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Im very sensitive to the notion that on AI we shouldnt do that, he continued, but if we try to overreach, we may come up with goose eggs meaning nothing.

Congress sat on the regulatory sidelines throughout the rise of the internet and social media, only to later discover widespread concerns including data privacy, hate speech, election interference, misinformation and market dominance. Even after years of tense hearings and legislative proposals, Warner acknowledges our record in social media is a big fat zippo.

He worries lawmakers will suffer a similar fate with artificial intelligence by trying to mitigate its full spectrum of risks with a single law comprehensive legislation that others, including Sen. Todd Young (R-Ind.) also doubt is realistic. Instead, Warners been selling his colleagues on first tackling narrowly focused issues: the potential for AI-generated deepfakes to disrupt elections and financial markets.

Where Im at on the regulatory front is we probably cant solve it all at once, Warner said. But where are the two most immediate areas where AI could have an almost existential threat tomorrow?

He said hes considering new regulations that would perhaps address concerns about bias or require labels for AI-generated deepfakes though Warner said he has reservations about allowing companies to apply labels.

Hes also weighing an increase in penalties under existing laws when AI is used to undermine elections or markets. But who might pay those penalties when technology is abused the tech company or their users has been a sore point for Congress. A law created at the dawn of the internet, known as Section 230, has largely shielded tech companies from liability for their users actions.

Even the biggest advocates of Section 230, in my conversations with them up here on the Hill, have said they dont expect Section 230 to carry over to AI, Warner said.

A targeted bill would still struggle to clear a sharply divided Congress, especially one that deals with election security, Warner said. But he argues it stands a better chance than some of the more sweeping ideas being considered, including the notion of creating a federal agency to oversee AI. Warner said hes not against that idea, but with a Republican-controlled House, I wouldnt put all my eggs in that basket.

Attempts to regulate technology with ties to China, in particular the video-sharing app TikTok, offer another cautionary tale, Warner said. Legislation that Warner introduced earlier this year with Senate Minority Whip John Thune (R-S.D.) that would give the Commerce Department more oversight of foreign-owned tech firms, called the RESTRICT Act, S. 686 (118), was lining up senators two by two, like Noahs Ark and had the White Houses blessing before stalling amid political attacks earlier this year.

Warner said he has less anxiety today about China dominating AI than he did a year ago, though concerns remain about Beijing using the technology to advance its military and intelligence operations. But if Congress cannot come to a bipartisan agreement on how to combat national security concerns posed by Chinese technology, he said, then the prospect for comprehensive AI legislation looks grim.

Its so important, this is more on the politics side than the substance side, to at least show we can do something now, Warner said. Even if industry and other groups think thats all Congress will do, he added, I will take that risk because weve been so pathetic on social media. Weve got to show that we can actually put some markers down that have the force of law.

Annie Rees contributed to this report.

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Warner on AI regulation: 'We probably can't solve it all at once' - POLITICO

But then, as statecraft in the real world came to favor game theory over traditional diplomacy, the metagame likewise shifted. Online players were no longer calling one another into solaria or billiards rooms to speechify about making the world safe for democracy. Games became shorter. Communication got blunter. Where someone playing Diplomacy by mail in the 1960s might have worked Iago-like angles to turn players against one another, a modern player might just text CON-BUL? (For Constantinople to Bulgaria?)

This is the current Diplomacy metagame. Game theory calculations undergird most utterances, and even humans communicate in code. Lerer joked that in modern-day online Diplomacy, even human players wouldnt pass the Turing test. Before Cicero, it seems, humans had already started playing like AIs. Perhaps, for an AI to win at Diplomacy, Diplomacy had to become a less human game.

Kostick, who won a European grand prix Diplomacy event in 2000 and was on the Irish team that took the Diplomacy National World Cup in 2012, misses the old style of gameplay. The whole purpose of Allan Calhamers design of the game, he told me, is to create a dynamic where the players all fear a stab and yet must deploy a stab or a lie to be the only person to reach 18.

Kostick believes that while he would have been delighted with the practical results of Ciceros website play, Metas project misses the mark. Ciceros glitches, Kostick believes, would make it easy to outwit with spam and contradictory inputs. Moreover, in Kosticks opinion, Cicero doesnt play real Diplomacy. In the online blitz, low-stab game Cicero does play, the deck is stacked in its favor, because players dont have to lie, which Cicero does badly. (As Lerer told me, Cicero didnt really understand the long-term cost of lying, so we ended up mostly making it not lie.) Kostick believes Ciceros metagame is off because it never knowingly advocates to a human a set of moves that it knows are not in the humans best interest. Stabbing, Kostick believes, is integral to the game. A Diplomacy player who never stabs is like a grandmaster at chess who never checkmates.

With some trepidation, I mentioned Kosticks complaint to Goff.

Unsurprisingly, Goff scoffed. He thinks its Kostick and his generation who misunderstand the game and give it its unfair reputation for duplicity. Cicero does stab, just rarely, Goff said. I reject outright that [compelling players to stab] was Calhamers intent.

I could tell we were in metagame territory when Goff and Kostick began arguing about the intent of the games creator, as if they were a couple of biblical scholars or constitutional originalists. For good measure, Goff bolstered his case by citing an axiom from high-level theory and invoking an elite consensus.

Regardless of Calhamers intent, game theory says, Dont lie, he told me. This is not controversial among any of the top 20 players in the world.

For one person or another to claim that their metagame is the real onebecause the founder wanted it that way, or all the best people agree, or universal academic theory says x or yis a very human way to try to manage a destabilizing paradigm shift. But, to follow Kuhn, such shifts are actually caused when enough people or players happen to align with one vision of reality. Whether you share that vision is contingent on all the vagaries of existence, including your age and temperament and ideology. (Kostick, an anarchist, tends to be suspicious of everything Meta does; Goff, a CFO of a global content company, believes clear, non-duplicitous communications can advance social justice.)

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What If the Robots Were Very Nice While They Took Over the World? - WIRED

Artificial intelligence (AI) is making headlines everywhere. Yet AI applications and implications for older adults, particularly older women, have not been adequately contemplated.

Its no longer a moonshot idea from a science fiction movie.AI is already part of our daily lives Apples Siri, Amazons Alexa, self-driving cars. And now ChatGPT, an AI chatbot that has human-like conversations, composes music, creates art and writes essays.It has disrupted the world as we know it. Pundits who are not easily impressed often describe these advancements as scary good.

Many leaders have asked for a pause on AI development until we gain a better understanding of its impact. This is a good idea but for reasons well beyond those often identified.

We need to ask: How can we ensure that AIs reach is considering the unique needs of different populations? For example, many countries are becoming super-aged societies where women make up the majority of the older population. Is AI taking the needs of older adults into account?

Without thinking through these questions, we may leave older adults, particularly women, and other marginalized populations, open to discriminatory outcomes.

The needs of older women are often invisible to decision-makers. Older women are a unique population and often gendered ageism discrimination based on their age and sex causes their needs to be neglected. Research has already demonstrated that older women are more likely to experience adverse health outcomes and facepovertyand discrimination based on age and sex.

AI perpetuates this discrimination in the virtual world by replicating discriminatory practices in the real world. Whats worse is that AI automates this discrimination speeds it up and makes the impact more widely felt.

AI models use historical data. In healthcare, large data sets composed of personal and biomedical information are currently being used to train AI, but these data have, in many cases,excluded older adultsand women, making technologies exclusionary by design.

For example, AI has a valuable use in drug research and development, which uses massive data sets or big data. But AI is only as good as the data it gets and much of the world has not collected drug data properly. In the United States,until the 1990s, women and minorities were not required to be included in National Institute of Health (NIH) funded studies. Andup until 2019, older adults were not required to be included in NIH funded studies leaving a gap in our understanding of the health needs of older women in particular.

Excluding older women from drug data collection has been specifically detrimental because they are more likely to have chronic conditions, conditions that may require drugs, and are more likely to experienceharmful side effectsfrom medications.

Also, AI powered systems are often designed based on ageist assumptions. Stereotypes such as older adults being technophobes result in their exclusion from participation in the design of advanced technologies.

For example, women make up majority of the residents in long-term care homes.A studyfound that biases held by technology developers towards older adults hindered the appropriate utilization of AI in long-term care.

There also needs to be further thought given to loss of autonomy and privacy, and the effects of limiting human companionship because of AI. Older women are more likely to experience loneliness, yet AI is already being used in the form of companion robots. Their impact on older womens wellbeing, especially loss of human contact, is not well studied.

This is how older women get left out from properly benefitting from advancements in technology.

The World Health Organizations (WHO) timelypolicy briefaddressesAgeism in Artificial Intelligence for Healthand outlines eight important considerations to ensure that AI technologies for health address ageism. These include participatory design of AI technology with older people and age-inclusive data.

We would add the need to consider the differences between women and men throughout.All levels of government also need to think about how AI is impacting our lives and get innovative with policy and legal frameworks to prevent systemic discrimination.

Ethical guidelines and the ongoing evaluation of AI systems can help prevent the perpetuation of gendered ageism and promote fair and equitable outcomes.

Its time we rethink our approach and reimagine our practices, so that everyone can participate and take advantage of what AI has to offer.

About the Authors: Surbhi Kalia is the Strategy Consultant andDr. Paula Rochon is a geriatrician and the founding directorof theWomens Age Labat Womens College Hospital.

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Why Artificial Intelligence Needs to Consider the Unique Needs of ... - Women's eNews

What is image-to-image translation?

Image-to-image translation is a generative artificial intelligence (AI) technique that translates a source image into a target image while preserving certain visual properties of the original image. This technology uses machine learning and deep learning techniques such as generative adversarial networks (GANs); conditional adversarial networks, or cGANs; and convolutional neural networks (CNNs) to learn complex mapping functions between input and output images.

Image-to-image translation allows images to be converted from one form to another while retaining essential features. The goal is to learn a mapping between the two domains and then generate realistic images in whatever style a designer chooses. This approach enables tasks such as style transfer, colorization and super-resolution, a technique that improves the resolution of an image.

The image-to-image technology encompasses a diverse set of applications in art, image engagement, data augmentation and computer vision, also known as machine vision. For instance, image-to-image translation allows photographers to change a daytime photo to a nighttime one, convert a satellite image into a map and enhance medical images to enable more accurate diagnoses.

Image processing systems using image-to-image translation require the following basic steps:

A critical aspect of image-to-image translation is ensuring the model generalizes well in response to previously unseen or unsupervised scenarios. Cycle consistency and unsupervised learning help to ensure that if an image is translated from one domain to another and then back, it returns to its original form. Deep learning architectures, such as U-Net and CNNs, are also commonly used because they can capture complex spatial relationships in images. In the training process, batch normalization and optimization algorithms are used to stabilize and expedite convergence.

The two main approaches to image-to-image translation are supervised and unsupervised learning.

Supervised methods rely on paired training data, where each input image has a corresponding target image. Using this approach, the generated image system learns the direct mapping that's required between the two domains. However, obtaining paired data can be challenging and time-consuming, especially when dealing with complex image transformation.

Unsupervised methods tackle the image-to-image translation problem without paired training examples. One prominent unsupervised approach is CycleGAN, which introduces the concept of cycle consistency. This involves two mappings: from the source domain to the target domain and vice versa. CycleGAN ensures the target domain is similar to the original source image.

Image-to-image translation and generative AI in general are touted for being cost-effective, but they're also criticized for lacking creativity. It's essential to research the various AI models that have been developed to handle image-to-image translation tasks, as each comes with its own unique benefits and drawbacks. Research groups such as Gartner also urge users and generative AI developers to look for trust and transparency when choosing and designing models.

Some of the most popular models include the following:

Image-to-image translation is a popular generative AI technology. Learn the eight biggest generative AI ethical concerns.

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What Is Image-to-Image Translation? | Definition from TechTarget - TechTarget

Dr. Tomas Chamorro-Premuzic and James Spiro

(Photo: Zoom/Sinay David)

On a philosophical or testimonial level, if you look at most of the mainstream science, neuroscience, behavioral science, there is probably 80% consensus that free will is actually overrated or overstated, said Dr. Tomas Chamorro-Premuzic, author of I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique. We think we are in control of the decisions we make, but actually there are so many serendipitous and biologically driven courses of our decision.

Dr. Tomas Chamorro-Premuzic is an organizational psychologist who works mostly in the areas of personality profiling, people analytics, talent identification, the interface between human and artificial intelligence, and leadership development. He is the Chief Innovation Officer at ManpowerGroup, a professor of business psychology at University College London and at Columbia University, co-founder of deepersignals.com, and an associate at Harvards Entrepreneurial Finance Lab.

He is the writer behind books such as Why Do So Many Incompetent Men Become Leaders?, The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right, and this years I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique. Joining CTech for its new BiblioTech video series, he discusses the integration of AI into our lives and how we can keep our unique creativity and value in an increasingly digital world.

Leaving aside these philosophical discussions what I highlight in the book is that if we get to a point where our decisions are so predictable that AI can make most of these decisions, even if we are not automated and replaced by AI, surely we need to question our sense of subjective free will?

Many of the topics that Chamorro-Premuzic addresses in the book relate to the impact that AI will have on our lives and how different generations might respond to the algorithms living beside us. For example, he cites tech leaders like Bill Gates and Elon Musk, who present concerning views of AI, but also respond positively to how Gen Z might learn to adopt such technologies.

One of the things that the digital age has introduced is ever more and more ADD-like behaviors, he continued. We are pressed to do things quicker and quicker. And therefore there are few rewards for pausing and thinking.

Even though he believes humans are perfectly capable of stopping and taking time to consider their thoughts and actions, most of the decisions today in the AI age are so fast that they become very predictable and therefore easily outsourced to machines.

Gen Z and the next generation will need to showcase their expertise in a different area or a different way, he told CTech. Expertise is mutating from knowing a lot of answers to asking the right questions - from memorizing and retrieving facts to knowing how, why, and where the facts are wrong Demonstrating and cultivating expertise is a big challenge for the young generations.

Tomas, in your book you tackle one of the biggest questions facing our species: "Will we use artificial intelligence to improve the way we work and live, or will we allow it to alienate us?" Why did you find that now was the moment that this question needed to be asked and why did your book come out when it did?

I wrote 4-5 years ago that AI could be a really powerful tool to translate data and make leadership selection more data-driven with my first book, Why Do So Many Incompetent Men Become Leaders? (And How to Fix it). Then came The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right, which was about practical advice on how organizations can do that. Then, I was already contracted to do a new book during the pandemic, and on a personal level I found myself interacting with AI so much and interacting with other humans so little, that I thought this thing was really about to take off especially if we will be in lockdown for a while.

I started to look at the wider impact of AI and human behavior. Coincidentally the book was due to launch when OpenAI released ChatGPT which I always say is good and bad. Its good because there is more interest now for a book that explores the implications for human intelligence and human creativity in an age where we can outsource much of our thinking to machines. And it's bad because I had to write it myself, I couldn't rely on ChatGPT to write it! I think the next one will probably be written by AI and I will edit it!

I'd like to highlight what some of the tech leaders of today have said about AI, which you address at the start of your book:

You comment that Bill Gates is concerned about super intelligence; Stephen Hawking noted that Super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we are in trouble. Finally, you highlight how Elon Musk labeled AI a fundamental risk to the existence of human civilization - although you point out it hasn't stopped him from trying to implant it into our brains.

Tomas, why are we pursuing such a scary and unknown technology?

We're pursuing it mostly for two reasons. First, over the past 10 years, we have amassed so much data that we dont have enough human resources or human intelligence available to analyze that data. Also, we had to rely on a large language model, or some version of AI, to help us make sense of the data and actually make decisions in a more efficient, quick, and effortless way which is needed in a work that is so complex.

The second reason is that human beings are very lazy. We love to optimize everything for familiarity, for predictability. You could either sit down to watch any movie that Netflix recommends to you and after five seconds youll be watching a movie, or you could do what I do which is dismiss the algorithm, dig deeper, and waste two hours of my life. By the time I actually find the movie I want to watch it is time to go to sleep. We are trading off efficiency, which means lazy, fast, and furious decision-making, for deep, thoughtful, and expert-like decisions.

It is the same whether we are choosing a job, a romantic partner, a restaurant, a hotel, or what we consume in terms of news. This is why AI has been introduced as a potential tool that can enhance our productivity. Even if we're not necessarily going to invest whatever savings we gain from the productivity that AI uses into more thoughtful, creative, and intellectually fulfilling activities. Therein lies the problem.

I want to address some of the more nefarious things you mention and some of the ways that AI is affecting us in ways we don't understand. We speak about AI in the world, but how much choice do we have and how much is just an illusion of choice?

On a philosophical or testimonial level, if you look at most of the mainstream science, neuroscience, behavioral science, there is probably around 80% consensus that free will is actually overrated or overstated. But it is mostly an illusion. We think we are in control of the decisions we make but actually, there are so many serendipitous and biologically driven courses of our decisions.

Leaving aside these philosophical discussions which are hard to verify and often don't mean much to the average consumer, it is clear to me: If we get to a point where our decisions are so predictable that AI can make most of these decisions, even if we are not automated and replaced by AI, surely we need to question our sense of subjective free will?

If when I'm writing an email to you and Googles auto-complete version is correct 95% of the time, then I have to wonder whether I really am an agentic creative human that still has some choice or whether it's more deterministic than we think. I think the way to think about these issues is that we are mostly free to choose, or at least we feel we are free to choose, but that doesn't necessarily mean we want to pause, think, and choose. One of the things that the digital age has introduced is even more and more ADD-like behaviors. We are pressed to do things quicker and quicker and therefore there are few rewards for pausing and thinking, which explains the rise of things like mindfulness movements, apps, and people who do digital detoxes.

We are perfectly capable of pausing and thinking, but most of the decisions we are making in the AI age are so fast that they become very predictable and therefore they can be outsourced to machines.

I'd like to elaborate on what you mention in the book which you call a "Crisis of Distractability". I think it really sums up where so many of us are today online. What did you mean by that and how is it manifesting itself in recent years?

Around 11 years ago I went to a digital marketing conference where you had all the big tech firms. For the first time, some people were introducing the notion of the second screen, which was very counterintuitive and bold at the time. People were watching TV and holding their iPads, or they were looking at their smartphones and now theres a second screen market.

Now, we all have 3-4 screens that we interact with all the time. Life itself has been downgraded to a distraction. You're almost distracted when you can't pay attention to your apps or your social media feeds. You get FOMO if you can't interact with people digitally and you have to pay attention to the analog world.

In terms of productivity, I think this is really important because even though we keep on arguing about whether technology and GenAI are going to lead to a productivity gain or the demise of human civilization, the tech firms keep telling us it will make us healthier, fitter, happier, and more productive.

Actually the productivity data is very clear. Our productivity went up between 2000-2008 in the first wave of the digital revolution, only to start to stagnate or stall after that, after the advent of social media. Roughly 60-75% of smartphone use occurs during working hours when they're working from home or in an office and 70% of workers report being distracted. In the U.S. alone, digital distractions cost the U.S. economy $650 billion dollars in productivity loss per year, which is 15 times more than the cost of absentees, turnover, and sickness. Multitasking, which we all do, results in a deficit of our intellectual cognitive performance of around 10 IQ points. It's basically as debilitating as smoking weed, presumably minus the benefits.

We think and fool ourselves into thinking that we can multitask, but every time you switch from one task to the other and you go back, youve lost the equivalent of 26 minutes of concentration on that task. Technology might improve productivity but sometimes you become more productive if you ignore or have the ability to resist technology as well.

There is a whole new generation in Gen Z who are growing up in the world youve been outlining - with AI and a search for uniqueness. What are some of the challenges they're going to have when trying to find their voice or establish their careers or relationships?

The main challenge will be to demonstrate social proof. If you just enter or start your career, no matter how smart you are, it is a very steep curve to demonstrate to others that you can provide more value than what you can get from AI. You're probably paying a lot of attention to ChatGPT and other forms of GenAI in terms of their ability to produce an article, or an opinion piece. Youre probably, in your area of expertise, able to spot the errors, but the reason you are adding value to that is because of your track record and experience, that actually you know your stuff.

If you're just starting, it's very difficult to persuade people that you have that expertise. Gen Z and the next generation will need to showcase their expertise in a different area or a different way. Expertise is mutating from knowing a lot of answers to asking the right questions - from memorizing and retrieving facts to knowing how, why, and where the facts are wrong. Fundamentally, to make decisions on the basis of information that might be correct or incorrect. Demonstrating and cultivating expertise is a big challenge for the young generations.

I heard that the future artists or engineers wont be coders, theyll be prompt engineers. Theyre going to know how to get the best out of the AI, which at the moment folks like me are walking around with our blindfolds not knowing what it's capable of.

There is an argument to be made that as soon as there's enough prompt engineers prompting AI, AI will learn to prompt itself then we will need to move to the next iteration. There is going to be a very intense cat-and-mouse race or game where as soon as we develop something it can be automated. And we have to develop something else and it can be automated.

Creativity is really critical. Spotify probably has enough data to automate 80% of its artists because it has an algorithm to understand what people like and most music can be pre-processed and done synthetically. Even if it automated 100% of its content, it probably wouldnt kill musicians. It would push artists to invent the next version of music. I think that's how we need to think about every form of performance that is intellectually fueled or creatively or artistically informed.

You touch on popular content in the book, such as Netflix's The Social Dilemma, the famous book Surveillance Capitalism, and of course Black Mirror, which is the modern-day Twilight Zone. What can readers learn from I, Human?

Hopefully they will learn a little bit about AI, especially if they don't have technical backgrounds on it. It's designed for people with no knowledge for people to understand what AI is and what it isnt - to understand how the algorithms that we interact with on a regular basis are reshaping our behavior.

Culture is always a big influence on how we behave. The average person today behaves differently from the average person in the Renaissance, medieval times, or in ancient Greece or Rome even though our hardware or DNA is the same. What I argue is that the current culture could be defined universally as the AI age, and with that comes certain behavioral traits and markers they will discover in their book.

The final part is a call to action, how we need to change if we want to ensure that the AI age is also the human AI age and that we use this technological invention to upgrade ourselves.It finishes on a relatively optimistic note with a call to action to rediscover some of the qualities that make us who we are. AI will probably not harm things like dep curiously, creativity, self-awareness, empathy, and EQ. The argument is that AI will probably win the IQ battle but the EQ battle could be won by humans.

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There is probably an 80% consensus that free will is actually ... - CTech