# What do you really believe?

Portrait of a Man with a Quilted Sleeve, Titian, c1509. Courtesy Wikipedia/National Gallery, London.

### Keith Frankish | Aeon Ideas

##### Edited by Nigel Warburton

Most of us have views on politics, current events, religion, society, morality and sport, and we spend a lot of time expressing these views, whether in conversation or on social media. We argue for our positions, and get annoyed if they are challenged. Why do we do this? The obvious answer is that we believe the views we express (ie, we think they are true), and we want to get others to believe them too, because they are true. We want the truth to prevail. That’s how it seems. But do we really believe everything we say? Are you always trying to establish the truth when you argue, or might there be other motives at work?

These questions might seem strange, offensive even. Am I suggesting that you are insincere or hypocritical in your views? No – at least I’m not suggesting that you are consciously so. But you might be unconsciously influenced by concerns other than truth. Nowadays, most psychologists agree that rapid, unconscious mental processes (sometimes called ‘System 1’ processes) play a huge role in guiding our behaviour. These processes are not thought of as Freudian ones, involving repressed memories and desires, but as ordinary, everyday judgments, motives and feelings that operate without conscious awareness, like a mental autopilot.

It seems plausible that such processes guide much of our speech. After all, we rarely give conscious thought to our reasons for saying what we do; the words just come to our lips. But if the motives behind our words are unconscious, then we must infer them from our behaviour, and might be mistaken about what they are. Again, this isn’t a revolutionary idea; for centuries, dramatists and novelists have depicted people deceived about their own motives. (For more on the nature and limits of self-knowledge, see my earlier Aeon article.)

It’s easy to think of motives that might prompt us to express a view we don’t really believe. We might want it to be true, and feel reassurance when we argue for it (think of the parents who insist that their missing child is still alive, despite the lack of evidence). We might associate it with people we admire, and assert it so as to be like them (think of how people are influenced by the views of celebrities). We might think that it will get us attention, and make us seem interesting (think of teenagers who adopt provocative views). We might profess it to fit in and gain social acceptance (think of a university student from a conservative background). Or we might feel that we have a duty to defend it because of our commitment to some creed or ideology (we sometimes call this attitude faith – belief in the religious sense).

Such motives might also be reinforced by other factors. As a society, we tend to admire people who know their own minds and stick to their principles. So, once we have expressed a view, for whatever reason, we might feel (again, unconsciously) that we are now committed to it, and should stick with it as a matter of integrity. At the same time, we might develop an emotional attachment to the view, a bit like an attachment to a sports team. It is now our view, the one we have publicly endorsed, and we want it to win out over its rivals just because it is ours. In this way, we might come to have a strong personal commitment to a claim, even if we don’t really believe it.

I am not suggesting that we are never guided by concerns for truth and knowledge (what philosophers call epistemic concerns), but I suspect that these sorts of emotional and social factors play a much larger role than we like to think. How else can we explain the vehemence with which people defend their views, and the hurt they feel when their views are challenged?

Is it bad if we sometimes say things we don’t believe? It might seem not. The aims I’ve mentioned – seeking social acceptance, for example, or cultivating a self-image – are not necessarily bad ones, and since they are unconscious it is arguable that we shouldn’t be held responsible for them anyway. There are dangers, however. For in order to achieve these aims we must convince our audience that we genuinely believe what we say. If they thought we were saying something merely in order to create an impression on them, then we wouldn’t succeed in creating that impression. And when our aim is to make some impression on ourselves – like the parents who insist that their child is still alive – we must convince ourselves that we believe it too. As a consequence, we might need to back up our words with deeds, acting as if we believe what we say. If there were a glaring disparity between what we said and did, our insincerity would be obvious. In this way, unconscious desires for acceptance, approval and reassurance can lead us to make choices on the basis of claims for which we have no good evidence, with obvious risks of frustration and failure.

Is there, then, any way of telling whether you really believe a claim? It might seem that conscious reflection would settle it. If you consciously entertain the claim, do you think it is true? Even this process might be unreliable, however. Many theorists hold that conscious thinking is simply talking to oneself in inner speech, in which case it can be guided by unconscious motives, just like outer speech. And, as I mentioned, unconscious desires can prompt us to deceive ourselves, telling ourselves that a claim is true even though we don’t really believe it.

Despite this, a thought experiment might help us detect what we genuinely believe to be true. In real life, there might be few contexts where truth really is our dominant concern: maintaining a comforting view or upholding a cherished ideology or self-image might almost always be more important to us than truth. But suppose you were being questioned by the Truth Demon – a super-powerful being who knows the truth on every topic, and will punish you horribly if you give a wrong answer or fail to answer at all. If you continue to assert a claim when the Truth Demon asks you if it is true, then you do really believe it, really think it is true. But if you give a different answer when under threat of torture by the all-knowing demon, then you don’t really believe the claim. This gives us a practical test for belief: imagine the situation just described as vividly as you can, and see what you would say about any of your views. But do be careful not to give too much conscious thought to the matter in case you start telling yourself what you want to hear.

Keith Frankish is an English philosopher and writer. He is a visiting research fellow with the Open University in the UK and an adjunct professor with the Brain and Mind Programme at the University of Crete. He lives in Greece.

This article was originally published at Aeon and has been republished under Creative Commons.

### Commentary

I like the gist of this article (along with the author’s previous article), but the Truth Demon (a.k.a. God) thought experiment could use some work. It’s too easy to deceive and delude oneself, even under imagined duress. It’s also unclear if many people would be “punished horribly” while expressing their false beliefs in good conscience. As mentioned by the author, a good judge of honest belief is one’s actions, but there is a difference between honest belief and truth itself (or is there?). Although the author’s final word is on point, I prefer Nietzsche’s thought experiment: “What, if some day or night a demon were to steal after you into your loneliest loneliness and say to you: ‘This life as you now live it and have lived it, you will have to live once more and innumerable times more’ … Would you not throw yourself down and gnash your teeth and curse the demon who spoke thus? Or have you once experienced a tremendous moment when you would have answered him: ‘You are a god and never have I heard anything more divine.”

We don’t know ourselves, we knowledgeable people—we are personally ignorant
about ourselves. And there’s good reason for that. We’ve never tried to find out who
we are. How could it ever happen that one day we’d discover our own selves? With
justice it’s been said that “Where your treasure is, there shall your heart be also.” Our treasure lies where the beehives of our knowledge stand. We are always busy with our knowledge, as if we were born winged creatures—collectors of intellectual honey. In our hearts we are basically concerned with only one thing, to “bring something home.” As far as the rest of life is concerned, what people call “experience”—which of us is serious enough for that? Who has enough time? In these matters, I fear, we’ve been “missing the point.”

Our hearts have not even been engaged—nor, for that matter, have our ears! We’ve
been much more like someone divinely distracted and self-absorbed into whose ear
the clock has just pealed the twelve strokes of noon with all its force and who all at
once wakes up and asks himself “What exactly did that clock strike?”—so we rub
ourselves behind the ears afterwards and ask, totally surprised and embarrassed “What have we really just experienced? And more: “Who are we really?” Then, as I’ve mentioned, we count—after the fact—all the twelve trembling strokes of the clock of our experience, our lives, our being—alas! in the process we keep losing the count. So we remain necessarily strangers to ourselves, we do not understand ourselves, we have to keep ourselves confused. For us this law holds for all eternity: “Each man is furthest from himself.” Where we ourselves are concerned, we are not “knowledgeable people.”

― Friedrich Nietzsche, On the Genealogy of Morals/Ecce Homo

When subjectivity, inwardness, is the truth, the truth becomes objectively determined as a paradox, and that it is paradoxical is made clear by the fact that subjectivity is truth, for it repels objectivity, and the expression for the objective repulsion is the intensity and measure of inwardness. The paradox is the objective uncertainty, which is the expression for the passion of inwardness, which is precisely the truth. This is the Socratic principle. The eternal, essential truth, that is, that which relates itself essentially to the individual because it concerns his existence (all other knowledge is, Socratically speaking, accidental, its degree and scope being indifferent), is a paradox. Nevertheless, the eternal truth is not essentially in itself paradoxical, but it becomes so by relating itself to an existing individual. Socratic ignorance is the expression of this objective uncertainty, the inwardness of the existential subject is the truth. To anticipate what I will develop later, Socratic ignorance is an analogy to the category of the absurd, only that there is still less objective certainty in the absurd, and therefore infinitely greater tension in its inwardness. The Socratic inwardness that involves existence is an analogy to faith, except that this inwardness is repulsed not by ignorance but by the absurd, which is infinitely deeper. Socratically the eternal, essential truth is by no means paradoxical in itself, but only by virtue of its relation to an existing individual.

― Søren Kierkegaard, Concluding Unscientific Postscript

# Scientism contra Philosophy

Many people mistake knowledge for wisdom because they are intimately related, and this is unfortunate because they are quite different in an important way. Knowledge is the accumulation of facts and information. Wisdom is the synthesis of knowledge and experiences into insights that deepen one’s understanding of relationships and the meaning of life. In other words, knowledge is a tool, and wisdom is the craft in which the tool is used.

If one understands this difference, he or she will also appreciate why it is vital to properly distinguish between the two. With the Internet, it is now relatively easy for a reasonably diligent person to quickly become knowledgeable in virtually any field of his or her choosing. We are literally awash in a sea of information! But having a hammer and knowing how to use it are two entirely different propositions. A hammer is amoral. Whether it is used for good or ill depends entirely on the wielder. Sadly, history is a lengthy record of the harms wrought by knowledgeable, well-meaning people who lacked wisdom.

In contrast to knowledge, wisdom is generally considered to be morally good. Why is this the case? Albert Einstein once said, ‘Wisdom is not a product of schooling but of the lifelong attempt to acquire it.’ Such a process is lengthy and arduous, which teaches the pursuer patience and humility. Seldom is a person unchanged by such a trial. When one finally uncovers a connection or insight that he or she believes to be universally applicable ‘truth,’ it often inspires awe akin to a spiritual experience.

‘Knowledge comes, but wisdom lingers,’ wrote Alfred, Lord Tennyson. Truths stay with a person for the rest of his or her life, coloring all subsequent thoughts and actions. Wisdom requires no law or threat of punishment to ensure compliance. The practitioner typically feels a strong compulsion to obey his or her own beliefs. The wise can still fall prey to indiscretions and questionable moral behavior–being flesh and blood like us all–however, if one tracks such statistics, the odds of such failings are likely to be very small compared to the general populace.

Society esteems the wise for their virtuosity and for their rarity. Subject matter experts number in the thousands, but the wise may only number in the tens or hundreds. And history records their names and achievements for posterity’s sake.

### . . . . .

This critical insight is brought to you by Justarius of Philoscifi.com

If scientific method is only one form of a general method employed in all human inquiry, how is it that the results of science are more reliable than what is provided by these other forms? I think the answer is that science deals with highly quantified variables and that it is the precision of its results that supplies this reliability. But make no mistake: Quantified precision is not to be confused with a superior method of thinking.

– James Blachowicz, The New York Times

Hawking contra Philosophy

# We Know So Little About So Much

I find it very interesting that even with the exponential growth in knowledge that we have witnessed from ancient times through the industrial age to the digital age, there still remains so much that is simply unknown to us. I have no doubt that the human spirit will in time conquer each and every question that it poses to itself. The following is a list of unsolved problems in various fields of scientific and philosophical inquiry. Aside from the solutions being interesting in themselves and offering the opportunity of contributing to our collective history, some of these problems even offer monetary incentives for proven solutions. So go ahead and tackle one!

We know very little, and yet it is astonishing that we know so much, and still more astonishing that so little knowledge can give us so much power.

– Bertrand Russell

## Unsolved Problems in Artificial Intelligence

In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI.[1] To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.

AI-complete problems are hypothesised to include computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.[2]

Currently, AI-complete problems cannot be solved with modern computer technology alone, but would also require human computation. This property can be useful, for instance to test for the presence of humans as with CAPTCHAs, and for computer security to circumvent brute-force attacks.[3][4]

### History

The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems.[5] Early uses of the term are in Erik Mueller’s 1987 Ph.D. dissertation[6] and in Eric Raymond‘s 1991 Jargon File.[7]

### AI-complete problems

AI-complete problems are hypothesised to include:

### Machine translation

Main article: Machine translation

To translate accurately, a machine must be able to understand the text. It must be able to follow the author’s argument, so it must have some ability to reason. It must have extensive world knowledge so that it knows what is being discussed — it must at least be familiar with all the same commonsense facts that the average human translator knows. Some of this knowledge is in the form of facts that can be explicitly represented, but some knowledge is unconscious and closely tied to the human body: for example, the machine may need to understand how an ocean makes one feel to accurately translate a specific metaphor in the text. It must also model the authors’ goals, intentions, and emotional states to accurately reproduce them in a new language. In short, the machine is required to have wide variety of human intellectual skills, including reason, commonsense knowledge and the intuitions that underlie motion and manipulation, perception, and social intelligence.Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.

### Software brittleness

Main article: Software brittleness

Current AI systems can solve very simple restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempt to “scale up” their systems to handle more complicated, real world situations, the programs tend to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation: they fail as unexpected circumstances outside of its original problem context begin to appear. When human beings are dealing with new situations in the world, they are helped immensely by the fact that they know what to expect: they know what all things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. A machine without strong AI has no other skills to fall back on.[8]

### Formalization

Computational complexity theory deals with the relative computational difficulty of computable functions. By definition it does not cover problems whose solution is unknown or has not been characterised formally. Since many AI problems have no formalisation yet, conventional complexity theory does not allow the definition of AI-completeness.

To address this problem, a complexity theory for AI has been proposed.[9] It is based on a model of computation that splits the computational burden between a computer and a human: one part is solved by computer and the other part solved by human. This is formalised by a human-assisted Turing machine. The formalisation defines algorithm complexity, problem complexity and reducibility which in turn allows equivalence classes to be defined.

The complexity of executing an algorithm with a human-assisted Turing machine is given by a pair $\langle\Phi_{H},\Phi_{M}\rangle$, where the first element represents the complexity of the human’s part and the second element is the complexity of the machine’s part.

### Results

The complexity of solving the following problems with a human-assisted Turing machine is:[9]

• Optical character recognition for printed text: $\langle O(1), poly(n) \rangle$
• Turing test:
• for an $n$-sentence conversation where the oracle remembers the conversation history (persistent oracle): $\langle O(n), O(n) \rangle$
• for an $n$-sentence conversation where the conversation history must be retransmitted: $\langle O(n), O(n^2) \rangle$
• for an $n$-sentence conversation where the conversation history must be retransmitted and the person takes linear time to read the query: $\langle O(n^2), O(n^2) \rangle$
• ESP game: $\langle O(n), O(n) \rangle$
• Image labelling (based on the Arthur–Merlin protocol): $\langle O(n), O(n) \rangle$
• Image classification: human only: $\langle O(n), O(n) \rangle$, and with less reliance on the human: $\langle O(\log n), O(n \log n) \rangle$.

### References

1. Jump up^ Shapiro, Stuart C. (1992). Artificial Intelligence In Stuart C. Shapiro (Ed.), Encyclopedia of Artificial Intelligence (Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on “AI-Complete Tasks”.)
2. Jump up^ Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness . In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) –In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3-17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf
3. Jump up^ Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford. CAPTCHA: Using Hard AI Problems for Security. In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294-311.
4. Jump up^ Bergmair, Richard (January 7, 2006). “Natural Language Steganography and an “AI-complete” Security Primitive”. CiteSeerX: 10.1.1.105.129. (unpublished?)
5. Jump up^ Mallery, John C. (1988), “Thinking About Foreign Policy: Finding an Appropriate Role for Artificially Intelligent Computers”, The 1988 Annual Meeting of the International Studies Association., St. Louis, MO.
6. Jump up^ Mueller, Erik T. (1987, March). Daydreaming and Computation (Technical Report CSD-870017) Ph.D. dissertation, University of California, Los Angeles. (“Daydreaming is but one more AI-complete problem: if we could solve any one artificial intelligence problem, we could solve all the others”, p. 302)
7. Jump up^ Raymond, Eric S. (1991, March 22). Jargon File Version 2.8.1 (Definition of “AI-complete” first added to jargon file.)
8. Jump up^ Lenat, Douglas; Guha, R. V. (1989), Building Large Knowledge-Based Systems, Addison-Wesley, pp. 1–5
9. Dafna Shahaf and Eyal Amir (2007) Towards a theory of AI completeness. Commonsense 2007, 8th International Symposium on Logical Formalizations of Commonsense Reasoning.

## Unsolved Problems in Biology

### References

1. Jump up^ Aniszewski, p. 142
2. Jump up^ Articleworld.org Blue Whale
3. Jump up^ Sejnowski, Terrence J.; Hemmen, J. L. van (2006). 23 problems in systems neuroscience (PDF). Oxford [Oxfordshire]: Oxford University Press. ISBN 0-19-514822-3.
4. Jump up^ Tononi, G; Koch, C. (2015). “Consciousness: Here, there and everywhere?” (PDF). Philosophical Transactions of the Royal Society London B.
5. Jump up^ Thomas N. Sherratt, David M. Wilkinson. Big questions in ecology and evolution. Oxford University Press US, 2009. ISBN 978-0-19-954861-3

## Unsolved Problems in Chemistry

Unsolved problems in chemistry tend to be questions of the kind “Can we make X chemical compound?”, “Can we analyse it?”, “Can we purify it?” and are commonly solved rather quickly, but may just as well require considerable efforts to be solved. However, there are also some questions with deeper implications. This article tends to deal with the areas that are the center of new scientific research in chemistry. Problems in chemistry are considered unsolved when an expert in the field considers it unsolved or when several experts in the field disagree about a solution to a problem.

### Organic chemistry problems

In addition to these, it is noteworthy that many mechanisms proposed for catalytic processes are poorly understood[which?] and often fail to explain all relevant phenomena.[citation needed]

### Biochemistry problems

• Enzyme kinetics: Why do some enzymes exhibit faster-than-diffusion kinetics?[8]
• Protein folding problem: Is it possible to predict the secondary, tertiary and quaternary structure of a polypeptide sequence based solely on the sequence and environmental information? Inverse protein-folding problem: Is it possible to design a polypeptide sequence which will adopt a given structure under certain environmental conditions?[5][9] This has been achieved for several small globular proteins in recent years.[10]
• RNA folding problem: Is it possible to accurately predict the secondary, tertiary and quaternary structure of a polyribonucleic acid sequence based on its sequence and environment?
• What are the chemical origins of life? How did non-living chemical compounds generate self-replicating, complex life forms?
• Protein design: Is it possible to design highly active enzymes de novo for any desired reaction?[11]
• Biosynthesis: Can desired molecules, natural products or otherwise, be produced in high yield through biosynthetic pathway manipulation?[12]

### References

1. The Future of Post-Human Chemistry: A Preface to a New Theory of Substances …, de Peter Baofu, page 285
2. Jump up^ The problem may actually occur at approximately Element 173, given the finite extension of nuclear-charge distribution.[citation needed] See the article on Extension of the periodic table beyond the seventh period, and the article section Relativistic effects of Atomic orbital.
3. Jump up^ Duffie, John A. (August 2006). Solar Engineering of Thermal Processes. Wiley-Interscience. p. 928. ISBN 978-0-471-69867-8.
4. Jump up^ Brabec, Christoph; Vladimir Dyakonov; Jürgen Parisi; Niyazi Serdar Sarıçiftçi (March 2006). Organic Photovoltaics: Concepts and Realization. Springer. p. 300. ISBN 978-3-540-00405-9.
5. “So much more to know”. Science 309 (5731): 78–102. July 2005. doi:10.1126/science.309.5731.78b. PMID 15994524.
6. Jump up^ S. Narayan, J. Muldoon, M.G. Finn, V.V. Fokin, H.C. Kolb, K.B. Sharpless,2005, “On Water: Unique Reactivity of Organic Compounds in Aqueous Suspension,” Angew. Chem. Int. Ed. 21:3157, see [onlinelibrary.wiley.com/doi/10.1002/anie.200462883/full]. accessed 15 December 2015.
7. Jump up^ Fredrik von Kieseritzky, 2013, “What is the true nature of gold-sulfur bonds?”, see [1], accessed 15 December 2014.
8. Jump up^ Hsieh M, Brenowitz M (August 1997). “Comparison of the DNA association kinetics of the Lac repressor tetramer, its dimeric mutant LacIadi, and the native dimeric Gal repressor”. J. Biol. Chem. 272 (35): 22092–6. doi:10.1074/jbc.272.35.22092. PMID 9268351.
9. Jump up^ King, Jonathan (2007). “MIT OpenCourseWare – 7.88J / 5.48J / 7.24J / 10.543J Protein Folding Problem, Fall 2007 Lecture Notes – 1”. MIT OpenCourseWare. RetrievedJune 22, 2013.
10. Jump up^ Dill KA; et al. (June 2008). “The Protein Folding Problem”. Annu Rev Biophys 37: 289–316. doi:10.1146/annurev.biophys.37.092707.153558. PMID 9268351.
11. Jump up^ http://depts.washington.edu/bakerpg/drupal/node/465
12. Jump up^ http://www.nature.com/nature/journal/v488/n7411/full/nature11478.html