The article is not mentioning that QE flooded the capital markets with money globally.
Money which mostly sits idle (they are so desperate that they buy bonds with negative interest rates).
This over supply of money and the lack of investment targets, eased raising of money for VCs.
More money, more VCs, more funding, more pressure on VCs to deliver returns, less interest in radical new ideas.
In other words VCs got an incentive to look for proven business models to invest in.
That also explains the success of angel.co.
I think the bigger problem is that capital allocation for new startups is broken. There is so much free capital in the market, and only a tiny percentage makes it into startups, globally ca $120bn per year. Compared to trillions of dollars floating free in the markets, that's a drop in the ocean.
Venture activity is also concentrated in a few places. It isn't widespread enough.
-> capital allocation for new ideas is ineffective and inefficient, the current model of investing isn't sufficient anymore.
The most (if not all) Big Data technologies are all based on the JVM (Java and/or Scala), why not just using JVM based languages, like Java, Scala and Clojure?
I have nothing against Python as such, but adding just another language is not simplifying the job.
Cool approach. I had some fights with Neo4J and taming IO. I did fall back on a 2nd Level Cache, which is nothing else than a huge hash map/KV store in memory.
There are some misconceptions in the article (imo):
The claim that game is already over is wrong, major car manufacturers like BMW, Audi, Mercedes, GM, Ford, PSA, are working on all possible approaches at once. They tested and tried any possible power train technology you can think of (except fission reactors and fusion reactors).
They are offering integrated services, even if they are moving slower than SV inspired companies, u should not underestimated their ability to move faster than expected. The automotive market is extremely competitive, companies that are succesful their, are succesful for a reason.
Cars are extremly complex, far more complex than an iPhone (there is a reason why they cost much more than an iPhone).
Teslas advantage is mainly that it can make money with selling certificates in California. This is good, but can not be applied worldwide.
The automotive industries supply chain is vast, they do operate globally, their relationships are much more complex than the electronics industry.
The devil is in the detail, Teslas much admired agility is no match for a Porsche or an i8. Building engines is an art.
Sales channels, selling a car is an art in itself (I do know something about selling cars, because my business idea is about selling cars, better of course than anyone else;-)). The sales channels are established and deep in the markets. Tesla has some trouble in China.
After Sales, a car needs after sales support.
Regulation, global car regulation is complex, wild and full of wonders.
Tastes, u will not believe how different tastes are globally. A Tesla looks like a Japanese Car to me as a German (which is a malus). A BMW, or Audi has it's own design language which is full of character and sophistication.
The hill to sell a car here is really really big.
But if Tesla and Apple bring some new ideas on the table, it would probably good for all participants.
Joke: may be you need a quantum singularity to get the intelligent machine singularity...
No I do not think that we do have black holes in our brains, and no we are not connected by worm holes...but it is nice idea. Likeminded people are connected by tiny worm holes.
The conclusion is that you need the quantum "layer" to get a intelligent machine.
Not the simulation of quants. Could be much easier than trying to simulate everything. Google tried to use DWave quantum computers for pattern matching in Google Glass.
But there is a debate that DWave computers are really quantum computers, some say they are not quantum computers.
Dualism is a part of it, but dualism is more an ethical question.
Indeterminism is more about emergence, quantum fields and such things. How the universe works.
My impression is that a lot of science that is applied to intelligent machines is based on a deterministic physical model based on Newton and LaPlace. The Bayesian networks was pioneered 200 years ago by LaPlace for example. But how is Einsteins relativity theory applied? Or quantum fields?
A chess board is a closed system with fixed rules, as far as I know you only need a lot of computing power to apply the min-max algorithm to solve any chess game.
In a chess game there are no probalities, AI is about to independently recognizing patterns in noise and develop assumptions out of it. The trick human brains are applying here is called intuition.
It was in my best intend to ignore all attempts before the Wright brothers to try to fly.
It is the same here, we have the wish to make intelligent machines, but we may lack an engine to do that. Besides the airplane design it was also the availability of powerful engines and other features to go the skies.
(I did read the Wikipedia article): in that sense I afraid we are more at stadium of Da Vincis concepts than an aeroplane.
You touched the core of the discussion, imo, that is there are two schools of thaught involved.
One sees the universe and anything else as a machine -> determinism
The others are seeing the universe as a discrete continuum -> indeterminism
That is also my fundamental criticism at the current approach, it is not even considering the possibility that the whole approach could be wrong.
I think intelligent machines are possible, but not without much more fundamental understanding. Hyping, cheering deep learning and all this stuff, is just irrational and illogical.
Where is your argumentation? Ditching together something that is looking like a neural net and boost it with statistical tricks, is just not a brain.
If you just define something as "intelligent" without even trying to benchmark it to original, well than you also can just a statistical experiment done with a lot of CPUs. I am sure Intel would be happy with that.
The singularity is a linear projection of computing power, in this projection anything that is questioning the singularity is pro-actively ignored.
What puzzles me most, is why it is ignored. For me it does not make sense to hope that just the amount of simulated neuronal complexity will be enough, that suddenly out of the complexity something intelligent emerges.
The whole approach is flawed. Something very essential is missing, that is a proven model how brains work and why they work, down to the last quantum state.
Money which mostly sits idle (they are so desperate that they buy bonds with negative interest rates).
This over supply of money and the lack of investment targets, eased raising of money for VCs.
More money, more VCs, more funding, more pressure on VCs to deliver returns, less interest in radical new ideas.
In other words VCs got an incentive to look for proven business models to invest in.
That also explains the success of angel.co.
I think the bigger problem is that capital allocation for new startups is broken. There is so much free capital in the market, and only a tiny percentage makes it into startups, globally ca $120bn per year. Compared to trillions of dollars floating free in the markets, that's a drop in the ocean.
Venture activity is also concentrated in a few places. It isn't widespread enough.
-> capital allocation for new ideas is ineffective and inefficient, the current model of investing isn't sufficient anymore.