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When Will an Artificially Intelligent Machine Display and Feel Human Emotions? Part 2/2

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In our last post, we raised the question: “Will an intelligent machine ever be able to completely replicate a human mind?” Let’s now address it.

Experts disagree. Some experts—such as English mathematical physicist, recreational mathematician, and philosopher Roger Penrose—argue there is a limit as to what intelligent machines can do. Most experts, however, including Ray Kurzweil, argue that it will eventually be technologically feasible to copy the brain directly into an intelligent machine and that such a simulation will be identical to the original. The implication is that the intelligent machine will be a mind and be self-aware.

This begs one big question: “When will the intelligent machines become self-aware?”

A generally accepted definition is that a person is conscious if that person is aware of his or her surroundings. If you are self-aware, it means you are self-conscious. In other words you are aware of yourself as an individual or of your own being, actions, and thoughts. To understand this concept, let us start by exploring how the human brain processes consciousness. To the best of our current understanding, no one part of the brain is responsible for consciousness. In fact neuroscience (the scientific study of the nervous system) hypothesizes that consciousness is the result of the interoperation of various parts of the brain called “neural correlates of consciousness” (NCC). This idea suggests that at this time we do not completely understand how the human brain processes consciousness or becomes self-aware.

Is it possible for a machine to be self-conscious? Obviously, since we do not completely understand how the human brain processes consciousness to become self-aware, it is difficult to definitively argue that a machine can become self-conscious or obtain what is termed “artificial consciousness” (AC). This is why AI experts differ on this subject. Some AI experts (proponents) argue it is possible to build a machine with AC that emulates the interoperation (i.e., it works like the human brain) of the NCC. Opponents argue that it is not possible because we do not fully understand the NCC. To my mind they are both correct. It is not possible today to build a machine with a level of AC that emulates the self-consciousness of the human brain. However, I believe that in the future we will understand the human brain’s NCC interoperation and build a machine that emulates it. Nevertheless this topic is hotly debated.

Opponents argue that many physical differences exist between natural, organic systems and artificially constructed (e.g., computer) systems that preclude AC. The most vocal critic who holds this view is American philosopher Ned Block (1942– ), who argues that a system with the same functional states as a human is not necessarily conscious.

The most vocal proponent who argues that AC is plausible is Australian philosopher David Chalmers (1966– ). In his unpublished 1993 manuscript “A Computational Foundation for the Study of Cognition,” Chalmers argues that it is possible for computers to perform the right kinds of computations that would result in a conscious mind. He reasons that computers perform computations that can capture other systems’ abstract causal organization. Mental properties are abstract causal organization. Therefore computers that run the right kind of computations will become conscious.

Source:  The Artificial Intelligence Revolution (2014), Louis A. Del Monte

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When Will an Artificially Intelligent Machine Display and Feel Human Emotions? Part 1/2

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Affective computing is a relatively new science. It is the science of programming computers to recognize, interpret, process, and simulate human affects. The word “affects” refers to the experience or display of feelings or emotions.

While AI has achieved superhuman status in playing chess and quiz-show games, it does not have the emotional equivalence of a four-year-old child. For example a four-year-old may love to play with toys. The child laughs with delight as the toy performs some function, such as a toy cat meowing when it is squeezed. If you take the toy away from the child, the child may become sad and cry. Computers are unable to achieve any emotional response similar to that of a four-year-old child. Computers do not exhibit joy or sadness. Some researchers believe this is actually a good thing. The intelligent machine processes and acts on information without coloring it with emotions. When you go to an ATM, you will not have to argue with the ATM regarding whether you can afford to make a withdrawal, and a robotic assistant will not lose its temper if you do not thank it after it performs a service. Highly meaningful human interactions with intelligent machines, however, will require that machines simulate human affects, such as empathy. In fact some researchers argue that machines should be able to interpret the emotional state of humans and adapt their behavior accordingly, giving appropriate responses for those emotions. For example if you are in a state of panic because your spouse is apparently having a heart attack, when you ask the machine to call for medical assistance, it should understand the urgency. In addition it will be impossible for an intelligent machine to be truly equal to a human brain without the machine possessing human affects. For example how could an artificial human brain write a romance novel without understanding love, hate, and jealousy?

Progress concerning the development of computers with human affects has been slow. In fact this particular computer science originated with Rosalind Picard’s 1995 paper on affective computing (“Affective Computing,” MIT Technical Report #321, abstract, 1995). The single greatest problem involved in developing and programming computers to emulate the emotions of the human brain is that we do not fully understand how emotions are processed in the human brain. We are unable to pinpoint a specific area of the brain and scientifically argue that it is responsible for specific human emotions, which has raised questions. Are human emotions byproducts of human intelligence? Are they the result of distributed functions within the human brain? Are they learned, or are we born with them? There is no universal agreement regarding the answers to these questions. Nonetheless work on studying human affects and developing affective computing is continuing.

There are two major focuses in affective computing.

1. Detecting and recognizing emotional information: How do intelligent machines detect and recognize emotional information? It starts with sensors, which capture data regarding a subject’s physical state or behavior. The information gathered is processed using several affective computing technologies, including speech recognition, natural-language processing, and facial-expression detection. Using sophisticated algorithms, the intelligent machine predicts the subject’s affective state. For example the subject may be predicted to be angry or sad.

2. Developing or simulating emotion in machines: While researchers continue to develop intelligent machines with innate emotional capability, the technology is not to the level where this goal is achievable. Current technology, however, is capable of simulating emotions. For example when you provide information to a computer that is routing your telephone call, it may simulate gratitude and say, “Thank you.” This has proved useful in facilitating satisfying interactivity between humans and machines. The simulation of human emotions, especially in computer-synthesized speech, is improving continually. For example you may have noticed when ordering a prescription by phone that the synthesized computer voice sounds more human as each year passes.

All current technologies to detect, recognize, and simulate human emotions are based on human behavior and not on how the human mind works. The main reason for this approach is that we do not completely understand how the human mind works when it comes to human emotions. This carries an important implication. Current technology can detect, recognize, simulate, and act accordingly based on human behavior, but the machine does not feel any emotion. No matter how convincing the conversation or interaction, it is an act. The machine feels nothing. However, intelligent machines using simulated human affects have found numerous applications in the fields of e-learning, psychological health services, robotics, and digital pets.

It is only natural to ask, “Will an intelligent machine ever feel human affects?” This question raises a broader question: “Will an intelligent machine ever be able to completely replicate a human mind?” We will address this question in part 2.

Source: The Artificial Intelligence Revolution (2014), Louis A. Del Monte

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How Do Intelligent Machines Learn?

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How and under what conditions is it possible for an intelligent machine to learn? To address this question, let’s start with a definition of machine learning. The most widely accepted definition comes from Tom M. Mitchell, a American computer scientist and E. Fredkin University Professor at Carnegie Mellon University. Here is his formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” In simple terms machine learning requires a machine to learn similar to the way humans do, namely from experience, and continue to improve its performance as it gains more experience.

Machine learning is a branch of AI; it utilizes algorithms that improve automatically through experience. Machine learning also has been a focus of AI research since the field’s inception. There are numerous computer software programs, known as machine-learning algorithms, that use various computational techniques to predict outcomes of new, unseen experiences. The algorithms’ performance is a branch of theoretical computer science known as “computational learning theory.” What this means in simple terms is that an intelligent machine has in its memory data that relates to a finite set of experiences. The machine-learning algorithms (i.e., software) access this data for its similarity to a new experience and use a specific algorithm (or combination of algorithms) to guide the machine to predict an outcome of this new experience. Since the experience data in the machine’s memory is limited, the algorithms are unable to predict outcomes with certainty. Instead they associate a probability to a specific outcome and act in accordance with the highest probability. Optical character recognition is an example of machine learning. In this case the computer recognizes printed characters based on previous examples. As anyone who has ever used an optical character-recognition program knows, however, the programs are far from 100 percent accurate. In my experience the best case is a little more than 95 percent accurate when the text is clear and uses a common font.

There are eleven major machine-learning algorithms and numerous variations of these algorithms. To study and understand each would be a formidable task. Fortunately, though, machine-learning algorithms fall into three major classifications. By understanding these classifications,we can gain significant insight into the science of machine learning. Therefore let us review the three major classifications:

  1. Supervised learning: This class of algorithms infers a function (a way of mapping or relating an input to an output) from training data, which consists of training examples. Each example consists of an input object and a desired output value. Ideally the inferred function (generalized from the training data) allows the algorithm to analyze new data (unseen instances/inputs) and map it to (i.e., predict) a high-probability output.
  2. Unsupervised learning: This class of algorithms seeks to find hidden structures (patterns in data) in a stream of input (unlabeled data). Unlike in supervised learning, the examples presented to the learner are unlabeled, which makes it impossible to assign an error or reward to a potential solution.
  3. Reinforcement learning: Reinforcement learning was inspired by behaviorist psychology. It focuses on which actions an agent (an intelligent machine) should take to maximize a reward (for example a numerical value associated with utility). In effect the agent receives rewards for good responses and punishment for bad ones. The algorithms for reinforcement learning require the agent to take discrete time steps and calculate the reward as a function of having taken that step. At this point the agent takes another time step and again calculates the reward, which provides feedback to guide the agent’s next action. The agent’s goal is to collect as much reward as possible.

In essence machine learning incorporates four essential elements.

  1. Representation: The intelligent machine must be able to assimilate data (input) and transform it in a way that makes it useful for a specific algorithm.
  2. Generalization: The intelligent machine must be able to accurately map unseen data to similar data in the learning data set.
  3. Algorithm selection: After generalization the intelligent machine must choose and/or combine algorithms to make a computation (such as a decision or an evaluation).
  4. Feedback: After a computation, the intelligent machine must use feedback (such as a reward or punishment) to improve its ability to perform steps 1 through 3 above.

Machine learning is similar to human learning in many respects. The most difficult issue in machine learning is generalization or what is often referred to as abstraction. This is simply the ability to determine the features and structures of an object (i.e., data) relevant to solving the problem. Humans are excellent when it comes to abstracting the essence of an object. For example, regardless of the breed or type of dog, whether we see a small, large, multicolor, long-hair, short-hair, large-nose, or short-nose animal, we immediately recognize that the animal is a dog. Most four-year-old children immediately recognize dogs. However, most intelligent agents have a difficult time with generalization and require sophisticated computer programs to enable them to generalize.

Machine learning has come a long way since the 1972 introduction of Pong, the first game developed by Atari Inc. Today’s computer games are incredibly realistic, and the graphics are similar to watching a movie. Few of us can win a chess game on our computer or smartphone unless we set the difficulty level to low. In general machine learning appears to be accelerating, even faster than the field of AI as a whole. We may, however, see a bootstrap effect, in which machine learning results in highly intelligent agents that accelerate the development of artificial general intelligence, but there is more to the human mind than intelligence. One of the most important characteristics of our humanity is our ability to feel human emotions.

This raises an important question. When will computers be capable of feeling human emotions? A new science is emerging to address how to develop and program computers to be capable of simulating and eventually feeling human emotions. This new science is termed “affective computing.”  We will discuss affective computing in a future post.

Source: The Artificial Intelligence Revolution (2014), Louis A. Del Monte

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Artificial Intelligence Interview Podcast

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Louis Del Monte on the Tom Barnard Show 7/23 discussing his new book, The Artificial Intelligence Revolution. During the interview we discuss  the future of AI and how it may impact humanity. You can listen to the complete interview at anytime via this link  https://www.tombarnardpodcast.com/july-23rd-2014-louis-del-monte-483-2/

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Artificial Intelligence Gives Rise to Intelligent Agents – Part 3/3 (Conclusion)

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In conclusion, let’s discuss the approaches that researchers pursued using electronic digital programmable computers.

From the 1960s through the 1970s, symbolic approaches achieved success at simulating high-level thinking in specific application programs. For example, in 1963, Danny Bobrow’s technical report from MIT’s AI group proved that computers could understand natural language well enough to solve algebra word problems correctly. The success of symbolic approaches added credence to the belief that symbolic approaches eventually would succeed in creating a machine with artificial general intelligence, also known as “strong AI,” equivalent to a human mind’s intelligence.

By the 1980s, however, symbolic approaches had run their course and fallen short of the goal of artificial general intelligence. Many AI researchers felt symbolic approaches never would emulate the processes of human cognition, such as perception, learning, and pattern recognition. The next step was a small retreat, and a new era of AI research termed “subsymbolic” emerged. Instead of attempting general AI, researchers turned their attention to solving smaller specific problems. For example researchers such as Australian computer scientist and former MIT Panasonic Professor of Robotics Rodney Brooks rejected symbolic AI. Instead he focused on solving engineering problems related to enabling robots to move.

In the 1990s, concurrent with subsymbolic approaches, AI researchers began to incorporate statistical approaches, again addressing specific problems. Statistical methodologies involve advanced mathematics and are truly scientific in that they are both measurable and verifiable. Statistical approaches proved to be a highly successful AI methodology. The advanced mathematics that underpin statistical AI enabled collaboration with more established fields, including mathematics, economics, and operations research. Computer scientists Stuart Russell and Peter Norvig describe this movement as the victory of the “neats” over the “scruffies,” two major opposing schools of AI research. Neats assert that AI solutions should be elegant, clear, and provable. Scruffies, on the other hand, assert that intelligence is too complicated to adhere to neat methodology.

From the 1990s to the present, despite the arguments between neats, scruffies, and other AI schools, some of AI’s greatest successes have been the result of combining approaches, which has resulted in what is known as the “intelligent agent.” The intelligent agent is a system that interacts with its environment and takes calculated actions (i.e., based on their success probability) to achieve its goal. The intelligent agent can be a simple system, such as a thermostat, or a complex system, similar conceptually to a human being. Intelligent agents also can be combined to form multiagent systems, similar conceptually to a large corporation, with a hierarchical control system to bridge lower-level subsymbolic AI systems to higher-level symbolic AI systems.

The intelligent-agent approach, including integration of intelligent agents to form a hierarchy of multiagents, places no restriction on the AI methodology employed to achieve the goal. Rather than arguing philosophy, the emphasis is on achieving results. The key to achieving the greatest results has proven to be integrating approaches, much like a symphonic orchestra integrates a variety of instruments to perform a symphony.

In the last seventy years, the approach to achieving AI has been more like that of a machine gun firing broadly in the direction of the target than a well-aimed rifle shot. In fits of starts and stops, numerous schools of AI research have pushed the technology forward. Starting with the loftiest goals of emulating a human mind, retreating to solving specific well-defined problems, and now again aiming toward artificial general intelligence, AI research is a near-perfect example of all human technology development, exemplifying trial-and-error learning, interrupted with spurts of genius.

Although AI has come a long way in the last seventy years and has been able to equal and exceed human intelligence in specific areas, such as playing chess, it still falls short of general human intelligence or strong AI. There are two significant problems associated with strong AI. First, we need a machine with processing power equal to that of a human brain. Second, we need programs that allow such a machine to emulate a human brain.

Source: The Artificial Intelligence Revolution (2014), Louis A. Del Monte