The MPs ignore it all. Nobody cares.
This culture has been encouraged by the most senior leaders. Whitehall now incentivises the promotion of courtier-fixers, not great managers like Groves and Mueller. Being in charge of massive screwups is no barrier to promotion.
Operational excellence is no requirement for promotion. The senior civil service now operates like a protected caste to preserve its power and privileges regardless of who the ignorant plebs vote for.
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You can see how crazy the incentives are when you consider elections. And now, when Brexit means the entire legal basis for procurement is changing, do these MPs, ministers and officials finally examine it and see how they could improve? No of course not! The top priority for Heywood et al viz Brexit and procurement has been to get hapless ministers to lock Britain into the same nightmare system even after we leave the EU — nothing must disrupt the gravy train! But nobody is even trying to do this and if some minister took a serious interest, they would soon find all sorts of things going wrong for them until the PermSec has a quiet word and the natural order is restored….
To put the failures of politicians and official in context, it is fascinating that most of the commercial world also ignores the crucial lessons from Groves et al!
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Most commercial megaprojects are over-schedule, over-budget, and over-promise. The data shows that there has been little improvement over decades. The authors have looked at a company data and concluded that, shock horror, operational excellence turns out to be vital after all! They conclude:. The study of management, like politics, is not a field with genuine expertise. Imagine the normal politician or senior official in Washington or London.
They have almost no exposure to genuinely brilliant managers or very well run organisations. Whitehall, as a subset of its general vandalism towards science, has successfully resisted all attempts at learning from ARPA for decades and this has been helped by the attitude of leading scientists themselves whose incentives push them toward supporting objectively bad funding models.
In science as well as politics, incentives can be destructive and stop learning. So, on one hand, radical improvements in non-military spheres would be a wonderful free lunch. We simply apply old lessons, scale them up with technology and there are massive savings for free. All unstable processes we shall control. But in China, the discipline is deeply integrated into national planning.
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The researchers have helped model resource management and other processes in smart cities powered by artificial intelligence. In Guangdong province, the researchers are evaluating how to develop a standardized approach for monitoring water use that might be extended to other smart cities. The goal is to shape behavior by using a mixture of carrots and sticks.
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In some citywide and commercial pilot projects already underway, individuals can be dinged for transgressions such as spreading rumors online. People who receive poor marks in the national system may eventually be barred from travel and denied access to social services, according to government documents…. A quarter-century ago, Qian proposed that society is a system comprising millions of subsystems: individual persons, in human parlance. Maintaining control in such a system is challenging because people have diverse backgrounds, hold a broad spectrum of opinions, and communicate using a variety of media, he wrote in in the Journal of Systems Engineering and Electronics.
His answer sounds like an early road map for the social credit system: to use then-embryonic tools such as artificial intelligence to collect and synthesize reams of data. Though influenced by U. Instead of shunning mathematical approaches, WSR tried to integrate them with softer inquiries, such as taking stock of what groups a project would benefit or harm. Thus metasynthesis from qualitative to quantitative approach is to unite organically the expert group, data, all sorts of information, and the computer technology, and to unite scien- tific theory of various disciplines and human experience and knowledge.
It is plausible that China could find a way within 15 years to find some nonlinear asymmetries that provide an edge while, channeling Marshal Ogarkov, it outthinks the Pentagon in management and operations. I blogged recently about Judea Pearl. He is one of the most important scholars in the field of causal reasoning.
Humans can interrogate stored representations of their environment with counter-factual questions: how to instantiate this in machines? Also economists, NB. The first step, one that will take place in maybe 10 years, is that conceptual models of reality will be programmed by humans. The next step will be that machines will postulate such models on their own and will verify and refine them based on empirical evidence.
That is what happened to science; we started with a geocentric model, with circles and epicycles, and ended up with a heliocentric model with its ellipses. We have to understand how to program them and what we gain out of it. For some reason, evolution has found this sensation of free will to be computationally desirable… Evidently, it serves some computational function. And the robot stops following them. Initially the robot does not know if it is a spider, a snake, an arm—it has no clue what its shape is.
The robot can then use that self-simulator internally to contemplate and adapt to different situations, handling new tasks as well as detecting and repairing damage in its own body …. The robot then used deep learning, a modern machine learning technique, to create a self-model. The first self-models were quite inaccurate, and the robot did not know what it was, or how its joints were connected. But after less than 35 hours of training, the self-model became consistent with the physical robot to within about four centimeters….
Press release HERE. This allows the user to generate realistic and coherent continuations about a topic of their choosing… Our model is capable of generating samples from a variety of prompts that feel close to human quality and show coherence over a page or more of text… These samples have substantial policy implications : large language models are becoming increasingly easy to steer towards scalable, customized, coherent text generation, which in turn could be used in a number of beneficial as well as malicious ways.
OpenAI has not released the full model yet because they take safety issues seriously. But there is another dimension to this trend. Look at this graph below.
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OpenAI blog. The AlphaZero project in the top right is the recent Deep Mind project in which an AI system a successor to the original AlphaGo that first beat human GO champions zoomed by centuries of human knowledge on GO and chess in about one day of training.
Many dramatic breakthroughs in machine learning, particularly using neural networks NNs , are open source. They are scaling up very fast. These NNs will keep demonstrating better than human performance in relatively narrowly defined tasks like winning games but these narrow definitions will widen unpredictably.
Past trends are not sufficient to predict how long the trend will continue into the future, or what will happen while it continues. But even the reasonable potential for rapid increases in capabilities means it is critical to start addressing both safety and malicious use of AI today. Foresight is essential to responsible policymaking and responsible technological development, and we must get out ahead of these trends rather than belatedly reacting to them.
This recent analysis of the extremely rapid growth of deep learning systems tries to estimate how long this rapid growth can continue and what interesting milestones may fall.
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Its rough answers are:. I think this analysis is wrong about the Manhattan Project MP. His argument is the MP represents a reasonable upper-bound for what America might spend. But the MP was not constrained by money — it was mainly constrained by theoretical and engineering challenges, constraints of non-financial resources and so on.
This is an important analogy. Some in Silicon Valley who already have many billions at their disposal are already calculating numbers for these budgets. Some at the leading edge of the field agree.
Once animal-level AI has been achieved, the move towards human-level AI may be small: it took billions of years to evolve smart animals, but only a few millions of years on top of that to evolve humans. Technological evolution is much faster than biological evolution, because dead ends are weeded out much more quickly.
Once we have animal-level AI, a few years or decades later we may have human-level AI , with truly limitless applications. Every business will change and all of civilisation will change…. A few million years later, AI will colonise the galaxy. We should be proud of being part of a grand process that transcends humankind.
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