical mechanics

Fundamentally, all neural networks work on a similar principle – to minimize energy. Neural networks use this principle to solve computing problems.

For example, imagine an image made up of pixels where you only can see a part of the picture. Some pixels are visible, while the rest are hidden. To determine what the image is, you consider all possible ways the hidden pixels could fit together with the visible pieces. From there, you would choose from among what statistical mechanics would say are the most likely states out of all the possible options.

Hopfield and Hinton developed a theory for neural networks based on the idea of statistical mechanics. Just like Ising before them, who modeled the collective interaction of atomic spins to solve the photo problem with a neural network, Hopfield and Hinton imagined collective interactions of pixels. They represented these pixels as neurons.

Just as in statistical physics, the energy of an image refers to how likely a particular configuration of pixels is. A Hopfield network would solve this problem by finding the lowest energy arrangements of hidden pixels.

However, unlike in statistical mechanics – where the energy is determined by known atomic interactions – neural networks learn these energies from data.

Hinton popularized the development of a technique called backpropagation. This technique helps the model figure out the interaction energies between these neurons, and this algorithm underpins much of modern AI learning.

The Boltzmann machine

Building upon Hopfield’s work, Hinton imagined another neural network, called the Boltzmann machine. It consists of visible neurons, which we can observe, and hidden neurons, which help the network learn complex patterns.

In a Boltzmann machine, you can determine the probability that the picture looks a certain way. To figure out this probability, you can sum up all the possible states the hidden pixels could be in. This gives you the total probability of the visible pixels being in a specific arrangement.

My group has worked on implementing Boltzmann machines in quantum computers for generative learning.

In generative learning, the network learns to generate new data samples that resemble the data the researchers fed the network to train it. For example, it might generate new images of handwritten numbers after being trained on similar images. The network can generate these by sampling from the learned probability distribution.

Generative learning underpins modern AI – it’s what allows the generation of AI art, videos and text.

Hopfield and Hinton have significantly influenced AI research by leveraging tools from statistical physics. Their work draws parallels between how nature determines the physical states of a material and how neural networks predict the likelihood of solutions to complex computer science problems.

About the Author:

Veera Sundararaghavan, Professor of Aerospace Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.