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Talking Digital Future: Artificial Intelligence

Talking Digital Future: Artificial Intelligence

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Quantum computing could potentially break much of the encryption algorithms and protocols that currently secure the internet and computational industry as they are.

I chose artificial intelligence as my next topic, as it can be considered as one of the most known technologies, and people imagine it when they talk about the future. But the right question would be: What is artificial intelligence?

Artificial intelligence is not something that just happened in 2015 and 2016. It’s been around for a hundred years as an idea, but as a science, we started seeing developments from the 1950s. So, this is quite an old tech topic already, but because of the kinds of technology that we have access to today — specifically, processing performance and storage — we’re starting to see significant leaps in AI development. 

When I started the course entitled, “Foundations of the Fourth Industrial Revolution (Industry 4.0),” I got deeper into the topic of artificial intelligence. One of the differences between the third industrial revolution — defined by the microchip and digitization — and the fourth industrial revolution is the scope, velocity and breakthroughs in medicine and biology, as well as widespread use of artificial intelligence across our society. Thus, AI is not only a product of Industry 4.0 but also an impetus as to why the fourth industrial revolution is currently happening and will continue to do so. I think there are two ways to understand AI: the first way is to try giving a quick definition of what it is, but the second is to also think about what it is not. 

Artificial intelligence as an imitation of humans

The definition that I’ve found useful in my own research is that AI is machines imitating humans. I believe this definition is important, because when many of us think about AI, we think of it as either being human or being better than human. I also think that’s not where we’re at today. 

There are different types of intelligence. One is artificial general intelligence, or superintelligence. The key here is in the word artificial, meaning an imitation. And it tries to replicate what humans do very well, but it can do it at an even better speed and at scale. 

So, if a human is given a 100-page book containing 100 photographs and is asked to circle every bicycle shown within, of course, the human will be able to do it and will do really well. Humans will probably do it with great accuracy, but they won’t be terribly quick at it. We’ll take the first picture, we’ll look at it, we’ll circle the bicycle, we’ll move onto the next picture, and so on until the 100th photograph. Humans barely have to think about it. It’s such a natural thing for us. But if you give this task to a computer today, it will do it in seconds, as it can imitate that human work remarkably faster. The speed really cannot be understated here. If you can combine the speed of processing with scale, you can start to augment what humans do, and find clever ways to do human processes, which many people have already been exposed to in the last few years. One of these is online recommendations. In Amazon’s early days, when you bought a book, you would be recommended some other books that you might like based on your preferences: People who bought this book also liked these books, so you might like them as well. This was a very clever usage of AI technology: Being able to look for patterns and make conclusions based on the conditions that humans set, and then serve them very quickly. 

Suddenly, this technological application became remarkably beneficial. Another example is getting from point A to point B. Today, this is a product, again, of fast processing and storage — thus, artificial intelligence. If you’re sitting in traffic while using a GPS device on your smartphone, and there’s a better way to get home to avoid traffic, that technology is going to evaluate many different permutations. You could do it as a human, but you can’t do it as fast and you certainly wouldn’t be able to consider all the tradeoffs between various routes. Whereas computers are very good at processing at this volume and scale.

Some useful applications for artificial intelligence

So, as a domain, artificial intelligence is going to continue to show up in many aspects of our lives. The fact that we’re all getting connected now means that you don’t have to have scale processing on your device, as you are connected to a query that is initiated through some activity being shot to the cloud. Like this, you have massive amounts of processing. In fact, we think that ultimately, quantum computing could manifest through cloud services. It’s very unlikely that we’re going to have a quantum chip or quantum computer on a smartphone. I don’t say never, but I just don’t think it will happen. What we’ll have is cloud-based quantum processing for artificial intelligence. 

This topic is especially cool in the healthcare domain. Think about how medicine works today. Medical practitioners go to school for many, many years, memorize a lot of information, then treat patients, get experience, and over the span of their career, become quite good at what they do. However, they are ultimately subject to the weaknesses of their own mortal existence. They can forget things; they can be absent-minded or, you know, just not connect the dots sometimes. Now, if we can equip a doctor physician with a computer to improve memory, options and optimization, the tools and the ability to provide medical aid suddenly change.

Let’s look at IBM’s AI initiative Watson combined with an oncologist treating a cancer patient, for example. Each patient is different, so the doctor wants to have as many details as possible about this type of cancer and the patient’s medical history to make the best treatment plan. An AI-augmented device produced for the doctor’s team could generate a scenario based on the data of every patient that has had this particular set of circumstances and that person’s characteristics. The patient could be a white male, 75 years old, living in a certain country and is of a certain genetic background. Suddenly, augmentation at an AI-level of scale and speed is changing the game.

So, my view is that we’re going to head into a world where there’s a lot more of that. Primary philosophical concerns for AI that will become very important in the future are, Can we ultimately displace the human so that these activities are just done by computers? Is there a role forward for humans in this AI future? The truth is, we’re finding that humans don’t really have value in a whole range of industries. So, how big and how massive would this displacement be?

Artificial intelligence for multiplanetary colonization 

Think of Elon Musk’s dream to create a colony on Mars. The problem is that people going there now would have a six-month journey, they would have to take all their possessions, food, seeds, medicine and so on, and after that, they would have to start healing Mars and creating a livable environment. Maybe this could serve as a great opportunity to realize that sending artificial intelligence first would be better and more quickly prepare new colonies for human beings. I think we’ll probably send robots first. Although, there is talk right now that sending humans is helpful for getting government funding, as it turns out to be a human achievement. It’s all about that.

When we were doing the Apollo missions to send humans to the moon in the 1960s, at first, we sent robots and rockets and we landed them. But in 1969, when we landed a human, when Neil Armstrong walked on the moon, it changed everything. It was a completely different experience.

Of course, we will attempt a multiplanetary existence if we want to extend the survival of humanity. For going beyond the Alpha Centauri and other solar systems, though, we might just send AI. Even at amazing speed, traveling these distances takes a long time — thousands of years. Eventually, one of these crafts might be fortunate enough to be intercepted by other intelligent life whose first exposure to humans on Earth will be robots and computers. They won’t actually meet a person, a biological being like you and I. 

So, this potential human future might actually be computers floating in the universe. It’s a more practical way for us to extend this idea. Otherwise, we would have to have multiple generations of humans born, raised and living on a spaceship.

Challenges for the development of artificial intelligence

The other possible course for AI development, which Elon Musk has also spoken about, is that maybe we have to separate human consciousness from the physical body. So your consciousness is basically uploaded to a computer and you can theoretically live forever, in a computer traveling through space. It boggles the mind. Really, it’s mind-expanding, because we don’t think in those terms at all. This is not how we think of the human experience — or existence at all — as we still have very old beliefs and religions about what it is to be human, what life is, the soul, and so on in our societies. Technology now is disrupting not only these beliefs, but also all our stereotypes, our understandings and our thoughts, which we as a species have had a long history with. 

Anyway, let’s continue to see how AI is embedded in everything we do. If you have a technological device, it’s probably going to have some form of AI, which will be powered by cloud services that continue to evolve and continue to groove. So, anything we do as humans that requires access to large amounts of data, expert systems, expert device optimization, etc., are invariably going to be AI.

Going back to an earlier point, the example of an AI’s performance on a human process outpacing that of a human may allude to artificial intelligence being intelligent or smart, but that assumption would not be correct today. AIs really don’t have any “smartness,” they are simply performing artificial mimicry — a parlor trick that makes them look smart. It simply has access to a lot of data, or big data, and uses some mathematics.

There is a type of learning called trial-and-error learning, in which one “successively tries various responses in a situation, seemingly at random, until one is successful in achieving the goal.” There was once a game on Google where users could spend time identifying pictures. A picture would show up on-screen, users playing against each other would be given four options as to what the picture depicted, and then the users would select the word closest to the picture. The whole idea was basically training the computer to associate difficult photos with English language terms. What machine learning effectively does as a subset of AI is using historical improvements in its database to make better suggestions and predictions going forward. 

This idea of predicting what happens is a sort of snowball: You can build new knowledge upon what you have already learned, which was also initially built upon what you had already learned. To add to this, the ability to predict gets much more precise over time, increasing our confidence and ambition with AI.

Artificial intelligence: Use cases

Today, apart from the takeoff and landing, a computer controls just about the entire flight of a commercial plane. We have a lot of confidence (or at least, the pilots do) that the plane can react to different circumstances and even know when to hand certain circumstances over to the pilots to contemplate. I say this with a little bit of ignorance, as a computer controlling takeoff and landing may already be in use. Today’s computers have enough processing power and information to make all the decisions necessary for flying a plane entirely. 

So, I think about the positive sides of where we are. There’s a whole set of things that can just get better. I think a lot about healthcare, and I believe AI is something that will enhance our ability to make big leaps, big innovations and major breakthroughs in healthcare, which has plateaued for many decades and is long-due for a transformation. 

We will certainly see improvements in production. We’ve got this notion of digital twins now: Making an artificial digital version or a copy of the real physical thing while putting sensors on the physical object. Let’s say again: A factory machine that produces a product and the digital version will entirely replicate what’s going on in the physical world. With the help of sensors and artificial intelligence, the machine can do things like predictive maintenance and even calibrate in real time. With this, we’ll be starting to get much more sophisticated, higher-quality tools of production. 

I think we’ll see better use of energy, things like energy distribution and optimization, where AI applications could dramatically change the industry for good. 

Another topic that I think is very important to humanity is our understanding of the weather and climate, which has a couple of major challenges on some remarkably complex topics that we’re still trying to figure out, particularly the climate. The computers that we have, which are based on classical computing systems, are not quite fast enough to even use the algorithms they’re provided to make predictions on what will happen. So, we’re going to need even faster processing. This is where, in my view, things get interesting — when we combine AI with quantum computing. The convergence of clever algorithms imitating humans driven by remarkable processing power can potentially start to solve some climate scenarios that are currently taking weeks and months to process on classical computers. 

There’s a whole lot of other interesting areas that are emerging. One of the areas that has remarkable potential but is yet to be realized is augmented virtual or mixed realities. These provide us with a kind of heads-up display for a whole range of things in work, life and in general, as well as the spheres of gaming and global collaboration. Again, AI is going to help with better rendering and better simulation. That isn’t just a fleeting topic, by the way, it means an AI plus hardware, faster processing, cloud functionality, connectivity, even visualization tools and the screens all need improvements. 

Artificial intelligence and other technologies

I think that brings me to my final point on this, which is: We shouldn’t think of AI alone. I think this is a mistake that many researchers and commentators often make when talking about the amazing benefits of AI, when we’re rather talking about software here. Combining AI with other technologies and industries is no less amazing.

So, I’d rather think about AI plus quantum, plus big data, plus blockchain, plus the supply chain, plus pharmaceuticals, plus healthcare. These are interesting prospects to think about, so convergence to me is really where the power of AI lies.

Think about the convergence of AI with blockchain, and how those technologies can be used together for the development of security, immutability, transparency and decentralization. I think of AI as an amplifier and blockchain as a powerful tool, and there is no doubt that the former will be involved in the set of technologies producing leading-edge solutions. No matter what, if blockchain is doing identity management or supply chain, or it is a very smart repository management system, AI baked into that architecture could make it better, faster and more precise. I can definitely see the intersections there.

One of the intersections of AI is with the Internet of Things. Let’s imagine a sensor that is part of the IoT: A complex, local sensor in a busy intersection that probably has a camera and perhaps another kind of environmental sensor embedded. Everything captured on the sensor in this IoT network is immediately sent to the cloud and processed, and maybe some action is sent back. 

The latency issues involved, though, don’t make this massively practical today. You can either have a sensor that does something simple, or you bring the computer-processing to the sensor itself, developing what’s called edge computing. A certain level of AI takes place on the device so that only the really meaningful data is sent back over the cloud to be processed, and then some action is taken. 

Not restricted to intersections, AI in the area of IoT sensors could be implemented in aircrafts, warehouses and cities, enabling very fast computation, filtering and decision-making at the edge. This creates a much faster and more efficient overall ecosystem.

Artificial intelligence and money

Another good question is, Does artificial intelligence need money? Let’s imagine a colony on Mars occupied by artificial intelligence alone. Would robots think that they still need to be paid? I think money is a very human thing. It has a very special role in the behavior of humans as well as strong relationships with reward, incentives and scarcity. These are all very human things.

What is needed for robots and AI is energy. We have to make the assumption that we’re talking about artificial intelligence here, and AI will work forever — as long as power is going to it. It doesn’t care for anything beyond that. It lacks an emotional component. 

This may change when we get to artificial superintelligence or artificial general intelligence, where we may see and call AI a type of consciousness, but let’s stick with AI, which is what we know today. To put it simply, the notion of bartering and incentives doesn’t seem to play a role; I would say what is most important is an energy source.

If an AI has an amazing solar power grid on the moon with really efficient batteries, it’s going to work for hundreds of years — assuming it can be maintained without such issues.

However, there is a twist to this. Imagine for a moment that we’re already at the point of everyone riding in self-driving cars and every car is equal on the road. No vehicle gets priority over another in terms of lanes or intersections. Basically, the main priority is safety, so when self-driving vehicles come to an intersection where they’re going to collide, they have an interaction between each other to avoid the collision, but there’s no prioritization other than rules of the road. 

Now imagine you get into one of these, and it’s an Uber of the future. The Uber has two fees: $10 for the trip and an optional $15. The difference is, if you pay that $15, your ride will get preference over lower-paying passengers. So let’s say you’ve paid an additional $15 and you get to this intersection — all other cars slow down. What happens is your car pays a premium to the other cars to slow down, giving yours the preferential route. So, these artificially intelligent cars — or, I’ve heard somebody call them “mobots” (mobile robots) — are going to start trading with each other, as they’re all going to have different pricing and a lot of different preferences. As they interact and have machine-to-machine communications, they will be sending cash or cryptocurrency.

It’s likely that, between the devices, preferential or less preferential treatment can be given or leased. Although it’s a little bit of a stretch in terms of the example above, robots paying for other robots to behave in certain ways based on different outcomes could become a reality. Ultimately, the question of risk in this example begins with: Where does the money go? Money trickles down to humans. Eventually, there will be shareholders or some organization incentivized to profit from this behavior.

Humans are basically the only creatures who create things symbolic of value, which then define our interactions.

Artificial intelligence and modern society

My next idea enters the paradigm of the post-truth society we live in. I worry a lot about the era we’re in right now. I like to think that in a few years, we’ll look back at this time and agree that it was a dark period.

Things started to get dark quickly in the years leading up to World War II in Europe. It didn’t even take decades. In the time that Adolf Hitler became known, moved through the ranks and became the chancellor, only three years passed. World War II raged for just six years, but in that brief window of time, Europe was destroyed almost beyond recognition and several millions of people were killed. Yet somehow out of that, we had the 1950s and the 1960s — periods of great optimism and change, liberalism and democracy, with a general positive hope for the future. At the time, anyone might have said that 1941 was the end or that there was no future for humanity, and yet here we are in the 21st century with a remarkably different world. 

These periods of darkness in the last few hundred years are just periods of massive uncertainty and instability. Yet, things have to get sorted out. The world wars happened because of empires, and then the empires collapsed and were rejigged. Finally, countries started to set borders in the 1950s and 1960s, and things like the United Nations and European Union began to emerge. 

Right now, the uncertainty is because suddenly, billions of people who never had a voice suddenly have one. Billions of people are connected and millions of them are moving to the middle class. Everybody wants a smartphone, a laptop, a car and a television, and more people want to meet. We are going through an incredible societal change while a dark cloud of climate change looms on the horizon. 

If you look at the data, though — education, rights for women, rights for minorities, the rate of diseases, wars — everything is much better. We pay attention to the darkness, but we forget the bigger picture is actually quite positive.

I do think there are some very unstable, unpredictable things happening right now, a lot of which is generated by our massive use of networks and collaboration of technologies. This is one of the manifestations of our post-truth world, and I think my comments here might be more personal than generalized. 

I think honesty and truth are essential not only to what it means to be human, but to creating stability in the world. So I am very concerned as both a citizen and as a human being that people who have power are not telling the truth, which encourages corporations and other entities to create untruths. 

One specific example of this is deepfakes — videos powered by artificial intelligence, processing and visual technology showing people saying or doing things they never said or did. Soon, we won’t be able to differentiate between what is fake and what is reality. I think we’ll even see a movie in a few years that features actors that are no longer alive, perhaps with Marlon Brando and Marilyn Monroe — and they will seem completely and utterly real, we won’t not be able to tell with our eyes that it’s all fake.

Artificial intelligence is a powerful tool that can improve our lives greatly, and it is already doing this. At the same time, AI could have a nefarious impact on our society. How we will use this powerful technology is only up to us, humans.

This is part two of a multi-part series on digital future and technological innovations, read part one about quantum computing here.

This article is from an interview held by Kristina Lucrezia Cornèr with Dr. Jonathan Reichental. It has been condensed and edited.

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Dr. Jonathan Reichental is the CEO of Human Future, a global business and technology education, advisory and investment firm. He is the former chief information officer for the City of Palo Alto, and is a multiple-award-winning technology leader whose 30-year career has spanned both the public and private sectors.

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“I Can’t Even Save”: Americans Are Getting Absolutely Crushed Under Enormous Debt Load

"I Can’t Even Save": Americans Are Getting Absolutely Crushed Under Enormous Debt Load

While Joe Biden insists that Americans are doing great…

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"I Can't Even Save": Americans Are Getting Absolutely Crushed Under Enormous Debt Load

While Joe Biden insists that Americans are doing great - suggesting in his State of the Union Address last week that "our economy is the envy of the world," Americans are being absolutely crushed by inflation (which the Biden admin blames on 'shrinkflation' and 'corporate greed'), and of course - crippling debt.

The signs are obvious. Last week we noted that banks' charge-offs are accelerating, and are now above pre-pandemic levels.

...and leading this increase are credit card loans - with delinquencies that haven't been this high since Q3 2011.

On top of that, while credit cards and nonfarm, nonresidential commercial real estate loans drove the quarterly increase in the noncurrent rate, residential mortgages drove the quarterly increase in the share of loans 30-89 days past due.

And while Biden and crew can spin all they want, an average of polls from RealClear Politics shows that just 40% of people approve of Biden's handling of the economy.

Crushed

On Friday, Bloomberg dug deeper into the effects of Biden's "envious" economy on Americans - specifically, how massive debt loads (credit cards and auto loans especially) are absolutely crushing people.

Two years after the Federal Reserve began hiking interest rates to tame prices, delinquency rates on credit cards and auto loans are the highest in more than a decade. For the first time on record, interest payments on those and other non-mortgage debts are as big a financial burden for US households as mortgage interest payments.

According to the report, this presents a difficult reality for millions of consumers who drive the US economy - "The era of high borrowing costs — however necessary to slow price increases — has a sting of its own that many families may feel for years to come, especially the ones that haven’t locked in cheap home loans."

The Fed, meanwhile, doesn't appear poised to cut rates until later this year.

According to a February paper from IMF and Harvard, the recent high cost of borrowing - something which isn't reflected in inflation figures, is at the heart of lackluster consumer sentiment despite inflation having moderated and a job market which has recovered (thanks to job gains almost entirely enjoyed by immigrants).

In short, the debt burden has made life under President Biden a constant struggle throughout America.

"I’m making the most money I've ever made, and I’m still living paycheck to paycheck," 40-year-old Denver resident Nikki Cimino told Bloomberg. Cimino is carrying a monthly mortgage of $1,650, and has $4,000 in credit card debt following a 2020 divorce.

Nikki CiminoPhotographer: Rachel Woolf/Bloomberg

"There's this wild disconnect between what people are experiencing and what economists are experiencing."

What's more, according to Wells Fargo, families have taken on debt at a comparatively fast rate - no doubt to sustain the same lifestyle as low rates and pandemic-era stimmies provided. In fact, it only took four years for households to set a record new debt level after paying down borrowings in 2021 when interest rates were near zero. 

Meanwhile, that increased debt load is exacerbated by credit card interest rates that have climbed to a record 22%, according to the Fed.

[P]art of the reason some Americans were able to take on a substantial load of non-mortgage debt is because they’d locked in home loans at ultra-low rates, leaving room on their balance sheets for other types of borrowing. The effective rate of interest on US mortgage debt was just 3.8% at the end of last year.

Yet the loans and interest payments can be a significant strain that shapes families’ spending choices. -Bloomberg

And of course, the highest-interest debt (credit cards) is hurting lower-income households the most, as tends to be the case.

The lowest earners also understandably had the biggest increase in credit card delinquencies.

"Many consumers are levered to the hilt — maxed out on debt and barely keeping their heads above water," Allan Schweitzer, a portfolio manager at credit-focused investment firm Beach Point Capital Management told Bloomberg. "They can dog paddle, if you will, but any uptick in unemployment or worsening of the economy could drive a pretty significant spike in defaults."

"We had more money when Trump was president," said Denise Nierzwicki, 69. She and her 72-year-old husband Paul have around $20,000 in debt spread across multiple cards - all of which have interest rates above 20%.

Denise and Paul Nierzwicki blame Biden for what they see as a gloomy economy and plan to vote for the Republican candidate in November.
Photographer: Jon Cherry/Bloomberg

During the pandemic, Denise lost her job and a business deal for a bar they owned in their hometown of Lexington, Kentucky. While they applied for Social Security to ease the pain, Denise is now working 50 hours a week at a restaurant. Despite this, they're barely scraping enough money together to service their debt.

The couple blames Biden for what they see as a gloomy economy and plans to vote for the Republican candidate in November. Denise routinely voted for Democrats up until about 2010, when she grew dissatisfied with Barack Obama’s economic stances, she said. Now, she supports Donald Trump because he lowered taxes and because of his policies on immigration. -Bloomberg

Meanwhile there's student loans - which are not able to be discharged in bankruptcy.

"I can't even save, I don't have a savings account," said 29-year-old in Columbus, Ohio resident Brittany Walling - who has around $80,000 in federal student loans, $20,000 in private debt from her undergraduate and graduate degrees, and $6,000 in credit card debt she accumulated over a six-month stretch in 2022 while she was unemployed.

"I just know that a lot of people are struggling, and things need to change," she told the outlet.

The only silver lining of note, according to Bloomberg, is that broad wage gains resulting in large paychecks has made it easier for people to throw money at credit card bills.

Yet, according to Wells Fargo economist Shannon Grein, "As rates rose in 2023, we avoided a slowdown due to spending that was very much tied to easy access to credit ... Now, credit has become harder to come by and more expensive."

According to Grein, the change has posed "a significant headwind to consumption."

Then there's the election

"Maybe the Fed is done hiking, but as long as rates stay on hold, you still have a passive tightening effect flowing down to the consumer and being exerted on the economy," she continued. "Those household dynamics are going to be a factor in the election this year."

Meanwhile, swing-state voters in a February Bloomberg/Morning Consult poll said they trust Trump more than Biden on interest rates and personal debt.

Reverberations

These 'headwinds' have M3 Partners' Moshin Meghji concerned.

"Any tightening there immediately hits the top line of companies," he said, noting that for heavily indebted companies that took on debt during years of easy borrowing, "there's no easy fix."

Tyler Durden Fri, 03/15/2024 - 18:00

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Sylvester researchers, collaborators call for greater investment in bereavement care

MIAMI, FLORIDA (March 15, 2024) – The public health toll from bereavement is well-documented in the medical literature, with bereaved persons at greater…

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MIAMI, FLORIDA (March 15, 2024) – The public health toll from bereavement is well-documented in the medical literature, with bereaved persons at greater risk for many adverse outcomes, including mental health challenges, decreased quality of life, health care neglect, cancer, heart disease, suicide, and death. Now, in a paper published in The Lancet Public Health, researchers sound a clarion call for greater investment, at both the community and institutional level, in establishing support for grief-related suffering.

Credit: Photo courtesy of Memorial Sloan Kettering Comprehensive Cancer Center

MIAMI, FLORIDA (March 15, 2024) – The public health toll from bereavement is well-documented in the medical literature, with bereaved persons at greater risk for many adverse outcomes, including mental health challenges, decreased quality of life, health care neglect, cancer, heart disease, suicide, and death. Now, in a paper published in The Lancet Public Health, researchers sound a clarion call for greater investment, at both the community and institutional level, in establishing support for grief-related suffering.

The authors emphasized that increased mortality worldwide caused by the COVID-19 pandemic, suicide, drug overdose, homicide, armed conflict, and terrorism have accelerated the urgency for national- and global-level frameworks to strengthen the provision of sustainable and accessible bereavement care. Unfortunately, current national and global investment in bereavement support services is woefully inadequate to address this growing public health crisis, said researchers with Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine and collaborating organizations.  

They proposed a model for transitional care that involves firmly establishing bereavement support services within healthcare organizations to ensure continuity of family-centered care while bolstering community-based support through development of “compassionate communities” and a grief-informed workforce. The model highlights the responsibility of the health system to build bridges to the community that can help grievers feel held as they transition.   

The Center for the Advancement of Bereavement Care at Sylvester is advocating for precisely this model of transitional care. Wendy G. Lichtenthal, PhD, FT, FAPOS, who is Founding Director of the new Center and associate professor of public health sciences at the Miller School, noted, “We need a paradigm shift in how healthcare professionals, institutions, and systems view bereavement care. Sylvester is leading the way by investing in the establishment of this Center, which is the first to focus on bringing the transitional bereavement care model to life.”

What further distinguishes the Center is its roots in bereavement science, advancing care approaches that are both grounded in research and community-engaged.  

The authors focused on palliative care, which strives to provide a holistic approach to minimize suffering for seriously ill patients and their families, as one area where improvements are critically needed. They referenced groundbreaking reports of the Lancet Commissions on the value of global access to palliative care and pain relief that highlighted the “undeniable need for improved bereavement care delivery infrastructure.” One of those reports acknowledged that bereavement has been overlooked and called for reprioritizing social determinants of death, dying, and grief.

“Palliative care should culminate with bereavement care, both in theory and in practice,” explained Lichtenthal, who is the article’s corresponding author. “Yet, bereavement care often is under-resourced and beset with access inequities.”

Transitional bereavement care model

So, how do health systems and communities prioritize bereavement services to ensure that no bereaved individual goes without needed support? The transitional bereavement care model offers a roadmap.

“We must reposition bereavement care from an afterthought to a public health priority. Transitional bereavement care is necessary to bridge the gap in offerings between healthcare organizations and community-based bereavement services,” Lichtenthal said. “Our model calls for health systems to shore up the quality and availability of their offerings, but also recognizes that resources for bereavement care within a given healthcare institution are finite, emphasizing the need to help build communities’ capacity to support grievers.”

Key to the model, she added, is the bolstering of community-based support through development of “compassionate communities” and “upskilling” of professional services to assist those with more substantial bereavement-support needs.

The model contains these pillars:

  • Preventive bereavement care –healthcare teams engage in bereavement-conscious practices, and compassionate communities are mindful of the emotional and practical needs of dying patients’ families.
  • Ownership of bereavement care – institutions provide bereavement education for staff, risk screenings for families, outreach and counseling or grief support. Communities establish bereavement centers and “champions” to provide bereavement care at workplaces, schools, places of worship or care facilities.
  • Resource allocation for bereavement care – dedicated personnel offer universal outreach, and bereaved stakeholders provide input to identify community barriers and needed resources.
  • Upskilling of support providers – Bereavement education is integrated into training programs for health professionals, and institutions offer dedicated grief specialists. Communities have trained, accessible bereavement specialists who provide support and are educated in how to best support bereaved individuals, increasing their grief literacy.
  • Evidence-based care – bereavement care is evidence-based and features effective grief assessments, interventions, and training programs. Compassionate communities remain mindful of bereavement care needs.

Lichtenthal said the new Center will strive to materialize these pillars and aims to serve as a global model for other health organizations. She hopes the paper’s recommendations “will cultivate a bereavement-conscious and grief-informed workforce as well as grief-literate, compassionate communities and health systems that prioritize bereavement as a vital part of ethical healthcare.”

“This paper is calling for healthcare institutions to respond to their duty to care for the family beyond patients’ deaths. By investing in the creation of the Center for the Advancement of Bereavement Care, Sylvester is answering this call,” Lichtenthal said.

Follow @SylvesterCancer on X for the latest news on Sylvester’s research and care.

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Article Title: Investing in bereavement care as a public health priority

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Copper Soars, Iron Ore Tumbles As Goldman Says “Copper’s Time Is Now”

Copper Soars, Iron Ore Tumbles As Goldman Says "Copper’s Time Is Now"

After languishing for the past two years in a tight range despite recurring…

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Copper Soars, Iron Ore Tumbles As Goldman Says "Copper's Time Is Now"

After languishing for the past two years in a tight range despite recurring speculation about declining global supply, copper has finally broken out, surging to the highest price in the past year, just shy of $9,000 a ton as supply cuts hit the market; At the same time the price of the world's "other" most important mined commodity has diverged, as iron ore has tumbled amid growing demand headwinds out of China's comatose housing sector where not even ghost cities are being built any more.

Copper surged almost 5% this week, ending a months-long spell of inertia, as investors focused on risks to supply at various global mines and smelters. As Bloomberg adds, traders also warmed to the idea that the worst of a global downturn is in the past, particularly for metals like copper that are increasingly used in electric vehicles and renewables.

Yet the commodity crash of recent years is hardly over, as signs of the headwinds in traditional industrial sectors are still all too obvious in the iron ore market, where futures fell below $100 a ton for the first time in seven months on Friday as investors bet that China’s years-long property crisis will run through 2024, keeping a lid on demand.

Indeed, while the mood surrounding copper has turned almost euphoric, sentiment on iron ore has soured since the conclusion of the latest National People’s Congress in Beijing, where the CCP set a 5% goal for economic growth, but offered few new measures that would boost infrastructure or other construction-intensive sectors.

As a result, the main steelmaking ingredient has shed more than 30% since early January as hopes of a meaningful revival in construction activity faded. Loss-making steel mills are buying less ore, and stockpiles are piling up at Chinese ports. The latest drop will embolden those who believe that the effects of President Xi Jinping’s property crackdown still have significant room to run, and that last year’s rally in iron ore may have been a false dawn.

Meanwhile, as Bloomberg notes, on Friday there were fresh signs that weakness in China’s industrial economy is hitting the copper market too, with stockpiles tracked by the Shanghai Futures Exchange surging to the highest level since the early days of the pandemic. The hope is that headwinds in traditional industrial areas will be offset by an ongoing surge in usage in electric vehicles and renewables.

And while industrial conditions in Europe and the US also look soft, there’s growing optimism about copper usage in India, where rising investment has helped fuel blowout growth rates of more than 8% — making it the fastest-growing major economy.

In any case, with the demand side of the equation still questionable, the main catalyst behind copper’s powerful rally is an unexpected tightening in global mine supplies, driven mainly by last year’s closure of a giant mine in Panama (discussed here), but there are also growing worries about output in Zambia, which is facing an El Niño-induced power crisis.

On Wednesday, copper prices jumped on huge volumes after smelters in China held a crisis meeting on how to cope with a sharp drop in processing fees following disruptions to supplies of mined ore. The group stopped short of coordinated production cuts, but pledged to re-arrange maintenance work, reduce runs and delay the startup of new projects. In the coming weeks investors will be watching Shanghai exchange inventories closely to gauge both the strength of demand and the extent of any capacity curtailments.

“The increase in SHFE stockpiles has been bigger than we’d anticipated, but we expect to see them coming down over the next few weeks,” Colin Hamilton, managing director for commodities research at BMO Capital Markets, said by phone. “If the pace of the inventory builds doesn’t start to slow, investors will start to question whether smelters are actually cutting and whether the impact of weak construction activity is starting to weigh more heavily on the market.”

* * *

Few have been as happy with the recent surge in copper prices as Goldman's commodity team, where copper has long been a preferred trade (even if it may have cost the former team head Jeff Currie his job due to his unbridled enthusiasm for copper in the past two years which saw many hedge fund clients suffer major losses).

As Goldman's Nicholas Snowdon writes in a note titled "Copper's time is now" (available to pro subscribers in the usual place)...

... there has been a "turn in the industrial cycle." Specifically according to the Goldman analyst, after a prolonged downturn, "incremental evidence now points to a bottoming out in the industrial cycle, with the global manufacturing PMI in expansion for the first time since September 2022." As a result, Goldman now expects copper to rise to $10,000/t by year-end and then $12,000/t by end of Q1-25.’

Here are the details:

Previous inflexions in global manufacturing cycles have been associated with subsequent sustained industrial metals upside, with copper and aluminium rising on average 25% and 9% over the next 12 months. Whilst seasonal surpluses have so far limited a tightening alignment at a micro level, we expect deficit inflexions to play out from quarter end, particularly for metals with severe supply binds. Supplemented by the influence of anticipated Fed easing ahead in a non-recessionary growth setting, another historically positive performance factor for metals, this should support further upside ahead with copper the headline act in this regard.

Goldman then turns to what it calls China's "green policy put":

Much of the recent focus on the “Two Sessions” event centred on the lack of significant broad stimulus, and in particular the limited property support. In our view it would be wrong – just as in 2022 and 2023 – to assume that this will result in weak onshore metals demand. Beijing’s emphasis on rapid growth in the metals intensive green economy, as an offset to property declines, continues to act as a policy put for green metals demand. After last year’s strong trends, evidence year-to-date is again supportive with aluminium and copper apparent demand rising 17% and 12% y/y respectively. Moreover, the potential for a ‘cash for clunkers’ initiative could provide meaningful right tail risk to that healthy demand base case. Yet there are also clear metal losers in this divergent policy setting, with ongoing pressure on property related steel demand generating recent sharp iron ore downside.

Meanwhile, Snowdon believes that the driver behind Goldman's long-running bullish view on copper - a global supply shock - continues:

Copper’s supply shock progresses. The metal with most significant upside potential is copper, in our view. The supply shock which began with aggressive concentrate destocking and then sharp mine supply downgrades last year, has now advanced to an increasing bind on metal production, as reflected in this week's China smelter supply rationing signal. With continued positive momentum in China's copper demand, a healthy refined import trend should generate a substantial ex-China refined deficit this year. With LME stocks having halved from Q4 peak, China’s imminent seasonal demand inflection should accelerate a path into extreme tightness by H2. Structural supply underinvestment, best reflected in peak mine supply we expect next year, implies that demand destruction will need to be the persistent solver on scarcity, an effect requiring substantially higher pricing than current, in our view. In this context, we maintain our view that the copper price will surge into next year (GSe 2025 $15,000/t average), expecting copper to rise to $10,000/t by year-end and then $12,000/t by end of Q1-25’

Another reason why Goldman is doubling down on its bullish copper outlook: gold.

The sharp rally in gold price since the beginning of March has ended the period of consolidation that had been present since late December. Whilst the initial catalyst for the break higher came from a (gold) supportive turn in US data and real rates, the move has been significantly amplified by short term systematic buying, which suggests less sticky upside. In this context, we expect gold to consolidate for now, with our economists near term view on rates and the dollar suggesting limited near-term catalysts for further upside momentum. Yet, a substantive retracement lower will also likely be limited by resilience in physical buying channels. Nonetheless, in the midterm we continue to hold a constructive view on gold underpinned by persistent strength in EM demand as well as eventual Fed easing, which should crucially reactivate the largely for now dormant ETF buying channel. In this context, we increase our average gold price forecast for 2024 from $2,090/toz to $2,180/toz, targeting a move to $2,300/toz by year-end.

Much more in the full Goldman note available to pro subs.

Tyler Durden Fri, 03/15/2024 - 14:25

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