Although we have been raising public awareness on climate change for years, this is not enough.
Despite the effects of climate change becoming more and more obvious, big polluting corporations – the ones responsible for the majority of carbon emissions – continue to carry on drilling for and burning fossil fuels.
Climate change is happening now, and it’s the most serious threat to life on our planet.
The global temperature increases day by day with much of Southern Europe and Northern Africa already in the grips of back-to-back heatwaves, which have caused wildfires and broken temperature records.
We all know that this warming causes harmful impacts such as the melting of Arctic sea ice, more severe weather events like heatwaves, floods and hurricanes, rising sea levels, spread of disease and the acidification of the ocean.
To date we have had around 26 global conferences resulting in agreements and promises, with insufficient actions to make any material changes to global temperatures rising.
Unless greenhouse gas emissions and global temperature are reduced within years, the world will face demanding consequences.
While every fraction of a degree making climate tipping points more likely the next UN Climate Change Conference will convene from 30 November to 12 December 2023 in Dubai, United Arab Emirates (UAE).
With signs that some climate tipping points are already approaching / irreversible we will witness once more the who’s the how’s and where while the melting of polar glaciers and sea ice, die-back of the Amazon rainforest and coral reef extinction are all on the edge of tipping over into a feedback loop of self-destruction, whereby their decline itself becomes a source of warming.
We can’t be sure exactly when tipping becomes inevitable.
Because of war in the Ukrain (which is affecting the world food supply) the climate targets will become looser and looser, higher and higher with world governments doing even less in the future.
We don’t have the policies in place, we don’t have the financing in place to reach any of the goals required.
Seven million people are already being killed by climate change around the world – as many as those killed by Covid. Yet progress by world governments has been achingly slow. it’s never been more important to demand that our leaders act.
Current policies are “totally inadequate” and you may rest assured that world leaders will once again make a “terrible mistake” in prioritising inflation, the pandemic and the Ukraine war over the climate.
We need concrete solutions to make it less uncomplicated to achieve any goals.
The world cannot be at “positive tipping point” in the fight against climate change without addressing the lack of financing. ( See previous posts)
Many commitments to reduce carbon emissions have been set, but few are binding and targets are often missed.
Climate change isn’t just a scientific problem or a political challenge its a distribution of wealth problem including technologies such as artificial intelligence.
It’s easy to feel overwhelmed, and to feel that climate change is too big to solve. It can be challenging to wrap your head around such a complex issue, These impacts are severe and far-reaching – both now and into the future – with no sign of slowing down unless drastic action is taken.
To work, all of these solutions need strong international cooperation between governments and businesses, including the most polluting sectors.
Many of the world’s biggest challenges, from poverty to wildlife extinction, are made more difficult by climate change.
But we already have the answers, now it’s a question of making them happen.
Mitigating greenhouse gas emissions requires changes in many areas, namely buildings, transportation, and the energy industry.
Governments want to be re-elected, and businesses can’t survive without customers. Demanding action from them is a powerful way to make change happen.
Transitioning to a sustainable future comes with a massive price tag, but it isn’t always clear who should foot the bill – or how the money should be spent.
Developing countries will increasingly be stuck with debts to pay for their climate solutions.
We are now facing an important crossroads. Make profit out of climate change or see it as a one-off, last-chance opportunity – to restructure economies at the pace and scale that climate science requires by integrating climate action into the economic recovery.
As the impacts of climate change add up, economists are trying to figure out what the true cost of a tonne of carbon really is. ” The most important figure you’ve never heard of”
It is basically a complete denial of climate science that underpinned the social cost of carbon.
Such as the cost of adapting to sea-level rise, or how increased temperatures affect labour productivity, and how crop yields will be affected. The impacts of climate change will be felt over many hundreds of years, whereas cutting emissions costs money now. A high discount rate suggests those alive today are worth more than future generations, whereas a low one suggests the opposite.
It defines how much society should pay to avert future damages caused by climate change. It also accounts for the impact that today’s emissions will have on future generations.
Instead of making assumptions about issues such as the relationship between temperature and human wellbeing at some abstract point in the future, there is now a lot of real-life data. If we pass certain climate tipping points, such as thawing permafrost and ice sheet disintegration, the runaway damage caused will increase the social cost of carbon. It will certainly affect the actions that people undertake.
It’s overwhelmingly accepted that climate change is a very significant threat to humanity.
We probably underestimated the consequences but every small step we take as individuals contributes.
So why not demand solar panel’s be put on every roof, free of costs, or that villages build solar farm to supply greed energy to their inhabitants, instead of military spending that will be worthless in the fight against rising tempts.
By financing renewable energy, “smart grid” technologies and other green innovations, of course things do not suddenly stabilise at 2030, but at the very least its a concrete step in the right direction.
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The biggest problem of our world today is not artificial intelligence but natural stupidity!
When it comes to climate change – profit seeking algorithms – and the Military race to send atomist drone killers into the battle field – Welcome to the perplexing world of collective stupidity!
The Trump campaign and Brexit – where we all woke up the next day astounded that “this could happen” are both prime examples of campaigns that leaned heavily on the emotions of anxiety, fear and tribalism. and collective stupidly.
Since then, there has been much unpacking of “what happened” and talk about “it could only have been “stupid” people” who could have voted that way.
But is this true?
Yes, profound lapses in logic can plague even the smartest mind.
There are intelligent people who are stupid. So why the paradox? Stupidity is not a lack of IQ.
Unconscious emotions drive our decisions – Intuitive feelings gave us an evolutionary advantage in caveman days, a survival way of dealing with information overload; and can still play a useful role as we on the precipice of a critical moment with AI.
All over the world, we are in the midst of a great shift. The data revolution has given way to the analytics movement. Press our emotional buttons and our judgement is derailed. Hence the temptation to choose the first solution that comes to mind, even if obviously flawed.
It seems that nothing encourages stupidity more than group culture.
An uncritical dependence on set rules often leads to absurd decisions, the-way-we-do-things-here, often not being the most intelligent way.
And the more intelligent someone is, the more disastrous the results of their stupidity.
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With generative AI technologies data-driven insights are reshaping outcomes without needing to write code, becoming truly intrusive, enabling decision-makers, analysts, data scientists and developers to collaborate and develop analytical insights in real time.
SO, WHAT CAN WE DO TO PROTECT OURSELVES FROM DOING STUPID THINGS?
Knowledge of our foolish nature, can help us escape its grasp.
We can step outside the group of Google algorithms knowledge to question where we are at and going.
and revert to culture-thinking that relies on that “everyone knows the true”
Stupidity is all around us. As long as there have been humans there has been human stupidity,
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Over the past decade, we’ve seen the volume of data available to decision-makers grow exponentially.
In this intelligence era, it’s no longer about how much data one company can generate, it’s about how they use it. Corporate leaders, academics, policymakers, and countless others are looking for ways to harness generative AI technology, which has the potential to transform the way we learn, work, and more.
Generative AI is evolving quickly, but to truly get the most benefits from this ground breaking technology, you need to manage the wide array of risks.
Why?
Because generative AI is so powerful and easy to use, it’s poised to change what is real and what is not.
Unlike earlier disruptions, the reality of the generative AI race is already looking out of control.
This could be the first “disruptive” new tech in a long time built and controlled largely by giants in the tech world which could entrench, rather than shake up, the status quo.
Right now, only a handful of companies — including Google, Meta, Amazon and Microsoft (through their $10 billion investment in Open-air) — are responsible for the world’s leading large language models.
So what can policymakers do about AI?
Is there a way to prevent the hottest new technology from simply cementing the power of the tech giants?
Virtual worlds should not become walled gardens.
It is abundantly clear that leaving it to the market to decide how these powerful technologies are used, and by whom, is a very risky proposition.
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For decades, many of the great scientific and philosophical minds had conceived of creating collective intelligence in the form of a globally connected space to pool our knowledge.
Social Media -Smart phones – are digitalizing citizens and their resulting emergent behaviour.
This is a phenomenon that occurs in complex adaptive systems. In such systems, simple components interact in such a way that the whole becomes greater than the sum of its parts.
Our collective intelligence has now become what can only be referred to as our collective stupidity.
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The Dark Side — Collective Stupidity.
Collective stupidity can be perplexing and is often harmless.
How is it possible that a group of smart individuals can sometimes make decisions so perplexing, it feels like the intelligence just evaporated?
How does collective stupidity happen?
Are we are better off by not underestimating the effects of this phenomenon?
A system based on generating clicks and interactions has created an environment for the outlandish and bizarre to flourish, with expertise falling by the wayside.
Broad, anonymous social networks breed collective stupidity.
In 2023, an estimated 4.9 billion people use social media across the world this number is expected to jump to approximately 5.85 billion users by 2027.
The driving force. The increasing global adoption of 5G technology.
These staggering numbers aren’t just statistics, either. They highlight the expansive influence and potential of social media platforms. Right now, 1.9 billion daily users access Facebook’s platform, Twitter has gained 319 new users per minute in 2020, while 500 hours of video are uploaded to YouTube in the same amount of time. Millions of businesses around the world rely on Facebook to connect with people.
The recent new platform Threads Meta’s new social network, had 100 million sign ups in its first five days.
With this much content being generated, how can experts possibly stand out from the crowd?
By emulating the human ability to forget some of the data, psychological AIs will transform algorithmic accuracy.
Machine learning, on the other hand, typically takes a different path: It sees reasoning as a categorization task with a fixed set of predetermined labels. It views the world as a fixed space of possibilities, enumerating and weighing them all.
Social media networks are not very sociable these days. Feeds are algorithmic, which means you see whatever the apps want to show you.
All this has eroded public confidence.
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We all have intelligence and expertise to offer, even if the internet leaves us feeling isolated at times.
With so much misguided thought and active disinformation online, it has become difficult for people with insight worth sharing to do so. Behind the anonymity of the web, anyone can claim to be an expert. When everybody is an expert, nobody is.
With online communities, the relationship between experts and their audience becomes a two-way street.
Many of the issues we throw billions of dollars at and attempt to solve with technology could be easily achieved if we were able to better utilize our collective intelligence.
Technology is the means, not the end; its potential is massive, but not as great as our own.
So we wildly overestimate our access to our own mind.
In essence, the same emergent behaviour that typically helps the group survive sometimes leads to collective stupidity and death.
The Internet gave us the ability to connect with people on a global scale.
But its click-baiting algorithms and lack of regulation also brought with them chaos. As social media came to dominate the landscape, it made using the internet for the purpose of collective intelligence increasingly difficult.
You see, with stupidity, or stupid people for that matter, protesting or reasoning doesn’t really work. This is mainly because of their strong prejudice. They simply disbelieve any facts or reasoning we provide. In most cases, they either simply deny the arguments. And if they can’t, then they call them trivial exceptions.
People are often made stupid under certain circumstances. Maybe they allow this to happen to themselves. It is a group phenomenon.
The nature of stupidity has its roots deep in the subconscious. It is largely driven by the fundamental mechanics of our experience. following the herd. It is arguably the most prominent one, and mostly it does make sense. If the information is lacking, doing what others are doing is probably the best bet. But this doesn’t work all the time.
In fact, herd behaviour is among the pre-eminent causes of stupidity.
It is not that intellect suddenly fails. But people are deprived of inner independence, so they give up autonomous positions under the overwhelming impact. We always feel that we are dealing with slogans, signs, buzzwords, and not with the real person. As if they are under the spell of someone or something.
As this happens, we are also creating (unknowingly) various risks to our socio-economic structure, civilization in general, and to some extent, for the human species.
Species-level risks are not evident yet; However, the other two, socio-economic and civilization level risks, are significant enough to be ignored.
So far, several significant building blocks have been developed and are in progress. When we stitch them together, AI’s capability will increase multifold, which should be a more significant concern for us.
It takes the already tiny amount of time we have to change our ways, and save the planet, and practically cuts it in half.
We have less than 27 years to get our collective act together and reshape how our entire civilisation operates. And I’m not sure if we can do that… The more concerning part is about the risks that we have not thought of yet. We may not be able to avoid all of them, but we can understand them to address them.
Our over-enthusiasm for new technologies has somehow colluded our quality expectations. So much so that we have almost stopped demanding the right quality solutions. We are so fond of this newness that we are ignoring flaws in new technologies.
The problem with these low-quality solutions is that subpar techs’ flaws do not surface until it is too late!
In many cases, the damage is already done and maybe be irreversible.
Misalignment between our goals and the machine’s goals could be dangerous. It is easier to correct a team of humans; doing that with a rampant machine could be a very tricky and arduous task.
Achieving a level of alignment with human-level common sense is quite tricky for a computerized system. Without having any balanced approach like a scorecard, this may not be achievable.
Technology is an answer to the “how” of the strategy, but without having the right “why” and “what” in place, it can do more damage than good. When AI systems do not know why, there will always be a lurking risk of discrimination, bias, or an illogical outcome.
Weapon systems equipped with AI are the most vulnerable to the right AI in wrong hand problems and therefore have the greatest risks. The Russian /Ukrain war is now the labourite of drone warfare. The possibility of AI systems being used to overpower others by some group or a country is a significant risk.
Overall, the right AI’s risk in the wrong hands is one of the critical challenges and warrants substantial attention to avoid it.
Extending AI and automation beyond logical limits could potentially alter our perception of what humans can do.
We still value human interaction, communication skills, emotional intelligence, and several other qualities in humans. What happens when an AI app takes over? What happened to AI doing mundane tasks and leaving time for us to do what we like and love?
The most important thing in artificial intelligence isn’t the fancy algorithms.
Let’s assume the worst case and we have a general purpose AI – that can do everything a human can.
What would happen?
Waiting for smartphone app to tell us what to do next and how we might be feeling now!
The enormous power carried by the grey matter in our heads may become blunt and eventually useless if we never exercise it, turning it into just some slush. The old saying, “use it or lose it,” is explicitly applicable in this case. Half knowledge is more dangerous than ignorance!
Trust me, a lot can happen in 24 hours. The lesson here is – in times like this, the first principles-based thinking is your best bet.
Our problem is that on one side, we have intelligent people, who are full of doubts, and on the other, we have stupid people full of confidence. Stupidity is not an intellectual failing, it’s a moral failing. And it happens because we believe only in feelings and not in facts or truthfulness
When we see and hear all this, we wonder if there is any antidote? If there is any way to stop this from happening?
The ultimate test of a moral society is the kind of world that it leaves to its children.
So the question now is, “How are we going to fight this AI pandemic?”
We will finally recognize that more computing power makes machines faster, not smarter.
If a problem is too difficult for a machine, it is we who will have to adapt to its limited abilities.
There is already a frustrating struggle for humans and machines to understand one another in natural language. Soon, we will live in a world where, regardless of your programming abilities, the main limitations are simply curiosity and imagination.
The Garland Test, inspired by dialog from the movie, is passed when a person feels that a machine has consciousness, even though they know it is a machine.
Will computers pass the Garland Test in 2023? I doubt it. But what I can predict is that claims like this will be made, resulting in yet more cycles of hype, confusion, and distraction from the many problems that even present-day AI is giving rise to.
This will force us to reconsider how our behaviours today might influence digital versions of ourselves set to outlive us.
Faced with this prospect of virtual immortality, 2023 will be the year we broaden our definition of what it means to live forever, a moral question that will fundamentally change how we live our day-to-day lives, but also what it means to be immortal stupid.
We tend to think we are the be all and end all—but we’re not. The sooner we can realize that the natural world goes its way, not our way, the better.” “I hope as a consequence that the needs and wonder and importance of the natural world are seen. We tend to think we are the be all and end all—but we’re not.
We’re both the victims and benefactors, and the sooner we can realize that the natural world goes its way, not our way, the better.” Sir David Attenborough.
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Limiting the damage requires rapid, radical change to the way the world works.
In this post I will lay out the true case for pessimism and the true case for (cautious) optimism.
“Is there hope?” is just a malformed question.
It mistakes the nature of the problem.
The atmosphere is steadily warming. Things are going to get worse for humanity the more it warms.
But there’s nothing magic about 2 degrees. It doesn’t mark a line between not-screwed and screwed.
We have some choice in how screwed we are, and that choice will remain open to us no matter how hot it gets.
Even if temperature rise exceeds 2 degrees, the basic structure of the challenge will remain the same.
It will still be warming. It will still get worse for humanity the more it warms. Two degrees will be bad, but three would be worse, four worse than that, and five worse still.
When temperatures reach 60c photosynthesis stops working and the need for sustainability becomes more urgent, not less. At that point, we will be flirting with non-trivial tail risks of species-threatening — or at least civilization-threatening — effects.
In sum:
Humanity faces the urgent imperative to reduce greenhouse gas emissions, then eliminate them, and then go “net carbon negative,” i.e., absorb and sequester more carbon from the atmosphere than it emits.
It will face that imperative for several generations to come, no matter what the temperature is.
What are the reasonable odds that the current international regime, the one that will likely be in charge for the next dozen crucial years, will reduce global carbon emissions enough to hit the 2 degree target?
Can we restrain and channel our collective development in a sustainable direction. NO
For any hope of hitting 2 degrees, global emissions must peak and begin rapidly falling within the next dozen years. And they must continue rapidly falling until humanity goes net carbon negative sometime around mid-century or shortly thereafter.
That means developed countries must go negative earlier, to allow for a slower and more difficult shift in developing countries.
Accomplishing that would require immediate, bold, sustained, coordinated action. And, well … look around. Look at how things are going. Look at who is running things. Look at the established economic regimes of the last half-century. Is this likely to happen, not on your nelly
As Enno Schröder and Servaas Storm of Delft University write in their blunt and unsettling recent paper, “the required degree and speed with which we have to decarbonize our economies and improve energy efficiency are quite difficult to imagine within the context of our present socioeconomic system.”
The dominant climate-economic models used to generate scenarios showing how to hit the 2 degree target produce a few key common outcomes.
One is that they require an extraordinary amount of energy efficiency. The bulk of the reduction in demand for fossil fuels through 2040 or so, in most successful 2 degree scenarios, is accomplished by reduction in overall energy demand. It is only around 2040 that displacement of fossil fuel energy by zero-carbon energy takes over as the dominant driver of fossil fuel reductions.
For centuries now, the growth of economies has been tightly coupled with rising energy demand and rising greenhouse gas emissions — a one-to-one correlation, more or less.
In recent years, however, several countries have seen their economies grow faster than their emissions.
The world’s current economies are not capable of the emission reductions required to limit temperature rise to 2 degrees. If world leaders insist on maintaining historical rates of economic growth, and there are no step-change advances in technology, hitting that target requires a rate of reduction in carbon intensity for which there is simply no precedent.
Despite all the recent hype about decoupling, there’s no historical evidence that current economies are decoupling at anything close to the rate required.
In fact, it’s worth noting that the vast majority of scenarios used by climate policymakers take continued economic growth as an unquestioned premise. And they also accept that historical technology improvement rates will hold in the future. The question they basically answer: “How much can we reduce emissions while continuing to grow our economies at historical rates, with technology developing at historical rates?”
Put simply, if we are determined to maintain the economic status quo, we cannot possibly mitigate climate change, so we must turn to adapting to it.
We have to come to terms with the impossibility of material, social, and political progress as a universal promise: life is going to be worse for most people in the 21st century in all these dimensions.
The political consequences of this are hard to predict.
The choice is radicalism today or disaster tomorrow, and from all signs, humanity is choosing the latter.
The fight to decarbonize and eventually go carbon negative will last beyond the lifetime of anyone reading this post. That is true no matter how high the temperature rises. The stakes will always be enormous; time will always be short; there will never be an excuse to stop fighting.
All of this needs collective action and a strong directional thrust which ‘markets’ or ‘private agents’ alone are unable to provide.
But rapid change is not just possible in technology. It is also possible in politics.
In both domains, there are “tipping points” after which change accelerates, rendering the once implausible inevitable.
We are rarely able to predict those tipping points.
Relying on them can seem like hoping for miracles. But our history is replete with miraculously rapid changes. They have happened; they can happen again. And the more we envision them, and work toward them, the more likely they become.
What other choice is there?
It will take close to half a million years before a ton of CO2 emitted today from burning fossil fuels is completely removed from the atmosphere naturally.
The world militaries contribution to green house gases ( and I am guessing ) alone is bigger than the economic out put of the whole of the African.
It has been 30 years since the Rio summit, when a global system was set up that would bring countries together on a regular basis to try to solve the climate crises.
The ink was hardly dry on the Glasgow pact when the world began to change in ways potentially disastrous for hopes of tackling the climate crisis. Energy and food price rises mean that governments face a cost of living and energy security crisis, with some threatening to respond by returning to fossil fuels, including coal.
Despite pledges made at climate summit the world is still nowhere near its goals on limiting global temperature rise. The next summit will be on different as no one wants to carry the financial can.
(In previous post I have suggested the establishment of a Perpetual green fund by placing 0.05% commission on all activities that are not sustainable.) This could spread the cost of tackling the climate crises Fairley.
We don’t have time to have unquestioned assumptions.
The real truth is that the earth in its billion of years of existence ( with our without us) has gone through many climate change disasters and survived.
We on the other had only need a further temperature rise to join a log list of extinction.
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≈ Comments Off on THE BEADY EYE SAY’S. Ten years from now, we may look back on this moment in history as a colossal mistake or it could be the greatest empowerment moment in human history.
This year, the world got a rude awakening to the insane power of AI when OpenAI unleashed ChatGPT4 onto the world. This AI text generator/chatbot seemed to be able to replicate human-generated content so well that even AI detection software struggled to tell the difference between the two.
This is not an alien invasion of intelligent machines; it’s the result of our own efforts to make our infrastructure and our way of life more intelligent.
It’s part of human endeavour. We merge with our machines. Ultimately, they will extend who we are.
Our mobile phone, for example, makes us more intelligent and able to communicate with each other. It’s really part of us already. It might not be literally connected to you, but nobody leaves home without one.
It’s like half your brain.
Thinking of AI as a futuristic tool that will lead to immeasurable good or harm is a distraction from the ways we are using it now.
How do we ensure that the AI we build, which might very well be significantly smarter than any person who has ever lived, is aligned with the interests of its creators and of the human race?
What if at some point in the near future, computer scientists build an AI that passes a threshold of superintelligence and can build other super intelligent AI.
An unaligned super intelligent AI could be quite a problem.
For example, we’ve been predicting for decades that AI will replace radiologists, but machine learning for radiology is still a complement for doctors rather than a replacement. Let’s hope this is a sign of AI’s relationship to the rest of humanity—that it will serve willingly as the ship’s first mate rather than play the part of the fateful iceberg.
No laws can prevent China ~ Russia ~ Terrorist network~ Rogue psychopath from developing the most manipulative and dishonest AI you could possibly imagine.
We can’t trust some speculative future technology to rescue us.
Climate change is already killing people, and many more people are going to die even in a best-case scenario, but we get to decide now just how bad it gets.
Action taken decades from now is much less valuable than action taken soon.
The first role AI can play in climate action is distilling raw data into useful information – taking big datasets, which would take too much time for a human to process, and pulling information out in real time to guide policy or private-sector action.
Everyone wants a silver bullet to solve climate change; unfortunately there isn’t one. But there are lots of ways AI can help fight climate change. While there is no single big thing that AI will do, there are many medium-sized things.
Most movies about AI have an “us versus them” mentality, but that’s really not the case.
Even if one were to stand on the side of curious skepticism, (which feels natural,) we ought to be fairly terrified by this nonzero chance of humanity inventing itself into extinction.
Whereas AI is, for now, pure software blooming inside computers. Someday soon, however, AI might read everything—like, literally every thing, swallowing everything into a black hole and not even god knows what it will be recycled.
Just shovel ever-larger amounts of human-created text into its maw, and wait for wondrous new skills to manifest. With enough data, this approach could perhaps even yield a more fluid intelligence, or a humanlike artificial mind akin to those that haunt nearly all of our mythologies of the future.
On the syllabus at the moment : Is a decent fraction of all the surviving text that we have ever produced.
To codify the philosophy in a set of wise laws and regulations to ensure the good behaviour of our super intelligent AI, like laws to make it illegal, for example, to develop AI systems that manipulate domestic or foreign actors. Is pie in the sky –
In the next decade, autocrats and terrorist networks could be able to cheaply build diabolical AI that can accomplish some of the goals outlined in the Yudkowsky story. (The key issue is not “human-competitive” intelligence (as his open letter puts it); It’s what happens after AI gets to smarter-than-human intelligence.
Key thresholds here may not be obvious.
We definitely can’t calculate in advance what happens when, and it currently seems imaginable that a research lab would cross critical lines without noticing.
AT THE MOMENT ALL WE HAVE IS A COPING MECCHANISM.
Like non-proliferation laws for nuclear weaponry that are hard to enforce.
Nuclear weapons require raw material that is scarce and needs expensive refinement. Software is easier, and this technology is improving by the month.
We have years to debate how education ought to change in response to these tools, but something interesting and important is undoubtedly happening.
If we figured out how people are going to share in the wealth that AI unlocks, then I think we could end up in a world where people don’t have to work to eat, and are instead taking on projects because they are meaningful to them.
But where do AI companies get this truly astonishing amount of high-quality data from?
Well, to put it bluntly, they steal it.
But as it stands, the AI boom might be approaching a flashpoint where these models can’t avoid consuming their own output, leading to a gradual decline in their effectiveness. This will only be accelerated as AI-generated content perfuses the internet over the coming years, making it harder and harder to source genuine human-made content.
AI is viewed as a strategic technology to lead us into the future.
So what should be done:
Many people lack a full understanding of AI and therefore are more likely to view it as a nebulous cloud instead of a powerful driving force that can create a lot of value for society;
Instead of writing off AI as too complicated for the average person to understand, we should seek to make AI accessible to everyone in society. It shouldn’t be just the scientists and engineers who understand it; through adequate education, communication and collaboration, people will understand the potential value that AI can create for the community.
We should democratize AI, meaning that the technology should belong to and benefit all of society; and we should be realistic about where we are in AI’s development.
Most of the achievements we have made are, in fact, based on having a huge amount of (labelled) data, rather than on AI’s ability to be intelligent on its own. Learning in a more natural way, including unsupervised or transfer learning, is still nascent and we are a long way from reaching AI supremacy.
From this point of view, society has only just started its long journey with AI and we are all pretty much starting from the same page. To achieve the next breakthroughs in AI, we need the global community to participate and engage in open collaboration and dialogue.
If this does not happen and happen (sooner than later) it will be AI that will be calling the shots
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At a time when the world is changing more quickly than ever before, we need a new vocabulary to help us grasp what’s happening.
I’m not sure that THE WORDS WE HAVE AT PRESENT TO DESCRIBE OUR WORLD hold anymore in the world-wide ‘web’ of meaning, we now inhabit (or are trapped in), with its exponentially increasing complexities.
Amid the whirlwind of our changing times, in which even the new language gurus cannot tell us where we’re going, there must be some universal value that can define us other than stupidity being digitalized.
Humanity is a blip in geologic history:
With social media words are just kind of disintegrate before your eyes or become a meaningless string of letters.
Like the word need which has become some kind of a fatigue sound, falling prey to semantic satiations, losing meaning for the listener, who then perceives the speech as repeated meaningless sounds.
Need is now repeated so much, that it is now as indistinct as the packages of generic Wal-Mart string cheese.
Take the language of politics, for example, it is becoming increasingly blurred.
Right and left, conservative and progressive, traditional and modern — these words have become so calcified that we often get lost in the labyrinth of ambiguity.
If words created the world, then words can also enrich or impoverish it, sanctify or demonize it.
Language is rich in words and meaning, but it can also become petrified while reality creatively evolves around it.
The power of words is such that they can spark a war or bring about peace. Everything begins with language.
So then, what does “artificial intelligence” actually mean (to use the latest buzzwords)?
Even the brainy scientists don’t really understand it. If so, what just happened to you is nothing new.
These days we have the capacity to look billions of years into the past but it seems that we can’t see beyond our own very noses, or hear, when it comes to the planet.
It used to be said that to name something is to begin understanding it but the veneer of linguistic facility of AI is not the same as actually comprehending human language.
AI has burst out of its academic bubble into the real world, and its lack of understanding of that world can have real and sometimes devastating consequences.
It might be possible to write down all the unwritten facts, rules and assumptions required for understanding text but not language. We let machines learn to understand language on their own, simply by ingesting vast amounts of written text and learning to predict words.
But has GPT-3 — trained on text from thousands of websites, books and encyclopaedia’s — transcended Watson’s veneer? Does it really understand the language it generates and ostensibly reasons about?
The crux of the problem, in my view, is that understanding language requires understanding the world, and a machine exposed only to language cannot gain such an understanding.
Humans rely on innate, pre-linguistic core knowledge of space, time and many other essential properties of the world in order to learn and understand language. If we want machines to similarly master human language, we will need to first endow them with the primordial principles humans are born with.
Machines that can genuinely comprehend what “it” refers to in a sentence, and everything else that understanding “it” entails.
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The world faces four main challenges: climate change, mistrust of leaders, increased geopolitical tension, and the dark side of the technological revolution. (Which is digitizing not just our imagination of our future’s by plundering the finite resources of the planet for profit.)
1) Climate change is the defining issue of our time,” It represents an “existential threat” to humankind. “The planet will not be destroyed. What will be destroyed is our capacity to live on the planet.
2) People believe that the fruits of globalization are not being fairly distributed. Seven in 10 people in the world live in countries where inequality is growing.
3) Increased geopolitical tensions are further exacerbated by weaknesses in institutions. For example, the UN Security Council’s “inability to take decisions” or to enforce the ones they do take, such as the arms embargo.
4) Artificial Intelligence that is owned by corporations are unbalancing the values that are common to us all. Turning Democracy into AI Totalitarianism Democratic Societies with mass surveillance.
Because in the age of the internet and super-connectivity, all of these things, like face recognition have been raised to sophisticated arts ( Clear View ) that, instead of being forced on us, have quietly colonised our lives.
In times past, when frustrating circumstances demanded new ways of expressing what it means to be alive here a few for present day use.
The internet/cyberspace is wonderful, because it gives people the freedom to augment or totally change their identities, and this is a marvellous new dawn for human expression, a new step in human evolution. Nah, it’s a false dawn, because the internet is essentially a libertarian arena, and as such an amoral one (lots of ‘freedoms’ but with no attendant social obligations); it is a new jungle where we must watch our backs and struggle for survival, surely a backward step in evolution.
The term ‘hyperobject’ was coined by the academic Timothy Morton, and it refers to phenomena that are so large and so far beyond the human frame of reference that they are not susceptible to reason but to AI.
Immigration. The realisation that racism never really went away, it just camouflaged its fundamental failure of empathy as tolerance – this is a contention of the Black Lives Matter movement. The term is now making the short jump to other second- (eg LGBT) and third- (eg feminism) phase civil rights movements equally lulled by the illusion of tolerance. The goal is to go beyond feeling tolerated to being fully accepted and welcomed.
3. Deletion. This word is likely to be bandied about much more frequently in the decades ahead, as social media users realise that the websites they are on are not merely neutral ‘platforms’ for ‘social interaction’ but more like a kind of flypaper to which people and all of their personal data stick. Moreover, these websites are specifically designed to be addictive –
4)Global capitalism is, by its unjust and shambolic nature, going to experience crashes of increasing severity throughout the 21st Century, leaving us all to survive with growing desperation amidst its wreckage.
5. Shadow banking. Nobody knows how large this sector is, but current estimates put shadow banking at (£124 trillion) and OTC transactions at (£412 trillion), or roughly twice and six-and-a-half times the GDP of the entire Earth, respectively. Both sectors were of course heavily involved in creating the 2008 crash, and both have remained almost unaltered since then.
6. Attention crisis. The fact that no one can take their eyes off their smartphones – James Williams writes that “the liberation of human attention may be the defining moral and political struggle of our time”. Our minds are being rewired for commercial purposes. His argument that the social contract, the idea of human rights, should be extended to cyberspace is gaining traction.
Was the creation of the internet not supposed to be the dawn of a technological and informational utopia? Even its father, Tim Berners-Lee, the inventor of the world wide web, is convinced it is failing us.
7. Post-human. It seems that history has caught up with us, for our identities now extend into cyberspace in many ways, we no longer merely rely on our brain cells but now store much of our knowledge in technological clouds that function as extensions of our minds, and we live with the corresponding hardware in such intimacy (in the form of portable devices that are linked to our minds and even metabolisms in many ways) that it sometimes feels like we are only a few steps away from being ‘cyborgs’ in the true sense of the term. Gender, though, is still a problem.
8. Masculinity. There was a time when you’d ask a man what masculinity was and his response would be something like ‘not feminine’ (pejorative) and ‘not queer’ (pejorative). Note all the negativity.
These days it is increasingly a good thing to be a woman (new, broad definition) and to be queer (new, broad definition). Both are eating away at the old territory occupied by masculinity, according to writers such as Hanna Rosin, Cordelia Fine or Grayson Perry. What’s left is something of a void, aka ‘the crisis of masculinity’.
The challenge ahead for men is to formulate what they are, and want to be, rather than what they aren’t. How to open up this frontier?
I have a suggestion. For generations feminists and queer activists have been fighting to draw attention to masculinity’s toxic side-effects. At long last, mainstream men seem on the verge of accepting that there is a problem. It remains for us all to take this a step further, and work to understand how this toxicity has also been poisoning men on the inside.
9. Generation Why? It applies to anyone born in the digital age.
To roughly clarify our terms here: Baby Boomers are the generation born after World War Two and before 1965; Generation X (Douglas Coupland) the cohort born between the mid-1960s and 1980; Generation Y (Millennials) includes people born between 1980-ish and 2000; Generation Z (Post-Millennials) is anyone born after 2000. These categories don’t really have global reach, but they are evocative as metaphors.
The gist of Smith’s argument is that Facebook and its like are reductive: they cut us down to size and reprogrammed us to suit their own ends, which are advertising and selling things – exploitation. “Five-hundred million sentient people entrapped in the recent careless thoughts of a Harvard sophomore,” she calls it.
Smith was writing a few years ago; the number of Facebook users has now passed 2 billion. Generations Y and Z have led lives saturated by the internet, by social media platforms and apps, which have claimed to make life complete and have all of the answers all of the time. Is this paraphernalia worthy of them? Are they content to be trapped in the reveries of Zuckerberg and the like? No. There are detectable tremors of disaffection and radicalisation. I suspect that as more and more post-millennials reach voting age, Generation Why may be giving us some loud answers.
10. The new weird An emerging genre of speculative, ‘post-human’ writing that blurs genre boundaries and conventions, pushes humanity and human-centred reason from the centre to the margins, and generally poses questions that may not be answerable in any terms we can understand (hence the ‘weird’). In the present era, where potent advertising and PR forces are doing everything in their power to make truth irrelevant and directly hack our minds, and where politicians no longer seem to acknowledge the existence of facts, the word has sinister new applications.
The COVID-19 pandemic is a tragic reminder of how deeply connected we are. There is a clear and urgent need for concrete multilateral solutions, based on common action across borders for the good of all humanity, starting with extend beyond national governments, to include more participation from local authorities, civil society, business leaders and others.
How close we are to destroying our world with dangerous technologies of our own making.
No one country can tackle the problem’s on their own no matter how large their population, how strong their economy or how feared their military.
Everyone sees change everywhere, and I think it’s important to figure out where are we going to be five to 10 years from now.
We’re going to see more automation. We’re going to see, unfortunately, more technological unemployment.
I don’t think they will be able to ignore the issue of inequality. We’re seeing social tensions and all sorts of frictions proliferate. The sooner we start tackling it, the better. We really need to start thinking outside of the box.
In the end it back to that word Need:
We need to be less wasteful. We need to economize our resources. We need to be more pro-environment in our own behaviour as consumers.
Let’s replace it with Yugen.
“We can either save our world or condemn humanity to a hellish future.”
All human comments appreciated. All like clicks and abuse chucked in the bin.
Artificial intelligence is already suffering from three key issues: privacy, bias and discrimination, which if left unchecked can start infringing on – and ultimately take control of – people’s lives.
As digital technology became integral to the capitalist market dystopia of the first decades of the 21st century, it not only refashioned our ways of communicating but of working and consuming, indeed ways of living.
Then along came the the Covid-19 pandemic which revealed not only the lack of investment, planning and preparation that underlay the scandalous slowness of the responses by states around the world, but also grotesque class and racial inequalities as it coursed its way through the population and the owners of high-tech corporations were enriched by tens of billions.
It’s already too late to get ahead of this generative AI freight train.
The growing use of AI has already transformed the way the global economy works.
In this backdrop, AI can be used to profile people like you and me to such a detail which may well become more than uncomfortable! And this is no exaggeration.
This is just a tip of the iceberg!
So what if anything can be done to ensure responsible and ethical practices in the field.
Concern over AI development has accelerated in recent months following the launch of OpenAI’s ChatGPT last year, which sparked the release of similar chatbots by other companies, including Google, Snap and TikTok. The growing realization that vast numbers of people can be fooled by the content chatbots gleefully spit out, now the clock is ticking to not just the collapse of values that enshrine human life but the very existence of the human race.
“This is not the future we want.”
Now there is no option but to put in place international laws, not mandatory regulations, before AI is infringing human rights. However as we are witnessing with climate change, to achieve any global cooperation is a bit of a problem.
From the climate crisis to our suicidal war on nature and the collapse of biodiversity, our global response is too little, too late. Technology is moving ahead without guard rails to protect us from its unforeseen consequences.
So we have two contrasting futures one of breakdown and perpetual crisis, and another in which there is a breakthrough, to a greener, safer future. This approach would herald a new era for multilateralism, in which countries work together to solve global problems.
In order to achieve these aims, the Secretary-General of the United nations recommends a Summit of the Future, which would “forge a new global consensus on what our future should look like, and how we can secure it”. The need for international co-operation beyond borders is something that makes a lot of sense, especially these days, because the role of the modern corporation in influencing the impact of AI is in conflict with the common values needed to survive.
The principle of working together, recognizing that we are bound to each other and that no community or country, however powerful, can solve its challenges alone.” Any national government is, of course, guided by its own set of localised values and realities.
But geopolitics, I would argue, always underlies any ambition. The immaturity of the ‘Geopolitics of AI’ field leaves the picture incomplete and unclear so it requires the introduction of agreed international common laws.
Let Ireland hold such a Summit.
This summit could coordinate efforts to bring about inclusive and sustainable policies that enable countries to offer basic services and social protection to their citizens with universal laws that defines the several capabilities of AI i.e. identify the ones that are more susceptible to misuse than the others.
(It is incredibly important for understanding the current environment in which any product is built or research conducted and it will be critical to forging a path forwards and towards safe and beneficial AI.)
The challenges are great, and the lessons of the past cannot be simply superimposed onto the present.
For example.
The designers of AI technologies should satisfy legal requirements for safety, accuracy and efficacy for well-defined use cases or indications. In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.
Another For example the collection of Data which is the backbone of AI.
Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.
It is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.
Laws to ensure that AI systems be designed to minimize their environmental consequences and increase energy efficiency.
If we want the elimination of black-box approach through mandatory explain ability for AI – Agreed or not agree should not be an option.
While AI can be extraordinarily useful it is already out of control with self learning algorithms that no one can understand or to be brought to account.
These profit seeking skewed algorithms owned by corporations are causing racial and gender-based discrimination.
I firmly believe that the Government must engage in meaningful dialogues with other countries on a common international laws that are now needed to subject developers to a rigorous evaluation process, and to ensure that entities using the technology act responsibly and are held accountable.
Having said that, governments must keep their roles limited and not assume absolute powers.
Multiple actors are jostling to lead the regulation of AI.
The question business leaders should be focused on at this moment, however, is not how or even when AI will be regulated, but by whom.
Governments have historically had trouble attracting the kind of technical expertise required even to define the kinds of new harms LLMs and other AI applications may cause.
Perhaps a licensing framework is needed to strike a balance between unlocking the potential of AI and addressing potential risks.
Or
AI ‘Nutrition Labels’ that would explain exactly what went into training an AI, and which would help us understand what a generative AI produces and why.
Or
Take the Meta’s open source approach which contrasts sharply with the more cautious, secretive inclinations of OpenAI and Google. With Open Source models like this and Stable Diffusion already out there, it may be impossible to get the Genie back into the bottle.
The metaverse is not well understood or appreciated by the media and the public. The metaverse is much, much bigger than one company, and weaving them together only complicates the matter.
Governments should never again face a choice between serving their people or servicing their debt.
Still, the most promising way not to provoke the sorcerer would be to avoid making too big a mess in the first place.
All human comments appreciated. All like clicks and abuse chucked in the bin
I am sure that unless you have being living on another planet it is becoming more and more obvious that the manner you live your life is being manipulate and influence by technologies.
So its worth pausing to ask why the use of AI for algorithm-informed decision is desirable, and hence worth our collective effort to think through and get right.
A huge amount of our lives – from what appears in our social media feeds to what route our sat-nav tells us to take – is influenced by algorithms. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms.
Artificial intelligence (AI) is naught but algorithms.
The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Algorithms are also at play, with most financial transactions today accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms.
Algorithms are aimed at optimizing everything.
Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.
Yes they can save lives, make things easier and conquer chaos, but when it comes both the commercial/ social world, there are many good reasons to question the use of Algorithms.
Why?
They can put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, while exploiting not just of you, but the very resources of our planet for short-term profits, destroying what left of democracy societies, turning warfare into face recognition, stimulating inequality, invading our private lives, determining our futures without any legal restrictions or transparency, or recourse.
The rapid evolution of AI and AI agents embedded in systems and devices in the Internet of Things will lead to hyper-stalking, influencing and shaping of voters, and hyper-personalized ads, and will create new ways to misrepresent reality and perpetuate falsehoods.
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As they are self learning, the problem is who or what is creating them, who owns these algorithms and what if there should be any controls in their usage.
Lets ask some questions that need to be ask now not later concerning them.
1) The outcomes the algorithm intended to make possible (and whether they are ethical)
2) The algorithm’s function.
3) The algorithm’s limitations and biases.
4) The actions that will be taken to mitigate the algorithm’s limitations and biases.
5) The layer of accountability and transparency that will be put in place around it.
There is no debate about the need for algorithms in scientific research – such as discovering new drugs to tackle new or old diseases/ pandemics, space travel, etc.
Out side of these needs the promise of AI is that we could have evidence-based decision making in the field:
Helping frontline workers make more informed decisions in the moments when it matters most, based on an intelligent analysis of what is known to work. If used thoughtfully and with care, algorithms could provide evidence-based policymaking, but they will fail to achieve much if poor decisions are taken at the front.
However, it’s all well and good for politicians and policymakers to use evidence at a macro level when designing a policy but the real effectiveness of each public sector organisation is now the sum total of thousands of little decisions made by algorithms each and every day.
First (to repeat a point made above), with new technologies we may need to set a higher bar initially in order to build confidence and test the real risks and benefits before we adopt a more relaxed approach. Put simply, we need time to see in what ways using AI is, in fact, the same or different to traditional decision making processes.
The second concerns accountability. For reasons that may not be entirely rational, we tend to prefer a human-made decision. The process that a person follows in their head may be flawed and biased, but we feel we have a point of accountability and recourse which does not exist (at least not automatically) with a machine.
The third is that some forms of algorithmic decision making could end up being truly game-changing in terms of the complexity of the decision making process. Just as some financial analysts eventually failed to understand the CDOs they had collectively created before 2008, it might be too hard to trace back how a given decision was reached when unlimited amounts of data contribute to its output.
The fourth is the potential scale at which decisions could be deployed. One of the chief benefits of technology is its ability to roll out solutions at massive scale. By the same trait it can also cause damage at scale.
In all of this it’s important to remember that while progress isn’t guaranteed transformational progress on a global scale normally takes time, generations even, to achieve but we pulled it off in less than a decade and spent another decade pushing the limits of what was possible with a computer and an Internet connection and, unfortunately, we are beginning running into limits pretty quickly such as.
No one wants to accept that the incredible technological ride we’ve enjoyed for the past half-century is coming to an end, but unless algorithms are found that can provide a shortcut around this rate of growth, we have to look beyond the classical computer if we are to maintain our current pace of technological progress.
A silicon computer chip is a physical material, so it is governed by the laws of physics, chemistry, and engineering.
After miniaturizing the transistor on an integrated circuit to a nanoscopic scale, transistors just can’t keep getting smaller every two years. With billions of electronic components etched into a solid, square wafer of silicon no more than 2 inches wide, you could count the number of atoms that make up the individual transistors.
So the era of classical computing is coming to an end, with scientists anticipating the arrival of quantum computing designing ambitious quantum algorithms that tackle maths greatest challenges an Algorithm for everything.
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Algorithms may be deployed without any human oversight leading to actions that could cause harm and which lack any accountability.
The issues the public sector deals with tend to be messy and complicated, requiring ethical judgements as well as quantitative assessments. Those decisions in turn can have significant impacts on individuals’ lives. We should therefore primarily be aiming for intelligent use of algorithm-informed decision making by humans.
If we are to have a ‘human in the loop’, it’s not ok for the public sector to become littered with algorithmic black boxes whose operations are essentially unknowable to those expected to use them.
As with all ‘smart’ new technologies, we need to ensure algorithmic decision making tools are not deployed in dumb processes, or create any expectation that we diminish the professionalism with which they are used.
Algorithms could help remove or reduce the impact of these flaws.
So where are we.
At the moment modern algorithms are some of the most important solutions to problems currently powering the world’s most widely used systems.
Here are a few. They form the foundation on which data structures and more advanced algorithms are built.
Google’s PageRank algorithm is a great place to start, since it helped turn Google into the internet giant it is today.
The PageRank algorithm so thoroughly established Google’s dominance as the only search engine that mattered that the word Google officially became a verb less than eight years after the company was founded. Even though PageRank is now only one of about 200 measures Google uses to rank a web page for a given query, this algorithm is still an essential driving force behind its search engine.
The Key Exchange Encryption algorithm does the seemingly impo
Backpropagation through a neural network is one of the most important algorithms invented in the last 50 years.
Neural networks operate by feeding input data into a network of nodes which have connections to the next layer of nodes, and different weights associated with these connections which determines whether to pass the information it receives through that connection to the next layer of nodes. When the information passed through the various so-called “hidden” layers of the network and comes to the output layer, these are usually different choices about what the neural network believes the input was. If it was fed an image of a dog, it might have the options dog, cat, mouse, and human infant. It will have a probability for each of these and the highest probability is chosen as the answer.
Without backpropagation, deep-learning neural networks wouldn’t work, and without these neural networks, we wouldn’t have the rapid advances in artificial intelligence that we’ve seen in the last decade.
Routing Protocol Algorithm (LSRPA) are the two most essential algorithms we use every day as they efficiently route data.
The two most widely used by the Internet, the Distance-Vector Routing Protocol Algorithm (DVRPA) and the Link-State traffic between the billions of connected networks that make up the Internet.
Compression is everywhere, and it is essential to the efficient transmission and storage of information.
Its made possible by establishing a single, shared mathematical secret between two parties, who don’t even know each other, and is used to encrypt the data as well as decrypt it, all over a public network and without anyone else being able to figure out the secret.
Searches and Sorts are a special form of algorithm in that there are many very different techniques used to sort a data set or to search for a specific value within one, and no single one is better than another all of the time. The quicksort algorithm might be better than the merge sort algorithm if memory is a factor, but if memory is not an issue, merge sort can sometimes be faster;
One of the most widely used algorithms in the world, but in that 20 minutes in 1959, Dijkstra enabled everything from GPS routing on our phones, to signal routing through telecommunication networks, and any number of time-sensitive logistics challenges like shipping a package across country. As a search algorithm, Dijkstra’s Shortest Path stands out more than the others just for the enormity of the technology that relies on it.
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At the moment there are relatively few instances where algorithms should be deployed without any human oversight or ability to intervene before the action resulting from the algorithm is initiated.
The assumptions on which an algorithm is based may be broadly correct, but in areas of any complexity (and which public sector contexts aren’t complex?) they will at best be incomplete.
Why?
Because the code of algorithms may be unviewable in systems that are proprietary or outsourced.
Even if viewable, the code may be essentially uncheckable if it’s highly complex; where the code continuously changes based on live data; or where the use of neural networks means that there is no single ‘point of decision making’ to view.
Virtually all algorithms contain some limitations and biases, based on the limitations and biases of the data on which they are trained.
Though there is currently much debate about the biases and limitations of artificial intelligence, there are well known biases and limitations in human reasoning, too. The entire field of behavioural science exists precisely because humans are not perfectly rational creatures but have predictable biases in their thinking.
Some are calling this the Age of Algorithms and predicting that the future of algorithms is tied to machine learning and deep learning that will get better and better at an ever-faster pace. There is something on the other side of the classical-post-classical divide, it’s likely to be far more massive than it looks from over here, and any prediction about what we’ll find once we pass through it is as good as anyone else’s.
It is entirely possible that before we see any of this, humanity will end up bombing itself into a new dark age that takes thousands of years to recover from.
The entire field of theoretical computer science is all about trying to find the most efficient algorithm for a given problem. The essential job of a theoretical computer scientist is to find efficient algorithms for problems and the most difficult of these problems aren’t just academic; they are at the very core of some of the most challenging real world scenarios that play out every day.
Quantum computing is a subject that a lot of people, myself included, have gotten wrong in the past and there are those who caution against putting too much faith in a quantum computer’s ability to free us from the computational dead end we’re stuck in.
The most critical of these is the problem of optimization:
How do we find the best solution to a problem when we have a seemingly infinite number of possible solutions?
While it can be fun to speculate about specific advances, what will ultimately matter much more than any one advance will be the synergies produced by these different advances working together.
Synergies are famously greater than the sum of their parts, but what does that mean when your parts are blockchain, 5G networks, quantum computers, and advanced artificial intelligence?
DNA computing, however, harnesses these amino acids’ ability to build and assemble itself into long strands of DNA.
It’s why we can say that quantum computing won’t just be transformative, humanity is genuinely approaching nothing short of a technological event horizon.
Quantum computers will only give you a single output, either a value or a resulting quantum state, so their utility solving problems with exponential or factorial time complexity will depend entirely on the algorithm used.
One inefficient algorithm could have kneecapped the Internet before it really got going.
It is now oblivious that there is no going back.
The question now is there anyway of curtailing their power.
This can now only be achieved with the creation of an open source platform where the users control their data rather than it being used and mined. (The uses can sell their data if the want.)
This platform must be owned by the public, and compete against the existing platforms like face book, twitter, what’s App, etc, protected by an algorithm that protects the common values of all our lives – the truth.
Of course it could be designed by using existing algorithms which would defeat its purpose.
It would be an open net-work of people a kind of planetary mind that has to always be funding biosphere-friendly activities.
A safe harbour perhaps called the New horizon. A digital United nations where the voices of cooperation could be heard.
So if by any chance there is a human genius designer out there that could make such a platform he might change the future of all our digitalized lives for the better.
All human comments appreciated. All like clicks and abuse chucked in the bin.
We all know that massive changes need to be made to the way we all live on the planet, due to climate change.
However most of us are not aware of the effects that artificial intelligence in having on our lives.
This post looks at our changing understanding of ourselves, due digitalized reasoning, which is turning us into digitalized
citizens, relying more on and more on digitalized reasoning for all aspects of living.
Does it help us understand what is going on? Or to work out what we can do about it?
It could be said that the climate is beyond our control, but AI remains within the realms of control.
Is this true?
It is true that the human race is in grave danger of stupidity re climate change which if not addressed globally could cause our extinction.
We know that using technology alone will not solve climate change, but it is necessary to gather information about what is happing to the planet, while our lives are monitored in minute detail by algorithms for profit.
There are many reasons why this is happing and the consequences of it will be far reaching and perhaps as dangerous if not more than what the climate is and will be bringing.
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While biology reasoning usually starts with an observation leading to a logical problem-solving with deductive conclusions
usually reliable, provided the premises are true.
Digital AI reasoning on the other hand is a cycle rather than any logically straight line.
It is the result of one go-round becomes feedback that improves the next round of question asking to ask machine
learning, with all programs and algorithms learning the result instantly.
Example One Drone to the next. One high-frequency trade to the next. One bank loan to the next. One human to the next.
Another words.
Digital Reasoning, is combining artificial intelligence and machine learning with all the biases program’s in the code in the first place without any supervision oversight, or global regulation
It combined volumes of data in real-time to remove the propose a hypothesis, to make a new hypothesis without conclusively prove that it’s correct. An iterative process of inductive reasoning extracts a likely (but not certain) premise from specific and limited observations. There is data, and then conclusions are drawn from the data; this is called inductive logic/ reasoning.
Inductive reasoning does not guarantee that the conclusion will be true.
In inductive inference, we go from the specific to the general. We make many observations, discern a pattern, make a generalization, and infer an explanation or a theory.
In other words, there is nothing that makes a guess ‘educated’ other than the learning program.
The differences between deductive reasoning and inductive reasoning.
Deductive reasoning is a top-down approach, while inductive reasoning is a bottom-up approach.
Inductive reasoning is used in a number of different ways, each serving a different purpose:
We use inductive reasoning in everyday life to build our understanding of the world.
Inductive reasoning, or inductive logic, is a type of reasoning that involves drawing a general conclusion from a set of specific observations. Some people think of inductive reasoning as “bottom-up” logic the one logic exercise we do nearly every day, though we’re scarcely aware of it. We take tiny things we’ve seen or read and draw general principles from them—an act known as inductive reasoning.
Inductive reasoning also underpins the scientific method: scientists gather data through observation and experiment, make hypotheses based on that data, and then test those theories further. That middle step—making hypotheses—is an inductive inference, and they wouldn’t get very far without it.
Inductive reasoning is also called a hypothesis-generating approach, because you start with specific observations and build toward a theory. It’s an exploratory method that’s often applied before deductive research.
Finally, despite the potential for weak conclusions, an inductive argument is also the main type of reasoning in academic life.
Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning, where you start with specific observations and form general conclusions.
Deductive reasoning is used to reach a logical and true conclusion. In deductive reasoning, you’ll often make an argument for a certain idea. You make an inference, or come to a conclusion, by applying different premises. Due to its reliance on inference, deductive reasoning is at high risk for research biases, particularly confirmation bias and other types of cognitive bias like belief bias.
In deductive reasoning, you start with general ideas and work toward specific conclusions through inferences. Based on theories, you form a hypothesis. Using empirical observations, you test that hypothesis using inferential statistics and form a conclusion.
In practice, most research projects involve both inductive and deductive methods.
However it can be tempting to seek out or prefer information that supports your inferences or ideas, with inbuilt bias creeping into research. Patients have a better chance of surviving, banks can ensure their employees are meeting the highest standards of conduct, and law enforcement can protect the most vulnerable citizens in our society.
However, there are important distinctions that separate these two pathways to a logical conclusion of what Digitized reasoning is going to do or replace human reasoning.
First there is no debate that Computers have done amazing calculations for us, but they have never solved a hard problem on their own.
The problem is the communication barrier between the language of humans and the language of computers.
A programmer can code in all the rules, or axioms, and then ask if a particular conjecture follows those rules. The computer then does all the work. Does it explain its work. No.
All that calculating happens within the machine, and to human eyes it would look like a long string of 0s and 1s. It’s impossible to scan the proof and follow the reasoning, because it looks like a pile of random data. “No human will ever look at that proof and be able to say, ‘I get it.’ They operate in a kind of black box and just spit out an answer.
Machine proofs may not be as mysterious as they appear. Maybe they should be made to explain.
I can see it becoming standard practice that if you want your paper/ codes/ algorithm to be accepted, you have to get it past an automatic checker – re transparency because efforts at the forefront of the field today aim to blend learning with reasoning.
After all, if the machines continue to improve, and they have access to vast amounts of data, they should become very good at doing the fun parts, too. “They will learn how to do their own prompts.”
company will enable customers to spot risks before they happen, maximize the scalability of supervision teams, and uncover strategic insights from large
The Limits of Reason.
Neural networks are able to develop an artificial style of intuition, leverage communications data to spot risks before they happen, and identify new insights to drive fresh growth initiatives, creating a large divide between firms investing to harvest data-driven insights and leverage data to manage risk, and those who are falling behind.
This will bear out in earnings and share prices in the years to come.
The challenge of automating reasoning in computer proofs as a subset of a much bigger field:
Natural language processing, which involves pattern recognition in the usage of words and sentences. (Pattern recognition is also the driving idea behind computer vision, the object of Szegedy’s previous project at Google.)
Like other groups, his team wants theorem provers that can find and explain useful proofs. He envisions a future in which theorem provers replace human referees at major journals.
Josef Urban thinks that the marriage of deductive and inductive reasoning required for proofs can be achieved through this kind of combined approach. His group has built theorem provers guided by machine learning tools, which allow computers to learn on their own through experience. Over the last few years, they’ve explored the use of neural networks — layers of computations that help machines process information through a rough approximation of our brain’s neuronal activity. In July, his group reported on new conjectures generated by a neural network trained on theorem-proving data.
Harris disagrees. He doesn’t think computer provers are necessary, or that they will inevitably “make human mathematicians obsolete.” If computer scientists are ever able to program a kind of synthetic intuition, he says, it still won’t rival that of humans.
“Even if computers understand, they don’t understand in a human way.”
I say the current Ukraine Russian war is the labourite of AI reasoning this war with all its consequence is telling us that AI should never be allowed near nuclear weapons or….dangerous pathogens.
An inductive argument is one that reasons in the opposite direction from deduction.
Given some specific cases, what can be inferred about the underlying general rule?
The reasoning process follows the same steps as in deduction.
The difference is the conclusions: an inductive argument is not a proof, but rather a probalistic inference.
When scholars use statistical evidence to test a hypothesis, they are using inductive logic.
The main objective of statistics is to test a hypothesis. A hypothesis is a falsifiable claim that requires verification.
Most progress in science, engineering, medicine, and technology is the result of hypothesis testing.
When a computer uses statistical evidence to test a hypothesis it’s assumption may or may not be true. To prove something is correct, we first need to take reciprocal of it and then try to prove that reciprocal is wrong which ultimately proves something is correct.
Finally this post has been written or generated by a human reasoning, that see the dangers of losing that reasoning to Digital reasoning of Enterprise Spock.
All human comments appreciated. All like clicks and abuse chucked in the bin.
Yes. Artificial Intelligence will most likely be needed to help us solve a lot of the big challenges facing society today, be that health, cures for diseases, climate change, etc. It is already predicting the shape of every protein in the human body.
However, in my mind it is deeply wrong that a small group of people ( under the skin of private technology enterprises) without any democratic oversight are making decisions with potentially to affect every life on earth.
Its time to take our blinkers off and let the world have a say in what they are doing.
Why?
Because a three-letter acronym ( God like AI) doesn’t capture the enormity of what Artificial General Intelligence (AIG) will represent, or do. This would be a force beyond our control or understanding and one that will usher in the obsolescence or destruction of the p
The Beady Eye has been bleating on about this and profit seeking algorithms, now for some considerable time, but from the number of comments on the subject it seems not many of us give a hoot for the need for transparencies, regulations, and overall safeties when it comes to technology. So we are running to the finishing line without any understanding of what on the other side.
Since the arrival of the internet/smart phone one only has to look at the state of the Planet to realise that we have gone training – AI ALGORITHMS / TECHNOLOGY TO GENERATE TEXTS/ RECONGISING EVEREYDAY IMAGES/ GENERATING REALISTIC PHOTOS/ AND REPLICATION OF VOICES, BY FEEDING THEM WITH THE ENTIRE INTERNET STRIP-MINING THE LIFEWORLD. (The focus on games and chatbots is sheltering people from the more serious implications of this work.)
The world already has many existential threats, but the threat posed by AIG is the number one risk of this century, with an engineered biological pathogen a close second. The potential for scams and misinformation is significant.
An God like super intelligent machine would be light out for all of us.
So the question is.
Why are these organisation racing to create God like AI ?
Is it that it gives an illusion of illimitable power.
For now the race is being driven by markets, with the Ukraine war the labourite of cyber wars, making private investment not the only driving force but nations also contributing to this contest.
If we put the wrong objects into a super intelligent machine we are bound to lose.
Unlike the human brain that grows large Ai systems are quite different.
They grow themselves with machine learning and their capabilities jump sharply.
We don’t yet full understand how they work and cannot demonstrate likely out comes in advance.
The present harms and AI/AIG are not mutually exclusive and overlap in important ways.
One of the most challenging aspects of thinking about this topic is working out which precedents we can draw on.
WE ARE NOT POWERLESS TO SLOW DOWN THIS RACE.
If we can get our governments to ask under oath about the timeline for developing God’s like AIG. To demand under law a complete record of the safety tests with evidence of understand how the system works, to ensure their aliment with our common values, we might save humanity, before we humans are cut out of the loop.
Unfortunately economics has not been flexible enough to take on this obvious truth The whole field and discipline of economics, by which we plan and justify what we do as a society, is simply riddled with absences, contradictions, logical flaws, and most important of all, false axioms and false gaols.
All human comments appreciated. All like clicks and abuse chucked in the bin.
Some things we can mitigate, some we can’t. Some things we can adapt to, some we cannot.
The question of how (or whether) we respond to climate change ultimately is a matter for policymakers to decide, but politics cannot (and should not) be separated from good science. And so on, and so on. We’ve heard them all.
As CLIMATE CHANGE impacts grow in frequency and severity, they will—and in many cases already have—create crises for people and nature around the world. If unchecked, these impacts will spread and worsen with more animal extinction and biodiversity loss, water shortages, and displaced communities.
Climate change is one of the most contentious issues facing society today.
Over the past several decades, we have seen not only increasing environmental degradation, but also the erosion of the concepts of the public good and collective responsibility to preserve nature.
In embracing the monetary valuation of nature as a strategy for mobilizing support for environmental conservation, environmentalists are resigning themselves to a political status quo that can only comprehend value in terms of money and markets.
By viewing ecosystems and their services through a pecuniary lens, monetization profoundly changes our relationship with nature, and, if taken to the point of commodification, can subject the fragility of nature’s balance to the destructive logic and volatility of markets.
Even though the trend toward the privatization of public goods has been pervasive over the past decades, we should not acquiesce so easily in allowing the privatization of the most basic public good of all—nature itself.
We must meet the grave environmental challenges of the twenty-first century with boldness and prudence, using the precautionary principle, along with the principles of fairness and democracy, to set boundaries that human action must not transgress.
Some argue that monetization, by revealing the economic contribution of nature and its services, can heighten public awareness and bolster conservation efforts. Others go beyond such broad conceptual calculations and seek to establish tradable prices for ecosystem services, claiming that markets can achieve what politics has not. Such an approach collapses nature’s complex functions into a set of commodities stripped from their social, cultural, and ecological context.
Although the path from valuation to commodification is not inevitable, it is indeed a slippery slope.
Do nature’s services need a monetary value?
Do conservation policy need an economic motive to get sufficient attention from policymakers and the public?
One approach seeks to monetize the value of nature simply in order to reveal its immense economic contribution to society.
Monetization is only meaningful and effective if there are markets to set prices for the ecosystem services in question. Markets for such commodified ecosystem services, they argue, can protect conservation policy from the vagaries of political will. Roll back bureaucratic red tape, and let the market work its magic to save nature.
The line between valuation and commodification, although clear in theory, becomes blurred in practice.
The monetization of any resource can cause long term problems for people.
To be sure, valuation alone does not inevitably entail the risks to the preservation of nature intrinsic to commodification. Nevertheless, it changes how we see and relate to nature and can inadvertently pave the way for the privatization of ecosystem services that the advocates of valuation often oppose.
Environmentalists, business leaders, and policymakers have all sought to make environmental protection an economic rather than just a political issue. The introduction of “no net loss” policies, which allow economic development to proceed as long as the net acreage of a specific type of ecosystem is maintained, has effected a paradigm shift in environmental policymaking. However, offsetting ignores how unique and interconnected biodiversity is, and it overlooks the importance of nature for local communities and the ways they suffer when their ecosystems are damaged. Land-use policies based on whether a company can pay for an offset, and not on what local communities and humanity need to survive, undermine basic rights and democratic principles
National economic accounts such as GDP remain blind to the services of nature. Such accounts likewise fail to distinguish between constructive and destructive economic activity with respect to human and ecological well-being. Needless to say, a deeper understanding and greater awareness of the relationship of society to nature is always welcome, but the rigor and usefulness of GDP-level information remains questionable.
Delineating an individual ecosystem from the complex fabric of nature poses numerous significant challenges. For example, the provision of oxygen for humans and animals to breathe is an ecosystem service of global scale.
But how do we value the contribution of individual sub-systems like a single forest to this global service?
We could all still breathe if one forest is cut down, but not if all forests were cut down.
Embarking upon the path of valuation also changes the way we see and understand nature.
The value of the whole ecosystem to society is more than the sum of its monetized parts:
Reducing its value to mere monetary terms, even if it were technically practical, strips away its cultural and spiritual value. A bad policy can be replaced, but the holistic functions of nature cannot.
Through disaggregation, each service can be rendered into a discrete monetizable “package” so that it can have its own market and its own price. Such an approach tilts policymaking in favour of the interests of the economically powerful. The least powerful actors—often local communities, indigenous peoples, women, small-scale farmers, etc.—get pushed to the margins, their voices ignored.
In order to prevent monetization from slipping into commodification, we must revisit one of the hallowed principles of environmental policy: the precautionary principle. It states that when an action or policy could pose a substantial risk to the environment, a very high burden of justification should fall on those seeking to take such an action. Like the classical mantra of medical ethics, the precautionary principle insists upon first doing no harm.
What if one of those billionaires manipulates the market by withholding or restricting the free flow of water?
71% of Earth’s surface is water.
There are 326 million trillion gallons of it on and in the planet. 96.5% of the water is ocean water, and just 3.5% is fresh water. Of that 3%, 69% of that water is locked up in glaciers. Another 30% of that freshwater is underground and usually requires costly extraction. That leaves 114 million billion gallons of readily accessible freshwater, not necessarily drinkable water, but water nonetheless. That sounds like enough, but it represents just 1% of the Earth’s water for every man, woman, child, and animal on the planet. That 1% of the water has to also serve every agricultural and industrial need on the planet. In most cases, it also needs to be filtered and treated before it is safely consumable.
So, though there is plenty of water on the planet, not very much of it is drinkable. Not very much of it is accessible, and the distribution methods are easily manipulated, legislated, and monetized. That’s never good for the common man. Nestle Water, for instance, extracted 36 million gallons of water from a national forest in California in 2015 to sell as bottled water, even as Californians were ordered to cut their water use because of a historic drought in the state.
Farmers, hedge funds, and municipalities alike can now hedge against — or bet on — future water.
While that may seem innocuous enough, the cost disparity probably gets passed on to the cities and individual consumers. It’s easy to imagine how many ways the monetization of water as a commodity is a dangerous first step in government and corporate overreach and intrusion. Mega-banks and investment firms such as Goldman Sachs, JP Morgan Chase, Citigroup, UBS, Deutsche Bank, Credit Suisse, Macquarie Bank, Barclays Bank, the Blackstone Group, Allianz, and HSBC Bank, among others, are consolidating their control over water.
Wealthy tycoons such as former President George H.W. Bush and his family, Hong Kong’s Li Ka-shing, Philippines’ Manuel V. Pangilinan, and others are also buying thousands of acres of land with aquifers, lakes, water rights, water utilities, and shares in water engineering and technology companies all over the world.
Complicit governments are legislating your rights to access and accumulate Earth’s free-flowing resources. It falls from the sky onto your property, but it is owned by someone else the minute it touches the Earth.
Water rights are conveyed as real property interests using the same formalities as real estate, but in most cases, everyone is tapped into the same source. If fracking, mining, or industrial operations pollute that source, they spoil it for everyone. So, merely having access to a water source is not enough.
The fact is that water is being restricted, legislated, and monetized more every year, and the rich are grabbing up the rights as fast as they can.
During periods of drought, when water levels are already low, it is easy to imagine how one person’s control over a large water area can lead to huge profits. This is why the super-wealthy are snapping up water, water contracts, water rights, and governments letting them do so all over the world. Two billion people now live in nations plagued by water problems, and almost two-thirds of the world could face water shortages in just four years. Even on a planet covered and steeped with water, water is a resource. As a resource, it can be monetized and controlled, and you could be denied or deprived of access to it.
Whatever you choose to call it, the most important thing is that we act to stop it.
If it is not the capitalization and exploitation of the resources of our planet with climate change will continue.
I can assure you, the super-wealthy are not buying up the water around the planet for altruistic purposes. They are doing so because they see a profit from it. Freshwater first then fresh air.
—–
CLIMATE CHANGE IS NOW A PRODUCT AND NET ZERO A SLOGAN.
There is now no stopping sea levels rising. A two meter rise would displace more than 2% of the world’s population and cut world food production/ supply by 25%. The rate of carbon emissions are the highest they’ve been in 66 million years and the amount of warming in the coming decades is expected to be 250 times greater than the average warming during the past century.
The rate of ocean acidification is the highest it has been in 300 million years!
Warming surface waters may be contributing to slowing ocean currents.
The warming climate is contributing to rising populations of insect / pests.
To mitigate the effects of climate change is going to cost quadrillions.
We are on course to match the worst extinction of earth species both on land and in the oceans.
We at a point where money will not suffice to make a difference.
There’s no consensus on global warming.
Many species are approaching—or have already reached—the limit of where they can go to find hospitable climates. In the polar regions, animals like polar bears that live on polar ice are now struggling to survive as that ice melts.
From straining agricultural systems to making regions less habitable, climate change is affecting people everywhere.
Climate change also exacerbates the threat of human-caused conflict resulting from a scarcity of resources like food and water that are less reliable as growing seasons change and seasons become less predictable. Around the globe, many of the poorest nations are being impacted first and most severely by climate change, even though they have contributed far less to the increase in carbon emissions that has caused the warming in the first place.
Higher temperatures are affecting the length of seasons and in some places, are already crossing safe levels for ecosystems and humans.
Now more than ever in order to enable a just transition to a low-carbon economy, gender and equality, human rights, and food security, with links to climate change we must use the power of the law to fight those who would harm our communities, our climate, and the natural world we value so deeply.
Recently, many countries have focused on mainstreaming net zero emissions targets: 138 states have now made a net zero pledge.
However, targets in all climate-related national laws and policies are currently far from the pledges made in NDCs (Simply put, an NDC, or Nationally Determined Contribution, is a climate action plan to cut emissions and adapt to climate impacts) and from enabling global warming to be limited to below 2 °C.
For NDCs to work, they need to be widely understood and used by businesses, civil society, academia and ordinary citizens. Each has roles to play, which is why many governments invite different constituencies to take part in defining NDC priorities.
For many reasons, including a lack of adequate finance, capacity and, in some cases, insufficient political commitment combined with the pandemic-related economic downturn is expected to constrain implementation.
For developing countries, moving forward depends on developed countries realizing their commitment to provide $100 billion in climate finance to developing countries. Dedicating half of this amount to adaptation, would help close significant financing shortfalls for vital measures to protect lives and livelihoods. Rapid policy developments are required to achieve this goal.
Climate change legislation is less a politically partisan issue than is commonly assumed:
Everyone is a climate actor and can be part of the change that needs to happen.
If we can slow or stop deforestation and manage natural land so that it is healthy, we could achieve up to one third of the emission reductions needed by 2030 to keep global temperatures from rising more than 2°C (3.6°C).
We must as a planet commit ourselves to reaching net zero carbon emissions by 2050.
The truth, however, is that even if we do successfully reach net zero carbon emissions by 2050, we will still have to address harmful climate impacts, and so the solution to climate change must also include measures to adapt to the impacts of global warming.
We need to increase renewable energy at least nine-fold from where it is today. This cannot be achieved without a major shift to renewable energy.
There is not a hard and fast deadline on climate action vs. inaction. There is no definitive line of demarcation that we can protect against; instead it is a matter of minimizing the effects of climate change.
We need to begin reducing carbon emissions RIGHT NOW to give our planet and our population the future that is least impacted.
The low carbon economy that we need to create will also give us cleaner air, better energy choices, new jobs and may even save us money. Likewise, many of the natural solutions that we need to adapt to even today’s climate change impacts benefit all of us: cleaner air and water, more natural recreation opportunities and jobs.
Nature, like climate, may be approaching irreversible tipping points where changes push systems into completely new states, even as more than half the global GDP depends on the planet’s natural systems.
For climate, the world has a clear net zero emissions goal.
But what’s the goal for nature? It hardly takes a genius to see things aren’t going well in the world or for our civilization.
When we actually look at the state of our civilization — in factual, empirical terms — the results are…well, you’ll be able to judge for yourself in just a moment.
Progress has flatlined and ground to a halt.
Living standards are declining in 90% of countries.
Each generation now does worse than the one before it,
Democracy’s in steep decline around the globe
The points above may in truth be small fry, compared to this one.
We are running out of our most basic, critical, fundamental resources.
People are more pessimistic now than at any point during the last century.
Anxiety, rage, anger, and despair are the defining sentiments of now — along with maybe the numbness of endlessly scrolling some algorithmically generated infotainment feed.
I could go on. But it’s hardly necessary. All the above are facts. They aren’t opinions, speculations, or even conclusions. They’re empirical truths about our civilization.
Each of the points above is its own crisis, and each one of them would be bad enough for any age, challenging, threatening, arduous enough.
But all of them, together, at once? That’s something new. They are painting a caricature without really thinking about the state of life as it is now.
All human comments appreciate. All like clicks and abuse chucked in the bin.
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