Google’s New AI Is a Master of Games, but How Does It Compare to the Human Mind?


After building AlphaGo to beat the world’s best Go players, Google DeepMind built AlphaZero to take on the world’s best machine players

AI Chess
Google’s new artificial intelligence program, AlphaZero, taught itself to play chess, shogi, and Go in a matter of hours, and outperforms the top-ranking AIs in the gameplay arena.

For humans, chess may take a lifetime to master. But Google DeepMind’s new artificial intelligence program, AlphaZero, can teach itself to conquer the board in a matter of hours.

Building on its past success with the AlphaGo suite—a series of computer programs designed to play the Chinese board game Go—Google boasts that its new AlphaZero achieves a level of “superhuman performance” at not just one board game, but three: Go, chess, and shogi (essentially, Japanese chess). The team of computer scientists and engineers, led by Google’s David Silver, reported its findings recently in the journal Science.

“Before this, with machine learning, you could get a machine to do exactly what you want—but only that thing,” says Ayanna Howard, an expert in interactive computing and artificial intelligence at the Georgia Institute of Technology who did not participate in the research. “But AlphaZero shows that you can have an algorithm that isn’t so [specific], and it can learn within certain parameters.”

AlphaZero’s clever programming certainly ups the ante on gameplay for human and machine alike, but Google has long had its sights set on something bigger: engineering intelligence.

The researchers are careful not to claim that AlphaZero is on the verge of world domination (others have been a little quicker to jump the gun). Still, Silver and the rest of the DeepMind squad are already hopeful that they’ll someday see a similar system applied to drug design or materials science.

So what makes AlphaZero so impressive?

Gameplay has long been revered as a gold standard in artificial intelligence research. Structured, interactive games are simplifications of real-world scenarios: Difficult decisions must be made; wins and losses drive up the stakes; and prediction, critical thinking, and strategy are key.

Encoding this kind of skill is tricky. Older game-playing AIs—including the first prototypes of the original AlphaGo—have traditionally been pumped full of codes and data to mimic the experience typically earned through years of natural, human gameplay (essentially, a passive, programmer-derived knowledge dump). With AlphaGo Zero (the most recent version of AlphaGo), and now AlphaZero, the researchers gave the program just one input: the rules of the game in question. Then, the system hunkered down and actively learned the tricks of the trade itself.

Go
AlphaZero is based on AlphaGo Zero, part of the AlphaGo suite designed to play the Chinese board game Go, pictured above. Early iterations of the original program were fed data from human-versus-human games; later versions engaged in self-teaching, wherein the software played games against itself to learn its own strategy.

This strategy, called self-play reinforcement learning, is pretty much exactly what it sounds like: To train for the big leagues, AlphaZero played itself in iteration after iteration, honing its skills by trial and error. And the brute-force approach paid off. Unlike AlphaGo Zero, AlphaZero doesn’t just play Go: It can beat the best AIs in the business at chess and shogi, too. The learning process is also impressively efficient, requiring only two, four, or 30 hours of self-tutelage to outperform programs specifically tailored to master shogi, chess, and Go, respectively. Notably, the study authors didn’t report any instances of AlphaZero going head-to-head with an actual human, Howard says. (The researchers may have assumed that, given that these programs consistently clobber their human counterparts, such a matchup would have been pointless.)

AlphaZero was also able to trounce Stockfish (the now unseated AI chess master) and Elmo (the former AI shogi expert) despite evaluating fewer possible next moves on each turn during game play. But because the algorithms in question are inherently different, and may consume different amounts of power, it’s difficult to directly compare AlphaZero to other, older programs, points out Joanna Bryson, who studies artificial intelligence at the University of Bath in the United Kingdom and did not contribute to AlphaZero.

Google keeps mum about a lot of the fine print on its software, and AlphaZero is no exception. While don’t know everything about the program’s power consumption, what’s clear is this: AlphaZero has to be packing some serious computational ammo. In those scant hours of training, the program kept itself very busy, engaging in tens or hundreds of thousands of practice rounds to get its board game strategy up to snuff—far more than a human player would need (or, in most cases, could even accomplish) in pursuit of proficiency.

This intensive regimen also used 5,000 of Google’s proprietary machine-learning processor units, or TPUs, which by some estimates consume around 200 watts per chip. No matter how you slice it, AlphaZero requires way more energy than a human brain, which runs on about 20 watts.

The absolute energy consumption of AlphaZero must be taken into consideration, adds Bin Yu, who works at the interface of statistics, machine learning, and artificial intelligence at the University of California, Berkeley. AlphaZero is powerful, but might not be good bang for the buck—especially when adding in the person-hours that went into its creation and execution.

Energetically expensive or not, AlphaZero makes a splash: Most AIs are hyper-specialized on a single task, making this new program—with its triple threat of game play—remarkably flexible. “It’s impressive that AlphaZero was able to use the same architecture for three different games,” Yu says.

So, yes. Google’s new AI does set a new mark in several ways. It’s fast. It’s powerful. But does that make it smart?

This is where definitions start to get murky. “AlphaZero was able to learn, starting from scratch without any human knowledge, to play each of these games to superhuman level,” DeepMind’s Silver said in a statement to the press.

Even if board game expertise requires mental acuity, all proxies for the real world have their limits. In its current iteration, AlphaZero maxes out by winning human-designed games—which may not warrant the potentially alarming label of “superhuman.” Plus, if surprised with a new set of rules mid-game, AlphaZero might get flummoxed. The actual human brain, on the other hand, can store far more than three board games in its repertoire.

What’s more, comparing AlphaZero’s baseline to a tabula rasa (blank slate)as the researchers do—is a stretch, Bryson says. Programmers are still feeding it one crucial morsel of human knowledge: the rules of the game it’s about to play. “It does have far less to go on than anything has before,” Bryson adds, “but the most fundamental thing is, it’s still given rules. Those are explicit.”

And those pesky rules could constitute a significant crutch. “Even though these programs learn how to perform, they need the rules of the road,” Howard says. “The world is full of tasks that don’t have these rules.”

When push comes to shove, AlphaZero is an upgrade of an already powerful program—AlphaGo Zero, explains JoAnn Paul, who studies artificial intelligence and computational dreaming at the Virginia Polytechnic Institute and State University and was not involved in the new research. AlphaZero uses many of the same building blocks and algorithms as AlphaGo Zero, and still constitutes just a subset of true smarts. “I thought this new development was more evolutionary than revolutionary,” she adds. “None of these algorithms can create. Intelligence is also about storytelling. It’s imagining things that are not yet there. We’re not thinking in those terms in computers.”

Part of the problem is, there’s still no consensus on a true definition of “intelligence,” Yu says—and not just in the domain of technology. “It’s still not clear how we are training critically thinking beings, or how we use the unconscious brain,” she adds.

To this point, many researchers believe there are likely multiple types of intelligence. And tapping into one far from guarantees the ingredients for another. For instance, some of the smartest people out there are terrible at chess.

With these limitations, Yu’s vision of the future of artificial intelligence partners humans and machines in a kind of coevolution. Machines will certainly continue to excel at certain tasks, she explains, but human input and oversight may always be necessary to compensate for the unautomated.

Of course, there’s no telling how things will shake out in the AI arena. In the meantime, we have plenty to ponder. “These computers are powerful, and can do certain things better than a human can,” Paul says. “But that still falls short of the mystery of intelligence.”

Science has outgrown the human mind and its limited capacities.


Cometh the man; Francis Bacon's insight was that the process of discovery was inherently algorithmic. <em>Photo courtesy NPG/Wikipedia</em>
Cometh the man; Francis Bacon’s insight was that the process of discovery was inherently algorithmic.

The duty of man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads and … attack it from every side. He should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency. 

– Ibn al-Haytham (965-1040 CE)

Science is in the midst of a data crisis. Last year, there were more than 1.2 million new papers published in the biomedical sciences alone, bringing the total number of peer-reviewed biomedical papers to over 26 million. However, the average scientist reads only about 250 papers a year.Meanwhile, the quality of the scientific literature has been in decline. Some recent studies found that the majority of biomedical papers were irreproducible.

The twin challenges of too much quantity and too little quality are rooted in the finite neurological capacity of the human mind. Scientists are deriving hypotheses from a smaller and smaller fraction of our collective knowledge and consequently, more and more, asking the wrong questions, or asking ones that have already been answered. Also, human creativity seems to depend increasingly on the stochasticity of previous experiences – particular life events that allow a researcher to notice something others do not. Although chance has always been a factor in scientific discovery, it is currently playing a much larger role than it should.

One promising strategy to overcome the current crisis is to integrate machines and artificial intelligence in the scientific process. Machines have greater memory and higher computational capacity than the human brain. Automation of the scientific process could greatly increase the rate of discovery. It could even begin another scientific revolution. That huge possibility hinges on an equally huge question: can scientific discovery really be automated?

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I believe it can, using an approach that we have known about for centuries. The answer to this question can be found in the work of Sir Francis Bacon, the 17th-century English philosopher and a key progenitor of modern science.

The first reiterations of the scientific method can be traced back many centuries earlier to Muslim thinkers such as Ibn al-Haytham, who emphasised both empiricism and experimentation. However, it was Bacon who first formalised the scientific method and made it a subject of study. In his book Novum Organum (1620), he proposed a model for discovery that is still known as the Baconian method. He argued against syllogistic logic for scientific synthesis, which he considered to be unreliable. Instead, he proposed an approach in which relevant observations about a specific phenomenon are systematically collected, tabulated and objectively analysed using inductive logic to generate generalisable ideas. In his view, truth could be uncovered only when the mind is free from incomplete (and hence false) axioms.

The Baconian method attempted to remove logical bias from the process of observation and conceptualisation, by delineating the steps of scientific synthesis and optimising each one separately. Bacon’s vision was to leverage a community of observers to collect vast amounts of information about nature and tabulate it into a central record accessible to inductive analysis. In Novum Organum, he wrote: ‘Empiricists are like ants; they accumulate and use. Rationalists spin webs like spiders. The best method is that of the bee; it is somewhere in between, taking existing material and using it.’

The Baconian method is rarely used today. It proved too laborious and extravagantly expensive; its technological applications were unclear. However, at the time the formalisation of a scientific method marked a revolutionary advance. Before it, science was metaphysical, accessible only to a few learned men, mostly of noble birth. By rejecting the authority of the ancient Greeks and delineating the steps of discovery, Bacon created a blueprint that would allow anyone, regardless of background, to become a scientist.

Bacon’s insights also revealed an important hidden truth: the discovery process is inherently algorithmic. It is the outcome of a finite number of steps that are repeated until a meaningful result is uncovered. Bacon explicitly used the word ‘machine’ in describing his method. His scientific algorithm has three essential components: first, observations have to be collected and integrated into the total corpus of knowledge. Second, the new observations are used to generate new hypotheses. Third, the hypotheses are tested through carefully designed experiments.

If science is algorithmic, then it must have the potential for automation. This futuristic dream has eluded information and computer scientists for decades, in large part because the three main steps of scientific discovery occupy different planes. Observation is sensual; hypothesis-generation is mental; and experimentation is mechanical. Automating the scientific process will require the effective incorporation of machines in each step, and in all three feeding into each other without friction. Nobody has yet figured out how to do that.

Experimentation has seen the most substantial recent progress. For example, the pharmaceutical industry commonly uses automated high-throughput platforms for drug design. Startups such as Transcriptic and Emerald Cloud Lab, both in California, are building systems to automate almost every physical task that biomedical scientists do. Scientists can submit their experiments online, where they are converted to code and fed into robotic platforms that carry out a battery of biological experiments. These solutions are most relevant to disciplines that require intensive experimentation, such as molecular biology and chemical engineering, but analogous methods can be applied in other data-intensive fields, and even extended to theoretical disciplines.

Automated hypothesis-generation is less advanced, but the work of Don Swanson in the 1980s provided an important step forward. He demonstrated the existence of hidden links between unrelated ideas in the scientific literature; using a simple deductive logical framework, he could connect papers from various fields with no citation overlap. In this way, Swanson was able to hypothesise a novel link between dietary fish oil and Reynaud’s Syndrome without conducting any experiments or being an expert in either field. Other, more recent approaches, such as those of Andrey Rzhetsky at the University of Chicago and Albert-László Barabási at Northeastern University, rely on mathematical modelling and graph theory. They incorporate large datasets, in which knowledge is projected as a network, where nodes are concepts and links are relationships between them. Novel hypotheses would show up as undiscovered links between nodes.

The most challenging step in the automation process is how to collect reliable scientific observations on a large scale. There is currently no central data bank that holds humanity’s total scientific knowledge on an observational level. Natural language-processing has advanced to the point at which it can automatically extract not only relationships but also context from scientific papers. However, major scientific publishers have placed severe restrictions on text-mining. More important, the text of papers is biased towards the scientist’s interpretations (or misconceptions), and it contains synthesised complex concepts and methodologies that are difficult to extract and quantify.

Nevertheless, recent advances in computing and networked databases make the Baconian method practical for the first time in history. And even before scientific discovery can be automated, embracing Bacon’s approach could prove valuable at a time when pure reductionism is reaching the edge of its usefulness.

Human minds simply cannot reconstruct highly complex natural phenomena efficiently enough in the age of big data. A modern Baconian method that incorporates reductionist ideas through data-mining, but then analyses this information through inductive computational models, could transform our understanding of the natural world. Such an approach would enable us to generate novel hypotheses that have higher chances of turning out to be true, to test those hypotheses, and to fill gaps in our knowledge. It would also provide a much-needed reminder of what science is supposed to be: truth-seeking, anti-authoritarian, and limitlessly free.

Quantum Engineer Talks Untapped Potential of Human Mind, Major Problems in Science Today


Dr. Garret Moddel teaches a course at the University of Colorado that explores psychic phenomena, such as remote viewing. Preliminary studies in his classroom have even seemed to suggest that students can accurately predict stock market changes!

His students have told him that the course has opened their minds, but not just in terms of becoming aware of psi phenomena (Psi refers to any psychic phenomenon, such as psychokinesis, telepathy, or clairvoyance). Perhaps more importantly, Dr. Moddel has taught them how science works, and how to think more critically about science.

Science by Consensus Is a Problem

If 97 percent of scientists say something is true, does that make it true? A large portion of the general public may think so. But, Dr. Moddel said, “That’s not the way that science works. It’s not a consensus sport. In fact, it’s often the lone mavericks who, in the end of it, are right.”

If 97 percent of scientists say something is true, does that make it true?

“We know based on historical examples that most of the science that we now believe is going to be modified, so nothing that we have is really cast in stone,” he said. “Scientific progress is slowed by cascading opinions.”

He watched a classic example of this take hold of his mother’s life. In the 1950s, nutritionist Ancel Keys conducted what is now known as the “Seven Countries Study,” commissioned by the U.S. Public Health Service. Keys found that countries with less dietary fat had healthier populations. “He was very influential, and once he stated that opinion, it stuck,” Dr. Moddel said.

For the past 50 years, the United States has been operating on this belief, with manufacturers cutting fat in foods, but sometimes adding sugar and other unhealthy elements to compensate for lost flavor. Though many studies after Keys’ showed his conclusions to be incorrect, it’s only in the past five years that the scientific community has really begun to recognize that sugar, not fat, is the enemy.

While many scientists who go against the grain are cut down by their colleagues, tenure thankfully allows many of today’s mavericks—including Dr. Moddel—to study psi and other not-quite-popular topics without professional repercussions.

His more mainstream work, in quantum engineering, has earned Dr. Moddel a position of respect in the scientific community. His conventionally minded colleagues are “respectful, at least on the surface” of his psi work, Dr. Moddel said. The university, after making him jump through some hoops to establish his psi-studies course as a critical-thinking course, now treats him with “benign neglect,” he said.

Though the security of tenure is far out of reach for Dr. Moddel’s students, some of them are eager to jump right into psi studies. “I’m the one who injects caution,” he said. He tells them, “You’ve got a career to think about. Yes, please look into this, but in order not to sabotage yourself, make sure you do really good mainstream work.”

Remote Viewing in Dr. Moddel’s Classroom

The United States government declassified documents in the 1990s showing it has extensively studied and used remote viewing. Dr. Moddel has brought Paul Smith, a U.S. Army-trained remote viewing researcher, into the class to help his students.

Dr. Moddel gave an example of a student project. The student wanted to see what would happen if he removed one of the two people involved in remote viewing and replaced the human with a machine. In remote viewing experiments, a person is usually asked to draw whatever image comes to mind. Two images have been preselected by those conducting the experiment, each corresponding to an event in the future.

For example, the image of a bowling ball could be designated as meaning the value of a particular stock will rise the following day. The image of a rabbit means the value of that stock will fall the following day.

So one person, unaware of which images have been chosen, draws an image that randomly comes to mind. The other person involved in the experiment is the judge. The judge looks at the picture the person has drawn and decides if it looks more like a bowling ball or a rabbit. If the images drawn seem to consistently correspond to the actual stock market outcomes of the following day, it would seem the person who drew the pictures is the one performing remote viewing. But, Dr. Moddel reminds us, the judge could also be exercising some psi ability.

Instead of having a person draw the images, the student had a machine do so. He used a random number generator (a machine designed to randomly create bits) to output the bit stream to a computer, which used the stream to form an image. The random number generator was situated next to a person who served as the viewer, and presumably the random number generator output was being influenced by the viewer.

Even with the machine, the student got some statistically significant results. In previous experiments, Dr. Moddel’s students have been able to predict changes in the stock market at a rate above chance (correctly seven times in seven attempts).

 Dr. Moddel’s students have been able to predict changes in the stock market at a rate above chance (correctly seven times in seven attempts).
He and fellow researchers are currently engaged in a crowdfunding campaign to further investigate this. He has talked before about the importance of intention in psi experiments—it’s been suggested by other experiments in his classroom that the enthusiasm or belief in psi of the subjects or the experimenters can affect the results—so we wondered what role intention might play in a stock market experiment

16 Things You Needs to Know About Your Mind


Human Mind is a wonderful masterpiece that has immense potentials. Most of its potentials, however, remain unused in most people, since it is not us who are in charge of things, but our Mind takes control of us. In order to control something, we first need to know the thing concerned — so, we must know our Mind so as to be in charge of it.

16 Things You Needs to Know About Your Mind

What do we need to know about the mind? Here are 16 important things everyone needs to know about the workings of the human mind.

1. The most important thing we need to about our Mind is that it is not something that exists separately, individually, like some inanimate object. The Mind is not an object – it is a process. The process of constantly streaming thoughts. This stream of the thoughts is what we perceive as the Mind. The very basic nature of thoughts is that they are in a constant move, and this motion, almost automatically, creates the Mind.

2. A characteristic feature of our Mind is that it keeps roaming, wandering; it operates in something like an automatic mode. Thoughts come and go all the time.

3. In most of our waking time, our Mind wanders either in the past or in the future, in our thoughts we deal with our experience of the past, offences we suffered in the past, or with our future plans, goals and fears.

4. In most cases, our Mind is locked up in the prison of the past by an event the outcome of which is unpleasant to us. Our thoughts turn toward that event in the past, we would like do change the course of the events, or we worry what others might think of us because of our improper behavior in that past moment.

5. Another way of becoming prisoners is when our Mind puts the spell of an imaginary future, or the image of a desired, idyllic state upon us. Then we mobilize all our energies to make those images come true, and we tend to pass by the opportunities offered by the present almost blindly.

6. The creative force of the Mind is only accessible in the present moment, in the here and now.

7. Our Mind is constantly evaluating things. It means that we do not simply live through our experiences, but we also categorize them as good or bad. We judge everything that happens to us and everybody we meet in our lives.

8. Our Mind is constantly producing stories. The entirety of these stories comprises our personal histories.

9. The Mind is usually rejecting of, or even hostile to, the present moment. We often think that this or that should not happen that way, I should be somewhere else now, in some much better place. Why do such things only happen to me all the time? Our mind is thus in a constant struggle with the present.

10. The conditioned mental patterns of the Mind are realized as various systems of beliefs and patterns of thoughts in our lives.

11. The conditioned mental patterns have been handed down to us by our parents, our community and the society in which we grow up, and we have also borrowed some from the media.

12. We very often accept these ready-made mental patterns and beliefs uncritically, without any thinking; what is more, we identify with these patterns that will, in this way, be incorporated into our personalities.

13. The conditioned mental patterns place the development of the Ego in the foreground, and make efforts to sustain that development until the end of the life of the individual. The programmings support the progress of the Ego, they urge us to develop a powerful and efficient Ego for ourselves, and they make us believe that it is the ultimate goal in human life.

14. Our Mind itself deems our own mental image of our personal development good. At the same time, this half of the Mind deems the other half, the one we wish to change, bad. Mental images fight against each other, trying to overcome each other, using the weapons of selective perception and story fabrication.

15. Our Mind is one of the most sophisticated, most complicated instruments in the world. In this modern, rushing world, however, the Mind is bombarded with information to the extent that it virtually overflows. On those occasions the amounts of unprocessed information whirl in the Mind so fast that we are sometimes afraid of going mad.

16. The purpose of the Mind is to serve as a means of connections, to connect us to the world to each other. Through the Mind, used with alert Consciousness, creative energies are released to the world, and create a wonderful harmony there.

Nowadays more and more of us begin to realize and experience that we are more than our Minds, more that our thoughts and emotions, and the personal history these thoughts and emotions build up. Our attention is no longer completely engaged by telling our personal history and identifying with that personal history, and we become more and more sensitive to the deeper dimensions of our life. We also begin to notice the breaks between thoughts, and we begin to turn towards these gates leading beyond the Mind.

The Relation Between the Moon and the Human Mind


Every thing around us has an effect on something within us – be it our exposure to the sun’s ultraviolet radiation (UVR), looking at the morning sky, taking a walk in the forest, or swimming in the sea – nature’s beauty prompts the flow of hormones and energy in our body. But not everything that’s present in nature is fundamentally good for human beings. The Moon, romanticised by poets and artists since ages, has a series of negative effects on the human mind, scientifically and spiritually. Several scientists and research institutions have conducted experiments over the years to study the effect of moon and full moon nights on the human mind and behaviour. Moon, Conscious and Sub-Conscious According to Quantum Physics, everything in the Universe – stars, planets, satellites or even the moon has an operating frequency. The frequency emanated by the moon affects the frequency of the mind that exerts control over our feelings, emotions and desires. The mind, which consists of conscious and sub-conscious mind, reacts to the standing and positioning of the moon in the sky. Neuroscience has recognized that the subconscious controls 95% of our lives.

chandra-asthami

Sub-conscious mind is the collective storehouse of impressions, memories and thoughts accumulated over the years and lifetime and it has a higher operating frequency in comparison to that of the moon. One needs constructive thinking and observatory skills to get into the realms of the sub-conscious mind. The moon frequencies have the power to make the thought frequencies in our sub-conscious mind to surface to the conscious mind. Since our sub-conscious mind consists of unnecessary and necessary, positive and negative imprints, their combined rise to the conscious mind, can leave us feeling exasperated, crazy and mindless. Moon, Tides and Human body Moon is the reason for tides on earth, as due to its gravitational pull the volatile objects (water) on earth tend to get disturbed. Aristotle and Roman historian Pliny the Elder suggested that the brain was the “moistest” organ in the body. Scientist went further on this line of thought and suggested that since the human body is made up of 80% water, it could be possible that moon does influence human’s state of mind and behaviour. However, to each research that proves the same, there is a contradictory research denying the same. But, there is concrete evidence that the moon affects human sleep patterns. The evidence was published based on the experiment where 33 adult volunteers (of both sexes) of different age groups were made to sleep for several nights in a sleep lab. Researchers studied and observed the volunteers’ brain activity, eye movements and hormone levels. Gradually, it was found out that on the nights closer to moon days, volunteers took an average of five minutes longer to fall asleep, and slept overall 20 minutes lesser than their usual sleeping hours. Additionally, melatonin (hormone that helps in regulating sleep cycle) level had dropped compared to other nights hence proving the fact that people are prone to insomnia during full moon nights. Practitioners of Ashtanga yoga, a traditional vigorous yoga style that is almost thousand years old, are asked to avoid doing yoga on moon days: full moon and new moon. The reason for the same is that one exhibits too much energy on full moon days, which might lead to injury and fracture in the body. It is, thus, advised, to indulge in   activities that calms the mind, for example — meditation. Countering the influence of the Moon In order to not let the moon vibrancies take over you, be vigilant about your own vibrations. Be watchful of your behaviour, impulses and thoughts on moon days. Since, new moon asks a part of your unconscious to rise up, you can use this as an opportunity to cleanse your mind off those thoughts which tend to bother and disturb you. New moon energy can be used in our favour if we choose to harness that energy to reflect and create a better self. Image sources and References A Database on Research of Human Behaviour with Regards to the Full Moon Moon and Mind Moon and sleep Spirituality and moon Effect of Moon How the moon messes with your sleep

Read more at: http://fractalenlightenment.com/29465/issues/the-relation-between-the-moon-and-the-human-mind | FractalEnlightenment.com

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