"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Jose Lopez is an AI/ML researcher at Intel Labs. Here, he explores the use of machine learning for audio understanding and industrial predictive maintenance. Intel operates six wafer fabrication (fab) sites and four test manufacturing locations globally, producing, on average, five billion transistors per second. Keeping these fabrication facilities running smoothly is a top priority as malfunctions can be costly. In addition, ensuring chip manufacturing production targets are met is vital due to the fact that some expect the chip shortage to continue into 2023.
Dementia symptoms are similar to the behavioral and cognitive signs of severe psychotic illnesses. Shared brain changes were still, and it hadn't been determined how important they are for patients with at-risk disease stages. For a study, researchers sought to compare the structural magnetic resonance imaging (MRI) patterns of behavioral-variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and schizophrenia using machine learning; to estimate predictability in bvFTD and schizophrenia patients using sociodemographic, clinical, and biological data; and to look at prognostic significance, genetic underpinnings, and progression in patients with clinical high-risk (CHR) states for psychosis or recent-onset depression (ROD). In order to identify and compare diagnostic patterns, the study included 1,870 participants from 5 cohorts, including patients with bvFTD (n 108), established AD (n 44), mild cognitive impairment or early-stage AD (n 96), schizophrenia (n 157), or major depression (n 102). Additionally, patients with CHR (n 160) or ROD (n 161) were included to examine the prognostic relevance and progression of the patterns.
ML is changing biological research. This has led to new discoveries in biotechnology and healthcare. Machine Learning and Artificial Intelligence are changing the way that people live and work. These fields have been praised and criticized. AI and ML, or as they are commonly known, have many applications and benefits across a wide variety of industries.
Hopsworks is organizing the second Feature Store Summit, a free online conference for Feature Engineering and managing data for AI, happening on October 11th, 2022. Whatever your area of expertise, and wherever you are in your career journey, the Feature Store Summit offers the opportunity to learn from leading innovators on the latest platforms, best practices, and use cases for managing your data for AI. This year's talks and sessions revolve around the theme of'Accelerating Production Machine Learning with Feature Stores' from companies such as Uber, Linkedin, Airbnb, Doordash, Disney Streaming and many more. By joining the event you will be able to hear from people who have seen the good, bad and ugly side of the feature stores and learn from their experiences. It will help you to understand the capabilities of a Feature Store and the various cutting-edge technologies that facilitate bringing ML models into production, as well as showcase ways to improve your ML platforms.
The Google team has developed a new AI model that can design complex chips in just hours. This is an incredibly difficult task and usually takes months for human engineers to accomplish. Let's look into what this new artificial intelligence microchip is and the potential impact it could make in the technology industry. A microchip is a small electronic device that controls and stores electronic data. It is made up of a silicon chip that has been fabricated into a very small size.
Then there is the ever-present pandemic that has been threatening this planet for years. It got me thinking: can technology be used to combat all these horrible diseases and improve patient outcomes. Is artificial intelligence going to play a role in this? We've achieved another milestone in Artificial Intelligence adoption: $6.9 Billion of market value and counting. The intelligent healthcare market will reach 67.4 Billion by 2027.
Artificial intelligence applications are frequently used without any mechanism for external testing or evaluation. Modern machine learning systems are opaque to outside stakeholders, including researchers, who can only probe the system by providing inputs and measuring outputs. Researchers, users, and regulators alike are thus forced to grapple with using, being impacted by, or regulating algorithms they cannot fully observe. This brief reviews the history of algorithm auditing, describes its current state, and offers best practices for conducting algorithm audits today. We identified nine considerations for algorithm auditing, including legal and ethical risks, factors of discrimination and bias, and conducting audits continuously so as to not capture just one moment in time.
The research team from DeepMind has recently published a paper where they introduce a new AI platform for improving mathematical algorithms. The new system is known under the name of AlphaTensor, which is an extension of AlphaZero to the discipline of mathematics. This tool takes classic fundamental algorithms as input and produces their improved versions. The team used a matrix multiplication algorithm as a basis for their study, where they analyzed a 50-year-old problem of finding the fastest way to multiply two matrices. Matrix multiplication may seem a very boring part of mathematics requiring lots of time spent multiplying and adding numbers.
Last year Google made a big change to its phone line with the introduction of its custom-designed Tensor chip. By focusing on increased AI and machine learning performance, the company was able to create more advanced apps and features for its handsets without needing help from the cloud. And now with the launch of the Pixel 7 and 7 Pro alongside the Tensor G2, it feels like Google is deepening the marriage between its hardware and software. On the outside, Google is using a similar template to what we got with the Pixel 6 with a couple of notable tweaks. On the Pixel 7, you get a screen made from Gorilla Glass Victus, while in back, there's an even more pronounced camera bar that now extends seamlessly from the phone's frame across the width of the device.