Beginner Machine Learning tutorial (TryHackMe Advent of Cyber Day 14)

Summary

このビデオ脚本は、AIと機械学習の基本を解説し、それらがサイバーセキュリティにどのように適用されるかについても説明しています。チャレンジの14日目にCTOが楽器の製造プロセスを妨害し、欠陥品が発生する問題を機械学習で解決しようとするストーリーを通じて、観客を引き込みます。脚本では、遺伝的アルゴリズム、粒子群、神経ネットワークなど、機械学習アルゴリズムの3つのタイプに触れています。さらに、教師あり学習と教師なし学習の2つのトレーニング方法も紹介されています。最後に、データセットの重要性と、ニューラルネットワークのトレーニングと検証プロセスについても語られており、サイバーセキュリティにおけるAIと機械学習の活用事例も詳しく説明されています。

Takeaways

• 🤖 AIと機械学習は、現在最もホットなトピックの1つであり、市場で非常に注目されています。
• 📚 このチュートリアルは、TryHackMeによって提供され、初心者が参加できる完全無料で楽しい教育的なチャレンジを提供しています。
• 🧐 AIと機械学習の違いを理解することが重要で、機械学習は人間知能を模倣するプロセスです。
• 🔍 問題を効果的に解決するためには、適切な機械学習アルゴリズムが必要です。3つの主要なタイプが紹介されています：遺伝子アルゴリズム、パーティクルスウォーム、ニューラルネットワーク。
• 🧠 ニューラルネットワークは人間の脳を模倣し、多数のニューロンが入力を受け取り出力を生成するプロセスを試みます。
• 📈 ニューラルネットワークをトレーニングする方法として、教師あり学習と教師なし学習の2つの方法が説明されています。
• 🔢 入力データを正規化し、重みと掛け合わせることでニューラルネットワークはどの入力がより重要なかを判断します。
• 📉 アクティベーション関数は、出力を一定の範囲内に保つために使用され、ランダムな数字ではなく比較可能な出力を保証します。
• ➡️ フィードフォワードループとバックプロパゲーションは、ニューラルネットワークをトレーニングするプロセスで、ネットワークが正しい決定を下すために使用されます。
• 📝 データセットはニューラルネットワークに与えられる情報で、十分な情報だけを与えることでオーバートレーニングを避けます。
• 🔑 サイバーセキュリティでは、AIと機械学習が異常行動の検出やユーザー行動分析に役立ち、迅速かつ正確な予測を提供できます。
• 🏆 最後に、チャレンジを解決し、正確な予測を行った場合、フラグを獲得できます。

Q & A

AIと機械学習の違いは何ですか？

AIは人工知能の総称であり、機械学習はその一つのプロセスです。機械学習は、人間のような意思決定を機械にさせるプロセスであり、データセットを用いて機械が正確な意思決定を下すように教えます。

遺伝子アルゴリズムとは何ですか？

遺伝子アルゴリズムは、自然選択と進化のプロセスを模倣したアルゴリズムです。生存競争に基づく単純でありながらも、実際の状況で実装すると複雑になる可能性があります。

ニューラルネットワークとは何ですか？

ニューラルネットワークは、人間の脳の神経細胞がどのように機能するかを模倣した機械学習の手法です。多くの入力を受信し、出力を生む細胞とニューロンを模倣しています。

教師あり学習とは何ですか？

教師あり学習は、ニューラルネットワークに学習させたい情報を提供する学習方法です。データセットを用いて、機械が正しい意思決定を下すように学習させます。

隠れ層とは何ですか？

隠れ層はニューラルネットワークの中間レイヤーで、数学的な計算やデータの解釈が行われます。複雑な計算を行うには多くのレイヤーが必要です。

活性化関数とは何ですか？

活性化関数は、ニューラルネットワークの出力を一定の範囲内に収めるための関数です。これにより、異なるトイを比較することができます。

フィードフォワードループとは何ですか？

フィードフォワードループは、ニューラルネットワークを訓練する手法の1つで、入力を正規化し、入力層にフィードし、答えを得るプロセスです。

バックプロパゲーションとは何ですか？

バックプロパゲーションは、ニューラルネットワークが正しい答えを出したかどうかをフィードバックするプロセスです。これにより、ネットワークはより良い意思決定を下す方法を学びます。

データセットとは何ですか？

データセットは、ニューラルネットワークにフィードする情報で、機械学習モデルを訓練するために使用されます。適切な量のデータがないと、ニューラルネットワークは新しい問題に対して適切な意思決定を下すことができません。

オーバートレインとは何ですか？

オーバートレインは、ニューラルネットワークに過剰な情報を与えることで、ネットワークが答えを覚えてしまい、ロジックを理解しなくなってしまう状態です。バリデーションを通じて、ネットワークが十分に学習しているかどうかを判断します。

AIと機械学習がサイバーセキュリティにどのように役立つか説明してください。

AIと機械学習は、異常行動の検出やマルウェアの検出、ユーザー行動分析など、サイバーセキュリティの様々な分野で役立ちます。機械学習は、通常のネットワークトラフィックやユーザーの行動パターンを学習し、異常を検出することができるため、サイバー攻撃の検出と対応において非常に役立ちます。

このチュートリアルで使用されたPythonスクリプトの目的は何ですか？

このチュートリアルで使用されたPythonスクリプトは、トレーニングデータセットとテストデータセットを使用してニューラルネットワークを構築し、トレーニングとバリデーションを実行し、最後にテストデータセットに対する予測を行って結果を出力するものです。

Outlines

00:00

🤖 AIと機械学習の基礎とそのサイバーセキュリティへの応用

この段落では、AIと機械学習の基本を解説し、それらがサイバーセキュリティでどのように使われるかについて説明しています。チュートリアルは、「Advent of CBA by try hack me」の一部であり、初心者向けの無料コンペ테ーションを通じて楽しく学ぶことができます。CTOがToy Pipelineを問題にし、制御エルフを配置することで問題に対処している物語を通じて、AIと機械学習の力を使って問題を解決しようとしています。また、AIと機械学習の違いや、機械学習アルゴリズムの3つのタイプ（遺伝的アルゴリズム、粒子群、神経ネットワーク）についても触れています。

05:00

🧠 ニューラルネットワークのトレーニングとデータセットの重要性

第2段落では、ニューラルネットワークのトレーニング方法とデータセットの扱いについて詳しく説明しています。ニューラルネットワークは3つのレイヤー（入力レイヤー、隠れレイヤー、出力レイヤー）から構成され、各レイヤーで行われる計算とその意味が解説されています。また、フィードフォワードループとバックプロパゲーションというトレーニング手法や、データセットを扱う際のバリデーションとテストデータの使い方についても説明されています。

10:02

🔍 パイソンスクリプトを使った機械学習の応用と課題解決

第3段落では、具体的なPythonスクリプトを使って機械学習モデルを構築し、トレーニングデータセットとテストデータセットを使って問題を解決する方法が説明されています。データの正規化、80/20の分割、そしてトレーニングとバリデーションのコードを用いた説明がされています。また、予測結果をファイルに保存し、それをURLにアップロードして正確さを検証するプロセスも紹介されています。

15:03

🛡️ AIと機械学習を用いたサイバーセキュリティの応用

最後の段落では、AIと機械学習がサイバーセキュリティにどのように役立つかについて語られています。異常行動の検知やユーザー行動分析など、AIが提供する高速な検知能力の活用方法が説明されています。また、コースの最終課題として、AIと機械学習に関する質問への答え方を解説し、最終的なフラッグの提出方法も紹介されています。

Keywords

💡人工知能（AI）

人工知能とは、機械が人間のように思考や判断を行う能力を持つように設計された技術です。このビデオでは、AIがサイバーセキュリティにどのように適用されるかについて解説し、問題のあるトイを特定するのに役立つと述べています。

💡機械学習

機械学習は、人工知能のサブセットであり、データを通じて機械が自己学習し、判断を行うプロセスを指します。ビデオでは、機械学習がトイの品質検査やサイバーセキュリティでの異常行動検知にどのように役立つかを説明しています。

💡遺伝的アルゴリズム

遺伝的アルゴリズムは、自然選択と進化のプロセスを模倣したアルゴリズムで、最適解を見つけるために使用されます。ビデオでは、これは機械学習アルゴリズムの1つとして触れられており、問題解決手法の一例として紹介されています。

💡パーティクルスウォーム

パーティクルスウォームは、鳥が集団で特定の地点に群がるプロセスを模倣したアルゴリズムです。ビデオでは、機械学習アルゴリズムの1つとして説明されており、複雑な問題解決に役立つとされています。

💡神経ネットワーク

神経ネットワークは、人間の脳の神経細胞がどのように機能するかを模倣したアルゴリズムです。ビデオでは、神経ネットワークがトイの検査プロセスで使用され、正常性と欠陥品を判断するのに役立つと述べています。

💡教師あり学習

教師あり学習は、機械学習の手法の1つで、ラベル付きのデータセットを使って機械が学習します。ビデオでは、この手法がニューラルネットワークをトレーニングし、正常なトイと欠陥品を区別するのに使用されると説明されています。

💡非教師あり学習

非教師あり学習は、機械が与えられたデータを独自に分析し、パターンを見つけるプロセスです。ビデオでは、この手法がトイのデータに対して適用され、ニューラルネットワークが自己学習を行う方法として触れられています。

💡隠れ層

隠れ層は、ニューラルネットワークの中間レイヤーで、入力データを処理して意味のある情報を作成します。ビデオでは、隠れ層がニューラルネットワークで計算が行われ、トイの正常性を判断する上で重要な役割を果たしていると述べています。

💡フィードフォワードループ

フィードフォワードループは、ニューラルネットワークの訓練プロセスで使用される手法で、入力データを順番に処理して予測結果を得ます。ビデオでは、このループがニューラルネットワークが自己学習を進める上で欠かせないプロセスであると説明されています。

💡バックプロパゲーション

バックプロパゲーションは、ニューラルネットワークの訓練プロセスで使用される手法で、ネットワークの予測が正確かどうかをフィードバックします。ビデオでは、このプロセスがニューラルネットワークがより正確な予測を行うように学習するのに必要であると述べています。

💡データセット

データセットは、機械学習モデルに入力されるデータの集まりで、モデルが学習・予測を行うための基礎です。ビデオでは、データセットがニューラルネットワークに情報を与え、トイの正常性を判断する訓練に使用されると説明されています。

Highlights

AI is one of the hottest topics in the market at the moment.

This tutorial is part of the Advent of Cyber Security Awareness (CBA) by TryHackMe, a free competition for beginners.

The basics of AI and machine learning will be covered, along with their application in cybersecurity.

The difference between AI and machine learning will be explained.

Machine learning is a process to teach a machine to mimic human intelligence.

Three types of machine learning algorithms will be explored: genetic algorithm, particle swarm, and neural network.

Neural networks are the most popular and mimic how neurons work in the human brain.

Two methods of training neural networks will be discussed: supervised learning and unsupervised learning.

Supervised learning provides the neural network with labeled data to learn from.

Unsupervised learning allows the neural network to find interesting patterns in unlabeled data.

The neural network consists of an input layer, hidden layer, and output layer.

The hidden layer is where the mathematical computations and decision-making occur.

The feedforward loop and backpropagation are key processes in training a neural network.

The importance of proper data preparation and avoiding overtraining will be discussed.

The example will use a supervised learning method to classify toys as defective or not.

The process of building and training a neural network model will be demonstrated using Python code.

AI and machine learning have practical applications in cybersecurity for detecting unusual behavior and user activity.

The tutorial provides a step-by-step guide to using AI and machine learning in solving a cybersecurity challenge.

The presenter shares his experience and insights on the evolution and effectiveness of AI in cybersecurity over the years.

Transcripts

AI is one of the hottest topics in the

Market at the moment and if you ever

wanted to learn about AI then this free

tutorial is for you this is part of the

Advent of CBA by try hack me which is a

completely free competition for

beginners to participate in fun and

educational challenges I will go through

the basics of AI and machine learning

but I will also explain to you how AI

can be used in cyber security and if

you're new to the channel I'm un next

guy and I'll help people learn their

first cyber security job even if they

don't have any degree or technical IT

background I have many videos where I

show you exactly how to land your first

cyber security job like this video for

example but wait a minute it's almost

Christmas time let's get into it so in

day 14 of the challenge the CTO has made

our toy pipeline go wrong that's not

unusual of CTO by the way by infecting

elves at key positions in the toy making

process he has poisoned the pipeline and

caused the elves to make defective toys

maxky has started to combat the problem

by placing control elves in the pipeline

these elves take measurements of the

toys to try and narrow down the exact

location of problematic elves in the

pipeline by comparing the measurements

of defective and perfect toys however

this is an incredibly tedious and

lengthy process so he's looking to use

machine learning so in this story the

CTO has messed up the process of making

toys and what we want to do is use the

power of AI and machine learning to try

and detect which toys are in good

condition and which toys are defective

but first things first we need to learn

the difference between Ai and machine

learning you see so many people use the

word AI by mistake because what they

really are referring to is what we call

machine learning machine learning is

essentially a process where we try and

teach a machine to mimic human

intelligence for example I want my

machine to be able to make decisions

accurately but the problem we face is

that sometimes we end up using F

statements for example if we're

producing toys and we want all the toys

to be red but one of the toys is blue

then our machine can detect if the toy

is blue because that's an easy process

we can just have an F statement that say

if the the toy is not thread then the

toy is defective but unfortunately in

the real world the problems that we Face

are a little bit more complicated for

example in our toys problem here it's

not just the color but sometimes it's

the height sometimes it's the weight or

the dimensions so to effectively solve

this problem we need to use proper

machine learning the process of

analyzing the input and trying to find

the problem this process and this flow

of decision we call it an algorithm now

there are so many different algorithms

for machine learning which go beyond

this video but here we will explore

three types of machine learning

algorithms so the first one is called

genetic algorithm this structure

essentially mimics the process of

natural selection and evolution it's

essentially an algorithm based on the

theory of survival for the Fest fairly

simplistic idea but it can be

complicated when we try to implement it

in real life the second type is called

particle swarm so this one aims to mimic

the process of how birds flock in a

group together at a specific point and

the third one is called the neural

network the neural network is by far the

most popular one in fact this is the one

that I learned long time ago in

University and if I'm honest with you I

forgot most of the stuff that I learned

because I didn't practice or apply it in

the real world but today with the AI

boom I find myself needing to go back to

those Concepts and learn them again

which is a lot of fun for me so

basically neural networks mimic the

process of how neurons work in our brain

neural networks try to replicate our

human brain so we have so many cells and

neurons in our brain and they receive

various inputs and they produce outputs

so this is the basic idea behind neural

networks and this is the one that we

will use in our example today and I

promise you as we go through the example

it will become a lot easier to learn and

understand so the neural network much

like the human brain needs to learn

before it can make decisions and there

are many ways to teach neural networks

how to make decisions in fact so many

PHD programs are based on specific way

of teaching neural networks and trying

to find the efficiency of that way in

this example here we will be learning

two methods of training neural networks

the first one is called supervised

learning learning and the second one is

called unsupervised learning the

supervised learning is where we provide

the neural network with the information

that we want it to learn so ideally we

need to create what we refer to as data

sets these data sets are essentially

information that we feed to the neural

network in order for the neural network

to be able to make decisions based on

this data for example if I want to teach

the newal network about toys my data set

should include information about what a

good toy looks like so that when the

neuron Network sees a toy that doesn't

look like the good toy then it can tell

you what it's defective or not the

second type is called unsupervised

learning and this one is a little bit

more complex we basically let the neural

network try and find interesting things

so in our toys example instead of

specifically telling the neural network

that this is a good toy what we do

instead is we feeded large amount of

information about toys in general and

then we let the neural networks try to

make correlations and decisions based on

that data as I said this is a complex

topic and in the challenge there are

links that you can follow where you can

read more about this later on in this

example that we will go through we will

use the supervised learning method so as

you can see in this example our machine

learning model here consist of three

layers we have the input layer and then

we have a hidden layer and we have then

the output layer so at the moment we

have a neural network that doesn't have

any information so the first step is to

give it some information that

information is fed to the neural network

at the input layer so in this example

you can see that the input layer have

the height width length color scheme

make ID and the check ID and at the end

of that model we have something called

the output layer this will simply tell

us whether the toy is defective or not

but then in the middle we have the

hidden layer this is where the magic

happens this is where the math where the

computation happens this is where the

neural network tries to make sense of

the data and the hidden layer itself can

be composed of so many layers in fact

the more complicated the calculations

are the more layers we need now this

example doesn't go deep into what a no

is but if I wants to simplify it for you

a node can be a server it can be a

laptop it even can be an application

instance or a thread think of a node is

like something that does computations so

the more computational nodes we have the

more servers we have the more computing

power we have the more mathematical

computation we can perform so if we have

a complex neural network that's doing

true artificial intelligence we really

need a lot of computational power and a

lot of mathematical calculations as well

now in this example we will zoom in a

little bit on the hidden layer to see

how the calculations are performed but

don't worry it's not complex math it's

very very basic simplified for you just

to know enough to understand how neural

networks actually work we don't just

take the inputs as they are in fact we

multiply the inputs by the weight now

this is important logic we need to

understand the relationship between the

height and the weight of the toy this

will help the neural network understand

which input contribute more to the

output than others so for example one

toy could be a lot taller than other

toys but once we factor in the weight

then we can see how much that height is

actually contributing simil similarly we

don't just take the output value as it

is but we take that output value and we

put it into what we refer to as an

activation function the purpose of the

activation function is that we don't

want the outputs to be just random

numbers we want these outputs to be

within one range so we can compare the

toys to one another so in this example

we want the output to be a decimal

number between 0 and 1 or it could be

between minus1 and 1 now that we have

some idea of how the neural network

operates the next step would be to train

the neural network this means we need

information to feed into the neural

network so it can start and make

decisions for us so the first method

here is called the feed forward Loop

this is the simplest form of training so

essentially the way it works the first

step is we normalize the input normalize

the input is what we talked about in the

previous step which is we multiplies it

by weight to help the neural network

decide which inputs are more important

and then we feed the inputs to our nodes

in the input layer as you can see in the

diagram and then we simply get the

answer so the more data we give to the

neural netk took the better the decision

will be because it will know what a

defective toy looks like but that's only

half of the equation we have been

feeding input to the Neal Network now we

need to do what we refer to as back

propagation this is where we simply tell

the neural network whether the answer

was correct or not and that's how the

neural network can learn so we will give

the neural network a certain input and

then the neural network will come and

tell you well I think this toy is good

but then you will look at it and say yes

it was and then you will look at the

answer and then you will tell the neural

network whether the answer was correct

or not and that's how the neural network

can be trained to make better decisions

now the last topic that we need to talk

about before we go into solving the

challenge is the information that we

feed to the neural network which we

refer to as data sets now the type of

data that we give to the neural network

is a huge topic on its own and it's not

straightforward so as tryck me trying to

explain it if you were at school and

your teacher have explained to you that

1 + 1 = 2 and 2 + 2 = 4 but then in the

exam you get the question of 3 + 3 now

we know that the answer is six but you

can only know that if you understood the

basic principle of addition if you just

memorized 1 + 1 = 2 and 2 + 2 = 4 then

you won't be able to calculate 3 + 3 you

have to understand the logic behind the

calculation the same thing applies to

machine learning we can simply give our

neural network so much information but

then the neural network end up

memorizing all of this information the

problem is when the neural network faces

a new problem that it hasn't seen before

then it can't make a decision so the way

we train our neuron network is we need

to train it in such a way that it

understands the underlying logic it's

not just cramming and memorizing things

so the way we fix this problem is that

we need to know how much information is

enough otherwise we will end up into

what we refer to as overtraining this is

when we give the neur network way too

much information that it ends up

memorizing the answers as opposed to

understanding the logic so to achieve

that we do something we refer to as

validation we essentially validate

whether the neural network has Lear

enough or not so to perform validation

we have to split the data set into three

data sets the first set is called the

training data this is the information

that we feed to the neural network but

then we also need validation data which

is what we will use to validate whether

the neural network understood our

training or not so after each training

round we need to send the validation

data through our Network to determine

the performance if the performance

starts to decline then we know that

we're overtraining our neuron Network

and finally we have testing data this

data set is simply used to calculate the

final performance of the network the

network shouldn't see this data until

we're completely done with the training

process so with all of this background

information now we can proceed to solve

our Challenge and I promise you the hot

part is done this challenge is

straightforward and I'll show you

exactly how to solve it so we

essentially have three files one is a

python script called detector and then

we have our training data set and we

have our testing data set we need

training data set we need validation

data set and we need testing data set in

this particular example the training and

the validation data sets are both in the

same file so we only have two files for

Simplicity now what we need to do is we

need to add some lines of codes to our

python script but don't be afraid you

don't actually need to know Python

Programming or anything because these

lines of codes are given to you all you

need to do is you need to start the

machine in the top right corner and this

will split our screen in half and then

we can copy the lines of codes into the

detector script so as you can see if we

walk through the codes the first thing

we do is we import python libraries that

are needed for our neural network here

we're using two libraries one is called

pandas and the other one is called py

learn for building our neuron Network

and the next thing is we need to load

the data set in our case we have two

Excel files and then once we get the

input data remember we normalize it so

in our previous example we had to

multiply it by the weight but also in

this example we need to make all the

inputs numerical so even the color we

need to just convert it to numbers so

that we get a simple decimal output

answer and finally we need to load the

data everything will be stored in the

variable we call x and testore x stores

the testing data for us the next step is

where you will need to copy some lines

of code so for our data set remember we

have one file for both the training and

the validation so we will split the data

in this example we will use an 80/20

split so what I will do I will simply

copy this line into the script sh I use

the Vim editor because as I was

recording it was a little bit slow

remember you can just use the graphical

interface you can simp simply double

click on the script and you can copy and

paste the line you don't actually need

to use the vi editor if that's easier

for you and the next step we need to

copy these lines to normalize the data

simply copy paste this line into the

script and then we need to start the

training process again all I will do is

just copy this line into the script then

we've got our classifier code and our

validation prediction code as well and

finally we need to insert a testing

prediction code as well once you're done

remember to save the file if you've

double clicked on the file and you edit

that just make sure to click save and

then we will simply run this python

script so we simply type the command

Python 3 space and then we type

detector. py you can copy this line of

code if it's easy for you and then we

can watch the magic happen a few moments

later now when you finish the prediction

will be saved to a file then we simply

need to upload the prediction to this

URL so open this URL within your testing

machine and then we upload the file so

let's see if our prediction were

accurate if our accuracy is above 90%

person then we will be awarded with the

flag and here we go winning we got the

flag I'm not going to lie it feels good

now when you run it you may not always

get 90% accuracy and if that happens

simply run it again and the explanation

for that is within neural networks there

is some Randomness built in remember

they try to mimic a real human brain a

real neural network so this is a an

issue to look out for now when it comes

to cyber security Ai and machine

learning are not a new topic in fact

even 5 years ago I was bombarded by by

vendors telling me how they have this

sophisticated Ai and machine learning

built into their products but when I go

and test them I get extremely

questionable results it was simply bad

however today they have gotten a lot

better now some of the security tools

that can make use of AI is tools we use

to detect unusual behavior and this is

important because neural network can be

really good at detecting things a lot

faster than us humans for example if you

have a malware on your network then the

traffic will look different than the

normal traffic so your neural network

can actually learn what a normal

day-to-day operation looks like and

whenever there is something unusual then

that AI or machine learning can generate

an alert for you this is extremely

useful for cyber security professionals

who work in detecting and responding to

cyber attacks another thing which is

also popular in the industry is what we

refer to as user Behavior analytics for

example if every day I log into my email

at 9:00 a.m. from Australia but all of a

sudden out of the blue my username logs

into my email email at 3:00 a.m. from

France this is an unusual behavior

because machine learning have learned

and observed my behavior over a very

long period of time and now it can

detect if I'm doing something that's

unusual now notice these are simplistic

examples but as you've learned in this

course if you provide really good data

sets into your machine learning it can

provide you with fairly accurate

predictions now the final part of the

challenge is to answer the questions so

the first question is what is the other

term given for artificial intelligence

this is an easy one machine learning the

second question is what machine

learnings aim to mimic the process of

natural selection it's called genetic

algorithm we talked about this at the

beginning of the lesson and then what's

the name of the learning style that

makes use of labeled data to train a

machine learning structure if we scroll

up this is called supervised learning

and what's the name of the layer between

the input and output layers of neural

network this is the hidden layer this is

where the magic happens and finally what

is the name of the process used to

provide feedback to the newal network on

how close it prediction was is called

back propagation this is where we tell

the network whether the prediction was

correct or not and finally we need to

submit the flag that we got so all we

need to do is copy paste the flag here

and submit now as I said Ai and machine

learning are an extremely Hot Topic in

cyber security and if you're trying to

land your first cyber security job then

I have a step by-step road map to take

you from absolute beginner all the way

to becoming a cyber security

professional in this video and I'll see

you there