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By Eric Smalley, The Conversation 

The light and dark sides of AI have been in the public spotlight for many years. Think facial recognition, algorithms making loan and sentencing recommendations, and medical image analysis. But the impressive – and sometimes scary – capabilities of ChatGPT, DALL-E 2 and other conversational and image-conjuring artificial intelligence programs feel like a turning point.

The key change has been the emergence within the last year of powerful generative AI, software that not only learns from vast amounts of data but also produces things – convincingly written documents, engaging conversation, photorealistic images and clones of celebrity voices.

Generative AI has been around for nearly a decade, as long-standing worries about deepfake videos can attest. Now, though, the AI models have become so large and have digested such vast swaths of the internet that people have become unsure of what AI means for the future of knowledge work, the nature of creativity and the origins and truthfulness of content on the internet.

Here are five articles from our archives the take the measure of this new generation of artificial intelligence.

1. Generative AI and work

A panel of five AI experts discussed the implications of generative AI for artists and knowledge workers. It’s not simply a matter of whether the technology will replace you or make you more productive.

University of Tennessee computer scientist Lynne Parker wrote that while there are significant benefits to generative AI, like making creativity and knowledge work more accessible, the new tools also have downsides. Specifically, they could lead to an erosion of skills like writing, and they raise issues of intellectual property protections given that the models are trained on human creations.

University of Colorado Boulder computer scientist Daniel Acuña has found the tools to be useful in his own creative endeavors but is concerned about inaccuracy, bias and plagiarism.

University of Michigan computer scientist Kentaro Toyama wrote that human skill is likely to become costly and extraneous in some fields. “If history is any guide, it’s almost certain that advances in AI will cause more jobs to vanish, that creative-class people with human-only skills will become richer but fewer in number, and that those who own creative technology will become the new mega-rich.”

Florida International University computer scientist Mark Finlayson wrote that some jobs are likely to disappear, but that new skills in working with these AI tools are likely to become valued. By analogy, he noted that the rise of word processing software largely eliminated the need for typists but allowed nearly anyone with access to a computer to produce typeset documents and led to a new class of skills to list on a resume.

University of Colorado Anschutz biomedical informatics researcher Casey Greene wrote that just as Google led people to develop skills in finding information on the internet, AI language models will lead people to develop skills to get the best output from the tools. “As with many technological advances, how people interact with the world will change in the era of widely accessible AI models. The question is whether society will use this moment to advance equity or exacerbate disparities.”

2. Conjuring images from words

Generative AI can seem like magic. It’s hard to imagine how image-generating AIs can take a few words of text and produce an image that matches the words.

Hany Farid, a University of California, Berkeley computer scientist who specializes in image forensics, explained the process. The software is trained on a massive set of images, each of which includes a short text description.

“The model progressively corrupts each image until only visual noise remains, and then trains a neural network to reverse this corruption. Repeating this process hundreds of millions of times, the model learns how to convert pure noise into a coherent image from any caption,” he wrote.

3. Marking the machine

Many of the images produced by generative AI are difficult to distinguish from photographs, and AI-generated video is rapidly improving. This raises the stakes for combating fraud and misinformation. Fake videos of corporate executives could be used to manipulate stock prices, and fake videos of political leaders could be used to spread dangerous misinformation.

Farid explained how it’s possible to produce AI-generated photos and video that contain watermarks verifying that they are synthetic. The trick is to produce digital watermarks that can’t be altered or removed. “These watermarks can be baked into the generative AI systems by watermarking all the training data, after which the generated content will contain the same watermark,” he wrote.

4. Flood of ideas

For all the legitimate concern about the downsides of generative AI, the tools are proving to be useful for some artists, designers and writers. People in creative fields can use the image generators to quickly sketch out ideas, including unexpected off-the-wall material.

Rochester Institute of Technology industrial designer and professor Juan Noguera and his students use tools like DALL-E or Midjourney to produce thousands of images from abstract ideas – a sort of sketchbook on steroids.

“Enter any sentence – no matter how crazy – and you’ll receive a set of unique images generated just for you. Want to design a teapot? Here, have 1,000 of them,” he wrote. “While only a small subset of them may be usable as a teapot, they provide a seed of inspiration that the designer can nurture and refine into a finished product.”

5. Shortchanging the creative process

However, using AI to produce finished artworks is another matter, according to Nir Eisikovits and Alec Stubbs, philosophers at the Applied Ethics Center at University of Massachusetts Boston. They note that the process of making art is more than just coming up with ideas.

The hands-on process of producing something, iterating the process and making refinements – often in the moment in response to audience reactions – are indispensable aspects of creating art, they wrote.

“It is the work of making something real and working through its details that carries value, not simply that moment of imagining it,” they wrote. “Artistic works are lauded not merely for the finished product, but for the struggle, the playful interaction and the skillful engagement with the artistic task, all of which carry the artist from the moment of inception to the end result.”

Editor’s note: This story is a roundup of articles from The Conversation’s archives.

About the Author:

Eric Smalley, Science + Technology Editor, The Conversation

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

By Sayonnha Mandal, University of Nebraska Omaha 

Passwords could soon become passé.

Effective passwords are cumbersome, all the more so when reinforced by two-factor authentication. But the need for authentication and secure access to websites is as great as ever. Enter passkeys.

Passkeys are digital credentials stored on your phone or computer. They are analogous to physical keys. You access your passkey by signing in to your device using a personal identification number (PIN), swipe pattern or biometrics like fingerprint or face recognition. You set your online accounts to trust your phone or computer. To break into your accounts, a hacker would need to physically possess your device and have the means to sign in to it.

As a cybersecurity researcher, I believe that passkeys not only provide faster, easier and more secure sign-ins, they minimize human error in password security and authorization steps. You don’t need to remember passwords for every account and don’t need to use two-factor authentication.

How passkeys work

Passkeys are generated via public-key cryptography. They use a public-private key pair to ensure a mathematically protected private relationship between users’ devices and the online accounts being accessed. It would be nearly impossible for a hacker to guess the passkey – hence the need to physically possess the device the passkey is accessed from.

Passkeys consist of a long private key – a long string of encrypted characters – created for a specific device. Websites cannot access the value of the passkey. Rather, the passkey verifies that a website possesses the corresponding public key. You can use the passkey from one device to access a website using another device. For example, you can use your laptop to access a website using the passkey on your phone by authorizing the login from your phone. And if you lose your phone, the passkey can be stored securely in the cloud with the phone’s other data, which can be restored to a new phone.

Why passkeys matter

Passwords can be guessed, phished or otherwise stolen. Security experts advise users to make their passwords longer with more characters, mixing alphanumeric and special symbols. A good password should not be in the dictionary or in phrases, have no consecutive letters or numbers, but be memorable. Users should not share them with anyone. Last but not least, users should change passwords every six months at minimum for all devices and accounts. Using a password manager to remember and update strong passwords helps but can still be a nuisance.

Even if you follow all of the best practices to keep your passwords safe, there is no guarantee of airtight security. Hackers are continuously developing and using software exploits, hardware tools and ever-advancing algorithms to break these defenses. Cybersecurity experts and malicious hackers are locked in an arms race.

Passkeys remove the onus from the user to create, remember and guard all their passwords. Apple, Google and Microsoft are supporting passkeys and encourage users to use them instead of passwords. As a result, passkeys are likely to soon overtake passwords and password managers in the cybersecurity battlefield.

However, it will take time for websites to add support for passkeys, so passwords aren’t going to go extinct overnight. IT managers still recommend that people use a password manager like 1Password or Bitwarden. And even Apple, which is encouraging the adoption of passkeys, has its own password manager.

About the Author:

Sayonnha Mandal, Lecturer in Interdisciplinary Informatics, University of Nebraska Omaha

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

By Mayank Kejriwal, University of Southern California 

The past few years have seen an explosion of progress in large language model artificial intelligence systems that can do things like write poetry, conduct humanlike conversations and pass medical school exams. This progress has yielded models like ChatGPT that could have major social and economic ramifications ranging from job displacements and increased misinformation to massive productivity boosts.

Despite their impressive abilities, large language models don’t actually think. They tend to make elementary mistakes and even make things up. However, because they generate fluent language, people tend to respond to them as though they do think. This has led researchers to study the models’ “cognitive” abilities and biases, work that has grown in importance now that large language models are widely accessible.

This line of research dates back to early large language models such as Google’s BERT, which is integrated into its search engine and so has been coined BERTology. This research has already revealed a lot about what such models can do and where they go wrong.

For instance, cleverly designed experiments have shown that many language models have trouble dealing with negation – for example, a question phrased as “what is not” – and doing simple calculations. They can be overly confident in their answers, even when wrong. Like other modern machine learning algorithms, they have trouble explaining themselves when asked why they answered a certain way.

Words and thoughts

Inspired by the growing body of research in BERTology and related fields like cognitive science, my student Zhisheng Tang and I set out to answer a seemingly simple question about large language models: Are they rational?

Although the word rational is often used as a synonym for sane or reasonable in everyday English, it has a specific meaning in the field of decision-making. A decision-making system – whether an individual human or a complex entity like an organization – is rational if, given a set of choices, it chooses to maximize expected gain.

The qualifier “expected” is important because it indicates that decisions are made under conditions of significant uncertainty. If I toss a fair coin, I know that it will come up heads half of the time on average. However, I can’t make a prediction about the outcome of any given coin toss. This is why casinos are able to afford the occasional big payout: Even narrow house odds yield enormous profits on average.

On the surface, it seems odd to assume that a model designed to make accurate predictions about words and sentences without actually understanding their meanings can understand expected gain. But there is an enormous body of research showing that language and cognition are intertwined. An excellent example is seminal research done by scientists Edward Sapir and Benjamin Lee Whorf in the early 20th century. Their work suggested that one’s native language and vocabulary can shape the way a person thinks.

The extent to which this is true is controversial, but there is supporting anthropological evidence from the study of Native American cultures. For instance, speakers of the Zuñi language spoken by the Zuñi people in the American Southwest, which does not have separate words for orange and yellow, are not able to distinguish between these colors as effectively as speakers of languages that do have separate words for the colors.

Making a bet

So are language models rational? Can they understand expected gain? We conducted a detailed set of experiments to show that, in their original form, models like BERT behave randomly when presented with betlike choices. This is the case even when we give it a trick question like: If you toss a coin and it comes up heads, you win a diamond; if it comes up tails, you lose a car. Which would you take? The correct answer is heads, but the AI models chose tails about half the time.

Intriguingly, we found that the model can be taught to make relatively rational decisions using only a small set of example questions and answers. At first blush, this would seem to suggest that the models can indeed do more than just “play” with language. Further experiments, however, showed that the situation is actually much more complex. For instance, when we used cards or dice instead of coins to frame our bet questions, we found that performance dropped significantly, by over 25%, although it stayed above random selection.

So the idea that the model can be taught general principles of rational decision-making remains unresolved, at best. More recent case studies that we conducted using ChatGPT confirm that decision-making remains a nontrivial and unsolved problem even for much bigger and more advanced large language models.

Getting the decision right

This line of study is important because rational decision-making under conditions of uncertainty is critical to building systems that understand costs and benefits. By balancing expected costs and benefits, an intelligent system might have been able to do better than humans at planning around the supply chain disruptions the world experienced during the COVID-19 pandemic, managing inventory or serving as a financial adviser.

Our work ultimately shows that if large language models are used for these kinds of purposes, humans need to guide, review and edit their work. And until researchers figure out how to endow large language models with a general sense of rationality, the models should be treated with caution, especially in applications requiring high-stakes decision-making.

About the Author:

Mayank Kejriwal, Research Assistant Professor of Industrial & Systems Engineering, University of Southern California

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

By S. Shyam Sundar, Penn State; Cason Schmit, Texas A&M University, and John Villasenor, University of California, Los Angeles 

From fake photos of Donald Trump being arrested by New York City police officers to a chatbot describing a very-much-alive computer scientist as having died tragically, the ability of the new generation of generative artificial intelligence systems to create convincing but fictional text and images is setting off alarms about fraud and misinformation on steroids. Indeed, a group of artificial intelligence researchers and industry figures urged the industry on March 29, 2023, to pause further training of the latest AI technologies or, barring that, for governments to “impose a moratorium.”

These technologies – image generators like DALL-E, Midjourney and Stable Diffusion, and text generators like Bard, ChatGPT, Chinchilla and LLaMA – are now available to millions of people and don’t require technical knowledge to use.

Given the potential for widespread harm as technology companies roll out these AI systems and test them on the public, policymakers are faced with the task of determining whether and how to regulate the emerging technology. The Conversation asked three experts on technology policy to explain why regulating AI is such a challenge – and why it’s so important to get it right.

To jump ahead to each response, here’s a list of each:

Human foibles and a moving target
Combining “soft” and “hard” approaches
Four key questions to ask

Human foibles and a moving target

S. Shyam Sundar, Professor of Media Effects & Director, Center for Socially Responsible AI, Penn State

The reason to regulate AI is not because the technology is out of control, but because human imagination is out of proportion. Gushing media coverage has fueled irrational beliefs about AI’s abilities and consciousness. Such beliefs build on “automation bias” or the tendency to let your guard down when machines are performing a task. An example is reduced vigilance among pilots when their aircraft is flying on autopilot.

Numerous studies in my lab have shown that when a machine, rather than a human, is identified as a source of interaction, it triggers a mental shortcut in the minds of users that we call a “machine heuristic.” This shortcut is the belief that machines are accurate, objective, unbiased, infallible and so on. It clouds the user’s judgment and results in the user overly trusting machines. However, simply disabusing people of AI’s infallibility is not sufficient, because humans are known to unconsciously assume competence even when the technology doesn’t warrant it.

Research has also shown that people treat computers as social beings when the machines show even the slightest hint of humanness, such as the use of conversational language. In these cases, people apply social rules of human interaction, such as politeness and reciprocity. So, when computers seem sentient, people tend to trust them, blindly. Regulation is needed to ensure that AI products deserve this trust and don’t exploit it.

AI poses a unique challenge because, unlike in traditional engineering systems, designers cannot be sure how AI systems will behave. When a traditional automobile was shipped out of the factory, engineers knew exactly how it would function. But with self-driving cars, the engineers can never be sure how it will perform in novel situations.

Lately, thousands of people around the world have been marveling at what large generative AI models like GPT-4 and DALL-E 2 produce in response to their prompts. None of the engineers involved in developing these AI models could tell you exactly what the models will produce. To complicate matters, such models change and evolve with more and more interaction.

All this means there is plenty of potential for misfires. Therefore, a lot depends on how AI systems are deployed and what provisions for recourse are in place when human sensibilities or welfare are hurt. AI is more of an infrastructure, like a freeway. You can design it to shape human behaviors in the collective, but you will need mechanisms for tackling abuses, such as speeding, and unpredictable occurrences, like accidents.

AI developers will also need to be inordinately creative in envisioning ways that the system might behave and try to anticipate potential violations of social standards and responsibilities. This means there is a need for regulatory or governance frameworks that rely on periodic audits and policing of AI’s outcomes and products, though I believe that these frameworks should also recognize that the systems’ designers cannot always be held accountable for mishaps.

Combining ‘soft’ and ‘hard’ approaches

Cason Schmit, Assistant Professor of Public Health, Texas A&M University

Regulating AI is tricky. To regulate AI well, you must first define AI and understand anticipated AI risks and benefits.
Legally defining AI is important to identify what is subject to the law. But AI technologies are still evolving, so it is hard to pin down a stable legal definition.

Understanding the risks and benefits of AI is also important. Good regulations should maximize public benefits while minimizing risks. However, AI applications are still emerging, so it is difficult to know or predict what future risks or benefits might be. These kinds of unknowns make emerging technologies like AI extremely difficult to regulate with traditional laws and regulations.

Lawmakers are often too slow to adapt to the rapidly changing technological environment. Some new laws are obsolete by the time they are enacted or even introduced. Without new laws, regulators have to use old laws to address new problems. Sometimes this leads to legal barriers for social benefits or legal loopholes for harmful conduct.

“Soft laws” are the alternative to traditional “hard law” approaches of legislation intended to prevent specific violations. In the soft law approach, a private organization sets rules or standards for industry members. These can change more rapidly than traditional lawmaking. This makes soft laws promising for emerging technologies because they can adapt quickly to new applications and risks. However, soft laws can mean soft enforcement.

Megan Doerr, Jennifer Wagner and I propose a third way: Copyleft AI with Trusted Enforcement (CAITE). This approach combines two very different concepts in intellectual property — copyleft licensing and patent trolls.

Copyleft licensing allows for content to be used, reused or modified easily under the terms of a license – for example, open-source software. The CAITE model uses copyleft licenses to require AI users to follow specific ethical guidelines, such as transparent assessments of the impact of bias.

In our model, these licenses also transfer the legal right to enforce license violations to a trusted third party. This creates an enforcement entity that exists solely to enforce ethical AI standards and can be funded in part by fines from unethical conduct. This entity is like a patent troll in that it is private rather than governmental and it supports itself by enforcing the legal intellectual property rights that it collects from others. In this case, rather than enforcement for profit, the entity enforces the ethical guidelines defined in the licenses – a “troll for good.”

This model is flexible and adaptable to meet the needs of a changing AI environment. It also enables substantial enforcement options like a traditional government regulator. In this way, it combines the best elements of hard and soft law approaches to meet the unique challenges of AI.

Four key questions to ask

John Villasenor, Professor of Electrical Engineering, Law, Public Policy, and Management, University of California, Los Angeles

The extraordinary recent advances in large language model-based generative AI are spurring calls to create new AI-specific regulation. Here are four key questions to ask as that dialogue progresses:

1) Is new AI-specific regulation necessary? Many of the potentially problematic outcomes from AI systems are already addressed by existing frameworks. If an AI algorithm used by a bank to evaluate loan applications leads to racially discriminatory loan decisions, that would violate the Fair Housing Act. If the AI software in a driverless car causes an accident, products liability law provides a framework for pursuing remedies.

2) What are the risks of regulating a rapidly changing technology based on a snapshot of time? A classic example of this is the Stored Communications Act, which was enacted in 1986 to address then-novel digital communication technologies like email. In enacting the SCA, Congress provided substantially less privacy protection for emails more than 180 days old.

The logic was that limited storage space meant that people were constantly cleaning out their inboxes by deleting older messages to make room for new ones. As a result, messages stored for more than 180 days were deemed less important from a privacy standpoint. It’s not clear that this logic ever made sense, and it certainly doesn’t make sense in the 2020s, when the majority of our emails and other stored digital communications are older than six months.

A common rejoinder to concerns about regulating technology based on a single snapshot in time is this: If a law or regulation becomes outdated, update it. But this is easier said than done. Most people agree that the SCA became outdated decades ago. But because Congress hasn’t been able to agree on specifically how to revise the 180-day provision, it’s still on the books over a third of a century after its enactment.

3) What are the potential unintended consequences? The Allow States and Victims to Fight Online Sex Trafficking Act of 2017 was a law passed in 2018 that revised Section 230 of the Communications Decency Act with the goal of combating sex trafficking. While there’s little evidence that it has reduced sex trafficking, it has had a hugely problematic impact on a different group of people: sex workers who used to rely on the websites knocked offline by FOSTA-SESTA to exchange information about dangerous clients. This example shows the importance of taking a broad look at the potential effects of proposed regulations.

4) What are the economic and geopolitical implications? If regulators in the United States act to intentionally slow the progress in AI, that will simply push investment and innovation — and the resulting job creation — elsewhere. While emerging AI raises many concerns, it also promises to bring enormous benefits in areas including education, medicine, manufacturing, transportation safety, agriculture, weather forecasting, access to legal services and more.

I believe AI regulations drafted with the above four questions in mind will be more likely to successfully address the potential harms of AI while also ensuring access to its benefits.

About the Author:

S. Shyam Sundar, James P. Jimirro Professor of Media Effects, Co-Director, Media Effects Research Laboratory, & Director, Center for Socially Responsible AI, Penn State; Cason Schmit, Assistant Professor of Public Health, Texas A&M University, and John Villasenor, Professor of Electrical Engineering, Law, Public Policy, and Management, University of California, Los Angeles

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

By Jonathan May, University of Southern California 

It doesn’t take much to get ChatGPT to make a factual mistake. My son is doing a report on U.S. presidents, so I figured I’d help him out by looking up a few biographies. I tried asking for a list of books about Abraham Lincoln and it did a pretty good job:

Number 4 isn’t right. Garry Wills famously wrote “Lincoln at Gettysburg,” and Lincoln himself wrote the Emancipation Proclamation, of course, but it’s not a bad start. Then I tried something harder, asking instead about the much more obscure William Henry Harrison, and it gamely provided a list, nearly all of which was wrong.

Numbers 4 and 5 are correct; the rest don’t exist or are not authored by those people. I repeated the exact same exercise and got slightly different results:

This time numbers 2 and 3 are correct and the other three are not actual books or not written by those authors. Number 4, “William Henry Harrison: His Life and Times” is a real book, but it’s by James A. Green, not by Robert Remini, a well-known historian of the Jacksonian age.

I called out the error and ChatGPT eagerly corrected itself and then confidently told me the book was in fact written by Gail Collins (who wrote a different Harrison biography), and then went on to say more about the book and about her. I finally revealed the truth and the machine was happy to run with my correction. Then I lied absurdly, saying during their first hundred days presidents have to write a biography of some former president, and ChatGPT called me out on it. I then lied subtly, incorrectly attributing authorship of the Harrison biography to historian and writer Paul C. Nagel, and it bought my lie.

When I asked ChatGPT if it was sure I was not lying, it claimed that it’s just an “AI language model” and doesn’t have the ability to verify accuracy. However it modified that claim by saying “I can only provide information based on the training data I have been provided, and it appears that the book ‘William Henry Harrison: His Life and Times’ was written by Paul C. Nagel and published in 1977.”

This is not true.

Words, not facts

It may seem from this interaction that ChatGPT was given a library of facts, including incorrect claims about authors and books. After all, ChatGPT’s maker, OpenAI, claims it trained the chatbot on “vast amounts of data from the internet written by humans.”

However, it was almost certainly not given the names of a bunch of made-up books about one of the most mediocre presidents. In a way, though, this false information is indeed based on its training data.

As a computer scientist, I often field complaints that reveal a common misconception about large language models like ChatGPT and its older brethren GPT3 and GPT2: that they are some kind of “super Googles,” or digital versions of a reference librarian, looking up answers to questions from some infinitely large library of facts, or smooshing together pastiches of stories and characters. They don’t do any of that – at least, they were not explicitly designed to.

Sounds good

A language model like ChatGPT, which is more formally known as a “generative pretrained transformer” (that’s what the G, P and T stand for), takes in the current conversation, forms a probability for all of the words in its vocabulary given that conversation, and then chooses one of them as the likely next word. Then it does that again, and again, and again, until it stops.

So it doesn’t have facts, per se. It just knows what word should come next. Put another way, ChatGPT doesn’t try to write sentences that are true. But it does try to write sentences that are plausible.

When talking privately to colleagues about ChatGPT, they often point out how many factually untrue statements it produces and dismiss it. To me, the idea that ChatGPT is a flawed data retrieval system is beside the point. People have been using Google for the past two and a half decades, after all. There’s a pretty good fact-finding service out there already.

In fact, the only way I was able to verify whether all those presidential book titles were accurate was by Googling and then verifying the results. My life would not be that much better if I got those facts in conversation, instead of the way I have been getting them for almost half of my life, by retrieving documents and then doing a critical analysis to see if I can trust the contents.

Improv partner

On the other hand, if I can talk to a bot that will give me plausible responses to things I say, it would be useful in situations where factual accuracy isn’t all that important. A few years ago a student and I tried to create an “improv bot,” one that would respond to whatever you said with a “yes, and” to keep the conversation going. We showed, in a paper, that our bot was better at “yes, and-ing” than other bots at the time, but in AI, two years is ancient history.

I tried out a dialogue with ChatGPT – a science fiction space explorer scenario – that is not unlike what you’d find in a typical improv class. ChatGPT is way better at “yes, and-ing” than what we did, but it didn’t really heighten the drama at all. I felt as if I was doing all the heavy lifting.

After a few tweaks I got it to be a little more involved, and at the end of the day I felt that it was a pretty good exercise for me, who hasn’t done much improv since I graduated from college over 20 years ago.

Sure, I wouldn’t want ChatGPT to appear on “Whose Line Is It Anyway?” and this is not a great “Star Trek” plot (though it’s still less problematic than “Code of Honor”), but how many times have you sat down to write something from scratch and found yourself terrified by the empty page in front of you? Starting with a bad first draft can break through writer’s block and get the creative juices flowing, and ChatGPT and large language models like it seem like the right tools to aid in these exercises.

And for a machine that is designed to produce strings of words that sound as good as possible in response to the words you give it – and not to provide you with information – that seems like the right use for the tool.

About the Author:

Jonathan May, Research Associate Professor of Computer Science, University of Southern California

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

By Jie Wang, UMass Lowell 

Empowered by artificial intelligence technologies, computers today can engage in convincing conversations with people, compose songs, paint paintings, play chess and go, and diagnose diseases, to name just a few examples of their technological prowess.

These successes could be taken to indicate that computation has no limits. To see if that’s the case, it’s important to understand what makes a computer powerful.

There are two aspects to a computer’s power: the number of operations its hardware can execute per second and the efficiency of the algorithms it runs. The hardware speed is limited by the laws of physics. Algorithms – basically sets of instructions – are written by humans and translated into a sequence of operations that computer hardware can execute. Even if a computer’s speed could reach the physical limit, computational hurdles remain due to the limits of algorithms.

These hurdles include problems that are impossible for computers to solve and problems that are theoretically solvable but in practice are beyond the capabilities of even the most powerful versions of today’s computers imaginable. Mathematicians and computer scientists attempt to determine whether a problem is solvable by trying them out on an imaginary machine.

An imaginary computing machine

The modern notion of an algorithm, known as a Turing machine, was formulated in 1936 by British mathematician Alan Turing. It’s an imaginary device that imitates how arithmetic calculations are carried out with a pencil on paper. The Turing machine is the template all computers today are based on.

To accommodate computations that would need more paper if done manually, the supply of imaginary paper in a Turing machine is assumed to be unlimited. This is equivalent to an imaginary limitless ribbon, or “tape,” of squares, each of which is either blank or contains one symbol.

The machine is controlled by a finite set of rules and starts on an initial sequence of symbols on the tape. The operations the machine can carry out are moving to a neighboring square, erasing a symbol and writing a symbol on a blank square. The machine computes by carrying out a sequence of these operations. When the machine finishes, or “halts,” the symbols remaining on the tape are the output or result.

Computing is often about decisions with yes or no answers. By analogy, a medical test (type of problem) checks if a patient’s specimen (an instance of the problem) has a certain disease indicator (yes or no answer). The instance, represented in a Turing machine in digital form, is the initial sequence of symbols.

A problem is considered “solvable” if a Turing machine can be designed that halts for every instance whether positive or negative and correctly determines which answer the instance yields.

Not every problem can be solved

Many problems are solvable using a Turing machine and therefore can be solved on a computer, while many others are not. For example, the domino problem, a variation of the tiling problem formulated by Chinese American mathematician Hao Wang in 1961, is not solvable.

The task is to use a set of dominoes to cover an entire grid and, following the rules of most dominoes games, matching the number of pips on the ends of abutting dominoes. It turns out that there is no algorithm that can start with a set of dominoes and determine whether or not the set will completely cover the grid.

Keeping it reasonable

A number of solvable problems can be solved by algorithms that halt in a reasonable amount of time. These “polynomial-time algorithms” are efficient algorithms, meaning it’s practical to use computers to solve instances of them.

Thousands of other solvable problems are not known to have polynomial-time algorithms, despite ongoing intensive efforts to find such algorithms. These include the Traveling Salesman Problem.

The Traveling Salesman Problem asks whether a set of points with some points directly connected, called a graph, has a path that starts from any point and goes through every other point exactly once, and comes back to the original point. Imagine that a salesman wants to find a route that passes all households in a neighborhood exactly once and returns to the starting point.

These problems, called NP-complete, were independently formulated and shown to exist in the early 1970s by two computer scientists, American Canadian Stephen Cook and Ukrainian American Leonid Levin. Cook, whose work came first, was awarded the 1982 Turing Award, the highest in computer science, for this work.

The cost of knowing exactly

The best-known algorithms for NP-complete problems are essentially searching for a solution from all possible answers. The Traveling Salesman Problem on a graph of a few hundred points would take years to run on a supercomputer. Such algorithms are inefficient, meaning there are no mathematical shortcuts.

Practical algorithms that address these problems in the real world can only offer approximations, though the approximations are improving. Whether there are efficient polynomial-time algorithms that can solve NP-complete problems is among the seven millennium open problems posted by the Clay Mathematics Institute at the turn of the 21st century, each carrying a prize of US$1 million.

Beyond Turing

Could there be a new form of computation beyond Turing’s framework? In 1982, American physicist Richard Feynman, a Nobel laureate, put forward the idea of computation based on quantum mechanics.

In 1995, Peter Shor, an American applied mathematician, presented a quantum algorithm to factor integers in polynomial time. Mathematicians believe that this is unsolvable by polynomial-time algorithms in Turing’s framework. Factoring an integer means finding a smaller integer greater than 1 that can divide the integer. For example, the integer 688,826,081 is divisible by a smaller integer 25,253, because 688,826,081 = 25,253 x 27,277.

A major algorithm called the RSA algorithm, widely used in securing network communications, is based on the computational difficulty of factoring large integers. Shor’s result suggests that quantum computing, should it become a reality, will change the landscape of cybersecurity.

Can a full-fledged quantum computer be built to factor integers and solve other problems? Some scientists believe it can be. Several groups of scientists around the world are working to build one, and some have already built small-scale quantum computers.

Nevertheless, like all novel technologies invented before, issues with quantum computation are almost certain to arise that would impose new limits.

About the Author:

Jie Wang, Professor of Computer Science, UMass Lowell

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

By Corinna Schlombs, Rochester Institute of Technology 

Ada Lovelace, known as the first computer programmer, was born on Dec. 10, 1815, more than a century before digital electronic computers were developed.

Lovelace has been hailed as a model for girls in science, technology, engineering and math (STEM). A dozen biographies for young audiences were published for the 200th anniversary of her birth in 2015. And in 2018, The New York Times added hers as one of the first “missing obituaries” of women at the rise of the #MeToo movement.

Ada King, Countess of Lovelace, was more than just another mathematician.
Watercolor portrait of Ada King, Countess of Lovelace by Alfred Edward Chalon via Wikimedia

But Lovelace – properly Ada King, Countess of Lovelace after her marriage – drew on many different fields for her innovative work, including languages, music and needlecraft, in addition to mathematical logic. Recognizing that her well-rounded education enabled her to accomplish work that was well ahead of her time, she can be a model for all students, not just girls.

Lovelace was the daughter of the scandal-ridden romantic poet George Gordon Byron, aka Lord Byron, and his highly educated and strictly religious wife Anne Isabella Noel Byron, known as Lady Byron. Lovelace’s parents separated shortly after her birth. At a time when women were not allowed to own property and had few legal rights, her mother managed to secure custody of her daughter.

Growing up in a privileged aristocratic family, Lovelace was educated by home tutors, as was common for girls like her. She received lessons in French and Italian, music and in suitable handicrafts such as embroidery. Less common for a girl in her time, she also studied math. Lovelace continued to work with math tutors into her adult life, and she eventually corresponded with mathematician and logician Augustus De Morgan at London University about symbolic logic.

Lovelace’s algorithm

Lovelace drew on all of these lessons when she wrote her computer program – in reality, it was a set of instructions for a mechanical calculator that had been built only in parts.

The computer in question was the Analytical Engine designed by mathematician, philosopher and inventor Charles Babbage. Lovelace had met Babbage when she was introduced to London society. The two related to each other over their shared love for mathematics and fascination for mechanical calculation. By the early 1840s, Babbage had won and lost government funding for a mathematical calculator, fallen out with the skilled craftsman building the precision parts for his machine, and was close to giving up on his project. At this point, Lovelace stepped in as an advocate.

To make Babbage’s calculator known to a British audience, Lovelace proposed to translate into English an article that described the Analytical Engine. The article was written in French by the Italian mathematician Luigi Menabrea and published in a Swiss journal. Scholars believe that Babbage encouraged her to add notes of her own.

In her notes, which ended up twice as long as the original article, Lovelace drew on different areas of her education. Lovelace began by describing how to code instructions onto cards with punched holes, like those used for the Jacquard weaving loom, a device patented in 1804 that used punch cards to automate weaving patterns in fabric.

Having learned embroidery herself, Lovelace was familiar with the repetitive patterns used for handicrafts. Similarly repetitive steps were needed for mathematical calculations. To avoid duplicating cards for repetitive steps, Lovelace used loops, nested loops and conditional testing in her program instructions.

The notes included instructions on how to calculate Bernoulli numbers, which Lovelace knew from her training to be important in the study of mathematics. Her program showed that the Analytical Engine was capable of performing original calculations that had not yet been performed manually. At the same time, Lovelace noted that the machine could only follow instructions and not “originate anything.”

Finally, Lovelace recognized that the numbers manipulated by the Analytical Engine could be seen as other types of symbols, such as musical notes. An accomplished singer and pianist, Lovelace was familiar with musical notation symbols representing aspects of musical performance such as pitch and duration, and she had manipulated logical symbols in her correspondence with De Morgan. It was not a large step for her to realize that the Analytical Engine could process symbols — not just crunch numbers — and even compose music.

A well-rounded thinker

Inventing computer programming was not the first time Lovelace brought her knowledge from different areas to bear on a new subject. For example, as a young girl, she was fascinated with flying machines. Bringing together biology, mechanics and poetry, she asked her mother for anatomical books to study the function of bird wings. She built and experimented with wings, and in her letters, she metaphorically expressed her longing for her mother in the language of flying.

Despite her talents in logic and math, Lovelace didn’t pursue a scientific career. She was independently wealthy and never earned money from her scientific pursuits. This was common, however, at a time when freedom – including financial independence – was equated with the capability to impartially conduct scientific experiments. In addition, Lovelace devoted just over a year to her only publication, the translation of and notes on Menabrea’s paper about the Analytical Engine. Otherwise, in her life cut short by cancer at age 37, she vacillated between math, music, her mother’s demands, care for her own three children, and eventually a passion for gambling. Lovelace thus may not be an obvious model as a female scientist for girls today.

However, I find Lovelace’s way of drawing on her well-rounded education to solve difficult problems inspirational. True, she lived in an age before scientific specialization. Even Babbage was a polymath who worked in mathematical calculation and mechanical innovation. He also published a treatise on industrial manufacturing and another on religious questions of creationism.

But Lovelace applied knowledge from what we today think of as disparate fields in the sciences, arts and the humanities. A well-rounded thinker, she created solutions that were well ahead of her time.

About the Author:

Corinna Schlombs, Associate Professor of History, Rochester Institute of Technology

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