MIT | Mad Scientist Laboratory

January 14, 2019January 11, 2019

112. A Strategy for Everything: Quantum Artificial Intelligence, Quantum Multiverse, and Feedback Loop of Finite Information

[Editor’s Note: Mad Scientist Laboratory is pleased to publish the following post by repeat guest blogger Mr. Victor R. Morris, addressing the relationship of Artificial Intelligence (AI), Robotic and Autonomous systems (RAS), and Quantum Information Science (QIS) to Quantum Artificial Intelligence (QAI), and why we should pursue a parallel QAI strategy in order to predict alternative possibilities in a quantum multiverse. Prepare to have your consciousness expanded — Read on! (Note: Some of the embedded links in this post are best accessed using non-DoD networks.)]

Introduction
The U.S. defense industry routinely analyzes emerging and potentially disruptive technological trends influencing long-term strategic competition. This post describes the greater defense community as public and private sectors responsible for national security and associated interests abroad. Interstate competition has implications for global order and disorder, according to the 2018 National Defense Strategy summary.

The three defense industry trends identified in this post are:

Artificial Intelligence (AI),
- Robotic and Autonomous systems (RAS), and
  - Quantum Information Science (QIS).

According to Paul Scharre‘s preface to Elsa Kania‘s paper on Battlefield Singularity, published by the Center for a New American Security (CNAS), “Artificial intelligence (AI) is fast heating up as a key area of strategic competition.” (N.B., both Mr. Scharre and Ms. Kania are proclaimed Mad Scientists whose works have previously graced this blog site). Furthermore, structured analysis identified interrelated aspects of these trends and the requirement for a multi-disciplinary strategy focused on Quantum Artificial Intelligence (QAI), anticipating the potential impact on global systems.

A number of countries have published AI Strategies during the past 21 months. China is routinely assessed as a leader in AI development and investment. Russia is finalizing its draft National AI Roadmap; the final version will be released mid-year 2019. The U.S. is scheduled to release its national strategy for artificial intelligence in early 2019. This intentionally or unintentionally follows the National Strategic Overview for Quantum Information Science released in September 2018. Additionally, around the same time period, both the Massachusetts Institute of Technology (MIT) and U.S. Defense Advanced Research Projects Agency (DARPA) announced billion dollar investment plans for AI research and technologies. The U.S. Army’s Robotic and Autonomous Systems Strategy publication coincided with Canada’s AI strategy in March 2017. Canada was the first country to announce an AI strategy and future investment in the AI ecosystem. The European Union followed up on recent strategic developments by announcing a plan fostering AI development and employment last month.

First, this post argues that AI, QIS, and RAS are components of a greater QAI ecosystem underpinned by the scientific notion of information discussed in detail later. Information does not measure what is known, rather it measures the number of possible alternatives for something. Combining AI and quantum computing applications potentially results in QAI, according to a variety of scientists and theorists in the field. Additionally, information is the nucleus or “quanta” of the entire QAI ecosystem. Understanding information is critical to understanding the natural world. Secondly, the post argues “keeping up with the Joneses” in AI is counterproductive and perpetuates misunderstanding of advancements and implications for the future.

The first section of this post briefly describes AI, Machine Learning (ML), RAS, QIS, and QAI, and their relationships with information. The second section describes theoretical interpretations of reality based on quantum mechanical properties.

Section 1 Overview
AI, sometimes called machine intelligence, includes the machine learning field enabling autonomous or independent functions and activity. QIS and computing are the next evolution of classical computing with implications for machine learning, reasoning, and autonomous systems behavior. As mentioned above, information is a fundamental consideration for all of these fields and the ability to perform parallel probabilistic tasks. “Probabilistic” refers to probabilities indirectly associated with randomness.

Artificial Intelligence (AI) and Machine Learning (ML)

AI involves computer systems performing tasks normally requiring human intelligence. In computer science, AI is the study of intelligent agents or autonomous entities perceiving and acting upon their environment. AI is intelligence exhibited by machines, enabled by machine learning algorithms in simpler terms. Algorithms are rule sets defining sequences of operations. ML is a field of AI and set of statistical techniques associated with machines performing intellectual, human tasks. ML includes deep learning and is critical to AI because it involves Artificial Neural Networks (ANN) like the human brain, enabling learning from large quantities of data to improve predictions and data driven decisions. ANNs are a framework for ML algorithms working together to process complex data sets.

Robotic and Autonomous Systems (RAS) Robots are one type of AI entity, while others include cyber agents, decision aids, and virtual assistants. Amazon’s Alexa and Apple’s Siri are good examples of AI-enabled virtual assistants using ML to perform tasks. RAS are technologies granted autonomy or level of independence to execute tasks in a prescribed environment in a military context. RAS examples include both land and air systems like explosive ordnance disposal robots and unmanned aerial vehicles commonly referred to as “drones.” Autonomous behavior is designed by humans through a combination of sensors and advanced computing processes. Advanced computing involves both environmental navigation and software enabled decision-making. RAS independence is a progressive spectrum, ranging from remote control to full autonomy.

Quantum Information Science (QIS)
According to the September 2018 United States Government’s National Strategic Overview for Quantum Information Science report, “Quantum information science (QIS) applies the best understanding of the sub-atomic world—quantum theory—to generate new knowledge and technologies.” Quantum theory, also called quantum mechanics, describes the smallest finite quantities, or “quanta,” making up the quantum fields composing the universe. QIS includes the quantum computing field using quantum mechanical properties to advance information processing, transmission, and measurement.For example, quantum computation uses the quantum analog of a bit, called a quantum bit, existing in multiple states due to quantum superposition. Superposition allows quantum systems the ability to simultaneously occupy different quantum states. This fundamental principle means qubits are described as a linear combination of 0 and 1 (composition of basis states), and not solely 0 or 1 as in classical computing before measurement.

Quantum Artificial Intelligence (QAI)
This section does not attempt to explain AI and QIS intersections in detail. Both areas are so extensive that unifying concepts are difficult to understand. This post sees QAI as a different element of the taxonomy and not a subset of classical AI. “Quantum physics is based on information theory and probability theory” according to Andreas Wichert, author of Principles of Quantum Artificial Intelligence. He presents both theories in his book, highlighting quantum physics’ relationship to AI through associative memory and Bayesian networks. Associate memory and Bayesian networks are applied later to QAI based on their access to information.

Section 2 Overview
This section outlines interpretations of information and quantum theories and AI intersections. Information is a finite measurement of possible alternatives existing in the multiverse. Quantum computing has the potential for reversible or time-invertible deep learning and associative memory based on quantum entanglement and superposition. Quantum AI has the potential to test the multiverse theory, because QAI networks process, transmit, and measure information across space-time.

Information Theory
Information takes many forms that differ from one another, like natural language, symbols, acoustic speech, and pictures. The scientific notion of information is more precise. Information theory, proposed by Claude E. Shannon, studies the quantification, storage, and communication of information. Once again, Information does not measure what is known, it measures the number of possible alternatives for something.

Carlo Rovelli uses a dice example in his book Reality Is Not What It Seems: The Journey to Quantum Gravity to illustrate this point. If a dice is thrown, it can land on one of six sides. When we observe it fall on a number, we have an amount of information where N=6 because the possible alternatives are six. Instead of “N” (number of alternatives), scientists measure information in terms of quantity deemed “S” after Shannon. Rovelli also states information is finite in nature based on quantum mechanical properties. New or “relevant” information cancels out “irrelevant” information in a physical system, therefore systems can always obtain new information from other systems. *This point is important for later.

Measuring Possible Alternatives
The fundamental unit of classical information is a “bit.” The natural unit of information, or “nat,” is a unit of information or entropy. Information entropy is the average rate information is produced by a random source of data. Information entropy can be measured in bits, nats, or decimal digits, depending on the base logarithm defining it. Once again, a binary digit, characterized as 0 and 1, represents information in classical computing. A quantum bit, or “qubit,” is the basic unit of quantum information in the quantum world. A qubit can be a coherent superposition of both 0 and 1 eigenstates according to quantum mechanical properties. A qubit can also hold more information than a classical bit. Lastly, probability amplitudes are complex numbers. They are the probability of a qubit to appear in its basis states.

Quantum Machine Learning through Quantum Information
Quantum ANNs potentially enable deep learning from large quantities of qubits. Qubits are information, so they measure possible alternatives. Quantum ANNs are like Bayesian networks graphically modelling probabilistic relationships in this specific interpretation. The quantum nature of these networks expand access to reciprocal or correlated information.

An interpretation of reciprocal information is discussed through quantum mechanical properties and quantum many-worlds, also called “multiverse” theory in the last part of this post. This specific interpretation is multiverses are finite because information is. This is loosely based on Steven Hawking and Thomas Hertog’s April 2018 article, A smooth exit from internal inflation? where they state, “eternal inflation does not produce an infinite fractal-like multiverse, but is finite and reasonably smooth.”

Quantum Many Worlds
Quantum computing has the potential to allow reversible or time-invertible deep learning and associative memory, based on quantum entanglement and superposition. Qubits contain entangled relevant and irrelevant (anti-correlated probabilities) information across space-time. This concept ensures a retro-causality loop of finite information exchange. Quantum associative memory is the ability to learn and remember correlations between seemingly unrelated items. This is possible because all “items” are correlated through quantum phenomena. Relevant information in one world or universe (macro possible alternative) is simultaneously irrelevant information in the adjacent world because of quantum states and finite quantity of information in nature. Quantum information cannot be copied according to the no-cloning theorem. Conversely, it cannot be deleted based on a time reversed dual called the “no-deleting theorem.”

Information is the quanta of consciousness. It is a measurement of awareness following all possible trajectories through the quantum multiverse ensuring the feedback loop of finite information that is reality.

This specific interpretation is based on Hugh Everett’s relative state or many-worlds interpretation (MWI) and information reality code concept. MWI states “all possible alternate histories and futures are real, each representing an actual world” or universe. The reality code behaves similarly to classical coding. Coding theory is the application of information theory manifesting efficient and reliable data transmission in a non-deterministic manner (where meaning is relative). Information in a data set is characterized by its Shannon entropy.

Summary of Key Points (You made it!)
• The QAI ecosystem is underpinned by the scientific notion of information
• Information does not measure what is known, it measures the number of possible alternatives for something
• Relevant information cancels out irrelevant information in a physical system, therefore systems can always obtain new information from other systems
• A qubit can be a coherent superposition of both 0 and 1 eigenstates, according to quantum mechanical properties
• Qubits contain entangled relevant and irrelevant information across the multiverse
• MWI states all possible alternate histories and futures are real, each representing an “actual world” or universe
• Multiverses are finite because information is
• Information is the quanta of consciousness and measurement of awareness

What’s Next?
One interpretation of AI is whoever becomes the leader in this sphere will become the ruler of the world. This is one possible alternative for QAI. Another possible alternative is the validation the many-worlds theory, providing insight into observable world alternate histories and optimized futures because information is available to QAI agent networks. The predictive nature of classical AI to support global superpower decision-making may not happen as planned either. Predictions in the observable world exist in other worlds, so AI predicting the observable future is relative. For example, when a dice lands on the number 1 in the observable world, it lands on the other five alternatives in alternate worlds. Additionally, unknown events in the observable world are known elsewhere in the quantum multiverse and vice versa (alternate histories and futures). Physicist David Deutsch, a proponent of the MWI, believes MWI will be testable through quantum computing. Based on this blog’s conjecture, developing a parallel QAI strategy is the first step in preparing for our changing understanding of the world.

If you enjoyed this mind-bending post, please see Mr. Morris’ previous guest blog posts:

And read Ms. Kania’s Quantum Surprise on the Battlefield?

Victor R. Morris is a civilian irregular warfare and threat mitigation instructor at the Joint Multinational Readiness Center (JMRC) in Germany.

October 11, 2018October 11, 2018

89. “The Queue”

[Editor’s Note: Mad Scientist Laboratory is pleased to present our September edition of “The Queue” – a monthly post listing the most compelling articles, books, podcasts, videos, and/or movies that the U.S. Army’s Training and Doctrine Command (TRADOC) Mad Scientist Initiative has come across during the past month. In this anthology, we address how each of these works either informs or challenges our understanding of the Future Operational Environment. We hope that you will add “The Queue” to your essential reading, listening, or watching each month!]

1. Can you tell a fake video from a real one? and How hard is it to make a believable deepfake? by Tim Leslie, Nathan Hoad, and Ben Spraggon, Australian Broadcast Corporation (ABC) News, 26 and 27 September 2018, respectively.

and

Deep Video Portraits by Hyeongwoo Kim, Pablo Garrido, Ayush Tewari, Weipeng Xu, Justus Thies, Matthias Nießner, Patrick Perez, Christian Richardt, Michael Zollhöfer, and Christian Theobalt, YouTube, 17 May 2018.

Mad Scientist has previously sounded the alarm regarding the advent and potential impact of “DeepFakes” – deceptive files created using artificial neural networks and graphics processors that yield nearly undetectably fake imagery and videos. When distributed via Social Media, these files have the potential to “go viral” — duping, deceiving, and manipulating whole populations of viewers.

ABC’s first news piece provides several video samples, enabling you to test your skills in trying to detect which of the videos provided are real or fake. ABC then goes on to warn that “We are careening toward an infocalypse,” where we may soon find ourselves in living in “A world without truth”.

In their second piece, ABC delves into the step-by-step mechanics of how DeepFakes are created, using former Australian PM Malcolm Turnbull as a use case, and posits placing this fabricated imagery into different, possibly compromising, scenes, manipulating reality for a credulous public.

The Deep Video Portraits YouTube video (snippets of which were used to illustrate both of the aforementioned ABC news pieces) was presented at the Generations SIGGRAPH conference, convened in Vancouver, BC, on 12-16 August 2018. In conjunction with the ABC articles, the combined narration and video in Deep Video Portraits provide a comprehensive primer on how photo realistic, yet completely synthetic (i.e., fictional) re-animations can be accomplished using source and target videos.

Source: Deep Video Portraits – SIGGRAPH 2018 via YouTube /
Christian Theobalt

When combined with the ubiquity of Social Media, these public domain AI algorithms (e.g., FakeApp, DerpFakes, DeepFakes) are democratizing an incredibly disruptive capability. The U.S. must develop and implement means (e.g., education) to “inoculate” its citizenry and mitigate this potentially devastating Gray Zone weapon.

“Attacking an adversary’s most important center of gravity — the spirit of its people — no longer requires massive bombing runs or reams of propaganda. All it takes is a smartphone and a few idle seconds. And anyone can do it.” — P.W. Singer and Emerson T. Brooking in LikeWar – The Weaponization of Social Media

2. “The first “social network” of brains lets three people transmit thoughts to each other’s heads,” by Emerging Technology from the arXiv, MIT Technology Review, 29 September 2018.

In 2015, scientists at University of Washington in Seattle connected two people via a brain to brain interface. The connected individuals were able to play a 20 questions game. Now these scientists have announced the first group brain to brain network. They call the network the “BrainNet” and the individuals were able to play a collaborative Tetris-like game.

Source: BrainNet: A Multi-Person Brain-to-Brain Interface for
Direct Collaboration Between Brains / https://arxiv.org/pdf/1809.08632.pdf

To date, our future operational environment has described the exploding Internet of Things and even the emerging concept of an Internet of Battle Things. The common idea here is connecting things – sensors, weapons, and AI to a human in the or on the loop. The idea of adding the brain to this network opens incredible opportunities and vulnerabilities. We should start asking ourselves questions about this idea: 1) Could humans control connected sensors and weapons with thought alone, 2) Could this be a form of secure communications in the future, and 3) Could the brain be hacked and what vulnerabilities does this add? (Read Battle of the Brain) There are many more questions, but for now maybe we should broaden our ideas about connectivity to the Internet of Everything and Everyone.

3. “Scientists get funding to grow neural networks in petri dishes,” Lehigh University, 14 September 2018.

An overview of running image recognition on living neuron testbed / Source: Xiaochen Guo / Lehigh University

The future of computing may not necessarily be silicon or quantum-based — it may be biological! The National Science Foundation (NSF) recently awarded an interdisciplinary team of biologists and computer engineers $500,000. This is in support of Understanding the Brain and the BRAIN Initiative, a coordinated research effort that seeks to accelerate the development of new neurotechnologies. The intent is to help computer engineers develop new ways to think about the design of solid state machines, and may influence other brain-related research using optogenetics, a biological technique that uses light to control cells — “spike train stimuli” — similar to a two-dimensional bar code. The encoding of the spike train will then be optically applied to a group of networked in vitro neurons with optogenetic labels. This hybrid project could lead to a better understanding of how organic computers and brains function. This suggests a radically different vision of future computing where, potentially, everything from buildings to computers could be grown in much the same way that we “grow” plants or animals today.

4. “These “Robotic Skins” Turn Everyday Objects into Robots,” by Rachael Lallensack, Smithsonian.com, 19 September 2018, reviewed by Ms. Marie Murphy.

A team of roboticists out of Yale University published a report announcing the development of OmniSkins, pliable material with embedded sensors that can animate ordinary, inert objects. OmniSkins turn ordinary objects into robots on contact. These removable sheets can be reused and reconfigured for a variety of functions, from making foam tubes crawl like worms to creating a device which can grab and hold onto objects out of static foam arms. Initially developed for NASA, demonstrations reveal that OmniSkins can make a stuffed animal walk when wrapped around its legs and correct the posture of a person when embedded in their shirt. While these are fun examples, the realistic military applications are vast and varied. OmniSkins could represent a new development in performance-enhancing exoskeletons, enabling them to be lighter and more flexible. These sheets can turn ordinary objects into useful machines in a matter of minutes and can be modified with cameras or other sensors to fit the needs of the mission.

5. “Movement-enhancing exoskeletons may impair decision making,” by Jennifer Chu, MIT, 5 October 2018.

PowerWalk / Source: Bionic Power Inc. via MIT

Researchers from MIT have discovered that the use of exoskeletons to enhance speed, power, and endurance could have a negative effect on attention, decision-making, and cognition. The researchers found that 7 out of 12 subjects actually performed worse on cognitive tasks while wearing an exoskeleton through an obstacle course. The researchers tested them on several cognitive tasks from responding to visual signals to following their squad leader at a defined distance. They concluded that more than half of the subjects wearing the exoskeleton showed a marked decline in reaction time to the various tests. This presents an interesting challenge for technology developers. Does a positive solution in one area negatively affect another, seemingly unrelated, area? Would the subjects in the test have performed better if they had prolonged training with the exoskeletons as opposed to a few days? If so, this presents an additional burden and training demand on Soldiers and the Army. Will a trade study involving not just physical measures, but cognitive ones now need to be integrated into all new Army technology developments and what does this do to the development timeline?

6. “Researchers Create “Spray On” 2-D Antennas,” by Michael Koziol, IEEE Spectrum, 21 September 2018.

Drexel’s MXene “Antenna Spray Paint” / Source: YouTube via IEEE Spectrum

Researchers from Drexel University have developed a novel solution to reducing the size and weight of traditional antennas. Using a metal like titanium or molybdenum, bonded with carbides or nitrides called MXene, they were able to produce a spray-on antenna solution. By dissolving the MXene in water, and using a commercial off-the-shelf spray gun, one can rapidly design, customize, and deploy a working antenna. The spray-on antenna is 100nm thick (versus a traditional copper antenna that is 3,000nm) and has a higher conductivity than carbon nanotubes – a previous solution to the small and thin antenna problem. On a hyperactive battlefield where Soldiers may need on-demand solutions in a compressed timeline, MXene spray-on antennas may be a potential game changer. How much time, materials, and processing can be saved in an operational environment if a Soldier can quickly produce a low profile antenna to a custom specification? What does this mean for logistics if repair parts for antennas no longer need to be shipped from outside the theater of operations?

7. “NASA’s Asteroid-Sampling Spacecraft Begins Its Science Work Today,” by Mike Wall, Space.com, 11 September 2018.

NASA Infographic on the OSIRIS-REx Mission / Source: **https://www.space.com/11808-nasa-asteroid-mission-osiris-rex-1999-rq36-infographic.html**

NASA’s OSIRIS-REx (short for Origins, Spectral Interpretation, Resource Identification, Security – Regolith Explorer) spacecraft commenced studying near-Earth asteroid Bennu’s dust plumes from afar on 11 September 2018. Once the probe achieves orbit around the ~500m-wide space rock on 31 December 2018, it will further explore that body’s dust, dirt, and gravel. Then, in mid-2020, OSIRIS-REx will swoop down to the surface to collect and return a sample of material to Earth in a special return capsule. While this piece represents very cool extraterrestrial science, it is also significant for what it bodes for the future Operational Environment, Multi-Dimensional Operations in the Space Domain, and our newly established Space Force.

“The $800 million OSIRIS-REx mission will … contribute to planetary-defense efforts. For example, the probe’s observations should help researchers better understand the forces that shape potentially dangerous asteroids’ paths through space… (Bennu itself is potentially hazardous; there’s a very small chance that it could hit Earth in the late 22nd century.)”

OSIRIS-REx is not the only probe sampling asteroids – Japan’s Hayabus2 spacecraft is preparing to touch down on the asteroid Ryugu this month. NASA has estimated the total value of resources locked in asteroids is equivalent to $100 Billion for every man, woman, and child on Earth.

This century’s new space race to capitalize on and exploit our solar system’s heretofore untapped mineral wealth, while defending critical space assets, will demand that the U.S. budgets for, develops, and maintains future space-based capabilities (initially unmanned, but eventually manned, as required by mission) to protect and defend our national and industrial space interests.

8. “Soldiers who obliterate enemy fighters with drones will be guided on the morality of their actions by specially trained army chaplains,” by Roy Tingle, Daily Mail Online, 25 September 2018.

Source: Defense Visual Information Distribution Service (DIVIDS)

In possibly an all-time record for the worst news article title, it has been revealed that the British Army is training ethicists to teach soldiers about the morality of killing with drones. Chaplains will spend one year studying and obtaining a Master’s degree in Ethics at Cardiff University so that they can instruct officers on the moral dilemmas involved in killing an enemy from thousands of miles away. Officials have long been concerned about the emotional trauma suffered by drone pilots, as well as the risk that they will be more likely to use deadly force if the confrontation is being played out on a computer screen. This is about the speed of future combat and the decisive action that will be needed on the battlefield in the future. War will remain a human endeavor, but our Soldiers will be stressed to exercise judgement and fight at the ever increasing machine speed. The Army must be prepared to enter new ethical territory and make difficult decisions about the creation and employment of cutting edge technologies. While the Army holds itself to a high ethical standard, new converging technologies may come at an ethical cost. Updating guidance, policy, and law must keep up with what is employed on the battlefield. Many of these ethical dilemmas and questions lack definite answers and are ethical considerations that most of our future adversaries are unlikely to consider.

If you read, watch, or listen to something this month that you think has the potential to inform or challenge our understanding of the Future Operational Environment, please forward it (along with a brief description of why its potential ramifications are noteworthy to the greater Mad Scientist Community of Action) to our attention at: usarmy.jble.tradoc.mbx.army-mad-scientist@mail.mil — we may select it for inclusion in our next edition of “The Queue”!