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By Paul S. Rosenbloom, University of Southern California; Christian Lebiere, Carnegie Mellon University, and John E. Laird, University of Michigan 

Progress in artificial intelligence has enabled the creation of AIs that perform tasks previously thought only possible for humans, such as translating languages, driving cars, playing board games at world-champion level and extracting the structure of proteins. However, each of these AIs has been designed and exhaustively trained for a single task and has the ability to learn only what’s needed for that specific task.

Recent AIs that produce fluent text, including in conversation with humans, and generate impressive and unique art can give the false impression of a mind at work. But even these are specialized systems that carry out narrowly defined tasks and require massive amounts of training.

It still remains a daunting challenge to combine multiple AIs into one that can learn and perform many different tasks, much less pursue the full breadth of tasks performed by humans or leverage the range of experiences available to humans that reduce the amount of data otherwise required to learn how to perform these tasks. The best current AIs in this respect, such as AlphaZero and Gato, can handle a variety of tasks that fit a single mold, like game-playing. Artificial general intelligence (AGI) that is capable of a breadth of tasks remains elusive.

Ultimately, AGIs need to be able to interact effectively with each other and people in various physical environments and social contexts, integrate the wide varieties of skill and knowledge needed to do so, and learn flexibly and efficiently from these interactions.

Building AGIs comes down to building artificial minds, albeit greatly simplified compared to human minds. And to build an artificial mind, you need to start with a model of cognition.

From human to Artificial General Intelligence

Humans have an almost unbounded set of skills and knowledge, and quickly learn new information without needing to be re-engineered to do so. It is conceivable that an AGI can be built using an approach that is fundamentally different from human intelligence. However, as three longtime researchers in AI and cognitive science, our approach is to draw inspiration and insights from the structure of the human mind. We are working toward AGI by trying to better understand the human mind, and better understand the human mind by working toward AGI.

From research in neuroscience, cognitive science and psychology, we know that the human brain is neither a huge homogeneous set of neurons nor a massive set of task-specific programs that each solves a single problem. Instead, it is a set of regions with different properties that support the basic cognitive capabilities that together form the human mind.

These capabilities include perception and action; short-term memory for what is relevant in the current situation; long-term memories for skills, experience and knowledge; reasoning and decision making; emotion and motivation; and learning new skills and knowledge from the full range of what a person perceives and experiences.

Instead of focusing on specific capabilities in isolation, AI pioneer Allen Newell in 1990 suggested developing Unified Theories of Cognition that integrate all aspects of human thought. Researchers have been able to build software programs called cognitive architectures that embody such theories, making it possible to test and refine them.

Cognitive architectures are grounded in multiple scientific fields with distinct perspectives. Neuroscience focuses on the organization of the human brain, cognitive psychology on human behavior in controlled experiments, and artificial intelligence on useful capabilities.

The Common Model of Cognition

We have been involved in the development of three cognitive architectures: ACT-R, Soar and Sigma. Other researchers have also been busy on alternative approaches. One paper identified nearly 50 active cognitive architectures. This proliferation of architectures is partly a direct reflection of the multiple perspectives involved, and partly an exploration of a wide array of potential solutions. Yet, whatever the cause, it raises awkward questions both scientifically and with respect to finding a coherent path to AGI.

Fortunately, this proliferation has brought the field to a major inflection point. The three of us have identified a striking convergence among architectures, reflecting a combination of neural, behavioral and computational studies. In response, we initiated a communitywide effort to capture this convergence in a manner akin to the Standard Model of Particle Physics that emerged in the second half of the 20th century.

This Common Model of Cognition divides humanlike thought into multiple modules, with a short-term memory module at the center of the model. The other modules – perception, action, skills and knowledge – interact through it.

Learning, rather than occurring intentionally, happens automatically as a side effect of processing. In other words, you don’t decide what is stored in long-term memory. Instead, the architecture determines what is learned based on whatever you do think about. This can yield learning of new facts you are exposed to or new skills that you attempt. It can also yield refinements to existing facts and skills.

The modules themselves operate in parallel; for example, allowing you to remember something while listening and looking around your environment. Each module’s computations are massively parallel, meaning many small computational steps happening at the same time. For example, in retrieving a relevant fact from a vast trove of prior experiences, the long-term memory module can determine the relevance of all known facts simultaneously, in a single step.

Guiding the way to Artificial General Intelligence

The Common Model is based on the current consensus in research in cognitive architectures and has the potential to guide research on both natural and artificial general intelligence. When used to model communication patterns in the brain, the Common Model yields more accurate results than leading models from neuroscience. This extends its ability to model humans – the one system proven capable of general intelligence – beyond cognitive considerations to include the organization of the brain itself.

We are starting to see efforts to relate existing cognitive architectures to the Common Model and to use it as a baseline for new work – for example, an interactive AI designed to coach people toward better health behavior. One of us was involved in developing an AI based on Soar, dubbed Rosie, that learns new tasks via instructions in English from human teachers. It learns 60 different puzzles and games and can transfer what it learns from one game to another. It also learns to control a mobile robot for tasks such as fetching and delivering packages and patrolling buildings.

Rosie is just one example of how to build an AI that approaches AGI via a cognitive architecture that is well characterized by the Common Model. In this case, the AI automatically learns new skills and knowledge during general reasoning that combines natural language instruction from humans and a minimal amount of experience – in other words, an AI that functions more like a human mind than today’s AIs, which learn via brute computing force and massive amounts of data.

From a broader AGI perspective, we look to the Common Model both as a guide in developing such architectures and AIs, and as a means for integrating the insights derived from those attempts into a consensus that ultimately leads to AGI.

About the Author:

Paul S. Rosenbloom, Professor Emeritus of Computer Science, University of Southern California; Christian Lebiere, Research Psychologist, Carnegie Mellon University, and John E. Laird, John L. Tishman Professor of Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Mark Flannery, University of Florida 

– I’ve studied finance and financial markets since the 1970s, and I have never seen the Federal Reserve’s monetary policy get such prominent news coverage as it has this past year.

And with good reason. What the Fed does has profound implications for companies, consumers and the U.S. economy, especially now as the U.S. central bank tries to tame the fastest jump in consumer prices in decades. In short, the Fed is jacking up interest rates in hopes that doing so slows the economy enough to bring down inflation.

The housing market is the sector most substantially influenced by interest rate changes, and as such, it’s a key indicator of whether the Fed’s plans are succeeding. To see why, I need only consider the experience of my son – or the many other Americans hunting for a new home at a time of rising interest rates.

What the Fed is doing

First, a little background.

The Federal Reserve is raising interest rates at the fastest pace in its 108-year history as part of its inflation battle. Today’s big policy steps are needed in part because the Fed and many others took awhile to understand what was causing the rise in inflation.

In fall 2021, while the pace of inflation was accelerating past 4% – double the Fed’s targeted rate – the prevailing view at the central bank and elsewhere was that it reflected temporary disruptions following two years of COVID-19-related slowdowns. The assumption was that inflation would abate automatically as supply chains worked themselves out.

Unfortunately, that assumption proved wrong because it did not recognize how much government COVID-19 relief spending had stimulated what economists call “aggregate demand” – in other words, the total demand for goods and services produced in an economy. Put another way, consumer spending spurred by government aid created strong demand across the economy.

And so consumer prices continued to accelerate. Russia’s war in Ukraine made the problem worse, especially by driving up global food and energy prices. As of June 2022, inflation was surging at 9.1%, the fastest pace since 1981.

While the Fed can’t do much about the war or other supply-chain issues, it can address domestic aggregate demand. That’s where higher interest rates come in.

Higher borrowing costs choke off consumer demand for homes, cars and other goods and services that typically require a loan, while companies pare back their investments in factories and hiring, which should ease overall inflation.

The Fed began its most recent tightening policy in March 2022 with a 0.25 percentage point increase in its target interest rate, which acts as a benchmark for other borrowing costs in the U.S. and around the world. Since then, the central bank has raised its target rate twice more – by 0.5 percentage point in May and 0.75 percentage point in June.

On July 27, the Fed is expected to raise the rate by another 0.75 percentage point, though some observers have predicted an unprecedented 1 point increase after the June consumer prices report showed inflation was still accelerating.

Why the housing market matters

The trick to reducing inflation is to choke off enough aggregate demand to tame inflation without driving the economy into recession. One of the main ways to see whether this is happening is to look at housing, which has always been particularly sensitive to rate changes and constitutes more than one-quarter of total U.S. wealth.

Because buying a house or apartment is such a large expenditure, nearly all purchasers must borrow a pretty big share of the purchase price. And just as record-low mortgages borrowing costs in 2021 helped fuel a housing market boom by lowering the cost of servicing that debt, higher rates increase the cost, discouraging housing purchases.

The average rate on a 30-year mortgage hit 5.81% in June, the highest level since 2008 and up from less than 3% throughout most of 2021. The rate currently stands at 5.54%. On a $200,000 mortgage, a 5.54% rate translates into over $400 in extra interest costs every month compared with 3%.

Confronted with such an increase, some house hunters – like my son – have stepped back and reconsidered whether now is the right time to buy.

Housing starting to stall

In other words, higher mortgage rates lead individuals to invest less in housing. And the effect of falling demand doesn’t stop with the house. When people buy a new house, they also tend to purchase new furniture, lawn equipment, televisions and so on. And buying a used home often requires hiring contractors and others to remodel the kitchen or build a new closet in the kids’ room.

So if people are buying fewer homes, they also are purchasing less furniture, electronics and lawnmowers and have less need for electricians and plumbers.

The drop in demand for all these goods and services should take a meaningful bite out of inflation. While it’s still too early to say if this part of the Fed plan is working, we can already see the effects of rising mortgage rates in recent housing data.

In recent months, fewer new houses are being built, fewer existing homes are being sold and homebuyers are walking away from signed deals at the highest rate since the start of the COVID-19 pandemic.

At the same time, consumers and investors are beginning to anticipate less inflationary pressure in the next year or so.

What it means for homebuyers

So as the Fed prepares to hike benchmark rates again, what does all this mean for U.S. consumers, and especially my son and other people looking for a new home?

For one thing, don’t expect long-term interest rates, including for mortgages, to rise much, and certainly not by the same amount of the Fed’s interest rate hike.

Investors tend to factor expected Fed policy changes into its market rates. So unless there is a surprise from the Fed, like a full 1-point hike, long-term rates are unlikely to change much. And they may even begin to fall soon, either because inflation is subdued or the U.S. slips into recession.

And while it would be nice to know how tighter monetary policy – that is, higher interest rates – will affect today’s stratospheric house prices, this is hard to predict. The withdrawal of some buyers from the market should depress house prices by reducing demand, but sellers may also simply decide to delay selling rather than accept a lower price.

The challenge for would-be homebuyers like my son and his family is to find a seller who cannot hold their house off the market and to offer a lower price than the house would have attracted a few months ago to offset its higher financing cost. The more that happens, the more the Fed will know its rate hikes are working.

About the Author:

Mark Flannery, Professor of Finance, University of Florida

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Elsabe Loots, University of Pretoria 

– Over the past 25 years there has been lots of research and debate about the concept, the history and state of globalisation, its various dimensions and benefits.

The World Economic Forum has set out the case that the world has experienced four waves of globalisation. In a 2019 publication it summarised them as follows.

The first wave is seen as the period since the late 19th century, boosted by the industrial revolution associated with the improvements in transportation and communication, and ended in 1914. The second wave commenced after WW2 in 1945 and ended in 1989. The third commenced with the fall of the Berlin Wall in 1989 and the disbanding of the former Soviet Union in 1991, and ended with the global financial crises in 2008.

The fourth wave kicked off in 2010 with the recovery of the impact of the global financial crises, the rising of the digital economy, artificial intelligence and, among others, the increasing role of China as a global powerhouse.

More recent debates on the topic focus on whether the world is now experiencing a retraction from the fourth wave and whether it is ready for the take-off of the fifth wave.

The similarities between the retraction period of the first wave and the current global dynamics a century later are startling. But do these similarities mean that a retraction from globalisation is evident? Is there sufficient evidence of de-globalisation or rather “slowbalisation”?

Parallels

The drawn-out retreat from globalisation during the 30-year period – 1914 to 1945 – was characterised by the geopolitical and economic impact of WWI and WWII. Other factors were the 1918-1920 Spanish Flu pandemic ; the Stock Market Crash of 1929 followed by the Great Depression of the 1930s; and the rise of the Communist Bloc under Stalin in the 1940s.

This period was further typified by protectionist sentiments, increases in tariffs and other trade barriers and a general retraction in international trade.

Looking at the current global context, the parallels are remarkable. The world is still fighting the COVID pandemic that had devastating effects on the world economy, global supply chains and people’s lives and well-being.

For its part, the Russia-Ukraine war has caused major global uncertainties and food shortages. It has also led to increases in gas and fuel prices, further disruptions in global value chains and political polarisation.

The increase in the price of various consumer goods and in energy have put pressure on the general price level. World inflation is aggressively on the rise for the first time in 40 years. Monetary authorities worldwide are trying to fight inflation.

Global governance institutions like the World Trade Organisation and the UN, which functioned well in the post-WWII period, now have less influence while the Russian-Ukraine war has split the world politically into three groups. They are the Russian invasion supporters, the neutral countries and those opposing, a group dominated by the US, EU and the UK. This split is contributing to complex geopolitical challenges, which are slowly leading to changes in trade partnerships and regionalism.

Europe is already looking for new suppliers for oil and gas and early indications of the potential expansion of the Chinese influence in Asia are evident.

A less connected world

De-globalisation is seen as

a movement towards a less connected world, characterised by powerful nation states, local solutions and border controls rather than global institutions, treaties, and free movement.

There’s now talk of slowbalisation. The term was first used by trendwatcher and futurologist Adjiedji Bakas in 2015 to describe the phenomenon as the

continued integration of the global economy via trade, financial and other flows, albeit at a significant slower pace.

The data on economic globalisation paint an interesting picture. They show that, even before the COVID pandemic hit the world in 2020, a deceleration in the intensity of globalisation is evident. The data which represent broad measures of globalisation, includes:

• World exports of goods and services. As a percentage of world GDP, these reached an all-time high of 31% in 2008 at the end of the third globalisation wave. Exports fell as a percentage of global GDP and only recovered to that level during the early stages of the fourth wave in 2011. Exports then slowly started to regress to 28% of global GDP in 2019 and further to a low of 26% during the first Covid-19 year in 2020.
• The volume of foreign direct investment inflows. These reached a peak of US$2 trillion in 2016 before trending lower, reaching US$1.48 trillion in 2019. Although the 2020 foreign direct investment inflows of US$963 billion are a staggering 20% below the 2009 financial crises level, they recovered to US$1.58 billion in 2021.
• Foreign direct investment as percentage of GDP started to increase from a mere 1% in 1989 to a peak of 5,3% in 2007. After a retraction following the global financial crises, it peaked again in 2015 and 2016 at around 3,5%. It then declined to 1,7% in 2019 and 1,4% in 2020.
• Multinational enterprises have been the major vehicle for economic globalisation over time. The number of them indicates the willingness of companies to invest outside their home countries. In 2008 the UN Conference on Trade and Development reported approximately 82 000. The number declined to 60 000 in 2017.
• Data on world private capital flows (including foreign direct investment, portfolio equity flows, remittances and private sector borrowing) are not readily available. However, Organisation for Economic Co-operation and Development data show that private capital flows for reporting countries reached an all-time high of US$414 billion in 2014, followed by a declining trend to US$229 billion in 2019 and a negative outflow of US$8 billion in 2020.

These declining trends are further substantiated by the evidence of deeper fragmentation in economic relations caused by Brexit and the problematic US/China relations, in particular during the Trump era.

What next?

The question now is whether the latest data is:

• indicative of either a retraction from globalisation similar to that experienced after the first wave a century ago;
• or it is merely a process of de-globalisation;
• or slowbalisation in anticipation of the world economy’s recovery from the impact of Covid-19 pandemic and the war in Ukraine?

The similarities between the first wave of globalisation and the existing global events are certainly significant, although embedded in a total different world order.

The current dynamics shaping the world such as the advancement of technology, the digital era and the speed with which technology and information is spread, will certainly influence the intensity of the retraction of the already embedded dependence on globalisation.

Nation states realise that blindly entering into contracts and agreements with companies in other countries, may be problematic and that trade and investment partners need to be chosen carefully. The events over the past three years have certainly shown that economies around the world are deeply integrated and, despite examples of protectionism and threats of more inward-looking policies, it will not be possible to retract in totality.

What may occur is fragmentation where supply chains becoming more regionalised. Nobel prize winning economist Joseph Stiglitz refers to the move to “friend shoring” of production, a phrase coined by US Treasury Secretary Janet Yellen.

It is becoming obvious that the process of globalisation certainly shows characteristics of both de-globalisation and slowbalisation. It’s also clear that the global external shocks require a total rethink, repurpose and reform of the process of globalisation. This will most probably lead the world into the fifth wave of globalisation.

About the Author:

Elsabe Loots, Professor of Economics and former Dean of the Faculty of Economic and Management Sciences, University of Pretoria

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Mayank Kejriwal, University of Southern California 

– The world is a networked place, literally and figuratively. The field of network science is used today to understand phenomena as diverse as the spread of misinformation, West African trade and protein-protein interactions in cells.

Network science has uncovered several universal properties of complex social networks, which in turn has made it possible to learn details of particular networks. For example, the network consisting of the international financial corruption scheme uncovered by the Panama Papers investigation has an unusual lack of connections among its parts.

But understanding the hidden structures of key elements of social networks, such as subgroups, has remained elusive. My colleagues and I have found two complex patterns in these networks that can help researchers better understand the hierarchies and dynamics of these elements. We found a way to detect powerful “inner circles” in large organizations simply by studying networks that map emails being sent among employees.

We demonstrated the utility of our methods by applying them to the famous Enron network. Enron was an energy trading company that perpetrated fraud on a massive scale. Our study further showed that the method can potentially be used to detect people who wield enormous soft power in an organization regardless of their official title or position. This could be useful for historical, sociological and economic research, as well as government, legal and media investigations.

From pencil and paper to artificial intelligence

Sociologists have been constructing and studying smaller social networks in careful field experiments for at least 80 years, well before the advent of the internet and online social networks. The concept is so simple that it can be drawn on paper: Entities of interest – people, businesses, countries – are nodes represented as points, and relationships between pairs of nodes are links represented as lines drawn between the points.

Using network science to study human societies and other complex systems took on new meaning in the late 1990s when researchers discovered some universal properties of networks. Some of these universal properties have since entered mainstream pop culture. One concept is the Six Degrees of Kevin Bacon, based on the famous empirical finding that any two people on Earth are six or fewer links apart. Similarly, versions of statements such as “the rich get richer” and “winner takes all” have also been replicated in some networks.

These global properties, meaning ones applying to the entire network, seemingly emerge from the myopic and local actions of independent nodes. When I connect with someone on LinkedIn, I am certainly not thinking of the global consequences of my connection on the LinkedIn network. Yet my actions, along with those of many others, eventually lead to predictable, rather than random, outcomes about how the network will evolve.

My colleagues and I have used network science to study human trafficking in the U.K., the structure of noise in artificial intelligence systems’ outputs, and financial corruption in the Panama Papers.

Groups have their own structure

Along with studying emergent properties like the Six Degrees of Kevin Bacon, researchers have also used network science to focus on problems such as community detection. Stated simply, can a set of rules, otherwise known as an algorithm, automatically discover groups or communities within a collection of people?

Today there are hundreds, if not thousands, of community detection algorithms, some relying on advanced AI methods. They are used for many purposes, including finding communities of interest and uncovering malicious groups on social media. Such algorithms encode intuitive assumptions, such as the expectation that nodes belonging to the same group are more densely connected to one another than nodes belonging to different groups.

Although an exciting line of work, community detection does not study the internal structure of communities. Should communities be thought of only as collections of nodes in networks? And what about communities that are small but particularly influential, such as inner circles and in-crowds?

Two hypothetical structures for influential groups

In a manner of speaking, you likely already have some inkling of the structure of very small groups in social networks. The truth of the adage that “a friend of my friend is also my friend” can be tested statistically in friendship networks by counting the number of triangles in the network and determining whether this number is higher than chance alone could explain. And indeed, many social network studies have been used to verify the claim.

Unfortunately, the concept starts breaking down when extended to groups with more than three members. Although motifs have been well studied in both algorithmic computer science and biology, they have not been reliably linked to influential groups in real communication networks.

Building on this tradition, my doctoral student Ke Shen and I found and presented two structures that seem elaborate but turn out to be quite common in real networks.

The first structure extends the triangle, not by adding more nodes, but by directly adding triangles. Specifically, there is a central triangle that is flanked by other peripheral triangles. Importantly, the third person in any peripheral triangle must not be linked to the third person on the central triangle, thereby excluding them from the true inner circle of influence.

The second structure is similar but assumes that there is no central triangle, and the inner circle is just a pair of nodes. A real-life example might be two co-founders of a startup like Sergey Brin and Larry Page of Google, or a power couple with joint interests, common in global politics, like Bill and Hillary Clinton.

Understanding influential groups in an infamous network

We tested our hypothesis on the Enron email network, which is well studied in network science, with nodes representing email addresses and links representing communication among those addresses. Despite being elaborate, not only were our proposed structures present in the network in greater numbers than chance alone would predict, but a qualitative analysis showed that there is merit to the claim that they represent influential groups.

The main characters in the Enron saga are well documented by now. Intriguingly, some of these characters do not seem to have had much official influence but may have wielded significant soft power. An example is Sherri Reinartz-Sera, who was the longtime administrative assistant of Jeffrey K. Skilling, the former chief executive of Enron. Unlike Skilling, Sera was only mentioned in a New York Times article following investigative reporting that took place during the course of the scandal. However, our algorithm discovered an influential group with Sera occupying a central position.

Dissecting power dynamics

Society has intricate structures at the levels of individuals, friendships and communities. In-crowds are not just ragtag groups of characters talking to one another, or a single ringleader calling all the shots. Many in-crowds, or influential groups, have a sophisticated structure.

While much still remains to be discovered about such groups and their influence, network science can help uncover their complexity.

About the Author:

Mayank Kejriwal, Research Assistant Professor of Industrial & Systems Engineering, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.