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The truth behind a successful social media platform

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Nowadays, just about anyone can launch their own social media platform to the public— but its simplicity doesn’t come without its tribulations. More than half the world uses social media on a daily basis, averaging 2 hours daily on platforms like FaceBook and Instagram. Each year, sites are seeing a 3% increase in user signups, indicating that social media is solidifying its presence in society on a global scale. While these aspects may sound promising to the next social media entrepreneur, it reveals the competitive nature behind the ever-growing technology industry. 

Currently, there are 7 platforms that dominate the social media market, with 4 being owned by META corporation. It is estimated that 4.88 billion identities are registered online as of July 2023, making social media the largest international digital connection. Most users are active on 6 or 7 platforms, meaning that a registered account doesn’t guarantee usage. Unlike other forms of media, social media relies on its users to generate content and spur activity, and inactivity guarantees the death of the platform. 

META’s recent social media creation, Threads, shows how easy it is for a platform to die out despite its large user base. In an attempt to rival Twitter, META’s biggest competitor, Zuckerberg and team decided to create a platform eerily similar, except users must register using their Instagram credentials and identity, binding the two platforms to each other. At first, Threads saw great success with 27.8 million downloads during the first week in the USA alone. In less than a week, Threads managed to gain half as many users as Twitter, a platform created back in 2006. While these reports seemed promising for the future of Threads, 70% of accounts were reported as inactive by its third week of existence. 

Photo by Julio Lopez via Pexels
Photo by Julio Lopez via Pexels

While there are various factors contributing to Threads’ decline, perhaps the most notable aspect was Threads dependence on its mother platform, Instagram. Forcing users to link their Instagram credentials to their Threads profile guarantees a lack of curated online identity, which often will lose users’ excitement as it provides nothing new. Content generation, curation, and maintenance is too similar to Instagram, leading to users disengaging. Why maintain another social media profile when Instagram already exists? 

This phenomena is certainly not new to the world of social media, yet it’s a cautionary tale about the trials and tribulations of a new social media platform. Even for well established companies like META, a new platform’s success is not guaranteed. It can be difficult to identify how the market will respond to a new social media platform as both technology and people’s desires seem to be changing at rapid rates. Therefore, one must go beyond the technical components in order to ensure their social media platform’s success, and maintain activity. 

One of the newest popular social media platforms, BeReal, has achieved enormous success due to its ability to target an aspect of the market missing on other platforms. Built upon “authentic use” , BeReal allows users to post one photo a day highlighting their lifestyle “in real time”. It is now the fastest growing social media app for Gen. Z users worldwide, with a 313% surge of signups this past year. The creators targeted those who felt that social media was “inauthentic”, which seems to be quite the lucrative market. In this instance, identifying a gap within the social media market for users has paid off greatly for BeReal. From its creation in 2020 to now, it seems BeReal is here to stay for many avid social media users. 

While social media’s fruitful rewards seem promising to a young entrepreneur, it takes a lot more than one may think to be successful. Take it from companies like META, a projected profit on paper is not always reality for the social media market. It takes great creativity, skill, and perhaps a bit of luck to crack into the ever-expanding world of social media. 

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Katie Williams, Rome Design Agency

Feature Image by Geri Tech via Pexels

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What is ‘ethical AI’ and how can companies achieve it?

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The rush to deploy powerful new generative AI technologies, such as ChatGPT, has raised alarms about potential harm and misuse. The law’s glacial response to such threats has prompted demands that the companies developing these technologies implement AI “ethically.”

But what, exactly, does that mean?

The straightforward answer would be to align a business’s operations with one or more of the dozens of sets of AI ethics principles that governments, multistakeholder groups and academics have produced. But that is easier said than done.

We and our colleagues spent two years interviewing and surveying AI ethics professionals across a range of sectors to try to understand how they sought to achieve ethical AI – and what they might be missing. We learned that pursuing AI ethics on the ground is less about mapping ethical principles onto corporate actions than it is about implementing management structures and processes that enable an organization to spot and mitigate threats.

This is likely to be disappointing news for organizations looking for unambiguous guidance that avoids gray areas, and for consumers hoping for clear and protective standards. But it points to a better understanding of how companies can pursue ethical AI.

Grappling with ethical uncertainties

Our study, which is the basis for a forthcoming book, centered on those responsible for managing AI ethics issues at major companies that use AI. From late 2017 to early 2019, we interviewed 23 such managers. Their titles ranged from privacy officer and privacy counsel to one that was new at the time but increasingly common today: data ethics officer. Our conversations with these AI ethics managers produced four main takeaways.

First, along with its many benefits, business use of AI poses substantial risks, and the companies know it. AI ethics managers expressed concerns about privacy, manipulation, bias, opacity, inequality and labor displacement. In one well-known example, Amazon developed an AI tool to sort résumés and trained it to find candidates similar to those it had hired in the past. Male dominance in the tech industry meant that most of Amazon’s employees were men. The tool accordingly learned to reject female candidates. Unable to fix the problem, Amazon ultimately had to scrap the project.

Generative AI raises additional worries about misinformation and hate speech at large scale and misappropriation of intellectual property.

Second, companies that pursue ethical AI do so largely for strategic reasons. They want to sustain trust among customers, business partners and employees. And they want to preempt, or prepare for, emerging regulations. The Facebook-Cambridge Analytica scandal, in which Cambridge Analytica used Facebook user data, shared without consent, to infer the users’ psychological types and target them with manipulative political ads, showed that the unethical use of advanced analytics can eviscerate a company’s reputation or even, as in the case of Cambridge Analytica itself, bring it down. The companies we spoke to wanted instead to be viewed as responsible stewards of people’s data.

The challenge that AI ethics managers faced was figuring out how best to achieve “ethical AI.” They looked first to AI ethics principles, particularly those rooted in bioethics or human rights principles, but found them insufficient. It was not just that there are many competing sets of principles. It was that justice, fairness, beneficence, autonomy and other such principles are contested and subject to interpretation and can conflict with one another.

This led to our third takeaway: Managers needed more than high-level AI principles to decide what to do in specific situations. One AI ethics manager described trying to translate human rights principles into a set of questions that developers could ask themselves to produce more ethical AI software systems. “We stopped after 34 pages of questions,” the manager said.

Fourth, professionals grappling with ethical uncertainties turned to organizational structures and procedures to arrive at judgments about what to do. Some of these were clearly inadequate. But others, while still largely in development, were more helpful, such as:

  • Hiring an AI ethics officer to build and oversee the program.
  • Establishing an internal AI ethics committee to weigh and decide hard issues.
  • Crafting data ethics checklists and requiring front-line data scientists to fill them out.
  • Reaching out to academics, former regulators and advocates for alternative perspectives.
  • Conducting algorithmic impact assessments of the type already in use in environmental and privacy governance.

Ethics as responsible decision-making

The key idea that emerged from our study is this: Companies seeking to use AI ethically should not expect to discover a simple set of principles that delivers correct answers from an all-knowing, God’s-eye perspective. Instead, they should focus on the very human task of trying to make responsible decisions in a world of finite understanding and changing circumstances, even if some decisions end up being imperfect.

In the absence of explicit legal requirements, companies, like individuals, can only do their best to make themselves aware of how AI affects people and the environment and to stay abreast of public concerns and the latest research and expert ideas. They can also seek input from a large and diverse set of stakeholders and seriously engage with high-level ethical principles.

This simple idea changes the conversation in important ways. It encourages AI ethics professionals to focus their energies less on identifying and applying AI principles – though they remain part of the story – and more on adopting decision-making structures and processes to ensure that they consider the impacts, viewpoints and public expectations that should inform their business decisions.

Ultimately, we believe laws and regulations will need to provide substantive benchmarks for organizations to aim for. But the structures and processes of responsible decision-making are a place to start and should, over time, help to build the knowledge needed to craft protective and workable substantive legal standards.

Indeed, the emerging law and policy of AI focuses on process. New York City passed a law requiring companies to audit their AI systems for harmful bias before using these systems to make hiring decisions. Members of Congress have introduced bills that would require businesses to conduct algorithmic impact assessments before using AI for lending, employment, insurance and other such consequential decisions. These laws emphasize processes that address in advance AI’s many threats.

Some of the developers of generative AI have taken a very different approach. Sam Altman, the CEO of OpenAI, initially explained that, in releasing ChatGPT to the public, the company sought to give the chatbot “enough exposure to the real world that you find some of the misuse cases you wouldn’t have thought of so that you can build better tools.” To us, that is not responsible AI. It is treating human beings as guinea pigs in a risky experiment.

Altman’s call at a May 2023 Senate hearing for government regulation of AI shows greater awareness of the problem. But we believe he goes too far in shifting to government the responsibilities that the developers of generative AI must also bear. Maintaining public trust, and avoiding harm to society, will require companies more fully to face up to their responsibilities.

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Dennis Hirsch, Professor of Law and Computer Science; Director, Program on Data and Governance; core faculty TDAI, The Ohio State University and Piers Norris Turner, Associate Professor of Philosophy & PPE Coordinator; Director, Center for Ethics and Human Values, The Ohio State University.

This article originally appeared in The Conversation under a Creative Commons License.

Feature image by Fauxels via Pexels.

a close-up of a building by Tim West

Why many Americans are terrified of AI

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AI is the talk of the town these days. But despite the technology’s impressive accomplishments—or perhaps because of them—not all of that talk is positive. There was a New York Times tech columnist’s piece about his unsettling interaction with ChatGPT in February; an open letter calling for a moratorium on AI research in March; “godfather of AI” Geoffrey Hinton’s dramatic resignation from Google and warning about the dangers of AI; and just this week, OpenAI CEO Sam Altman’s testimony before Congress, in which he said his “worst fear is we cause significant harm to the world” and encouraged legislation around the technology (though he also argued that generative AI should be treated differently, which would be convenient for his company).

It seems these warnings (along with all the other media circulating on the topic) have reached the American public loud and clear, and people don’t quite know what to think—but many are getting nervous. A poll carried out last week by Reuters revealed that more than half of Americans believe AI poses a threat to humanity’s future.

The poll was conducted online between May 9 and May 15, with 4,415 adults participating, and the results were published yesterday. More than two-thirds of respondents expressed concern about possible negative impacts of AI, while 61 percent believe it could be a threat to civilization.

“It’s telling such a broad swatch of Americans worry about the negative effects of AI,” said Landon Klein, director of US policy at the Future of Life Institute, the organization behind the previously mentioned open letter. “We view the current moment similar to the beginning of the nuclear era, and we have the benefit of public perception that is consistent with the need to take action.”

One nebulous aspect of the poll, and of many of the headlines about AI we see on a daily basis, is how the technology is defined. What are we referring to when we say “AI”? The term encompasses everything from recommendation algorithms that serve up content on YouTube and Netflix, to large language models like ChatGPT, to models that can design incredibly complex protein architectures, to the Siri assistant built into many iPhones.

A blender made hand by Simon Lee. Courtesy of Unsplash.
Simon Lee, courtesy of Unsplash.

IBM’s definition is simple: “a field which combines computer science and robust datasets to enable problem-solving.” Google, meanwhile, defines it as “a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.”

It could be that peoples’ fear and distrust of AI comes partly from a lack of understanding of it, and a stronger focus on unsettling examples than positive ones. The AI that can design complex proteins may help scientists discover stronger vaccines and other drugs, and could do so on a vastly accelerated timeline.

In fact, biotechnology and medicine are two fields for which AI holds enormous promise, be it by modeling millions of proteins, coming up with artificial enzymes, powering brain implants that help disabled people communicate, or helping diagnose conditions like Alzheimer’s.

Sebastian Thrun, a computer science professor at Stanford who founded Google X, pointed out that there’s not enough public awareness of the potential for positive impact AI has. “The concerns are very legitimate, but I think what’s missing in the dialogue in general is why are we doing this in the first place?” he said. “AI will raise peoples’ quality of life, and help people be more competent and more efficient.”

While 61 percent of the poll’s respondents said AI could be a risk to humanity, only 22 percent said it won’t be a risk; the other 17 percent weren’t sure.

However, the (sort of?) good news is that AI isn’t the biggest thing Americans are losing sleep over. The top worry at the moment is, unsurprisingly, the economy (82 percent of respondents fear a looming recession), with crime coming in second (77 percent said they support increasing police funding to fight crime).

If an AI solution came along that could, say, point out economic strategies humans haven’t yet thought of, would that make people less wary of it?

Given everything else the tech can do, this doesn’t seem like such a long shot.

^^^

Vanessa Bates Ramirez, senior editor of Singularity Hub.

This article originally appeared in Singularity Hub under a Creative Commons license.

Feature image by Tim West, courtesy of Unsplash.

Michael Timothy Bennett / Generated using MidJourney

Can Machines Be Self-Aware? New Research Explains How This Could Happen

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To build a machine, one must know what its parts are and how they fit together. To understand the machine, one needs to know what each part does and how it contributes to its function. In other words, one should be able to explain the “mechanics” of how it works.

According to a philosophical approach called mechanism, humans are arguably a type of machine – and our ability to think, speak and understand the world is the result of a mechanical process we don’t understand.

To understand ourselves better, we can try to build machines that mimic our abilities. In doing so, we would have a mechanistic understanding of those machines. And the more of our behaviour the machine exhibits, the closer we might be to having a mechanistic explanation of our own minds.

This is what makes AI interesting from a philosophical point of view. Advanced models such as GPT4 and Midjourney can now mimic human conversation, pass professional exams and generate beautiful pictures with only a few words.

Yet, for all the progress, questions remain unanswered. How can we make something self-aware, or aware that others are aware? What is identity? What is meaning?

Although there are many competing philosophical descriptions of these things, they have all resisted mechanistic explanation.

In a sequence of papers accepted for the 16th Annual Conference in Artificial General Intelligence in Stockholm, I pose a mechanistic explanation for these phenomena. They explain how we may build a machine that’s aware of itself, of others, of itself as perceived by others, and so on.

Intelligence and intent

A lot of what we call intelligence boils down to making predictions about the world with incomplete information. The less information a machine needs to make accurate predictions, the more “intelligent” it is.

For any given task, there’s a limit to how much intelligence is actually useful. For example, most adults are smart enough to learn to drive a car, but more intelligence probably won’t make them a better driver.

My papers describe the upper limit of intelligence for a given task, and what is required to build a machine that attains it.

I named the idea Bennett’s Razor, which in non-technical terms is that “explanations should be no more specific than necessary”. This is distinct from the popular interpretation of Ockham’s Razor (and mathematical descriptions thereof), which is a preference for simpler explanations.

The difference is subtle, but significant. In an experiment comparing how much data AI systems need to learn simple maths, the AI that preferred less specific explanations outperformed one preferring simpler explanations by as much as 500%.

Futuristic Woman, courtesy of Unsplash
Futuristic Woman, courtesy of Unsplash

Exploring the implications of this discovery led me to a mechanistic explanation of meaning – something called “Gricean pragmatics”. This is a concept in philosophy of language that looks at how meaning is related to intent.

To survive, an animal needs to predict how its environment, including other animals, will act and react. You wouldn’t hesitate to leave a car unattended near a dog, but the same can’t be said of your rump steak lunch.

Being intelligent in a community means being able to infer the intent of others, which stems from their feelings and preferences. If a machine was to attain the upper limit of intelligence for a task that depends on interactions with a human, then it would also have to correctly infer intent.

And if a machine can ascribe intent to the events and experiences befalling it, this raises the question of identity and what it means to be aware of oneself and others.

Causality and identity

I see John wearing a raincoat when it rains. If I force John to wear a raincoat on a sunny day, will that bring rain?

Of course not! To a human, this is obvious. But the subtleties of cause and effect are more difficult to teach a machine (interested readers can check out The Book of Why by Judea Pearl and Dana Mackenzie).

To reason about these things, a machine needs to learn that “I caused it to happen” is different from “I saw it happen”. Typically, we’d program this understanding into it.

However, my work explains how we can build a machine that performs at the upper limit of intelligence for a task. Such a machine must, by definition, correctly identify cause and effect – and therefore also infer causal relations. My papers explore exactly how.

The implications of this are profound. If a machine learns “I caused it to happen”, then it must construct concepts of “I” (an identity for itself) and “it”.

The abilities to infer intent, to learn cause and effect, and to construct abstract identities are all linked. A machine that attains the upper limit of intelligence for a task must exhibit all these abilities.

This machine does not just construct an identity for itself, but for every aspect of every object that helps or hinders its ability to complete the task. It can then use its own preferences as a baseline to predict what others may do. This is similar to how humans tend to ascribe intent to non-human animals.

So what does it mean for AI?

Of course, the human mind is far more than the simple program used to conduct experiments in my research. My work provides a mathematical description of a possible causal pathway to creating a machine that is arguably self-aware. However, the specifics of engineering such a thing are far from solved.

For example, human-like intent would require human-like experiences and feelings, which is a difficult thing to engineer. Furthermore, we can’t easily test for the full richness of human consciousness. Consciousness is a broad and ambiguous concept that encompasses – but should be distinguished from – the more narrow claims above.

I have provided a mechanistic explanation of aspects of consciousness – but this alone does not capture the full richness of consciousness as humans experience it. This is only the beginning, and future research will need to expand on these arguments.

^^^

Michael Timothy Bennett, PhD Student, School of Computing, Australian National University.

Feature image by Michael Timothy Bennett.

This article originally appeared in The Conversation under a Creative Commons License.

avi-richards-YksAiGhWn5w-unsplash

Big Tech Companies Are Becoming More Powerful Than Nation-States

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They are already richer than many countries, and the rise of AI looks set to increase their influence.

A few years ago, I was having dinner with a friend who worked at Google. As we were discussing the ins and outs of the tech world, he casually remarked: “Google is going to take over the world you know.” Driving home and reflecting on that remark I thought: “How curious.” But now, as I contemplate the shambles our democracy has become, I’m more inclined to think: “How prescient.”

Democracy is under threat—not just in the U.S. but in many other countries as well. But the precipitous actions of newly minted authoritarian leaders and the turmoil in Western democracies are just a few of the puzzle pieces needed to figure out how we got to this point. Another less discussed trend is that U.S. citizens are being subjected to a relentless onslaught from intrusive technologies that have become embedded in the everyday fabric of our lives, creating unprecedented levels of social and political upheaval.

Advanced computer technology and the internet have given us many wonderful gifts when rightly applied. But we now know they can also be terrible taskmasters, impersonal forces that can dehumanize our personal interactions, cause severe mental health problems (especially for teenage girls), and serve as a de-facto wealth transfer mechanism to the billionaire class. Still, we accept the negatives because of the positive benefits. In this sense, we might even call hyper-technology a Grand Seduction. Now, AI has exploded onto the scene and threatens to monkey-wrench our lives in unimaginable ways.

The limitations of these widely used technologies are well known. They include social media and what Harvard professor Shoshanna Zuboff calls “surveillance capitalism”—the buying and selling of our personal info and even our DNA in the corporate marketplace. But powerful new ones are poised to create another wave of radical change. Under the mantle of the “Fourth Industrial Revolution,” these include artificial intelligence or AI, the metaverse, the Internet of Things, the Internet of Bodies (in which our physical and health data is added into the mix to be processed by AI), and my personal favorite, police robots. All of these technologies will be enhanced and amplified by the use of 5G and 6G communications via a rapidly expanding satellite system provided by Elon Musk.

This is a two-pronged effort involving both powerful corporations and government initiatives. These tech-based systems are operating “below the radar” and are rarely discussed in the mainstream media. In addition to corporate surveillance, governments are also busy beefing up their own systems. While we tend to associate these sorts of initiatives with the NSA and the Department of Homeland Security, a groundbreaking article in the Boston Globe has described how pervasive and intrusive surveillance has become even at the state level. The article methodically details how law enforcement agencies in Massachusetts operate a huge apparatus of drones, license plate readers, and devices called cell-site simulators, which pretend to be cell towers in order to capture cellphone signals to pinpoint the location of individuals.

The AI “Arms Race”

AI’s precipitous and dramatic entry into the technology mix has ushered in what Time magazine and other mainstream publications are calling an “AI Arms Race.” The designation is telling, given that AI has been developed with significant funding from the defense and government sectors. This accelerated deployment is happening without the benefit of thoughtful political oversight because elected officials, often at a disadvantage in the face of technologies they don’t completely understand, are providing little guidance, regulation, or pushback.

Parsing the subtler impact of technology in our lives is tricky. That’s because it sneaks up on us. It doesn’t happen by a vote or by some distinct series of events. Rather, it just creeps along, establishing itself in maddeningly minute increments. The sum total of these technological intrusions, fostered by government and corporations often working together, constitutes a semi-invisible overlay of technocratic governance that has no central organizing principle, unlike the traditional government structures we’re familiar with. Just because these systems are “distributed” (to use a little computer jargon) doesn’t mean that they are any less powerful. And while the internet presents the appearance of democratized participation, it’s important to remember that its ultimate Oz-like control is centralized in the deep corridors of Big Tech companies.

Goodbye Nation States?

As we see democratic principles slowly vaporize even in Western nations, the fact that Big Tech continues to consolidate its power globally over and above that of nation-states is deeply concerning. However (just to keep things nice and confusing) sometimes it does this in cooperation with governments via public/private partnerships, a kind of Faustian bargain.

The Time magazine article cited above offered this startling observation: “Even if computer scientists succeed in making sure the AIs don’t wipe us out, their increasing centrality to the global economy could make the Big Tech companies who control it vastly more powerful. They could become not just the richest corporations in the world—charging whatever they want for commercial use of this critical infrastructure—but also geopolitical actors to rival nation-states.”

Some might argue that this has already happened and the nexus of world power is now corporate-leaning. The world’s biggest tech companies are now richer and more powerful than most countries. According to an article in PC Week in 2021 discussing Apple’s dominance: “By taking the current valuation of Apple, Microsoft, Amazon, and others, then comparing them to the GDP of countries on a map, we can see just how crazy things have become… Valued at $2.2 trillion, the Cupertino company is richer than 96% of the world. In fact, only seven countries currently outrank the maker of the iPhone financially.”

For the moment, these trends appear to be unstoppable, given the levels of corporate investment already at stake and the supine posture and dependency of governments on their largesse. The best available response for the moment is simply greater public awareness and a commitment to face the contours of this brave new technocratic world head-on and with a clear vision. Given the astonishingly out-of-control power of the Big Tech sector, it’s also crucial to realize that simply regulating these systems while allowing them to continue to siphon off the power of traditional governments will not be enough to preserve our quality of life going forward.

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Tom Valovic is a journalist and the author of Digital Mythologies. 

This article originally appeared in Common Dreams under a Creative Commons license.

Feature image of Apple’s Cupertino headquarters by Avi Richards on Unsplash.