Spelade
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I don’t like to admit it, but I’m dependant on caffeine to get motivated in the mornings.
On an intellectual level, I don’t like it. But emotionally, I don’t know any other way to get by.
So if you’re in the same boat as me…
I think you’ll enjoy my guest today Nathaniel Solace. We talk about heaps of different ways to kick your caffeine habit and feel more energized.
I was first drawn to Nathaniel after I listened to a few podcasts he did on Dopamine Detoxes.
It’s pretty crazy how this little neurotransmitter can seriously affect our lives (for good and bad). Frankly, it's a topic not spoken about enough.
So If you’re like me and you’re into optimizing your health and getting the most out of your energy.
You’ll love this episode.
Here’s what we chat about today.
***What to do if you can’t function unless you’re caffeinated. Nathaniel discusses proven ways to get off the coffee and feel even more ENERGIZED.
**Why the number one thing that blocks all momentum in any entrepreneurs’ business...is themselves. Hear about the simple exercise that injects more excitement and pleasure into your day.
**Three simple hacks to dramatically boost your mental clarity and energy.
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CONNECT WITH NATHANIEL
https://www.instagram.com/nathaniel_solace/?hl=en
WORK WITH NATHANIEL
https://www.nathanielsolace.com/links
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Gateway Drug Intro Jules' World? Checkout This Video on YouTube 'How to Build & Monetise an Email List in 2023 [COMPLETE GUIDE]'
https://youtu.be/ewTDzVbWpkw?si=RsRK3aJ0M5Bv-XF8
About Jules Dan.
Are you a coach, consultant or info-marketer that’s looking to ramp up your sales & conversions with email marketing?
If so, then you’re in the right place. This podcast is designed to help you with three core areas of your marketing.
How to get more customers
How to make MORE per customer
How to keep them coming back
And I’ll show you how to do this with the power of the pen…aka selling with your words.
So who am I and why should you trust me?
I’ve been in the trenches of freelance copywriting for 2.5 years. I’ve made plenty of mistakes. I’ve made heaps of stupid decisions.
However on my journey, I’ve also generated my clients over $15 million dollars in results.
This is the place to learn from someone in the trenches every day...
'How to make it rain with your email list'
So whether you've just got a taste for list building...
Or you're an established list owner that's curious how to profitably maximise the profit potential of every lead...
Then stick around, because you’re in good hands on Email List Profit Secrets.
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If you're a long-time listener of Storytelling Secrets...
You've probably heard me say this timeless piece of wisdom...
"People want to see your scars. Not your wounds".
Which basically means, when it comes to publishing content...
It's best you tell people about your struggles...after you've gotten through them. That way you can inspire them and give them hope that they can do it too.
That to me is strategic storytelling.
Naturally, I have to lead by example for you.
And today, I hit a major personal milestone. It's been four years in the making and I wanted to share it with you today.
Let's dive in.
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Gateway Drug Intro Jules' World? Checkout This Video on YouTube 'How to Build & Monetise an Email List in 2023 [COMPLETE GUIDE]'
https://youtu.be/ewTDzVbWpkw?si=RsRK3aJ0M5Bv-XF8
About Jules Dan.
Are you a coach, consultant or info-marketer that’s looking to ramp up your sales & conversions with email marketing?
If so, then you’re in the right place. This podcast is designed to help you with three core areas of your marketing.
How to get more customers
How to make MORE per customer
How to keep them coming back
And I’ll show you how to do this with the power of the pen…aka selling with your words.
So who am I and why should you trust me?
I’ve been in the trenches of freelance copywriting for 2.5 years. I’ve made plenty of mistakes. I’ve made heaps of stupid decisions.
However on my journey, I’ve also generated my clients over $15 million dollars in results.
This is the place to learn from someone in the trenches every day...
'How to make it rain with your email list'
So whether you've just got a taste for list building...
Or you're an established list owner that's curious how to profitably maximise the profit potential of every lead...
Then stick around, because you’re in good hands on Email List Profit Secrets.
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Marc Wallert, Trainer, Redner und Bestsellerautor im Interview. Im Jahr 2000 hat Marc 140 Tage als Geisel im philippinischen Dschungel überlebt. 15 Jahre wirkte er als Führungskraft in internationalen Unternehmen und heute kombiniert er Führungserfahrung mit Entführungserfahrung. Er inspiriert Menschen und Organisationen, wie sie stark durch Krisen kommen und gestärkt aus ihnen hervorgehen. Aktuell erschien sein Buch: Stark durch Krisen - Von der Kunst, nicht den Kopf zu verlieren.
Wir stecken mitten in der Coronakrise und sind Geiseln eines Virus. Marc und ich sprechen in dieser Folge darüber, welche Bilder, Ziele und welches Denken uns hilft, um nicht den Kopf zu verlieren und stark zu bleiben. Warum ist ZU positives Denken in einer Krise nicht hilfreich? Wie kommt man raus aus der Opferrolle? Wie hat die Kommunikation mit den asiatischen Geiselnehmern funktioniert? Ist „Krönchen richten und weiter“ nach der Krise eine hilfreiche Strategie? Welche Dschungelstrategien überträgt Marc in den Businessalltag?
▶️ Mehr Infos zu Marc Wallert finden Sie auf seiner Webseite: https://marcwallert.com
▶️ Hier gehts zu Marcs Buch "Stark durch Krisen": https://marcwallert.com/buch/
▶️ Buchen Sie einen Platz in meinem nächsten Intensiv-Seminar "Professionell Auftreten" in Berlin https://www.birgit-schuermann.com/seminare
▶️ Sie möchten sich gemeinsam mit mir (live oder via Zoom & Co.) auf Ihren nächsten Redebeitrag vorbereiten? Schreiben Sie an: https://www.birgit-schuermann.com/kontakt
▶️ Ob über Zoom & Co oder live auf Ihrer Bühne, hier gehts zu meinen Vorträgen: https://www.birgit-schuermann.com/vortraege
▶️ Lesen Sie viele praktische Rhetorik-Tipps in meinem Rhetorik-Blog "Einfach reden.": https://www.birgit-schuermann.com/blog
▶️ Weitere Infos zur aktuellen Folge erhalten Sie über meinen Hörerservice: https://www.birgit-schuermann.com/podcast
▶️ Vernetzen Sie sich mit mir über LinkedIn: https://www.linkedin.com/in/birgit-schürmann-9a4408168/
▶️ Folgen Sie mir auf Instagram: https://www.instagram.com/birgit_schuermann/
▶️ Sie haben Fragen, Anregungen oder Themenwünsche? Nur zu! Schreiben Sie mir unter: [email protected]
🎤 Verbess´re Deine Sprache, Deine Rede, damit sie nicht Dein Glück verdirbt!
Shakespeare
Ihre Birgit Schürmann
Folge direkt herunterladen -
Professor Mark Bishop does not think that computers can be conscious or have phenomenological states of consciousness unless we are willing to accept panpsychism which is idea that mentality is fundamental and ubiquitous in the natural world, or put simply, that your goldfish and everything else for that matter has a mind. Panpsychism postulates that distinctions between intelligences are largely arbitrary.
Mark’s work in the ‘philosophy of AI’ led to an influential critique of computational approaches to Artificial Intelligence through a thorough examination of John Searle's 'Chinese Room Argument'
Mark just published a paper called artificial intelligence is stupid and causal reasoning wont fix it. He makes it clear in this paper that in his opinion computers will never be able to compute everything, understand anything, or feel anything.
00:00:00 Tim Intro
00:15:04 Intro
00:18:49 Introduction to Marks ideas
00:25:49 Some problems are not computable
00:29:57 the dancing was Pixies fallacy
00:32:36 The observer relative problem, and its all in the mapping
00:43:03 Conscious Experience
00:53:30 Intelligence without representation, consciousness is something that we do
01:02:36 Consciousness helps us to act autonomously
01:05:13 The Chinese room argument
01:14:58 Simulation argument and computation doesn't have phenomenal consciousness
01:17:44 Language informs our colour perception
01:23:11 We have our own distinct ontologies
01:27:12 Kurt Gödel, Turing and Penrose and the implications of their work
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Today we are going to talk about the *Data-efficient image Transformers paper or (DeiT) which Hugo is the primary author of. One of the recipes of success for vision models since the DL revolution began has been the availability of large training sets. CNNs have been optimized for almost a decade now, including through extensive architecture search which is prone to overfitting. Motivated by the success of transformers-based models
in Natural Language Processing there has been increasing attention in applying these approaches to vision models. Hugo and his collaborators used a different training strategy and a new distillation token to get a massive increase in sample efficiency with image transformers.
00:00:00 Introduction
00:06:33 Data augmentation is all you need
00:09:53 Now the image patches are the convolutions though?
00:12:16 Where are those inductive biases hiding?
00:15:46 Distillation token
00:21:01 Why different resolutions on training
00:24:14 How data efficient can we get?
00:26:47 Out of domain generalisation
00:28:22 Why are transformers data efficient at all? Learning invariances
00:32:04 Is data augmentation cheating?
00:33:25 Distillation strategies - matching the intermediatae teacher representation as well as output
00:35:49 Do ML models learn the same thing for a problem?
00:39:01 How is it like at Facebook AI?
00:41:17 How long is the PhD programme?
00:42:03 Other interests outside of transformers?
00:43:18 Transformers for Vision and Language
00:47:40 Could we improve transformers models? (Hybrid models)
00:49:03 Biggest challenges in AI?
00:50:52 How far can we go with data driven approach?
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Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods can be used to discover knowledge, to debug or justify the model and its predictions, and to control and improve the model, reason about potential bias in models as well as increase the social acceptance of models. But Interpretability methods can also be quite esoteric, add an additional layer of complexity and potential pitfalls and requires expert knowledge to understand. Is it even possible to understand complex models or even humans for that matter in any meaningful way?
Introduction to IML [00:00:00]
Show Kickoff [00:13:28]
What makes a good explanation? [00:15:51]
Quantification of how good an explanation is [00:19:59]
Knowledge of the pitfalls of IML [00:22:14]
Are linear models even interpretable? [00:24:26]
Complex Math models to explain Complex Math models? [00:27:04]
Saliency maps are glorified edge detectors [00:28:35]
Challenge on IML -- feature dependence [00:36:46]
Don't leap to using a complex model! Surrogate models can be too dumb [00:40:52]
On airplane pilots. Seeking to understand vs testing [00:44:09]
IML Could help us make better models or lead a better life [00:51:53]
Lack of statistical rigor and quantification of uncertainty [00:55:35]
On Causality [01:01:09]
Broadening out the discussion to the process or institutional level [01:08:53]
No focus on fairness / ethics? [01:11:44]
Is it possible to condition ML model training on IML metrics ? [01:15:27]
Where is IML going? Some of the esoterica of the IML methods [01:18:35]
You can't compress information without common knowledge, the latter becomes the bottleneck [01:23:25]
IML methods used non-interactively? Making IML an engineering discipline [01:31:10]
Tim Postscript -- on the lack of effective corporate operating models for IML, security, engineering and ethics [01:36:34]
Explanation in Artificial Intelligence: Insights from the Social Sciences (Tim Miller 2018)
https://arxiv.org/pdf/1706.07269.pdf
Seven Myths in Machine Learning Research (Chang 19)
Myth 7: Saliency maps are robust ways to interpret neural networks
https://arxiv.org/pdf/1902.06789.pdf
Sanity Checks for Saliency Maps (Adebayo 2020)
https://arxiv.org/pdf/1810.03292.pdf
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.
https://christophm.github.io/interpretable-ml-book/
Christoph Molnar:
https://www.linkedin.com/in/christoph-molnar-63777189/
https://machine-master.blogspot.com/
https://twitter.com/ChristophMolnar
Please show your appreciation and buy Christoph's book here;
https://www.lulu.com/shop/christoph-molnar/interpretable-machine-learning/paperback/product-24449081.html?page=1&pageSize=4
Panel:
Connor Tann https://www.linkedin.com/in/connor-tann-a92906a1/
Dr. Tim Scarfe
Dr. Keith Duggar
Video version:
https://youtu.be/0LIACHcxpHU
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Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Research, INRIA and Yale. His bachelors is in computer science where he achieved the highest GPA in his cohort.
Ishan is fast becoming a prolific scientist, already with more than 3000 citations under his belt and co-authoring with Yann LeCun; the godfather of deep learning. Today though we will be focusing an exciting cluster of recent papers around unsupervised representation learning for computer vision released from FAIR. These are; DINO: Emerging Properties in Self-Supervised Vision Transformers, BARLOW TWINS: Self-Supervised Learning via Redundancy Reduction and PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with
Support Samples. All of these papers are hot off the press, just being officially released in the last month or so. Many of you will remember PIRL: Self-Supervised Learning of Pretext-Invariant Representations which Ishan was the primary author of in 2019.
References;
Shuffle and Learn - https://arxiv.org/abs/1603.08561
DepthContrast - https://arxiv.org/abs/2101.02691
DINO - https://arxiv.org/abs/2104.14294
Barlow Twins - https://arxiv.org/abs/2103.03230
SwAV - https://arxiv.org/abs/2006.09882
PIRL - https://arxiv.org/abs/1912.01991
AVID - https://arxiv.org/abs/2004.12943 (best paper candidate at CVPR'21 (just announced over the weekend) - http://cvpr2021.thecvf.com/node/290)
Alexei (Alyosha) Efros
http://people.eecs.berkeley.edu/~efros/
http://www.cs.cmu.edu/~tmalisie/projects/nips09/
Exemplar networks
https://arxiv.org/abs/1406.6909
The bitter lesson - Rich Sutton
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Machine Teaching: A New Paradigm for Building Machine Learning Systems
https://arxiv.org/abs/1707.06742
POET
https://arxiv.org/pdf/1901.01753.pdf
