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Singularity

The Inevitability Of A Computer Smarter Than Humanity

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In my last post, I predicted that the world would experience the singularity between 2040 -2045, an artificially intelligent machine that exceeds the combined cognitive intelligence of the entire human race. In this post, I will delineate my predictions leading to the singularity. Please note their simplicity. I have worked hard to strip away all non-essential elements and only focus on those that represent the crucial elements leading to the singularity. I will state my rationale, and you can judge whether to accept or reject each prediction. Here are my predictions:

Prediction 1: Computer hardware, with computational power greater than a human brain (estimated at 36.8 petaflops), will be in the hands of governments and wealthy companies by the early 2030s.

Rationale: My reasoning for this is straightforward. We are already at the point that governments utilize computers close to the computational power of the human brain.  They are IBM’s Sequoia (16.32 petaflops), Cray’s Titan (17.59 petaflops), and China’s Tianhe-2 (33.86 petaflops). Given the state of current computer technology, we can use Moore’s law to reach the inescapable conclusion that by the early 2030s, governments and wealthy companies will own supercomputers with computational capability greater than a human brain.

Prediction 2: Software will exist that not only emulates but also exceeds the cognitive processes of the human brain by the early 2040s.

Rationale: Although no computer-software combination has passed the Turing test (i.e., essentially conversing with a computer is equivalent to conversing with another human), several have come close. For example, in 2015, a program called Eugene was able to convince 10 of 30 judges from the Royal Society that it was human. Given Moore’s law, by 2025, computer-processing power will have increased by over 100 fold. I view Moore’s law to be applicable in a larger context than raw computer processing power. I believe it is an observation regarding the trend of human creativity as it applies to technology. However, is Moore’s law applicable to software improvement? Historically, software development has not followed Moore’s law. The reason behind this was funding. Computer hardware costs dominated the budget of most organizations. The software had traditionally taken a backseat to hardware, but that trend is changing. With the advent of ubiquitous, cost-effective computer hardware, there is more focus on producing high-quality software. This emphasis led to software engineering development, which since the early 1980s has become widely recognized as a profession on par with other engineering disciplines. Numerous companies and government agencies employ highly educated software engineers. As a result, state-of-the-art computer software is closing the gap and becoming a near-follower of state-of-the-art computer hardware. How near? Based on my judgment, which I offer only as a rough estimate, software prowess is approximately one decade behind computer processing power. My rationale for this is straightforward. Even if computer hardware and software receive equal funding, the computer hardware will still lead the software simply because you need the hardware for the more sophisticated software to function. Is my estimation that software lags hardware by ten years correct? If anything, I think it is conservative. If you agree, it is reasonable to accept that vastly more capable computer software will follow within a decade in addition to the vastly increased computer processing power. Based on this, it is not a stretch to judge one or more computers will pass the Turing Test by 2025-2030. Even if software development progresses on a linear trend, as opposed to the exponential trend predicted by Moore’s law, we can expect computer software to improve 10 fold from 2030 to 2040. In my judgment, this will be sufficient to exceed the cognitive processes of the human brain.

Prediction 3: A computer will be developed in the 2040-2045 timeframe that exceeds the cognitive intelligence of all humans on Earth.

Rationale: This last prediction is, in effect, predicting the timeframe of the singularity. It requires predictions 1 and 2 to be correct and that a database that represents all human knowledge be available to store in a computer’s memory. To understand this last point, let us consider a hypothetical question. Will there be a digital database by the early 2040s equivalent to all knowledge known to humanity? In my view, the answer is yes. Databases like this almost exist today. For example, consider the data that Google has indexed. In addition to indexing online content, Google began an ambitious project in 2004, namely to scan and index the world’s paper books and make them searchable online. If we assume that by 2040 they complete this task, their database would contain all the information in books up to that point and all online information. Would that be all the knowledge of humanity? Perhaps! There is no way of knowing if Google alone will be the digital repository of all human knowledge in 2040. The crucial point is there are likely to be digital databases in 2040 that, if integrated, represent the total of all human knowledge. Google may only be one of them. These databases can be stored in a computer’s memory. With early 2040 state-of-the-art software, a supercomputer in early 2040 will be able to access those databases and cognitively exceed the intelligence of the entire human race, which is by definition the point of the singularity.

Many contemporary futurists typically predict numerous details leading to the singularity and attempt to attach a timeframe to each detail. I have set that approach aside since it is not relevant to predicting the singularity. That includes, for example, predicting computer brain implants, nanotech-based manufacturing, as well as a laundry list of other technological marvels. However, I think the singularity will only require accurately predicting the three events delineated above. As simple as they appear, they satisfy two crucial requirements. One, they are necessary, and two, they are sufficient to predict the singularity.

In making the above predictions, I made one critical assumption. I assumed that humankind would continue the “status quo.” I am ruling out world-altering events, such as large asteroids striking Earth, leading to human extinction, or a nuclear exchange that renders civilization impossible. Is assuming the “status quo” reasonable? We’ll discuss that in the next post.

A circular image of the center of a building.

Predicting the Singularity

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Futurists differ on the technical marvels and cultural changes that will precede the singularity. In this context, let us define the singularity as a point in time when an artificially intelligent machine exceeds the combined cognitive intelligence of the entire human race. In effect, there is no widely accepted vision of the decade leading to the singularity. There are reasons why this is the case.

The most obvious reason is that futurists differ on when the singularity will occur. Respected artificial intelligence technology futurists, like Ray Kurzweil and the late James Martin (1933 – 2013), predict the singularity will occur on or about 2045. At the 2012 Singularity Summit, Stuart Armstrong, a University of Oxford James Martin research fellow, conducted a poll regarding artificial generalized intelligence (AGI) predictions (i.e., the timing of the singularity) and found a median value of 2040. If you scour the Internet, you can find predictions that are substantially earlier and a century later. Therefore, let me preface everything I say with “caveat emptor,” Latin for “Let the buyer beware.” In this context, you may interpret it, “Let the reader be skeptical.” Although I strongly believe that my predictions regarding the singularity are correct, I also caution that the reader be skeptical and examine each prediction using their own judgment to ascertain its validity.

After much research and thought, I have concluded that the world will experience the singularity between 2040 -2045. In effect, I agree with Kurzweil, Martin, and the 2012 Armstrong survey. That suggests that the singularity will occur within the next twenty-five years. In the next post, I’ll explain how I arrived at my projection in the next post.

A colorful star with many lines coming out of it.

China’s Laser Weapons

Categories, War, War At The Speed Of Light, ,

This is an edited excerpt from my new book, War At The Speed Of Light.

Significant evidence indicates that China is developing laser weapons. Jane’s 360 reported, “Chinese media have reported that a prototype laser weapon is being tested by the People’s Liberation Army Navy (PLAN). An article published on 5 April [2019] on the Sina news website contains several screengrabs taken from footage broadcast by China Central Television (CCTV) showing a trainable optical device mounted on a mobile chassis with a large main lens.”

China’s laser weapon appeared in a promotional video broadcast by state-run channel CCTV. The transmission shows it in a ground-based, vehicle-mounted application. According to Sina.com, China intends both land and sea deployment, including aboard its destroyers, as an alternative to their short-range surface-to-air missile. This last statement implies it has a range of about three miles. Beyond talking about potential applications, China provides no evidence of the laser’s capabilities.

China is using espionage to obtain any information it can on the US Navy’s developments. The Maritime Executive, a source for breaking maritime and marine news, reported, “[The] U.S. Navy has uncovered evidence of widespread and persistent hacking by Chinese actors targeting naval technology. According to a recent internal review ordered by Navy Secretary Richard Spencer, the service’s broader R&D ecosystem is “under cyber siege,” primarily by Chinese hacking teams.”

My view is that China is doing all within its capability to develop laser weapons. Given their tenacity to hack their way into the US’ most crucial intelligence information, combined with their government’s funding of advanced weapons, it is only a matter of time before they weaponize lasers. Indeed, according to ZeeNews, “The Indian and US satellites are vulnerable to China’s ground-based lasers as according to some analysts China has acquired the full capability to destroy the enemy’s satellite sensors through its lasers. China can cause great damage to Indian and US satellites during wartime.” If this last statement is true, it means China has become a laser power.

artificial intelligence equal to human intelligence

How Will We Know IF Artificial Intelligence Equals Human Intelligence?

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Today, we find many different opinions regarding what constitutes human intelligence. There is no one widely accepted answer. Here are two definitions that have found some acceptance among the scientific community.

  1. “A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—‘catching on,’ ‘making sense of things,” or ‘figuring out’ what to do” (“Mainstream Science on Intelligence,” an editorial statement by fifty-two researchers, The Wall Street Journal, December 13, 1994).
  2. “Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by thinking. Although these individual differences can be substantial, they are never entirely consistent: a given person’s intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of ‘intelligence’ attempt to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions.” (“Intelligence: Knowns and Unknowns,” a report published by the Board of Scientific Affairs of the American Psychological Association, 1995).

Now that we have some basis for defining human intelligence, let us attempt to define a test that we could use to assert that artificial intelligence emulates human intelligence.

Alan Turing is widely considered the father of theoretical computer science and artificial intelligence. He became prominent for his pivotal role in developing a computer that cracked the daily settings for the Enigma machine, Germany’s technology for coding messages during World War II. This breakthrough allowed the Allies to defeat the Nazis in many crucial engagements. Some credit Turing’s work, as a cryptanalyst, for shortening the war in Europe by as many as two to four years. After World War II, in 1950, Alan Turing turned his attention to artificial intelligence and proposed the now-famous Turing test. The Turing test is a methodology to test the intelligence of a computer. The Turing test requires a human “judge” to engage both a human and a computer with strong AI in a natural-language conversation. None of the participants, however, can see each other. If the judge cannot distinguish between the human and strong AI computer, the computer passes the Turing test and is equivalent to human intelligence. This test does not require that the answers be correct, just indistinguishable. Passing the Turing test requires almost all the major capabilities associated with strong AI to be equivalent to those of a human brain. It is a challenging test, and to date, no intelligent agent has passed it. However, over the years, there have been numerous attempts to pass the Turing Test, with associated claims of success. Here is a summary of major attempts to pass the Turing Test:

  • In 1966, Joseph Weizenbaum created the ELIZA program, which examined a user’s typed comments for keywords. If the program found a keyword, its algorithm used a rule to return a reply. Although Weizenbaum and others claim success, their claim is highly contentious. In effect, this is the same type of algorithm (i.e., a set of rules followed in problem-solving operations by a computer) early search engines used to provide search returns before Google’s use of “link popularity” (i.e., the number of links that point to a website using an imbedded keyword) to improve search return relevance.
  • In 1972, Kenneth Colby created PARRY, which was characterized as “ELIZA with attitude.” The PARRY program took the ELIZA algorithm and additionally modeled the behavior of a paranoid schizophrenic. Once again, the results were disappointing. It was not able to consistently convince professional psychiatrists that it was a real patient.
  • In 2015, the developers of a program called Eugene made a claim it passed the Turing Test. However, their claim turned out to be bogus. Eugene was able to convince 10 of 30 judges from the Royal Society that it was human. Although augmentative, there is a strong consensus based on the test conditions and results that Eugene did not pass the Turing test.

Although other tests claim to go beyond the Turing Test, no new test has gained wide support in the scientific community. Therefore, even today, the Turing Test remains the gold standard concerning an AI machine emulating human intelligence. Despite recent claims to the contrary, no AI machine has been able to pass the Turing Test.

AI is approaching human intelligence

Artificial Intelligence Is Approaching Human Intelligence

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According to Moore’s law, computer-processing power doubles every eighteen months. Using Moore’s law and simple mathematics suggest that in ten years, the processing power of our personal computers will be over a hundred times greater than the computers we currently are using. Military and consumer products using top-of-the-line computers running state-of-the-art AI software will likely exceed our desktop computer performance by factors of ten. In effect, artificial intelligence in top-of-the-line computers running state-of-the-art AI software will eventually be equivalent to and may actually exceed human intelligence.

Given the above, let us ask, “What should we expect from AI technology in ten years?” Here are some examples:

·       In military systems, expect autonomous weapons, including fighter drones, robotic Navy vessels, and robotic tanks.

·       In consumer products, expect personal computers that become digital assistants and even digital friends. Expect to be able to add “driverless” as an option to the car you buy. Expect productivity to increase by factors of ten in every human endeavor, as strong AI shoulders the “heavy lifting.”

·       In medical technology, expect surgical systems, like the da Vinci Surgical System, robotic platforms designed to expand the surgeon’s capabilities and offer a state-of-the-art minimally invasive option for major surgery, to become completely autonomous. Also, expect serious, if not life-threatening, technical issues as the new surgical systems are introduced, similar to the legal issues that plagued the da Vinci Surgical System, from 2012 through 2014. Expect prosthetic limbs to be directly connected to your brain via your nervous system and perform as well as the organic limb it replaced. Expect new pharmaceutical products that cure (not just treat) cancer and Alzheimer’s disease. Expect human life expectancy to increase by decades. Expect to see brain implants (i.e., technology that is implanted into the brain) become common, such as brain implants to rehabilitate stroke victims, by bypassing the damaged area of the brain.

·       On the world stage, expect cybercrime and cyber terrorism to become the number one issue that technologically advanced countries like the United States will have to fight. Expect significant changes in employment. When robots, embedded with strong AI computers can do the work currently performed by humans, it is not clear what type of work humans will do. Expect leisure to increase dramatically. Expect unemployment issues.       

The above examples are just the tip of a mile-long spear and highly likely to become realities. Most of what I cited is already off the drawing boards and being tested. AI is dramatically changing our lives already, and I project it will approach human intelligence in the next ten years. This is arguably optimistic. However, the majority of researchers project AI will be equivalent to human intelligence by mid-2021. Therefore, expect AI to be equivalent to human intelligence between 2030-2050.

Integrated Circuit

How Moore’s Law Ended the Second AI Winter

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In our last post, I stated, “While AI as a field of research experienced funding surges and recessions, the infrastructure that ultimately fuels AI, integrated circuits, and computer software continued to follow Moore’s law. In the next post, we’ll discuss Moore’s law and its role in ending the second AI Winter.” This post will describe how Moore’s law ended the second AI Winter.

Intel co-founder Gordon E. Moore was the first to note a peculiar trend: the number of components in integrated circuits had doubled every year from the 1958 invention of the integrated circuit until 1965. In 1970 Caltech professor, VLSI (i.e., Very-Large-Scale Integration) pioneer, and entrepreneur Carver Mead coined the term “Moore’s law,” referring to Gordon E. Moore’s observation, and the phrase caught on within the scientific community. In 1975, Moore revised his prediction regarding the number of components in integrated circuits doubling every year to doubling every two years. Intel executive David House noted that Moore’s latest prediction would cause computer performance to double every eighteen months due to the combination of more transistors and the transistors themselves becoming faster.

This means that while the research field of AI experienced surges and recessions, the fundamental building blocks of AI, namely integrated circuit computer components, continued their exponential growth. Even today, Moore’s law is still applicable. In fact, many semiconductor companies use Moore’s law to plan their long-term product offerings. There is a deeply held belief in the semiconductor industry that they must adhere to Moore’s law must remain competitive. In effect, it has become a self-fulfilling prophecy.

In the strictest sense, Moore’s law is not a physical law of science. Rather, it delineates a trend or a general rule. This begs a question, “How long will Moore’s law continue to apply?” For approximately the last half-century, each estimate has predicted that Moore’s law would hold for another decade at various points in time. This has been occurring for almost five decades. I worked in the semiconductor industry for more than thirty years and over 20 years as a director of engineering for Honeywell’s Solid State Electronics Center, which developed and manufactured state-of-the-art integrated circuits for computers, missiles, and satellites. As a director of engineering, I was responsible for developing some of the world’s most sophisticated integrated circuits and sensors. During my over thirty years in the semiconductor industry, Moore’s law always appeared as if it would reach an impenetrable barrier. This, however, did not happen. New technologies constantly seemed to provide a stay of execution. We know that the trend may change at some point, but no one really has made a definitive case as to when this trend will end. The difficulty in predicting the end has to do with how one interprets Moore’s law. In my judgment, Moore’s law is not about integrated circuits, but rather it is an observation about human creativity as it relates to technology development. In fact, American author and Google’s director of engineering, Ray Kurzweil, showed via historical analysis that technological change is exponential. He termed this “The Law of Accelerating Returns” (Reference: The Age of Spiritual Machines, 1999, Ray Kurzweil).

As computer hardware and software continued its relentless exponential improvement, the AI field focused its development on “intelligent agents” or, as it often referred to, “smart agents.” The smart agent is a system that interacts with its environment and takes calculated actions to achieve its goal. Smart agents also can be combined to form multi-agent systems, with a hierarchical control system to bridge lower-level AI systems to higher-level AI systems. This became the game-changer. Using smart agents, AI technology has equal and exceed human intelligence in specific areas, such as playing chess. However, the current state of AI technology still falls short of general human intelligence, but this will change in the coming decades. We’ll discuss this further in the next post.

A large piece of ice on the beach

What Caused the Second “AI Winter”?

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In our last post, we stated, “When the early founders of AI set extremely high expectations, they invited scrutiny. With the passing years, it became obvious that the reality of artificial intelligence fell short of their predictions. In 1974, funding for AI research began to dry up, both in the United States and Britain, which led to a period called the ‘AI Winter,’ and optimism regarding AI turned to skepticism. The first AI Winter lasted until the early 1980s.”

In early the 1980s, researchers in AI began to abandon the monumental task of developing strong AI and began to focus on expert systems. An expert system, in this context, is a computer system that emulates the decision-making ability of a human expert. This meant the computer software allowed the machine to “think” equivalently to an expert in a specific field, like chess for example. Expert systems became a highly successful development path for AI. By the mid-1980s, the funding faucet for AI research was flowing at more than a billion dollars per year.

Unfortunately, the funding faucet began to run dry again by 1987, starting with the failure of the Lisp machine market that same year. MIT AI lab programmers Richard Greenblatt and Thomas Knight, who formed the company Lisp Machines Inc., developed the Lisp machine in 1973. The Lisp machine was the first commercial, single-user, high-end microcomputer, which used Lisp programming (a specific high-level programming language) to tackle specific technical applications.

Lisp machines pioneered many commonplace technologies, including laser printing, windowing systems and high-resolution bit-mapped graphics, to name a few. However, the market reception for these machines was dismal, with only about seven thousand units sold by 1988, at about $70,000 per machine. In addition, the company, Lisp Machines Inc., suffered from severe internal politics regarding how to improve its market position. This internal strife caused divisions in the company. To make matters worse, cheaper desktop PCs soon were able to run Lisp programs even faster than Lisp machines. Most companies that produced Lisp machines went out of business by 1990, which led to a second and longer-lasting AI Winter.

If you are getting the impression that being an AI researcher from the 1960s through the late 1990s was akin to riding a roller coaster, your impression is correct. Life for AI researchers during that timeframe was a feast or famine-type existence.

While AI as a field of research experienced funding surges and recessions, the infrastructure that ultimately fuels AI, integrated circuits, and computer software, continued to follow Moore’s law. In the next post, we’ll discuss Moore’s law and its role in ending the second AI Winter.

A view of the mountains from above.

What Caused the First “AI Winter”?

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The real science of artificial intelligence (AI) began with a small group of researchers, John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon. In 1956, these researchers founded the field of artificial intelligence at a conference held at Dartmouth College. Their work and their students’ work soon amazed the world, as their computer programs taught computers to solve algebraic word problems, provide logical theorems, and even speak English.

By the mid-1960s, the Department of Defense began pouring money into AI research. Along with this funding, unprecedented optimism and expectations regarding the capabilities of AI technology became common. In 1965, Dartmouth’s Herbert Simon helped fuel the unprecedented optimism and expectations by predicting, “Machines will be capable, within twenty years, of doing any work a man can do.” In 1967, Minsky not only agreed but also added, “Within a generation…the problem of creating ‘artificial intelligence’ will substantially be solved.”

Had the early founders been correct in their predictions, all human toil would have ceased by now, and our civilization would be a compendium of technological wonder. It is possible to speculate that every person would have a robotic assistant to ease their way through their daily chores, including cleaning their houses, driving them to any destination, and anything else that fills our daily lives with toil. However, as you know, that is not the case.

Obviously, Simon and Minsky had grossly underestimated the level of hardware and software required to achieve AI that replicates the intelligence of a human brain (i.e., strong artificial intelligence). Strong AI is also synonymous with general AI. Unfortunately, underestimating the level of hardware and software required to achieve strong artificial intelligence continues to plague AI research even today.

When the early founders of AI set extremely high expectations, they invited scrutiny. With the passing years, it became obvious that the reality of artificial intelligence fell short of their predictions. In 1974, funding for AI research began to dry up, both in the United States and Britain, which led to a period called the “AI Winter,” and optimism regarding AI turned to skepticism.

The first AI Winter lasted until the early 1980s. In the next post, we’ll discuss the second AI Winter.

c-war

The Pace Of Warfare Is Increasing From Hyperwar To C-War

Categories, Technology, War, War At The Speed Of Light, , ,

In my latest book, War At The Speed Of Light, I coined a new term, “c-war.” This is an excerpt from the book’s introduction and explains the rationale behind this term.

The pace of warfare is accelerating. In fact, according to the Brookings Institution, a nonprofit public policy organization, “So fast will be this process [command and control decision-making], especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.”

The term “hyperwar” adequately describes the quickening pace of warfare resulting from the inclusion of AI into the command, control, decision-making, and weapons of war. However, to my mind, it fails to capture the speed of conflict associated with directed energy weapons. To be all-inclusive, I would like to suggest the term “c-war.” In Einstein’s famous mass-energy equivalent equation, E = mc2, the letter “c” is used to denote the speed of light in a vacuum. [For completeness, E means energy and m mass.] Surprisingly, the speed of light in the Earth’s atmosphere is almost equal to its velocity in a vacuum. On this basis, I believe c-war more fully captures the new pace of warfare associated with directed energy weapons.

A red light is shining on the dark background.

Here’s Why The US Is Pursuing Directed Energy Weapons

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This is an excerpt from the introduction of my new book, War At The Speed Of Light.

As the US’ most capable potential adversaries deploy missile defenses that could threaten its advanced weapons systems, such as Ford-class aircraft carriers and B-2 stealth bombers, the US is developing countermeasures. Current countermeasures rely on anti-ballistic missile defense systems, such as the Terminal High Altitude Area Defense (THAAD). These countermeasures primarily use missiles to destroy missiles, which is akin to using bullets to stop bullets.

Unfortunately, these countermeasures do not cover the complete threat spectrum. For example, THAAD is only effective against short-, medium- and intermediate-range ballistic missiles, not against intercontinental ballistic missiles. Also, the countermeasures can be an expensive deterrent. For example, in 2017, a US ally used a Patriot missile, priced at about three million dollars, to shoot down a small enemy quadcopter drone, available on Amazon for about two hundred dollars. Of course, the quadcopter drone had no chance against the Patriot, a radar-targeted missile more commonly used to shoot down enemy aircraft and ballistic missiles. The military terms this “overkill.” In theory, the enemy could order more of the two hundred dollar quadcopter drones from Amazon or eBay until they exhaust the US and its allies’ stock of Patriot missiles.

Given the expense of using missiles to counter enemy missiles and drones, along with their ineffectiveness across the entire threat spectrum, the US military is turning to laser and other directed energy weapons. While the price tag for hypersonic missiles continues to soar, approaching six hundred million per missile, the cost per laser pulse continues to drop, approaching about one dollar per shot. In addition, the US military feels that directed energy weapons will be effective against the entire threat spectrum, from intercontinental ballistic missiles to drone swarms.