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The rise of powerful generative AI tools like ChatGPT has been described as this generations iPhone moment. In March, the OpenAI website, which lets visitors try ChatGPT, reportedly reached847 million unique monthly visitors. Amid this explosion of popularity, the level of scrutiny placed on gen AI has skyrocketed, with several countries acting swiftly to protect consumers.

In April, Italy became the first Western country toblockChatGPT on privacy grounds, only to reverse the ban four weeks later. Other G7 countries areconsidering a coordinated approachto regulation.

The UK will host thefirst global AI regulation summitin the fall, with Prime Minister Rishi Sunak hoping the country can drive the establishment of guardrails on AI. Itsstated aimis to ensure AI is developed and adopted safely and responsibly.

Regulation is no doubt well-intentioned. Clearly, many countries are aware of the risks posed by gen AI. Yet all this talk of safety is arguably masking a deeper issue: AI bias.

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Although the term AI bias can sound nebulous, its easy to define. Also known as algorithm bias, AI bias occurs when human biases creep into the data sets on which the AI models are trained. This data, and the subsequent AI models, then reflect any sampling bias, confirmation bias and human biases (against gender, age, nationality, race, for example) and clouds the independence and accuracy of any output from the AI technology.

As gen AI becomes more sophisticated, impacting society in ways it hadnt before, dealing with AI bias is more urgent than ever. This technology isincreasingly usedto inform tasks like face recognition, credit scoring and crime risk assessment. Clearly, accuracy is paramount with such sensitive outcomes at play.

Examples of AI bias have already been observed in numerous cases. When OpenAIs Dall-E 2, a deep learning model used to create artwork, wasasked to create an imageof a Fortune 500 tech founder, the pictures it supplied were mostly white and male. When asked if well-known Blues singer Bessie Smith influenced gospel singer Mahalia Jackson, ChatGPTcould not answer the question without further prompts, raising doubts about its knowledge of people of color in popular culture.

Astudyconducted in 2021 around mortgage loans discovered that AI models designed to determine approval or rejection did not offer reliable suggestions for loans to minority applicants.These instances prove that AI bias can misrepresent race and gender with potentially serious consequences for users.

AI that produces offensive results can be attributed to the way the AI learns and the dataset it is built upon. If the data over-represents or under-represents a particular population, the AI will repeat that bias, generating even more biased data.

For this reason, its important that any regulation enforced by governments doesnt view AI as inherently dangerous. Rather, any danger it possesses is largely a function of the data its trained on. If businesses want to capitalize on AIs potential, they must ensure the data it is trained on is reliable and inclusive.

To do this, greater access to an organizations data to all stakeholders, both internal and external, should be a priority. Modern databases play a huge role here as they have the ability to manage vast amounts of user data, both structured and semi-structured, and have capabilities to quickly discover, react, redact and remodel the data once any bias is discovered. This greater visibility and manageability over large datasets means biased data is at less risk of creeping in undetected.

Furthermore, organizations must train data scientists to better curate data while implementing best practices for collecting and scrubbing data. Taking this a step further, the data training algorithms must be made open and available to as many data scientists as possible to ensure that more diverse groups of people are sampling it and can point out inherent biases. In the same way modern software is often open source, so too should appropriate data be.

Organizations have to be constantly vigilant and appreciate that this is not a one-time action to complete before going into production with a product or a service. The ongoing challenge of AI bias calls for enterprises to look at incorporating techniques that are used in other industries to ensure general best practices.

Blind tasting tests borrowed from the food and drink industry, red team/blue team tactics from the cybersecurity world or the traceability concept used in nuclear power could all provide valuable frameworks for organizations in tackling AI bias. This work will help enterprises to understand the AI models, evaluate the range of possible future outcomes and gain sufficient trust with these complex and evolving systems.

In previous decades, talk of regulating AI was arguably putting the cart before the horse. How can you regulate something whose impact on society is unclear? A century ago, no one dreamt of regulating smoking because it wasnt known to be dangerous. AI, by the same token, wasnt something under serious threat of regulation any sense of its danger was reduced tosci-fi filmswith no basis in reality.

But advances in gen AI and ChatGPT, as well as advances towards artificial general Intelligence (AGI), have changed all that. Some national governments seem to be working in unison to regulate AI, while paradoxically, others are jockeying for position as AI regulators-in-chief.

Amid this hubbub, its crucial that AI bias doesnt become overly politicized and is instead viewed as a societal issue that transcends political stripes. Across the world, governments alongside data scientists, businesses and academics must unite to tackle it.

Ravi Mayuram is CTO of Couchbase.

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As regulators talk tough, tackling AI bias has never been more urgent - VentureBeat

We are seeing a progression of Generative AI applications powered by large language models (LLM) from prompts to retrieval augmented generation (RAG) to agents. Agents are being talked about heavily in industry and research circles, mainly for the power this technology provides to transform Enterprise applications and provide superior customer experiences. There are common patterns for building agents that enable first steps towards artificial general intelligence (AGI).

In my previous article, we saw a ladder of intelligence of patterns for building LLM powered applications. Starting with prompts that capture problem domain and use LLM internal memory to generate output. With RAG, we augment the prompt with external knowledge searched from a vector database to control the outputs. Next by chaining LLM calls we can build workflows to realize complex applications. Agents take this to a next level by auto determining how these LLM chains are to be formed. Let's look in detail.

A key pattern with agents is that they use the language understanding power of LLM to make a plan on how to solve a given problem. The LLM understands the problem and gives us a sequence of steps to solve the problem. However, it doesn't stop there. Agents are not a pure support system that will provide you recommendations on solving the problem and then pass on the baton to you to take the recommended steps. Agents are empowered with tooling to go ahead and take the action. Scary right!?

If we ask an agent a basic question like this:

Human: Which company did the inventor of the telephone start?

Following is a sample of thinking steps that an agent may take.

Agent (THINKING):

Agent (RESPONSE): Alexander Graham Bell co-founded AT&T in 1885

You can see that the agent follows a methodical way of breaking down the problem into subproblems that can be solved by taking specific Actions. The actions here are recommended by the LLM and we can map these to specific tools to implement these actions. We could enable a search tool for the agent such that when it realizes that LLM has provided search as an action, it will call this tool with the parameters provided by the LLM. The search here is on the internet but can as well be redirected to search an internal knowledge base like a vector database. The system now becomes self-sufficient and can figure out how to solve complex problems following a series of steps. Frameworks like LangChain and LLaMAIndex give you an easy way to build these agents and connect to toolings and API. Amazon recently launched their Bedrock Agents framework that provides a visual interface for designing agents.

Under the hood, agents follow a special style of sending prompts to the LLM which make them generate an action plan. The above Thought-Action-Observation pattern is popular in a type of agent called ReAct (Reasoning and Acting). Other types of agents include MRKL and Plan & Execute, which mainly differ in their prompting style.

For more complex agents, the actions may be tied to tools that cause changes in source systems. For example, we could connect the agent to a tool that checks for vacation balance and applies for leave in an ERP system for an employee. Now we could build a nice chatbot that would interact with users and via a chat command apply for leave in the system. No more complex screens for applying for leaves, a simple unified chat interface. Sounds exciting!?

Now what if we have a tool that invokes transactions on stock trading using a pre-authorized API. You build an application where the agent studies stock changes (using tools) and makes decisions for you on buying and selling of stock. What if the agent sells the wrong stock because it hallucinated and made a wrong decision? Since LLM are huge models, it is difficult to pinpoint why they make some decisions, hence hallucinations are common in absence of proper guardrails.

While agents are all fascinating you probably would have guessed how dangerous they can be. If they hallucinate and take a wrong action that could cause huge financial losses or major issues in Enterprise systems. Hence Responsible AI is becoming of utmost importance in the age of LLM powered applications. The principles of Responsible AI around reproducibility, transparency, and accountability, try to put guardrails on decisions taken by agents and suggest risk analysis to decide which actions need a human-in-the-loop. As more complex agents are being designed, they need more scrutiny, transparency, and accountability to make sure we know what they are doing.

Ability of agents to generate a path of logical steps with actions gets them really close to human reasoning. Empowering them with more powerful tools can give them superpowers. Patterns like ReAct try to emulate how humans solve the problem and we will see better agent patterns that will be relevant to specific contexts and domains (banking, insurance, healthcare, industrial, etc.). The future is here and technology behind agents is ready for us to use. At the same time, we need to keep close attention to Responsible AI guardrails to make sure we are not building Skynet!

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Decoding Opportunities and Challenges for LLM Agents in ... - Unite.AI

LG presented a vision of what autonomous vehicles (AVs) could be like in the future - and its all about having more "me time" on the move.

Its been a stressful day at work so you decide to linger in the car and take a breath before getting out and facing the task of preparing dinner or tackling the household chores.

You recline your seat, listening to the soothing sounds of nature while it gives you a heated massage. Or maybe you opt for counselling from the onboard artificial intelligence (AI) to wind down and clear your head after a hectic day.

Compared with your current daily commute sitting in stop-start traffic, the concept might seem lightyears from reality. However, it is just one vision of what autonomous driving could look like proposed by South Korean electronics giant LG.

The technology behind autonomous vehicles (AVs) is currently geared towards the mechanics of getting the car to move and navigate independently while the onboard experience of passengers is, for now at least, relegated to a secondary talking point.

LG, on the other hand, is now actively turning its focus to the sensory elements of being inside the autonomous cars of the future, believing the perspective should shift to the opportunities that AVs will give to improve the driving experience.

"There have been a lot of discussions about future mobility in terms of physical transformation and the role of the car. However, despite many discussions, it is still unclear how the changes will exactly happen," William Cho, the companys CEO, said this week at IAA Mobility - one of the worlds largest trade fairs of its kind - in Munich.

"As we all know, the mobility industry is evolving dramatically, changing our traditional beliefs on cars. Our in-depth customer research directed us to see mobility through the lens of customer experience, focusing on expanding space in the car and quality of time spent on the road".

The companys idea? To redefine the car from a means of travel to a "personalised digital cave" for its occupant.

To date, billions have been invested in developing the technology to produce robot vehicles controlled and piloted by AI-powered computer systems, but prototypes so far all require human inputs.

AVs in the US are subject to standards set by SAE International, formerly known as the Society of Automotive Engineers, with level 0 being no automation and level 5 being the highest rating, full vehicle autonomy in all conditions and locations.

Teslas driver assistance system Autopilot, for example, which offers partial automation, is classified at level 2. The US carmakers basic Autopilot, which is available in all models, offers lane centring and assisted steering while more advanced systems, like Enhanced Autopilot and Full Self-Driving Capacity, have functions to help park, stop and summon the vehicle.

Earlier this summer, Mercedes-Benz announced its Drive Pilot system had been given level 3 approval, attaining the highest SAE rating for a commercial vehicle to date.

Unlike level 2, cars classified as level 3 can handle most driving situations but again, a driver must intervene to make inputs to avoid potentially dangerous incidents.

Last month, Cruise, an arm of US automaker General Motors, was granted a licence in California - along with Alphabet-owned company Waymo - to expand its existing fleet of self-driving taxis in San Francisco and operate on a 24/7 basis.

Unlike commercial vehicles, these taxis are operating at level 4 - in other words, near-complete autonomy. They are programmed to drive in a preset area - known as geofencing - in which they can negotiate their environment through a combination of cameras, sensors, machine learning algorithms and artificial intelligence (AI), determining their location, real-time information on pedestrians and traffic and how each is likely to behave.

If a difficult circumstance arises, a human operator is able to step in remotely to guide or stop the vehicle.

And difficulties do arise. Just 10 days after it was granted its latest licence, Cruise was asked to reduce its fleet following a series of accidents, including a collision with a fire engine.

According to data from the US National Highway Traffic Safety Administration (NHTSA), self-driving Tesla vehicles have also been involved in 736 crashes in the US since 2019, resulting in 17 known fatalities.

Despite the rollout of services like Cruise and Tesla's Autopilot, and the major investment in research, development and testing by the automotive industry, its unlikely a level 5 vehicle will be on the market anytime soon.

Cho believes, however, that electrification will only accelerate the shift to autonomous driving.

"Today's mobility is shifting towards software-defined vehicles [SDVs]. This means social mobility will transform into highly sophisticated electronic devices and can be seen as one of moving space to provide new experiences," he said.

LGs vision for these mobile experiences is theoretical for now but the company plans to design and produce technologies for future AVs based on three core themes collectively known as "Alpha-able": Transformable, Explorable and Relaxable.

For the first, LG predicts that cars will become personalised digital caves, spaces that will be able to easily adapt to suit different purposes and occasions. It could be a restaurant to dine in with your partner, a home office on wheels where you can make business deals in private or even recline and watch a film in a cinema on wheels.

For the second theme, LG is aiming to incorporate augmented reality (AR) and advanced AI to improve the interactions with the vehicle; whether this be voice assistants who recommend content based on the duration of the determined route to your destination or interactive windscreens made from OLED displays that show information about your location and journey.

And of course, the driving experience should be relaxing, through sensory stimuli such as films, massages, meditative music and so on through the cars infotainment system.

While level 5 AVs are yet to materialise, LG says it is already at work on the necessary technology to achieve its three-pronged objectives, including opening a new factory in Hungary in a joint venture with Magna International to produce e-powertrains, the power source of EVs.

"We strongly believe future mobility should focus on the mission to deliver another level of customer experience. LG, with innovative mobility solutions, is more than committed to this important mission," Cho said.

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Heated massages, AI counselling and films on the go: Will ... - Euronews

2023 may well be the year we look back on as a time of significant change with regards to the exponential growth of artificial intelligence. AI platforms and tools are starting to have a major impact on our daily lives and virtually every conceivable industry is starting to sit up and take notice.

While the doom mongers might be concerned that these superintelligent machines pose a genuine threat to humanity, and concern grows about our future here on planet earth, many experts point to the enormous potential and benefits such sophisticated technology could have on the world.

Just recently, the co-founder of Google DeepMind, Mustafa Suleyman said that he believes within the next five years, everybody is going to have their own AI-powered personal assistant as the technology becomes cheaper and more widespread.

While on the other hand, Godfather of AI Geoffrey Hinton quit his job at Google because hes concerned at the rate of improvement in AI development and what this means for society as a whole.

One thing is for certain, the world we live in is going to change drastically in the coming years. Thankfully, Im able to call upon one of the smartest human beings I know and someone who is not only at the forefront of this shift, but who also cares deeply about the ethical, political and social ramifications of AI development, and is focussed on the goal of creating benevolent AI systems.

Dr Ben Goertzel is a cross-disciplinary scientist, futurist, author and entrepreneur, who has spent the best part of his working life focused on creating benevolent superhuman artificial general intelligence (AGI).

In fact, Ben has been credited with popularising the term AGI in our mainstream thinking and has published a dozen scientific books, 150 technical papers, and numerous journal articles, making him one of the worlds foremost experts in this rapidly expanding field.

In 2017, Ben founded SingularityNet with the goal of creating a decentralised, democratic, inclusive and beneficial AGI which has become the worlds leading decentralised AI marketplace.

At SingularityNET, Bens goal is to create an AGI that is not dependent on any central entity, is accessible to anyone, and not restricted to the narrow goals of a single corporation or even a single country.

The platform is an open and decentralised network of AI services on a blockchain where developers publish their services to the network, and can be used by anyone with an internet connection.

SingularityNets latest project is Zarqa, a supercharged, intelligent, neural-symbolic, large language model on a massive scale that promises to not only take on OpenAIs ChatGPT, but go much, much further.

Such advanced neural-symbolic AI techniques will revolutionise and disrupt every industry, taking a giant step towards AGI.

Ive come to the conclusion that to make decentralised AGI really work. We have to launch something thats way smarter than ChatGPT and launch that on a decentralised infrastructure.

In a broader sense, Ben does of course concede that there are risks in building machines that are capable of learning anything and everything, including how to reprogram itself to become an order of magnitude more intelligent than any human.

I think the implications of superintelligence are huge and hard to foresee. Its like asking nomads living in early human tribes what civilisation is going to be like. They could foresee a few aspects of it, but to some extent, you just have to discover when you get there.

Moreover, Ben highlights a more pressing concern is the risk that selfish people and big business will use AI to exert their own greed and control over other people. Its a fascinating conundrum, and there is so much to consider, something that Ben has spent more time than most thinking about.

Ben truly believes that our focus should be on building AI systems that make the world a more compassionate, more just, and more sustainable place right now and moving forward into the future.

I really enjoy sitting down for these chats with Ben, theres so much to learn, and if youre interested in the technology that is shaping our future world or looking for an investment opportunity, then make sure to tune in. The economic potential of AI is huge and over the next decade is expected to generate multiple trillions of dollars.

Im optimistic about the potential for beneficial AGI, and decentralised is important because centralisation of control tends to bring with it some narrow motivational system separate from the ethics of whats best for everyone.

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Dr Ben Goertzel - A.I. Wars: Google Fights Back Against OpenAI's ... - London Real

Suppose I told you that in 10 years, the world as you know it will be over. You will live in a sort of paradise. You wont get sick, or age, or die. Eternal life will be yours! Even better, your mind will be blissfully free of uncertainty youll have access to perfect knowledge. Oh, and youll no longer be stuck on Earth. Instead, you can live up in the heavens.

If I told you all this, would you assume that I was a religious preacher or an AI researcher?

Either one would be a pretty solid guess.

The more you listen to Silicon Valleys discourse around AI, the more you hear echoes of religion. Thats because a lot of the excitement about building a superintelligent machine comes down to recycled religious ideas. Most secular technologists who are building AI just dont recognize that.

These technologists propose cheating death by uploading our minds to the cloud, where we can live digitally for all eternity. They talk about AI as a decision-making agent that can judge with mathematical certainty whats optimal and whats not. And they envision artificial general intelligence (AGI) a hypothetical system that can match human problem-solving abilities across many domains as an endeavor that guarantees human salvation if it goes well, even as it spells doom if it goes badly.

These visions are almost identical to the visions of Christian eschatology, the branch of theology that deals with the end times or the final destiny of humanity.

Christian eschatology tells us that were all headed toward the four last things: death, judgment, and heaven or hell. Although everyone whos ever lived so far has died, well be resurrected after the second coming of Christ to find out where well live for all eternity. Our souls will face a final judgment, care of God, the perfect decision-maker. That will guarantee us heaven if it goes well, but hell if it goes badly.

Five years ago, when I began attending conferences in Silicon Valley and first started to notice parallels like these between religion talk and AI talk, I figured there was a simple psychological explanation. Both were a response to core human anxieties: our mortality; the difficulty of judging whether were doing right or wrong; the unknowability of our lifes meaning and ultimate place in this universe or the next one. Religious thinkers and AI thinkers had simply stumbled upon similar answers to the questions that plague us all.

So I was surprised to learn that the connection goes much deeper.

The intertwining of religion and technology is centuries old, despite the people wholl tell you that science is value-neutral and divorced from things like religion, said Robert Geraci, a professor of religious studies at Manhattan College and author of Apocalyptic AI. Thats simply not true. It never has been true.

In fact, historians tracing the influence of religious ideas contend that we can draw a straight line from Christian theologians in the Middle Ages to the father of empiricism in the Renaissance to the futurist Ray Kurzweil to the tech heavyweights hes influenced in Silicon Valley.

Occasionally, someone there still dimly senses the parallels. Sometimes I think a lot of the breathless enthusiasm for AGI is misplaced religious impulses from people brought up in a secular culture, Jack Clark, co-founder of the AI safety company Anthropic, mused on Twitter in March.

Mostly, though, the figures spouting a vision of AGI as a kind of techno-eschatology from Sam Altman, the CEO of ChatGPT-maker OpenAI, to Elon Musk, who wants to link your brain to computers express their ideas in secular language. Theyre either unaware or unwilling to admit that the vision theyre selling derives much of its power from the fact that its plugging into age-old religious ideas.

But its important to know where these ideas come from. Not because religious is somehow pejorative; just because ideas are religious doesnt mean theres something wrong with them (the opposite is often true). Instead, we should understand the history of these ideas of virtual afterlife as a mode of salvation, say, or moral progress understood as technological progress so we see that theyre not immutable or inevitable; certain people came up with them at certain times to serve certain purposes, but there are other ideas out there if we want them. We dont have to fall prey to the danger of the single story.

We have to be careful with what narratives we buy into, said Elke Schwarz, a political theorist at Queen Mary University of London who studies the ethics of military AI. Whenever we talk about something religious, theres something sacred at play. Having something thats sacred can enable harm, because if something is sacred its worth doing the worst things for.

In the Abrahamic religions that shaped the West, it all goes back to shame.

Remember what happens in the book of Genesis? When Adam and Eve eat from the tree of knowledge, God expels them from the garden of Eden and condemns them to all the indignities of flesh-and-blood creatures: toil and pain, birth and death. Humankind is never the same after that fall from grace. Before the sin, we were perfect creatures made in the image of God; now were miserable meat sacks.

But in the Middle Ages, Christian thinkers developed a radical idea, as the historian David Noble explains in his book The Religion of Technology. What if tech could help us restore humanity to the perfection of Adam before the fall?

The influential ninth-century philosopher John Scotus Eriugena, for example, insisted that part of what it meant for Adam to be formed in Gods image was that he was a creator, a maker. So if we wanted to restore humanity to the God-like perfection of Adam prior to his fall, wed have to lean into that aspect of ourselves. Eriugena wrote that the mechanical arts (a.k.a. technology) were mans links with the Divine, their cultivation a means to salvation.

This idea took off in medieval monasteries, where the motto ora et labora prayer and work began to circulate. Even in the midst of the so-called Dark Ages, some of these monasteries became hotbeds of engineering, producing inventions like the first known tidal-powered water wheel and impact-drilled well. Catholics became known as innovators; to this day, engineers have four patron saints in the religion. Theres a reason why some say the Catholic Church was the Silicon Valley of the Middle Ages: It was responsible for everything from metallurgy, mills, and musical notation to the wide-scale adoption of clocks and the printing press, as I noted in a 2018 Atlantic article.

This wasnt tech for techs sake, or for profits sake. Instead, tech progress was synonymous with moral progress. By recovering humanitys original perfection, we could usher in the kingdom of God. As Noble writes, Technology had come to be identified with transcendence, implicated as never before in the Christian idea of redemption.

The medieval identification of tech progress with moral progress shaped successive generations of Christian thinkers all the way into modernity. A pair of Bacons illustrates how the same core belief that tech would accomplish redemption influenced both religious traditionalists and those who adopted a scientific worldview.

In the 13th century, the alchemist Roger Bacon, taking a cue from biblical prophecies, sought to create an elixir of life that could achieve something like the Resurrection as the apostle Paul described it. The elixir, Bacon hoped, would give humans not just immortality, but also magical abilities like traveling at the speed of thought. Then in the 16th century, Francis Bacon (no relation) came along. Superficially he seemed very different from his predecessor he critiqued alchemy, considering it unscientific yet he prophesied that wed one day use tech to overcome our mortality for the glory of the Creator and the relief of mans estate.

By the Renaissance, Europeans dared to dream that we could remake ourselves in the image of God not only by inching toward immortality, but also by creating consciousness out of inanimate matter.

The possibility to make new life is, other than defeating death, the ultimate power, Schwarz pointed out.

Christian engineers created automata wooden robots that could move around and mouth prayers. Muslims were rumored to create mechanical heads that could talk like oracles. And Jewish folktales spread about rabbis who brought to life clay figures, called golems, by permuting language in magical ways. In the stories, the golem sometimes offers salvation by saving the Jewish community from persecution. But other times, the golem goes rogue, killing people and using its powers for evil.

If all of this is sounding distinctly familiar well, it should. The golem idea has been cited in works on AI risk, like the 1964 book God & Golem, Inc. by mathematician and philosopher Norbert Wiener. You hear the same anxieties today in the slew of open letters released by technologists, warning that AGI will bring upon us either salvation or doom.

Reading these statements, you might well ask: why would we even want to create AGI, if AGI threatens doom as much as it promises salvation? Why not just limit ourselves to creating narrower AI which could already work wonders in applications like curing diseases and stick with that for a while?

For an answer to that, come with me on one more romp through history, and well start to see how the recent rise of three intertwined movements have molded Silicon Valleys visions for AI.

A lot of people assume that when Charles Darwin published his theory of evolution in 1859, all religious thinkers instantly saw it as a horrifying, heretical threat, one that dethroned humans as Gods most godly creations. But some Christian thinkers embraced it as gorgeous new garb for the old spiritual prophecies. After all, religious ideas never really die; they just put on new clothes.

A prime example was Pierre Teilhard de Chardin, a French Jesuit priest who also studied paleontology in the early 1900s. He believed that human evolution, nudged along with tech, was actually the vehicle for bringing about the kingdom of God, and that the melding of humans and machines would lead to an explosion of intelligence, which he dubbed the omega point. Our consciousness would become a state of super-consciousness where we merge with the divine and become a new species.

Teilhard influenced his pal Julian Huxley, an evolutionary biologist who was president of both the British Humanist Association and the British Eugenics Society, as author Meghan OGieblyn documents in her 2021 book God, Human, Animal, Machine. It was Huxley who popularized Teilhards idea that we should use tech to evolve our species, calling it transhumanism.

That, in turn, influenced the futurist Ray Kurzweil, who made basically the same prediction as Teilhard: Were approaching a time when human intelligence merges with machine intelligence, becoming unbelievably powerful. Only instead of calling it the omega point, Kurzweil rebranded it as the singularity.

The human species, along with the computational technology it created, will be able to solve age-old problems and will be in a position to change the nature of mortality in a postbiological future, wrote Kurzweil in his 1999 national bestseller The Age of Spiritual Machines. (Strong New Testament vibes there. Per the book of Revelation: Death shall be no more, neither shall there be mourning nor crying nor pain any more, for the former things have passed away.)

Kurzweil has copped to the spiritual parallels, and so have those whove formed explicitly religious movements around worshiping AI or using AI to move humanity toward godliness, from Martine Rothblatts Terasem movement to the Mormon Transhumanist Association to Anthony Levandowskis short-lived Way of the Future church. But many others, such as Oxford philosopher Nick Bostrom, insist that unlike religion, transhumanism relies on critical reason and our best available scientific evidence.

These days, transhumanism has a sibling, another movement that was born in Oxford and caught fire in Silicon Valley: effective altruism (EA), which aims to figure out how to do the most good possible for the most people. Effective altruists also say their approach is rooted in secular reason and evidence.

Yet EA actually mirrors religion in many ways: functionally (it brings together a community built around a shared vision of moral life), structurally (its got a hierarchy of prophet-leaders, canonical texts, holidays, and rituals), and aesthetically (it promotes tithing and favors asceticism). Most importantly for our purposes, it offers an eschatology.

EAs eschatology comes in the form of its most controversial idea, longtermism, which Musk has described as a close match for my philosophy. It argues that the best way to help the most people is to focus on ensuring that humanity will survive far into the future (as in, millions of years from now), since many more billions of people could exist in the future than in the present assuming our species doesnt go extinct first.

And heres where we start to get the answer to our question about why technologists are set on building AGI.

To effective altruists and longtermists, just sticking with narrow AI is not an option. Take Will MacAskill, the Oxford philosopher known as the reluctant prophet of effective altruism and longtermism. In his 2022 book What We Owe the Future, he explains why he thinks a plateauing of technological advancement is unacceptable. A period of stagnation, he writes, could increase the risks of extinction and permanent collapse.

He cites his colleague Toby Ord, who estimates that the probability of human extinction through risks like rogue AI and engineered pandemics over the next century is one in six Russian roulette. Another fellow traveler in EA, Holden Karnofsky, likewise argues that were living at the hinge of history or the most important century a singular time in the story of humanity when we could either flourish like never before or bring about our own extinction. MacAskill, like Musk, suggests in his book that a good way to avoid extinction is to settle on other planets so we arent keeping all our eggs in one basket.

But thats only half of MacAskills moral case for space settlement. The other half is that we should be trying to make future human civilization as big and utopian as possible. As MacAskills Oxford colleague Bostrom has argued, the colonization of the universe would give us the area and resources with which to run gargantuan numbers of digital simulations of humans living happy lives. The more space, the more happy (digital) humans! This is where the vast majority of moral value lies: not in the present on Earth, but in the future in heaven Sorry, I meant in the virtual afterlife.

When we put all these ideas together and boil them down, we get this basic proposition:

Any student of religion will immediately recognize this for what it is: apocalyptic logic.

Transhumanists, effective altruists, and longtermists have inherited the view that the end times are nigh and that technological progress is our best shot at moral progress. For people operating within this logic, it seems natural to pursue AGI. Even though they view AGI as a top existential risk, they believe we cant afford not to build it given its potential to catapult humanity out of its precarious earthbound adolescence (which will surely end any minute!) and into a flourishing interstellar adulthood (so many happy people, so much moral value!). Of course we ought to march forward technologically because that means marching forward morally!

But is this rooted in reason and evidence? Or is it rooted in dogma?

The hidden premise here is technological determinism, with a side dash of geopolitics. Even if you and I dont create terrifyingly powerful AGI, the thinking goes, somebody else or some other country will so why stop ourselves from getting in on the action? OpenAIs Altman exemplifies the belief that tech will inevitably march forward. He wrote on his blog in 2017 that unless we destroy ourselves first, superhuman AI is going to happen. Why? As we have learned, scientific advancement eventually happens if the laws of physics do not prevent it.

Have we learned that? I see no evidence to suggest that anything that can be invented necessarily will be invented. (As AI Impacts lead researcher Katja Grace memorably wrote, Consider a machine that sprays shit in your eyes. We can technologically do that, but probably nobody has ever built that machine.) It seems more likely that people tend to pursue innovations when there are very powerful economic, social, or ideological pressures pushing them to.

In the case of the AGI fever thats gripped Silicon Valley, recycled religious ideas in the garb of transhumanism, effective altruism, and longtermism have supplied the social and ideological pressures. As for the economic, profit-making pressure, well, thats always operative in Silicon Valley.

Now, 61 percent of Americans believe AI may threaten human civilization, and that belief is especially strong among evangelical Christians, according to a Reuters/Ipsos poll in May. To Geraci, the religious studies scholar, that doesnt come as a surprise. Apocalyptic logic, he noted, is very, very, very powerful in American Protestant Christianity to the point that 4 in 10 US adults currently believe that humanity is living in the end times.

Unfortunately, apocalyptic logic tends to breed dangerous fanaticism. In the Middle Ages, when false messiahs arose, people gave up their worldly possessions to follow their prophet. Today, with talk of AGI doom suffusing the media, true believers drop out of college to go work on AI safety. The doom-or-salvation, heaven-or-hell logic pushes people to take big risks to ante up and go all in.

In an interview with me last year, MacAskill disavowed extreme gambles. He told me he imagines that a certain type of Silicon Valley tech bro, thinking theres a 5 percent chance of dying from some AGI catastrophe and a 10 percent chance AGI ushers in a blissful utopia, would be willing to take those odds and rush ahead with building AGI.

Thats not the sort of person I want building AGI, because they are not responsive to the moral issues, MacAskill told me. Maybe that means we have to delay the singularity in order to make it safer. Maybe that means it doesnt come in my lifetime. That would be an enormous sacrifice.

When MacAskill told me this, I pictured a Moses figure, looking out over the promised land but knowing he would not reach it. The longtermist vision seemed to require of him a brutal faith: You personally will not be saved, but your spiritual descendants will.

Theres nothing inherently wrong with believing that tech can radically improve humanitys lot. In many ways, it obviously already has.

Technology is not the problem, Ilia Delio, a Franciscan sister who holds two PhDs and a chair in theology at Villanova University, told me. In fact, Delio is comfortable with the idea that were already in a new stage of evolution, shifting from Homo sapiens to techno sapiens. She thinks we should be open-minded about proactively evolving our species with techs help.

But shes also clear that we need to be explicit about which values are shaping our tech so that we can develop the technology with purpose and with ethical boundaries, she said. Otherwise, technology is blind and potentially dangerous.

Geraci agrees. If a ton of people in Silicon Valley are going, Hey, Im in for this technology because its going to make me immortal, thats a little bit terrifying, he told me. But if somebody says, Im in for this technology because I think were going to be able to use it to solve world hunger those are two very different motives. It would impact the types of products you try to design, the population for which you are designing, and the way you try to deploy it in the world around you.

Part of making deliberate decisions about which values animate tech is also being keenly aware of who gets the power to decide. According to Schwarz, the architects of artificial intelligence have sold us on a vision of necessary tech progress with AI and set themselves up as the only experts on it, which makes them enormously powerful arguably more powerful than our democratically elected officials.

The idea that developing AGI is a kind of natural law becomes an ordering principle, and that ordering principle is political. It gives political power to some and a lot less to most others, Schwarz said. Its so strange to me to say, We have to be really careful with AGI, rather than saying, We dont need AGI, this is not on the table. But were already at a point when power is consolidated in a way that doesnt even give us the option to collectively suggest that AGI should not be pursued.

We got to this point in large part because, for the past thousand years, the West has fallen prey to the danger of the single story: the story equating tech progress with moral progress that we inherited from medieval religious thinkers.

Its the one narrative we have, Delio said. That narrative has made us inclined to defer to technologists (who, in the past, were also spiritual authorities) on the values and assumptions being baked into their products.

What are alternatives? If another narrative were to say, Just the dynamism of being alive is itself the goal, then we might have totally different aspirations for technology, Delio added. But we dont have that narrative! Our dominant narrative is to create, invent, make, and to have that change us.

We need to decide what kind of salvation we want. If were generating our enthusiasm for AI through visions of transcending our earthbound limits and our meat-sack mortality, that will create one kind of societal outcome. But if we commit to using tech to improve the well-being of this world and these bodies, we can have a different outcome. We can, as Noble put it, begin to direct our astonishing capabilities toward more worldly and humane ends.

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Why Silicon Valley AI prophecies just feel like repackaged religion - Vox.com