To make new leaps in advancing artificial intelligence, AI would, as author Jun Wu puts it in Forbes, have to ‘learn to learn’. What would that mean?
As Wu explains, “humans have the unique ability to learn from any situation or surrounding.” Humans can adapt their process of learning. To be able to have such a flexible quality AI needs Artificial General Intelligence – it would have to learn about the learning process, what is called Meta-Learning.
There is one very specific contrast in the learning process between humans and artificial intelligence. While the human capacity for learning is limited, AI has many more resources such as its computational power. Human brainpower has its limits and it also has limited time to learn. But, while AI “learns from more data than the data our human brains use, processing these vast amounts of data requires immense computational power.”
Wu explains that“as the complexity of AI’s tasks grows, there’s also an exponential increase in computational power.” This would mean that even if the cost of computational power is low, “exponential increase is never the scenario that we want.” This is the main reason that at the moment “AI is designed to be specific-purpose learners,” making their learning process more efficient.
But as AI started to learn more, “learning to learn” it started to “infer from data with increasing complexity.” To avoid the exponential increase in computational power, a more efficient learning path had to be devised, and AI had to remember that path.
The whole problem got even more complex when researchers and technologists started to assign multi-tasking problems to AI. To be able to do that, AI “needs to be able to evaluate independent sets of data in parallel. It also needs to relate pieces of data and infers connections on that data.” As one task is being done, AI needs to update its knowledge so that it can apply it in other situations. “Since tasks are interrelated, the evaluations for the tasks will need to be done by the whole network.”
Google developed one such model, MultiModel, which is an AI system that “learned to perform eight different tasks simultaneously. MultiModel can detect objects in images, provide captions, recognize speech, translate between four pairs of languages, and perform grammatical constituency parsing.
While Google’s achievement is a big leap forward, AI still needs to make further strides so that it can become a general-purpose learner. To be able to achieve this it would need to further develop meta-reasoning and meta-learning. As Wu explains, “meta-reasoning focuses on the efficient use of cognitive resources. Meta-learning focuses on human’s unique ability to efficiently use limited cognitive resources and limited data to learn.”
Currently, there are studies being conducted to figure out the gaps between human cognition and the way AI learns such as awareness of internal states, the accuracy of memory or confidence.
All this means that “becoming an artificial generalized learner requires extensive research on how humans learn as well as research on how AI can mimic the way that humans learn. To adapt to new situations such as having the ability to “multitask”, and the ability to make “strategic decisions” with limited resources, are just a few of the hurdles that AI researchers will overcome along the way.”
Charles J. Simon, Author, Will Computers Revolt? – Interview Series
Charles J. Simon, BSEE, MSCS, nationally-recognized entrepreneur, software developer and manager. With a broad management and technical expertise and degrees in both Electrical Engineering and Computer Sciences Mr. Simon has many years of computer experience in industry including pioneering work in AI and CAD (two generations of CAD).
He is also the author of ‘Will Computers Revolt‘, which offers an in-depth view at the future possibility of Artificial General Intelligence (AGI).
What was it that originally attracted you to AI, and specifically to AGI?
I’ve been fascinated by the question, “Can machines think?” ever since I first read Alan Turing’s seminal 1950 paper which begins with that question. So far, the answer is clearly, “No,” but there is no scientific reason why not. I joined the AI community with the initial neural network boom in the late 1980s and since then AI has made great strides. But the intervening thirty years haven’t brought understanding to our machines, an ability which would catapult numerous apps to new levels of usefulness.
You stated that you share the option of MIT AI expert Rodney Brooks who says, ‘that without interaction with an environment – without a robotic body as you will – machines will never exhibit AGI.’ This is basically stating that with insufficient inputs from a robotic body, the AI will never develop AGI capabilities. Outside of computer vision, what types of inputs are needed to develop AGI?
Today’s AI needs to be augmented with basic concepts like the physical existence of objects in a reality, the passage of time, cause and effect—concepts clear to any three-year-old. A toddler uses multiple senses to learn these concepts by touching and manipulating toys, moving through the home, learning language, etc. While it is possible to create an AGI with more limited senses, just as there are deaf people and blind people who are perfectly intelligent but more senses and abilities to interact makes solving the AGI problem easier.
For completeness my simulator can provide senses of smell and taste. It remains to be seen if these will also prove important to AGI.
You stated that ‘A Key Requirement for intelligence is an environment which is external to the intelligence’. The example you gave is that ‘it is unreasonable to expect IBM’s Watson to “understand” anything if it has no underlying idea of what a “thing” is’. This clearly plays in the current limitations of narrow AI, especially natural language processing. How can AI developers best overcome this current limitation of AI?
A key factor is storing knowledge which is not specifically verbal, visual, or tactile but as abstract “Things” which can have verbal, visual, and tactile attributes. Consider something as simple as the phrase, “a red ball”. You know what these words mean because of your visual and tactile experiences. You also know the meaning of related actions like throwing, bouncing, kicking, etc. which all come to mind to some extent when you hear the phrase. Any AI system which is specifically word-based or specifically image-based will miss out on the other levels of understanding.
I have implemented a Universal Knowledge Store which stores any kind of information in a brain-like structure where Things are analogous to neurons and have many attribute references to other Things—references are analogous to synapses. Thus, red and ball are individual Things and a red ball is a Thing which has attribute references to the red Thing and the ball Thing. Both red and ball have references to the corresponding Things for the words “red” and “ball”, each of which, in turn, have references to other Things which define how the words are heard, spoken, read, or spelled as well as possible action Things.
You’ve reached the conclusion that brain simulation of general intelligence is a long way off while AGI may be (relatively) just around the corner. Based on this statement, should we move on from attempting to emulate or create a simulation of the human brain, and just focus on AGI?
Today’s deep learning and related technologies are great for appropriate applications but will not spontaneously lead to understanding. To take the next steps, we need to add techniques specifically targeted at solving the problems which are within the capacity of any three-year-old.
Taking advantage of the intrinsic abilities of our computers can be orders of magnitude more efficient than the biological equivalent or any simulation of it. For example, your brain can store information in the chemistry of biological synapses over several iterations requiring 10-100 milliseconds. A computer can simply store the new synapse value in a single memory cycle, a billion times faster.
In developing AGI software, I have done both biological neural simulation and more efficient algorithms. Carrying forward with the Universal Knowledge Store, when simulated in simulated biological neurons, each Thing requires a minimum of 10 neurons and usually many more. This puts the capacity of the human brain somewhere between ten and a hundred million Things. But perhaps an AGI would appear intelligent if it comprehends only one million Things—well within the scope of today’s high-end desktop computers.
A key unknown is how much of the robot’s time should be allocated to processing and reacting to the world versus time spent imagining and planning. Can you briefly explain the importance of imagination to an AGI?
We can imagine many things and then only act on the ones we like, those which further our internal goals, if you will. The real power of imagination is being able to predict the future—a three-year-old can figure out which sequences of motion will lead her to a goal in another room and an adult can speculate on which words will have the greatest impact on others.
An AGI similarly will benefit from going beyond being purely reactive to speculating on various complex actions and choosing the best.
You believe that Asimov’s three laws of robotics are too simple and ambiguous. In your book you shared some ideas for recommended laws to be programmed in robots. Which laws do you feel are most important for a robot to follow?
New “laws of robotics” will evolve over years as AGI emerges. I propose a few starters:
- Maximize internal knowledge and understanding of the environment.
- Share that knowledge accurately with others (both AGI and human).
- Maximize the well-being of both AGIs and humans as a whole—not just as an individual.
You have some issues with the Turing Test and the concept behind it. Can you explain how you believe the Turing Test is flawed?
The Turing Test has served us well for fifty years as an ad-hoc definition of general intelligence but as AGI nears, we need to hone the definition and we need a clearer definition. The Turing Test is actually a test of how human one is, not how intelligent one is. The longer a computer can maintain the deception, the better it performs on the test. Obviously, asking the question, “Are you a computer?” and related proxy questions such as, “What is your favorite food?” are dead giveaways unless the AGI is programmed to deceive—a dubious objective at best.
Further, the Turing Test has motivated AI development into areas of limited value with (for example) chatbots with vast flexibility in responses but no underlying comprehension.
What would you do differently in your version of the Turing Test?
Better questions could probe specifically into the understanding of time, space, cause-and-effect, forethought, etc. rather than random questions without any particular basis in psychology, neuroscience, or AI. Here are some examples:
- What do you see right now? If you stepped back three feet, what differences would you see?
- If I [action], what would your reaction be?
- if you [action], what will my likely reactions be?
- Can you name three things which are like [object]?
Then, rather than evaluating responses as to whether they are indistinguishable from human responses, they should be evaluated in terms of whether or not they are reasonable responses (intelligent) based on the experience of the entity being tested.
You’ve stated that when faced with demands to perform some short-term destructive activity, properly programmed AGIs will simply refuse. How can we ensure that the AGI is properly programmed to begin with?
Decision-making is goal-based. In combination with an imagination, you (or an AGI) consider the outcome of different possible actions and choose the one which best achieves the goals. In humans, our goals are set by evolved instincts and our experience; an AGI’s goals are entirely up to the developers. We need to ensure that the goals of an AGI align with the goals of humanity as opposed to the personal goals of an individual. [Three possible goals as listed above.]
You’ve stated that it’s inevitable that humans will create an AGI, what’s your best estimate for a timeline?
Facets of AGI will begin to emerge within the coming decade but we won’t all agree that AGI has arrived. Eventually, we will agree that AGI has arrived when they exceed most human abilities by a substantial margin. This will take two or three decades longer.
For all the talks of AGI will it be real consciousness as we know it?
Consciousness manifests in a set of behaviors (which we can observe) which are based on an internal sensation (which we can’t observe). AGIs will manifest the behaviors; they need to in order to make intelligent decisions. But I contend that our internal sensation is largely dependent on our sensory hardware and instincts and so I can guarantee that whatever internal sensations an AGI might have, they will be different from a human’s.
The same can be said for emotions and our sense of free will. In making decisions, one’s belief in free will permeates every decision we make. If you don’t believe you have a choice, you simply react. For an AGI to make thoughtful decisions, it will likewise need to be aware of its own ability to make decisions.
Last question, do you believe that an AGI has more potential for good or bad?
I am optimistic that AGIs will help us to move forward as a species and bring us answers to many questions about the universe. The key will be for us to prepare and decide what our relationship will be with AGIs as we define their goals. If we decide to use the first AGIs as tools of conquest and enrichment, we shouldn’t be surprised if, down the road, they become their own tools of conquest and enrichment against us. If we choose that AGIs are tools of knowledge, exploration, and peace, then that’s what we’re likely to get in return. The choice is up to us.
Thank you for a fantastic interview exploring the future potential of building an AGI. For readers who wish to learn more they may read ‘Will Computers Revolt‘ or visit Charle’s website futureai.guru.
Noah Schwartz, Co-Founder & CEO of Quorum – Interview Series
Noah is an AI systems architect. Prior to founding Quorum, Noah spent 12 years in academic research, first at the University of Southern California and most recently at Northwestern as the Assistant Chair of Neurobiology. His work focused on information processing in the brain and he has translated his research into products in augmented reality, brain-computer interfaces, computer vision, and embedded robotics control systems.
Your interest in AI and robotics started as a little boy. How were you first introduced to these technologies?
The initial spark came from science fiction movies and a love for electronics. I remember watching the movie, Tron, as an 8-year old, followed by Electric Dreams, Short Circuit, DARYL, War Games, and others over the next few years. Although it was presented through fiction, the very idea of artificial intelligence blew me away. And even though I was only 8-years old, I felt this immediate connection and an intense pull toward AI that has never diminished in the time since.
How did your passions for both evolve?
My interest in AI and robotics developed in parallel with a passion for the brain. My dad was a biology teacher and would teach me about the body, how everything worked, and how it was all connected. Looking at AI and looking at the brain felt like the same problem to me – or at least, they had the same ultimate question, which was, How is that working? I was interested in both, but I didn’t get much exposure to AI or robotics in school. For that reason, I initially pursued AI on my own time and studied biology and psychology in school.
When I got to college, I discovered the Parallel Distributed Processing (PDP) books, which was huge for me. They were my first introduction to actual AI, which then led me back to the classics such as Hebb, Rosenblatt, and even McCulloch and Pitts. I started building neural networks based on neuroanatomy and what I learned from biology and psychology classes in school. After graduating, I worked as a computer network engineer, building complex, wide-area-networks, and writing software to automate and manage traffic flow on those networks – kind of like building large brains. The work reignited my passion for AI and motivated me to head to grad school to study AI and neuroscience, and the rest is history.
Prior to founding Quorum, you spent 12 years in academic research, first at the University of Southern California and most recently at Northwestern as the Assistant Chair of Neurobiology. At the time your work focused on information processing in the brain. Could you walk us through some of this research?
In a broad sense, my research was trying to understand the question: How does the brain do what it does using only what it has available? For starters, I don’t subscribe to the idea that the brain is a type of computer (in the von Neumann sense). I see it as a massive network that mostly performs stimulus-response and signal-encoding operations. Within that massive network there are clear patterns of connectivity between functionally specialized areas. As we zoom in, we see that neurons don’t care what signal they’re carrying or what part of the brain they’re in – they operate based on very predictable rules. So if we want to understand the function of these specialized areas, we need to ask a few questions: (1) As an input travels through the network, how does that input converge with other inputs to produce a decision? (2) How does the structure of those specialized areas form as a result of experience? And (3) how do they continue to change as we use our brains and learn over time? My research tried to address these questions using a mixture of experimental research combined with information theory and modeling and simulation – something that could enable us to build artificial decision systems and AI. In neurobiology terms, I studied neuroplasticity and microanatomy of specialized areas like the visual cortex.
You then translated your work into augmented reality, and brain-computer interfaces. What were some of the products you worked on?
Around 2008, I was working on a project that we would now call augmented reality, but back then, it was just a system for tracking and predicting eye movements, and then using those predictions to update something on the screen. To make the system work in realtime, I built a biologically-inspired model that predicted where the viewer would based on their microsaccades – tiny eye movements that occur just before you move your eye. Using this model, I could predict where the viewer would look, then update the frame buffer in the graphics card while their eyes were still in motion. By the time their eyes reached that new location on the screen, the image was already updated. This ran on an ordinary desktop computer in 2008, without any lag. The tech was pretty amazing, but the project didn’t get through to the next round of funding, so it died.
In 2011, I made a more focused effort at product development and built a neural network that could perform feature discovery on streaming EEG data that we measured from the scalp. This is the core function of most brain-computer interface systems. The project was also an experiment in how small of a footprint could we get this running on? We had a headset that read a few channels of EEG data at 400Hz that were sent via Bluetooth to an Android phone for feature discovery and classification, then sent to an Arduino-powered controller that we retrofitted into an off-the-shelf RC car. When in use, an individual who was wearing the EEG headset could drive and steer the car by changing their thoughts from doing mental math to singing a song. The algorithm ran on the phone and created a personalized brain “fingerprint” for each user, enabling them to switch between a variety of robotic devices without having to retrain on each device. The tagline we came up with was “Brain Control Meets Plug-and-Play.”
In 2012, we extended the system so it operated in a much more distributed manner on smaller hardware. We used it to control a multi-segment, multi-joint robotic arm in which each segment was controlled by an independent processor that ran an embedded version of the AI. Instead of using a centralized controller to manipulate the arm, we allowed the segments to self-organize and reach their target in a swarm-like, distributed manner. In other words, like ants forming an ant bridge, the arm segments would cooperate to reach some target in space.
We continued moving in this same direction when we first launched Quorum AI – originally known as Quorum Robotics – back in 2013. We quickly realized that the system was awesome because of the algorithm and architecture, not the hardware, so in late 2014, we pivoted completely into software. Now, 8 years later, Quorum AI is coming full-circle, back to those robotics roots by applying our framework to the NASA Space Robotics Challenge.
Quitting your job as a professor to launch a start-up had to have been a difficult decision. What inspired you to do this?
It was a massive leap for me in a lot of ways, but once the opportunity came up and the path became clear, it was an easy decision. When you’re a professor, you think in multi-year timeframes and you work on very long-range research goals. Launching a start-up is the exact opposite of that. However, one thing that academic life and start-up life have in common is that both require you to learn and solve problems constantly. In a start-up, that could mean trying to re-engineer a solution to reduce product development risk or maybe studying a new vertical that could benefit from our tech. Working in AI is the closest thing to a “calling” as I’ve ever felt, so despite all the challenges and the ups and downs, I feel immensely lucky to be doing the work that I do.
You’ve since then developed Quorum AI, which develops realtime, distributed artificial intelligence for all devices and platforms. Could you elaborate on what exactly this AI platform does?
The platform is called the Environment for Virtual Agents (EVA), and it enables users to build, train, and deploy models using our Engram AI Engine. Engram is a flexible and portable wrapper that we built around our unsupervised learning algorithms. The algorithms are so efficient that they can learn in realtime, as the model is generating predictions. Because the algorithms are task-agnostic, there is no explicit input or output to the model, so predictions can be made in a Bayesian manner for any dimension without retraining and without suffering from catastrophic forgetting. The models are also transparent and decomposable, meaning they can be examined and broken apart into individual dimensions without losing what has been learned.
Once built, the models can be deployed through EVA to any type of platform, ranging from custom embedded hardware or up to the cloud. EVA (and the embeddable host software) also contain several tools to extend the functionality of each model. A few quick examples: Models can be shared between systems through a publication/subscription system, enabling distributed systems to achieve federated learning over both time and space. Models can also be deployed as autonomous agents to perform arbitrary tasks, and because the model is task-agnostic, the task can be changed during runtime without retraining. Each individual agent can be extended with a private “virtual” EVA, enabling the agent to simulate models of other agents in a scale-free manner. Finally, we’ve created some wrappers for deep learning and reinforcement learning (Keras-based) systems to enable these models to operate on the platform, in concert with more flexible Engram-based systems.
You’ve previously described the Quorum AI algorithms as “mathematical poetry”. What did you mean by this?
When you’re building a model, whether you’re modeling the brain or you’re modeling sales data for your enterprise, you start by taking an inventory of your data, then you try out known classes of models to try and approximate the system. In essence, you are creating rough sketches of the system to see what looks best. You don’t expect things to fit the data very well, and there’s some trial and error as you test different hypotheses about how the system works, but with some finesse, you can capture the data pretty well.
As I was modeling neuroplasticity in the brain, I started with the usual approach of mapping out all the molecular pathways, transition states, and dynamics that I thought would matter. But I found that when I reduced the system to its most basic components and arranged those components in a particular way, the model got more and more accurate until it fit the data almost perfectly. It was like every operator and variable in the equations were exactly what they needed to be, there was nothing extra, and everything was essential to fitting the data.
When I plugged the model into larger and larger simulations, like visual system development or face recognition, for instance, it was able to form extremely complicated connectivity patterns that matched what we see in the brain. Because the model was mathematical, those brain patterns could be understood through mathematical analysis, giving new insight into what the brain is learning. Since then, we’ve solved and simplified the differential equations that make up the model, improving computational efficiency by multiple orders of magnitude. It may not be actual poetry, but it sure felt like it!
Quorum AI’s platform toolkit enables devices to connect to one another to learn and share data without needing to communicate through cloud-based servers. What are the advantages of doing it this way versus using the cloud?
We give users the option of putting their AI anywhere they want, without compromising the functionality of the AI. The status quo in AI development is that companies are usually forced to compromise security, privacy, or functionality because their only option is to use cloud-based AI services. If companies do try to build their own AI in-house, it often requires a lot of money and time, and the ROI is rarely worth the risk. If companies want to deploy AI to individual devices that are not cloud-connected, the project quickly becomes impossible. As a result, AI adoption becomes a fantasy.
Our platform makes AI accessible and affordable, giving companies a way to explore AI development and adoption without the technical or financial overhead. And moreover, our platform enables users to go from development to deployment in one seamless step.
Our platform also integrates with and extends the shelf-life of other “legacy” models like deep learning or reinforcement learning, helping companies repurpose and integrate existing systems into newer applications. Similarly, because our algorithms and architectures are unique, our models are not black boxes, so anything that the system learns can be explored and interpreted by humans, and then extended to other areas of business.
It’s believed by some that Distributed Artificial Intelligence (DAI), could lead the way to Artificial General Intelligence (AGI). Do you subscribe to this theory?
I do, and not just because that’s the path we’ve set out for ourselves! When you look at the brain, it’s not a monolithic system. It’s made up of separate, distributed systems that each specialize in a narrow range of brain functions. We may not know what a particular system is doing, but we know that its decisions depend significantly on the type of information it’s receiving and how that information changes over time. (This is why neuroscience topics like the connectome are so popular.)
In my opinion, if we want to build AI that is flexible and that behaves and performs like the brain, then it makes sense to consider distributed architectures like those that we see in the brain. One could argue that deep learning architectures like multi-layer networks or CNNs can be found in the brain, and that’s true, but those architectures are based on what we knew about the brain 50 years ago.
The alternative to DAI is to continue iterating on monolithic, inflexible architectures that are tightly coupled to a single decision space, like those that we see in deep learning or reinforcement learning (or any supervised learning method, for that matter). I would suggest that these limitations are not just a matter of parameter tweaking or adding layers or data conditioning – these issues are fundamental to deep learning and reinforcement learning, at least as we define them today, so new approaches are required if we’re going to continue innovating and building the AI of tomorrow.
Do you believe that achieving AGI using DAI is more likely than reinforcement learning and/or deep learning methods that are currently being pursued by companies such as OpenAI and DeepMind?
Yes, although from what they’re blogging about, I suspect OpenAI and DeepMind are using more distributed architectures than they let on. We’re starting to hear more about multi-system challenges like transfer learning or federated/distributed learning, and coincidentally, about how deep learning and reinforcement learning approaches aren’t going to work for these challenges. We’re also starting to hear from pioneers like Yoshua Bengio about how biologically-inspired architectures could bridge the gap! I’ve been working on biologically-inspired AI for almost 20 years, so I feel very good about what we’ve learned at Quorum AI and how we’re using it to build what we believe is the next generation of AI that will overcome these limitations.
Is there anything else that you would like to share about Quorum AI?
We will be previewing our new platform for distributed and agent-based AI at the Federated and Distributed Machine Learning Conference in June 2020. During the talk, I plan to present some recent data on several topics, including sentiment analysis as a bridge to achieving empathic AI.
I would like to give a special thank you to Noah for these amazing answers, and I would recommend that you visit the Quorum to learn more.
China Beginning to Implement AI in Court System
China is beginning to experiment with artificial intelligence and other technologies within their court system. It is another example of how artificial intelligence is becoming important in every aspect of society.
The nation is using artificial intelligence judges, cyber courts, and verdicts that are delivered on chat apps in developing their new system. Digitization helps streamline case-handling, and cyberspace and technologies such as blockchain and cloud computing are being used as well.
These new developments were announced by the nation’s Supreme People’s Court in a new policy paper.
One of the aspects of the new court system is a “mobile court” that is offered on WeChat, a social media platform popular in the country. WeChat has already taken up over 3 million legal cases or other judicial procedures since it began in March, according to the Supreme People’s Court.
In a demonstration that was given, authorities showed the way the Hangzhou Internet Court operates. It has an online interface that contains an AI judge portrayed with an on-screen avatar. Litigants appear before the AI judge on video chat, and it prompts them to present their cases.
“Does the defendant have any objection to the nature of the judicial blockchain evidence submitted by the plaintiff?” the virtual judge said as it sat under China’s national emblem.
“No objection,” a human plaintiff answered.
Some of the cases that are handled at the Hangzhou court include online trade disputes, copyright cases and e-commerce product liability claims. The civil complaints can be registered by litigants, and they can later log on to attend their court hearing.
According to officials, the simple functions fall into the realm of the virtual judge, which helps the human justices who are responsible for making the major rulings in each case.
One of the main reasons for implementing digitization into the court system is to help keep up with the growing number of cases that come from mobile payments and e-commerce in China. The nation has about 850 million mobile internet users, the most of any nation in the world.
“(Concluding cases) at a faster speed is a kind of justice, because justice delayed is justice denied,” Hangzhou Internet Court Vice President Ni Defeng said.
According to Ni, blockchain technology is especially useful. It helps streamline and create clearer records of the legal process.
Similar chambers have now been created in Beijing and Guangzhou in the south. In total, the branches have accepted 118,764 cases and concluded 88,401, according to the Supreme People’s Court.
The “mobile court” option on WeChat allows users to complete case filings, hearings, and evidence exchange without every physically appearing in a courtroom.
The program has been launched in 12 provinces and regions, and courts all around the nation are using other online tools as well. Zhou Qiang, chief justice and president of the Supreme People’s Court, told a panel that 90 percent of China’s courts had handled some type of online case since October.
At the same time, President Xi Jinping is pushing China forward in the AI space, and he is trying to turn them into the world’s leader in technology. All of this is done with a direct link to the government, and that is what concerns other nations like the United States.
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