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Mind reading is common among us humans. Not in the ways that psychics claim to do it, by gaining access to the warm streams of consciousness that fill every individuals experience, or in the ways that mentalists claim to do it, by pulling a thought out of your head at will. Everyday mind reading is more subtle: We take in peoples faces and movements, listen to their words and then decide or intuit what might be going on in their heads.

Among psychologists, such intuitive psychology the ability to attribute to other people mental states different from our own is called theory of mind, and its absence or impairment has been linked to autism, schizophrenia and other developmental disorders. Theory of mind helps us communicate with and understand one another; it allows us to enjoy literature and movies, play games and make sense of our social surroundings. In many ways, the capacity is an essential part of being human.

What if a machine could read minds, too?

Recently, Michal Kosinski, a psychologist at the Stanford Graduate School of Business, made just that argument: that large language models like OpenAIs ChatGPT and GPT-4 next-word prediction machines trained on vast amounts of text from the internet have developed theory of mind. His studies have not been peer reviewed, but they prompted scrutiny and conversation among cognitive scientists, who have been trying to take the often asked question these days Can ChatGPT do this? and move it into the realm of more robust scientific inquiry. What capacities do these models have, and how might they change our understanding of our own minds?

Psychologists wouldnt accept any claim about the capacities of young children just based on anecdotes about your interactions with them, which is what seems to be happening with ChatGPT, said Alison Gopnik, a psychologist at the University of California, Berkeley and one of the first researchers to look into theory of mind in the 1980s. You have to do quite careful and rigorous tests.

Dr. Kosinskis previous research showed that neural networks trained to analyze facial features like nose shape, head angle and emotional expression could predict peoples political views and sexual orientation with a startling degree of accuracy (about 72 percent in the first case and about 80 percent in the second case). His recent work on large language models uses classic theory of mind tests that measure the ability of children to attribute false beliefs to other people.

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.

A famous example is the Sally-Anne test, in which a girl, Anne, moves a marble from a basket to a box when another girl, Sally, isnt looking. To know where Sally will look for the marble, researchers claimed, a viewer would have to exercise theory of mind, reasoning about Sallys perceptual evidence and belief formation: Sally didnt see Anne move the marble to the box, so she still believes it is where she last left it, in the basket.

Dr. Kosinski presented 10 large language models with 40 unique variations of these theory of mind tests descriptions of situations like the Sally-Anne test, in which a person (Sally) forms a false belief. Then he asked the models questions about those situations, prodding them to see whether they would attribute false beliefs to the characters involved and accurately predict their behavior. He found that GPT-3.5, released in November 2022, did so 90 percent of the time, and GPT-4, released in March 2023, did so 95 percent of the time.

The conclusion? Machines have theory of mind.

But soon after these results were released, Tomer Ullman, a psychologist at Harvard University, responded with a set of his own experiments, showing that small adjustments in the prompts could completely change the answers generated by even the most sophisticated large language models. If a container was described as transparent, the machines would fail to infer that someone could see into it. The machines had difficulty taking into account the testimony of people in these situations, and sometimes couldnt distinguish between an object being inside a container and being on top of it.

Maarten Sap, a computer scientist at Carnegie Mellon University, fed more than 1,000 theory of mind tests into large language models and found that the most advanced transformers, like ChatGPT and GPT-4, passed only about 70 percent of the time. (In other words, they were 70 percent successful at attributing false beliefs to the people described in the test situations.) The discrepancy between his data and Dr. Kosinskis could come down to differences in the testing, but Dr. Sap said that even passing 95 percent of the time would not be evidence of real theory of mind. Machines usually fail in a patterned way, unable to engage in abstract reasoning and often making spurious correlations, he said.

Dr. Ullman noted that machine learning researchers have struggled over the past couple of decades to capture the flexibility of human knowledge in computer models. This difficulty has been a shadow finding, he said, hanging behind every exciting innovation. Researchers have shown that language models will often give wrong or irrelevant answers when primed with unnecessary information before a question is posed; some chatbots were so thrown off by hypothetical discussions about talking birds that they eventually claimed that birds could speak. Because their reasoning is sensitive to small changes in their inputs, scientists have called the knowledge of these machines brittle.

Dr. Gopnik compared the theory of mind of large language models to her own understanding of general relativity. I have read enough to know what the words are, she said. But if you asked me to make a new prediction or to say what Einsteins theory tells us about a new phenomenon, Id be stumped because I dont really have the theory in my head. By contrast, she said, human theory of mind is linked with other common-sense reasoning mechanisms; it stands strong in the face of scrutiny.

In general, Dr. Kosinskis work and the responses to it fit into the debate about whether the capacities of these machines can be compared to the capacities of humans a debate that divides researchers who work on natural language processing. Are these machines stochastic parrots, or alien intelligences, or fraudulent tricksters? A 2022 survey of the field found that, of the 480 researchers who responded, 51 percent believed that large language models could eventually understand natural language in some nontrivial sense, and 49 percent believed that they could not.

Dr. Ullman doesnt discount the possibility of machine understanding or machine theory of mind, but he is wary of attributing human capacities to nonhuman things. He noted a famous 1944 study by Fritz Heider and Marianne Simmel, in which participants were shown an animated movie of two triangles and a circle interacting. When the subjects were asked to write down what transpired in the movie, nearly all described the shapes as people.

Lovers in the two-dimensional world, no doubt; little triangle number-two and sweet circle, one participant wrote. Triangle-one (hereafter known as the villain) spies the young love. Ah!

Its natural and often socially required to explain human behavior by talking about beliefs, desires, intentions and thoughts. This tendency is central to who we are so central that we sometimes try to read the minds of things that dont have minds, at least not minds like our own.

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Have AI Chatbots Developed Theory of Mind? What We Do and Do Not Know. - The New York Times

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

Illustration of the Support Vector Machine (SVM) Algorithm. The Black Circles and Triangles Indicate Unaffected Individuals and Patients with CVD, Respectively. A: Normal people and CVD patients are linearly separable. B: Normal people and CVD patients are nonlinearly separable. C: Normal people and CVD patients are mapped into high-dimensional space and separated by a decision surface. Credit: Cardiovascular Innovations and Applications (2023). DOI: 10.15212/CVIA.2023.0011

Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data.

Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.

This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.

ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

The study is published in the journal Cardiovascular Innovations and Applications.

More information: Jiawei Zhou et al, Machine Learning Methods in Real-World Studies of Cardiovascular Disease, Cardiovascular Innovations and Applications (2023). DOI: 10.15212/CVIA.2023.0011

Provided by Compuscript Ltd

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Machine learning methods in real-world studies of cardiovascular disease - Medical Xpress

In aglobal reportissued by S&P, 95% of enterprises across various industries said that Artificial Intelligence (AI) adoption is an important part of their digital transformation journey. Were seeing expanded interest in the adoption of AI for many reasons, including lowering costs, increasing sales, and improving worker productivity. At the same time, if youre keeping up with the news on AI these days, you know were also seeing considerable focus placed on explaining how AI models work and why explainability is important. But our question as two AI practitioners Is explainability that important? Or does it lead to a false sense of security?

Explainable Artificial Intelligence (XAI), as summed up by IBM Watson, is a set of processes and methods that allows human users to comprehend and trust the results and outputcreated by machine learning algorithms.Many believe that XAI promotes model transparency and trust, making people more comfortable with the risk of improper learning and incorrect predictions that can occur with machine learning models.

Its human nature to seek explanations as a means of better understanding unknown subjects. We lean on explainability even more when the stakes are high. Asrecently concluded by two Dartmouth researchers, if the explanation is visually supported by pretty charts, we are partial to it. Explanations can give us a feeling of security when it comes to making informed decisions. Take, for example, a patient who asks a doctor for an explanation of a diagnosis. Even when the explanation is hard to grasp, the more scientific the doctor sounds, the better the patient may feel. It can be the same with AI. The more detail end users are given about how it works, the more likely they are to accept the outcome as valid and feel confident about doing so.

Are explanations sufficient? Some things are complex, and merely having an explanation is not a sufficient and necessary condition to derive utility.

And with many businesses considering avenues for AI adoption, we have to ask about the risks associated with relying so heavily on explainability. What if the explainer is not sufficiently knowledgeable? Users could be fed incorrect information without realizing it. What if theres not familiarity with the topic to fully grasp the explanation? It is quite possible that when it comes to new topics like AI models, users such as business stakeholders, regulators, and even domain experts may end up with only a superficial understanding of the explanation provided. They may not be able to discern if and how the model was incorrect in the first place, which means even with explanations, users can still end up with disastrous decision making.

In many use cases, a more accurate model is better than having an explanation. After all, what better evidence of utility than a model that gives the right outcome? Hence, we must question if we should be going after explainability, as is the rage right now in XAI, or after truthfulness?

Truthfulness comes from accuracy measures, which give us an indication of how much reliance we can place on the system. Accuracy is directly linked to the quality of the underlying data. The progression of data quality and accuracy over time goes hand and hand. Many AI models are used in dynamic settings where data drift is the norm. Asking crucial questions about the distribution of training data and out of sample data is elemental to having accurate models that can be relied on.

Forget explanations and reasoning for a moment and picture a system that can establish a high degree of truthfulness by means of doing well on a large test dataset across different real-world distributions. Seems too good to be true, right?

Let us examine this concept using a real-life scenario. Have you ever had to ask your colleague or friend for an explanation of how they recognized you in just a nanosecond of time? No, because of the truthfulness of the outcome. It never crosses your mind to understand the how, because the end result is correct with a high degree of accuracy. Similarly in AI, when we transition to a phase where the models accuracy beats the human baseline, and we reach that high degree of accuracy, explainability will become less relevant. So, what is the alternative to explainability? Simplified, business-friendly metrics. As AI practitioners, we need to recognize that it is difficult for non-practitioners to make sense of our different analytical metrics, such as: F1 Score, Rouge Score, Perplexity, Bleu Score, WER, Confusion Matrix, etc. We need a simplified, business-friendly metric that can be readily understood, like Googles use of Sensibleness and Specificity Average (SSA) Score in their evaluation score for Meena.[1]While it may not be easy to develop simplified metrics in all instances, its imperative we do so whenever possible to limit the need for model explanations and ultimately lead to better decision-making for AI end users.

Link:
The illusion of explainability in machine learning models - Finextra

Its no secret that OpenAIs ChatGPT has some incredible capabilities for instance, the chatbot can write poetry that resembles Shakespearean sonnets or debug code for a computer program. These abilities are made possible by the massive machine-learning model that ChatGPT is built upon. Researchers have found that when these types of models become large enough, extraordinary capabilities emerge.

But bigger models also require more time and money to train. The training process involves showing hundreds of billions of examples to a model. Gathering so much data is an involved process in itself. Then come the monetary and environmental costs of running many powerful computers for days or weeks to train a model that may have billions of parameters.

Its been estimated that training models at the scale of what ChatGPT is hypothesized to run on could take millions of dollars, just for a single training run. Can we improve the efficiency of these training methods, so we can still get good models in less time and for less money? We propose to do this by leveraging smaller language models that have previously been trained, says Yoon Kim, an assistant professor in MITs Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Rather than discarding a previous version of a model, Kim and his collaborators use it as the building blocks for a new model. Using machine learning, their method learns to grow a larger model from a smaller model in a way that encodes knowledge the smaller model has already gained. This enables faster training of the larger model.

Their technique saves about 50 percent of the computational cost required to train a large model, compared to methods that train a new model from scratch. Plus, the models trained using the MIT method performed as well as, or better than, models trained with other techniques that also use smaller models to enable faster training of larger models.

Reducing the time it takes to train huge models could help researchers make advancements faster with less expense, while also reducing the carbon emissions generated during the training process. It could also enable smaller research groups to work with these massive models, potentially opening the door to many new advances.

As we look to democratize these types of technologies, making training faster and less expensive will become more important, says Kim, senior author of a paper on this technique.

Kim and his graduate student Lucas Torroba Hennigen wrote the paper with lead author Peihao Wang, a graduate student at the University of Texas at Austin, as well as others at the MIT-IBM Watson AI Lab and Columbia University. The research will be presented at the International Conference on Learning Representations.

The bigger the better

Large language models like GPT-3, which is at the core of ChatGPT, are built using a neural network architecture called a transformer. A neural network, loosely based on the human brain, is composed of layers of interconnected nodes, or neurons. Each neuron contains parameters, which are variables learned during the training process that the neuron uses to process data.

Transformer architectures are unique because, as these types of neural network models get bigger, they achieve much better results.

This has led to an arms race of companies trying to train larger and larger transformers on larger and larger datasets. More so than other architectures, it seems that transformer networks get much better with scaling. Were just not exactly sure why this is the case, Kim says.

These models often have hundreds of millions or billions of learnable parameters. Training all these parameters from scratch is expensive, so researchers seek to accelerate the process.

One effective technique is known as model growth. Using the model growth method, researchers can increase the size of a transformer by copying neurons, or even entire layers of a previous version of the network, then stacking them on top. They can make a network wider by adding new neurons to a layer or make it deeper by adding additional layers of neurons.

In contrast to previous approaches for model growth, parameters associated with the new neurons in the expanded transformer are not just copies of the smaller networks parameters, Kim explains. Rather, they are learned combinations of the parameters of the smaller model.

Learning to grow

Kim and his collaborators use machine learning to learn a linear mapping of the parameters of the smaller model. This linear map is a mathematical operation that transforms a set of input values, in this case the smaller models parameters, to a set of output values, in this case the parameters of the larger model.

Their method, which they call a learned Linear Growth Operator (LiGO), learns to expand the width and depth of larger network from the parameters of a smaller network in a data-driven way.

But the smaller model may actually be quite large perhaps it has a hundred million parameters and researchers might want to make a model with a billion parameters. So the LiGO technique breaks the linear map into smaller pieces that a machine-learning algorithm can handle.

LiGO also expands width and depth simultaneously, which makes it more efficient than other methods. A user can tune how wide and deep they want the larger model to be when they input the smaller model and its parameters, Kim explains.

When they compared their technique to the process of training a new model from scratch, as well as to model-growth methods, it was faster than all the baselines. Their method saves about 50 percent of the computational costs required to train both vision and language models, while often improving performance.

The researchers also found they could use LiGO to accelerate transformer training even when they didnt have access to a smaller, pretrained model.

I was surprised by how much better all the methods, including ours, did compared to the random initialization, train-from-scratch baselines. Kim says.

In the future, Kim and his collaborators are looking forward to applying LiGO to even larger models.

The work was funded, in part, by the MIT-IBM Watson AI Lab, Amazon, the IBM Research AI Hardware Center, Center for Computational Innovation at Rensselaer Polytechnic Institute, and the U.S. Army Research Office.

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Learning to grow machine-learning models | MIT News | Massachusetts Institute of Technology - MIT News

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