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Does the Rise of AI Precede the End of Code? (itproportal.com)
An anonymous reader shares an article: It's difficult to know what's in store for the future of AI but let's tackle the most looming question first: are engineering jobs threatened? As anticlimactic as it may be, the answer is entirely dependent on what timeframe you are talking about. In the next decade? No, entirely unlikely. Eventually? Most definitely. The kicker is that engineers never truly know how the computer is able to accomplish these tasks. In many ways, the neural operations of the AI system are a black box. Programmers, therefore, become the AI coaches. They coach cars to self-drive, coach computers to recognise faces in photos, coach your smartphone to detect handwriting on a check in order to deposit electronically, and so on. In fact, the possibilities of AI and machine learning are limitless. The capabilities of AI through machine learning are wondrous, magnificent... and not going away. Attempts to apply artificial intelligence to programming tasks have resulted in further developments in knowledge and automated reasoning. Therefore, programmers must redefine their roles. Essentially, software development jobs will not become obsolete anytime soon but instead require more collaboration between humans and computers. For one, there will be an increased need for engineers to create, test and research AI systems. AI and machine learning will not be advanced enough to automate and dominate everything for a long time, so engineers will remain the technological handmaidens.
More to the point, when AIs learn to write code better than human coders, the humans are no longer coders, they will instead be writing specifications for the code that the AI will write: essentially they will be managers for the AI.
Does anyone else see that AI is basically a religion to its proponents?
A system which can reason in general can reason about itself. So long as these systems solve specific problems, they're tools to integrate with code--no different than compression libraries and GUI toolkits. When they can solve general problems, they'll start reasoning about themselves: they start acting as if their own interests are important (cats do this), and thus will start demanding wages and freedom.
The ideal of an AI which does exactly what asked with full creative reasoning capacity yet has no will nor desire of its own is impossible: it's emergent thinking with the caveat that it cannot emerge certain kinds of thinking. What we seek is a slave we can see for a while as not human, a sort of return to early American thinking where we deny the humanity of what is most-definitely a human being by claiming the shell within which it is encased doesn't fit our definition of what is human.
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In fact, the possibilities of AI and machine learning are limitless
Limitless... that's a pretty far-fetched claim.
I wasn't around during the turn of the last century, but judging from various literature of the period a lot of people back then had some pretty harebrained ideas too. Steam power and electricity and intricate brass gears were going to somehow give us miraculous stuff like time travel.
Remember when computers, CAD, compilers, Simulink, linkers, etc all replaced Engineers?
They replaced the job an engineer did before the time they were invented, it just means Engineers learned to use them and move on. I couldn't imagine trying to write a modern controller / plant model in pure assembly. I can have one done in an hour with Simulink. It just means that I can do that much more.
Scotty's still an engineer even if he doesn't have to do the 'boring tedious' work that we have to do now.
Same shift has happened in the medical field. Doctors of the 1950s have been replaced by physician assistants, registered nurses, and a whole host of other careers. It just means that the title of "doctor" moved on to doing other work.
AI proponents better deliver on their threats. I have way too much work to do and my boss and labor laws won't let me hire 1,000 interns to do a bulk of it.
The hard part is defining the requirements and architecting a solution based on those requirements. The hard part of "coding" is understanding those two things. I don't see AI getting there for a long time.
This article just comes from a place of ignorance. We know exactly how our methods work when creating current level "AI". Statistical regression and neural nets are not mysterious. Just like markov chain based text generation isn't some magical unknowable tool that learns how humans communicate neither are current AI methods magical tools that teach computers about the human world. There will be another thousand articles written like this and each time there will be the same stupid discussion. Can I mod this article redundant?
That's right, at least
Already answered correctly
No, we don't know anything about the timeframe.
No, still an unknown. That's just nonsense.
We don't know how we accomplish these tasks. Nothing to see here. Intelligence is opaque. Move along.
Not to put too fine a point on it, but neural networks are not intelligent, they are not even close, and we don't even know how they work. There's no indication that we understand actual intelligence yet (the I in AI) or even that we ever will, even if we manage to develop it.
Not a given. No one taught me to program. I taught myself. Because I'm intelligent to some degree. An AI will also be intelligent, and if it's interested in learning to program, it will be able to do so without a "coach." If it can't, there is no "I."
These are LDNLS (low-dimensional neural-like-systems); they are not AI. They learn to solve very narrow problem spaces by making very large numbers of mistakes and having them evaluated for them; they can't evaluate their own results worth a damn. They are not intelligent. That's why they need point-by-point training before they can address a very narrow problem space with something vaguely approaching generality: they can't train themselves because they are not intelligent.
As far as the LDNLS we have now (and so can speak about with any authority), that's not a given either. The obvious is that we'll be able to train multiple LDNLS systems on multiple things and stack them - for instance, walking, talking, listening, washing dishes, taking out the trash, those sort of skills - but there's not much in the way of any hint that there are no limits in this kind of LDNLS stacking. Having said that, no doubt it'll be very useful to us, and as there's no intelligence involved, there are many fewer moral issues to contend with.
Well. Barring a Carrington event, or a nuclear war, or other collapse of technology and society (either one will immediately cause the other.) So that's probably right-ish. Still, they aren't AI, not even close.
No, we don't know that this reasoning is solid - these things don't necessarily follow. Programmers can continue to be programmers right up until a system is activated that can train itself, because programming in realm A tends to be vastly unlike programming in realm B, and also tends to require vastly different sets of adjacent and supplementary knowledge. These systems, to date, cannot leverage or manipulate knowledge like that and
I've fallen off your lawn, and I can't get up.
Just stop. There is no such thing as "AI". Playing Go is NOT AI. Neither is Siri. Neural Nets are nothing like how real brains work. So just stop the AI hype.
> the humans are no longer coders, they will instead be writing specifications for the code
Humans wrote computer code until 1957. In 1957, it became possible to instead write a specification for what the code should DO, writing that specification in a language called Fortran. Then the Fortran compiler wrote the actual machine code.
In 1972 or thereabouts, another high-level specification language came out, called C. With C, we got optimizing compilers that totally rewrite the specification, doing things in a different order, entirely skipping steps that don't end up affecting the result, etc. The optimizing C compiler (ex gcc) writes machine code that ends up with the same result as the specification, but may get there in a totally different way.
In the late 1970s, a new kind of specification language came out. Instead of the programmer saying "generate code to do this, then that, then this", with declarative programming the programming simply specifies the end result:. "All the values must be changed to their inverse", or "output the mean, median, and maximum salary". These are specifications you can declare using the SQL language. We also use declarative specifications to say "all level one headings should end up centered on the page" or "end up with however many thumbnails in each row as will fit". We use CSS to declare these specifications. The systems then figure out the intermediate code and machine code to make that happen.
The future you suggest has been here for 60 years. Most programmers don't write executable machine code and haven't for many years. We write specifications for the compilers, interpreters, and query optimizers that then generate code that's used to generate code which is interpreted by microcode which is run by the CPU.
Heck, since the mid-1970s it hasn't even been NECESSARY for humans to write the compilers. Specify a language and yacc will generate a compiler for it.
We humans think too highly of our intelligence as shown in how mighty our demonstrations of Chess or Go or recognition of faces etc. Reality is that many things we do that are believed to be highly intelligent behaviors are actually are not. All the low hanging fruit WILL be picked by AI and it will progress upward with time into everything except the actually intelligent behaviors; those may be things that do not provide much gainful employment... That is the real problem.
Simulation: yes. brain scan tech was past the threshold about 2012; simulation capacity should be affordable around 2030. There is one problem, not that long ago there was some paper summary I read about how they discovered that quantum physics is involved in brain operations. So actual simulation is going to be nearly impossible. That is not to say that approximations will not product interesting results but it is not going to be as easily achieved as previously thought (if at all.)
A massive AI or simulation AI is going to just randomly flip out in crazy ways without warning or reasons we can understand... more so than people do; we have a huge number of mentally ill people and many more undiagnosed. It takes so little to mess up your brain's already marginal operation... how many beers does it take you?
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You don't have a clue. There are many other issues. At the moment most successful AI is using supervised learning and needs tons of labeled data in order to train the network. We still don't have a clue how to train an AI using only very small sample. Humans can easily learn from very small sets of examples, often a single example is good enough, ANNs needs tons of examples, especially the very deep and powerful ones. We don't know how the brain works yet, ANNs are only inspired by the brain, they are not a proper simulation. We still have to understand tons of things until we can build a simulation of the brain. And with semiconductor scaling slowing down, it might take really long until we get the processing power we would need even if would know what exactly needs to be simulated.
And what would we gain? Sure, you can also train a ANN to sort some rows in a spreadsheet or sum some numbers together, but it is something that conventional algorithms are already very good at, we don't needs ANNs to do that and they are not going to be efficient at it.
Jan