今日が人生で一番若い！
道産子フリーランスのこーだいです。
今回は、「僕の人生史上、最悪の失敗談」を語ります。
最初に、注意事項があります。
この動画は、
- 個人の力で稼ぎ自由に生きたい！
- 平凡な人生は嫌だ！
- 会社に縛られる人生は嫌だ！
- もっと稼いで親孝行したい！
- 一度きりの人生、好きな事していきたい！
そう本気で思っている方だけ見てください！
僕は、札幌出身の２５歳で、
現在、物販、ブログ、FX、アフィリエイトなどネットで稼ぎつつ、
個人で稼ぐことに特化したスクール 『NeFreカレッジ』を運営し、自分で会社を経営しています。
複数の収入の柱があるので、収入は月に数百万円あります。
もちろん、上には上がいるので僕もまだ、道の途中で何も成し遂げていないと思っています。
「良いな！順風満帆な人生で」
といわれることがありますが、
うまくいっていることばかりではありません。
むしろ沢山の失敗をしてきました。
そして、今日話すのは、数ある失敗の中でも一番の絶望エピソードです。
僕は親友を見殺しにしました。
今、そんな彼と一緒に事業をやっているのが、不思議なくらいです。
当時の僕はさすがに落ち込んで、ご飯が食べれないほど、追い詰められていました。
そして、今回の動画は、ただ失敗談を話して終わりではありません。
最後には、
「今日から、道産子のあなたの人生が変わる究極のアクションプラン」をお伝えします。
この動画を通じて一人でも多くの人に、個人で稼ぐに挑戦する勇気と喜びを届けたいです。
ぜひ、最後まで飛ばさず見て頂けると嬉しいです。
ではいきましょう。
時は2018年6月に遡ります
会社を２ヶ月で辞め、タイに行った僕は猛烈に作業していました！
もうやるしかない状況だったんです。
タイに行く時に、SNSなどは全て消去しましたが、
ある日、ちょっとした自分の甘さから、インスタグラムをダウンロードして、友人たちの投稿をみました。
すると、１件DMが来てました。
「俺も稼ぎたいんだけど教えてくれないか？」
でも僕は、
「こんなに必死にやって、稼げるようになったのに教えたくない。俺はお前が遊んでいる間努力して稼げるようになったんだ」
そう思ったんです。
そして、僕は「今度な！」そう冷たく返し、スマホを閉じました。
時は2019年12月
そうして、僕は瞬く間に個人で稼げるようになり、地元に帰省しました。
帰省した際、久しぶりに地元の友人に会うと
なんと、あの時連絡をくれた大切な友人が詐欺にあっていました。
100万以上借金をしていて、大学生の時の僕と全く同じ状況です。
「マジか・・・」
僕はそれ以上の言葉が出ませんでした。
知識があるのに友人を救えなかったことが本当にショックでした。
そして、僕の中で「価値観のパラダイムシフト」が起こったんです。
パラダイムシフトというのは
これまでの当たり前と思っていた常識が崩れ去って、新しい常識が生まれることです。
その新しい常識とは一体何かというと
「自分のためじゃなくて、誰かのために頑張ろう」という価値観です。
正直、これまではずっと「自分が稼ぎたい！」ということだけを考えていました。
もう、頭の中が100％自分だったんです。自己満足だけで生きてました。
そこに、「誰かの為にというスパイスも入れてみよう」
そう思ったんです。
考えてみれば、自分一人で稼げるようになっても孤独です。
僕は、タイに住む寂しい日本人のお金持ちの人をたくさん見てきました。
そうして、
親友に自分が学んできた物販やアフィリエイト、FXを教えました。
すると彼は、みるみる成長し、わずか半年で月収100万円も稼げるようになりました。
でも、彼から言われたのは、
まだまだ北海道には、
・個人で稼ぎたいけど何を学んだら良いか分からない人
・再現性の低いノウハウを学んで消耗している人
・騙されて借金している人
がたくさんいるということです。
そレを知り、
そこからもっといろんな人に僕が学んできた集大成を教えることになりました。
そうして偶然できたのが・・・
友人を救ったことがきっかけで、偶然生まれたのが僕が運営する
「NeFre大学」です。
全ての道産子に挑戦する喜びを。という理念を掲げ、
下は18歳〜上は70歳まで一緒に学んでいます。
サブテーマは、「田舎のおばあちゃんでも稼げる」です。
・再現性の低いノウハウで消耗しお金も友達も失う
・個人で稼ぐに挑戦せずに貧乏なことを嘆く
のは今ココで終わりにしましょう。
今日から人生が変わる究極のアクションプラン
断言します。
もし道産子のあなたが、本気で稼ぎ人生を変えたいと思っているなら、
僕らと学びましょう！
「結局宣伝かよ！」「そんなの偽善だ」「なんか胡散臭い！」と思う人もいるでしょう。
でも、僕らは本気です。
断言します。北海道にコレ以上の環境はないです。
もし道産子のあなたが、本気で稼ぎ人生を変えたいと思っているなら、
僕らと一緒に学び豊かで自由な人生を手に入れましょう！
想像してみてください。あなたはどんな人生を歩みたいですか？
5秒間、目を閉じて、理想の人生を思い描いてみてください。
僕は、
19歳の時に、
パソコン１台で稼いで、世界中どこでも自由に生きたい。
そう強く思い、その夢を叶えました。
そして今は、
全ての道産子に挑戦する喜びを。
この理念を掲げ、自由に生きる仲間を増やしています。
感情を揺さぶる
ありきたりな言葉ですが、
人生は１度きりです。
1回しかない人生、
好きなことやって、後悔なく生きたいですよね。
あなたは、嫌な仕事をして、一生過ごしたいですか？
もし仮ににあなたの 収入源が1つだと、
それがダメになった時、人生が崩壊します。
だからもし、
あなたが今、会社やアルバイトの給料など、一つの収入源に頼ってるとするなら危険です。
僕は５つ以上の強固な収入の柱があります。
そんな僕の思考、ノウハウ全てを、ここまで見てくれたあなたに伝授し来ます！
もし、あなたが今の生活に満足できず、
- 個人の力で稼ぎ自由に生きたい！
- 平凡な人生は嫌だ！
- 会社に縛られる人生は嫌だ！
- もっと稼いで親孝行したい！
- 一度きりの人生、好きな事していきたい！
そう本気で思っているなら、僕らが力になります。
概要欄のLINEを追加してメッセージください^^
あなたの夢を一緒に叶えていきましょう！
最後まで読んでいただきありがとうございました。
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The proportional-integral-spinoff (PID) controller may
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The models are optimized for latency — a essential prerequisite for immersive,
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The use cases earlier mentioned show some applications, but AI technology use can extend to any complicated difficulty that could be modeled using simulations.
The types of troubles in which the technology may be
applied are demonstrated in Figure 8. A handful
of other industry challenges that fall into the
groups listed are:
Everyone is talking about AI, It appears. However, if
you are feeling overwhelmed or unsure about what
the hell people are talking about, don’t stress.
I’ve received you.
AI-created data continues to be only a part of the puzzle.
The long-phrase vision should be to take all these techniques—and others not but invented—and hand
them over to an AI coach that controls how artificial brains are wired, how They’re qualified, and what They’re properly trained on.
As well as the previous year has seen a raft of projects in which AI has become properly trained on automatically created data.
Face-recognition systems are being skilled with
AI-generated faces, for example.
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“This doesn’t mean that they are able to’t write anything
unique … but we should always hope them being inaccurate and fabricate info.
If we do that, then the errors make any difference fewer
since our usage and their applications might be modified appropriately,” she
suggests. 5. Don’t anthropomorphize
what is now called artificial general intelligence (Goertzel and
Pennachin 2007). The stated plans of this movement
“I have abruptly switched my views on no matter if these things are going for being
more intelligent than us.”
The tech sector also tends to overstate the capabilities of their solutions.
“Be skeptical; be cynical,” claims Douglas Heaven.
I asked a few of the best AI journalists from the business
to share their major tips on how to talk about AI with self confidence.
My colleagues and I expend our days obsessing over the tech, listening to
AI people and then translating what they say into clear, relatable language with significant context.
I’d say we know a thing or two about what we’re talking about.
Rui Wang thinks human-designed challenges are going to be a bottleneck and
that real development in AI will require AI to return up with
its very own.
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Maximizing item high quality and minimizing off-spec
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In this situation, the expertise of human operators is needed
to operate gear at appropriate ailments.
In the final 10 years, Large Tech has become really good at some things: language,
prediction, personalization, archiving, text parsing,
and data crunching. But it surely’s even now surprisingly lousy at catching, labeling, and eradicating hazardous content.
One just needs to recall the spread of conspiracy theories about
elections and vaccines inside the United States over the past two years to
understand the real-world problems this causes.
Artificial intelligence is often a technological advancement that will involve programming technology to problem resolve.
Artificial intelligence is frequently talked about in conjunction with machine learning
or deep learning and large data.
Finish-to-close processes of building and AI
learning brokers for advanced process controls are: coaching the agent in simulation, teaching the agent throughout
multiple optimization aims and situations,
assessing the agent, and finally deploying the agent to the edge being a production control system.
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But there’s another crucial observation below. Intelligence was hardly ever an endpoint for evolution, something to goal for.
Instead, it emerged in many different forms from numerous very small remedies to challenges
that allowed residing things to survive and take on future challenges.
Intelligence is definitely the current high place within an ongoing and
open-finished process. In this perception, evolution is
kind of different from algorithms the best way people typically consider them—as signifies to an stop.
His own ambitions tie back into his early interests in human intelligence and how it developed.
His grand vision is to established things up so that machines may someday see their very own intelligence—or intelligences—emerge and improve through
innumerable generations of demo and error, guided by algorithms with no
top blueprint in mind.
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Once the brain has handed the assessment take a look at, it can be
deployed. While you’ll find many modes of deployment, the distinctive benefit
of using tag-based simulators used for screening control systems is they may be used as the middleware
to combine the brain with the control system.
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I’ve read Massive Tech say this many times ahead of about one particular rule
or another. Most of the time, the chance of losing out on revenue in the world’s second-largest
trading bloc is simply too massive, they usually figure something out.
The apparent caveat Here’s that many companies have picked not to function, or to have a restrained existence, in China.
But that’s also a very different circumstance.
(Time)
To wrap up dialogue of CRA, we make two quick factors, to wit:
Inspite of The boldness in the likes of Pollock about the eventual
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“I have folks who’re at the exact same level as I’m, who did full-time MBAs and have
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As an example, if a value is controlled by a combination of a few valves, what comes about if one particular valve has become unavailable?
Can the brain do something affordable if among the list of valves is trapped,
or out for maintenance? This is in which simulator models made for operator trainer systems
or control system tests is usually adapted.
NVIDIA NeMo™ — for building, customizing and deploying language
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The first approach, invented individually by Wang and her colleagues at DeepMind and by a team at OpenAI at within the exact same time, uses recurrent neural networks.
This type of network might be educated in this kind of way that the activations of their neurons—approximately akin into
the firing of neurons in biological brains—encode
any type of algorithm.
It’s as in case you experienced an school inspector watching over a bunch of academics, Every single offering different learning techniques.
The inspector checks which techniques help the students receive the best scores and tweaks them appropriately.
Through these approaches, researchers are building AI that is more strong, more generalized, and capable of
learn faster with considerably less data. For example, Finn would like a robot that has learned to walk on flat ground to be able to transition, with minimal
extra training, to walking on the slope or on grass or even though carrying a load.
Once the brain has passed the assessment test,
it may be deployed. When you’ll find many modes of deployment, the unique benefit of using tag-based simulators used for screening control
systems is they may be used as the middleware to combine the
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These models supply a method to develop elaborate process simulations and are
faster during run-time, both of which allow a more successful technique to develop the brain. Tag-based process simulators are known for any simple design, simplicity of use and ability to adapt to an array
of simulation needs, which suit the necessities of the simulation model needed
to teach DRL-based brains.
AIs may also be learning how you can prepare Every other.
In one recent example, two robot arms worked jointly, with one arm learning to set more durable and more durable block-stacking challenges that experienced
the other to grip and grasp objects.
DeepMind and OpenAI took advantage of this to prepare a recurrent neural
network to produce reinforcement-learning algorithms,
which tell an AI the way to behave to attain given goals.
Tiku endorses watching YouTube explainers on concepts and AI models.
“Skip the AI influencers to the more subdued hosts,
like Computerphile,” she states. “IBM Technology is great for those who’re looking for
something short and simple. There’s no channel aimed at informal observers, but it really can help demystify the process.
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But for computer science students in college,
I do think a vital thing that future engineers need to realize is when to demand
input and the way to talk throughout disciplinary boundaries to get at frequently tricky-to-quantify notions of protection, fairness, fairness, and
many others.
Prabu Parthasarathy concluded his PhD in mechanical engineering and started
as an engineer at Wood over eighteen years in the past, inevitably growing his function into operations
and management. Prabu seems to be following a portfolio of software
items and services delivering digital options globally in while in the upstream,
midstream, downstream, manufacturing and multiple other industries.
Computer eyesight. In AI, computer eyesight makes use of sample recognition and
deep learning to understand a picture or video.
This usually means the machine can look around and take pictures or videos in real time,
and interpret the surroundings.
It may well look basic in the mean time, but
for Wang and A few other researchers, POET hints in a revolutionary new method
to create supersmart machines: by finding AI to make itself.
Wang’s former colleague Jeff Clune is Amongst the biggest boosters of
this notion.
A number of use conditions and corresponding exclusive,
challenges or applications are offered For instance the power of DRL-based brains.
For example, Should you be registered for classes from both equally the School of Information Studies and also the
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Beyond the hype of a “digital twin,” learning agents must have access to a directionally precise simulation model to practice on and suggests of deploying a learning agent to make selections within the plant.
If you need to develop your knowledge about AI or have your own questions,
this article is usually a worthwhile source of information.
I hope you enjoy reading the article and gain new insights into Artificial
Intelligence. Feel free to share this article with your colleagues who could also be interested in this topic.
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AI-based controllers may even use sound signals and vibration sensor inputs to determine how you can make process
decisions, just like sounds human operators are issue to.
A chance to process Visible information, like
the size of the flare, differentiates and reveals DRL-based controllers’ capabilities.
Hao agrees. She states it’s also helpful to keep in mind that AI development is very
political and will involve huge amounts of money and many factions of researchers with competing interests:
“Sometimes the conversation all over AI is much
less about the technology and more about the people.”
Clune’s bold eyesight is grounded by more than OpenAI’s
investment. The history of AI is filled with examples in which human-designed remedies gave approach to
machine-learned types. Take computer vision: ten years ago, the
large breakthrough in image recognition arrived when current hand-crafted systems have been replaced by kinds that taught themselves from scratch.
It’s the exact same for many AI successes. One of the interesting
things about AI, and machine learning specifically, is its power to come across remedies that
humans haven’t discovered—to surprise us. An oft-cited
example is AlphaGo (and its successor AlphaZero), which defeat the
best humanity has to offer at the ancient, beguiling sport
of Go by utilizing seemingly alien strategies.
This information will dive deeper into what artificial intelligence is, the way it’s used, and what we are able to be expecting from it inside
the future.
be traced each of the way back to Aristotle’s theory from the syllogism.[fifteen] In the case of uncertain reasoning,
the question isn’t
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Greater access to the code powering generative models
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Clune also stresses the significance of thinking about the ethics of The brand new technology from the start.
There is a good likelihood that AI-designed neural networks and
algorithms will be even more difficult to understand than today’s already opaque black-box
systems.
Wang thinks that receiving AI to work through
this problem for itself avoids a few of the demo and error of the hand-designed approach:
“We are able to’t quite possibly count on to come across the proper answer instantly.”
Within the process, she hopes, we will also learn more about how brains work.
“There’s nevertheless so much we don’t understand about the best
way that humans and animals learn,” she states.
Astonishingly, not only did AutoML Zero spontaneously build a neural
network, but it surely came up with gradient descent,
the most common mathematical technique that human designers use to practice a network.
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I asked several of the best AI journalists inside the business
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