This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
At Apple, we believe privacy is a fundamental human right. And we believe in giving our users a great experience while protecting their privacy. For years, weve used techniques like differential privacy as part of our opt-in device analytics program. This lets us gain insights into how our products are used, so we can improve them, while protecting user privacy by preventing Apple from seeing individual-level data from those users.
The canonical deep learning approach for learning requires computing a gradient term at each layer by back-propagating the error signal from the output towards each learnable parameter. Given the stacked structure of neural networks, where each layer builds on the representation of the layer below, this approach leads to hierarchical representations.
Regression is a powerful statistical method that plays a critical role in machine learning, particularly when it comes to making predictions and understanding the relationships between variables. By analyzing past data, regression helps us draw insights and foresight into future trends, making it invaluable across numerous fields such as economics, medicine, and meteorology.
Jack Dorsey, co-founder of Twitter (now X) and Square (now Block), sparked a weekends worth of debate around intellectual property, patents, and copyright, with a characteristically terse post declaring, delete all IP law. Xs current owner Elon Musk quickly replied, I agree.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Out-of-distribution (OOD) samples pose a significant challenge in the realm of machine learning, particularly for deep neural networks. These instances differ from the training data and can lead to unreliable predictions. Understanding how to identify and manage OOD data is essential in building robust AI systems capable of handling diverse and unforeseen inputs.
A new suggestion that complexity increases over time, not just in living organisms but in the nonliving world, promises to rewrite notions of time and evolution.
The machine learning lifecycle is an intricate series of stages that guides the development and deployment of machine learning models. Through understanding each phase, teams can effectively harness data to create solutions that address specific problems. Numerous factors contribute to the success of this process, making it essential for data scientists and stakeholders to comprehend the lifecycle comprehensively.
The machine learning lifecycle is an intricate series of stages that guides the development and deployment of machine learning models. Through understanding each phase, teams can effectively harness data to create solutions that address specific problems. Numerous factors contribute to the success of this process, making it essential for data scientists and stakeholders to comprehend the lifecycle comprehensively.
Psychology has been instrumental in the evolution of artificial intelligence, offering foundational insights into learning, cognition, and behavior that have shaped key AI technologies.
DENVER Artificial intelligence is quietly transforming how doctors interact with patients and it might already be in use during your next visit to the doctors office. Thousands of physicians across the country are using a form of AI called ambient listening, surveys show.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
The next wave of AI transformation will be driven by agents. Rather than simply asking questions or following prompts, these AI tools will carry out complex tasks and act with far more autonomy. This will change a lot of things as we become able to delegate more and more tasks to machines.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
The Maltese archipelago is a small island chain that is among the most remote in the Mediterranean. Humans were not thought to have reached and inhabited such small and isolated islands until the regional shift to Neolithic lifeways, around 7.5 thousand years ago (ka)1. In the standard view, the limited resources and ecological vulnerabilities of small islands, coupled with the technological challenges of long-distance seafaring, meant that hunter-gatherers were either unable or unwilling to mak
Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
In todays column, I explore how to use generative AI and large language models (LLMs) to learn and hone your negotiation skills. The deal is this. Life is filled with a constant stream of negotiations, yet few people seem to know what it means to be a good negotiator.
Reducing body weight to improve metabolic health and related comorbidities is a primary goal in treating obesity1,2. However, maintaining weight loss is a considerable challenge, especially as the body seems to retain an obesogenic memory that defends against body weight changes3,4. Overcoming this barrier for long-term treatment success is difficult because the molecular mechanisms underpinning this phenomenon remain largely unknown.
As another AI-driven trend gained traction, artists countered by sharing their own human-made takes on the ChatGPT-generated action figures that circulated online in recent days.
Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. It’s crucial for applications like spam detection, disease diagnosis, and customer segmentation, improving decision-making and operational efficiency across various sectors. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
A German experiment has found that people are likely to continue working full-time even if they receive no-strings-attached universal basic income payments.
In the accounting world, staying ahead means embracing the tools that allow you to work smarter, not harder. Outdated processes and disconnected systems can hold your organization back, but the right technologies can help you streamline operations, boost productivity, and improve client delivery. Dive into the strategies and innovations transforming accounting practices.
Ive, the designer of the iPhone, has a startup that's reportedly developing a range of AI-powered devices. ChatGPT maker OpenAI is reportedly looking into a potential acquisition of an artificial intelligence startup co-founded by former Apple design chief Jony Ive and OpenAI CEO Sam Altman.
Speaker: Chris Townsend, VP of Product Marketing, Wellspring
Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?
Input your email to sign up, or if you already have an account, log in here!
Enter your email address to reset your password. A temporary password will be e‑mailed to you.
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content