It is widely believed that the methods of science somehow “prove” scientific facts. You hear reference to “scientific proof” in the news media, and even scientists will use the word “proof” informally to describe a very strong set of evidence.

Some theories, such as the theory of gravity,
F = M * m * G / x^2

(Force equals mass of the object, times the mass of the second object, times the gravitational constant, divided by the square of the distance)

are so reliable that we say that this theory “explains” the fact, as if this theory were an inherent property of nature, and not just our assumptions about it. Thus, chalk falls to the ground because there is this equation written on the board. In ordinary language, our everyday experiences (observations, facts, phenomena) of the external world are the logical consequence of arguments whose premises are NATURAL LAW. What is the difference between a theory and a NATURAL LAW? A natural law is theory with so few exceptions that we’re willing to give it complete acceptance. If I drop my pen and it falls and I ask you why, you say it’s because of the law of gravity, as if the theory exists prior to and independently of the fact. We actually believe that the theory of gravity is part of the intrinsic structure of the universe, and so it follows that in this particular case, I drop the pen, and, it falls. However, if you look more closely at how scientific reasoning works, it becomes clear that even the “laws” of nature are never proven.

In fact, logically, the proof goes in the other direction. That is, hypotheses are predictions about nature that are logically derived from theories. This kind of inference is known as deduction. A deductive inference is one in which the conclusion about particulars follows necessarily from general or universal premises.

For instance, a famous deductive inference goes: “Socrates is a man. All men are mortal. Therefore, Socrates is a mortal.” In this argument, “All men are mortal” serves the same role as a scientific theory. “Socrates is a mortal” serves the same role as a hypothesis would. The state of Athens tested this hypothesis by sentencing Socrates to death.

Deductive inferences are well-understood in philosophy. Deductive inferences are either valid or invalid. They succeed entirely or fail completely, and there are no shades of grey in between.

So, as a generalization about nature, theories serve as premises or assumptions from which specific facts (observations, phenomena, hypotheses or, to use really fancy words, empirical test implications) can be deduced as a conclusion.

An argument consists of one or more premises, an inference and a conclusion.

Premises are themselves are either assumed to be true, or else they must be the conclusion some other argument which requires its own premises and inference. Ultimately, then, every method of inference (argument, justification, explanation) requires some premises which themselves remain unproven. This is a consequence not of anything particular about science but of logic alone. If you’ve heard of Godel’s incompleteness theorem of mathematics, this is what it boils down to.

So, as I said before, logic is never a source of truth. Valid logical inferences are not truth-creating, but merely truth-conserving, transferring the assumed truth of the premises to the conclusions.

When scientific creationists claim that evolution by natural selection is “just an unproven theory,” they are right in the trivial sense that no scientific theories are ever proven. Scientific theories are established by a different kind of inference called induction.

An inductive inference is one in which a generalized conclusion follows from the truth of particular premises. Thus, Newton’s Universal Law of Gravitation was derived by observing numerous objects falling with a given velocity with respect to time, mass, etc. Inductive inferences are either strong or weak, and there is a lot of grey area in between that is NOT well-understood in philosophy. In fact, all inductive inferences are INVALID because it is not logically necessary (nor empirically certain) that all members of any class can be characterized by observing only some of them. The fact that scientific progress requires this invalid inference– that only observation and experiment may decide upon the acceptance or rejection of scientific laws and theories– is called the PROBLEM OF INDUCTION in philosophy. David Hume described the problem of induction by saying, just because the sun has risen every morning for as long as I can remember, is no guarantee that it will rise tomorrow.

Let’s review how scientific inference works. By the “scientific method” as commonly taught in high schools, science begins with some practical problem or disturbing fact, from which we INDUCE or generalize a theory.

From that theory, we DEDUCE specific hypotheses or empirical test implications, about situations which are members of the same class, but have not yet been observed. If these test implications are true, then, by induction, the theory is stronger. If an empirical test implication is false, the observation is called an ANOMALY. If you have anomalous observations, you can either reject the theory, or you can qualify it with AUXILIARY ASSUMPTIONS. Auxiliary assumptions explain why the theory is true, but only appears to be false under certain conditions. For instance, an auxiliary assumption about wind resistance has to be added to Newton’s law of gravity to explain why feathers fall more slowly than rocks. If a hypothesis deduced from the auxiliary assumption is true, then, by induction, the theory grows stronger.

One factor contributing to the strength of an inductive inference or theory is its POWER. The power, or scope, of a theory is the number of independent empirical implications that are confirmed by observation. Another desirable property of a theory is known as PARSIMONY, or simplicity. That is, a theory or inductive inference is stronger if it makes fewer independent assumptions. William of Ockham summarized this principle in the fourteenth century in a famous dictum known as OCKHAM’S RAZOR: “Pluralitas non est ponenda sine neccesitate”, which translates as “entities should not be multiplied without necessity.” The anthropologist Marvin Harris summarized power and parsimony by saying, “The theory that explains the largest number of facts while making the smallest number of independent unverified assumptions will be given priority.”

Power and parsimony are important concepts in the philosophy of science, but let me return to the main point, which is that scientific inference actually works in the backwards direction to how we commonly conceptualize scientific explanation. That is, the theories that are put forward as explanations for empirical observations are, in fact, the conclusions of inductive inferences for which empirical observations are the premises. Understanding how and why induction is problematic forces us to admit that all scientific theories are inventions rather than discoveries, which is an essential step in thinking critically about science.

The fact that all scientific theories (indeed, all logical arguments) necessarily include assumptions that are irreducibly irrational creates a never-ending opportunity for scientific progress.

In The Structure of Scientific Revolutions, Thomas Kuhn described some of the irrational forces at work in the history of science. Sometimes a theory is so successful that it attracts a large enduring number of scientists from the field. Kuhn called these socially popular theories PARADIGMS. For example, behaviorism (the theory that only reinforcement contingencies matter; brain and mind don’t) was a paradigm that dominated psychology for much of the 20th century. A paradigm is not just a theory however. They are also sociological movements. Scientists invest many years of their lives, and institutions like UH invest a huge amount of money into particular scientific methods. Paradigms are often not just theories but a set of methods that are accepted as “scientifically sound.” If for some reason, the basic premises of a paradigm are called into question, human beings will resist accepting that all of their training and all of their equipment are obsolete. Scientists sometimes poke fun at religion by pointing out how Church officials refused to look through Galileo’s telescope. In fact, we are often guilty of doing the same thing to our colleagues.

Under the shadow of a strong paradigm, the only way a scientist can get funding and maintain a career in science is to propose experiments that confirm and extend the paradigm. So, rather than inventing new theories which are usually more risky, scientists will often follow in the footsteps of someone else’s outstanding achievement. This is what Thomas Kuhn called NORMAL SCIENCE. Normal science is the process by which large numbers of the members of a discipline systematically test, one after another, all of the empirical hypotheses that are entailed by a paradigm (e.g., filling in another element on the periodic table, or sequencing another gene). Normal science is contrasted with REVOLUTIONARY SCIENCE, which involves throwing out the old theory and explaining the same facts with a new theory that explains the same facts while making new predictions.

It is of interest the difference between normal and revolutionary science correspond roughly to the difference between deduction and induction. It also roughly to the difference between science and philosophy, in that scientists (even the revolutionary ones) spend most of their time doing observation (testing the hypotheses or conclusions deduced from theories) while philosophers spend most of their time examining fundamental assumptions (the premises from which hypotheses are derived.)

Kuhn argues that normal science has the advantage of focusing the attention of the scientific community on an important problem, but it has the disadvantage of shutting out numerous other opportunities for discovery. I think the lesson to be drawn from Kuhn’s analysis is that we need to look at our own work like anthropologists. That is, it is easy to see how people in distant times or places get fixated on arbitrary beliefs, but it’s more important to examine whether there are more powerful alternatives to our own beliefs.

There is another point of view in scientific philosophy called the CRITICAL SOCIAL SCIENCE perspective, which approaches the economy of science from an anthropological point of view. The key question to a critical social scientist is, “Who Benefits?” from the dominant paradigm. As a scientist who studies the efficacy of EEG biofeedback for psychological disorders, I take a critical social science view of how poorly funded research on biofeedback is compared to studies of prescription drugs. However, when we look at how little research is done on possible therapeutic applications of certain non-prescription drugs like marijuana and LSD compared to studies aimed at finding negative effects, many of us take a critical social science view of the influence of the “War on Drugs,” and the massive police-prison-industrial complex it maintains.

So, the fact that every argument requires premises that remain unproven-that every scientific theory involves assumptions that are only supported by the invalid inference known as induction-helps us to see the irrational, social, and political dimensions underlying the agenda of research. It may also help us to see how science, far from being a cold, detached, objective inquiry, requires ethical reflection and action at the most basic level.

As Heisenberg said, what we know about nature depends upon what we ask.

Futurist Ray Kurzweil explains how the boundary between man and machine is quickly disappearing. PLUS: A gallery of today’s most mind-blowing ‘bots

http://www.popsci.com/popsci/technology/d6a188432263d010vgnvcm1000004eecbccdrcrd.html

Human experience is marked by a refusal to obey our limitations. We’ve escaped the ground, we’ve escaped the planet, and now, after thousands of years of effort, our quest to build machines that emulate our own appearance, movement and intelligence is leading us to the point where we will escape the two most fundamental confines of all: our bodies and our minds. Once this point comes—once the accelerating pace of technological change allows us to build machines that not only equal but surpass human intelligence—we’ll see cyborgs (machine-enhanced humans like the Six Million Dollar Man), androids (human-robot hybrids like Data in Star Trek) and other combinations beyond what we can even imagine.

Although the ancient Greeks were among the first to build machines that could emulate the intelligence and natural movements of people (developments invigorated by the Greeks’ musings that human intelligence might also be governed by natural laws), these efforts flowered in the European Renaissance, which produced the first androids with lifelike movements. These included a mandolin-playing lady, constructed in 1540 by Italian inventor Gianello Torriano. In 1772 Swiss watchmaker Pierre Jacquet-Droz built a pensive child named L’Écrivain (The Writer) that could write passages with a pen. L’Écrivain’s brain was a mechanical computer that was impressive for its complexity even by today’s standards.

Such inventions led scientists and philosophers to speculate that the human brain itself was just an elaborate automaton. Wilhelm Leibniz, a contemporary of Isaac Newton, wrote around 1700: “What if these theories are really true, and we were magically shrunk and put into someone’s brain while he was thinking. We would see all the pumps, pistons, gears and levers working away, and we would be able to describe their workings completely, in mechanical terms, thereby completely describing the thought processes of the brain. But that description would nowhere contain any mention of thought! It would contain nothing but descriptions of pumps, pistons, levers!”

Leibniz was on to something. There are indeed pumps, pistons and levers inside our brain—we now recognize them as neurotransmitters, ion channels and the other molecular components of the neural machinery. And although we don’t yet fully understand the details of how these little machines create thought, our ignorance won’t last much longer.

The word “robot” originated almost a century ago. Czech dramatist Karel Capek first used the term in his 1921 play R.U.R. (for “Rossum’s Universal Robots”), creating it from the Czech word “robota,” meaning obligatory work. In the play, he describes the invention of intelligent biomechanical machines intended as servants for their human creators. While lacking charm and goodwill, his robots brought together all the elements of machine intelligence: vision, touch sensitivity, pattern recognition, decision making, world knowledge, fine motor coordination and even a measure of common sense.

Capek intended his intelligent machines to be evil in their perfection, their perfect rationality scornful of human frailty. These robots ultimately rise up against their masters and destroy all humankind, a dystopian notion that has been echoed in much science fiction since.

The specter of machine intelligence enslaving its creators has continued to impress itself on the public consciousness. But more significantly, Capek’s robots introduced the idea of the robot as an imitation or substitute for a human being. The idea has been reinforced throughout the 20th century, as androids engaged the popular imagination in fiction and film, from Rosie to C-3PO and the Terminator.

The first generation of modern robots were, however, a far cry from these anthropomorphic visions, and most robot builders have made no attempt to mimic humans. The Unimate, a popular assembly-line robot from the 1960s, was capable only of moving its one arm in several directions and opening and closing its gripper. Today there are more than two million Roomba robots scurrying around performing a task (vacuuming) that used to be done by humans, but they look more like fast turtles than maids. Most robots will continue to be utilitarian devices designed to carry out specific tasks. But when we think of the word “robot,” Capek’s century-old concept of machines made in our own image still dominates our imagination and inspires our goals.

The aspiration to build human-level androids can be regarded as the ultimate challenge in artificial intelligence. To do it, we need to understand not just human cognition but also our physical skill—it is, after all, a critical part of what the brain does. Coordinating intention with movement in a complex environment is largely the responsibility of the cerebellum, which comprises more than half the neurons in the brain. And the body itself represents much of our complexity: There is more information in the human genome, which describes the human body, than in the design of the brain.
We are making tremendous strides toward being able to understand how the brain works. The performance/price ratio, capacity and bandwidth of every type of information technology, electronic and biological alike, is doubling about every year. I call this pervasive phenomenon the law of accelerating returns. Our grasp of biology is proceeding at an accelerating pace, also exponentially increasing every year. It took scientists five years to be able to sequence HIV, for example, but the SARS virus required only 31 days. The amount of genetic data that’s been sequenced has doubled every year since the human genome project began in 1990, and the cost per base pair has come down by half each year, from $10 in 1990 to about a penny today. We are making comparable gains in understanding how the genome expresses itself in proteins and in understanding how a broad range of biological mechanisms work. Indeed, we are augmenting and re-creating nearly every organ and system in the human body: hearts and pancreases, joints and muscles.

The same progression applies to our knowledge of the human brain. The three-dimensional resolution of brain scans has been exponentially increasing, and the latest generation of scanners can image individual neuronal connections firing in real time. The amount of data that scientists are gathering on the brain is similarly increasing every year. And they are showing that this information can be understood by converting it into models and simulations of brain regions, some two dozen of which have already been completed. IBM also recently began an ambitious effort to model a substantial part of the cerebral cortex in incredible detail.

If we are to re-create the powers of the human brain, we first need to understand how complex it is. There are 100 billion neurons, each with thousands of connections and each connection containing about 1,000 neural pathways. I’ve estimated the amount of information required to characterize the state of a mature brain at thousands of trillions of bytes: a lot of complexity.

But the design of the brain is a billion times as simple as this. How do we know? The design of the human brain—and body—is stored in the genome, and the genome doesn’t contain that much information. There are three billion rungs of DNA in the human genome: six billion bits, or 800 million bytes. It is replete with redundancies, however; one lengthy sequence called ALU is repeated 300,000 times. Since we know the genome’s structure, we can compress its information to only 30 million to 100 million bytes, which is smaller than the code for Microsoft Word. About half of this contains the design of the human brain.

The brain can be described in just 15 million to 50 million bytes because most of its wiring is random at birth. For example, the trillions of connections in the cerebellum are described by only a handful of genes. This means that most of the cerebellum wiring in the infant brain is chaotic. The system is designed to be self-organizing, though, so as the child learns to walk and talk and catch a fly ball, the cerebellum gets filled with meaningful information.

My point is not that the brain is simple, but that the design is at a level of complexity that we can fathom and manage. And by applying the law of accelerating returns to the problem of analyzing the brain’s complexity, we can reasonably forecast that there will be exhaustive models and simulations of all several hundred regions of the human brain within about 20 years.

Once we understand how the mind operates, we will be able to program detailed descriptions of these principles into inexpensive computers, which, by the late 2020s, will be thousands of times as powerful as the human brain—another consequence of the law of accelerating returns. So we will have both the hardware and software to achieve human-level intelligence in a machine by 2029. We will also by then be able to construct fully humanlike androids at exquisite levels of detail and send blood-cell-size robots into our bodies and brains to keep us healthy from inside and to augment our intellect. By the time we succeed in building such machines, we will have become part machine ourselves. We will, in other words, finally transcend what we have so long thought of as the ultimate limitations: our bodies and minds.

more about this great man here:

http://en.wikipedia.org/wiki/Raymond_Kurzweil

http://www.sciam.com/article.cfm?chanID=sa003&articleID=0002491F-755F-1473-B55F83414B7F0000

The smoke from burning marijuana leaves contains several known carcinogens and the tar it creates contains 50 percent more of some of the chemicals linked to lung cancer than tobacco smoke. A marijuana cigarette also deposits four times as much of that tar as an equivalent tobacco one. Scientists were therefore surprised to learn that a study of more than 2,000 people found no increase in the risk of developing lung cancer for marijuana smokers.

“We expected that we would find that a history of heavy marijuana use–more than 500 to 1,000 uses–would increase the risk of cancer from several years to decades after exposure to marijuana,” explains physician Donald Tashkin of the University of California, Los Angeles, and lead researcher on the project. But looking at residents of Los Angeles County, the scientists found that even those who smoked more than 20,000 joints in their life did not have an increased risk of lung cancer.

The researchers interviewed 611 lung cancer patients and 1,040 healthy controls as well as 601 patients with cancer in the head or neck region under the age of 60 to create the statistical analysis. They found that 80 percent of those with lung cancer and 70 percent of those with other cancers had smoked tobacco while only roughly half of both groups had smoked marijuana. The more tobacco a person smoked, the greater the risk of developing cancer, as other studies have shown.

But after controlling for tobacco, alcohol and other drug use as well as matching patients and controls by age, gender and neighborhood, marijuana did not seem to have an effect, despite its unhealthy aspects. “Marijuana is packed more loosely than tobacco, so there’s less filtration through the rod of the cigarette, so more particles will be inhaled,” Tashkin says. “And marijuana smokers typically smoke differently than tobacco smokers; they hold their breath about four times longer allowing more time for extra fine particles to deposit in the lungs.”

The study does not reveal how marijuana avoids causing cancer. Tashkin speculates that perhaps the THC chemical in marijuana smoke prompts aging cells to die before becoming cancerous. Tashkin and his colleagues presented the findings yesterday at a meeting of the American Thoracic Society in San Diego.