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So there are a couple of buzzword technologies that are supposedly changing our economies in a major way. And some of them have already achieved remarkable things, so they cannot be discounted right away.
But the promises some of these technologies are making are gonna be hard to fulfill.
Example 1: Blockchain
Blockchain has been the hot thing for a couple of years now, but has not delivered a lot of tangible benefits outside of crypto currencies.
Study goes looking for successful blockchain projects, finds none:
https://www.theregister.co.uk/2018/11/30/blockchain_study_finds_0_per_cent_success_rate/
No really, none. Not few. Not a single one.
"We found a proliferation of press releases, white papers, and persuasively written articles," Burg et al wrote on Thursday. "However, we found no documentation or evidence of the results blockchain was purported to have achieved in these claims. We also did not find lessons learned or practical insights, as are available for other technologies in development."
(...)
Blockchain vendors were keen to puff the merits of the technology, but when the three asked for proof of success in the field, it all went very quiet.
Therefore, this statement by Australia's Digital Transformation Agency is hardly surprising:
for every use of blockchain you would consider today, there is a better technology
https://www.zdnet.com/article/dta-d...-for-every-use-there-is-a-better-alternative/
Example 2: Artificial Intelligence
We've all read about the impressive feats e.g. in the area of the game Go, and many applications today have implemented some AI features already. I myself have been leading an AI project for the last couple of months and I'm quite optimistic.
But many successful applications of "AI" are actually just applications of Bayesian statistics. And when we get into the area of machine learning and deep learning, it gets a bit muddy. It seems that generally speaking, you need to expect most AI projects to fail:
https://blogs.wsj.com/cio/2017/12/1...ll-in-the-enterprise-despite-potential-panel/
For example, Monsanto's Director of Digital Partnerships said that
A 99% failure rate with a current slate of 50-plus deep-learning projects is acceptable because “that 1% is going to bring exponential gain"
So yes, AI is likely going to bring benefits, even though - and that's the key issue with AI - we won't really be able to tell why single decisions or proposals built on AI have come about. Nobody's gonna be held accountable for fuckups because it's "been the AI". These implications of AI are not discussed enough in my opinion.
Example 3: Quantum computing
Here, I'm just going to quote from a very interesting article. You can read the whole thing though, it's good:
https://spectrum.ieee.org/computing/hardware/the-case-against-quantum-computing
Quantum computing is all the rage. It seems like hardly a day goes by without some news outlet describing the extraordinary things this technology promises. Most commentators forget, or just gloss over, the fact that people have been working on quantum computing for decades—and without any practical results to show for it.
We’ve been told that quantum computers could “provide breakthroughs in many disciplines, including materials and drug discovery, the optimization of complex manmade systems, and artificial intelligence.” We’ve been assuredthat quantum computers will “forever alter our economic, industrial, academic, and societal landscape.” We’ve even been told that “the encryption that protects the world’s most sensitive data may soon be broken” by quantum computers. It has gotten to the point where many researchers in various fields of physics feel obliged to justify whatever work they are doing by claiming that it has some relevance to quantum computing. [...]
In light of all this, it’s natural to wonder: When will useful quantum computers be constructed? The most optimistic experts estimate it will take 5 to 10 years. More cautious ones predict 20 to 30 years. (Similar predictions have been voiced, by the way, for the last 20 years.) I belong to a tiny minority that answers, “Not in the foreseeable future.” Having spent decades conducting research in quantum and condensed-matter physics, I’ve developed my very pessimistic view. It’s based on an understanding of the gargantuan technical challenges that would have to be overcome to ever make quantum computing work.
[...]
To my mind, quantum computing researchers should still heed an admonition that IBM physicist Rolf Landauer made decades ago when the field heated up for the first time. He urged proponents of quantum computing to include in their publications a disclaimer along these lines: “This scheme, like all other schemes for quantum computation, relies on speculative technology, does not in its current form take into account all possible sources of noise, unreliability and manufacturing error, and probably will not work.”
My point here? Don't believe the hype just yet, at least in the case of Blockchain and Quantum computing. And be aware that AI will often not work, and if it does, and you are involved in a business context, it does not absolve you from making decisions based on analysis, experience, and common sense.
But the promises some of these technologies are making are gonna be hard to fulfill.
Example 1: Blockchain
Blockchain has been the hot thing for a couple of years now, but has not delivered a lot of tangible benefits outside of crypto currencies.
Study goes looking for successful blockchain projects, finds none:
https://www.theregister.co.uk/2018/11/30/blockchain_study_finds_0_per_cent_success_rate/
No really, none. Not few. Not a single one.
"We found a proliferation of press releases, white papers, and persuasively written articles," Burg et al wrote on Thursday. "However, we found no documentation or evidence of the results blockchain was purported to have achieved in these claims. We also did not find lessons learned or practical insights, as are available for other technologies in development."
(...)
Blockchain vendors were keen to puff the merits of the technology, but when the three asked for proof of success in the field, it all went very quiet.
Therefore, this statement by Australia's Digital Transformation Agency is hardly surprising:
for every use of blockchain you would consider today, there is a better technology
https://www.zdnet.com/article/dta-d...-for-every-use-there-is-a-better-alternative/
Example 2: Artificial Intelligence
We've all read about the impressive feats e.g. in the area of the game Go, and many applications today have implemented some AI features already. I myself have been leading an AI project for the last couple of months and I'm quite optimistic.
But many successful applications of "AI" are actually just applications of Bayesian statistics. And when we get into the area of machine learning and deep learning, it gets a bit muddy. It seems that generally speaking, you need to expect most AI projects to fail:
https://blogs.wsj.com/cio/2017/12/1...ll-in-the-enterprise-despite-potential-panel/
For example, Monsanto's Director of Digital Partnerships said that
A 99% failure rate with a current slate of 50-plus deep-learning projects is acceptable because “that 1% is going to bring exponential gain"
So yes, AI is likely going to bring benefits, even though - and that's the key issue with AI - we won't really be able to tell why single decisions or proposals built on AI have come about. Nobody's gonna be held accountable for fuckups because it's "been the AI". These implications of AI are not discussed enough in my opinion.
Example 3: Quantum computing
Here, I'm just going to quote from a very interesting article. You can read the whole thing though, it's good:
https://spectrum.ieee.org/computing/hardware/the-case-against-quantum-computing
Quantum computing is all the rage. It seems like hardly a day goes by without some news outlet describing the extraordinary things this technology promises. Most commentators forget, or just gloss over, the fact that people have been working on quantum computing for decades—and without any practical results to show for it.
We’ve been told that quantum computers could “provide breakthroughs in many disciplines, including materials and drug discovery, the optimization of complex manmade systems, and artificial intelligence.” We’ve been assuredthat quantum computers will “forever alter our economic, industrial, academic, and societal landscape.” We’ve even been told that “the encryption that protects the world’s most sensitive data may soon be broken” by quantum computers. It has gotten to the point where many researchers in various fields of physics feel obliged to justify whatever work they are doing by claiming that it has some relevance to quantum computing. [...]
In light of all this, it’s natural to wonder: When will useful quantum computers be constructed? The most optimistic experts estimate it will take 5 to 10 years. More cautious ones predict 20 to 30 years. (Similar predictions have been voiced, by the way, for the last 20 years.) I belong to a tiny minority that answers, “Not in the foreseeable future.” Having spent decades conducting research in quantum and condensed-matter physics, I’ve developed my very pessimistic view. It’s based on an understanding of the gargantuan technical challenges that would have to be overcome to ever make quantum computing work.
[...]
To my mind, quantum computing researchers should still heed an admonition that IBM physicist Rolf Landauer made decades ago when the field heated up for the first time. He urged proponents of quantum computing to include in their publications a disclaimer along these lines: “This scheme, like all other schemes for quantum computation, relies on speculative technology, does not in its current form take into account all possible sources of noise, unreliability and manufacturing error, and probably will not work.”
My point here? Don't believe the hype just yet, at least in the case of Blockchain and Quantum computing. And be aware that AI will often not work, and if it does, and you are involved in a business context, it does not absolve you from making decisions based on analysis, experience, and common sense.
