AI News, BOOK REVIEW: The Computer Revolution/Artificial Intelligence/Diagnosis

The Computer Revolution/Artificial Intelligence/Diagnosis

A computer can be plugged in and, after copying the information recorded by the sensors, make a determination as to any problems that fall outside the normal range of use.

It is important for computer system algorithms to be able to determine which part of a system is failing, where that part is located, the type of fault in the part and many systems now provide the steps to fix the problems.

In time it is likely that artificial intelligence will be able to diagnoses with a high degree of accuracy, not only technical systems with the necessary number of sensors, but organic matter as well.

The Dark Secret at the Heart of AI

Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey.

The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence.

Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems.

The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation.

There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.

But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users.

But banks, the military, employers, and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable.

“Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.” There’s already an argument that being able to interrogate an AI system about how it reached its conclusions is a fundamental legal right.

The resulting program, which the researchers named Deep Patient, was trained using data from about 700,000 individuals, and when tested on new records, it proved incredibly good at predicting disease.

Without any expert instruction, Deep Patient had discovered patterns hidden in the hospital data that seemed to indicate when people were on the way to a wide range of ailments, including cancer of the liver.

If something like Deep Patient is actually going to help doctors, it will ideally give them the rationale for its prediction, to reassure them that it is accurate and to justify, say, a change in the drugs someone is being prescribed.

Many thought it made the most sense to build machines that reasoned according to rules and logic, making their inner workings transparent to anyone who cared to examine some code.

But it was not until the start of this decade, after several clever tweaks and refinements, that very large—or “deep”—neural networks demonstrated dramatic improvements in automated perception.

It has given computers extraordinary powers, like the ability to recognize spoken words almost as well as a person could, a skill too complex to code into the machine by hand.

The same approach can be applied, roughly speaking, to other inputs that lead a machine to teach itself: the sounds that make up words in speech, the letters and words that create sentences in text, or the steering-wheel movements required for driving.

The resulting images, produced by a project known as Deep Dream, showed grotesque, alien-like animals emerging from clouds and plants, and hallucinatory pagodas blooming across forests and mountain ranges.

In 2015, Clune’s group showed how certain images could fool such a network into perceiving things that aren’t there, because the images exploit the low-level patterns the system searches for.

The images that turn up are abstract (imagine an impressionistic take on a flamingo or a school bus), highlighting the mysterious nature of the machine’s perceptual abilities.

It is the interplay of calculations inside a deep neural network that is crucial to higher-level pattern recognition and complex decision-making, but those calculations are a quagmire of mathematical functions and variables.

“But once it becomes very large, and it has thousands of units per layer and maybe hundreds of layers, then it becomes quite un-understandable.” In the office next to Jaakkola is Regina Barzilay, an MIT professor who is determined to apply machine learning to medicine.

The diagnosis was shocking in itself, but Barzilay was also dismayed that cutting-edge statistical and machine-learning methods were not being used to help with oncological research or to guide patient treatment.

She envisions using more of the raw data that she says is currently underutilized: “imaging data, pathology data, all this information.” How well can we get along with machines that are

After she finished cancer treatment last year, Barzilay and her students began working with doctors at Massachusetts General Hospital to develop a system capable of mining pathology reports to identify patients with specific clinical characteristics that researchers might want to study.

Barzilay and her students are also developing a deep-learning algorithm capable of finding early signs of breast cancer in mammogram images, and they aim to give this system some ability to explain its reasoning, too.

The U.S. military is pouring billions into projects that will use machine learning to pilot vehicles and aircraft, identify targets, and help analysts sift through huge piles of intelligence data.

A silver-haired veteran of the agency who previously oversaw the DARPA project that eventually led to the creation of Siri, Gunning says automation is creeping into countless areas of the military.

But soldiers probably won’t feel comfortable in a robotic tank that doesn’t explain itself to them, and analysts will be reluctant to act on information without some reasoning.

A chapter of Dennett’s latest book, From Bacteria to Bach and Back, an encyclopedic treatise on consciousness, suggests that a natural part of the evolution of intelligence itself is the creation of systems capable of performing tasks their creators do not know how to do.

But since there may be no perfect answer, we should be as cautious of AI explanations as we are of each other’s—no matter how clever a machine seems.“If it can’t do better than us at explaining what it’s doing,” he says, “then don’t trustit.”

Diagnosis (artificial intelligence)

As a subfield in artificial intelligence, Diagnosis is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct.

If the system is not functioning correctly, the algorithm should be able to determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing.

The computation is based on observations, which provide information on the current behaviour.

The expression diagnosis also refers to the answer of the question of whether the system is malfunctioning or not, and to the process of computing the answer.

This word comes from the medical context where a diagnosis is the process of identifying a disease by its symptoms.

The mechanic will first try to detect any abnormal behavior based on the observations on the car and his knowledge of this type of vehicle.

If he finds out that the behavior is abnormal, the mechanic will try to refine his diagnosis by using new observations and possibly testing the system, until he discovers the faulty component;

The expert diagnosis (or diagnosis by expert system) is based on experience with the system.

Using this experience, a mapping is built that efficiently associates the observations to the corresponding diagnoses.

slightly different approach is to build an expert system from a model of the system rather than directly from an expertise.

An example is the computation of a diagnoser for the diagnosis of discrete event systems.

This approach can be seen as model-based, but it benefits from some advantages and suffers some drawbacks of the expert system approach.

In particular, the faulty behaviour is generally little-known, and the faulty model may thus not be represented.

Given observations of the system, the diagnosis system simulates the system using the model, and compares the observations actually made to the observations predicted by the simulation.

b

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system is said to be diagnosable if whatever the behavior of the system, we will be able to determine without ambiguity a unique diagnosis.

The problem of diagnosability is very important when designing a system because on one hand one may want to reduce the number of sensors to reduce the cost, and on the other hand one may want to increase the number of sensors to increase the probability of detecting a faulty behavior.

another class looks for sets of sensors that make the system diagnosable, and optionally comply to criteria such as cost optimization.

In applications using model-based diagnosis, such a model is already present and doesn't need to be built from scratch.

methods of developing systems that display aspects of intelligent behaviour.

are designed to imitate the human capabilities of thinking and sensing.

that is, fully specified step-by-step procedures that define a solution to the problem.

The actions of a knowledge-based AI system depend to a far greater degree on the situation

that is, fully specified step-by-step procedures that define a solution to the problem.

The actions of a knowledge-based AI system depend to a far greater degree on the situation

on disciplines such as computer science, biology, psychology, linguistics, mathematics,

associated with human intelligence, such as reasoning, learning, and problem solving.

1950 Turing Test - a machine performs intelligently if an

interrogator using remote terminals cannot distinguish its responses from those of a human. Result:

knowledge about its application domain and uses an inferencing (reason) procedure to

solve problems that would otherwise require human competence or expertise.

systems stems primarily from the specific knowledge about a narrow domain stored in the

knowledge to bear on the facts of the particular situation at hand.

of an ES also contains heuristic knowledge - rules of thumb used by human

These include areas such as high-risk credit decisions, advertising decision

Categories of Expert System Applications 1950 Turing Test - a machine performs intelligently if an

interrogator using remote terminals cannot distinguish its responses from those of a human.

Result: General problem-solving methods 1960 AI established as research field.

Result: Knowledge-based expert systems 1970 AI commercialization began Result: Transaction processing and decision support systems

in the human brain 1990 Intelligent agents Result: Software that performs assigned tasks on the users

behalf 11.2 Capabilities of Expert Systems: General View The most important applied area of AI is the field of expert

knowledge about its application domain and uses an inferencing (reason) procedure to

solve problems that would otherwise require human competence or expertise.

systems stems primarily from the specific knowledge about a narrow domain stored in the

knowledge to bear on the facts of the particular situation at hand.

of an ES also contains heuristic knowledge - rules of thumb used by human

11.3 Applications of Expert Systems The test outlines some illustrative minicases of expert systems

These include areas such as high-risk credit decisions, advertising decision

process control, design, scheduling and planning, and generation of options.

- an organized collection of facts and heuristics about the system's domain. An

ES is built in a process known as knowledge engineering, during which knowledge

about the domain is acquired from human experts and other sources by knowledge engineers.

- an organized collection of facts and heuristics about the system's domain. An

ES is built in a process known as knowledge engineering, during which knowledge

about the domain is acquired from human experts and other sources by knowledge engineers.

The accumulation of knowledge in knowledge bases, from which

conclusions are to be drawn by the inference engine, is the hallmark of an expert system.

taken under circumstances, causality, time, dependencies, goals, and other higher-level concepts.

the attributes of a complex object and frames for various object types have specified

the attributes of a complex object and frames for various object types have specified

the explanation may be either in a natural language or simply a listing of rule numbers.

knowledge contained in the knowledge base to come up with a recommendation.

expert system, the inference engine controls the order in which production rules

which acts as a blackboard, accumulating the knowledge about the case at hand.

The inference engine repeatedly applies the rules to the working memory, adding new information

(obtained from the rules conclusions) to it, until a goal state is produced or confirmed. Figure

Forward chaining The facts of the given case are entered into the working memory,

which acts as a blackboard, accumulating the knowledge about the case at hand.

The inference engine repeatedly applies the rules to the working memory, adding new information

(obtained from the rules conclusions) to it, until a goal state is produced or confirmed.

facts available in the working memory and by the premises that can be satisfied.

engine attempts to match the condition (IF) part of each rule in the knowledge base

open-ended problems of a design or planning nature, such as, for example, establishing

- the inference engine attempts to match the assumed (hypothesized)

conclusion - the goal or subgoal state - with the conclusion (THEN) part of the

If such a rule is found, its premise becomes the new subgoal.

type systems, in which each of several possible conclusions can be checked to see

conclusion - the goal or subgoal state - with the conclusion (THEN) part of the

If such a rule is found, its premise becomes the new subgoal.

review until a goal state that can be supported by the premises is encountered.

type systems, in which each of several possible conclusions can be checked to see

reasoning since it allows for approximate values and inferences and incomplete or ambiguous

Fuzzy logic is a method of choice for handling uncertainty in some

important things to keep in mind when selecting ES tools include: 1.

important things to keep in mind when selecting ES tools include:

Specific expert systems Expert systems technologies include:

developer with the inference engine, user interface, and the explanation and knowledge acquisition

expert systems, which require much less effort in order to field an actual system.

developer with the inference engine, user interface, and the explanation and knowledge acquisition

- these systems expand the capabilities of shells in various

LISP into a procedural language more commonly found in the commercial environment, such

- knowledge engineer works with the expert to place the initial

of work the individual must do to solve a problem, and they do leave people with the

are costly and require significant development time and computer resources.

are costly and require significant development time and computer resources.

The most advanced AI sensory system is compute vision, or visual scene recognition.

The most advanced AI sensory system is compute vision, or visual scene recognition.

as opposed to systems that recognize words or short phrases spoken one at a time or

can automatically change itself in order to perform the same tasks more efficiently and more

can automatically change itself in order to perform the same tasks more efficiently and more

this case, a system is able to generate its knowledge, represented as rules.

brain's mesh-like network of interconnected processing elements, called neurons.

neural networks are much simpler than the human brain (estimated to have more than 100

certain patterns and then apply what it learned to new cases where it can discern

Artificial intelligence

AI research follows two distinct, and to some extent competing, methods, the symbolic (or “top-down”) approach, and the connectionist (or “bottom-up”) approach.

The top-down approach seeks to replicate intelligence by analyzing cognition independent of the biological structure of the brain, in terms of the processing of symbols—whence the symbolic label.

(Tuning adjusts the responsiveness of different neural pathways to different stimuli.) In contrast, a top-down approach typically involves writing a computer program that compares each letter with geometric descriptions.

In The Organization of Behavior (1949), Donald Hebb, a psychologist at McGill University, Montreal, Canada, suggested that learning specifically involves strengthening certain patterns of neural activity by increasing the probability (weight) of induced neuron firing between the associated connections.

This hypothesis states that processing structures of symbols is sufficient, in principle, to produce artificial intelligence in a digital computer and that, moreover, human intelligence is the result of the same type of symbolic manipulations.

This App Can Diagnose Your Car Trouble

says developer and mechanical engineer Joshua Siegel, referring to the hosts of the long-running NPR program Car Talk, who could famously diagnose car problems by listening to callers imitate whatever strange noise their car was making.

“I stood in awe of the friends and family surrounding me who had a knack for being able to identify subtle problems within vehicles, from slight changes in pitch to minute vibrations in the suspension…I reasoned that if trained individuals could detect these problems accurately, mobile phones possessing the same ‘sensors’

The app’s powers are currently limited to certain common problems that can be easily detected by smartphone sensors, including wheel imbalance, engine misfires, improper tire pressure and clogged air filters.

Charles Sanville, a master certified Volkswagen technician from outside Raleigh, North Carolina, says that a given car problem might present as a 'plink' in the majority of cars, but a significant minority of cars will make a totally different sound, despite having the same problem. This is where an experienced mechanic is needed.

When Sanville is diagnosing a noise in the air-conditioning, for example, he’ll first sit in the driver’s seat to listen, then move to the passenger seat, then stick his head under the dashboard, then change all the settings on the climate control system, all to see if the noise changes.

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