Artificial Intelligence: Toward a Theory of Minds

One thing that differentiates humans from animals is a concept called “Theory of Mind”. Theory of Mind describes how individuals are able to understand what other humans are thinking and feeling so that they can make an appropriate response. This idea is increasingly important for implementations of artificial intelligence.  Theory of Mind is an understanding of the intellectual and emotional state, motivations, and probable actions of another individual based upon internal hypotheses about how that individual must be thinking. It demands creating a model of another mind. To date, this concept has been considered mainly in the realm of developing humanoid robots, but it has important consequences across all AI.

How is this important? It has two components with different effects. First, it is about developing an AI Theory of Mind to promote more precise interaction with human beings. Intelligent machines will need to anticipate human response to understand the broader context of questions and required actions. Second, it is about having a Theory of Mind for each AI that we, as humans, encounter (Theory of AI Mind). This will become increasingly important for interactions between people and machines.

AI Theory of Minds

AI Theory of Human Mind

The next evolution of an AI will require each AI to have a Theory of Human Mind, and an ability to understand and act according to human characteristics, thought patterns, and biases. This is a part of current developments. It is difficult, however, since it requires machines to understand something that we strain to understand ourselves. Philosophers have struggled endlessly with the lack of capability to externalize human thought or obtain an objective understanding. Still, as with the Turing test, the attribution of a Theory of Mind is proven if the machine is able to anticipate human thoughts, actions, or desires and act upon them in a way that signifies understanding rather than pre-programmed behavior.

Theory of Mind is likely to emerge as a clear differentiator between generations of AI, and an important milestone along the way to true general intelligence. It will also mark the possibility of full collaboration with humans with each side capable of understanding possible actions by the other.

Human Theory of AI Mind

The other side of the coin is the more immediately important human Theory of AI Mind. We are intuitively aware of this when question a digital assistant. Before making a query, we consider the “intelligence” of the machine and its probable response, and frame questions to produce a useful response. We then evaluate the response based on the same model.  If it is a weak or very limited AI, this is taken into account; if it is a strong AI, then questions of capability, bias, focus, and domain come into play. We create a model of AI response. Theory of AI mind is also important in considering responsibility for an action; a concept which will be extremely important for insurance in applications such as autonomous vehicles.

Theory of AI Mind will become increasingly important for user interface in moving toward ubiquitous AI. As machines grow “smarter”, we will expect more of them, and more differences between them. As with understanding of human Theory of Mind and personality, we will hold mental models of specific AI’s and interact with them according to that model.

Implications for Theories of General Intelligence

With further development of both sides of AI Theory of Mind will come a greater understanding of how people interact with other intelligent entities–including other humans. AI provides an intelligent “Other” that is not necessarily subject to biases of human thought. People have inbuilt biases of many types and from many sources including heredity, society, brain function, mental limitations, the conscious/subconscious split, and  innumerable other factors. If we abstract intelligence from this matrix, it becomes more possible to understand the specific things which make human intelligence special. For the first time, we may be able to step aside from human capabilities and reach a broader understanding of intelligence itself.

From a technology perspective, Theory of Mind will become a competitive factor in AI development. Machines of limited intelligence, creativity, or capability will be perceived as such and will have caveats put upon their operation. This is similar to human interactions with animals, which are perceived as having a small intelligence and fixed responses to many stimuli. Competition to meet such perceptual criteria will empower the race toward artificial general intelligence. It will also drive development of intelligence toward more human-like interaction. Some of this is already happening as we move toward intelligent humanoids which are made to appear like rational creatures and satisfy some of the requirements of a theory of mind.

Conclusion

Theory of Mind is likely to become more critical in coming years. In a sense, it provides an evaluative concept for AI which steps beyond the Turing Test. In the Turing test it is possible to create a nebulous interaction which appears on the surface to be somewhat human and somewhat able to fool individuals. But, if a machine is able to interact on an intellectual level and provide the framework for an adequate Theory of Mind ,then it becomes something more than just a machine with interesting and creative responses. Perhaps, a Theory of Mind is the basis for consciousness, or a preliminary step in that direction. But, in any case, this criteria, commonly used to differentiate between humans and animals, must be turned on its head to distinguish between humans and AI.


 


Agricultural Robots and AI in Farming; Promises and Concerns

With constant attention given to Industry 4.0, autonomous vehicles and industrial robots, there is one significant area of robotics that is often under-reported—the growing use of autonomous agricultural robots and AI-driven smart systems in agriculture. Although automation has been practiced on the farm for many years, it is not been as widely visible as its cousins on the shop floor. But technology being deployed on farms today is likely to have far reaching consequences.

We are on the edge of an explosion in robotics that will change the face of agriculture around the world, affecting labor markets, society, and the wealth of nations. Moreover, developments today are global in extent, with solutions being created in the undeveloped world as well as in the developed world, stretching across every form of agriculture from massive row crop agribusiness and livestock management, down to precision farming and crop management in market gardens and enclosed spaces.

Agriculture Robots Today

Agriculture is vital to the health of the ever-expanding human population, and to the wide range of interrelated industries that make up the agricultural sector. Processes include everything from planting, seeding, weeding, milking, herding, and gathering, to transportation, processing, logistics, and ultimately to the market—be it the supermarket, or increasing retail online. The UN predicts that world population will rise from 7.3 billion to 9.7 billion in 2050. Robots and intelligent systems will be critical in improving food supplies. Analyst company Tractica estimates shipments of agricultural robots will increase from 32,000 units in 2016 to 594,000 units annually by 2024—when the market is expected to reach $74.1 billion per year.

While automation has been in place for some time and semi-autonomous tractors are increasingly common, farms pose particular problems for robots. Unlike highway travel, which is difficult enough for autonomous vehicles, agricultural robots need to be able to respond to unforeseen events, plus handle and manipulate objects within their environment. AI makes it possible to identify weeds and crops; discern crop health and status; and to be able to take action delicately enough to avoid damage in actions such as picking. At the same time, these robots must navigate irregular surfaces and pathways, find locations on a very fine scale,  and sense specific plant conditions across the terrain.

Agricultural robots using AI technologies are responding to economies in the agricultural sector as well as to rising labor costs and immigration restrictions. The first areas of general impact are in large businesses, since robots have high investment and maintenance costs and there is a lack of skilled operators. Conditions will change as robots become cheaper, more widely available, and capable of performing more diverse tasks. This will require evolution of AI technologies, expansion of collaboration abilities among robots; and man-machine combined operations. The ability of robots to work with humans could be particularly significant due to the wide range of discrimination tasks involved in food safety, quality control, and weed and pest removal. Robots will be guided by human supervisors with skills in agriculture and knowledge of robotic and agricultural systems.

Opportunities and Growth

According to the International Federation of Robotics, agricultural robots are likely to be the fastest growing robotics sector by 2020. Different sectors of agricultural markets will respond differently. Large businesses with row crops are early responders, since they have funds to invest and shrinking margins. For these companies, there are huge benefits in reducing labor costs and instituting more precise farming methods. As picking and weed killing and pest removal systems become more widely available, citrus orchards and difficult-to-pick crops are likely to be next. Robots capable of picking citrus, berries, and other delicate fruit in difficult locations are already starting appear. There are applications in virtually every part of the agricultural sector.

Other uses will appear as robots become more common and less expensive. Robots can make a difference not only in harvesting, but also in precision of water application and fertilizer. In areas where water is contentious, such as California and the Middle East, more efficient watering will make it possible to grow larger crops with greater efficiency and less water, avoiding creation of political and social crises.

In the developing world, opportunities are enormous but individual farmers have fewer resources. For this reason, smaller robots and robot clusters are likely to be more widely used, possibly with emerging Robot-as-a-Service (RaaS) operators providing  labor on a per-usage or rental basis. Robotics will be able to save enormously on chemical costs and water used for irrigation, which will have significant economic impacts, as well as environmental benefits.

Progress and Caution

As use of agricultural robots continues to expand, they will take on increasinly complex tasks, and replace a larger portion of agricultural labor–a critical component of global employment. In many countries, particularly in the developing world, this will create shifts in employment which will empower trends such as rural migration to cities, and reduce overall availability of labor–intensive jobs. More training will be needed by more people; this will impact education, socialization, and finance; particularly in countries with large populations.

There are many implications as AI and agricultural robots are deployed. New ideas are blossoming, startups are on the rise, and we can expect a wide range of consequences as  next generation agricultural robotics and AI continue to emerge.


 


Evolving the Cognitive Man-Machine

Human intelligence and artificial intelligence will increasingly interact as we extend the range of mechanical cognition to include sensory interpretation, role playing, and sentiment (Affective Computing, Intersecting Sentiment and AI: The VideoShifting the Boundaries of Human Computer Interaction with AI: The Video). Such advancement will both create new conflicts and increase our understanding of the human mind by providing an objective platform for comparison.

But the cross-pollination of human and machine understanding does not stop there. As digital assistant roles progress, robots will need to understand and influence people. They will need to win negotiations, and devise strategies of engagement. AI will need to become increasingly cognizant of human thought patterns and social characteristics. This will make them a part of the greater “human conversation.”

As cognitive systems are assigned roles in which they must take the lead or suggest actions, AI will be playing a human game with human pieces. Social interaction is a construct: Knowing the rules, anyone can play. This will lead to competition and friction between automata and humans across a wide range of activities.

Even as AI continues to advance, human capabilities will be amplified through integrated advisers, prostheses, and avatars that will vastly increase our ability to process information, remember and assemble concepts, travel to remote locations, and communicate–all at the speed of light.

Robots and mankind are locked in a co-evolution that will ultimately lead to hybridization. We can add new robotic capabilities much faster than we can evolve them on our own. Simple toolmaking was the first step along this path; the final step will be where the intersection of humanity and machine becomes blurred, and finally, almost invisible.

Organisms adapt to fill a niche; when they can no longer adapt, their cousins take over. Evolution is about survival of the fittest, not of the strongest or the largest or even the smartest. Technology is an evolution of tools to fit a world defined by humans, that will continue to be shaped by human thought. Hybridization is inevitable, because it will augment human capability. Technology can evolve and be adapted much quicker than native biology; so further evolution of the species will be based on technology.

At present, we are barely on the doorstep of hybridization. We have clumsy “wearables,” limited but promising smart prostheses; the beginnings of AR concepts from Google Glass to HoLolens; social industrial robots that can work with people; digital assistants that can insert themselves into social settings; and an increasing range of smart devices that bridge the human context and the IoT.

In a somewhat distant future, we will likely view this as simply “making better  tools.” The alarming possibilities we envision today will be the commonplace realities.  As with the unknown Chinese inventor of printing blocks for text, we will ignore revolutionary change and create a narrative in which everything is consistently normal.

Twilight of the Gods? Perhaps. For the present, we are faced with the problem of understanding these changes and applying new technologies in a way that society continues to benefit, and the multitude of interstices are filled. This will create great opportunity, but it will also demand innovation directed specifically toward human-machine interaction.

Ultimately, of course, this solves the problem of “the Singularity,” and a robotic Apocalypse. To quote Walt Kelly’s Pogo comic strip, “we have met the enemy and he is us.”


 


Brain Machine Interface: Cornerstone of the Next AI

As we move into the era of AI, we can expect changes across a broad range of areas. AI will vie with extension of human intelligence and human-machine hybridization. As we bind the complex and supremely powerful human intelligence to the array of capabilities available in AI, there will be an inevitable transformation in human thought and action. This transformation will require a close interface between humans and machines. We have already seen the beginning of a close connection between Internet search and familiar thought processes. People are able to react more rapidly to information through rapid and real-time access to supporting data. But processes of cognition demand much faster response to integrate the vast incoming data streams with other cognitive functions.

Because of this connection between AI and device interface, development of a range of prostheses will occur as this interface grows in importance. There is already an impetus to provide a direct connection between mechanical systems and the brain going back decades–even further, perhaps, if we include experiments from the 19th century on electro-stimulation of animals. To date, these experiments have remained relatively limited in function, involving only simple messages and simple capabilities endowed with limited human awareness and response. However, as understanding of thought processes and possibilities of interface devices continue to increase in complexity, we will see radical advances that lead to a new era of human-machine interaction.

Recent developments in this area suggest the possibility of an explosion in capability, led by ability to read biological thought through invasive and noninvasive techniques; to act upon human thought streams through the same mechanisms; and to comprehend a much wider scope of thought processes and action potential as a result.

The chief direct human-machine interface possibilities come from two areas. These are insertion of a chip of minute size in a biological system; and use of external sensors and headsets to read electronic activity in the brain and interpret or act upon it. Thus, recent developments have shown capability to move artificial limbs; to retain sensation in artificial prostheses; capability to understand images in dreams; to understand emotional states; and ability to understand and act upon thought streams from individual neurons or neural matrices. Growth in this area has led to artificial smart limbs; artificial cornea implants; cochlear implants; and a range of similar helpful devices, each of which requires specific input from neural systems and provide an output based upon processing of some form.

The capabilities of these devices grow as the range increases. Recent developments have included cheaper local, and even some remote, electrical brain reading capabilities. On the implant side, sensors have become smaller and more suitable to insertion in areas of the brain or other portions of the body; most recently, the possibility of using a tiny chip with stent-like expansion and noninvasive insertion in the bloodstream, situated remotely, is a DARPA project. On another level of human brain interface, we have seen capability to grow biological neuron nets and integrate them with other systems. This increases the possibilities of a variety of direct brain interface devices as well as helping to improve our overall understanding of the requirements to build an intelligence.

The need to create a closer integration between man and machine is driven by the same requirements as increased AI within the enterprise. AI and big data demand vast increases in real-time computation which leads directly to improved networking and the capability to rapidly draw resources into the cognitive mix. Humans will need to keep up with this ever increasing processing of information. This will provide a competitive market for interface devices which make it easier for individuals to become first responders in an artificially cognitive world. Such an interface will also provide subconscious control possibilities for intelligent systems; to engage fight or flight reflexes in avoiding danger, and with enhanced capabilities linked directly between devices and implants. Additionally, the capability to read and respond to human thought will require new languages of machine communication, new forms of security, and create the ability to link human beings together on a thought processing level with the possibility of instantaneous problem-solving through immediate ad-hoc networks of human and AI components.

What does this mean ethically? Human thought is evolving quickly, and human values will always drive this innovation. Philosophically, we live within a human-centered world where the definitions and the actions are all circumscribed by human beings with their genetic predispositions. We do not fully understand the nature of human world construction due to our direct immersion in it. This makes it more difficult to determine the end result. But, we are unlikely to yield world-building resources and capabilities to a machine. With control of the definitions and the possibilities, human beings will continue to reign supreme.

In other respects, it is important that this is a long-term evolution. Concepts of jobs and work location are all capable of change. These are human definitions and fit the world which was created after the Neolithic Revolution. They have been further molded by the Industrial Revolution. For example, we think of cities as central to culture; but there is no longer the same need for cities. Likewise, there is no longer a need for jobs as we have defined them; yet society must channel people to perform activities that are beneficial to society.

In the immediate sense, the further development of human-AI initiatives creates new possibilities for autonomous and semi-autonomous robots, faster and more efficient business processes, and possibly greater innovation and collaboration. Companies need to understand the possibility for this type of interface to offer specific advantages to their firm. They could make processing of information much quicker; but will also raise ethical and legal issues that need to be explored. The possibilities are truly earthshaking. But the challenges are enormous, and the repercussions across social, economic, and political areas are likely to be revolutionary.


Autonomous Social Robots: The Video

Autonomous robots are difficult and impressive. The drive to autonomy is particularly evident in transportation, where we are on the verge of entering the era of self-driving cars. But autonomous social robots–robots that act independently and cooperate to perform a task–are of particular importance, and have been advancing gradually in the wake of developments in robotics, big data analytics, and deep learning. They are strongly related to machine learning, in fact, as a kind of physical manifestation of a neural net.

Autonomous social robots are extremely important in understanding how AI and robotics will influence the future. As with many such concepts, however, the possibilities are only really grasped in action. Here, we’ve assembled a set of four videos from diverse sources on the web that offer an engaging visual exploration of the subject.

Feel free to comment or suggest analytics videos that make a compelling case or provide an appealing representation of the technology.

Termite-Inspired Robots Can Build Unsupervised (National Geographic)

Autonomous Robots Self Assemble And Fly

Flying robots, the builders of tomorrow (Reuters)

Programmable self-assembly in a thousand-robot swarm (Harvard)


Human-Centered Robotics–The Video

Human-Centered Robotics is a somewhat broadly defined area looking at interactions between AI-driven robots and humans. Robots and humans must coexist in society, in the home, and in the workplace. An issue of particular urgency is how process robots, particularly in manufacturing, will operate efficiently without harming people, and without requiring complex programming skills to function in a changing environment.

Robotics companies and academic institutions are working to ease the way for an interactive robotics future. Here are a few videos of the current state of the human-robot interaction art:

Automatica 2016

University of Texas at Austin

DIAG Robotics Lab, Sapienza University of Rome

Nao Robot Task Learning