Category: AI

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A one-up on motion capture

A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.
 

MIT researchers used the RISP method to predict the action sequence, joint stiffness, or movement of an articulated hand, like this one, from a target image or video.

From “Star Wars” to “Happy Feet,” many beloved films contain scenes that were made possible by motion capture technology, which records movement of objects or people through video. Further, applications for this tracking, which involve complicated interactions between physics, geometry, and perception, extend beyond Hollywood to the military, sports training, medical fields, and computer vision and robotics, allowing engineers to understand and simulate action happening within real-world environments.

As this can be a complex and costly process — often requiring markers placed on objects or people and recording the action sequence — researchers are working to shift the burden to neural networks, which could acquire this data from a simple video and reproduce it in a model. Work in physics simulations and rendering shows promise to make this more widely used, since it can characterize realistic, continuous, dynamic motion from images and transform back and forth between a 2D render and 3D scene in the world. However, to do so, current techniques require precise knowledge of the environmental conditions where the action is taking place, and the choice of renderer, both of which are often unavailable.

Now, a team of researchers from MIT and IBM has developed a trained neural network pipeline that avoids this issue, with the ability to infer the state of the environment and the actions happening, the physical characteristics of the object or person of interest (system), and its control parameters. When tested, the technique can outperform other methods in simulations of four physical systems of rigid and deformable bodies, which illustrate different types of dynamics and interactions, under various environmental conditions. Further, the methodology allows for imitation learning — predicting and reproducing the trajectory of a real-world, flying quadrotor from a video.

“The high-level research problem this paper deals with is how to reconstruct a digital twin from a video of a dynamic system,” says Tao Du PhD '21, a postdoc in the Department of Electrical Engineering and Computer Science (EECS), a member of Computer Science and Artificial Intelligence Laboratory (CSAIL), and a member of the research team. In order to do this, Du says, “we need to ignore the rendering variances from the video clips and try to grasp of the core information about the dynamic system or the dynamic motion.”

Du’s co-authors include lead author Pingchuan Ma, a graduate student in EECS and a member of CSAIL; Josh Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; Wojciech Matusik, professor of electrical engineering and computer science and CSAIL member; and MIT-IBM Watson AI Lab principal research staff member Chuang Gan. This work was presented this week the International Conference on Learning Representations.

While capturing videos of characters, robots, or dynamic systems to infer dynamic movement makes this information more accessible, it also brings a new challenge. “The images or videos [and how they are rendered] depend largely on the on the lighting conditions, on the background info, on the texture information, on the material information of your environment, and these are not necessarily measurable in a real-world scenario,” says Du. Without this rendering configuration information or knowledge of which renderer is used, it’s presently difficult to glean dynamic information and predict behavior of the subject of the video. Even if the renderer is known, current neural network approaches still require large sets of training data. However, with their new approach, this can become a moot point. “If you take a video of a leopard running in the morning and in the evening, of course, you'll get visually different video clips because the lighting conditions are quite different. But what you really care about is the dynamic motion: the joint angles of the leopard — not if they look light or dark,” Du says.

In order to take rendering domains and image differences out of the issue, the team developed a pipeline system containing a neural network, dubbed “rendering invariant state-prediction (RISP)” network. RISP transforms differences in images (pixels) to differences in states of the system — i.e., the environment of action — making their method generalizable and agnostic to rendering configurations. RISP is trained using random rendering parameters and states, which are fed into a differentiable renderer, a type of renderer that measures the sensitivity of pixels with respect to rendering configurations, e.g., lighting or material colors. This generates a set of varied images and video from known ground-truth parameters, which will later allow RISP to reverse that process, predicting the environment state from the input video. The team additionally minimized RISP’s rendering gradients, so that its predictions were less sensitive to changes in rendering configurations, allowing it to learn to forget about visual appearances and focus on learning dynamical states. This is made possible by a differentiable renderer.

The method then uses two similar pipelines, run in parallel. One is for the source domain, with known variables. Here, system parameters and actions are entered into a differentiable simulation. The generated simulation’s states are combined with different rendering configurations into a differentiable renderer to generate images, which are fed into RISP. RISP then outputs predictions about the environmental states. At the same time, a similar target domain pipeline is run with unknown variables. RISP in this pipeline is fed these output images, generating a predicted state. When the predicted states from the source and target domains are compared, a new loss is produced; this difference is used to adjust and optimize some of the parameters in the source domain pipeline. This process can then be iterated on, further reducing the loss between the pipelines.

To determine the success of their method, the team tested it in four simulated systems: a quadrotor (a flying rigid body that doesn’t have any physical contact), a cube (a rigid body that interacts with its environment, like a die), an articulated hand, and a rod (deformable body that can move like a snake). The tasks included estimating the state of a system from an image, identifying the system parameters and action control signals from a video, and discovering the control signals from a target image that direct the system to the desired state. Additionally, they created baselines and an oracle, comparing the novel RISP process in these systems to similar methods that, for example, lack the rendering gradient loss, don’t train a neural network with any loss, or lack the RISP neural network altogether. The team also looked at how the gradient loss impacted the state prediction model’s performance over time. Finally, the researchers deployed their RISP system to infer the motion of a real-world quadrotor, which has complex dynamics, from video. They compared the performance to other techniques that lacked a loss function and used pixel differences, or one that included manual tuning of a renderer’s configuration.

In nearly all of the experiments, the RISP procedure outperformed similar or the state-of-the-art methods available, imitating or reproducing the desired parameters or motion, and proving to be a data-efficient and generalizable competitor to current motion capture approaches.

For this work, the researchers made two important assumptions: that information about the camera is known, such as its position and settings, as well as the geometry and physics governing the object or person that is being tracked. Future work is planned to address this.

“I think the biggest problem we're solving here is to reconstruct the information in one domain to another, without very expensive equipment,” says Ma. Such an approach should be “useful for [applications such as the] metaverse, which aims to reconstruct the physical world in a virtual environment," adds Gan. “It is basically an everyday, available solution, that’s neat and simple, to cross domain reconstruction or the inverse dynamics problem,” says Ma.

This research was supported, in part, by the MIT-IBM Watson AI Lab, Nexplore, DARPA Machine Common Sense program, Office of Naval Research (ONR), ONR MURI, and Mitsubishi Electric.

Lauren Hinkel | MIT-IBM Watson AI Lab

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The case for hybrid artificial intelligence

Cognitive scientist Gary Marcus believes advances in artificial intelligence will rely on hybrid AI, the combination of symbolic AI and neural networks.

Deep learning, the main innovation that has renewed interest in artificial intelligence in the past years, has helped solve many critical problems in computer vision, natural language processing, and speech recognition. However, as the deep learning matures and moves from hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.

This is a reality that many of the pioneers of deep learning and its main component, artificial neural networks, have acknowledged in various AI conferences in the past year. Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, the three “godfathers of deep learning,” have all spoken about the limits of neural networks.

The question is, what is the path forward?

At NeurIPS 2019, Bengio discussed system 2 deep learning, a new generation of neural networks that can handle compositionality, out of order distribution, and causal structures. At the AAAI 2020 Conference, Hinton discussed the shortcomings of convolutional neural networks (CNN) and the need to move toward capsule networks.

But for cognitive scientist Gary Marcus, the solution lies in developing hybrid models that combine neural networks with symbolic artificial intelligence, the branch of AI that dominated the field before the rise of deep learning. In a paper titled “The Next Decade in AI: Four Steps Toward Robust Artificial Intelligence,” Marcus discusses how hybrid artificial intelligence can solve some of the fundamental problems deep learning faces today.

Connectionists, the proponents of pure neural network–based approaches, reject any return to symbolic AI. Hinton has compared hybrid AI to combining electric motors and internal combustion engines. Bengio has also shunned the idea of hybrid artificial intelligence on several occasions.

But Marcus believes the path forward lies in putting aside old rivalries and bringing together the best of both worlds.

What’s missing in deep neural networks?

The limits of deep learning have been comprehensively discussed. But here, I would like to generalization of knowledge, a topic that has been widely discussed in the past few months. While human-level AI is at least decades away, a nearer goal is robust artificial intelligence.

Here’s how Marcus defines robust AI: “Intelligence that, while not necessarily superhuman or self-improving, can be counted on to apply what it knows to a wide range of problems in a systematic and reliable way, synthesizing knowledge from a variety of sources such that it can reason flexibly and dynamically about the world, transferring what it learns in one context to another, in the way that we would expect of an ordinary adult.”

Those are key features missing from current deep learning systems. Deep neural networks can ingest large amounts of data and exploit huge computing resources to solve very narrow problems, such as detecting specific kinds of objects or playing complicated video games in specific conditions.

However, they’re very bad at generalizing their skills. “We often can’t count on them if the environment differs, sometimes even in small ways, from the environment on which they are trained,” Marcus writes.

Case in point: An AI trained on thousands of chair pictures won’t be able to recognize an upturned chair if such a picture was not included in its training dataset. A super-powerful AI trained on tens of thousands of hours of StarCraft 2 gameplay can play at championship level, but only under limited conditions. As soon as you change the map or the units in the game, its performance will take a nosedive. And it can’t play any game that is similar to StarCraft 2, such as Warcraft or Command & Conquer.

AI AlphaStar StarCraft II
A deep learning algorithm that plays championship-level StarCraft can’t play a similar game. It won’t even be able to maintain its level of gameplay if the settings are changed the slightest bit.

The current approach to solve AI’s generalization problem is to scale the models: Create bigger neural networks, gather larger datasets, use larger server clusters, and train the reinforcement learning algorithms for longer hours.

“While there is value in such approaches, a more fundamental rethink is required,” Marcus writes in his paper.

In fact, the “bigger is better” approach has yielded modest results at best while creating several other problems that remain unsolved. For one thing, the huge cost of developing and training large neural networks is threatening to centralize the field in the hands of a few very wealthy tech companies.

When it comes to dealing with language, the limits of neural networks become even more evident. Language models such as OpenAI’s GPT-2 and Google’s Meena chatbot each have more than a billion parameters (the basic unit of neural networks) and have been trained on gigabytes of text data. But they still make some of the dumbest mistakes, as Marcus has pointed out in an article earlier this year.

“When sheer computational power is applied to open-ended domain—such as conversational language understanding and reasoning about the world—things never turn out quite as planned. Results are invariably too pointillistic and spotty to be reliable,” Marcus writes.

What’s important here is the term “open-ended domain.” Open-ended domains can be general-purpose chatbots and AI assistants, roads, homes, factories, stores, and many other settings where AI agents interact and cooperate directly with humans. As the past years have shown, the rigid nature of neural networks prevents them from tackling problems in open-ended domains. In his paper, Marcus discusses this topic in detail.

Why we need to combine symbolic AI and neural networks?

Connectionists believe that approaches based on pure neural network structures will eventually lead to robust or general AI. After all, the human brain is made of physical neurons, not physical variables and class placeholders and symbols.

But as Marcus points out in his essay, “Symbol manipulation in some form seems to be essential for human cognition, such as when a child learns an abstract linguistic pattern, or the meaning of a term like sister that can be applied in an infinite number of families, or when an adult extends a familiar linguistic pattern in a novel way that extends beyond a training distributions.”

Marcus’ premise is backed by research from several cognitive scientists over the decades, including his own book The Algebraic Mind and the more recent Rebooting AI. (Another great read in this regard is the second chapter of Steven Pinker’s book How the Mind Works, in which he lays out evidence that symbol manipulation is an essential part of the brain’s functionality.)

We already have proof that symbolic systems work. It’s everywhere around us. Our web browsers, operating systems, applications, games, etc. are based on rule-based programs. “The same tools are also, ironically, used in the specification and execution of virtually all of the world’s neural networks,” Marcus notes.

Decades of computer science and cognitive science have proven that being able to store and manipulate abstract concepts is an essential part of any intelligent system. And that is why symbol-manipulation should be a vital component of any robust AI system.

“It is from there that the basic need for hybrid architectures that combine symbol manipulation with other techniques such as deep learning most fundamentally emerges,” Marcus says.

Examples of hybrid AI systems

human brain

The benefit of hybrid AI systems is that they can combine the strengths of neural networks and symbolic AI. Neural nets can find patterns in the messy information we collect from the real world, such as visual and audio data, large corpora of unstructured text, emails, chat logs, etc. And on their part, rule-based AI systems can perform symbol-manipulation operations on the extracted information.

Despite the heavy dismissal of hybrid artificial intelligence by connectionists, there are plenty of examples that show the strengths of these systems at work. As Marcus notes in his paper, “Researchers occasionally build systems containing the apparatus of symbol-manipulation, without acknowledging (or even considering the fact) that they have done so.” Marcus iterates several examples where hybrid AI systems are silently solving vital problems.

One example is the Neuro-Symbolic Concept Learner, a hybrid AI system developed by researchers at MIT and IBM. The NSCL combines neural networks to solve visual question answering (VQA) problems, a class of tasks that is especially difficult to tackle with pure neural network–based approaches. The researchers showed that NCSL was able to solve the VQA dataset CLEVR with impressive accuracy. Moreover, the hybrid AI model was able to achieve the feat using much less training data and producing explainable results, addressing two fundamental problems plaguing deep learning.

Google’s search engine is a massive hybrid AI that combines state-of-the-art deep learning techniques such as Transformers and symbol-manipulation systems such as knowledge-graph navigation tools.

AlphaGo, one of the landmark AI achievements of the past few years, is another example of combining symbolic AI and deep learning.

“There are plenty of first steps towards building architectures that combine the strengths of the symbolic approaches with insights from machine learning, in order to develop better techniques for extracting and generalizing abstract knowledge from large, often noisy data sets,” Marcus writes.

The paper goes into much more detail about the components of hybrid AI systems, and the integration of vital elements such as variable binding, knowledge representation and causality with statistical approximation.

“My own strong bet is that any robust system will have some sort of mechanism for variable binding, and for performing operations over those variables once bound. But we can’t tell unless we look,” Marcus writes.

Lessons from history

One thing to commend Marcus on is his persistence in the need to bring together all achievements of AI to advance the field. And he has done it almost single-handedly in the past years, against overwhelming odds where most of the prominent voices in artificial intelligence have been dismissing the idea of revisiting symbol manipulation.

Marcus sticking to his guns is almost reminiscent of how Hinton, Bengio, and LeCun continued to push neural networks forward in the decades where there was no interest in them. Their faith in deep neural networks eventually bore fruit, triggering the deep learning revolution in the early 2010s, and earning them a Turing Award in 2019.

It will be interesting to see where Marcus’ quest for creating robust, hybrid AI systems will lead to.

 


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Hybrid AI Will Go Mainstream in 2022

Analysts predict an AI boom, driven by possibilities and record funding. While challenges remain, a hybrid approach combining the best of the realm may finally send it sailing into the mainstream.

Artificial intelligence (AI) is becoming the dominant trend in data ecosystems around the world, and by all counts, it will accelerate as the decade unfolds. The more the data community learns about AI and what it can do, the faster it empowers IT systems and structures. This is primarily why IDC predicts the market to top $500 billion as early as 2024, with penetration across virtually all industries driving a wealth of applications and services designed to make work more effective. In fact, CB Insights Research reported that at the close of Q3 2021, funding for AI companies had already surpassed 2020 levels by roughly 55%, setting a global record for the fourth consecutive quarter.

In 2022, we can expect AI to become better in solving practical problems that hamper unstructured language data-driven processes, thanks to improvements in complex cognitive tasks such as natural language understanding (NLU). At the same time, there will be increased scrutiny into how and why AI does what it does, such as ongoing efforts by the U.S. National Institutes of Standards and Technology (NIST) aimed at more explainable AI. This will require greater transparency into AI’s algorithmic functions without diminishing its performance or raising costs.

You shall know a word by the company it keeps

Of all the challenges that AI must cope with, understanding language is one of the toughest. While most AI solutions can crunch massive volumes of raw numbers or structured data in the blink of an eye, the multitude of meanings and nuances in language, based on the context they are in is another matter entirely. More often than not, words are contextual, which means they convey different understandings in different circumstances. Something easy and natural for our brains is not that easy for any piece of software.

 

This is why the development of software that can interpret language correctly and reliably has become a critical factor in the development of AI across the board. Achieving this level of computational prowess would literally unleash the floodgates of AI development by allowing it to access and ingest virtually any kind of knowledge.

NLU is a vital piece of this puzzle by virtue of its ability to leverage the wealth of language-based information. Language inhabits all aspects of enterprise activity, which means that an AI approach cannot be complete without extracting as much value as possible from this type of data.

A knowledge-based, or symbolic AI approach, leverages a knowledge graph which is an open box. Its structure is created by humans and is understood to represent the real world where concepts are defined and related to each other by semantic relationships. Thanks to knowledge graphs and NLU algorithms, you can read and learn from any text, out-of-the-box, and gain a true understanding of how data is being interpreted and conclusions are being drawn from that interpretation. This is similar to how we as humans are able to create our own specific, domain-oriented knowledge, and it enables AI projects to link its algorithmic results to explicit representations of knowledge.

In 2022, we should see a definitive shift toward this kind of AI approach combining both different techniques. Hybrid AI leverages different techniques to improve overall results and better tackle complex cognitive problems. Hybrid AI is an increasingly popular approach for NLU and natural language processing (NLP). Bringing together the best of AI-based knowledge or symbolic AI and learning models (machine learning, ML) is the most effective way to unlock the value of unstructured language data with the accuracy, speed and scale required by today’s businesses.

Not only will the use of knowledge, symbolic reasoning and semantic understanding produce more accurate results and a more efficient, effective AI environment, it will also reduce the need for cumbersome and resource-intensive training, based on wasteful volumes of documents on expensive, high-speed data infrastructure. Domain-specific knowledge can be added through subject matter experts and/or machine learning algorithms leveraging the analysis of small and pinpointed training sets of data to produce highly accurate, actionable results quickly and efficiently. 

The world of hybrid AI

But why is this transition happening now? Why hasn’t AI been able to harness language-based knowledge previously? We have been led to believe that learning approaches can solve any of our problems. In some cases, they can, but just because ML does well with certain needs and specific contexts doesn’t mean it is always the best method. And we see this all too often when it comes to the ability to understand and process language. Only in the past few years have we seen significant advancements in NLU based on hybrid (or composite) AI approaches.

Rather than throwing one form of AI, with its limited set of tools, at a problem, we can now utilize multiple, different approaches. Each can target the problem from a different angle, using different models, to evaluate and solve the issue in a multi-contextual way. And since each of these techniques can be evaluated independently of one another, it becomes easier to determine which ones deliver the most optimal outcomes.

With the enterprise already having gotten a taste of what AI can do, this hybrid approach is poised to become a strategic initiative in 2022. It produces significant time and cost benefits, while boosting the speed, accuracy and efficiency of analytical and operational processes. To take just one example, the process of annotation is currently performed by select experts, in large part due to the difficulty and expense of training. By combining the proper knowledge repositories and graphs, however, the training can be vastly simplified so that the process itself can be democratized among the knowledge workforce.

More to Come

Of course, research in all forms of AI is ongoing. But we will see particular focus on expanding the knowledge graph and automating ML and other techniques because enterprises are under constant pressure to leverage vast amounts of data quickly and at low cost.

As the year unfolds, we will see steady improvements in the way organizations apply these hybrid models to some of their most core processes. Business automation in the form of email management and search is already in sight. The current keyword-based search approach, for instance, is inherently incapable of absorbing and interpreting entire documents, which is why they can only extract basic, largely non-contextual information. Likewise, automation email management systems can rarely penetrate meaning beyond simple product names and other points of information. In the end, users are left to sort through a long list of hits trying to find the salient pieces of knowledge. This slows down processes, delays decision-making and ultimately hampers productivity and revenue.

Empowering NLU tools with symbolic comprehension under a hybrid framework will give all knowledge-based organizations the ability to mimic the human ability to comprehend entire documents across their intelligent, automated processes.

By , CTO at expert.ai on March 2, 2022 in Artificial Intelligence

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What is Hybrid AI?

 

Researchers are working to combine the strengths of symbolic AI and neural networks to develop Hybrid AI.

 
As the research community makes progress in artificial intelligence and deep learning, scientists are increasingly feeling the need to move towards hybrid artificial intelligence. Hybrid AI is touted to solve fundamental problems that deep learning faces today. 

Hybrid AI brings together the best aspects of neural networks and symbolic AI. Combining huge data sets (visual and audio, textual, emails, chat logs, etc.) allows neural networks to extract patterns. Then, rule-based AI systems can manipulate the retrieved information by using algorithms to manipulate symbols.

Researchers are working to develop hybrid AI systems that can figure out simple abstract relations between objects and the reason behind them as effortlessly as a human brain. 

What is symbolic AI?

During the 1960s and 1970s, new technological advances were met with researchers’ increasing desire to understand how machines and nature interact. Researchers believed that using symbolic approaches would inevitably produce an artificially intelligent machine, which was seen as their discipline’s long-term goal.

The “good old-fashioned artificial intelligence” or “GOFAI” was coined by John Haugeland in his 1985 book ‘Artificial Intelligence: The Very Idea‘ that explored artificial intelligence’s ethical and philosophical implications. Since the initial efforts to build thinking computers in the 1950s, research and development in the AI field have followed two parallel approaches: symbolic AI and connectionist AI. 

Symbolic AI (also known as Classical AI) is an area of artificial intelligence research that focuses on attempting to express human knowledge clearly in a declarative form, that is, facts and rules. From the mid-1950s until the late 1980s, there was significant use of symbolic artificial intelligence. On the other hand, in recent years, a connectionist approach such as machine learning with deep neural networks has come to the forefront.

Combining symbolic AI and neural networks 

 

There has been a shift from the symbolic approach in the past few years due to its technical limits. 

According to David Cox, IBM Director at MIT-IBM Watson AI Lab, deep learning and neural networks excel at the “messiness of the world,” but symbolic AI does not. Neural networks meticulously study and compare a large number of annotated instances to discover significant relationships and create corresponding mathematical models. 

Several prominent IT businesses and academic labs have put significant effort into the use of deep learning. Neural networks and deep learning excel at tasks where symbolic AI fails. As a result, it’s being used to tackle complex challenges today. For example, deep learning has made significant contributions to the computer vision revolution with use cases in facial recognition and tuberculosis detection. Language-related activities have also benefited from deep learning breakthroughs.

There are, however, certain limits to deep learning and neural networks. One argument is that the availability of large volumes of data depends on it. In addition, neural networks are also vulnerable to hostile instances, often known as adversarial data, which can manipulate an AI model’s behaviour in unpredictable and harmful ways.

However, when combined with each other, symbolic AI and neural networks can form a good base for developing hybrid AI systems.

Future of hybrid AI 

The hybrid AI model utilises the neural network’s ability to process and evaluate unstructured data while also using symbolic AI techniques. Connectivist viewpoints argue that techniques based on neural networks will eventually provide sophisticated and broadly applicable AI. In 2019, International Conference on Learning Representations (ICLR) featured a paper in which the researchers combined neural networks with rule-based artificial intelligence to create an AI model. This approach has been called the “Neuro-Symbolic Concept Learner” (NCSL); it claims to overcome the difficulties AI faces and to be superior to the sum of its parts. NCSL, a hybrid system of AI developed by researchers at MIT and IBM tackles visual question answering (VQA) problems; the NSCL uses neural networks in conjunction with neural networks with remarkable accuracy. The researchers demonstrated that NCSL was able to handle the VQA dataset CLEVR. Even more important, the hybrid AI model could make outstanding achievements with less training data and overcome two long-standing deep learning challenges.

Even Google search engine is a complex, all-in-one AI system made up of cutting-edge deep learning tools such as Transformers and advanced symbol manipulation tools like the knowledge graph.

BY RITIKA SAGAR

PUBLISHED ON JULY 21, 202


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The Identity Paradigm

Tony Gregory intercultual psychologist

In 1962, Thomas Kuhn published the most important intellectual work of the 20th century, The Structure of Scientific Revolutions. In it he argued against the long-held belief that evolution was an uninterrupted and steady continuum. He posited instead that progress came in jerks and starts – long periods of calm that were managed according to widely accepted beliefs and customs interspersed with brief violent periods of enormous change, like the renaissance, when all that had been accepted before was challenged and frequently overthrown. He called these violent brief periods 'paradigm shifts,' and since that time it has become an accepted part of how we see our world.

It was not long after that that Alvin Toffler wrote Future Shock, in which he argued that not only was Kuhn correct, but that the periods of relative stability between the brief and violent episodes of change were becoming shorter, so short in fact that it challenged out ability as humans to adjust to one set of revolutionary changes before another set was already upon us.

He gave as an example the impact of railroads on history. When Julius Caesar marched his legions south from France to Italy to conquer Rome in the first century AD it took more or less the same time as it took Napoleon to cover the same distance seventeen hundred years later. But it was only forty years after that when the railroad linking France and Italy was completed, cutting the journey from two months to three days. When Lincoln was assassinated in 865, it was only noon the next day that they heard about it in San Francisco. I saw the assassination of Robert Kennedy live – at the same time it happened – a century later. There are many examples you can give, but the impact is similar – changes coming at such a fast pace produce stress, and stress is the handmaiden of paradigm change.

One of the most important insights about paradigm shifts is that the animals that did well following the rules of the previous paradigm did not do well in the new one if they continued to follow those same rules because all the rules had changed (just ask the dinosaurs). People that owned stables during the age of agriculture were no longer at the center of things when the automobile replaced the horse as the accepted means of transportation. Quite clearly, there is a clear message here – if the paradigm changes and you don't, your future looks bleak.

But it is important to point out that not all paradigm changes are the same. The industrial revolution was a definite change in paradigms, and economic power in the world shifted dramatically from an emphasis on ownership of land to an emphasis on access to raw materials and the means of production. Yet the family structure survived the change, as did religion and nationalism.

The change from the ice age to the Holocene period which we presently inhabit was also a paradigm shift, but one far more powerful than the movement from agriculture to industry. When the glaciers finally retreated and the planet warmed, our species (Homo sapiens in case you forgot) spread around the globe and our numbers exploded because it became possible for us to sustain ourselves in far larger groups, which in turn allowed us to do things we had never done before, like build permanent dwellings and use the land to provide us with food on a continual basis, which we called agriculture.

We actually started recording events then, some ten thousand years ago – we call it history. The concentration of our species in such large numbers created a need to order things, to solve disputes and regulate affairs, and that led to the birth of customs, religion and culture and the domestication of animals. I could go on but I think you get the point – the change was so dramatic that nothing that had been true before remained. It was a transformation.

The other thing to point out is that all of this happened slowly, over the period of more than one lifetime. The people that came south after the glaciers retreated were long gone before the first cities were built and the first empires were formed. Akkadia was the first human empire, formed in Mesopotamia 4300 years ago, and that’s a full five thousand years after the glaciers began to retreat. We had time to adjust, time to consider how to respond to our new reality, time to try different ways of approaching things, and time to fail and try something else and still survive (unlike the Neanderthals).

Now, at the beginning of what we call our twenty-first century since we started writing stuff down, it appears that we are on the verge of a new paradigm shift, and possibly one as dramatic as that last big one when the ice retreated. If that is true, then we should remember that insight from so long ago – nothing that went before remained. That is the mark of a complete transformation.

It's tough for us to think about that because whether we like it or not we are children of our current paradigm, formed by its assumptions, educated in its customs and brainwashed accordingly. We find it difficult to think of ourselves without these things we are wedded to. Look, when Copernicus stepped forward in 1543 and said "Uh…I just want to point out that the earth is not the center , it’s the sun" even very smart people had a hard time wrapping their heads around that. It took literally a hundred years before it was accepted as a scientific proof (except in parts of the United States where science is still not accepted until this day). That is called denial of reality, and back then a lot of people were in that state for an extended period of time.

So when I step up and suggest that everything is about to change, not just the small stuff, I imagine that a lot of people – smart people – will find that hard to accept. Nevertheless, I think our ice age is about to end, and, in the spirit of Alvin Toffler, I think the new paradigm will be upon us so quickly that we will not have a lot of time to react. So, with that proviso, here is my preview of the next paradigm. Please forgive me if not all of the changes are of the same magnitude and if I leave some out. I, too, am a child of our current paradigm, and like everyone else my vision to see ahead is both limited and subjective.

We have become accustomed to identifying ourselves in relation to other people, to our geographical location, our membership in some political group ( a nation) and to our occupation, and to what we believe, which the more extreme among us label 'the truth.' So, I say I am a father, a husband, a member of a certain family, a citizen of a community and a nation, and I work as a psychologist - and all of that is about to change.

WORK

let's start with the easy one – work. There is not enough of it to go around. In our current paradigm we regard unemployment as some sort of negative state, a disease that needs to be treated. We talk about work moving around the world and call it outsourcing. We act as if the lack of jobs in North America means those same jobs have somehow magically moved to Asia and it is the cause of a great deal of unrest. None of that is true.

What is true is that human work, as we have come to know it during the last three centuries, is disappearing. What was once done by human labor is now done by machines. In a report on Automation in 2020, the World Economic Forum predicted by the year 2025, 53% of work would be performed by humans and 47% by machines, a 14% increase from the year the report was issued. If you carry that ratio forward then all work will be done by machines before the year 2060. But forget the numbers game. The impact of automation is that work will cease to be the center of life as it has been during the last three centuries.

It's not only that people will not physically move to find work, like they moved from the country to the cities at the start of the industrial revolution. It means there will be no place to move to. The family will not have to sacrifice some part of their life so that the wage earner can do his job, there simply will be no wage earner. People's income from work will not have to be supplemented by government spending when it is not enough because there will be no income from work. That is the nature of a complete transformation.

Income will not be apportioned on the basis of achievement (higher salary for work that is valued more highly) but existentially– you will not get money because of what you do but rather because of who you are. Iran was the first country to install universal basic income in 2010, and the practice is now prevalent throughout northern Europe. In an economic sense it is inevitable. If people depend on work for income, when there is no work, people starve, and when people starve, they revolt and topple governments (Just ask Louis the XVI). Every government on earth will take steps to prevent that.

Once work is no longer a benchmark of identification, the status distributed on the basis of occupation or position will cease to exist. A manager will not be more important than a laborer; a doctor will not have higher status than a janitor because these jobs will cease to exist.  The subtle but unmistakable prejudice of assigning credibility based on occupation (doctors must be smarter than gardeners) will slowly fade away and people will be judged on who they really are rather than the work they perform.

Organizations will look completely different, and all the silly talk about organizational 'culture' will cease (thank God) because machines aren't in need of culture. The center of life will not be the place of work, there will be no traffic jams nor daily disruption of activities because of the physical need to move from one place to another, and identity will have to emanate from something other than where you work, because there will be no such thing.

Some things will remain. There will probably be teachers to some extent, though most instruction will be provided by machines, and there will be caretakers for more intimate human contact, though again, basic medical functions will be fully automated. Entertainment may remain a human occupation in some form, though it is important to point out that today most of the most popular entertainment is now animation (80% of top box office receipts in 2019 came from Disney studios and the most popular films tend to feature cartoon characters rather than human beings).

The clincher in all of this is time. We had eons to adjust from a nomadic life style to living in permanent communities. We will have just decades to adjust from a world with work to a world without work and it will leave literally billions of people gasping to find something to do. Some people like to compare what will happen to that old experiment of putting the frog in a pot of lukewarm water and heating it slowly so the frog doesn’t notice until he's cooked, but that isn't what will happen. The changes will be so fast that we will feel ourselves cooking, and it won't be pleasant.

FAMILY

Family has been the anchor of our identity for longer than work, probably for the last fifteen to twenty thousand years. It is without doubt the most emotionally-charged part of our identity, and most of our great works of literature deal with it from Oedipus to Anna Karenina. There is a natural inclination for a species to nurture its young; this is not exclusive to mammals. What is exclusive is the tendency of mammals to remain in units defined by a common blood line for an extended period of time, and among the mammals we humans are the champs. We extend our families for generations and we have made them the center of our lives, once again, for good and ill.

Part of the reason for this is survival. In the beginning if you were sick or injured you would not survive unless there were other people around you who cared enough to tend to you. More recently, the bond of survival has not been exclusively physical but also economic. Especially in the current generation, children in the west in particular are less well-off financially than their parents and without that support they would not make it. Like the man said, family is the place that when you go there they have to take you in.

There is an attendant pride that accompanies family identity, particularly when the family is adept either at maintaining a certain status (aristocracy, for example) or occupation (the military, for example). So, there are families of hostlers, shoemakers, haberdashers, iron-workers, doctors, and so on, and the connection between familial and occupational identity makes these families stronger over time. They exert pressure on their young to 'follow in their footsteps' and to adopt their ideals and beliefs, and believe this continuity has great value.

The industrial revolution weakened this bond for all but the wealthiest, causing as it did displacement of millions of people who found it necessary to move away from their place of origin to another community in order to secure employment, and the division of labor into employers and employees weakened the family ties of the latter and in millions of cases made it impossible for them to maintain the occupation or trade of the previous generations. The evolution of humanity from family-based to community-based dates from this time, about three hundred years ago.

But the real dismemberment of the family has been prosperity. As people become wealthier, on the top of their agenda is the desire to distance themselves from others. This has now arrived at a situation in which one out of every seven households in the United States is listed as a single person residence, and the situation in many major European cities is even more pronounced. In popular culture the familial bond has been replaced by the comradely bond, i.e. people you meet are closer to you than people of your same blood. In turn, this has led to a decrease in marriages and birthrates, and it becomes a self-propagating loop.

The coming identity paradigm holds a future in which the individual will replace the family as the basic social unit. Clearly, this is such a revolution that it is difficult for most people to imagine, but it is on the way, supported by the development of virtual relationships as a replacement for close physical relationships, meaning the sensation of being close to a person without ever being in the same room with him or her.

This is already well underway, egged on by social media, which encourages the individual to remain isolated from others in a physical sense in preference of a virtual connection. It is a common sight now to see a group of people 'together' in a public place not speaking to each other but rather managing a dialogue with a cell phone with somebody else who is not in the room.

Unlike the loss of work, which is a phenomenon not dictated or controlled by personal choice, this movement toward the individual in place of the family unit will take time, tempered by economic factors as well as strong cultural opposition, but it is coming nonetheless and will be the norm for most places on the planet by the end of the century.  There are already sections of big cities like Tokyo that are intended for the exclusive use of young people, as well as adult communities restricted to those over the age of 65.

Multi-generational living arrangements are already largely a thing of the past globally, particularly beyond the nuclear family. The cultural consequences of this change are immense and frankly frightening for me to contemplate. Practically, it means that we will need to find new ways to transfer property and assign responsibility (designated driver will replace parent). Emotionally, we will go through a hard time when we dismember old axioms like 'blood is thicker than water,' because quite clearly, with all of its attraction, collegial ties will never take on the commitment that blood ties have.  In the new identity paradigm, the family will disappear.

BELONGING

Belonging is such a central pillar of our current paradigm that it has been enshrined as a key component of mental health. People who shun contact with others are not just considered anti-social; they are labeled as mentally unwell (autistic). Mass movements were a central feature of the last two centuries, both political and social. Whether they were as benign as scouting organizations or as controversial as political protests, being part of some action which involved thousands of other people gathering together was a mainstay of life in every country on the planet. This is now coming to an end.

People will still voice their opinions, but they will do so online. Even dating has become a virtual activity rather than a night out; you check out a person's profile in the privacy of your own home long before you meet them.  The same is true of voting and all forms of political activity. Not only can it be done from the home, it is being done from the home. The key to watch here is sporting events, one of the more acceptable reasons to mix physically with thousands of other people. When people begin to prefer viewing the events on a screen rather than sitting in a stadium, public participation will be terminated because it will become unprofitable.

Again, there will still be instances where thousands if not millions of people will express their opinions on a common topic, but this will be done in real time, surveys conducted by pressing a button on your phone rather than driving to a common location.

The mental health community will be forced to redesign conclusions about what it means to be alone. Indeed, loneliness itself will need to be redefined. Are you really alone (not lonely) if you are physically removed from everyone else but your cell phone is by your side? There will be a whole new list of mental conditions when the common living situation is one person alone. Clearly, there will be fewer problems resulting from interpersonal conflict (like domestic violence) because there will be fewer people living together. On the other hand, a whole new list of ailments will pop up because there will not be that other person in the room that can tell you when you are wrong. It will be a new world.

ARTIFICIAL INTELLIGENCE

Our present paradigm has been flavored with our conceit that we are masters of the world, that we could bend the natural laws to our will, that we had some sort of irresistible control over everything. I suppose that the climate crisis is enough evidence to demonstrate what a mistake that was, but there is something even closer to home that will shake us to our roots in the new paradigm – we are no longer calling the shots.

Artificial intelligence will be the driving force in the new paradigm, and algorithms will make decisions in a distinctly different way than human beings. The lead elements of this new force are already changing the buying and selling of stocks and bonds and the application of medical procedures in hospitals all over the world. In the space of a few decades, all transportation will be directed by artificial intelligence, and drones and driver-less vehicles will be the norm (There will be no more human drivers or pilots because they are too dangerous). Manufacturing is already there, but there will be complete automation by the middle of the century.

AI will take the lead in education and customer service and the last pathetic attempts to suggest that the room for human work is just moving to other occupations will fall mute. In the new paradigm we will cease to make decisions about anything other than what we want personally, and that too will be limited. This is the one that scares me the most, but unless I take advantage of the next big change I won't be around, so it won't matter.

Human beings are used to making decisions. For a long time our ability to do this well was intimately tied to our survival. The idea that this will be taken from us because AI will do it better is a conclusion that many of us will find hard to swallow, and we will be reaching for that phantom limb long after it has been removed. Old people who believe they can drive just as well at the age of eighty as they did when they were 20 is a hint of what it will feel like. When the reality sets in that this is not rue it will likely be accompanied by a depression that will be very difficult to deal with, maybe even tied to the meaning of life. It will be a global emotional crisis that more than likely will trigger new forms of belief.

MORTALITY

Yuval Harari has been writing for some time about the conquest of death. At the present time, eight vital organs can be transplanted: the heart, kidneys, liver, lungs, pancreas, intestine, thymus and uterus. Artificial limbs are now commonplace, as well as eye transplants, artificial bladder implants, inner ear implants, and deep brain stimulation. The practicality of replacing the entire body, other than some higher functions of the brain, is now a distinct possibility before the middle of the century.

That means that your body no longer defines who you are, nor are you limited to a specific number of years before you 'die.' 'Life' will have to be redefined when it is not followed by the modifier 'time.' Immortality is a daunting moral and philosophical challenge, but it is no longer a physical one. It is very likely that the possibility of living longer will have a dramatic effect on birthrates, as the idea of passing the torch to a new generation, what Richard Dawkins called The Selfish Gene, will become a remnant of thinking from the previous paradigm, because that thinking is based on the assumption that the existing organism cannot sustain itself beyond a certain date.

No doubt the conquest of mortality will also lead to significant changes in relationships that were previous thought of (at least in theory) as life time commitments, like marriage and even parenthood. It will also be marked by the development of a whole new industry dedicated to the total replacement of the body, possibly with gender changes thrown in for a little spice – live eighty years as a man and another eighty years as a woman.

Immortality combined with artificial intelligence will demand an entire rethinking of the role of Homo sapiens on the planet, as well as how we define spirituality (if all of us are immortal how does this change the status of deities?).  It is a daunting prospect. Things that we regarded as one-time decisions will lose that distinction, and almost everything will become choice-determined. Death itself will become a decision, not inevitability, and this alone will completely reshape philosophy and morality.

NATIONALITY

For the past several centuries we have defined ourselves as members of one nationality or another to such an extent that human beings were willing to die to protect or extend that abstract concept, something that commanded our loyalty even more than family or religion.

Most of us tend to forget our previous participation in smaller political units like tribes and regions, and for the most part these remain as romantic abstractions, lacking the full force of what it means to be a system of a country. Those pictures of Uncle Sam pointing his finger at you and calling you to enlist are not just propaganda, they are the expression of the belief of the country that it has the right to demand that its citizens give their lives to protect it. In the country in which I live this is a reality, and the state is by law authorized to exert its domain over the private lives of its citizens.

Because of the maximum commitment it involves, most of us are highly emotional about what we call our national identity. Yet nations, too, may not be a part of the next paradigm, as difficult as it is to believe. There is a contractual need for people to align themselves with a large political entity that manages an infrastructure. We need water, electricity, transportation systems and supply chains, and these are arrangements beyond the power or resources of any individual. But they are definitely contractual, and by no mean the exclusive rights or ability of nations.

In practice – not theory, practice – power companies in the United States can supply energy to all the homes of North America and maybe South America as well. The practice of ending the power grid at a country's borders is a political decision, not a technological one.

There is also no practical reason why a person living in Caracas cannot contract with a company half way around the globe, say India, for the supply of needed services, if that supplier is capable of meeting the demand. When it becomes clear that the supply of services that were formally relegated only to nations – security, welfare, transportation, health, energy, waste disposal, and more – can be supplied to individuals by a more effective alternative, then the grip of nations on individuals will slip.

The people of Catalonia do not want to be part of Spain, and the people of California have their doubts about the United States, yet this dissatisfaction with the larger national unity is still just a little step, the dismantling of larger political bodies into smaller ones.

There is a real possibility that the next paradigm holds a much more dramatic change in store – the alliance of the individual with an organizing structure beyond nations. Instead of a process of unification that produces ever bigger political bodies, think of it in the other direction – the existence of thousands of service providers making direct contact with consumers directly on a non-geographical basis, and not using a government as an agent.

So, for example, the person living in London might receive his mail from a supplier in Delhi, his power from a supplier in Norway, his security from a company in Scotland, and his health from an organization in Switzerland. He may still consider himself English, but this will have more to do with his physical surroundings than with the political structure associated with it.

Quite clearly such a dramatic change has immeasurable implications for property ownership and civil legislation of every kind, and the number of lawyers required to work it out I don't even want to think about, but the point is that on a practical level it is indeed possible. It is only the abstract concept of nations for which so many people laid down their lives in the previous century that keeps it from happening. Nations have traditionally promoted themselves by their opposition to other nations, a practice which was expensive and bloody (we are better than they are; they want to kill us, so let's kill them first). If there is a business model that proves to be much more cost-efficient than the national one (and less bloody), it will come to pass, and within the next one hundred years, though I know how hard that is to believe. Yes, nations may be a thing of the past.

There will be a lot of gnashing of teeth when contemplating the alternatives, and there will remain a true need for the collection of public money in order to finance projects for the good of all (taxes), and there will always be disagreements over decisions made and the need to handle the losers so that they do not act to disrupt the system – all of that is true, but there is no natural law that says this must be the work of nations. The fact is that many nations are artificial in the extreme, the deformed children of colonialism, places like Pakistan and India and many states in Africa. The attempt to supplant such constructions with something else more effective is a positive idea, and it will be pursued.

RELIGION

The final pillar of identity that will be challenged in the new paradigm is belief. For the last millennium, many individuals have defined who they are as members of some religious movement, with Christianity and Islam being the most prominent recent examples. More blood has been spilled trying to sway different parts of the world to one religion or another over the last millennium than any other cause. This was challenged a half millennium ago when Christianity finally started to come apart into disparate elements of Protestantism and Catholicism and has been echoed more recently with the division of Islam into Sunni and Shiite. Still, many nations are defined by their religion. There are more than 80 nations today that officially give preference to one religion over another, including the one in which I reside.

Yet that, too, will be challenged by the impact of the new identity paradigm. In 2020, church membership in the United States dropped below 50% for the first time since the Gallup Poll began reporting. The American Mosque Survey reported a similar decline in the number of African Americans attending mosques in the United States. Similar situations are found in Europe. The Muslim population in Asia is still growing, but at a slower rate than was true half a century ago. Christianity in Latin America is becoming increasing more Pentecostal and less Catholic.

This does not mean that in the new paradigm religion will not play a role, but it does seem to indicate that the role will be much more individualized and much less public. In other words, the practice of mass movements of people professing the same belief who attempt to forcibly take over various parts of the world to install that belief seems to be coming at an end. It will take some time to realize that, but certainly most everyone can see that religious leaders today of whatever ilk are less influential in their ability to sway global events than they were even a hundred years ago.

Nations like Iran may still claim some sort of religious intent in their dealings with other nations, but this will become much less convincing during the next few decades, and most people will see it for what it really is – a political movement masquerading as a belief. A recent survey conducted in Iran suggested that about 40% of the country identified itself as actively Muslim in opposition to the official state claim of 99%.

***

Imagine for a moment a human being who is not defined by his nationality, place in a family, age, and membership in a religion, race, occupation, status or gender. How, then, is he to be defined? - Purely by his or her actions, emotions and thoughts, and what he or she makes from them? It would be true individuality, an identity that would make grouping impossible and therefore defy prejudice or assumptions. You would need to assess each person you meet in depth to really get to know them, because there would be no basis on which to make assumptions.

Patterns of course would eventually develop, they always do, but the base for these patterns would be different. We will no longer here things like "all women are…" or "Blacks are always…" or "Jews all are…" because there will be no meaning to these old distinctions. It would be like saying all Huguenots are the same or all Wares are the same, because these groups no longer exist. Some people will think alike, have the same taste, wear similar fashions, believe similar things, but those like-minded people will come from a wide variety of what used to be called mutually exclusive groups in the old paradigm, our paradigm.

I know that these observations may make some people uncomfortable; I know they make me uncomfortable. We are creatures of our times, and many of us have gotten ahead by following closely the rules that our paradigm gave us. So why is it that we need a new paradigm when so many of us are comfortable with the one we have even with all of its flaws?

Well, I don't think anyone did a survey of the woolly mammoths before the end of the ice age. It turned out that the paradigm shift was beyond their control, and their extinction was one of the unfortunate consequences of it. The truth is that many of the decisions we made over the last few centuries have consequences that we did not intend nor want, but they are consequences nonetheless. Who could have predicted that prosperity would lead to a desire to separate and not to join? Yet this is where the evolution of our species has led us – to a complete redefinition of who we are. We are subject to the consequences of our own actions, intentional or not.

I suppose in the middle of the feudal millennium many smart people would have found it hard to believe that there could be a world one day without masters or peasants, but it came to pass. Similarly, many of us may find it hard to believe today that there could be a world without marriage or the concept of children as the property of their parents until a certain age, or that people have a duty to sacrifice their lives for a nation's aspirations, but there is an equal likelihood that these things too will come to pass.

I guess the real question is if we will end up like the woolly mammoths, buried in the tundra to be excavated years hence by some other species that made the transformation to the new paradigm more successfully than us, or we will somehow transform ourselves to the new rules and realities... Time will tell.

But get ready. The first winds of the new paradigm are already whipping up the leaves around us. There will be rain after that and thunder and lightning. It will be a real storm, one like we have never experienced before. It won't work to close all the shutters and wait for the storm to pass, because this is a transformation, not a period of chaos after which everything will return to what it was before. This is the identity paradigm, and it is the invitation to define anew who we are.

Imagine there's no heaven

It's easy if you try

No hell below us

Above us, only sky

Imagine all the people

Livin' for today

Ah

Imagine there's no countries

It isn't hard to do

Nothing to kill or die for

And no religion, too

 -John Lennon

Imagine…

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