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These are some things that I stumbled on that motivate me: https://youtube.com/shorts/q1txLXeFyLg?si=0cSsSBFv93QzMN6I

I have not listed the resources in any particular order(say these are the resources for keeping update with research, these are the resources for latest news, etc). The resources are listed paltform wise. These resources are the best resources to keep updated with research, controversies, latest news, etc all rolled into one place(That is how we keep the things intresting) Youtube https://www.youtube.com/@1littlecoder https://www.youtube.com/@3blue1brown https://www.youtube.com/@WesRoth https://www.youtube.com/@AICoffeeBreak https://www.youtube.com/@misraturp https://www.youtube.com/@intheworldofai https://www.youtube.com/@TheoreticallyMedia https://www.youtube.com/@TwoMinutePapers https://www.youtube.com/@VoloBuilds https://www.youtube.com/@MervinPraison https://www.youtube.com/@WelchLabsVideo https://www.youtube.com/@Deeplearningai https://www.youtube.com/@underfitted https://www.youtube.com/@matthew_berman https://www.youtube.com/@TheAiGrid https://www.youtube.com/@visualkernel https://www.youtube.com/@t3dotgg https://www.youtube.com/@SkillLeapAI https://www.youtube.com/@SebastianRaschka https://www.youtube.com/@VisuallyExplained https://www.youtube.com/@MattVidPro https://www.youtube.com/@WhatsAI https://www.youtube.com/@ritvikmath https://www.youtube.com/@theorants https://www.youtube.com/@engineerprompt https://www.youtube.com/@aiadvantage https://www.youtube.com/@DavidOndrej https://www.youtube.com/@umarjamilai https://www.youtube.com/@TheoThrowaways https://www.youtube.com/@OpenAI https://www.youtube.com/@OlivioSarikas https://www.youtube.com/@rileybrownai https://www.youtube.com/@replit https://www.youtube.com/@outliier https://www.youtube.com/@NeuralNine https://www.youtube.com/@szymonozog7862 https://www.youtube.com/@SirajRaval https://www.youtube.com/@sedetweiler https://www.youtube.com/@avb_fj https://www.youtube.com/@what-is-math https://www.youtube.com/@replicatehq https://www.youtube.com/@aiexplained-official https://www.youtube.com/@AIJasonZ https://www.youtube.com/@dlByManish https://www.youtube.com/@mreflow https://www.youtube.com/@neuralink https://www.youtube.com/@FlowiseAI https://www.youtube.com/@NicholasRenotte https://www.youtube.com/@OnyxStudiosInteractive https://www.youtube.com/@YannicKilcher https://www.youtube.com/@RobertMilesAI https://www.youtube.com/@jamesbriggs https://www.youtube.com/@rowanch https://www.youtube.com/@MachineLearningWithJay https://www.youtube.com/@unitreerobotics https://www.youtube.com/@VercelHQ https://www.youtube.com/@qilinxue989 https://www.youtube.com/@kishalaydas https://www.youtube.com/@bycloudAI https://www.youtube.com/@elvissaravia https://www.youtube.com/@YTomS https://www.youtube.com/sciencephiletheai https://www.youtube.com/@MachineLearningSimulation https://www.youtube.com/@EdenMarco https://www.youtube.com/@Fireship https://www.youtube.com/@Tech-Rogue https://www.youtube.com/@SimonHoiberg https://www.youtube.com/@PixelArtistry_ https://www.youtube.com/@leonvanzyl https://www.youtube.com/@soroushmehraban https://www.youtube.com/@prathoshap5226 https://www.youtube.com/@simplyexplained https://www.youtube.com/@TechWithTim https://www.youtube.com/@howardjeremyp https://www.youtube.com/@NormalizedNerd https://www.youtube.com/@ShusenWangEng https://www.youtube.com/@TheFutureThinker https://www.youtube.com/@code4AI https://www.youtube.com/@eyeonai3425 https://www.youtube.com/@robmulla https://www.youtube.com/@figureai https://www.youtube.com/@HuggingFace https://www.youtube.com/@SmithaKolan https://www.youtube.com/@Langflow https://www.youtube.com/@animatedai https://www.youtube.com/@TheFocusedCoder https://www.youtube.com/@echohive https://www.youtube.com/@BostonDynamics https://www.youtube.com/@lexfridman https://www.youtube.com/@SerranoAcademy https://www.youtube.com/@LaCarnevali https://www.youtube.com/@campusx-official https://www.youtube.com/@MicroConf https://www.youtube.com/@anthropic-ai https://www.youtube.com/@MachineLearningStreetTalk https://www.youtube.com/@jbhuang0604 https://www.youtube.com/@LangChain https://www.youtube.com/@joshfpocock https://www.youtube.com/@CodeEmporium https://www.youtube.com/@deepbean https://www.youtube.com/@RobShocks https://www.youtube.com/@digitallearninghub-imperia3540 https://www.youtube.com/@aipapersacademy https://www.youtube.com/@FlowiseAI https://www.youtube.com/@DevelopersDigest https://www.youtube.com/@ThePyCoach https://www.youtube.com/@AndrejKarpathy https://www.youtube.com/@lethal-intelligence https://www.youtube.com/@BobDoyleMedia https://www.youtube.com/@KapilSachdeva https://www.youtube.com/@SamuelAlbanie1 https://www.youtube.com/@arp_ai https://www.youtube.com/@ariseffai https://www.youtube.com/@LevendeStreg https://www.youtube.com/@DreamingAIChannel https://www.youtube.com/@indydevdan https://www.youtube.com/@theAIsearch https://www.youtube.com/@algorithmicsimplicity https://www.youtube.com/@Aitrepreneur https://www.youtube.com/@DevelopersHutt https://www.youtube.com/@ArtOfTheProblem https://www.youtube.com/@IntuitiveMachineLearning https://www.youtube.com/@GregIsenberg https://www.youtube.com/@CNETHighlights https://www.youtube.com/@bhancock_ai https://www.youtube.com/@DwarkeshPatel https://www.youtube.com/@OptimisticFuturology https://www.youtube.com/@hu-po https://www.youtube.com/@daveebbelaar https://www.youtube.com/@controlaltai https://www.youtube.com/@Explaining-AI https://www.youtube.com/@lewingtonn https://www.youtube.com/@donatocapitella https://www.youtube.com/@DenisDmitrievDeepRobotics https://www.youtube.com/@BryanJohnson https://www.youtube.com/@EmergentGarden https://www.youtube.com/@glebkrasmedia https://www.youtube.com/@DrTrefor https://www.youtube.com/@AIBites https://www.youtube.com/@FuturMinds https://www.youtube.com/@AssemblyAI https://www.youtube.com/@Computerphile https://www.youtube.com/@Green-Code https://www.youtube.com/@ArxivInsights https://www.youtube.com/@CreatorMagicAI https://www.youtube.com/@gautamiruvanti7592 https://www.youtube.com/@AAmini https://www.youtube.com/@HeduAI https://www.youtube.com/@AIBeyondInfinity https://www.youtube.com/@gabrielmongaras https://www.youtube.com/@AI.Tooltip https://www.youtube.com/@CodingwithGPT https://www.youtube.com/@calendarG https://www.youtube.com/@CNET https://www.youtube.com/@ariseffai https://www.youtube.com/@beyondfireship https://www.youtube.com/@GoogleQuantumAI https://www.youtube.com/@DeepFindr https://www.youtube.com/@TheAIEpiphany https://www.youtube.com/@chatwithdata https://www.youtube.com/@AdamLucek https://www.youtube.com/@borismeinardus https://www.youtube.com/@edrickdch https://www.youtube.com/@AIAnimationStudio https://www.youtube.com/@menloparklab https://www.youtube.com/@henrikkniberg https://www.youtube.com/@Gonkee https://www.youtube.com/@DubStepZz https://www.youtube.com/@allin https://www.youtube.com/@4tuneGuide_official https://www.youtube.com/@AISciencesLearn https://www.youtube.com/@AladdinPersson https://www.youtube.com/@Augmented_AI

Given that Seems that AI is improving at a rapid pace Following jobs will be lost Text to video models etc will replace Actors, producers, writers etc. Text to speech models will replace Singers AI pimps will replace Only fans, Ticktok creators etc Tools like Claude computer use will replace all remote workers Indie hackers relying on microsaas will be replaced by AI. Infact most SaaS will be replaced by AI. Bloggers and most sites will be replaced by SearchGPTs. All consultants like lawyers who do not go to court, accountants etc. I cannot find a job where you do 9 to 5 on a computer and you not getting replaced by AI. 99% of all book authors except for people like Marcus Aurelius will be replaced by AI. Online courses, teachers even nice people like Khan Academy will be replaced by AI. Heck i do not think that even great colleges like IISC and IITs teaching computer science will be spared. Copywriters Affiliate marketers because of tools like perplexity shopping If we are able to build robots If we are able to get robots which also seems likely then even jobs that require physical actions will also be replaced by AI. How to prepare for this brave new world Invest all your money to buy AI tools and tech upgrades. Be a super learner. Obviously it will become easier to make a software/videos/agents etc in the future but you still have to push the current tools to absolute limits - so that you are prepared for the future when these tools mature. These effors will not be in vain Building an audience on X. Keeping up with AI progress. Learning about AI. Evergreen phylosophy etc Spending time with family and friends. I building AI agents will be the only thing that matters if there was one thing that mattered. What i think will still be valuable Bitcoin, Ethereum maybe Land I will not bet that thousand year old trend of gold being valueable will be replaced. I feel that somehow these jobs will still be there Sports I would have written artists, actors etc but most of them are crooks in real life. We do not watch them because we are inspired by them in real life. Contarary to popular belief I think AI will be able to empatize even better than humans. These are the things that excite me AR glasses with a personal coach/secretary. GPT 5/6 level AI agents.

What is a Programming Language? A programming language is a formal system that allows humans to give instructions to computers. It acts as a bridge between human-readable commands and machine code. Python, which we’ll focus on in this article, is a popular high-level programming language known for its readable syntax and extensive library ecosystem. What is a Module? A module in Python is a file containing Python code that can be imported and used in other Python programs. It’s a fundamental way to organize and reuse code by grouping related functions, classes, and variables together. ...

Recall Recall from our discussion on boosting we defined the ensemble model $H_T(x)$ as: $$ H_T(x) = \sum_{t=1}^T \alpha_t h_t(x) $$ the problem of finding the next weak learner reduces to: $$ h_T = \arg\min_{h \in H} \sum_{i=1}^n r_i \cdot h(x_i) $$ where $$r_i = -\frac{\partial R(H_{T-1}(x_i))}{\partial H_{T-1}(x_i)}$$ Problem setting Let the dataset be $D = {(x_i, y_i)}_{i=1}^n$ where $y_i \in {-1, 1}$. The loss function is given by: $$L(H(x_i), y_i) = \exp(-y_i H(x_i))$$ ...

Why did we not have such a problem in EM algorithm? The Variational Autoencoder (VAE) architecture can be visualized as follows: Before expanding the KL divergence term, recall that: latent variable $q_{\phi}(z|x)$ is the approximate posterior (i.e., how likely the latent variable $z$ is given the input $x$). $p_{\theta}(z|x)$ is the true posterior (i.e., the actual distribution of $z$ given $x$ under the model). The KL divergence measures how much $q_{\phi}(z|x)$ deviates from $p_{\theta}(z|x)$: ...

Anderson’s result states that for a forward SDE of the form: $$dx = f(x,t)dt + g(t)dB_t$$ The corresponding reverse SDE takes the form: $$dx = \left(f(x,t) - g^2(t)\nabla_x(\log p_t(x))\right)dt + g(t)dB_t$$ where: $f(x,t)$ is the drift term of the forward process $g(t)$ is the diffusion coefficient $\nabla_x(\log p_t(x))$ is the score function The reverse process has the same diffusion term $g(t)dB_t$ as the forward process This result shows that to reverse a diffusion process, we need to: ...

Notations $L$: number of layers in the network. $w_{jk}^l$: weight connecting $k^{th}$ neuron of $(l-1)^{th}$ layer to $j^{th}$ neuron of $l^{th}$ layer $b_j^l$: bias of $j^{th}$ neuron in $l^{th}$ layer $a_j^l$: output of $j^{th}$ neuron in $l^{th}$ layer $z_j^l$: preactivation output of $j^{th}$ neuron in $l^{th}$ layer Here: $$z_j^l = \sum_k w_{jk}^l a_k^{l-1} + b_j^l$$ Also: $$a_j^l = \sigma(z_j^l)$$ $$a_j^l = \sigma(\sum_k w_{jk}^l a_k^{l-1} + b_j^l)$$ Derivation Let’s assume we use squared loss function: $$L = \frac{1}{2} |a^L - y|^2$$ ...

Bagging Let the original data be D. Create n new datasets by sampling with replacement from D. Each new dataset will have the same number of samples as the original dataset, but some samples will be repeated, and some will be excluded. Train a model on each of the new datasets. The final prediction is made by aggregating the predictions of all models: For classification, the final prediction is made by taking the majority vote of the predictions of all models. For regression, the final prediction is made by taking the average of the predictions of all models. Random Forest - Bagged descion trees Bagging of decision trees. Each decision tree is trained on a random subset of the features. The final prediction is made by taking the majority vote of the predictions of all decision trees.

Let the data be D = {($x_1$, $y_1$), ($x_2$, $y_2$), …, ($x_n$, $y_n$)} where $y_i$ is the label of the feature vector $x_i$. Let the hypothesis space be $H = {h | h: X \to Y}$. The Bayes classifier is defined as: $$h_B(x) = \begin{cases} 1 & \text{if } P(y=1 | x=x_i) > P(y=0 | x=x_i) \ 0 & \text{if } P(y=1 | x=x_i) \leq P(y=0 | x=x_i) \end{cases}$$ Notations $P(y)$ is also called the prior probability of class $y$ or class prior probability. $P(x|y)$ is also called the likelihood of $x$ given class $y$. $P(y|x)$ is also called the posterior probability of class $y$ given $x$. $P(x)$ is also called the evidence. Bayes classifier is the best classifier for 0-1 loss function To prove that the Bayes classifier is the best classifier for the 0-1 loss function, we need to show that it minimizes the expected risk (error) for any given input $x$. ...