Ask HN: What up and coming technology has you most excited?

Roller coasters and skydiving are exciting in a similar way.

We already play god.

People are just having sex without thinking twice.

I'm married and in the appropriate age and i have thought about having kids a ton.

I'm not sure if i'm even allowed to create a new thinking human being only for the motivation of having a kid.

The crispr thing takes all our medical advantages just an additional level up.

You see people argue about if you should do a gen test for down syndrome (don't get me wrong here! A human being is a human being. But yes there is a consensus in our society that you become a human after a period of 3 month, at least in germany) and other complications but would never think about denying a doctor to use a incubator for a baby which would never ever be alive today without the medical advances.

There is an unbelievable huge hypocritical thinking in this. Yes tons of human beings would not be alive today without medicine but on the other hand the responsibility coming from all this knowledge doesn't matter?

I believe that in a moral/ethical right way you would need to do your BEST for your kid and this means making sure that by using crispr you do that.

I have no idea where this journey will bring us and yes there is tons of things to figure out but we will be able to overtake natures randomness for future live. And we have to.

If someone has a blood cancer (i.e. they are damaged at a DNA level) and can only currently be cured by a danagerous stem cell transplant and then the ability comes along to fix their DNA back to what it previously was at the genetic level wouldnt you agree it would be more unethical to not allow them access to that treatment?

You don't think ridding ourselves of genetic diseases is an extremely large good? Millions of humans have incurable genetic diseases which vastly limit their quality of life and even lifespan itself. To me, this ALONE would be exciting. But when you add on the benefits to editing crops, livestock, microbes, and human traits not tied to disease, the ramifications become downright mind-boggling. I don't think it's exciting in a purely positive way, I honestly don't know if it's going to be good or bad on the whole. I'd wager that it starts off very good and slowly becomes less good, since the best use cases will all be targeted first.

This is all optimistic speculation. Being able to remove diseases from our genome is not equivalent to being able to create mega-geniuses with blonde hair. The "spelling-checked" persons DNA is just that, their DNA. It says nothing about how they're going to be raised, environmental factors etc. Before we can even start to think about "engineering" ourselves we still need to figure out how we're engineered to begin with and how that engineering changes throughout the span of our lives.

How would that work in practice? Of course, you could eliminate all hereditary diseases, make people smarter, remove certain genetic predispositions; but I'm not convinced that the "perfection" thing is possible.

The most obvious being, what exactly is perfection? If you ask 100 people to describe their perfect partner, you'd get 100 completely different answers. If our species would converge to any one of those descriptions, those who have a different idea of perfection would find themselves in a objectively worse world. Converging to some kind of average wouldn't be any better either.

Just take a look at the standards of beauty even over the last, say, 40 years. There's no constant, purely objective measurement of beauty, so to me it seems impossible to create perfection.

That's falling pretty strongly on the nature side of the nature vs nurture debate.

We've modified adult cells of Huntingtons patients [0] but I can't find any reports outside of hackerspaces of performing it on full human adults.

[0]: http://www.businessinsider.com/researchers-use-crispr-to-edi...

Sadly I imagine appearance will be the fashionable choices. Fitness be damned.

Wow IPKISS looks like something I would want to use. Ive been trying to write some of this myself and it's annoying. Thanks a bunch!

I'm working (like, on the side) on building a fully photonic computer. The progress is very slow, however some things fall into place quite a bit. Most photonic computing designs still use bits, mine would use signals which makes the computer much more powerful.

Only because no search engines understand them.

I would love to ask specific questions instead of translating everything into multiple queries of "search engine speak".

Every time we find an edge case (or a noob question) that lands us on stack exchange, we could just ask an AI directly to look up the relevant documentation or scour through thousands of bug reports and workarounds.

Stage two describing the solution you want generated code for, or stage three stating the problem, or stage 4 finding problems and solving them.

The reason PC and Mobile hasn't converged is because of the lack of good inputs that aren't mouse and keyboard. As long as there's no good way to input text on mobile, there will never be a convergence of those two verticals.

The Librem 5 from puri.sm is nice in that respect. The phone runs Linux and will have a docking station to attach monitor, keyboard, mouse, then use as laptop.

It does look like a "shit show" but with the proper (a lot) adjustments it can have its upsides

I would prefer a beautiful robot built brick house over modular housing, but I do not know a ton about pre-built houses.

> You need to build density to reduce the share of land cost per dwelling

Or (meaningfully) make proximity less important.

I think this is the biggest part, and less the "sleep in your car while you commute for 5 hours each way!" which sounds fucking terrible.

Imagine the amount of space and time saved by having 1 enormous lot on the outskirt of town, like an airport. You walk around your dense urban area, call your car, 10 minutes it's there, it drops you off, and parks itself. No circling the block, no terrible big box concrete hellscapes, just Park Slope without the frustration.

Cheap, too. Building parking in NYC costs ~$100k a spot https://www.nytimes.com/2007/07/12/us/12parking.html

Yes it does. Here's a pretty in depth analysis of it. (Take the hyperbole with a pinch of salt but the tech is correct.)

https://www.cringely.com/2018/04/06/the-space-race-is-over-a...

Initially I think due to disruption to banking and government monopolies. Outrageous fees, pitiful interest rates, and financial services only accessible Monday through Friday 9-5 and sometimes on Saturday seem pretty archaic today. People who never had access to a bank can send money to anyone peer-to-peer like cash. Millenials largely prefer to invest their money into cryptocurrencies over traditional assets, which I believe has largely been monopolized by a firmly established few.

I don't think it's a long-term view due to the numbers involved. For example, everyday developers might have access to 64-bit laptop computer with 4 cores @ 4 ghz and 16 GB of RAM (literally millions of people use such a machine, zero exaggeration).

When working on their own desktops, AI researches might use an 6-core or 8-core desktop with 64 GB of RAM or, rarely, 128 GB of RAM.

Access to 2-32 terabytes of RAM are not in the realm of what most AI researchers get to run their algorithms on on a daily basis.

Google's AlphaGo ran on[1] 1202 CPU's, 176 GPU's, and I didn't immediately find the amount of memory but on the order of magnitude you would think it is from 1 terabyte (if it's 1 GB of RAM per CPU) up to (reasonably) about 128 terabytes, with my guess being 4 TB-16 TB. However I didn't find this figure.

This is hardware that exists today, but AI researchers would have to wait a few years to have normal everyday access to this kind of thing in their normal everyday devices. Right now only datacenters match the processing ability of the human mind. Although they certainly exist in one cluster, they're far from the type of thing that is in everyone's hands yet.

On the other hand, once it is set up these clusters can be far faster than the human brain, which runs at really slow analog speeds. (Look up the propagation speed of neural signals.) The human brain is just slightly ahead of the curve right now without any specialized hardware.

There are 7 billion extant examples of intelligent human brains. There are probably less than 10,000 clusters AI researchers can play around with for months, which have 16-128 TB of RAM and aren't used for anything more important, just whatever they feel like running on it. (Versus easily more than 15 million computers with just 16 GB of RAM, though that may not be enough.)

I don't think this is very far-off.

[1] http://uk.businessinsider.com/heres-how-much-computing-power...

You raise a good and apt point.

I would say firstly, we don't have to know how to achieve it exactly, since once we have the hardware we can just experiment. The human source code is about seven hundred megabytes (fairly precisely, DNA is almost precisely 2 bits per base pair) so somewhere in that is the encoding for human intelligence, since humans inherit it. So we know that there is some < 700 MB program. Now 700 MB is a ton of source code, but it's not an unimaginable amount. We know some of the compiled results, as we can see how human brains are built actually. So there's lots of indications that we will be able to get similar results once we have the hardware for it - which we do.

Secondly I would point to many of the specific advances reached using machine learning as excellent indications that we are quite near the kind of algorithmic breakthroughs that are necessary. Since the topology of these algorithms is in many cases closely inspired by human neural topology, and many algorithms achieve many similar results, it seems quite likely that we would be able to reproduce some of these effects experimentally.

More than "likely", in many domains the computers have done just that.

>what if AI is totally possible, but our sacks of meat in skulls are not able to achieve it?

Won't be a problem if we use CRISPR to make ourselves smarter first!

All this talk of meat is making me hungry...stupid auto correct.... :<

It's exceedingly slow. Neurons fire at not 4 gigahertz, 400 megahertz, 40 megahertz, 4 megahertz, 400 kilohertz, 40 kilohertz, 4 kilohertz, or 0.4 kilohertz (400 hertz.) They fire at < 1 hertz to about 200 hertz. [1]

That is literally 8 orders of magnitude slower than processers. 8 orders of magnitude is the size of difference between a grain of sand and 1.2 short tons.

In the time that your neuron moves 1 grain of sand, a computer can move 1.2 short tons worth of sand 1 grain at a time (talking about just cache here) - because it's switching at 4 GHz instead of 10 hertz.

That is enough to make a lot of back and forth trips. Now, you have to make a lot of these back and forth trips because the brain is extremely parallel. But the difference between these extremely slow biochemical firing speeds and gigahertz light speed enables this.

The speed of propagation in the brain is about 100 meters/second [2]. The speed of propagation between two fiber optic end-points is about 299 792 458 meters/second. That gives you 7 orders of magnitude to play with. Or, in other words, it can let you take seven orders of magnitude more trips across a data center (or go that much farther) before you miss a real-time deadline for when you need to address a neuron by.

In the future it's pretty clear that computers are not going to think as fast as the human brain when doing similar calculations using a similar topology, but, for example, 10,000 times faster than real-time.

This really shouldn't surprise anyone growing up in technology. Brains are slow, mechanical devices. They're not digital and they don't do signal processing at the speed of light.

It's taken us some time to catch up because a hundred billion neurons is still a lot of neurons, and algorithm development using similar topologies has been slow.

[1] https://aiimpacts.org/rate-of-neuron-firing/#Estimates_of_ra...

[2] https://hypertextbook.com/facts/2002/DavidParizh.shtml

my sibling post lists some of the numbers involved, but you should especially look the number of neurons, their connectedness, firing speed, and above all realize how incredibly slow this analog biochemical signal propagation is. You can look at how many parts make it up. (About a hundred billion.) Finally you can know for sure that the algorithm can be encoded in well under 700 MB, since that is literally the entire blue print of the human genome (bit-perfect, at 2 bits per base pair), which (obviously) encodes human intelligence as just a small part of it. (Since it includes everything else too, such as your entire rest-of-your-body.)

Look at the number of "mechanical" (biochemical) parts making up the human brain, their blueprint, the size they're contained in.

Finally look at the results that analogous machine learning algorithms are making with (albeit much simpler) neural net topology models.

The idea that nobody will ever match it is like looking at a mechanical watch and saying no digital model of it could ever come up with similar emulated results. What.

When you look at the size of the datasets involved you will see that it is exactly the sort of thing we can expect to be emulating now.