If he cannot arm-twist OPEC, he may unleash America’s Special Petroleum Reserve
.. markets are being buffeted by three countervailing forces unleashed by President Donald Trump:
- his geopolitical agenda, particularly sanctions on Iran;
- his domestic political agenda, to lower American petrol prices before the mid-term elections; and
- his looming trade war with China.
If he does not get his way, he may have a dangerous weapon up his sleeve—America’s Strategic Petroleum Reserve (SPR). His meddling risks making OPEC, the oil cartel that is a focus of his wrath, look like a paragon of predictability.
.. adding fuel to the price rally is the Trump administration’s pressure on America’s allies to cut oil imports from Iran to zero by November 4th, or face punishment for violating American sanctions. This is more draconian than expected.
.. on July 2nd that more than 50 international firms, including energy ones, had agreed to pull out of Iran. Though America may allow some countries—possibly Turkey, France and others—to reduce imports rather than cut them completely, it will not grant any waivers.
.. a “zero-barrel” response could see between 800,000 and 1.05m b/d of Iranian crude come off the market, with the squeeze starting in September, 60 days of shipping time before the sanctions kick in.
.. In an interview on Fox TV aired on July 1st, he ordered OPEC to stop manipulating the market, threatening some of its members with the loss of American protection if they do not.
.. the highest level of production Saudi Aramco, the state-owned oil giant, has tried out for any length of time is 11m b/d (it is about 10.3m b/d at the moment). But keeping production at that level for several months would damage its reservoirs. Pumping 12m b/d would also take spare capacity in the global oil market to uncharted lows, exposing it dangerously to supply shocks.
.. Complicating things is the imminent risk of an America-China trade war. China has threatened tariffs on American oil imports if retaliation meets more retaliation.
.. China may pay no heed to American sanctions on Iran, which would further stoke tension between the two.
.. These factors, some bullish for oil prices, some bearish, may offset each other. But they have already had the unfortunate consequence of putting Mr Trump alongside the rulers of Saudi Arabia and Russia in the driving seat of global oil policy. Shale producers, who cannot respond to price signals anything like quickly enough to please Mr Trump, are sidelined
.. Analysts predict that if petrol prices continue to rise ahead of the mid-terms, Mr Trump will use a release of up to 30m barrels from the SPR to flood the market. That would be tantamount to launching an oil war against OPEC and Russia, in addition to the trade war. But it cannot be ruled out.
US “Defense” Budget About Control of Resources & Dominance
The Souls of Poor Folk identifies the United States’ irrational attachment to war:
The massive U.S. defense budget has never actually been about “defense.” . . . Rather, their goals are to consolidate U.S. corporations’ control over oil, gas, other resources and pipelines; to supply the Pentagon with military bases and strategic territory to wage more wars; to maintain military dominance over any challenger(s); and to continue to provide justification for Washington’s multi-billion-dollar military industry. [4]
The Souls of Poor Folk: Auditing America 50 Years After the Poor People’s Campaign Challenged Racism, Poverty, the War Economy/Militarism and Our National Morality, https://www.
poorpeoplescampaign.org/audit/ , 11. See the report for detailed and well-researched data.
Ways to think about machine learning
I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:
- Data is the new oil
- Google and China (or Facebook, or Amazon, or BAT) have all the data
- AI will take all the jobs
- And, of course, saying AI itself.
More useful things to talk about, perhaps, might be:
- Automation
- Enabling technology layers
- Relational databases.
.. Before relational databases appeared in the late 1970s, if you wanted your database to show you, say, ‘all customers who bought this product and live in this city’, that would generally need a custom engineering project. Databases were not built with structure such that any arbitrary cross-referenced query was an easy, routine thing to do. If you wanted to ask a question, someone would have to build it. Databases were record-keeping systems; relational databases turned them into business intelligence systems.
This changed what databases could be used for in important ways, and so created new use cases and new billion dollar companies. Relational databases gave us Oracle, but they also gave us SAP, and SAP and its peers gave us global just-in-time supply chains – they gave us Apple and Starbucks.
.. with each wave of automation, we imagine we’re creating something anthropomorphic or something with general intelligence. In the 1920s and 30s we imagined steel men walking around factories holding hammers, and in the 1950s we imagined humanoid robots walking around the kitchen doing the housework. We didn’t get robot servants – we got washing machines.
.. Washing machines are robots, but they’re not ‘intelligent’. They don’t know what water or clothes are. Moreover, they’re not general purpose even in the narrow domain of washing – you can’t put dishes in a washing machine, nor clothes in a dishwasher.. machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company... Machine learning is not going to create HAL 9000 (at least, very few people in the field think that it will do so any time soon), but it’s also not useful to call it ‘just statistics’... this might be rather like talking about SQL in 1980 – how do you get from explaining table joins to thinking about Salesforce.com? It’s all very well to say ‘this lets you ask these new kinds of questions‘, but it isn’t always very obvious what questions.
- .. Machine learning may well deliver better results for questions you’re already asking about data you already
- .. Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for ‘angry’ emails, or ‘anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches,
- .. machine learning opens up new data types to analysis – computers could not really read audio, images or video before and now, increasingly, that will be possible.
.. Within this, I find imaging much the most exciting. Computers have been able to process text and numbers for as long as we’ve had computers, but images (and video) have been mostly opaque.
.. Now they’ll be able to ‘see’ in the same sense as they can ‘read’. This means that image sensors (and microphones) become a whole new input mechanism – less a ‘camera’ than a new, powerful and flexible sensor that generates a stream of (potentially) machine-readable data. All sorts of things will turn out to be computer vision problems that don’t look like computer vision problems today.
.. I met a company recently that supplies seats to the car industry, which has put a neural network on a cheap DSP chip with a cheap smartphone image sensor, to detect whether there’s a wrinkle in the fabric (we should expect all sorts of similar uses for machine learning in very small, cheap widgets, doing just one thing, as described here). It’s not useful to describe this as ‘artificial intelligence’: it’s automation of a task that could not previously be automated. A person had to look.
.. one of my colleagues suggested that machine learning will be able to do anything you could train a dog to do
.. Ng has suggested that ML will be able to do anything you could do in less than one second.
.. I prefer the metaphor that this gives you infinite interns, or, perhaps, infinite ten year olds.
.. Five years ago, if you gave a computer a pile of photos, it couldn’t do much more than sort them by size. A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say ‘this one’s really interesting’. Today, with ML, the computer will match the ten year old and perhaps the fifteen year old. It might never get to the intern. But what would you do if you had a million fifteen year olds to look at your data? What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?
.. machine learning doesn’t have to match experts or decades of experience or judgement. We’re not automating experts. Rather, we’re asking ‘listen to all the phone calls and find the angry ones’. ‘Read all the emails and find the anxious ones’. ‘Look at a hundred thousand photos and find the cool (or at least weird) people’.
.. this is what automation always does;
- Excel didn’t give us artificial accountants,
- Photoshop and Indesign didn’t give us artificial graphic designers and indeed
- steam engines didn’t give us artificial horses. ..
Rather, we automated one discrete task, at massive scale.
.. Where this metaphor breaks down (as all metaphors do) is in the sense that in some fields, machine learning can not just find things we can already recognize, but find things that humans can’t recognize, or find levels of pattern, inference or implication that no ten year old (or 50 year old) would recognize.
.. This is best seen Deepmind’s AlphaGo. AlphaGo doesn’t play Go the way the chess computers played chess – by analysing every possible tree of moves in sequence. Rather, it was given the rules and a board and left to try to work out strategies by itself, playing more games against itself than a human could do in many lifetimes. That is, this not so much a thousand interns as one intern that’s very very fast, and you give your intern 10 million images and they come back and say ‘it’s a funny thing, but when I looked at the third million images, this pattern really started coming out’.
.. what fields are narrow enough that we can tell an ML system the rules (or give it a score), but deep enough that looking at all of the data, as no human could ever do, might bring out new results?
Hitting Putin Where It Hurts
But the prime minister should have gone in harder. If she really wanted to teach Russia a lesson, she should have announced measures allowing her government to scrutinize the billions of dollars invested in Britain by Russian oligarchs and their associates, some of whom have criminal or intelligence backgrounds. This kind of transparency would hit President Vladimir Putin and his allies where it hurts most: their bank accounts.
To this day, anybody from Mexican cartels to Saudi arms dealers to Russian oligarchs (and even American real estate magnates) can invest money in Britain through anonymous companies registered in Crown Overseas Territories like the British Virgin Islands. In London’s central borough of Westminster alone, some 10,000 apartments and houses are owned by companies whose proprietors are entirely unknown to the government.
.. In 2016, Mrs. May’s predecessor, David Cameron, was preparing legislationto force anonymous companies to reveal their real owners. Then he lost the referendum on Britain’s membership in the European Union.
.. Mrs. May probably has her reasons for not going forward with the law. She is the country’s weakest prime minister to assume office since World War II. Brexit has not only polarized public opinion but also created bitter divisions in her cabinet, and several ministers are open about their desire to take her place.
Because of this, she has had to handle the relationship with Russia after the murder attempt with great care. If she gets it wrong, her already enfeebled administration could collapse.
.. Mr. Putin may have realized how weak Mrs. May is, which is why he would decide to act now to take revenge on a man he sees as a traitor and to cause Britain new headaches.Russia’s motivation is understandable. Its economy faces serious structural problems, including a dangerous overreliance on oil and gas. At the same time, business leaders are worried about the country’s long-term demographic decline. Mr. Putin seeks to bolster his domestic popularity by looking powerful as he sows discord with the West... The government is reaping the dubious rewards of having opened the City of London since the late 1990s to foreign capital with no questions asked about its origin.The initial aim of this permissive approach was to persuade investors that London — rather than New York — was best suited to be the world’s financial capital. Among the many to take advantage of the light-touch regulations were oligarchs, spies and gangsters... Russian oligarchs have made an indelible mark on London. Some own newspapers, others our most successful soccer clubs, while many more own huge chunks of high-end property in the most fashionable parts of the capital... And some of those characters are close collaborators and friends of President Putin... If Mrs. May is convinced that Russia is behind this attack, then she needs to devise a way of getting to President Putin’s friends and collaborators. And that means great transparency. She should reintroduce the stalled proposal to force anonymous companies to reveal the sources of their cash.