Hop-off, hip-out, hip-op, hip-in, hop-on, hip-hop!
Pub-walking, tipsy-talking, camaraderie-chalking!
Day-grey, sun-ray, clay-way, may-stay, walkers-away, ok-ole!
Toff with cough makes off in drop-top driven by flop-mop.
Park life with the heart life,
Watch charmed life with the chilled life,
And see success mugged by half life.
As haunted life fights with fake life and low life,
So new life fights for this life and right life.
Trump to Kim:
Let’s just make this a flash in the pan, not a flash in the sky.
Trump to the media:
Fake news – it’s my story and I’m sticking to it!
This drama about statues is a hiding in plain sight!
I had a word to my Whitehouse maintenance crew. All my doors are revolving doors now.
When it comes to meetings with heads of state, I keep trusted advisers close and my junior family closer.
Trump to women:
Comb over and see me sometime!
Trump supporters: big em up and fake em up,
Trump press: write em up and send em up,
Post Trump campaigners: label em up and wrap em up!
In the current debate about UK austerity, what’s missing from the choice (not the fake choice between austerity and no austerity, but the hard choice between Social and Economic austerity) are two important other options (Productivity improvements and Philanthropy).
To elaborate, the current debate about austerity should be about the mix of four things:
(1) Social austerity – realisable tax rises for some or all current UK tax payers). Of course, history shows us that raising taxes encourages tax avoidance and discourages incentive to work harder.
(2) Economic austerity – alleviating current austerity through borrowing to burden future citizens with greater austerity.
(3) Productivity improvements – workers choosing (through a combination of after-hours study and after-hours volunteering?) to up-skill, to raise their productivity to ultimately alleviate austerity. When we change our expectations, build on small successes to boost our confidence and reframe current problems in a different way using personal flexibility, then there is every chance to better ourselves. If the future is about portfolio careers, and in the age of smart machines, ‘keeping our skin in the game’ through clever design, then up-skilling starts today. After all, process automation and machine learning won’t wait for us, but proceeds at its own pace. A final question about labour productivity at the national level. Which is better – fewer people employed but them generating higher average labour productivity (the French model, relative to the UK model) or, more people employed but with lower average labour productivity (the UK model, relative to the French model).
(4) Philanthropy – particularly high-net-worth individuals forming consortiums, to alleviate UK social deprivation through charitable foundation activity.
The best solution will probably come from a better combination of all four things.
One great opportunity with philanthropy is developing ‘hospital charities’ to build city hospitals that are entirely charity-funded and can take some ongoing pressure off the NHS, care homes and private hospitals. Such hospitals could offer a more selective range of treatments (target elective-surgeries with long waiting lists?), than the NHS.
Food for thought?
If the pace of technological advancement is speeding up, the pace of human consensus-building cannot afford to slow down.
At some point, AI decision-making will have to intervene. AI concerned with countering the ‘natural’ tendencies towards wealth concentration, human corruption and human greed (greedy because we can be).
Will religion, which used to counter these things, cope with an AI world?
I recently read an interesting article (see weblink above) by Jonathan Rochelle. Mr Rochelle is the head of the product management team for Google’s education outreach arm, called Google for Education.
Expert machine programming/AI development helps machines learn and machines (increasingly) help students learn. The question is, will conventional teaching cope?
Without doubt, machine learning is high growth off a low base. With a good deal more investment-return uncertainty, machine-assisted student learning is high growth off a low installation base.
Meanwhile, in the land of traditional education methods, the effectiveness of human teachers in fostering high learning growth from students is experiencing far more sluggish improvement. Some of the reasons arguably include the following (in no particular order):
(1) a lack of agreement inside schools on what’s causing the attainment gap problem. Is it a shortage of the best teachers, or the best teaching practices? Is it the poor parent-school partnership or the lack of school boundaries?
(2) resistance to learning from the students. Students and their parents may have a different view from the school about the best teaching style, or the best learning style for the student. Are teachers, who are passionate about their subject, making it relevant enough to the students’ future lives?
(3) the need to build suitable physical facilities to support student learning. Will far more conventional classrooms need to become computer suites, perhaps with virtual reality apparatus?
(4) budget funding constraints
(5) confusion on the institutions’ own goals (too many targets?).
As online education software increasingly provides a more complete teaching solution in the classroom, what can human educators do? Start planning now for the changeover (move to a variable cost workforce and shorter shelf-life classroom facilities), immerse students in the online systems world (so student graduates can partner with it later) and offer school curriculum choices in subjects that will be slowest to become obsolete i.e. subjects that remain valued by future employers who hire student graduates.
Lastly, how long before the Chinese equivalent of Google matches Google’s audacious plans for transforming global education?
Education and Work
Students stay at school longer, then graduate to do what? Make better and more informed decisions. Decisions on the things that AI can’t or won’t yet do.
People concede that they need the extra education to understand analysis. Year 14 Maths is compulsory.
Jobs open up reconciling and debugging AI systems, until such time as they merge. Counselling, drug rehab and mental health jobs prosper.
Politics and governance
Politics between 2020 and 2030 becomes largely concerned with social wealth distribution. Taxation and investment decisions are reformed.
Political referendums become more prevalent as the technology to host them becomes more cost-effective, but then disappear as governance identifies that issues can’t be resolved piecemeal, but that wholesale ecosystem policy reform is needed.
Hedge fund AI resources are harnessed to government policy making? How? Indirectly via consulting firms and higher education computer resources. Governments commission most complex policy problems to be solved using AI. AI resources are rented as needed to deliver the output.
The serious and super-complex problems become resolved by groups of AI’s acting together. Monitoring systems progressively merge.
Trade becomes less physical movement and more trade credits for the IP on items exported and imported between countries.
AI performs increasingly more of the services that currently occur between people.
Most financial currencies consolidate to align with the half a dozen large trading blocs that emerge.
Celebration of human endeavour is highlighted, tapping the human need to cheer the underdog. e.g. music contests, the Olympics and sports leagues, even as AI controls more of our functioning World.
Basic healthcare receives priority attention. People are actively counselling about healthy lifestyle choices.
Junk food and confectionery companies sponsor medical research into fat cell inhibiting medications and finally succeed, making their profits soar.
Mental health counselling aided by AI diagnostics achieves a quiet revolution, creating a happier but more aware society.
We’d plan tax reform BEFORE income distribution undergoes the full onslaught of machine automation.
The UN would fund and deploy aerial nano-bots that fly around the World destroying unregistered guns.
Religious opinion leaders would MODERNISE religious doctrine to accommodate future technological change.
We’d REFORM things in society before the flat part of the (technology) exponential curve turns into the steep part of the exponential curve.
Government social services would MANAGE people’s expectations in a honest way upfront, not make excuses in a patronising way afterwards. Prevention is usually cheaper that cure.
We’d APPOINT lobby groups to represent the animal kingdom and not pretend that humans and corporates have all the votes and all the freedom to act.
We’d ENCOURAGE people to self-learn to cope with global changes in progress.