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Javid first joined the Bravo universe in 2012 as an original cast member of “Shahs of Sunset.”

What AI Doesn't Know - And Why It Matters

Authored by Richard Porter via RealClearPolitics,

Artificial intelligence has taken the wired world by storm, but the backlash came almost as fast. Progressives complain of job losses, environmentalists question the ecological impacts of huge data centers, and local activists are clamoring for assurances that household utility bills won’t skyrocket because of the centers’ voracious electricity requirements. Others simply worry that the technology will overwhelm humans’ ability to control it.

At least in part, these reactions stem from the overselling of AI.

AI is super cool, but it’s not superhuman nor is it super intelligent. AI is simply very fast processing of vast amounts of data.

Intelligence, knowledge, understanding and wisdom are all different concepts; the distinction between them elucidates the scope and limits of both human and electronic “intelligence.”

Intelligence is the ability to process information into an internally coherent framework that’s useful and adds or detracts from knowledge to the extent it is more or less accurate. Knowledge is the accumulation of information organized into coherent frames or models that help us understand. Understanding is awareness of the significance, purpose, or meaning of accumulated knowledge.

And wisdom is judgment seasoned by experience and the awareness that intelligence, knowledge, and understanding are limited, inherently flawed, and useful only to the extent they advance a worthwhile purpose.

Nearly 2,500 years ago, the Oracle of Delphi reportedly declared that no man was wiser than Socrates. Socrates claimed to be stunned by this because he was keenly aware of how much he didn’t know. But after talking to others widely acclaimed to be knowledgeable, such as the leading politicians, poets, philosophers, and artisans of his day, he discerned this Delphic wisdom: Those claiming knowledge were ignorant of their own ignorance, whereas Socrates knew he knew nothing.

For this insight, Socrates was put to death for impiety and corrupting the youth of Athens, thereby proving for all time both the foolishness of his accusers’ certainty and the wisdom of Socratic questioning.

This bears repeating today, as we enter the Age of Artificial Intelligence: it’s wise to question the “intelligence” of machines, the “knowledge” they propagate, and our understanding of the significance and limits of the technology.

AI models are amazing and useful despite being incomprehensible to most of us, but AI is not infallible. AI will expand human knowledge and understanding of the world only if and to the extent that human users are encouraged to question AI results, processes, and functions.

People make mistakes, as do the people making and training the machines. Still, people tend to trust machines more than people, especially with respect to processing information that’s harder to process. For example, tennis players have more faith in electronic line calls over human line calls, although that faith in the new technology has been shaken by errors, such as when ball marks are inconsistent with the electronic line calls.

As AI use spreads, people will increasingly rely on AI and trust its results for routine tasks (like Google searches), while most people remain more skeptical of AI results for more complex tasks and do not trust AI to act to handle certain tasks for its users without human intervention.

It’s wise to question AI’s results; errors are common even in routine searches.

Examples of AI errors, hallucinations and political bias are rife. A Northwestern University business school professor of my acquaintance recently asked ChatGPT for advice evaluating investment alternatives. ChatGPT recommended he invest in a particular fund and described in detail that fund’s returns, risks, and assets. When the professor went to invest in ChatGPT’s recommended fund, he discovered the fund did not actually exist; ChatGPT made it all up (a phenomenon commonly referred to as “AI hallucination”). 

Indeed, AI can screw up even mundane tasks: In my research for this piece, a Google AI summary ascribed quotes to Socrates that are not supported by any historical record.

Artificial intelligence – like human intelligence – is prone to error and is not always reliable, but that’s to be expected, especially in a fledgling technology. AI is artificial intelligence, not artificial knowledge, understanding, or wisdom.  AI is a processor, a very fast processor, that organizes and distills information – and organized information is easier to evaluate and use by humans than vast amounts of unorganized information.

Properly understood, AI supplements and does not replace human intelligence, knowledge, or understanding; plus, the limitations and faults within these amazing models remind us that human intelligence is limited, too. Human intelligence imperfectly organizes the imperfect data to which a human has access and frames data in a subjective, not an objective, manner.

Many of us expect the machines that humans make to have “better” intelligence than the intelligence of its human creators – more objective, more comprehensive, more insightful. This is a naïve hope. In one sense, it is “better.” AI organizes more information faster than humans can. But who do they think programmed the thing? Every AI model is regurgitating imperfect information collected, created, and input by imperfect, subjective human beings.

What to make of all this?

First, perhaps the math nerds creating AI are mistakenly training machines to handle information processing on human topics as if human topics are math problems with a specific answer.  Perhaps instead, machines should be trained to suggest questions to consider instead of answers to accept with respect to human inquiries relating to politics, economics, psychology, child rearing, crop science – the full range of arts, humanities, and social sciences.

Second, people training these machines should be explicit about the biases and perspectives being built into how the AI organizes, sorts, and frames information. (My own bias on this topic is that I believe American AI companies should be building AI with quintessentially American framing.) 

Third, AI creators should consider the political, regulatory, and legal risks of “overselling” what AI is and what it can do. For example, should AI creators anticipate a duty to warn users of shortcomings with AI’s results and/or disclaimers of warranties?

Fourth, AI creators need to consider improving the quality of data upon which the systems are being trained, recognizing that many online data sources intentionally mislead to advance political agendas. Perfectly “unbiased” information is impossible to obtain, but some information is more accurate and less biased than other information; trainers should exercise better judgement about data.

The creation of AI large language models is an incredible feat of engineering. It’s quite useful, and will soon be essential, but it is still a product of human invention. As such, we need to recognize that AI is ultimately just the latest, greatest – but still imperfect – implement invented and used by homo sapiens to make life better for homo sapiens.

Richard Porter is a member of the Board of Directors of the Alfa Institute, a platform for ideas, policy proposals and new technology integration pertaining to artificial intelligence

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Tyler Durden Thu, 04/16/2026 - 21:45

The offseason could not have played out more favorably for Deonte Banks thus far.

Because Statcast has not yet pursued a way to quantify how tightly a hitter grasps a bat, chasing pitches outside of the zone might be the most concrete manner to measure whether a player or team is pressing.

See all the celebrity fashion.

See all the celebrity fashion.

Hannah Einbinder is all of us when it comes to that Jesse McCartney cameo in Hacks.

The PGA Tour likely is ready to make a strategic move to bring back the top stars that left to go to LIV — most notably Bryson DeChambeau and Jon Rahm.

"And I see a kid lying dead, and I know him from coming over here to play basketball. He's a good basketball player. Cool kid," AJ told The Post.

“This is the birthplace of a little idea called ‘no tax on tips,’” Trump said at a roundtable with workers benefitting from the new deduction.

Why The Crash Was Delayed

Authored by Robert Aro via Mises Institute,

Whatever happened to the mother of all crashes that was supposed to arrive when the Federal Reserve began tightening its balance sheet back in 2022? For several years, I’ve been scratching my head, convinced that draining the balance sheet by trillions of dollars should have triggered a systemic banking failure or some other Black Swan event. In the past, crises like Lehman/AIG or the 2020 lockdowns took the blame, when in reality, the root cause was always monetary.

From the peak in June 2022 to the trough in December 2025, the asset side of the Fed’s balance sheet shrank by roughly $2.3 trillion. That was the front door. But through the back door, something else was happening on the liability side: the Fed’s Overnight Reverse Repo Facility (RRP) was releasing $2.5 trillion of previously frozen private liquidity back into the financial system. 

If Quantitative Tightening (QT) removed liquidity, the RRP added it back... plus interest.

To recap: during QT, the Fed allows its holdings of Treasury securities and mortgage-backed securities (MBS) to mature. Financial intermediaries repay the Fed, and the Fed literally deletes that money from the system. This is the classic setup that exposes malinvestments, stresses credit markets, and reveals the imbalances described in Austrian Business Cycle Theory. 

But this time it really was different because of the Reverse Repo Facility.

By mid-2023, the (March 2023) Silicon Valley Bank crisis had passed and the Fed’s Bank Term Funding Program was alive and well; then the hikes finally tapped out. Eventually, the 1-Month (4-Week) Market Yield on U.S. Treasuries outpaced the Fed’s RRP rate, and the incentive changed. Fund managers began a stampede out of the Fed’s facility and rotated into T-bills to chase a higher risk-free return.

In less than two years, the RRP withdrawals injected around $100 to $200 billion+ a month into the financial system at its peak. This was effectively a backdoor stimulus program that bypassed the Fed’s official QT narrative and funded the government’s deficit. Correlation does not equal causation, but it’s also not surprising that the Dow Jones broke out to new highs at almost the exact moment the RRP began to unwind.

The system was running on stored liquidity thanks to a giant buffer accumulated during the pandemic stimulus era. But as of 2026, that buffer is gone. The RRP liability has flatlined at essentially zero, meaning that the trillion-dollar offset to QT has been fully exhausted.

Perhaps it was no coincidence that once the RRP hit empty, the Fed’s tightening ended. On December 11, 2025, the Federal Reserve Bank of New York announced it would begin Reserve Management Purchases (RMP’s) at a pace of approximately $40 billion per month. While they use Fedspeak to avoid the term Quantitative Easing (QE), in reality, they’ve returned to official balance sheet expansion. They are being forced to replace the lost RRP liquidity with fresh money printing.

The math remains staggering. Since June 2022, the Fed was slashing its balance sheet by embarking on a QT narrative. The result? A net liquidity injection to the tune of $200 billion. And they called it “tightening.”

With the RRP buffer now empty, we are entering uncharted territory. The Fed’s $40 billion a month balance sheet expansion is several times less than what was entering the system via the RRP drain. Ironically, what the Fed hopes will act as QE might feel more like QT. We are about to find out just how long the system can survive a true monetary contraction.

Tyler Durden Thu, 04/16/2026 - 20:55

This is a golden opportunity for Bronny James. He can prove himself. He can silence his detractors. He can show the world he belongs.

Mitchell Robinson has seen it all as a Knick, from toiling on the league’s worst team to now being the X factor on a contender.

Froyd previously spoke out against Russini after the latter was photographed holding hands with Patriots coach Mike Vrabel.

Froyd previously spoke out against Russini after the latter was photographed holding hands with Patriots coach Mike Vrabel.

The Devils are going all in on their pick to lead the team's front office.

The stage is set for an extra edition of the Hudson River Derby in just a few weeks. 

Who is or was the toughest guy in sports? 

US Army Trials Unmanned Hunter Wolf Robot With Gun, Radar In Combat Drills

The U.S. Army is quietly putting armed robots through their paces alongside real soldiers - and new footage suggests these machines could soon be a regular sight on tomorrow’s battlefields.

Wolf-X robotic combat vehicle by HDT Global.Blade HDT

Fresh imagery dropped on Monday by the Defense Visual Information Distribution Service shows a Hunter Wolf unmanned ground vehicle rolling with the 101st Airborne Division during a full-on combat simulation at the Joint Readiness Training Center (JRTC) in Louisiana. The display amounted to a serious stress test in one of the Army’s roughest training environments - where ideas either prove they work or get ditched fast.

The Hunter Wolf’s appearance at JRTC marks a significant shift - as units aren’t just playing around with unmanned gear in isolated experiments anymore; they’re dropping it straight into realistic, chaotic scenarios. Elements of the 101st used the vehicle for logistics runs and security tasks throughout the exercise. Photos show it fitted with a remotely operated .50-caliber machine gun, which hints that the Army is testing it for more than just hauling supplies—it’s being eyed for actual tactical roles too.

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A post shared by HDT Global (@hdtglobal)

The Hunter Wolf was originally picked up under the Army’s Small Multipurpose Equipment Transport program to take some of the crushing load off soldiers’ backs. But at Fort Polk, they ran it with a remote weapon station and EchoShield radar, turning it into a rolling set of eyes and teeth. The combo lets a unit push sensors and firepower forward without putting troops in the open. The robot can scout ahead, scan for threats, and even lay down fire while the soldiers stay under cover.

At the same time, it still hauls the basics - ammo, water, batteries, comms gear - so small units can stay mobile and supplied across wide, contested spaces. In today’s fights, logistics and security are blurring together anyway. A robot that can do both fits right in.

Defense analyst Teoman S. Nicanci (Army Recognition Group) points out that the real story here is the Army choosing a high-intensity training rotation like JRTC instead of a safe, staged test. It shows they’re serious about folding this tech into actual formations and missions, not just checking boxes.

For units like the 101st, where speed and mobility are everything, these unmanned platforms help keep that edge without burning out the troops or exposing them unnecessarily. Future battles are going to be packed with drones, artillery, and precision strikes—anything that cuts risk while keeping the pressure on is worth its weight.

Bottom line: the Hunter Wolf isn’t science fiction anymore. The Army is learning, right now, how to weave robots into the fight so soldiers can move faster, hit harder, and come home safer.

h/t Interesting Engineering

Tyler Durden Thu, 04/16/2026 - 20:30