#  SmileyNet \- Towards the Prediction of the Lottery
by Reading Tea Leaves with AI

Andreas Birk
Constructor University Bremen
School of Computer Science and Engineering
Campus Ring 1, 28759 Bremen
<EMAIL_ADDRESS>
###### Abstract

We introduce SmileyNet, a novel neural network with psychic abilities. It is
inspired by the fact that a positive mood can lead to improved cognitive
capabilities including classification tasks. The network is hence presented in
a first phase with smileys and an encouraging loss function is defined to bias
it into a good mood. SmileyNet is then used to forecast the flipping of a coin
based on an established method of Tasseology, namely by reading tea leaves.
Training and testing in this second phase are done with a high-fidelity
simulation based on real-world pixels sampled from a professional tea-reading
cup. SmileyNet has an amazing accuracy of 72% to correctly predict the flip of
a coin. Resnet-34, respectively YOLOv5 achieve only 49%, respectively 53%. It
is then shown how multiple SmileyNets can be combined to win the lottery.

##  1 Introduction

Modern AI is one of the greatest if not the greatest scientific breakthrough
of all times. The publication of the structure of DNA by Watson and Crick in
Nature [1] has only achieved slightly more than 19,000 citations by summer
2024 according to GoogleScholar. And it has taken 71 years. AI [2, 3] has led
to many important papers, which received many more citations within a much
shorter time-frame; see, e.g., Tab. 1 for a few examples based on citations
according to GoogleScholar.

| DNA | AI
---|---|---
publication | [1] | [4] | [5] | [6] | [7] | [8] | [2] | [9] | [10] | [11] | [12]
year | 1953 | 2016 | 2014 | 2012 | 2001 | 2014 | 2015 | 2014 | 2015 | 2009 | 2015
citations (x 1K) | 19 | 232 | 189 | 159 | 133 | 127 | 81 | 79 | 76 | 71 | 61
citations/year (x 1K) | 0.27 | 29 | 19 | 13 | 6 | 13 | 9 | 8 | 8 | 5 | 7
Table 1: The scientific relevance of AI compared to the discovery of DNA.

Given the relevance of AI for our modern world, it is surprising that there
are areas of high economic relevance where it has not been applied, yet. We
explore here - up to our knowledge for the very first time - the potential of
AI for the psychic services industry, which is a multi-billion dollar
business. This industry is estimated to have reached a revenue of well above
$2 billion in the USA in 2023. And it experiences a steady further growth.
This holds especially for psychic online services that managed an exponential
growth in recent years according to market-reports.

We suspect that AI may already be used in some real-world psychic services,
especially in the online branches. But the methods and the results are not
academically published up to our knowledge - likely due to the large economic
potential they possess. While this is somewhat understandable, we believe in
Open Science [13] and we share our insights due to the large benefits Psychic
AI can provide to humanity.

A first medium-term goal is to win the lottery to further fund research in the
novel field of Psychic AI. A well-established method of Tasseology (also known
as Tasseomancy or Tasseography) [14] is used to this end, namely reading tea
leaves [15, 16]. In the spirit of divide and conquer, the simpler problem of
predicting the outcome of flipping a coin is tackled first. We then show how
this can be used to predict the numbers for winning the lottery jackpot.

As an important basic research result, it is proven in this paper, up to our
knowledge for the very first time, that a neural network can possess psychic
powers. Concretely, it is shown that the novel SmileyNet can predict the
flipping of a coin with a precision well above chance.

##  2 SmileyNet

SmileyNet is a novel bio-inspired network. It is motivated by the fact that
cognitive processes can be positively influenced by a positive mood [17, 18].
This includes especially also classification tasks [19].

Figure 1: The smiley in form of the Unicode emoticon ”U+1F642” that is used to
bias the network into a positive mood.

Furthermore, it is well-known that visual stimuli are well suited to elicit
emotions [20, 21]. This includes in particular a smiling face [22]. We hence
first present the network with smileys as input to bias it into a good mood.
More precisely, the Unicode emoticon ”U+1F642” for the classical smiley,
officially known as ”Slightly Smiling Face” is used to this end (Fig.1).

But stress is a negative factor for cognition [23, 24]. We do not want to
loose the positive effects of the good mood by putting the network under
stress in the smiley training phase. Hence, it gets positive feedback [25]
that it is doing fine, i.e., that it is doing nothing wrong.

More precisely, the training in the smileyfication phase is done as follows. A
quadratic loss function ${\cal L}_{smile}()$ is used. To avoid putting the
network under stress and to give a further positive endorsement, the expected
output $\hat{y}$ is set to be the actual output $y$ whenever a smiley is
presented to the net, i.e., $\hat{y}=f_{I}(y)$ where $f_{I}()$ is the identity
function. So,

${\cal L}_{smile}()=\frac{1}{2}(y-\hat{y})^{2}\text{ with }\hat{y}=f_{I}(y)$

This method of smileyfication can be applied to any network ${\cal N}_{old}$
to turn it into a novel one ${\cal N}_{new}$, which is expected to perform
better than the previous ${\cal N}_{old}$.

Often, there may only be small improvements and it can happen that multiple
trials are needed until some improvement is seen for the first time, which
should then be immediately documented. Also, it can be useful to take
additional factors into account. For example, the effects of smileyfication
may only be seen on a sunny day as, e.g., the weather has an influence on the
mood [26, 27].

##  3 Related Work

YOLOv5 [28] and Resnet34 [4] are well-known networks for image classification.
They are used for comparison to SmileyNet in Sec. 5. There are also many other
networks. But as YOLOv5 and Resnet32 are very popular, we concentrate here on
those two.

Figure 2: YOLOv5 [29] Figure 3: Resnet [4]

YOLOv5 is the fifth iteration of the revolutionary ”You Only Look Once” object
detection model. It is designed to deliver high-speed, high-accuracy results
in real-time. Built on PyTorch, this powerful deep learning framework has
garnered immense popularity for its versatility, ease of use, and high
performance. Fig. 2 shows how it works.

ResNet-34 is a convolutional neural network in PyTorch. It is has 34 layers.
It addresses the problem of vanishing gradient. This is done with the identity
shortcut connection that skips one or more layers. Fig. 3 shows how it works.

##  4 High-Fidelity Simulation

The constellation of tea leaves and the predicted event are linked by cosmic
energy. We use this fact to generate high-fidelity training data. This is not
easy in the context of psychic powers and it may be one reason why AI has not
been used - up to our knowledge - in this economically highly relevant
business up to now.

The core idea of our approach for training data is to use the reverse
direction of the link. The coin is flipped in a deterministic way, i.e., it is
so to say turned by hand in rounds $r$ from one side to the other. The related
constellation of tea-leaves, i.e., the image $I_{r}$ is then generated
afterwards.

In the software implementation, ”heads” is presented with a ”1” and ”tails”
with a ”0” for the sake of convenience and wlog. Also wlog, we start with
”tails” facing up as initialization of the flipping process. In the very first
round $r=1$, the coin is turned and it hence shows ”heads”. The coin is then
turned again and it now shows ”tails” in round $r=2$, and so on. More
formally, it holds for the outcome $o_{flip}(r)$ in round $r$ that

$o_{flip}(r)=\begin{cases}\text{heads }&\text{if }(r+1)\;{mod}\;2=0\\\
\text{tails }&\text{otherwise}\end{cases}$

The images $I_{r}$ in our high-fidelity simulation are based on a real-world
image (Fig. 4)111wikimedia.org; curid=17659832; user: MochaSwirl; Public
Domain of tea-leaves in a cup used by a fortune-telling professional. From
this professional image, real-world pixels $p_{tea}$ belonging to tea and
real-world pixels $p_{cup}$ belonging to the cup are extracted. This forms the
base image $I_{0}$ with $100\times 100$ pixels (Fig. 5(a)). For the sake of
simplicity, $p_{tea}$-pixels are shown in black and white is used for
$p_{cup}$-pixels .

Figure 4: A real-world example of a tea-leaves-reading image.

The first image $I_{1}$ is then generated right after the coin was flipped for
the first time. So, $I_{1}$ is linked to producing ”head” according to the
laws of Tasseology. In general, each image $I_{r}$ is generated from $I_{r-1}$
by randomly selecting $k_{change}$ coordinates
$[x,y]_{j}:x,y\in\\{0,...,99\\}$ that are altered as follows

$I_{r}[x][y]=\begin{cases}p_{tea}&\text{if }I_{r-1}[x][y]=p_{cup}\\\
p_{cup}&\text{if }I_{r-1}[x][y]=p_{tea}\end{cases}$

(a) The starting image $I_{0}$ at $r=0$.

(b) $r=1$.

(c) $r=2$.

(d) $r=3$.

(e) $r=4$.

(f) $r=5$.

(g) $r=6$.

Figure 5: An illustration of the process that generates the images $I_{r}$.

Each $I_{r}$ is generated right after doing the flip of the coin in round $r$,
i.e., it is linked to the outcome $o_{flip}(r)$ of flipping the coin in that
round. According to our experience, small to medium values for $k_{change}$
work best. The process is illustrated in Fig. 5. As mentioned, the $p_{cup}$,
respectively $p_{tea}$ pixel are shown as white, respectively black pixel in
Fig. 5 for the sake of simplicity.

##  5 Experiments and Results

The high-fidelity simulation presented in Sec. 4 is used to generate 500
images. When it is needed by the network, the images are scaled to fit the
requirements of PyTorch. The set is split into the first 300 images that are
used for training and the following 200 images are used for validation.

| YOLOv5 | Resnet-34 | ours
---|---|---|---
accuracy | 53% | 49% | 72%
Table 2: The accuracy of the three networks. SmileyNet is by far the best.  |  ours compared to
---|---
| YOLOv5 | Resnet-34
accuracy increase | 35.38% | 46.43%
data reduction | 66% | 66%
Table 3: The performance gains of SmileyNet compared to YOLOv5 and Resnet-34.

Tab. 2 shows the results for YOLOv5 and Resnet-34. Resnet-34 has only 98 out
of 200 cases correctly predicted. YOLOv5 does a bit better and it manages 106
correct predictions on the testing set.

Due to the better performance of YOLOv5 compared to Resnet-34, it is used for
a smileyfication to generate SmileyNet version 1. As SmileyNet is expected to
have an improved performance, it is trained with less data. Only every fifth
image from the data-set is used. Note that this is still an alternating
sequence of ”heads” and ”tails”, i.e., it is exactly the same principle as
before, but just 100 training images. The remaining 400 images are used for
testing.

SmileyNet manages an amazing accuracy of 71.75%, i.e., 287 out of 400 cases
are correctly predicted. This is well above the performance of YOLOv5 and
Resnet-34 (Tab. 2). Most importantly, it is well above chance and we therefore
prove - up to our knowledge - for the very first time that AI can possess
psychic powers. Note that it can be shown that the improvement is
statistically significant, i.e., it is a thorough and sound result.

Tab. 3 summarizes the performance gains compared to YOLOv5 and Resnet-34.
SmileyNet has an accuracy of 71.75%, i.e., there is an increase by 35.38% from
the 53% of YOLOv5. The performance gain is in the comparison with Resnet-34
with 46.43% even higher. SmileyNet requires only 100 training images while
YOLOv5 and Resnet-34 had 300 images. So, there is a reduction by 200 images or
66% in comparison to the other two nets.

##  6 SmileyNet for Lottery Forecasts

The forecasting of throwing a coin can already be a useful psychic power. But
the forecast of lotteries has a very straightforward economical potential.
Lotteries exist in most countries around the globe with high jackpots for the
correctly predicted numbers. It is shown in the following with the example of
the German lottery how SmileyNet can be used to this end.

In the German lottery, six numbers $win_{1},...,win_{6}$ are drawn out of a
pool with the numbers $1,...,49$. The psychic power of SmileyNet can forecast
the flip of a coin, i.e., 1 bit. We therefore simply combine multiple
SmileyNets to encode the numbers $1,...,49$. Then, one after the other
$win_{i}$ is predicted.

More precisely, let $b_{j}$ denote the forecasting output of SmileyNet ${\cal
N}_{j}$ and $\left\langle b_{1},...,b_{n}\right\rangle_{2}$ denotes the number
$x$ encoded by the bits $b_{1},...,b_{n}$, i.e.,

$x=\left\langle b_{1},...,b_{n}\right\rangle_{2}=\sum_{i=1}^{n}{b_{i}\cdot
2^{n-i}}$

The $i$-th number $win_{i}$ of a lottery draw is then predicted with ten
SmileyNets ${\cal N}_{j}$ with outputs $b_{j}$ as follows

$win_{i}=\left\langle b_{1},...,b_{5}\right\rangle_{2}+\left\langle
b_{6},...,b_{9}\right\rangle_{2}+\left\langle b_{10}\right\rangle_{2}$

As mentioned, the winning numbers $win_{1},...,win_{6}$ are simply generated
one after the other. It must of course be ensured that fresh images of tea-
leaves from a just emptied cup are provided in every round. Nonetheless,
SmileyNet does not have perfect psychic power. It may hence happen in rare
occasions that a number $win_{j+k}$ has been predicted before as $win_{j}$.
But this is not a problem. Then, the current $win_{j+k}$ is simply discarded
and an other round is added to determine a new $win_{j+k}$.

Given the predictive power of SmileyNet to correctly forecast a bit with
$p_{\cal N}=0.7175$, the exact likelihood $p_{win}$ of forecasting the German
lottery with the above method can be computed as

$\displaystyle p_{win}$ $\displaystyle={p_{\cal N}}^{5}\cdot{p_{\cal
N}}^{4}\cdot p_{\cal N}$ $\displaystyle=0.03615922$
$\displaystyle=3.615922\cdot 10^{-2}$

This likelihood may look small at first glance, but it is by six orders of
magnitude better than just chance with

$\displaystyle p_{chance}$ $\displaystyle={49\choose 6}^{-1}$
$\displaystyle=\left(\frac{49!}{6!(49-6)!}\right)^{-1}$
$\displaystyle=\frac{1}{13,983,816}$ $\displaystyle=7.15112\cdot 10^{-8}$
$\displaystyle\ll p_{win}$

This is an important result that demonstrates the huge potential of the novel
field of Psychic AI. Note that it is a thorough result as it can be shown that
it is statistically significant.

But before we apply these results in practice, we still want to improve the
performance of our approach. This holds first and foremost for the likelihood
$p_{win}$ of predicting the correct numbers for winning the jackpot:

  * •

One option is simply more training data and more computation power for a
larger network to turn SmileyNet into a foundation model for psychic AI.

  * •

Furthermore, we expect a transformer to be more accurate given that the
drawing of lottery numbers is a weekly event in Germany. This is left for
future work.

Also, a reduction of the required number of SmileyNets would be nice before
applying it in practice as it is a bit inconvenient to drink so much tea.

##  7 Conclusions

SmileyNet version 1 was introduced. It is based on smileyfication, i.e., the
fact that a positive mood can have a positive influence on cognitive functions
including classification. It was shown how SmileyNet can be used to predict
the lottery by reading tea-leaves. First, the flipping of a coin was predicted
with a very good accuracy, namely 72%. State of the art networks like YOLOv5
and Resnet-34 perform much worse. Second, it was shown how multiple SmileyNets
can be combined for lottery forecasts. In addition to the potential practical
implications, it is a major breakthrough to show that AI can have psychic
powers.

##  8 Acknowledgments

We thank our anonymous reviewers for their feedback. Special thanks go to
reviewer 2, who pointed out that the drawing of lottery numbers tends to be a
weekly or at least regular event, and that the use of a transformer should be
beneficial.

## Epilog

This is a satirical accumulation of misconceptions, mistakes, and flawed
reasoning I have encountered in recent times as a reviewer and sometimes even
as a reader of published papers. I hope it is entertaining and useful in the
context of the education of BSc, MSc, and PhD students in Machine Learning,
Artificial Intelligence, and Cognitive Science.

For an easy start, here a few questions to reflect upon. First and foremost,
there is of course the following puzzle:

  * •

Where does the ”magic” hide that is presented here? How can SmileyNet
”predict” the flip of a coin better than chance?

As a small hint: there is a single conceptual mistake that explains it.

There are also many other bugs, respectively misconceptions and flawed
reasoning in this paper. Here some further food for thought, to hopefully
teach a good lesson by setting a bad example:

  * •

Can AI beat natural laws? What are the pros and cons of a detailed pre-
analysis of the possible information content of the data?

  * •

How much academic novelty is in SmileyNet? How much does it differ from the
net it is build upon?

  * •

How do we prevent smileyfication in our own work? How can we properly show
that an ”extension” or ”modification” of an existing approach leads to a
substantial improvement and study the related trade-offs?

  * •

What is a proper assessment of the state of the art? How do we find,
understand, and discuss related work?

  * •

What is a proper statistical performance analysis? What is statistical
significance and how should it be interpreted?

  * •

When and how do I expect my own work to be of relevance? Why do I publish it?

  * •

What are Artificial Intelligence and Machine Learning? Which sub-areas within
these disciplines exist? What are commonly used methodologies within the sub-
areas?

  * •

What is the role of training data in supervised machine learning? What are the
pros and cons of simulation?

  * •

What is Open Science? What are its principles? What does it imply for the
quality of the underlying work? What are the pros and cons if others,
respectively oneself adheres to it?

  * •

What is a bio-inspired design? How can insights from Cognitive Science be used
and which pitfalls exist?

  * •

What are the pros and cons of bibliometrics? How can they help us to identify
relevant work? And where are their constraints and limitations?

This list is a bit concentrated on the ”big questions”. It is of course also
possible to just concentrate on the (many) methodological mistakes in the
SmileyNet paper. So, the above list is neither meant to be exhaustive nor to
be without dispensable aspects depending on the context. It is just a possible
starting point for using the SmileyNet in the class-room, or to just meditate
about it on your own, or…

Enjoy and have fun…

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