๐๐๐ ๐ข๐ฌ ๐๐จ๐ซ๐ ๐๐ก๐๐ง ๐๐ก๐๐ญ๐๐๐ ๐ ๐๐ซ๐ญ๐ข๐๐ข๐๐ข๐๐ฅ ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ (๐๐): Visual Perception: AI that sees and interprets images. Intelligent Robotics: Smart robots that perform tasks. Speech Recognition ๐ฃ๏ธ: Understanding spoken words. Natural Language Processing (NLP) ๐ฌ: Understanding and generating human language. Automated Programming ๐ป: AI that writes code. ๐ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ : Decision Trees ๐ณ: Decision-making models. Naive Bayes Classification ๐: Simple probabilistic classifiers. K-Nearest Neighbors ๐ซ: Finding similar data points. Principal Component Analysis (PCA) ๐: Reducing data dimensions. Anomaly Detection ๐จ: Identifying unusual patterns. ๐ง ๐๐๐ฎ๐ซ๐๐ฅ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค๐ฌ: Multilayer Perceptrons ๐: Basic neural networks. Hopfield Networks ๐: Memory storage networks. Modular Neural Networks ๐ธ๏ธ: Multiple specialized networks. Boltzmann Machines ๐งฉ: Networks for learning probabilities. Radial Basis Function Networks ๐ฏ: Networks using radial functions. ๐ค ๐๐๐๐ฉ ๐๐๐๐ซ๐ง๐ข๐ง๐ : Convolutional Neural Networks (CNN) ๐ท: Image processing networks. Recurrent Neural Networks (RNN) โพ๏ธ: Sequence prediction networks. Generative Adversarial Networks (GAN) ๐จ: AI generating new data. Autoencoders ๐ ๏ธ: Compressing and reconstructing data. Self-Organizing Maps ๐บ๏ธ: Data visualization networks. โจ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐: BERT ๐: Understanding language context. GPT ๐ง : Generating human-like text. One Shot Learning ๐ธ: Learning from few examples. Transfer Learning ๐: Applying knowledge to new tasks. Multimodal AI ๐ผ๏ธ: Combining text, images, and more. ๐ค Follow Generative AI for the Latest Updates on AI ๐ค #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI
Rob Fielding
1w
with that said, using a good ChatBot that can write code; you can get all of these concepts wired together. i find it a little strange to be hand-writing pytorch code, and the pipeline that feeds it by hand, with hand-tuned hyper-parameters.
Poprouser
1w
Most people should never have to learn deeper than Generative AI user facing level. They engage with AI to the extent that it automates tasks, answers questions, and solves problems. HOWEVER, at the enterprise level, you have the baseline use cases for AI in Automated Programming to reduce the cost of software development and testing, AI-aided decision making, transmodal AI in rapid marketing deployment, and contextualization (BERT) that should improve language understanding over time. And that's just baseline use cases.
Appreciate your efforts. Just for info. the diagram is not accurate. Many terms are fuzzy, i.e. to say overlapping terminologies. E.g. NLP is general, can be under ML, NN, DL or AGI. MLP is not only under NN but also under DL, since "deep" in DL comes from no. of layers. Problem solving is only under AI not in ML, NN... seriously bro!
Steve King
1w
At Revenue Growth Associates, I am definitely seeing many in the enterprise space - at least at present - still equate AI to ChatGPT or to the new 'AI capabilities' released in traditional tools being used. These, of course, ARE examples of using AI under current business paradigms. However, as soon as the curtain is pulled back and the broader AI ecosystem is exposed, I see two different camps of people emerge: 1) Some tune out because it looks too complicated at a technical level, and 2) Some dive in with a curiosity for learning as much as they can about the technology. The challenge is that those in Camp #1 are often the business users, and those in Camp #2 are often the IT-focused people. The gap between these two groups (opportunity gap) represents the potential use cases that are just waiting to be discovered. Which raises a question. Should everyone be an AI expert or does this risk creating 'too many cooks in the kitchen'? A recent blog explores this question: https://revenuegrowthassociates.com/should-everyone-be-an-ai-expert/
The representation shows Generative AI as a subset of Deep Learning, which might be an oversimplification. Generative AI uses Deep Learning techniques, such as generative adversarial networks (GANs) and autoregressive models (like GPT), but it can also include other techniques that are not necessarily deep. Additionally, some might argue that Generative AI should be considered a cross-disciplinary field that utilizes multiple advanced AI techniques, not just those from Deep Learning. In summary from my personal point of view, while Generative AI often uses Deep Learning techniques, it is not entirely accurate to consider it exclusively a subset of Deep Learning. It may be more appropriate to consider it a standalone field that leverages various advanced AI methodologies, including but not limited to Deep Learning.
Is there any AI which would form questions of maths and science? Any suggestions?
Sydelab
1w
Of course itโs more then that : https://sydelab.com/category/categories/ai/
I am sorry. If youโre going to put โepochsโ as a part of AI you might as well put data preprocessing, hyperparameter tuning, learning curves, optimizer, activation functions in there (im being sarcastic). Who even made this??
From the VC perspective, the flood of money into GenAI is a bit surprising. Many in VC are investing in AI that is not likely to support the building of great companies. A lot of what GenAI can do is quickly becoming ubiquitous. That said, we're interested in Objective AI โ the ability of computers to think and understand context. We're on the cusp of what we can do in the next years with how computers can think and help make decisions.