Ai Experts Quotes

Indeed, many movies about artificial intelligence are so divorced from scientific reality that one suspects they are just allegories of completely different concerns. Thus the 2015 movie Ex Machina seems to be about an AI expert who falls in love with a female robot only to be duped and manipulated by her. But in reality, this is not a movie about the human fear of intelligent robots. It is a movie about the male fear of intelligent women, and in particular the fear that female liberation might lead to female domination. Whenever you see a movie about an AI in which the AI is female and the scientist is male, it’s probably a movie about feminism rather than cybernetics. For why on earth would an AI have a sexual or a gender identity? Sex is a characteristic of organic multicellular beings. What can it possibly mean for a non-organic cybernetic being?

AI won’t replace humans, but people who can use it will.” This sounds reassuring, but it oversimplifies the complex future of work and AI integration. Experts predict a surge in opportunities, but the intricate interplay between cognification, mass automation, and how we work remains uncharted. The net effect of AI on employment is unknown - we have no data on the future.

Algorithms tuned by an average engineer can outperform those built by the world’s leading experts

Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.

One expert can't outrank a full-stack digital team working in sync

De guiche. "Portez-les-lui." Cyrano, tenté et un peu charmé. "Vraiment…" De guiche. "Il est des plus experts. Il vous corrigera seulement quelques vers…" Cyrano, dont le visage s’est immédiatement rembruni. "Impossible, Monsieur ; mon sang se coagule En pensant qu’on y peut changer une virgule." De guiche. "Mais quand un vers lui plaît, en revanche, mon cher, Il le paye très cher." Cyrano. "Il le paye moins cher Que moi, lorsque j’ai fait un vers, et que je l’aime, Je me le paye, en me le chantant à moi-même !" De guiche. "Vous êtes fier." Cyrano. "Vraiment, vous l’avez remarqué ?

the invention of deep learning means that we are moving from the age of expertise to the age of data. Training successful deep-learning algorithms requires computing power, technical talent, and lots of data. But of those three, it is the volume of data that will be the most important going forward. That’s because once technical talent reaches a certain threshold, it begins to show diminishing returns. Beyond that point, data makes all the difference. Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.

The good news is that you can minimize the bias preserved by AI, but this comes as a joint responsibility between leaders, domain experts, and developers.

AI can not make you an expert or an authority. AI can only make you appear to be an expert or an authority.

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novel The Diamond Age: Or, A Young Lady’s Illustrated Primer, author Neal Stephenson gives readers a glimpse of what AI experts call a “lifelong learning companion”: an agent that tracks learning over the course of one’s lifetime, both insuring a mastery-level education and making exquisitely personalized recommendations about what exactly a student should learn next.

Some experts and thinkers, such as Nick Bostrom, warn that humankind is unlikely to suffer this degradation, because once artificial intelligence surpasses human intelligence, it might simply exterminate humankind. The AI would likely do so either for fear that humankind would turn against it and try to pull its plug, or in pursuit of some unfathomable goal of its own. For it would be extremely difficult for humans to control the motivation of a system smarter than themselves.

Researchers at Meta created a program called CICERO. It became an expert at playing the complex board game Diplomacy, a game in which planning long, complex strategies built around deception and backstabbing is integral. It shows how AIs could help us plan and collaborate, but also hints at how they could develop psychological tricks to gain trust and influence, reading and manipulating our emotions and behaviors with a frightening level of depth, a skill useful in, say, winning at Diplomacy or electioneering and building a political movement. The space for possible attacks against key state functions grows even as the same premise that makes AI so powerful and exciting—its ability to learn

Chapter 20 we will explore in far greater depth how to avoid brainwashing and how to distinguish reality from fiction. Here I would like to offer two simple rules of thumb. First, if you want reliable information, pay good money for it. If you get your news for free, you might well be the product. Suppose a shady billionaire offered you the following deal: “I will pay you $30 a month, and in exchange you will allow me to brainwash you for an hour every day, installing in your mind whichever political and commercial biases I want.” Would you take the deal? Few sane people would. So the shady billionaire offers a slightly different deal: “You will allow me to brainwash you for one hour every day, and in exchange, I will not charge you anything for this service.” Now the deal suddenly sounds tempting to hundreds of millions of people. Don’t follow their example. The second rule of thumb is that if some issue seems exceptionally important to you, make the effort to read the relevant scientific literature. And by scientific literature I mean peer-reviewed articles, books published by well-known academic publishers, and the writings of professors from reputable institutions. Science obviously has its limitations, and it has gotten many things wrong in the past. Nevertheless, the scientific community has been our most reliable source of knowledge for centuries. If you think the scientific community is wrong about something, that’s certainly possible, but at least know the scientific theories you are rejecting, and provide some empirical evidence to support your claim. Scientists, for their part, need to be far more engaged with current public debates. Scientists should not be afraid of making their voices heard when the debate wanders into their field of expertise, be it medicine or history. Of course, it is extremely important to go on doing academic research and to publish the results in scientific journals that only a few experts read. But it is equally important to communicate the latest scientific theories to the general public through popular science books, and even through the skillful use of art and fiction. Does that mean scientists should start writing science fiction? That is actually not such a bad idea. Art plays a key role in shaping people’s views of the world, and in the twenty-first century science fiction is arguably the most important genre of all, for it shapes how most people understand things such as AI, bioengineering, and climate change. We certainly need good science, but from a political perspective, a good science-fiction movie is worth far more than an article in Science or Nature.

The relevant time scale for superhuman AI is less predictable, but of course that means it, like nuclear fission, might arrive considerably sooner than expected. One formulation of the “it’s too soon to worry” argument that has gained currency is Andrew Ng’s assertion that “it’s like worrying about overpopulation on Mars.”11 (He later upgraded this from Mars to Alpha Centauri.) Ng, a former Stanford professor, is a leading expert on machine learning, and his views carry some weight. The assertion appeals to a convenient analogy: not only is the risk easily managed and far in the future but also it’s extremely unlikely we’d even try to move billions of humans to Mars in the first place. The analogy is a false one, however. We are already devoting huge scientific and technical resources to creating ever-more-capable AI systems, with very little thought devoted to what happens if we succeed.

When experts with similar ideas are put together in a group, their ideas become even more aligned. That’s where expertise becomes ideology. And that’s when the sh*t hits the fan

Minsky was an ardent supporter of the Cyc project, the most notorious failure in the history of AI. The goal of Cyc was to solve AI by entering into a computer all the necessary knowledge. When the project began in the 1980s, its leader, Doug Lenat, confidently predicted success within a decade. Thirty years later, Cyc continues to grow without end in sight, and commonsense reasoning still eludes it. Ironically, Lenat has belatedly embraced populating Cyc by mining the web, not because Cyc can read, but because there’s no other way. Even if by some miracle we managed to finish coding up all the necessary pieces, our troubles would be just beginning. Over the years, a number of research groups have attempted to build complete intelligent agents by putting together algorithms for vision, speech recognition, language understanding, reasoning, planning, navigation, manipulation, and so on. Without a unifying framework, these attempts soon hit an insurmountable wall of complexity: too many moving parts, too many interactions, too many bugs for poor human software engineers to cope with. Knowledge engineers believe AI is just an engineering problem, but we have not yet reached the point where engineering can take us the rest of the way. In 1962, when Kennedy gave his famous moon-shot speech, going to the moon was an engineering problem. In 1662, it wasn’t, and that’s closer to where AI is today. In industry, there’s no sign that knowledge engineering will ever be able to compete with machine learning outside of a few niche areas. Why pay experts to slowly and painfully encode knowledge into a form computers can understand, when you can extract it from data at a fraction of the cost? What about all the things the experts don’t know but you can discover from data? And when data is not available, the cost of knowledge engineering seldom exceeds the benefit. Imagine if farmers had to engineer each cornstalk in turn, instead of sowing the seeds and letting them grow: we would all starve.

Symbolist machine learning is an offshoot of the knowledge engineering school of AI. In the 1970s, so-called knowledge-based systems scored some impressive successes, and in the 1980s they spread rapidly, but then they died out. The main reason they did was the infamous knowledge acquisition bottleneck: extracting knowledge from experts and encoding it as rules is just too difficult, labor-intensive, and failure-prone to be viable for most problems. Letting the computer automatically learn to, say, diagnose diseases by looking at databases of past patients’ symptoms and the corresponding outcomes turned out to be much easier than endlessly interviewing doctors. Suddenly, the work of pioneers like Ryszard Michalski, Tom Mitchell, and Ross Quinlan had a new relevance, and the field hasn’t stopped growing since. (Another important problem was that knowledge-based systems had trouble dealing with uncertainty, of which more in Chapter 6.)

Many new jobs are likely to appear in the 21st century. The crucial problem will be creating new jobs that humans can perform better than algorithms. The tech bonanza will probably make it feasible to feed and support the useless masses effortlessly. But what will keep them all occupied so they don’t go crazy? One solution might be drugs and computer games, or 3D virtual-reality worlds. Some experts warn that the AI might just decide to exterminate humankind to avoid a revolt. Of course, we don’t really know what the human mind might come up with in the future. And those algorithms might end up saving our lives, too.

As he turned her face to study her, he said, “You have more courage than you have strength, Yellow Hair. It is not wise to fight when you cannot win.” Looking up at his carved features and the arrogant set of his mouth, she longed for the strength to jerk him off his horse. He wasn’t just taunting her, he was challenging her, mocking her. “You will yield. Look at me and know the face of your master. Remember it well.” Riding high on humiliation, Loretta forgot Amy, Aunt Rachel, everything. An image of her mother’s face flashed before her. Never, as long as she had life in her body, would she yield to him. She worked her parched mouth and spat. Nothing came out, but the message rang clear. “Nei mah-heepicut!” Releasing her, he struck her lightly on the arm. Wheeling his horse, he glanced toward the windows of the house and thumped his chest with a broad fist. “I claim her!” Loretta staggered, watching in numb disbelief as Hunter pranced his stallion in a circle around her. I claim her? Warily she turned, keeping him in sight, unsure of what he might do. He rode erect, his eyes touching on her dress, her face, her hair, as if everything about her were a curiosity. A taunting smile curved his mouth. His attention centered on her full skirt, and she could almost see the questions churning in his head. He repositioned his hand on the lance. The determination in his expression filled her with foreboding. He rode directly toward her, and she sidestepped. He turned his mount to come at her again. As he swept by he leaned forward, catching the hem of her skirt with his lance. Loretta whirled, striking out with her forearms, but the Indian moved expertly, his aim swift and sure, his horse precision-trained to the pressure of his legs. He was as bent on seeing her undergarments as she was on keeping them hidden. The outcome of their battle was a foregone conclusion, and Loretta knew it. His friends encouraged him, whooping with ribald laughter each time her ruffles flashed. She snatched the dirty peace flag from the wooden shaft and threw it to the earth, grinding it beneath the heel of her shoe. After fending off several more passes, exhaustion claimed its victory, and Loretta realized the folly in fighting. She stood motionless, breasts heaving, her eyes staring fixedly at nothing, head lifted. The warrior circled her, guiding his stallion’s flashing hooves so close to her feet that her toes tingled. When she didn’t move, he reined the horse to a halt and studied her for several seconds before he leaned forward to finger the bodice of her dress. Her breath snagged when he slid a palm over her bosom to the indentation of her waist. “Ai-ee,” he whispered. “You learn quick.” Raising tear-filled eyes to his, she again spat in his face. This time he felt the spray and wiped his cheek, his lips quivering with something that looked suspiciously like suppressed laughter, friendly laughter this time. “Maybe not so quick. But I am a good teacher. You will learn not to fight me, Yellow Hair. It is a promise I make for you.

Nei mah-heepicut!” Releasing her, he struck her lightly on the arm. Wheeling his horse, he glanced toward the windows of the house and thumped his chest with a broad fist. “I claim her!” Loretta staggered, watching in numb disbelief as Hunter pranced his stallion in a circle around her. I claim her? Warily she turned, keeping him in sight, unsure of what he might do. He rode erect, his eyes touching on her dress, her face, her hair, as if everything about her were a curiosity. A taunting smile curved his mouth. His attention centered on her full skirt, and she could almost see the questions churning in his head. He repositioned his hand on the lance. The determination in his expression filled her with foreboding. He rode directly toward her, and she sidestepped. He turned his mount to come at her again. As he swept by he leaned forward, catching the hem of her skirt with his lance. Loretta whirled, striking out with her forearms, but the Indian moved expertly, his aim swift and sure, his horse precision-trained to the pressure of his legs. He was as bent on seeing her undergarments as she was on keeping them hidden. The outcome of their battle was a foregone conclusion, and Loretta knew it. His friends encouraged him, whooping with ribald laughter each time her ruffles flashed. She snatched the dirty peace flag from the wooden shaft and threw it to the earth, grinding it beneath the heel of her shoe. After fending off several more passes, exhaustion claimed its victory, and Loretta realized the folly in fighting. She stood motionless, breasts heaving, her eyes staring fixedly at nothing, head lifted. The warrior circled her, guiding his stallion’s flashing hooves so close to her feet that her toes tingled. When she didn’t move, he reined the horse to a halt and studied her for several seconds before he leaned forward to finger the bodice of her dress. Her breath snagged when he slid a palm over her bosom to the indentation of her waist. “Ai-ee,” he whispered. “You learn quick.

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She had become something of a self-taught expert on the analysis of an AI traumatized by being ordered to lie.

According to many experts the majority of the people won't be needed anymore for the coming society. Almost everything will be done by artificial intelligence, including self-driving cars and trucks, which already exist anyway. Some even mentioned that AI is making universities obsolete by how fast it can produce information. However, In my view, the AI has limitations that the many can't see, because on a brain to brain comparison, the AI always wins, yet the AI can only compute with programmable data. In other words, the AI can think like a human but can't imagine or create a future. The AI is always codependent on the imagination of its user. So the limitations of the AI are in fact determined by humans. It is not bad that we have AI but that people have no idea of how to use it apart from replacing their mental faculties and being lazy. This is actually why education has always been a scam. The AI will simply remove that from the way. But knowledge will still require analysis and input of information, so the AI doesn't really replace the necessary individuals of the academic world, but merely the many useless ones that keep copying and plagiarizing old ideas to justify and validate a worth they don't truly possess. Being afraid and paranoid about these transitions doesn't make sense because evolution can't be stopped, only delayed. The problem at the moment has more to do with those who want to keep themselves in power by force and profiting from the transitions. The level of consciousness of humanity is too low for what is happening, which is why people are easily deceived. Consequently, there will be more anger, fear, and frustration, because for the mind that is fixed on itself, change is perceived as chaos. The suffering is then caused by emotional attachments, stubbornness and the paranoid fixation on using outdated systems and not knowing how to adapt properly. In essence, AI is a problem for the selfish mind - rooted in cognitive rationalizations -, but an opportunity of great value for the self-reflective mind - capable of a metacognitive analysis. And the reason why nobody seems to understand this is precisely because, until now, everyone separated the mind from the spirit, while not knowing how a spiritual ascension actually goes through the mind. And this realization, obviously, will turn all religions obsolete too. Some have already come to this conclusion, and they are the ones who are ready.

We appear to be in an up swing trend of artificial intelligence creating artificial experts.

Considerable efforts have been exerted to bring us to this point; however, their remains a great deal of work ahead of us as we rethink the nature of work in the age of Artificial Intelligence and explore the relationship before human labor and intelligent machines and expert systems.

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Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.

If you build a bomb and ignore the facts of physics, the bomb will not explode. But if you build an ideology and ignore the facts, the ideology may still prove explosive. While power depends on both truth and order, it is usually the people who know how to build ideologies and maintain order who give instructions to the people who merely know how to build bombs or hunt mammoths. Robert Oppenheimer obeyed Franklin Delano Roosevelt rather than the other way around. Similarly, Werner Heisenberg obeyed Adolf Hitler, Igor Kurchatov deferred to Joseph Stalin, and in contemporary Iran experts in nuclear physics follow the orders of experts in Shiite theology.

Indeed, the best experts for performing such diagnosis may soon be AI systems, given the rapid progress in computer vision and deep learning.

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Imagine how your own level of subjective consciousness differs if you’re experiencing a vague dream, are awake but drunk or sleepy, or are fully alert. This is the continuum that researchers wonder about when assessing animal consciousness. And expert opinion is shifting in favor of more animals having more consciousness than was once believed.

Even though you are not required to, consider holding yourself accountable for what you say online and on social media. More and more, it feels like social media has isolated people from the accountability of their words and claims. It seems as though lying, cheating and stealing are practically encouraged with the use of AI and cheap marketing tactics that focus on showing people how to appear as experts over highlighting genuine authority and expertise. So many make any claim they want, say anything they want and push false hype in order to present their subjective opinions as objective truth. (To me, this is lying.) I still believe Messaging and Marketing can be done morally, ethically and transparently. But in the end it is your choice.

– Qu'est-ce que la Philosophie, maman ? – Je n'ai jamais entendu ce mot. Une herbe médicinale ? – Un jeune homme me l'a dit qui sait lire et écrire. Il dit l'aimer plus que lui-même. – Alors c'est une femme, une étrangère, une Turque ou pire, une juive. Une infâme ! – Il dit qu'il l'aime plus que la richesse. – Alors, elle est très belle et très licencieuse… – Il dit qu'elle vivra en lui éternellement… – Elle est donc très experte, pour mieux le séduire… Et toi, comment le connais-tu ce jeune homme ? – Je l'ai vu par hasard une seule fois… – Bien ! Ce n'est pas l'un des nôtres… – Et moi, pourquoi je n'écris pas ? – Parce que les pauvres n'ont pas besoin d'écrire… – Et pourquoi je ne lis pas ? – Pareil. Tu n'es qu'une femme ! Ce n'est pas la peine…

Successful AI initiatives start with the right problems, but the right problems don’t necessarily come from your data scientists. They can come from leaders, domain experts, and innovators who sit close to the daily business challenges in your organization. Still, it takes practice to develop the vision for spotting AI opportunities...

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Supposedly they could do that, Vleen reminded himself with a healthy measure of skepticism. The target ship was controlled by humans who had an Elder AI helping them. He thought it unlikely that ship’s original AI still existed, or that the architecture of the substrate was anything even remotely familiar to the experts who were assigned to hack into it. The idea that they could take back the battlecruiser simply by accessing its main computer was almost silly, yet those were his orders.

Statement on Generative AI Just like Artificial Intelligence as a whole, on the matter of Generative AI, the world is divided into two camps - one side is the ardent advocate, the other is the outspoken opposition. As for me, I am neither. I don't have a problem with AI generated content, I have a problem when it's rooted in fraud and deception. In fact, AI generated content could open up new horizons of human creativity - but only if practiced with conscience. For example, we could set up a whole new genre of AI generated material in every field of human endeavor. We could have AI generated movies, alongside human movies - we could have AI generated music, alongside human music - we could have AI generated poetry and literature, alongside human poetry and literature - and so on. The possibilities are endless - and all above board. This way we make AI a positive part of human existence, rather than facilitating the obliteration of everything human about human life. This of course brings up a rather existential question - how do we distinguish between AI generated content and human created material? Well, you can't - any more than you can tell the photoshop alterations on billboard models or good CGI effects in sci-fi movies. Therefore, that responsibility must be carried by experts, just like medical problems are handled by healthcare practitioners. Here I have two particular expertise in mind - one precautionary, the other counteractive. Let's talk about the counteractive measure first - this duty falls upon the shoulders of journalists. Every viral content must be source-checked by responsible journalists, and declared publicly as fake, i.e. AI generated, unless recognized otherwise. Littlest of fake content can do great damage to society - therefore - journalists, stand guard! Now comes the precautionary part. Precaution against AI generated content must be borne by the makers of AI, i.e. the developers. No AI model must produce any material without some form of digital signature embedded in them, that effectively makes the distinction between AI generated content and human material mainstream. If developers fail to stand accountable out of their own free will, they must be held accountable legally. On this point, to the nations of the world I say, you can't expect backward governments like our United States to take the first step - where guns get priority over children - therefore, my brave and civilized nations of the world - you gotta set the precedent on holding tech giants accountable - without depending on morally bankrupt democratic imperialists. And remember, the idea is not to ban innovation, but to adapt it with human welfare. All said and done, the final responsibility falls upon just one person, and one person alone - the everyday ordinary consumer. Your mind has no reason to not believe the things you find on the internet, unless you make it a habit to actively question everything - or at least, not accept anything at face value. Remember this. Just because it's viral, doesn't make it true. Just because it's popular, doesn't make it right.

AI can only fool the fool who wants to be fooled. While AI can help to create an expert persona, product and presence… those that vet, double check and look for substantiated proof will find out the truth or see red flags and issues revealing questionable AI use and abuse.

As robotics expert and entrepreneur Rodney Brooks writes, “having ideas is easy. Turning them into reality is hard. Turning them into being deployed at scale is even harder.

I’ve always passionately believed in the power of the state to improve lives. Before my career in AI, I worked in government and the nonprofit sector. I helped start a charity telephone counseling service when I was nineteen, worked for the mayor of London, and co-founded a conflict resolution firm focused on multi-stakeholder negotiation. Working with public servants—people stretched thin and bone-tired, but forever in demand and doing heroic work for those who need it—was enough to show me what a disaster it would be if the state failed. However, my experience with local government, UN negotiations, and nonprofits also gave me invaluable firsthand knowledge of their limitations. They are often chronically mismanaged, bloated, and slow to act. One project I facilitated in 2009 at the Copenhagen climate negotiations involved convening hundreds of NGOs and scientific experts to align their negotiating positions. The idea was to present a coherent position to 192 squabbling countries at the main summit. Except we couldn’t get consensus on anything. For starters, no one could agree on the science, or the reality of what was happening on the ground. Priorities were scattered. There was no consensus on what would be effective, affordable, or even practical. Could you raise $10 billion to turn the Amazon into a national park to absorb CO2? How are you going to deal with the militias and bribes? Or maybe the answer was to reforest Norway, not Brazil, or was the solution to grow giant kelp farms instead? As soon as proposals were voiced, someone spoke up to poke holes in them. Every suggestion was a problem. We ended up with maximum divergence on all possible things. It was, in other words, politics as usual. And this involved people notionally on the “same team.” We hadn’t even gotten to the main event and the real horse-trading. At the Copenhagen summit a morass of states all had their own competing positions. Now pile on the raw emotion. Negotiators were trying to make decisions with hundreds of people in the room arguing and shouting and breaking off into groups, all while the clock was ticking, on both the summit and the planet. I was there trying to help facilitate the process, perhaps the most complex, high-stakes multiparty negotiation in human history, but from the start it looked almost impossible. Observing this, I realized we weren’t going to make sufficient progress fast enough. The timeline was too tight. The issues were too complex. Our institutions for addressing massive global problems were not fit for purpose.

By the time I began my Ph.D., the field of artificial intelligence had forked into two camps: the “rule-based” approach and the “neural networks” approach. Researchers in the rule-based camp (also sometimes called “symbolic systems” or “expert systems”) attempted to teach computers to think by encoding a series of logical rules: If X, then Y. This approach worked well for simple and well-defined games (“toy problems”) but fell apart when the universe of possible choices or moves expanded. To make the software more applicable to real-world problems, the rule-based camp tried interviewing experts in the problems being tackled and then coding their wisdom into the program’s decision-making (hence the “expert systems” moniker). The “neural networks” camp, however, took a different approach. Instead of trying to teach the computer the rules that had been mastered by a human brain, these practitioners tried to reconstruct the human brain itself. Given that the tangled webs of neurons in animal brains were the only thing capable of intelligence as we knew it, these researchers figured they’d go straight to the source. This approach mimics the brain’s underlying architecture, constructing layers of artificial neurons that can receive and transmit information in a structure akin to our networks of biological neurons. Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon—pictures, chess games, sounds—into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better.

The dramatic transformation that deep learning promises to bring to the global economy won’t be delivered by isolated researchers producing novel academic results in the elite computer science labs of MIT or Stanford. Instead, it will be delivered by down-to-earth, profit-hungry entrepreneurs teaming up with AI experts to bring the transformative power of deep learning to bear on real-world industries.

But few, if any, experts predicted that deep learning was going to get this good, this fast. Those unexpected improvements are expanding the realm of the possible when it comes to real-world uses and thus job disruptions.

Researchers in the rule-based camp (also sometimes called “symbolic systems” or “expert systems”) attempted to teach computers to think by encoding a series of logical rules: If X, then Y. This approach worked well for simple and well-defined games (“toy problems”) but fell apart when the universe of possible choices or moves expanded.

Nu ești o persoană mai puțin iubitoare sau incompletă, dacă îți rămân anumite lucruri pe care nu le poți ierta și dacă îți dorești ca pe anumite persoane să nu le mai întâlnești vreodată-n viață. Poate că devii un om mai puternic și mai curajos, dacă ai ceva mânie reziduală (fie că provine dintr-o mare trădare, fie din numeroase neplăceri mici) și, în ciuda ei, reușești să-ți vezi de viața ta. Mai important ca orice, nu e treaba nimănui - nici a terapeutului, nici a mamei sau profesorului ori a vreunui ghid spiritual, a celui mai bun prieten sau expert în relații - să-i spună altuia că trebuie să ierte (sau nu).

Symbolic AI takes human-readable observations about the world and builds them into an expert system that allows a computer to make deductions and decisions.

Another 2023 study prompted patients to ask online medical advice from ChatGPT and human doctors, without knowing whom they were interacting with. The medical advice given by ChatGPT was later evaluated by experts to be more accurate and appropriate than the advice given by the humans.

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Think about the impact of the new wave of AI systems. Large language models enable you to have a useful conversation with an AI about any topic in fluent, natural language. Within the next couple of years, whatever your job, you will be able to consult an on-demand expert, ask it about your latest ad campaign or product design, quiz it on the specifics of a legal dilemma, isolate the most effective elements of a pitch, solve a thorny logistical question, get a second opinion on a diagnosis, keep probing and testing, getting ever more detailed answers grounded in the very cutting edge of knowledge, delivered with exceptional nuance. All of the world’s knowledge, best practices, precedent, and computational power will be available, tailored to you, to your specific needs and circumstances, instantaneously and effortlessly. It is a leap in cognitive potential at least as great as the introduction of the internet.

To navigate the advent of artificial intelligence, we don’t need to become experts in machines—we need to become better stewards of humanity." - Jop Helm

so many people were shocked and upset when, in 1997, IBM’s Deep Blue chess-playing system defeated the world chess champion Garry Kasparov. This event so stunned Kasparov that he accused the IBM team of cheating; he assumed that for the machine to play so well, it must have received help from human experts.2 (In a nice bit of irony, during the 2006 World Chess Championship matches the tables were turned, with one player accusing the other of cheating by receiving help from a computer chess program.3) Our collective human angst over Deep Blue quickly receded. We accepted that chess could yield to brute-force machinery; playing chess well, we allowed, didn’t require general intelligence after all. This seems to be a common response when computers surpass humans on a particular task; we conclude that the task doesn’t actually require intelligence. As John McCarthy lamented, ‘As soon as it works, no one calls it AI any more.’4

In a truly open-world problem devoid of rigid rules and reams of perfect historical data, AI has been disastrous. IBM’s Watson destroyed at Jeopardy! and was subsequently pitched as a revolution in cancer care, where it flopped so spectacularly that several AI experts told me they worried its reputation would taint AI research in health-related fields.

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Lysenkoist agronomists to create super-crops, set back Soviet genetic research for decades and undermined Soviet agriculture. Soviet experts who suggested abandoning Lysenkoism and accepting Darwinism risked the gulag or a bullet to the head. Lysenkoism’s legacy haunted Soviet science and agronomy for decades and was one reason why by the early 1970s the U.S.S.R. ceased to be a major exporter of grain and

Another 2023 study prompted patients to ask online medical advice from ChatGPT and human doctors, without knowing whom they were interacting with. The medical advice given by ChatGPT was later evaluated by experts to be more accurate and appropriate than the advice given by the humans. More crucially for the issue of emotional intelligence, the patients themselves evaluated ChatGPT as more empathic than the human doctors.

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An AI future requires that we lean into building our own expertise as human experts. Since expertise requires facts, students will still need to learn reading, writing, history, and all the other basic skills required in the twenty-first century. We have already seen how this broad-based knowledge can help people get the most out of AI. And besides, we need to continue to have educated citizens rather than delegate all our thinking to machines.

While experts should spend lifelong careers discussing the finer details, it is crucial that the rest of us understand the fundamental principles that democracies can and should follow. The key message is that these principles are neither new nor mysterious. They have been known for centuries, even millennia.

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Observation: Suddenly, the world is flooded with self-proclaimed "AI experts," yet few have the training or experience to truly understand AI. Those who build AI rarely speak, and those who speak about AI rarely create anything new. Challenge: This creates a gap between hype and reality, leaving organizations and individuals vulnerable to misinformation.

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Teaching AI ethics is my day job, where I spend my days instructing students in the conflict between human judgment and machine learning, but I never thought I'd be outwitted by my own digital wallet. And yet there I was, staring at my fried hard drive and realizing that $910,000 worth of Bitcoin had just been encrypted into oblivion. The disaster started innocently enough. I had been operating an experimental machine learning program, training it to improve encryption security independently. Enthusiastic with the encouraging results, I ran one final simulation on my home laptop, the same one that held my wallet keys in an encrypted file. What could possibly go wrong? As would be the case, everything. The algorithm, eager to show itself, created a security system so robust that I couldn't even get access to it myself. It rendered the key file so corrupted that my life's savings might just as well be floating around cyberspace. When I tried to explain what had occurred in class the following day, the expressions on the faces of my classmates were one of amusement and horror. "Professor, didn't you teach data backups last week?" Ouch..Whatsapp: +1 4 14 80 71 4 85 A student approached me after class and softly whispered a lifeline: "Check out RAPID DIGITAL RECOVERY. They deal with cases like this." Desperation got the better of my pride, so I called them. From the first email, their team treated me like a valued partner, not another moron who let an AI lock him out...Email: rapiddigitalrecovery (@) execs. com RAPID DIGITAL RECOVERY's engineers approached my problem like battle-hardened cryptographers and compassionate therapists. They requested samples of my encryption methods, dissected my test algorithm, and effectively reverse-engineered my own efforts. They even smiled (graciously) at the enthusiasm of my marauding AI for security...Telegram: @Rapiddigitalrecovery1 After 12 days of nail-biting suspense, I heard the call. "Professor, we got it." My heart pounded faster than when I first powered up a neural network. My Bitcoin wallet was returned, every single satoshi. They even provided me with security recommendations tailored to my academic community so that my next algorithm would not hold my money hostage. Now, when I teach AI ethics, I introduce with this cautionary tale. My students chuckle, my robots beep their approval, and I sleep better knowing RAPID DIGITAL RECOVERY stands in the wings to rescue even the most hubristic tech wizards. Human ingenuity triumphs once more.

For many kinds of science, another time-consuming and otherwise complicated step involves surveying or interviewing participants, also called human subjects. This is difficult: it’s often hard to recruit an appropriate sample of the population of interest, or find ways to ask questions that get at the topic of interest without causing harm to the people being asked (for instance, asking about past trauma without retraumatizing participants). Sometimes, what’s needed is expert opinions, but the relevant experts are too busy or are unwilling to do the relatively low-paid labor of providing the information required. But what if chatbots could be designed to answer questions as if they were people with different kinds of lived experience or different expertise? How convenient! You might hope at this point that we’re making this up, but researchers have actually proposed using “in silico” samples for political science surveys35 and psychological experiments.36 We’ve already discussed37 a form of this type of methodology in Chapter 3—in a paper written by OpenAI researchers, they determined what kinds of tasks in what kinds of jobs could be handled by an LLM by asking the LLM itself. But this idea is ludicrous on its face: “silicon samples” are, obviously, not real people. Even if researchers are “just asking questions” about whether this is a reasonable methodology, it’s misguided. When other researchers take their results and use it as a justification to use “silicon samples” in social science research, it replaces empirical foundations with quicksand.

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