AI - Applied Use Cases
The release of PyTorch 1.3 includes support for model deployment to mobile devices, quantization, and front-end improvements, like the ability to name tensors. We’re also launching tools and libraries for improved model interpretability and multimodal development.
The release of PyTorch 1.3 includes support for model deployment to mobile devices, quantization, and front-end improvements, like the ability to name tensors. We’re also launching tools ...
RT @OpenAI: We've trained an AI system to solve the Rubik's Cube with a human-like robot hand. This is an unprecedented level of dexterity for a robot, and is hard even for humans to do. The system trains in an imperfect simulation and quickly adapts to reality: https://t.co/O04izt3KvO https://t.co/8lGhU2pPckOpen
We've trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand.
RT @VR_ARTech: Deep learning is the name for multilayered neural networks, which are networks composed of several “hidden layers” of nodes between the input and output. Link >> https://t.co/SpvrpcMXSN @DZone via @antgrasso #AI #ML #DeepLearning https://t.co/tyUf9zsvUIOpen
Learn about AI, machine learning, supervised learning, unsupervised learning, classification, decision trees, clustering, deep learning, and algorithms.
Comcast has a tremendous amount of streaming data, and still, it was able to build a robust Enterprise AI Platform with open-source solutions like MLFlow and Kubernetes. Check out the details. #AI #ML https://t.co/7zR7OqJJTjOpen
Kubernetes, specifically Kubeflow, ArgoCD, and Seldon Core, for model deployment. Data transformation and normalization can be ensured by the Data Transformation pod and the Data ...
RT @FinMKTG: The Seven #Patterns Of #AI https://t.co/oRovUZ9NtA #fintech #ArtificialIntelligence #MachineLearning #DeepLearning v/ @SpirosMargaris @kath0134 @CogWorldHub @HaroldSinnott @jblefevre60 @pierrepinna @mclynd @Ronald_vanLoon @horstwilmes @terence_mills @SabineVdL https://t.co/Q9SlYkZHRROpen
From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many.
Transferability is a major challenge in street navigation. Our work @ICCV19 proposed a cross-view policy learning approach that utilises top-down aerial view imagery to enable agents to learn faster and better in unseen street areas. https://t.co/wRgWdbyXeR https://t.co/9sHHMj2X1rOpen
We further re- formulate the transfer learning paradigm into three stages: 1) cross-modal training, when the agent is initially trained on multiple city regions, 2) aerial view-only ...
RT @Julez_Norton: The #DataScience hierarchy of needs. #MachineLearning #AI #DeepLearning #bigdata #Analytics https://t.co/I5qmeO9auh https://t.co/RSs55rSzhn @julez_norton @FrRonconi @jerome_joffre @ronald_vanloon @HeinzvHoenen @mvollmer1 @alvinfooOpen
Untangling data pipelines with a streaming platform.
AI is quite the 🐝 buzzword 🐝 but 𝘩𝘰𝘸 𝘤𝘢𝘯 𝘺𝘰𝘶 𝘮𝘢𝘬𝘦 𝘈𝘐 𝘸𝘰𝘳𝘬 𝘧𝘰𝘳 𝘺𝘰𝘶? We got you covered: https://t.co/7iUkwDsdOa https://t.co/D7bG9gd9VTOpen
Check out Einstein's Guide to AI Use Cases—it's an interactive website that helps you get started on your journey with AI.
RT @cloud_comp_news: How #AI developers are driving new demand for IT vendor services https://t.co/mB6d6UUbYx https://t.co/6dSNtrJERxOpen
Preparing for the adoption of new technologies is challenging for many large enterprise organisations - yet investment in AI systems and services will continue on a high-growth trajectory.
RT @IanLJones98: How Machine Learning, #BigData & AI Are Changing Healthcare Forever by @BernardMarr via @Forbes #AI #ML #DL #Robotics cc @YIbnM @DrJDrooghaag @KaiGrunwitz @evankirstel @FmFrancoise @NevilleGaunt @MasterofIoT @JohnNosta @BillMew https://t.co/aTa2fbLo1eOpen
While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and ...
This article by @vboykis about Siraj is thoughtful & nuanced, includes lots of background, and hits on many broader issues: https://t.co/X5B3xjRc0HOpen
I first came across Siraj Raval around 2016, when a data scientist friend sent me his video. “This is ridiculous,” he said, and I had to agree that it was. But, it was also hilarious. Who ...
Deep Learning: https://t.co/KgtHR2B9Vk NLP: https://t.co/zC31JsKLwz Comp Linear Algebra: https://t.co/qgQTDyxMit Bias, Ethics, & AI: https://t.co/CR3THkKmy6 Debunk Pipeline Myth: https://t.co/qIW64dWkUg AI Needs You: https://t.co/xUAv2eZLls Ethics Center: https://t.co/uCyv7xEt26Open
The course teaches a blend of traditional NLP topics (including regex, SVD, naive bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the ...
BREAKING: Machine-learning system can identify likely "serial hijackers" of IP addresses: https://t.co/4wN22GTHeQ (joint work @MIT & @SDSC_UCSD) https://t.co/OnUPOWl26jOpen
Model from the Computer Science and Artificial Intelligence Laboratory identifies “serial hijackers” of internet IP addresses.
We’ve summarized the key ideas of the research paper “AutoML: the survey of the state-of-the-art” from the Hong Kong Baptist University. Check it out to know, on which tasks AutoML already outperforms human-designed models. #AI #AutoML #ML https://t.co/3Perrkg1zcOpen
Following the structure of the original paper, we’ll touch upon the available AutoML techniques, summarize existing approaches to Neural Architecture Search (NAS), provide you with the ...
RT @DataInstituteSF: Join us for the Center for Applied Data Ethics (CADE) Tech Policy Workshop Nov 16 & 17 featuring @ruchowdh @noUpside @Combsthepoet @cbracy @KLdivergence and more! Check out the schedule and register: https://t.co/BESV1N7kboOpen
Sunday, March 10 to Tuesday, March 12, 2019
"Perhaps we’d all be better off if the gadgets around us could tell us what they’re thinking. But do they all need to?” My article on explainable AI for @iftf. Thanks @m_c_elish, @rao2z, @iammarkhammond, @freddylecue, @DARPA, David Danks, Dave Gunning. https://t.co/7S9QsqN2Y0Open
“A lot of new technologies bypass traditional forms of accountability,” says Madeleine Elish, an anthropologist at the Data & Society Research Institute, “including sometimes legal ...
RT @twainus: @SpiekSarah @PaulNemitz @nettwerkerin @sonstso_sk @PrivacyLab_WU @bendrath @DS_Stiftung @rennersen @johnchavens Given the current data structures and 2300+ years of defective logic since Aristotle (which John McCarthy himself identified in 1973), algorithmic totalitarianism and systemic biases of Machine Logic against Humans Reasoning is inevitable. * https://t.co/3vcdo0a5vWOpen
Do natural language processing tools from Amazon and Google contain racial and gender bias? Charles Earl investigates.
RT @srush_nlp: PyTorch-Struct (v0.2 https://t.co/wNWBTmvst6). CRF distributions API, documentation, parsing datasets, new structured models, Tree/Span-LSTM, DGL adapters, and perf. Fun example: RL for learning tree network over math (ListOps) https://t.co/QQ5Whby653Open
A library of vectorized implementations of core structured prediction algorithms (HMM, Dep Trees, CKY, ..,) - harvardnlp/pytorch-struct
RT @cloud_comp_news: What’s new in Gartner’s 2019 hype cycle for #AI – and what businesses need to know about https://t.co/poH6N53ADm https://t.co/IHB3v9oBF0Open
Enterprises are making progress with AI as it grows more widespread - and they are also making more mistakes that contribute to their accelerating learning curve.