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Saturday, 1 September 2018

Fear dumb people not smart machines?

Bill Dembski on the AI Boogeyman, and the Real AI Danger
Evolution News @DiscoveryCSC

On a new episode of ID the Future, Andrew McDiarmid reads an excerpt from a speech prepared by philosopher, mathematician, and trailblazing design theorist William Dembski for the launch of the 


Dr. Dembski asks whether we need to worry about an AI takeover. He says no, there’s no evidence that artificial intelligence (AI) could reach that level, or achieve consciousness, while on the other hand there is mounting evidence from both philosophy and the field of artificial intelligence technology that it cannot and will not.

“The real worry,” Dembski says, “isn’t that we’ll raise machines to our level, but that we’ll lower humanity to the level of machines.”

Big tobacco:A prelude to big marijuana?

Philip Morris v. Uruguay: Will investor-State arbitration send restrictions on tobacco marketing up in smoke?

How the consensus' gatekeepers got egg on their face.

James Tour on OOL science's circus.

On Origin of Life, Synthetic Chemist James Tour Delivers Chastisement to Jeremy England
David Klinghoffer | @d_klinghoffer  

As a postscript to Brian Miller’s  reply to MIT physicist Jeremy England, see this from the famed synthetic organic chemist James Tour, writing for the online journal Inference. InAn Open Letter to My Colleagues,” Tour sets out this way:

Life should not exist. This much we know from chemistry. In contrast to the ubiquity of life on earth, the lifelessness of other planets makes far better chemical sense. Synthetic chemists know what it takes to build just one molecular compound. The compound must be designed, the stereochemistry controlled. Yield optimization, purification, and characterization are needed. An elaborate supply is required to control synthesis from start to finish. None of this is easy. Few researchers from other disciplines understand how molecules are synthesized.

His colleagues are fooling themselves if they imagine otherwise. He gets around to England, not naming him except in a footnote, at the end:

If one understands the second law of thermodynamics, according to some physicists,15 “You [can] start with a random clump of atoms, and if you shine light on it for long enough, it should not be so surprising that you get a plant.”16

The quote, remarkably, is from Jeremy England in an interview with Natalie Wolchover for Quanta. Tour also cites England’s article “Statistical Physics of Self-Replication,” in the Journal of Chemical Physics, and one of the most absurdly titled God-bashing articles we’ve come across, “God is on the Ropes: The Brilliant New Science That Has Creationists and the Christian Right Terrified,” by Paul Rosenberg writing for Salon. Rosenberg quotes England from the same Quanta article, “[U]nder certain conditions, matter inexorably acquires the key physical attribute associated with life.” Oh, really, does it?

Tour goes on, referring to the notion that random atoms will become a plant if given plenty of light and plenty of time:

The interactions of light with small molecules is well understood. The experiment has been performed. The outcome is known. Regardless of the wavelength of the light, no plant ever forms.

We synthetic chemists should state the obvious. The appearance of life on earth is a mystery. We are nowhere near solving this problem. The proposals offered thus far to explain life’s origin make no scientific sense.

Beyond our planet, all the others that have been probed are lifeless, a result in accord with our chemical expectations. The laws of physics and chemistry’s Periodic Table are universal, suggesting that life based upon amino acids, nucleotides, saccharides and lipids is an anomaly. Life should not exist anywhere in our universe. Life should not even exist on the surface of the earth.17


It’s somehow more satisfying that England isn’t identified in the body of the article, but only in a footnote. That is a memorable instance of a senior scientist quietly taking a junior colleague out behind the woodshed. For more on the general subject, see Tour’s slashing 2016 lecture,  The Origin of Life: An Inside Story.”

Darwinism's quest for a free lunch hits yet another dead end?

Conservation of Information and Coevolution: New BIO-Complexity Article by Ewert and Marks
Brian Miller  

In a  previous article I described Winston Ewert, Robert Marks, and William Dembski’s book Introduction to Evolutionary Informatics which identifies the limitations of evolutionary algorithms to find solutions for complex problems. The book demonstrates that this class of programs is only capable of achieving non-trivial results unless information about desired outcomes is programmed into them. For instance, a program designed to find the best strategy for playing checkers must have detailed information about the game programmed into its search method. It could not develop a strategy to play chess without altering the underlying algorithm to include new chess-related information.

This constraint is a direct result of No Free Lunch (NFL) theorems and the related law of  Conservation of InformationAttempts have been make to overcome this challenge by appealing to what are termed coevolutionary searches. However, as Ewert and Marks demonstrate in a new article for the journal BIO-Complexitythese algorithms do not escape the NFL barrier, so they are no more efficient on average than random searches.

Evolutionary algorithms typically follow a standard set of steps. They generate trial solutions to a problem, and then assign each trial some “fitness” value. These values are then used to determine how the next iteration of trials is generated for testing. The process continues until a target is found.

Ewert and Marks use the illustration of generating recipes for making pancakes. In this example, a trial recipe represents a list of the specific amounts of each ingredient and details of the cooking, such as burner setting and times. The assigned value corresponds to the prepared pancake’s taste, and it determines how new recipes are generated. The process continues until a pancake is created that meets some taste standard. The set of values associated with all possible trials is described as a fitness landscape, and the search algorithm must navigate its terrain looking for targets. For standard algorithms, all information needed to assign a fitness value is known. In contrast, for coevolutionary algorithms information is not fully known, so incomplete or “subjacent” information must often be used. That’s because the fitness landscape continuously changes due to the presence of other organisms or other contingent factors.

This difference can be illustrated in terms of an examples from biological evolution. A standard evolutionary algorithm would correspond to an organism having a specific “fitness” which remains fairly constant in most situations. For instance, that of a desert plant might relate to such innate abilities as conserving water and processing sunlight. A computer program could model the plant’s fitness using related variables, such as the plant’s mass and the amount of chlorophyll produced in its leaves. These variables would fully determine the assigned fitness value. In contrast, a coevolutonary process would correspond to the fitness changing over time due to such factors as interactions with other species and details of the physical environment. For instance, chemicals in the skin could provide greater or lesser protection from different predators, and the shape of the plant could prove more or less helpful in different settings.

To model this increased complexity, the algorithms generate a query matrix where a row is assigned to each candidate solution, and each column corresponds to a different factor affecting fitness such as interactions with a particular species. Many cells in this matrix are often not known, whether due to computational or other practical limitations, so various methods are employed to assign each trial solution (row) an aggregate value based on the limited knowledge. The algorithm then proceeds as with traditional models. Many have claimed that coevolutionary programs can find solutions to a wide variety of problems more quickly on average than random searches, thus overcoming the restrictions of the NFL theorems. In other words, they eliminate the need for programmers to provide problem-specific “active information” to guide searches.

To test this claim, Ewert and Marks measured the performance of various coevolutionary algorithms for a variety of problems. They found that they could at best match the performance of traditional “full-search” methods, and they typically performed worse. Their article also describes how claims to the contrary were based on research that focused on solving very simple problems or that designed experiments in such a way as to provide hidden information to assist in finding targets. Therefore, coevolutionary processes cannot overcome NFL limitations, as they also require problem-specific information to perform properly.

These results have direct implications for Darwinian evolution. Biologists often claim that coevolutionary interactions between different species or species and the environment can alter the underlying fitness landscape in such a way as to drive evolutionary changes. A classic example is the proposed coevolution of bees and flowering plantsMany plants need insects as carriers for their pollen. As a result, the presence of insects places selective pressure on the plants to produce smells, colors, and nectar to attract them. In turn, the presence of the plants places selective pressures on the insects to move toward the plants’ signals to obtain the food supply. In addition, insects are selected for thicker hairs on their legs to capture more pollen. They can then fertilize more plants resulting in a greater food supply. Such scenarios may sound plausible, but they only result in trivial adjustments to preexisting structures.

In contrast, complex innovations, such as new body plans, radical innovations which, in turn, require large amounts of new information.The environment is often claimed to provide this new information, which is believed to be hidden in the fitness landscapes coupled to coevolutionary interactions. However, this research by Ewert and Marks directly challenges the claim. The search space corresponding to biological forms is vastly greater than what could be searched through random mutations in the offspring of any species. And natural selection cannot help without being provided large amounts of information on where new forms reside. For coevolutionary processes are no more efficient at solving problems than traditional evolutionary algorithms, and the latter are no more efficient on average than random searches.