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The Rise of Image Recognition AI in Medical Diagnostics

The Rise of Image Recognition AI in Medical Diagnostics

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Medical Imaging Diagnostics

Deep learning has revolutionized image recognition and analysis, making unprecedented performance leaps between 2010-2014. These rapid advancements enabled the development of automated, accurate, accessible, and cost-effective medical diagnostics.  Over 60 entities including 40 new firms globally have set out to capitalize on these technological advances, seeking to commercialize AI-based diagnostics services in fields such as cancer and cardiovascular disease (CVD). IDTechEx forecasts the market for AI-enabled image-based medical diagnostics to exceed $3 billion by 2030.

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In this article, IDTechEx examines the future market for image recognition AI in medical diagnostics. The article considers the progress thus far and assesses how each segment of the market is likely to evolve. Next, it considers the competitive landscape, examining investment patterns by disease area, company readiness levels by application, and the trends in focus areas. Finally, it provides an outlook about the future of this market.

This article draws from the brand new IDTechEx report, “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts”. It develops detailed quantitative benchmarks and examines the real status of technological capabilities today. It provides realistic roadmaps, showing what challenges the technology faces and how it is likely to evolve. Furthermore, it analyses the competitive landscape, identifying and reviewing more than 60 companies and leading research institutes. The strengths of the companies’ technology are benchmarked, as well as their product positioning, value proposition, and more.

Finally, it estimates and forecasts the total addressable market per disease type in scan volume. It provides realistic market penetration and cost evolution projections per disease type, as well as segmented market forecasts in scanned volume and market value. The details of IDTechEx’s findings can be found here www.IDTechEx.com/IRAI.

The Rise of Image Recognition AI in Medical Diagnostics

The use of image visualization and limited recognition software in medical diagnostics started over 20 years ago. This technology had however nearly reached its performance limits when deep learning (DL) and convolutional neural networks (CNNs) were developed, heralding a step-change in the capability and performance of machine vision.

This progress demonstrates that image recognition AI technology can match or even exceed human-level performance (in terms of accuracy, sensitivity, and specificity) in many disease areas and on many imaging modalities. The technical threshold for the automation of these diagnostic tasks has already been reached, laying the groundwork for commercial growth in the short and long term.

This is shown in the market projections below. Here, the bars represent estimates and growth forecasts from IDTechEx for total scan volumes per disease type. This is essentially the addressable market for AI. Note that accelerated growth in scan volumes can be expected when the AI is widely available and when the imaging equipment itself is low cost – e.g., RGB camera vs CT or MRI scanner.

The continuous line shows market penetration forecasts. The chart shows the average value weighted across all disease categories. In its report, IDTechEx has developed a different penetration curve for each segment, reflecting the state of technological readiness, clinical testing stage, the added value over existing methods, and so on.

Note that AI algorithms are already deployed with notable volumes. Nonetheless, an inflection point – as an average across all categories- is expected to occur around 2023-2024. All in all, AI usage in medical image diagnostics is anticipated to grow by nearly 10,000% until 2040 whilst the global addressable market (scan volumes regardless of processing method) will grow by 50%.

For a detailed segment-by-segment analysis, please visit www.IDTechEx.com/IRAI.

Medical Imaging Diagnostics

Source: IDTechEx Research - “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts

Company Landscape Segmentation: Target Markets and Geography

Given the high growth potential of image recognition AI, it is no surprise that competition in this market is heating up. The pie charts below show how the competitive landscape currently segments in terms of focus area and geography.

Medical Imaging Diagnostics

Source: IDTechEx Research - “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts

The most active and crowded market for image recognition AI, in terms of the number of players, is cancer. The value-added – be it in accelerating triage or in improving diagnostic - is very high, as early and accurate detection is vital to maximizing chances of survival.

Within this category, breast, lung, and skin cancer have been the main focus. Here, the high prevalence and relatively simple and fast image acquisition equipment creates high scan volumes, which in turn results in large demand.

These spaces are becoming increasingly crowded. Companies must move well beyond just meeting the minimum technical milestones to compete in these markets. Many have already passed clinical trials for specific algorithms. This is thus a prerequisite, but not a point of differentiation.

Respiratory diseases and CVD each make up about a fifth of the company landscape.  Interestingly, COVID-19 provided a boost for firms active in respiratory diseases. This is because COVID-19 detection is largely based on algorithms built for respiratory disease detection. Companies in this sector were quick to adapt their existing software to detect signs of COVID-19 as it provided them publicity at a time when overwhelmed hospitals were desperate for any kind of support.

In terms of geography, the USA is undoubtedly emerging as the central hub of medical image recognition AI technology. The annual scan volume in the USA per 1000 population is 3-5 times higher that of other countries. This, and the generally higher prices, make this an attractive market.

More details are available in the brand new IDTechEx report, “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts”.

Money Is Flowing in as Over $2.2 Billion Have Been Invested to Date

Interest in image recognition AI technology in medical diagnostics has soared in the last decade and this is reflected by the level of investments it has generated. Combined investments since 2017 are over 200% higher than the total since the start of the decade. IDTechEx found that total funding exceeds $2.2 billion. This figure mainly accounts for start-ups and medium-sized companies and excludes key players such as Google and Microsoft, for which the funding for AI radiology projects is excluded.

Medical Imaging Diagnostics

Source: IDTechEx Research - “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts

Differentiation, Scale, and the Fatal Perils of Being a Me-Too

Each company, or each product of a company, sits at a different stage of readiness. The chart below provides a snapshot for commercialization stage in the cancer detection segment (note: those most advanced do not necessarily have the best or most wide-ranging technology).

Although multiple firms are already selling, this alone does not guarantee success. Companies are trying one or multiple of the following approaches to succeed:

  • Towards wider applicability: The days of leaps in performance of image recognition are over, barring radical innovation in algorithm techniques. The gains in precision, recall and other metrics will henceforth be incremental. As such, the emphasis has shifted to other points. Of importance is showing that the AI is applicable to as wide a population set as possible.

This improves outcomes and widens market applicability. Whilst this requires no technological breakthrough, it is difficult to implement from execution, cost, and business model points of view.  One needs to place a robust data acquisition and learning loop via partnerships and customer relationships at the heart of the product development.  When successful, this will serve to erect moats around the business, acting as a large barrier to entry for newcomers. This will also serve as a filter separating out those who can evolve from a good tech story towards a viable business and those stuck at the tech demonstration level.

  • Evolving beyond simple abnormality identification towards super-human insights: Whilst there is a spread in what different algorithms are offering, most are positioned as decision support tools. As a minimum, they need to detect (recognize + localize) the anomaly of interest. In some cases, they offer detailed pixel-level segmentation. The evolution is now to provide further information and explanations alongside object detection and instant segmentation. Some are even aiming to suggest treatment options, although this is generally further down the line. In short, the goal is to raise the AI complexity beyond object detection.

Furthermore, the algorithms today offer what humans do, but may do so faster and/or better, thus unleashing the automation wave. In the future, with more digitization of patient data, more data fusion can be expected, perhaps enabling AI to offer insights beyond human capability. This could be a game-changer.

  • Scale: The IDTechEx view is that scale will matter in this business. This is because large scale, if done right, (a) means more access to data, which translates into an ever-widening performance gap against competitors in terms of algorithm accuracy, versatility, and applicability; (b) creates a one-stop-shop proposition, helping with the sales and customer acquisition process; (c) results in larger technical teams that can aid the on-site into-work-flow integration process, which in turn boosts installed base and acts a lock-in mechanism. In general, scale can help the winners drive consolidation.

Note that the elephant in the room here are the big software firms (Google and Microsoft). They have a big program on these topics but are yet, for various reasons, to take the plunge into this competitive landscape.

  • Aggressive pricing: The pricing model right now is based on either a tiered subscription model or a pay per use. Few are also proposing a one-off fee. However, the amortized development costs and the installation fees resemble more like a fixed cost, whilst the supplier’s computing costs vary with the scanning volume so some type of per-use model will prevail. In general, the trend will be towards ever more competitive pricing.  This is likely to be leveraged as a tool by well-funded organizations to force out smaller poorly capitalized competitors.

MID

Source: IDTechEx Research - “AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts

 

The post The Rise of Image Recognition AI in Medical Diagnostics appeared first on ValueWalk.

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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.

# # #

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

DOI: 10.1016/S2468-2667(24)00030-6

Authors: The complete list of authors is included in the paper.

Funding: The authors received funding from the National Cancer Institute (P30 CA240139 Nimer) and P30 CA008748 Vickers).

Disclosures: The authors declared no competing interests.

# # #


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Separating Information From Disinformation: Threats From The AI Revolution

Separating Information From Disinformation: Threats From The AI Revolution

Authored by Per Bylund via The Mises Institute,

Artificial intelligence…

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Separating Information From Disinformation: Threats From The AI Revolution

Authored by Per Bylund via The Mises Institute,

Artificial intelligence (AI) cannot distinguish fact from fiction. It also isn’t creative or can create novel content but repeats, repackages, and reformulates what has already been said (but perhaps in new ways).

I am sure someone will disagree with the latter, perhaps pointing to the fact that AI can clearly generate, for example, new songs and lyrics. I agree with this, but it misses the point. AI produces a “new” song lyric only by drawing from the data of previous song lyrics and then uses that information (the inductively uncovered patterns in it) to generate what to us appears to be a new song (and may very well be one). However, there is no artistry in it, no creativity. It’s only a structural rehashing of what exists.

Of course, we can debate to what extent humans can think truly novel thoughts and whether human learning may be based solely or primarily on mimicry. However, even if we would—for the sake of argument—agree that all we know and do is mere reproduction, humans have limited capacity to remember exactly and will make errors. We also fill in gaps with what subjectively (not objectively) makes sense to us (Rorschach test, anyone?). Even in this very limited scenario, which I disagree with, humans generate novelty beyond what AI is able to do.

Both the inability to distinguish fact from fiction and the inductive tether to existent data patterns are problems that can be alleviated programmatically—but are open for manipulation.

Manipulation and Propaganda

When Google launched its Gemini AI in February, it immediately became clear that the AI had a woke agenda. Among other things, the AI pushed woke diversity ideals into every conceivable response and, among other things, refused to show images of white people (including when asked to produce images of the Founding Fathers).

Tech guru and Silicon Valley investor Marc Andreessen summarized it on X (formerly Twitter): “I know it’s hard to believe, but Big Tech AI generates the output it does because it is precisely executing the specific ideological, radical, biased agenda of its creators. The apparently bizarre output is 100% intended. It is working as designed.”

There is indeed a design to these AIs beyond the basic categorization and generation engines. The responses are not perfectly inductive or generative. In part, this is necessary in order to make the AI useful: filters and rules are applied to make sure that the responses that the AI generates are appropriate, fit with user expectations, and are accurate and respectful. Given the legal situation, creators of AI must also make sure that the AI does not, for example, violate intellectual property laws or engage in hate speech. AI is also designed (directed) so that it does not go haywire or offend its users (remember Tay?).

However, because such filters are applied and the “behavior” of the AI is already directed, it is easy to take it a little further. After all, when is a response too offensive versus offensive but within the limits of allowable discourse? It is a fine and difficult line that must be specified programmatically.

It also opens the possibility for steering the generated responses beyond mere quality assurance. With filters already in place, it is easy to make the AI make statements of a specific type or that nudges the user in a certain direction (in terms of selected facts, interpretations, and worldviews). It can also be used to give the AI an agenda, as Andreessen suggests, such as making it relentlessly woke.

Thus, AI can be used as an effective propaganda tool, which both the corporations creating them and the governments and agencies regulating them have recognized.

Misinformation and Error

States have long refused to admit that they benefit from and use propaganda to steer and control their subjects. This is in part because they want to maintain a veneer of legitimacy as democratic governments that govern based on (rather than shape) people’s opinions. Propaganda has a bad ring to it; it’s a means of control.

However, the state’s enemies—both domestic and foreign—are said to understand the power of propaganda and do not hesitate to use it to cause chaos in our otherwise untainted democratic society. The government must save us from such manipulation, they claim. Of course, rarely does it stop at mere defense. We saw this clearly during the covid pandemic, in which the government together with social media companies in effect outlawed expressing opinions that were not the official line (see Murthy v. Missouri).

AI is just as easy to manipulate for propaganda purposes as social media algorithms but with the added bonus that it isn’t only people’s opinions and that users tend to trust that what the AI reports is true. As we saw in the previous article on the AI revolution, this is not a valid assumption, but it is nevertheless a widely held view.

If the AI then can be instructed to not comment on certain things that the creators (or regulators) do not want people to see or learn, then it is effectively “memory holed.” This type of “unwanted” information will not spread as people will not be exposed to it—such as showing only diverse representations of the Founding Fathers (as Google’s Gemini) or presenting, for example, only Keynesian macroeconomic truths to make it appear like there is no other perspective. People don’t know what they don’t know.

Of course, nothing is to say that what is presented to the user is true. In fact, the AI itself cannot distinguish fact from truth but only generates responses according to direction and only based on whatever the AI has been fed. This leaves plenty of scope for the misrepresentation of the truth and can make the world believe outright lies. AI, therefore, can easily be used to impose control, whether it is upon a state, the subjects under its rule, or even a foreign power.

The Real Threat of AI

What, then, is the real threat of AI? As we saw in the first article, large language models will not (cannot) evolve into artificial general intelligence as there is nothing about inductive sifting through large troves of (humanly) created information that will give rise to consciousness. To be frank, we haven’t even figured out what consciousness is, so to think that we will create it (or that it will somehow emerge from algorithms discovering statistical language correlations in existing texts) is quite hyperbolic. Artificial general intelligence is still hypothetical.

As we saw in the second article, there is also no economic threat from AI. It will not make humans economically superfluous and cause mass unemployment. AI is productive capital, which therefore has value to the extent that it serves consumers by contributing to the satisfaction of their wants. Misused AI is as valuable as a misused factory—it will tend to its scrap value. However, this doesn’t mean that AI will have no impact on the economy. It will, and already has, but it is not as big in the short-term as some fear, and it is likely bigger in the long-term than we expect.

No, the real threat is AI’s impact on information. This is in part because induction is an inappropriate source of knowledge—truth and fact are not a matter of frequency or statistical probabilities. The evidence and theories of Nicolaus Copernicus and Galileo Galilei would get weeded out as improbable (false) by an AI trained on all the (best and brightest) writings on geocentrism at the time. There is no progress and no learning of new truths if we trust only historical theories and presentations of fact.

However, this problem can probably be overcome by clever programming (meaning implementing rules—and fact-based limitations—to the induction problem), at least to some extent. The greater problem is the corruption of what AI presents: the misinformation, disinformation, and malinformation that its creators and administrators, as well as governments and pressure groups, direct it to create as a means of controlling or steering public opinion or knowledge.

This is the real danger that the now-famous open letter, signed by Elon Musk, Steve Wozniak, and others, pointed to:

“Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization?”

Other than the economically illiterate reference to “automat[ing] away all the jobs,” the warning is well-taken. AI will not Terminator-like start to hate us and attempt to exterminate mankind. It will not make us all into biological batteries, as in The Matrix. However, it will—especially when corrupted—misinform and mislead us, create chaos, and potentially make our lives “solitary, poor, nasty, brutish and short.”

Tyler Durden Fri, 03/15/2024 - 06:30

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