April, 2022

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Face detection using the Caffe model

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this section, we will build a face detection algorithm using Caffe model, but only OpenCV is not involved this time. Instead, along with the computer vision techniques, deep learning skills will also be required, i.e. We will use the deep learning […]. The post Face detection using the Caffe model appeared first on Analytics Vidhya.

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Designing Societally Beneficial Reinforcement Learning Systems

BAIR

Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMindā€™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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How to Measure and Mitigate Position Bias

Eugene Yan

Introducing randomness and/or learning from inherent randomness to mitigate position bias.

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What is Model Risk and Why Does it Matter?

DataRobot Blog

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.

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How To Get Promoted In Product Management

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

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When a passion for bass and brass help build better tools

DeepMind

We caught up with Kevin Millikin, a software engineer on the DevTools team. Heā€™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.

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Introduction to SQL for Data Engineering

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we will be looking for a very common yet very important topic i.e. SQL also pronounced as Ess-cue-ell. So this time I’ll be answering some of the factual questions about SQL which every beginner needs to know before getting […]. The post Introduction to SQL for Data Engineering appeared first on Analytics Vidhya.

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Offline RL Made Easier: No TD Learning, Advantage Reweighting, or Transformers

BAIR

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.

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Counterfactual Evaluation for Recommendation Systems

Eugene Yan

Thinking about recsys as interventional vs. observational, and inverse propensity scoring.

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Finding the Signal in the Noise: Talking Racing Strategy with McLaren Racingā€™s Randy Singh

DataRobot Blog

F1 is headed Down Under next week for the first time since 2019. The Australian Grand Prix ā€“ taking place in Melbourneā€™s Albert Park Circuit ā€“ will showcase a new track layout and will be a homecoming for Australiaā€™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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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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When a passion for bass and brass help build better tools

DeepMind

We caught up with Kevin Millikin, a software engineer on the DevTools team. Heā€™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.

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Picaroons Contract Review

StreamHacker

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.

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Is Quantum Computing the Future of Artificial Intelligence?

Analytics Vidhya

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 […].

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How to Use Computer Vision in Sports?

Dlabs.ai

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 ā€” and itā€™s forecast to reach $18.13bn by 2028 , a 14.07% increase. Whatā€™s more, thereā€™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 athletesā€™ performance to the top level.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. Itā€™s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow

Explosion

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.

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AI Impact Statements ā€“ Empathy, Imperfection, and Responsibility

DataRobot Blog

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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An empirical analysis of compute-optimal large language model training

DeepMind

We ask the question: ā€œWhat is the optimal model size and number of training tokens for a given compute budget?ā€ 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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Measuring Goodhartā€™s law

OpenAI

Goodhartā€™s law famously says: ā€œWhen a measure becomes a target, it ceases to be a good measure.ā€ Although originally from economics, itā€™s something we have to grapple with at OpenAI when figuring out how to optimize objectives that are difficult or costly to measure.

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How to Improve Email Deliverability and Optimize Each Send

Learn how to optimize email deliverability and drive greater email ROI. What lands your email in the customerā€™s inbox? Understanding those factors, otherwise known as email deliverability, is critical to getting the most return on your campaign investments. But the ā€œrulesā€ around which factors land you in the spam folder arenā€™t always easy to keep up with.

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Object Detection Using Haar Cascade: OpenCV

Analytics Vidhya

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 havenā€™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.

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The Algorithm Can Tell If A Pig Is Happy Or Sad

Dlabs.ai

Doesnā€™t time fly?! Just like that ā€” itā€™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.

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Taking the White (Sugar) Pill

NLPhilia

Four placebo pills a day work better than two. Blue placebo pills are superior at improving sleep; youā€™ll want green placebo pills for reducing anxiety. But placebo capsules beat placebo pillsā€”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?

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HIMSS 2022: Looking Forward

DataRobot Blog

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 ā€“ most importantly ā€“ had hundreds of conversations surrounding the impact of AI and the substantial support for the healthcare industry.

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Brick & Mortar Retail Relevance: How to Stay Ahead of the Curve

Speaker: Jay Black, Senior Account Executive

Let's set the record straight: in-store retail isn't dead - it's evolving! Faced with the digital age and the demands of omnichannel shopping, some retailers are thriving while others are struggling to adapt. Join Jay Black in this exclusive session as he explores the strategies that set successful stores apart, including: Crafting unique and unforgettable in-store experiences 🛍ļø Mastering the art of retail demands 🛒 Navigating inventory challenges in today's climate 📦 an

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An empirical analysis of compute-optimal large language model training

DeepMind

We ask the question: ā€œWhat is the optimal model size and number of training tokens for a given compute budget?ā€ 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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Stanford AI Lab Papers and Talks at ICLR 2022

The Stanford AI Lab Blog

The International Conference on Learning Representations (ICLR) 2022 is being hosted virtually from April 25th - April 29th. Weā€™re excited to share all the work from SAIL thatā€™s being presented, and youā€™ll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work thatā€™s happening at Stanford!

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Building Vehicle Counter System Using OpenCV

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we are going to build a vehicle counter system using OpenCV in Python using the concept of Euclidean distance tracking and contours. In the last article, we talked about object detection in OpenCV using haar cascades, if you havenā€™t […]. The post Building Vehicle Counter System Using OpenCV appeared first on Analytics Vidhya.

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Introducing spaCy v3.3

Explosion

spaCy v3.3 improves the speed of core pipeline components, adds a new trainable lemmatizer, and introduces trained pipelines for Finnish, Korean and Swedish.

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How Embedded Analytics Gets You to Market Faster with a SAAS Offering

Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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Should I Use Offline RL or Imitation Learning?

BAIR

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.

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Forecasting Solar Radiation using DataRobot to Optimize Power Generation

DataRobot Blog

ā€œIā€™d put my money on the sun and solar energy,ā€ said Thomas Edison to Henry Ford and Harvey Firestone. Indeed, the race to renewable power generation is catching pace and solar power is one of the cleanest power generation techniques in the renewable energy space. Like with any power generation methodology, solar power generation needs to be consumed without waste; however, the availability of sunlight is limited.

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Tackling multiple tasks with a single visual language model

DeepMind

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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