Lifeboat Foundation: Safeguarding Humanity
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Category: finance – Page 132

Creating rejuvenation will probably be quite expensive, but that’s no reason to give up on it. We can pull it off.

The first thing to realise is that, when you wonder how much something will cost, you’re actually wondering how many resources and how many people doing how much work it will take to do that something. That’s all that really matters. The problem is that we have a sucky economic system such that even if we do have more than enough people and resources to do the job, the monetary cost of it could be so high that you can’t get the job done without creating financial problems left and right. This should be a hint that the problem, if it exists, lies in our crappy economic system, not in rejuvenation itself or whatever other thing we may create.

Apart from the obvious fact that other hysterically expensive endeavours (such as space missions) are pulled off despite their costs, we must take into account that desperate circumstances call for desperate measures. We don’t need to tear apart our economic system and replace it with another before we create rejuvenation, and neither would we if faced with another health crisis (such as a pandemic) or a planetary crisis, but we need to get the job done despite its costs and the consequences they may have. We can’t give up on rejuvenation on the grounds that it may be too expensive to create, just like we wouldn’t in the case of an existential risk. Can you imagine that? There’s a huge asteroid on a collision course with Earth, and our only hope is a spectacularly expensive space mission to destroy it before it’s too late. Just who in their right mind would step up and say: ‘Nah, too expensive. Let’s not do it.

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Ice-nine is a fictitious alternative structure of water that is solid at room temperature. When a crystal of ice-nine contacts liquid water, it becomes a seed crystal that makes the molecules of liquid water arrange themselves into the solid form, ice-nine. Felix Hoenikker’s reason to create this substance was to aid in the military’s plight of wading through mud and swamp areas while fighting. That is, if ice-nine could reduce the wetness of the areas to a solid form, soldiers could easily maneuver across without becoming entrapped or slowed. (Cat’s Cradle by Kurt Vonnegut) Source: https://en.wikipedia.org/wiki/Cat%27s_Cradle

Jim Rickards uses ICE 9 in his latest work “The Road to Ruin” to warn investors of a potential ICE 9 event in which the financial system literally freezes up in a domino type event as he describes in a recent interview:

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Deep learning owes its rising popularity to its vast applications across an increasing number of fields. From healthcare to finance, automation to e-commerce, the RE•WORK Deep Learning Summit (27−28 April) will showcase the deep learning landscape and its impact on business and society.

Of notable interest is speaker Jeffrey De Fauw, Research Engineer at DeepMind. Prior to joining DeepMind, De Fauw developed a deep learning model to detect Diabetic Retinopathy (DR) in fundus images, which he will be presenting at the Summit. DR is a leading cause of blindness in the developed world and diagnosing it is a time-consuming process. De Fauw’s model was designed to reduce diagnostics time and to accurately identify patients at risk, to help them receive treatment as early as possible.

Joining De Fauw will be Brian Cheung, A PhD student from UC Berkeley, and currently working at Google Brain. At the event, he will explain how neural network models are able to extract relevant features from data with minimal feature engineering. Applied in the study of physiology, his research aims to use a retinal lattice model to examine retinal images.

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And yet, as impressive and powerful as these new technologies and machines are—and they’re becoming more so all the time—I believe they’re an opportunity to be embraced by accountants.

Computers and software have evolved to a point where they can populate spreadsheets, crunch numbers, and generate financial statements and earnings reports more quickly and accurately than any human accountant. In fact, machines are already taking on many of an accountant’s old, routine, administrative chores—on-line tax returns, and book-keeping software, are great examples of routine work that accountants no longer have to do.

This is a good thing. It is already allowing for human accountants to be more sophisticated advisors and planners. In this way, technology can be best used as a tool that gives humans more space to focus on analysis, interpretation, and strategy. In other words, computers have enormous potential to empower—rather than displace—accountants.

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In fact, when speaking with many AI experts across academia and industry, the consensus was unanimous: the development of AI cannot benefit only the few.

Income inequality is a well recognized problem. The gap between the rich and poor has grown over the last few decades, but it became increasingly pronounced after the 2008 financial crisis. While economists debate the extent to which technology plays a role in global inequality, most agree that tech advances have exacerbated the problem.

In an interview with the MIT Tech Review, economist Erik Brynjolfsson said, “My reading of the data is that technology is the main driver of the recent increases in inequality. It’s the biggest factor.”

Which begs the question: what happens as automation and AI technologies become more advanced and capable?

Continue reading “Artificial Intelligence And Income Inequality” | >

Hedge funds have been trying to teach computers to think like traders for years.

Now, after many false dawns, an artificial intelligence technology called deep learning that loosely mimics the neurons in our brains is holding out promise for firms. WorldQuant is using it for small-scale trading, said a person with knowledge of the firm. Man AHL may soon begin betting with it too. Winton and Two Sigma are also getting into the brain game.

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The prospect that artificial intelligence (AI) might one day surpass human intelligence is one that many people, including a number of notable personalities, are terrified of. And it’s not hard to see where that fear is coming from.

As it is, deep learning machines have already shown a number of ways where they outperform humans. So far, they can play video games, recognize faces, and even do stock market trading. There’s one area, though, where humans are still superior, and that’s the speed at which we learn.

Right now, humans learn at a rate that’s 10 times faster than that of a deep learning machine. And it is this ‘superiority’ that has kept that ‘AI taking over humans’ apocalyptic view in the background. Thanks (or no thanks?) to Google, however, this status quo is about to change.

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