Analytics Vidhya
APRIL 14, 2022
This article was published as a part of the Data Science Blogathon. Source: Forbes.com Introduction It is not hidden from the audience that quantum computing is the future of data processing. Tech giants like IBM, Google, and Microsoft are all aggressively pursuing quantum computing technology for a good reason. The massive speedups and power savings of quantum […].
BAIR
APRIL 29, 2022
Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind’s work on controlling a nuclear reactor or on improving Youtube video compression , or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning.
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Eugene Yan
APRIL 16, 2022
Introducing randomness and/or learning from inherent randomness to mitigate position bias.
DataRobot Blog
APRIL 29, 2022
With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. When business decisions are made based on bad models, the consequences can be severe.
Speaker: David Warren and Kevin O’Neill Stoll
Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng
DeepMind
APRIL 27, 2022
We introduce Flamingo, a single visual language model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended multimodal tasks.
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Analytics Vidhya
APRIL 4, 2022
This article was published as a part of the Data Science Blogathon. Introduction Nowadays, Machine learning is being used in various areas in the health business, including the development of improved medical processes, the management of patient records and data, and the treatment of chronic diseases. Healthcare firms may use machine learning to meet rising demand, […].
BAIR
APRIL 25, 2022
Figure 1: Summary of our recommendations for when a practitioner should BC and various imitation learning style methods, and when they should use offline RL approaches. Offline reinforcement learning allows learning policies from previously collected data, which has profound implications for applying RL in domains where running trial-and-error learning is impractical or dangerous, such as safety-critical settings like autonomous driving or medical treatment planning.
Eugene Yan
APRIL 9, 2022
Thinking about recsys as interventional vs. observational, and inverse propensity scoring.
DataRobot Blog
APRIL 7, 2022
F1 is headed Down Under next week for the first time since 2019. The Australian Grand Prix – taking place in Melbourne’s Albert Park Circuit – will showcase a new track layout and will be a homecoming for Australia’s own, McLaren Racing driver Daniel Ricciardo. The track changes, which Ricciardo consulted on , are the first modifications since 1996 and are aimed at reducing lap times, increasing speeds, and bringing the cars closer together.
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DeepMind
APRIL 11, 2022
We ask the question: “What is the optimal model size and number of training tokens for a given compute budget?” To answer this question, we train models of various sizes and with various numbers of tokens, and estimate this trade-off empirically. Our main finding is that the current large language models are far too large for their compute budget and are not being trained on enough data.
StreamHacker
APRIL 23, 2022
Creyzies is the official companion collect to mfers , by Reylarsdam. Every mfers holder received an equivalent amount of Creyzies in a surprise airdrop on 4/20/2022 , with all expenses paid by Sartoshi , the creator of mfers. The contract was written by West Coast NFT , who also wrote the mfers contract. Let’s take a look at the code , which begins with a giant ascii creyzie.
Analytics Vidhya
APRIL 1, 2022
This article was published as a part of the Data Science Blogathon. Introduction We can clearly see that sentiment analysis is getting more and more popular as e-commerce, SaaS solutions, and digital technologies advance. We’ll go through how this works and look at some of the most common corporate applications. We’ll also discuss the analysis’ […].
BAIR
APRIL 20, 2022
A demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP. It uses no TD learning, advantage reweighting, or Transformers! Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning. However, many recent algorithms reframe RL as a supervised learning problem.
Speaker: Simran Kaur, Founder & CEO at Tattva Health Inc.
The healthcare landscape is being revolutionized by AI and cutting-edge digital technologies, reshaping how patients receive care and interact with providers. In this webinar led by Simran Kaur, we will explore how AI-driven solutions are enhancing patient communication, improving care quality, and empowering preventive and predictive medicine. You'll also learn how AI is streamlining healthcare processes, helping providers offer more efficient, personalized care and enabling faster, data-driven
Dlabs.ai
APRIL 21, 2022
One of the fastest-growing technologies in artificial intelligence is computer vision. The market size for computer vision alone was estimated at $7.04bn in 2020 — and it’s forecast to reach $18.13bn by 2028 , a 14.07% increase. What’s more, there’s little doubt that technologies like computer vision are playing a crucial role in changing the face of sports, with applications used in training and analysis to take athletes’ performance to the top level.
DataRobot Blog
APRIL 22, 2022
In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. We also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it.
DeepMind
APRIL 27, 2022
We caught up with Kevin Millikin, a software engineer on the DevTools team. He’s in Salt Lake City this week to present at PyCon US, the largest annual gathering for those using and developing the open-source Python programming language.
StreamHacker
APRIL 23, 2022
Creyzies is the official companion collect to mfers , by Reylarsdam. Every mfers holder received an equivalent amount of Creyzies in a surprise airdrop on 4/20/2022 , with all expenses paid by Sartoshi , the creator of mfers. The contract was written by West Coast NFT , who also wrote the mfers contract. Let’s take a look at the code , which begins with a giant ascii creyzie.
Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
Analytics Vidhya
APRIL 1, 2022
This article was published as a part of the Data Science Blogathon. Introduction In this article, we will discuss how to implement a haar cascade for object detection in OpenCV. In the last article, we discussed real-time object classification, if you haven’t read it yet, the link is here. Source: Link Identifying a custom object […]. The post Object Detection Using Haar Cascade: OpenCV appeared first on Analytics Vidhya.
Explosion
APRIL 12, 2022
The use of a low-code annotation software tool [Prodigy] allows the rapid creation of a custom annotation dataset to train a NER model to identify clinical procedures stored in free-text electronic clinical notes.
Dlabs.ai
APRIL 14, 2022
Doesn’t time fly?! Just like that — it’s Spring! But don’t go thinking this good weather has made us lazy. We’ve been racing around the internet to find the most interesting news from the world of artificial intelligence. So let’s see what happened in March. This Algorithm Can Tell If A Pig Is Happy Or Sad The University of Copenhagen has developed a method for inferring pigs’ emotions based on their grunts using artificial intelligence.
DataRobot Blog
APRIL 11, 2022
If you follow the media stories about AI , you will see two schools of thought. One school is utopian, proclaiming the amazing power of AI, from predicting quantum electron paths to driving a race car like a champion. The other school is dystopian, scaring us with crisis-ridden stories that range from how AI could bring about the end of privacy to self-driving cars that almost immediately crash.
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DeepMind
APRIL 27, 2022
We caught up with Kevin Millikin, a software engineer on the DevTools team. He’s in Salt Lake City this week to present at PyCon US, the largest annual gathering for those using and developing the open-source Python programming language.
StreamHacker
APRIL 22, 2022
The Picaroons is a new NFT collection by Alex Lucas , with support from Pranksy. Holders of certain NFTs by Alex were able to mint up to 2 tokens, then during public mint anyone could mint 2 tokens (and previous minters could mint 2 more). The 10k collection quickly sold out during public mint, and is now only available on secondary marketplaces. An interesting thing about this contract is the extra effort into preventing bots from minting directly from the contract.
Analytics Vidhya
APRIL 12, 2022
This article was published as a part of the Data Science Blogathon. Introduction In this article, we will build a machine learning pipeline that is a Car Price Predictor using Spark in Python. We have already learned the basics of Pyspark in the last article. If you haven’t checked it yet, here is the link. […]. The post Building a Car Price Predictor Using Spark in Python appeared first on Analytics Vidhya.
OpenAI
APRIL 13, 2022
Goodhart’s law famously says: “When a measure becomes a target, it ceases to be a good measure.” Although originally from economics, it’s something we have to grapple with at OpenAI when figuring out how to optimize objectives that are difficult or costly to measure.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.
NLPhilia
APRIL 25, 2022
Four placebo pills a day work better than two. Blue placebo pills are superior at improving sleep; you’ll want green placebo pills for reducing anxiety. But placebo capsules beat placebo pills—and placebo injections were even better. Oh, and expensive, brand-name placebos beat cheap generic ones. Huh? Why would the method of administration make such a difference when the (inactive) substance delivered was always the same?
DataRobot Blog
APRIL 20, 2022
Another year at HIMSS has come and gone and DataRobot remains as energized as ever! This year was a big moment for the DataRobot team. We debuted our growing and dedicated healthcare organization, offered a preview of the AI Cloud DataRobot 8.0 release, and – most importantly – had hundreds of conversations surrounding the impact of AI and the substantial support for the healthcare industry.
DeepMind
APRIL 11, 2022
We ask the question: “What is the optimal model size and number of training tokens for a given compute budget?” To answer this question, we train models of various sizes and with various numbers of tokens, and estimate this trade-off empirically. Our main finding is that the current large language models are far too large for their compute budget and are not being trained on enough data.
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