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Quantum computing is like Forrest Gump’s box of chocolates: You never know what you’re gonna get. Quantum phenomena – the behavior of matter and energy at the atomic and subatomic levels – are not definite, one thing or another. They are opaque clouds of possibility or, more precisely, probabilities. When someone observes a quantum system, it loses its quantum-ness and “collapses” into a definite state.

Quantum phenomena are mysterious and often counterintuitive. This makes quantum computing difficult to understand. People naturally reach for the familiar to attempt to explain the unfamiliar, and for quantum computing this usually means using traditional binary computing as a metaphor. But explaining quantum computing this way leads to major conceptual confusion, because at a base level the two are entirely different animals.

This problem highlights the often mistaken belief that common metaphors are more useful than exotic ones when explaining new technologies. Sometimes the opposite approach is more useful. The freshness of the metaphor should match the novelty of the discovery.

The uniqueness of quantum computers calls for an unusual metaphor. As a communications researcher who studies technology, I believe that quantum computers can be better understood as kaleidoscopes.

This image could give you a better grasp of how quantum computers work.
Crystal A Murray/Flickr, CC BY-NC-SA

Digital certainty vs. quantum probabilities

The gap between understanding classical and quantum computers is a wide chasm. Classical computers store and process information via transistors, which are electronic devices that take binary, deterministic states: one or zero, yes or no. Quantum computers, in contrast, handle information probabilistically at the atomic and subatomic levels.

Classical computers use the flow of electricity to sequentially open and close gates to record or manipulate information. Information flows through circuits, triggering actions through a series of switches that record information as ones and zeros. Using binary math, bits are the foundation of all things digital, from the apps on your phone to the account records at your bank and the Wi-Fi signals bouncing around your home.

In contrast, quantum computers use changes in the quantum states of atoms, ions, electrons or photons. Quantum computers link, or entangle, multiple quantum particles so that changes to one affect all the others. They then introduce interference patterns, like multiple stones tossed into a pond at the same time. Some waves combine to create higher peaks, while some waves and troughs combine to cancel each other out. Carefully calibrated interference patterns guide the quantum computer toward the solution of a problem.

Achieving a quantum leap, conceptually

The term “bit” is a metaphor. The word suggests that during calculations, a computer can break up large values into tiny ones – bits of information – which electronic devices such as transistors can more easily process.

Using metaphors like this has a cost, though. They are not perfect. Metaphors are incomplete comparisons that transfer knowledge from something people know well to something they are working to understand. The bit metaphor ignores that the binary method does not deal with many types of different bits at once, as common sense might suggest. Instead, all bits are the same.

The smallest unit of a quantum computer is called the quantum bit, or qubit. But transferring the bit metaphor to quantum computing is even less adequate than using it for classical computing. Transferring a metaphor from one use to another blunts its effect.

The prevalent explanation of quantum computing is that while classical computers can store or process only a zero or one in a transistor or other computational unit, quantum computers supposedly store and handle both zero and one and other values in between at the same time through the process of superposition.

Superposition, however, does not store one or zero or any other number simultaneously. There is only an expectation that the values might be zero or one at the end of the computation. This quantum probability is the polar opposite of the binary method of storing information.

Driven by quantum science’s uncertainty principle, the probability that a qubit stores a one or zero is like Schroedinger’s cat, which can be either dead or alive, depending on when you observe it. But the two different values do not exist simultaneously during superposition. They exist only as probabilities, and an observer cannot determine when or how frequently those values existed before the observation ended the superposition.

Leaving behind these challenges to using traditional binary computing metaphors means embracing new metaphors to explain quantum computing.

Peering into kaleidoscopes

The kaleidoscope metaphor is particularly apt to explain quantum processes. Kaleidoscopes can create infinitely diverse yet orderly patterns using a limited number of colored glass beads, mirror-dividing walls and light. Rotating the kaleidoscope enhances the effect, generating an infinitely variable spectacle of fleeting colors and shapes.

The shapes not only change but can’t be reversed. If you turn the kaleidoscope in the opposite direction, the imagery will generally remain the same, but the exact composition of each shape or even their structures will vary as the beads randomly mingle with each other. In other words, while the beads, light and mirrors could replicate some patterns shown before, these are never absolutely the same.

Using the kaleidoscope metaphor, the solution a quantum computer provides – the final pattern – depends on when you stop the computing process. Quantum computing isn’t about guessing the state of any given particle but using mathematical models of how the interaction among many particles in various states creates patterns, called quantum correlations.

Each final pattern is the answer to a problem posed to the quantum computer, and what you get in a quantum computing operation is a probability that a certain configuration will result.

New metaphors for new worlds

Metaphors make the unknown manageable, approachable and discoverable. Approximating the meaning of a surprising object or phenomenon by extending an existing metaphor is a method that is as old as calling the edge of an ax its “bit” and its flat end its “butt.” The two metaphors take something we understand from everyday life very well, applying it to a technology that needs a specialized explanation of what it does. Calling the cutting edge of an ax a “bit” suggestively indicates what it does, adding the nuance that it changes the object it is applied to. When an ax shapes or splits a piece of wood, it takes a “bite” from it.

Metaphors, however, do much more than provide convenient labels and explanations of new processes. The words people use to describe new concepts change over time, expanding and taking on a life of their own.

When encountering dramatically different ideas, technologies or scientific phenomena, it’s important to use fresh and striking terms as windows to open the mind and increase understanding. Scientists and engineers seeking to explain new concepts would do well to seek out originality and master metaphors – in other words, to think about words the way poets do.

About the Author:

Sorin Adam Matei, Associate Dean for Research, Purdue University

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

By Megan Stubbs-Richardson, Mississippi State University; Devon Brenner, Mississippi State University; Lauren Etheredge, Mississippi State University, and MacKenzie Paul, Baylor University 

AI – or artificial intelligence – is often used as a way to summarize data and improve writing. But AI tools also represent a powerful and efficient way to analyze large amounts of text to search for patterns. In addition, AI tools can assist with developing research products that can be shared widely.

It’s with that in mind that we, as researchers in social science, developed a new database of AI tools for the field. In the database, we compiled information about each tool and documented whether it was useful for literature reviews, data collection and analyses, or research dissemination. We also provided information on the costs, logins and plug-in extensions available for each tool.

When asked about their perceptions of AI, many social scientists express caution or apprehension. In a sample of faculty and students from over 600 institutions, only 22% of university faculty reported that they regularly used AI tools.

From combing through lengthy transcripts or text-based data to writing literature reviews and sharing results, we believe AI can help social science researchers – such as those in psychology, sociology and communication – as well as others get the most out of their data and present it to a wider audience.

Analyze text using AI

Qualitative research often involves poring over transcripts or written language to identify themes and patterns. While this kind of research is powerful, it is also labor-intensive. The power of AI platforms to sift through large datasets not only saves researchers time, but it can also help them analyze data that couldn’t have been analyzed previously because of the size of the dataset.

Specifically, AI can assist social scientists by identifying potential themes or common topics in large, text-based data that scientists can interrogate using qualitative research methods. For example, AI can analyze 15 million social media posts to identify themes in how people coped with COVID-19. These themes can then give researchers insight into larger trends in the data, allowing us to refine criteria for a more in-depth, qualitative analysis.

AI tools can also be used to adapt language and scientists’ word choice in research designs. In particular, AI can reduce bias by improving the wording of questions in surveys or refining keywords used in social media data collection.

Identify gaps in knowledge

Another key task in research is to scan the field for previous work to identify gaps in knowledge. AI applications are built on systems that can synthesize text. This makes literature reviews – the section of a research paper that summarizes other research on the same topic – and writing processes more efficient.

Research shows that human feedback to AI, such as providing examples of simple logic, can significantly improve the tools’ ability to perform complex reasoning. With this in mind, we can continually revise our instructions to AI and refine its ability to pull relevant literature.

However, social scientists must be wary of fake sources – a big concern with generative AI. It is essential to verify any sources AI tools provide to ensure they come from peer-reviewed journals.

Share research findings

AI tools can quickly summarize research findings in a reader-friendly way by assisting with writing blogs, creating infographics and producing presentation slides and even images.

Our database contains AI tools that can also help scientists present their findings on social media. One tool worth highlighting is BlogTweet. This free AI tool allows users to copy and paste text from an article like this one to generate tweet threads and start conversations.

Be aware of the cost of AI tools

Two-thirds of the tools in the database cost money. While our primary objective was to identify the most useful tools for social scientists, we also sought to identify open-source tools and curated a list of 85 free tools that can support literature reviews, writing, data collection, analysis and visualization efforts.

In our analysis of the cost of AI tools, we also found that many offer “freemium” access to tools. This means you can explore a free version of the product. More advanced versions of the tool are available through the purchase of tokens or subscription plans.

For some tools, costs can be somewhat hidden or unexpected. For instance, a tool that seems open source on the surface may actually have rate limits, and users may find that they’ve run out of free questions to ask the AI.

The future of the database

Since the release of the Artificial Intelligence Applications for Social Science Research Database on Oct. 5, 2023, it has been downloaded over 400 times across 49 countries. In the database, we found 131 AI tools useful for literature reviews, summaries or writing. As many as 146 AI tools are useful for data collection or analysis, and 108 are useful for research dissemination.

We continue to update the database and hope that it can aid academic communities in their exploration of AI and generate new conversations. The more that social scientists use the database, the more they can work toward consensus of adopting ethical approaches to using AI in research and analysis.

About the Authors:

Megan Stubbs-Richardson, Assistant Research Professor at the Social Science Research Center, Mississippi State University; Devon Brenner, Professor of education, Mississippi State University; Lauren Etheredge, Research associate in sociology, Mississippi State University, and MacKenzie Paul, Doctoral student in psychology, Baylor University

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

During her chemistry Nobel Prize lecture in 2018, Frances Arnold said, “Today we can for all practical purposes read, write and edit any sequence of DNA, but we cannot compose it.” That isn’t true anymore.

Since then, science and technology have progressed so much that artificial intelligence has learned to compose DNA, and with genetically modified bacteria, scientists are on their way to designing and making bespoke proteins.

The goal is that with AI’s designing talents and gene editing’s engineering abilities, scientists can modify bacteria to act as mini factories producing new proteins that can reduce greenhouse gases, digest plastics or act as species-specific pesticides.

As a chemistry professor and computational chemist who studies molecular science and environmental chemistry, I believe that advances in AI and gene editing make this a realistic possibility.

Gene sequencing – reading life’s recipes

All living things contain genetic materials – DNA and RNA – that provide the hereditary information needed to replicate themselves and make proteins. Proteins constitute 75% of human dry weight. They make up muscles, enzymes, hormones, blood, hair and cartilage. Understanding proteins means understanding much of biology. The order of nucleotide bases in DNA, or RNA in some viruses, encodes this information, and genomic sequencing technologies identify the order of these bases.

The Human Genome Project was an international effort that sequenced the entire human genome from 1990 to 2003. Thanks to rapidly improving technologies, it took seven years to sequence the first 1% of the genome and another seven years for the remaining 99%. By 2003, scientists had the complete sequence of the 3 billion nucleotide base pairs coding for 20,000 to 25,000 genes in the human genome.

However, understanding the functions of most proteins and correcting their malfunctions remained a challenge.

AI learns proteins

Each protein’s shape is critical to its function and is determined by the sequence of its amino acids, which is in turn determined by the gene’s nucleotide sequence. Misfolded proteins have the wrong shape and can cause illnesses such as neurodegenerative diseases, cystic fibrosis and Type 2 diabetes. Understanding these diseases and developing treatments requires knowledge of protein shapes.

Before 2016, the only way to determine the shape of a protein was through X-ray crystallography, a laboratory technique that uses the diffraction of X-rays by single crystals to determine the precise arrangement of atoms and molecules in three dimensions in a molecule. At that time, the structure of about 200,000 proteins had been determined by crystallography, costing billions of dollars.

AlphaFold, a machine learning program, used these crystal structures as a training set to determine the shape of the proteins from their nucleotide sequences. And in less than a year, the program calculated the protein structures of all 214 million genes that have been sequenced and published. The protein structures AlphaFold determined have all been released in a freely available database.

To effectively address noninfectious diseases and design new drugs, scientists need more detailed knowledge of how proteins, especially enzymes, bind small molecules. Enzymes are protein catalysts that enable and regulate biochemical reactions.

AlphaFold3, released May 8, 2024, can predict protein shapes and the locations where small molecules can bind to these proteins. In rational drug design, drugs are designed to bind proteins involved in a pathway related to the disease being treated. The small molecule drugs bind to the protein binding site and modulate its activity, thereby influencing the disease path. By being able to predict protein binding sites, AlphaFold3 will enhance researchers’ drug development capabilities.

AI + CRISPR = composing new proteins

Around 2015, the development of CRISPR technology revolutionized gene editing. CRISPR can be used to find a specific part of a gene, change or delete it, make the cell express more or less of its gene product, or even add an utterly foreign gene in its place.

In 2020, Jennifer Doudna and Emmanuelle Charpentier received the Nobel Prize in chemistry “for the development of a method (CRISPR) for genome editing.” With CRISPR, gene editing, which once took years and was species specific, costly and laborious, can now be done in days and for a fraction of the cost.

AI and genetic engineering are advancing rapidly. What was once complicated and expensive is now routine. Looking ahead, the dream is of bespoke proteins designed and produced by a combination of machine learning and CRISPR-modified bacteria. AI would design the proteins, and bacteria altered using CRISPR would produce the proteins. Enzymes produced this way could potentially breathe in carbon dioxide and methane while exhaling organic feedstocks, or break down plastics into substitutes for concrete.

I believe that these ambitions are not unrealistic, given that genetically modified organisms already account for 2% of the U.S. economy in agriculture and pharmaceuticals.

Two groups have made functioning enzymes from scratch that were designed by differing AI systems. David Baker’s Institute for Protein Design at the University of Washington devised a new deep-learning-based protein design strategy it named “family-wide hallucination,” which they used to make a unique light-emitting enzyme. Meanwhile, biotech startup Profluent, has used an AI trained from the sum of all CRISPR-Cas knowledge to design new functioning genome editors.

If AI can learn to make new CRISPR systems as well as bioluminescent enzymes that work and have never been seen on Earth, there is hope that pairing CRISPR with AI can be used to design other new bespoke enzymes. Although the CRISPR-AI combination is still in its infancy, once it matures it is likely to be highly beneficial and could even help the world tackle climate change.

It’s important to remember, however, that the more powerful a technology is, the greater the risks it poses. Also, humans have not been very successful at engineering nature due to the complexity and interconnectedness of natural systems, which often leads to unintended consequences.

About the Author:

Marc Zimmer, Professor of Chemistry, Connecticut College

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

By Selcuk Uluagac, Florida International University 

You probably know better than to click on links that download unknown files onto your computer. It turns out that uploading files can get you into trouble, too.

Today’s web browsers are much more powerful than earlier generations of browsers. They’re able to manipulate data within both the browser and the computer’s local file system. Users can send and receive email, listen to music or watch a movie within a browser with the click of a button.

Unfortunately, these capabilities also mean that hackers can find clever ways to abuse the browsers to trick you into letting ransomware lock up your files when you think that you’re simply doing your usual tasks online.

I’m a computer scientist who studies cybersecurity. My colleagues and I have shown how hackers can gain access to your computer’s files via the File System Access Application Programming Interface (API), which enables web applications in modern browsers to interact with the users’ local file systems.

The threat applies to Google’s Chrome and Microsoft’s Edge browsers but not Apple’s Safari or Mozilla’s Firefox. Chrome accounts for 65% of browsers used, and Edge accounts for 5%. To the best of my knowledge, there have been no reports of hackers using this method so far.

My colleagues, who include a Google security researcher, and I have communicated with the developers responsible for the File System Access API, and they have expressed support for our work and interest in our approaches to defending against this kind of attack. We also filed a security report to Microsoft but have not heard from them.

Double-edged sword

Today’s browsers are almost operating systems unto themselves. They can run software programs and encrypt files. These capabilities, combined with the browser’s access to the host computer’s files – including ones in the cloud, shared folders and external drives – via the File System Access API creates a new opportunity for ransomware.

Imagine you want to edit photos on a benign-looking free online photo editing tool. When you upload the photos for editing, any hackers who control the malicious editing tool can access the files on your computer via your browser. The hackers would gain access to the folder you are uploading from and all subfolders. Then the hackers could encrypt the files in your file system and demand a ransom payment to decrypt them.

Ransomware is a growing problem. Attacks have hit individuals as well as organizations, including Fortune 500 companies, banks, cloud service providers, cruise operators, threat-monitoring services, chip manufacturers, governments, medical centers and hospitals, insurance companies, schools, universities and even police departments. In 2023, organizations paid more than US$1.1 billion in ransomware payments to attackers, and 19 ransomware attacks targeted organizations every second.

It is no wonder ransomware is the No. 1 arms race today between hackers and security specialists. Traditional ransomware runs on your computer after hackers have tricked you into downloading it.

New defenses for a new threat

A team of researchers I lead at the Cyber-Physical Systems Security Lab at Florida International University, including postdoctoral researcher Abbas Acar and Ph.D. candidate Harun Oz, in collaboration with Google Senior Research Scientist Güliz Seray Tuncay, have been investigating this new type of potential ransomware for the past two years. Specifically, we have been exploring how powerful modern web browsers have become and how they can be weaponized by hackers to create novel forms of ransomware.

In our paper, RøB: Ransomware over Modern Web Browsers, which was presented at the USENIX Security Symposium in August 2023, we showed how this emerging ransomware strain is easy to design and how damaging it can be. In particular, we designed and implemented the first browser-based ransomware called RøB and analyzed its use with browsers running on three different major operating systems – Windows, Linux and MacOS – five cloud providers and five antivirus products.

Our evaluations showed that RøB is capable of encrypting numerous types of files. Because RøB runs within the browser, there are no malicious payloads for a traditional antivirus program to catch. This means existing ransomware detection systems face several issues against this powerful browser-based ransomware.

We proposed three different defense approaches to mitigate this new ransomware type. These approaches operate at different levels – browser, file system and user – and complement one another.

The first approach temporarily halts a web application – a program that runs in the browser – in order to detect encrypted user files. The second approach monitors the activity of the web application on the user’s computer to identify ransomware-like patterns. The third approach introduces a new permission dialog box to inform users about the risks and implications associated with allowing web applications to access their computer’s file system.

When it comes to protecting your computer, be careful about where you upload as well as download files. Your uploads could be giving hackers an “in” to your computer.

About the Author:

Selcuk Uluagac, Professor of Computing and Information Science, Florida International University

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

A chatbot turns hostile. A test version of a Roomba vacuum collects images of users in private situations. A Black woman is falsely identified as a suspect on the basis of facial recognition software, which tends to be less accurate at identifying women and people of color.

These incidents are not just glitches, but examples of more fundamental problems. As artificial intelligence and machine learning tools become more integrated into daily life, ethical considerations are growing, from privacy issues and race and gender biases in coding to the spread of misinformation.

The general public depends on software engineers and computer scientists to ensure these technologies are created in a safe and ethical manner. As a sociologist and doctoral candidate interested in science, technology, engineering and math education, we are currently researching how engineers in many different fields learn and understand their responsibilities to the public.

Yet our recent research, as well as that of other scholars, points to a troubling reality: The next generation of engineers often seem unprepared to grapple with the social implications of their work. What’s more, some appear apathetic about the moral dilemmas their careers may bring – just as advances in AI intensify such dilemmas.

Aware, but unprepared

As part of our ongoing research, we interviewed more than 60 electrical engineering and computer science masters students at a top engineering program in the United States. We asked students about their experiences with ethical challenges in engineering, their knowledge of ethical dilemmas in the field and how they would respond to scenarios in the future.

First, the good news: Most students recognized potential dangers of AI and expressed concern about personal privacy and the potential to cause harm – like how race and gender biases can be written into algorithms, intentionally or unintentionally.

One student, for example, expressed dismay at the environmental impact of AI, saying AI companies are using “more and more greenhouse power, [for] minimal benefits.” Others discussed concerns about where and how AIs are being applied, including for military technology and to generate falsified information and images.

When asked, however, “Do you feel equipped to respond in concerning or unethical situations?” students often said no.

“Flat out no. … It is kind of scary,” one student replied. “Do YOU know who I’m supposed to go to?”

Another was troubled by the lack of training: “I [would be] dealing with that with no experience. … Who knows how I’ll react.”

Other researchers have similarly found that many engineering students do not feel satisfied with the ethics training they do receive. Common training usually emphasizes professional codes of conduct, rather than the complex socio-technical factors underlying ethical decision-making. Research suggests that even when presented with particular scenarios or case studies, engineering students often struggle to recognize ethical dilemmas.

‘A box to check off’

Accredited engineering programs are required to “include topics related to professional and ethical responsibilities” in some capacity.

Yet ethics training is rarely emphasized in the formal curricula. A study assessing undergraduate STEM curricula in the U.S. found that coverage of ethical issues varied greatly in terms of content, amount and how seriously it is presented. Additionally, an analysis of academic literature about engineering education found that ethics is often considered nonessential training.

Many engineering faculty express dissatisfaction with students’ understanding, but report feeling pressure from engineering colleagues and students themselves to prioritize technical skills in their limited class time.

Researchers in one 2018 study interviewed over 50 engineering faculty and documented hesitancy – and sometimes even outright resistance – toward incorporating public welfare issues into their engineering classes. More than a quarter of professors they interviewed saw ethics and societal impacts as outside “real” engineering work.

About a third of students we interviewed in our ongoing research project share this seeming apathy toward ethics training, referring to ethics classes as “just a box to check off.”

“If I’m paying money to attend ethics class as an engineer, I’m going to be furious,” one said.

These attitudes sometimes extend to how students view engineers’ role in society. One interviewee in our current study, for example, said that an engineer’s “responsibility is just to create that thing, design that thing and … tell people how to use it. [Misusage] issues are not their concern.”

One of us, Erin Cech, followed a cohort of 326 engineering students from four U.S. colleges. This research, published in 2014, suggested that engineers actually became less concerned over the course of their degree about their ethical responsibilities and understanding the public consequences of technology. Following them after they left college, we found that their concerns regarding ethics did not rebound once these new graduates entered the workforce.

Joining the work world

When engineers do receive ethics training as part of their degree, it seems to work.

Along with engineering professor Cynthia Finelli, we conducted a survey of over 500 employed engineers. Engineers who received formal ethics and public welfare training in school are more likely to understand their responsibility to the public in their professional roles, and recognize the need for collective problem solving. Compared to engineers who did not receive training, they were 30% more likely to have noticed an ethical issue in their workplace and 52% more likely to have taken action.

Over a quarter of these practicing engineers reported encountering a concerning ethical situation at work. Yet approximately one-third said they have never received training in public welfare – not during their education, and not during their career.

This gap in ethics education raises serious questions about how well-prepared the next generation of engineers will be to navigate the complex ethical landscape of their field, especially when it comes to AI.

To be sure, the burden of watching out for public welfare is not shouldered by engineers, designers and programmers alone. Companies and legislators share the responsibility.

But the people who are designing, testing and fine-tuning this technology are the public’s first line of defense. We believe educational programs owe it to them – and the rest of us – to take this training seriously.

About the Author:

Elana Goldenkoff, Doctoral Candidate in Movement Science, University of Michigan and Erin A. Cech, Associate Professor of Sociology, University of Michigan

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